From 6befd2d50c9305f455e1f4708476832be9ffc749 Mon Sep 17 00:00:00 2001
From: Jakub Polec <jpolec@gmail.com>
Date: Fri, 13 Jun 2025 07:25:59 +0200
Subject: [PATCH] Session_01

---
 README.md                                     |    99 +-
 Session_01/.DS_Store                          |   Bin 0 -> 6148 bytes
 Session_01/Data/BTCUSD-1h-data.csv            | 83955 ++++++++++++++++
 .../01_creating_dataframes.ipynb              |   391 +
 .../02_basic_operations.ipynb                 |   523 +
 .../03_selecting_filtering.ipynb              |   593 +
 .../04_grouping_aggregation.ipynb             |  1137 +
 .../05_adding_modifying_columns.ipynb         |   733 +
 .../06_handling_missing_data.ipynb            |   916 +
 .../07_merging_joining.ipynb                  |   937 +
 .../08_sorting_ranking.ipynb                  |  1408 +
 .../09_pivot_tables.ipynb                     |  1978 +
 .../10_time_series_analysis.ipynb             |  1149 +
 .../11_string_operation.ipynb                 |  1059 +
 .../12_data_visualization.ipynb               |  1126 +
 .../13_advanced_data_cleaning.ipynb           |   815 +
 Session_01/ohlcv_analysis.ipynb               |  1301 +
 Session_01/ohlcv_analysis_advanced.ipynb      |  1733 +
 18 files changed, 99852 insertions(+), 1 deletion(-)
 create mode 100644 Session_01/.DS_Store
 create mode 100755 Session_01/Data/BTCUSD-1h-data.csv
 create mode 100755 Session_01/PandasDataFrame-exmples/01_creating_dataframes.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/02_basic_operations.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/03_selecting_filtering.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/04_grouping_aggregation.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/05_adding_modifying_columns.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/06_handling_missing_data.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/07_merging_joining.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/08_sorting_ranking.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/09_pivot_tables.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/10_time_series_analysis.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/11_string_operation.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/12_data_visualization.ipynb
 create mode 100755 Session_01/PandasDataFrame-exmples/13_advanced_data_cleaning.ipynb
 create mode 100755 Session_01/ohlcv_analysis.ipynb
 create mode 100755 Session_01/ohlcv_analysis_advanced.ipynb

diff --git a/README.md b/README.md
index f89f387..d6ac98b 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,98 @@
-# crypto_bot_training
+# Build Your Own Crypto Trading Bot – Course Repository
+
+Welcome to the private repository for the **"Build Your Own Crypto Trading Bot – Hands-On Course with Alex"** by QuantJourney.
+
+This repository contains materials, templates, and code samples used during the 6 live sessions held in June 2025.
+
+> ⚠️ This repository is for registered participants only.
+
+---
+
+## Content Overview
+
+**Session 1: Foundations & Data Structures**
+- Set up Python, IDE, and required libraries
+- Pandas basics for financial time series
+- Understanding OHLCV format
+- Create your first crypto DataFrame with sample data
+
+**Session 2: Data Acquisition & Exchange Connectivity**
+- WebSocket basics for real-time crypto feeds (Binance focus)
+- Fail-safe reconnection logic and error handling
+- Logging basics for live systems
+- Build tools: order flow scanner, liquidation monitor, funding rate tracker
+
+**Session 3: Data Processing & Technical Analysis**
+- API access using CCXT
+- Handle rate limits and API error scenarios
+- Reconnect & retry mechanisms
+- Use pandas-ta to compute SMA, EMA, RSI
+- Create your own indicator pipeline
+
+**Session 4: Strategy Development & Backtesting**
+- Overview of strategy types (trend, mean reversion)
+- Backtesting with `backtesting.py`
+- Compute Sharpe ratio, drawdown, profit factor
+- Add position sizing, SL/TP, and walk-forward logic
+- Adjust for fees, slippage, and latency
+
+**Session 5: Bot Architecture & Implementation**
+- Bot system design: event-driven vs loop-based
+- Core components: order manager, position tracker, error handler
+- Risk constraints: daily limits, max size
+- Logging & monitoring structure
+- Write the engine core for your bot
+
+**Session 6: Live Trading & Deployment**
+- API keys and secure credential handling
+- Deployment targets: local, VPS, cloud (e.g., Hetzner)
+- Running 24/7: restart logic, alerting
+- Final bot launch + testing in production
+- Send alerts via Telegram or email
+
+---
+
+## 🤖 AI-Enhanced Trading
+Bonus section:
+- Use ChatGPT/Claude for strategy suggestions
+- Integrate AI-based filters or signal generation
+- Let LLMs help you refactor and extend your logic
+
+---
+
+## 📁 Repository Structure
+
+```text
+/Session_01/      # Foundations & DataFrame Handling
+/Session_02/      # WebSockets & Real-Time Feed Tools
+/Session_03/      # Indicators & Analysis
+/Session_04/      # Backtesting + Strategy Logic
+/Session_05/      # Trading Bot Core Engine
+/Session_06/      # Live Deployment and Monitoring
+/templates/     # Starter and final bot code
+/utils/         # Helper scripts for logging, reconnection, etc.
+README.md       # You are here
+```
+
+---
+
+## 🛠 Requirements
+- Python 3.10+
+- Install dependencies per session in each folder or via a top-level `requirements.txt` (provided)
+
+---
+
+## 📫 Support
+You can reach Alex directly at [alex@quantjourney.pro](mailto:alex@quantjourney.pro) for post-course support (1 week included).
+
+---
+
+## ⚠️ Disclaimer
+This project is for **educational use only**. No financial advice. Always trade with caution and use proper risk management.
+
+---
+
+Happy coding – and trade smart.
+
+QuantJourney Team
+
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+2025-04-08 02:00:00,79867.46,80849.98,80577.15,79907.63,397.35657515
diff --git a/Session_01/PandasDataFrame-exmples/01_creating_dataframes.ipynb b/Session_01/PandasDataFrame-exmples/01_creating_dataframes.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 1: Creating DataFrames\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Understand different methods to create pandas DataFrames\n",
+    "- Learn to create DataFrames from dictionaries, lists, and NumPy arrays\n",
+    "- Practice with various data types and structures\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Basic Python knowledge\n",
+    "- Understanding of lists and dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pandas version: 2.2.3\n",
+      "NumPy version: 2.2.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "print(f\"Pandas version: {pd.__version__}\")\n",
+    "print(f\"NumPy version: {np.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Method 1: Creating DataFrame from Dictionary\n",
+    "\n",
+    "This is the most common and intuitive way to create a DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Student DataFrame:\n",
+      "      Name  Age Grade  Score\n",
+      "0    Alice   23     A     95\n",
+      "1      Bob   25     B     87\n",
+      "2  Charlie   22     A     92\n",
+      "3    Diana   24     C     78\n",
+      "4      Eve   23     B     89\n",
+      "\n",
+      "Shape: (5, 4)\n",
+      "Data types:\n",
+      "Name     object\n",
+      "Age       int64\n",
+      "Grade    object\n",
+      "Score     int64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating DataFrame from dictionary\n",
+    "student_data = {\n",
+    "    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],\n",
+    "    'Age': [23, 25, 22, 24, 23],\n",
+    "    'Grade': ['A', 'B', 'A', 'C', 'B'],\n",
+    "    'Score': [95, 87, 92, 78, 89]\n",
+    "}\n",
+    "\n",
+    "df_students = pd.DataFrame(student_data)\n",
+    "print(\"Student DataFrame:\")\n",
+    "print(df_students)\n",
+    "print(f\"\\nShape: {df_students.shape}\")\n",
+    "print(f\"Data types:\\n{df_students.dtypes}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Method 2: Creating DataFrame from Lists\n",
+    "\n",
+    "You can create DataFrames from separate lists by combining them in a dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cities DataFrame:\n",
+      "       City  Population_Million    Country\n",
+      "0  New York                 8.4        USA\n",
+      "1    London                 8.9         UK\n",
+      "2     Tokyo                13.9      Japan\n",
+      "3     Paris                 2.1     France\n",
+      "4    Sydney                 5.3  Australia\n",
+      "\n",
+      "Index: [0, 1, 2, 3, 4]\n",
+      "Columns: ['City', 'Population_Million', 'Country']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating DataFrame from separate lists\n",
+    "cities = ['New York', 'London', 'Tokyo', 'Paris', 'Sydney']\n",
+    "populations = [8.4, 8.9, 13.9, 2.1, 5.3]\n",
+    "countries = ['USA', 'UK', 'Japan', 'France', 'Australia']\n",
+    "\n",
+    "df_cities = pd.DataFrame({\n",
+    "    'City': cities,\n",
+    "    'Population_Million': populations,\n",
+    "    'Country': countries\n",
+    "})\n",
+    "\n",
+    "print(\"Cities DataFrame:\")\n",
+    "print(df_cities)\n",
+    "print(f\"\\nIndex: {df_cities.index.tolist()}\")\n",
+    "print(f\"Columns: {df_cities.columns.tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Method 3: Creating DataFrame from NumPy Array\n",
+    "\n",
+    "This method is useful when working with numerical data or when you need random data for testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random DataFrame:\n",
+      "      Column_A  Column_B  Column_C\n",
+      "Row1        52        93        15\n",
+      "Row2        72        61        21\n",
+      "Row3        83        87        75\n",
+      "Row4        75        88        24\n",
+      "Row5         3        22        53\n",
+      "\n",
+      "Summary statistics:\n",
+      "        Column_A   Column_B   Column_C\n",
+      "count   5.000000   5.000000   5.000000\n",
+      "mean   57.000000  70.200000  37.600000\n",
+      "std    32.272279  29.693434  25.530374\n",
+      "min     3.000000  22.000000  15.000000\n",
+      "25%    52.000000  61.000000  21.000000\n",
+      "50%    72.000000  87.000000  24.000000\n",
+      "75%    75.000000  88.000000  53.000000\n",
+      "max    83.000000  93.000000  75.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating DataFrame from NumPy array\n",
+    "np.random.seed(42)  # For reproducible results\n",
+    "random_data = np.random.randint(1, 100, size=(5, 3))\n",
+    "\n",
+    "df_random = pd.DataFrame(random_data, \n",
+    "                        columns=['Column_A', 'Column_B', 'Column_C'],\n",
+    "                        index=['Row1', 'Row2', 'Row3', 'Row4', 'Row5'])\n",
+    "\n",
+    "print(\"Random DataFrame:\")\n",
+    "print(df_random)\n",
+    "print(f\"\\nSummary statistics:\")\n",
+    "print(df_random.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Method 4: Creating DataFrame with Custom Index\n",
+    "\n",
+    "You can specify custom row labels (index) when creating DataFrames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Products DataFrame with Custom Index:\n",
+      "         Product  Price  Stock\n",
+      "PROD001   Laptop   1200     15\n",
+      "PROD002    Phone    800     50\n",
+      "PROD003   Tablet    600     30\n",
+      "PROD004  Monitor    300     20\n",
+      "\n",
+      "Accessing by index label 'PROD002':\n",
+      "Product    Phone\n",
+      "Price        800\n",
+      "Stock         50\n",
+      "Name: PROD002, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating DataFrame with custom index\n",
+    "product_data = {\n",
+    "    'Product': ['Laptop', 'Phone', 'Tablet', 'Monitor'],\n",
+    "    'Price': [1200, 800, 600, 300],\n",
+    "    'Stock': [15, 50, 30, 20]\n",
+    "}\n",
+    "\n",
+    "# Custom index using product codes\n",
+    "custom_index = ['PROD001', 'PROD002', 'PROD003', 'PROD004']\n",
+    "df_products = pd.DataFrame(product_data, index=custom_index)\n",
+    "\n",
+    "print(\"Products DataFrame with Custom Index:\")\n",
+    "print(df_products)\n",
+    "print(f\"\\nAccessing by index label 'PROD002':\")\n",
+    "print(df_products.loc['PROD002'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Method 5: Creating Empty DataFrame and Adding Data\n",
+    "\n",
+    "Sometimes you need to start with an empty DataFrame and add data incrementally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame:\n",
+      "Empty DataFrame\n",
+      "Columns: [Date, Temperature, Humidity, Pressure]\n",
+      "Index: []\n",
+      "Shape: (0, 4)\n",
+      "\n",
+      "DataFrame after adding data:\n",
+      "         Date  Temperature  Humidity  Pressure\n",
+      "0  2024-01-01         22.5        65    1013.2\n",
+      "1  2024-01-02         24.1        68    1015.1\n",
+      "2  2024-01-03         21.8        72    1012.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating empty DataFrame with specified columns\n",
+    "columns = ['Date', 'Temperature', 'Humidity', 'Pressure']\n",
+    "df_weather = pd.DataFrame(columns=columns)\n",
+    "\n",
+    "print(\"Empty DataFrame:\")\n",
+    "print(df_weather)\n",
+    "print(f\"Shape: {df_weather.shape}\")\n",
+    "\n",
+    "# Adding data row by row (not recommended for large datasets)\n",
+    "weather_data = [\n",
+    "    ['2024-01-01', 22.5, 65, 1013.2],\n",
+    "    ['2024-01-02', 24.1, 68, 1015.1],\n",
+    "    ['2024-01-03', 21.8, 72, 1012.8]\n",
+    "]\n",
+    "\n",
+    "for row in weather_data:\n",
+    "    df_weather.loc[len(df_weather)] = row\n",
+    "\n",
+    "print(\"\\nDataFrame after adding data:\")\n",
+    "print(df_weather)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Try these exercises to reinforce your learning:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Create a DataFrame from dictionary with employee information\n",
+    "# Include: Employee ID, Name, Department, Salary, Years of Experience\n",
+    "\n",
+    "# Your code here:\n",
+    "employee_data = {\n",
+    "    # Add your data here\n",
+    "}\n",
+    "\n",
+    "# df_employees = pd.DataFrame(employee_data)\n",
+    "# print(df_employees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Create a DataFrame using NumPy with 6 rows and 4 columns\n",
+    "# Use column names: 'A', 'B', 'C', 'D'\n",
+    "# Use row indices: 'R1', 'R2', 'R3', 'R4', 'R5', 'R6'\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Create a DataFrame with mixed data types\n",
+    "# Include at least one string, integer, float, and boolean column\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Dictionary method** is most intuitive for creating DataFrames\n",
+    "2. **NumPy arrays** are useful for numerical data and testing\n",
+    "3. **Custom indices** provide meaningful row labels\n",
+    "4. **Empty DataFrames** can be useful but avoid adding rows one by one for large datasets\n",
+    "5. Always check the **shape** and **data types** of your DataFrame after creation\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/02_basic_operations.ipynb b/Session_01/PandasDataFrame-exmples/02_basic_operations.ipynb
new file mode 100755
index 0000000..cf67c42
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/02_basic_operations.ipynb
@@ -0,0 +1,523 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 2: Basic Operations\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Learn essential methods to explore DataFrame structure\n",
+    "- Understand how to get basic information about your data\n",
+    "- Master data inspection techniques\n",
+    "- Practice with summary statistics\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lesson 1: Creating DataFrames\n",
+    "- Basic understanding of pandas DataFrames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "# Set display options for better output\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.width', None)\n",
+    "pd.set_option('display.max_colwidth', 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Sample Dataset\n",
+    "\n",
+    "Let's create a comprehensive sales dataset to practice basic operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sales Dataset Created!\n",
+      "Dataset shape: (20, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a comprehensive sales dataset\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "sales_data = {\n",
+    "    'Date': pd.date_range('2024-01-01', periods=20, freq='D'),\n",
+    "    'Product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Phone'] * 4,\n",
+    "    'Sales': [1200, 800, 600, 1100, 850, 1300, 750, 650, 1250, 900,\n",
+    "              1150, 820, 700, 1180, 880, 1220, 780, 620, 1300, 850],\n",
+    "    'Region': ['North', 'South', 'East', 'West', 'North'] * 4,\n",
+    "    'Salesperson': ['John', 'Sarah', 'Mike', 'Lisa', 'Tom'] * 4,\n",
+    "    'Commission_Rate': [0.10, 0.12, 0.08, 0.11, 0.09] * 4\n",
+    "}\n",
+    "\n",
+    "df_sales = pd.DataFrame(sales_data)\n",
+    "print(\"Sales Dataset Created!\")\n",
+    "print(f\"Dataset shape: {df_sales.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Viewing Data\n",
+    "\n",
+    "These methods help you quickly inspect your data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View first few rows\n",
+    "print(\"First 5 rows (default):\")\n",
+    "print(df_sales.head())\n",
+    "\n",
+    "print(\"\\nFirst 3 rows:\")\n",
+    "print(df_sales.head(3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View last few rows\n",
+    "print(\"Last 5 rows (default):\")\n",
+    "print(df_sales.tail())\n",
+    "\n",
+    "print(\"\\nLast 3 rows:\")\n",
+    "print(df_sales.tail(3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random sample of 5 rows:\n",
+      "         Date Product  Sales Region Salesperson  Commission_Rate\n",
+      "0  2024-01-01  Laptop   1200  North        John             0.10\n",
+      "17 2024-01-18  Tablet    620   East        Mike             0.08\n",
+      "15 2024-01-16  Laptop   1220  North        John             0.10\n",
+      "1  2024-01-02   Phone    800  South       Sarah             0.12\n",
+      "8  2024-01-09  Laptop   1250   West        Lisa             0.11\n",
+      "\n",
+      "Random sample with different random state:\n",
+      "         Date Product  Sales Region Salesperson  Commission_Rate\n",
+      "7  2024-01-08  Tablet    650   East        Mike             0.08\n",
+      "10 2024-01-11  Laptop   1150  North        John             0.10\n",
+      "5  2024-01-06  Laptop   1300  North        John             0.10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample random rows\n",
+    "print(\"Random sample of 5 rows:\")\n",
+    "print(df_sales.sample(5))\n",
+    "\n",
+    "print(\"\\nRandom sample with different random state:\")\n",
+    "print(df_sales.sample(3, random_state=10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. DataFrame Information\n",
+    "\n",
+    "Get detailed information about your DataFrame structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comprehensive information about the DataFrame\n",
+    "print(\"DataFrame Info:\")\n",
+    "df_sales.info()\n",
+    "\n",
+    "print(\"\\nMemory usage:\")\n",
+    "df_sales.info(memory_usage='deep')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic properties\n",
+    "print(f\"Shape (rows, columns): {df_sales.shape}\")\n",
+    "print(f\"Number of rows: {len(df_sales)}\")\n",
+    "print(f\"Number of columns: {len(df_sales.columns)}\")\n",
+    "print(f\"Total elements: {df_sales.size}\")\n",
+    "print(f\"Dimensions: {df_sales.ndim}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Column and index information\n",
+    "print(\"Column names:\")\n",
+    "print(df_sales.columns.tolist())\n",
+    "\n",
+    "print(\"\\nData types:\")\n",
+    "print(df_sales.dtypes)\n",
+    "\n",
+    "print(\"\\nIndex information:\")\n",
+    "print(f\"Index: {df_sales.index}\")\n",
+    "print(f\"Index type: {type(df_sales.index)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Summary Statistics\n",
+    "\n",
+    "Understand your data through statistical summaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Summary statistics for numerical columns\n",
+    "print(\"Summary statistics:\")\n",
+    "print(df_sales.describe())\n",
+    "\n",
+    "print(\"\\nRounded to 2 decimal places:\")\n",
+    "print(df_sales.describe().round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Summary statistics for all columns (including non-numeric)\n",
+    "print(\"Summary for all columns:\")\n",
+    "print(df_sales.describe(include='all'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Individual statistics\n",
+    "print(\"Individual Statistical Measures:\")\n",
+    "print(f\"Mean sales: {df_sales['Sales'].mean():.2f}\")\n",
+    "print(f\"Median sales: {df_sales['Sales'].median():.2f}\")\n",
+    "print(f\"Standard deviation: {df_sales['Sales'].std():.2f}\")\n",
+    "print(f\"Minimum sales: {df_sales['Sales'].min()}\")\n",
+    "print(f\"Maximum sales: {df_sales['Sales'].max()}\")\n",
+    "print(f\"Sales range: {df_sales['Sales'].max() - df_sales['Sales'].min()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quantiles and percentiles\n",
+    "print(\"Quantiles for Sales:\")\n",
+    "print(f\"25th percentile (Q1): {df_sales['Sales'].quantile(0.25)}\")\n",
+    "print(f\"50th percentile (Q2/Median): {df_sales['Sales'].quantile(0.50)}\")\n",
+    "print(f\"75th percentile (Q3): {df_sales['Sales'].quantile(0.75)}\")\n",
+    "print(f\"90th percentile: {df_sales['Sales'].quantile(0.90)}\")\n",
+    "\n",
+    "print(\"\\nCustom quantiles:\")\n",
+    "quantiles = df_sales['Sales'].quantile([0.1, 0.3, 0.7, 0.9])\n",
+    "print(quantiles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Counting and Unique Values\n",
+    "\n",
+    "Understand the distribution of categorical data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count unique values in each column\n",
+    "print(\"Number of unique values per column:\")\n",
+    "print(df_sales.nunique())\n",
+    "\n",
+    "print(\"\\nUnique values in 'Product' column:\")\n",
+    "print(df_sales['Product'].unique())\n",
+    "\n",
+    "print(\"\\nValue counts for 'Product':\")\n",
+    "print(df_sales['Product'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Product distribution (counts and percentages):\n",
+      "         Count  Percentage\n",
+      "Product                   \n",
+      "Laptop       8        40.0\n",
+      "Phone        8        40.0\n",
+      "Tablet       4        20.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Value counts with percentages\n",
+    "print(\"Product distribution (counts and percentages):\")\n",
+    "product_counts = df_sales['Product'].value_counts()\n",
+    "product_percentages = df_sales['Product'].value_counts(normalize=True) * 100\n",
+    "\n",
+    "distribution = pd.DataFrame({\n",
+    "    'Count': product_counts,\n",
+    "    'Percentage': product_percentages.round(1)\n",
+    "})\n",
+    "print(distribution)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cross-tabulation\n",
+    "print(\"Cross-tabulation of Product vs Region:\")\n",
+    "crosstab = pd.crosstab(df_sales['Product'], df_sales['Region'])\n",
+    "print(crosstab)\n",
+    "\n",
+    "print(\"\\nWith percentages:\")\n",
+    "crosstab_pct = pd.crosstab(df_sales['Product'], df_sales['Region'], normalize='all') * 100\n",
+    "print(crosstab_pct.round(1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Data Quality Checks\n",
+    "\n",
+    "Essential checks for data quality and integrity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check for missing values\n",
+    "print(\"Missing values per column:\")\n",
+    "print(df_sales.isnull().sum())\n",
+    "\n",
+    "print(\"\\nPercentage of missing values:\")\n",
+    "missing_percentages = (df_sales.isnull().sum() / len(df_sales)) * 100\n",
+    "print(missing_percentages.round(2))\n",
+    "\n",
+    "print(\"\\nAny missing values in dataset?\", df_sales.isnull().any().any())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check for duplicates\n",
+    "print(f\"Number of duplicate rows: {df_sales.duplicated().sum()}\")\n",
+    "print(f\"Any duplicate rows? {df_sales.duplicated().any()}\")\n",
+    "\n",
+    "# Check for duplicates based on specific columns\n",
+    "print(f\"\\nDuplicate combinations of Date and Salesperson: {df_sales.duplicated(['Date', 'Salesperson']).sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Quick Data Exploration\n",
+    "\n",
+    "Rapid exploration techniques to understand your data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quick exploration function\n",
+    "def quick_explore(df, column_name):\n",
+    "    \"\"\"Quick exploration of a specific column\"\"\"\n",
+    "    print(f\"=== Quick Exploration: {column_name} ===\")\n",
+    "    col = df[column_name]\n",
+    "    \n",
+    "    print(f\"Data type: {col.dtype}\")\n",
+    "    print(f\"Non-null values: {col.count()}/{len(col)}\")\n",
+    "    print(f\"Unique values: {col.nunique()}\")\n",
+    "    \n",
+    "    if col.dtype in ['int64', 'float64']:\n",
+    "        print(f\"Min: {col.min()}, Max: {col.max()}\")\n",
+    "        print(f\"Mean: {col.mean():.2f}, Median: {col.median():.2f}\")\n",
+    "    else:\n",
+    "        print(f\"Most common: {col.mode().iloc[0] if not col.mode().empty else 'N/A'}\")\n",
+    "        print(f\"Sample values: {col.unique()[:5].tolist()}\")\n",
+    "    print()\n",
+    "\n",
+    "# Explore different columns\n",
+    "for col in ['Sales', 'Product', 'Region']:\n",
+    "    quick_explore(df_sales, col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Test your understanding with these exercises:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Create a larger dataset and explore it\n",
+    "# Create a dataset with 100 rows and at least 5 columns\n",
+    "# Include different data types (numeric, categorical, datetime)\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Write a function that provides a complete data profile\n",
+    "# Include: shape, data types, missing values, unique values, and basic stats\n",
+    "\n",
+    "def data_profile(df):\n",
+    "    \"\"\"Provide a comprehensive data profile\"\"\"\n",
+    "    # Your code here:\n",
+    "    pass\n",
+    "\n",
+    "# Test your function\n",
+    "# data_profile(df_sales)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Find interesting insights from the sales data\n",
+    "# Questions to answer:\n",
+    "# 1. Which product has the highest average sales?\n",
+    "# 2. Which region has the most consistent sales (lowest standard deviation)?\n",
+    "# 3. What's the total commission earned by each salesperson?\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **`.head()` and `.tail()`** are essential for quick data inspection\n",
+    "2. **`.info()`** provides comprehensive DataFrame structure information\n",
+    "3. **`.describe()`** gives statistical summaries for numerical columns\n",
+    "4. **`.nunique()` and `.value_counts()`** help understand categorical data\n",
+    "5. **Always check for missing values** and duplicates in your data\n",
+    "6. **Statistical measures** (mean, median, std) provide insights into data distribution\n",
+    "7. **Cross-tabulation** helps understand relationships between categorical variables\n",
+    "\n",
+    "## Common Gotchas\n",
+    "\n",
+    "- `.describe()` only includes numeric columns by default (use `include='all'` for all columns)\n",
+    "- Missing values can affect statistical calculations\n",
+    "- Large datasets might need memory-efficient exploration techniques\n",
+    "- Always verify data types are correct for your analysis"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/03_selecting_filtering.ipynb b/Session_01/PandasDataFrame-exmples/03_selecting_filtering.ipynb
new file mode 100755
index 0000000..33dfb63
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/03_selecting_filtering.ipynb
@@ -0,0 +1,593 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 3: Selecting and Filtering Data\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Master column and row selection techniques\n",
+    "- Learn boolean indexing for data filtering\n",
+    "- Understand the difference between `.loc[]` and `.iloc[]`\n",
+    "- Practice complex filtering conditions\n",
+    "- Handle edge cases in data selection\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-2\n",
+    "- Understanding of Python boolean operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "# Create sample dataset\n",
+    "np.random.seed(42)\n",
+    "sales_data = {\n",
+    "    'Date': pd.date_range('2024-01-01', periods=20, freq='D'),\n",
+    "    'Product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Phone'] * 4,\n",
+    "    'Sales': [1200, 800, 600, 1100, 850, 1300, 750, 650, 1250, 900,\n",
+    "              1150, 820, 700, 1180, 880, 1220, 780, 620, 1300, 850],\n",
+    "    'Region': ['North', 'South', 'East', 'West', 'North'] * 4,\n",
+    "    'Salesperson': ['John', 'Sarah', 'Mike', 'Lisa', 'Tom'] * 4,\n",
+    "    'Commission_Rate': [0.10, 0.12, 0.08, 0.11, 0.09] * 4\n",
+    "}\n",
+    "\n",
+    "df_sales = pd.DataFrame(sales_data)\n",
+    "print(\"Dataset loaded:\")\n",
+    "print(df_sales.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Selecting Columns\n",
+    "\n",
+    "Different ways to select columns from a DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Single column (Product) - Returns Series:\n",
+      "Type: <class 'pandas.core.series.Series'>\n",
+      "0    Laptop\n",
+      "1     Phone\n",
+      "2    Tablet\n",
+      "3    Laptop\n",
+      "4     Phone\n",
+      "Name: Product, dtype: object\n",
+      "\n",
+      "Single column with dot notation:\n",
+      "0    Laptop\n",
+      "1     Phone\n",
+      "2    Tablet\n",
+      "3    Laptop\n",
+      "4     Phone\n",
+      "Name: Product, dtype: object\n",
+      "\n",
+      "Single column as DataFrame (note the double brackets):\n",
+      "Type: <class 'pandas.core.frame.DataFrame'>\n",
+      "  Product\n",
+      "0  Laptop\n",
+      "1   Phone\n",
+      "2  Tablet\n",
+      "3  Laptop\n",
+      "4   Phone\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Single column selection (returns Series)\n",
+    "print(\"Single column (Product) - Returns Series:\")\n",
+    "product_series = df_sales['Product']\n",
+    "print(f\"Type: {type(product_series)}\")\n",
+    "print(product_series.head())\n",
+    "\n",
+    "print(\"\\nSingle column with dot notation:\")\n",
+    "print(df_sales.Product.head())\n",
+    "\n",
+    "print(\"\\nSingle column as DataFrame (note the double brackets):\")\n",
+    "product_df = df_sales[['Product']]\n",
+    "print(f\"Type: {type(product_df)}\")\n",
+    "print(product_df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Multiple column selection\n",
+    "print(\"Multiple columns:\")\n",
+    "selected_cols = df_sales[['Product', 'Sales', 'Region']]\n",
+    "print(selected_cols.head())\n",
+    "\n",
+    "print(\"\\nUsing a list variable:\")\n",
+    "columns_to_select = ['Date', 'Salesperson', 'Sales']\n",
+    "selected_df = df_sales[columns_to_select]\n",
+    "print(selected_df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Column selection with conditions\n",
+    "print(\"Selecting columns by data type:\")\n",
+    "numeric_cols = df_sales.select_dtypes(include=[np.number])\n",
+    "print(\"Numeric columns:\")\n",
+    "print(numeric_cols.head())\n",
+    "\n",
+    "print(\"\\nSelecting columns by name pattern:\")\n",
+    "# Columns containing 'S'\n",
+    "s_columns = [col for col in df_sales.columns if 'S' in col]\n",
+    "print(f\"Columns with 'S': {s_columns}\")\n",
+    "print(df_sales[s_columns].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Selecting Rows\n",
+    "\n",
+    "Different methods to select specific rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Row selection by index position\n",
+    "print(\"First row (index 0):\")\n",
+    "print(df_sales.iloc[0])\n",
+    "\n",
+    "print(\"\\nRows 2 to 4 (positions 1, 2, 3):\")\n",
+    "print(df_sales.iloc[1:4])\n",
+    "\n",
+    "print(\"\\nLast 3 rows:\")\n",
+    "print(df_sales.iloc[-3:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Row selection by label/index\n",
+    "print(\"Using .loc with index labels:\")\n",
+    "print(df_sales.loc[0:2])  # Note: includes endpoint with .loc\n",
+    "\n",
+    "print(\"\\nSpecific rows by index:\")\n",
+    "specific_rows = df_sales.loc[[0, 5, 10, 15]]\n",
+    "print(specific_rows[['Product', 'Sales', 'Region']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Random sampling\n",
+    "print(\"Random sample of 5 rows:\")\n",
+    "random_sample = df_sales.sample(n=5, random_state=42)\n",
+    "print(random_sample[['Product', 'Sales', 'Salesperson']])\n",
+    "\n",
+    "print(\"\\nRandom 25% of the data:\")\n",
+    "percentage_sample = df_sales.sample(frac=0.25, random_state=42)\n",
+    "print(f\"Sample size: {len(percentage_sample)} rows\")\n",
+    "print(percentage_sample[['Product', 'Sales']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Boolean Indexing and Filtering\n",
+    "\n",
+    "Filter data based on conditions using boolean indexing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simple boolean conditions\n",
+    "print(\"Sales greater than 1000:\")\n",
+    "high_sales = df_sales[df_sales['Sales'] > 1000]\n",
+    "print(high_sales[['Product', 'Sales', 'Region']])\n",
+    "\n",
+    "print(\"\\nSpecific product filter:\")\n",
+    "laptops_only = df_sales[df_sales['Product'] == 'Laptop']\n",
+    "print(laptops_only[['Date', 'Sales', 'Salesperson']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Multiple conditions with AND (&)\n",
+    "print(\"Laptops with sales > 1100:\")\n",
+    "laptop_high_sales = df_sales[(df_sales['Product'] == 'Laptop') & (df_sales['Sales'] > 1100)]\n",
+    "print(laptop_high_sales[['Date', 'Product', 'Sales', 'Region']])\n",
+    "\n",
+    "print(\"\\nNorth region with commission rate >= 0.10:\")\n",
+    "north_high_commission = df_sales[(df_sales['Region'] == 'North') & (df_sales['Commission_Rate'] >= 0.10)]\n",
+    "print(north_high_commission[['Product', 'Sales', 'Commission_Rate', 'Salesperson']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Multiple conditions with OR (|)\n",
+    "print(\"Laptops OR high sales (>1200):\")\n",
+    "laptop_or_high = df_sales[(df_sales['Product'] == 'Laptop') | (df_sales['Sales'] > 1200)]\n",
+    "print(laptop_or_high[['Product', 'Sales', 'Region']])\n",
+    "\n",
+    "print(\"\\nNorth OR South regions:\")\n",
+    "north_or_south = df_sales[(df_sales['Region'] == 'North') | (df_sales['Region'] == 'South')]\n",
+    "print(north_or_south[['Product', 'Sales', 'Region']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using .isin() for multiple values\n",
+    "print(\"Products: Laptop or Phone\")\n",
+    "laptop_phone = df_sales[df_sales['Product'].isin(['Laptop', 'Phone'])]\n",
+    "print(laptop_phone[['Product', 'Sales', 'Region']].head())\n",
+    "\n",
+    "print(\"\\nSpecific salespersons:\")\n",
+    "selected_salespeople = df_sales[df_sales['Salesperson'].isin(['John', 'Sarah'])]\n",
+    "print(selected_salespeople[['Salesperson', 'Product', 'Sales']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOT conditions using ~\n",
+    "print(\"NOT Tablets:\")\n",
+    "not_tablets = df_sales[~(df_sales['Product'] == 'Tablet')]\n",
+    "print(not_tablets['Product'].value_counts())\n",
+    "\n",
+    "print(\"\\nNOT in North region:\")\n",
+    "not_north = df_sales[~df_sales['Region'].isin(['North'])]\n",
+    "print(not_north['Region'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Advanced Selection with .loc and .iloc\n",
+    "\n",
+    "Powerful selection methods for precise data access."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# .loc for label-based selection\n",
+    "print(\".loc examples - Label-based selection:\")\n",
+    "\n",
+    "# Select specific rows and columns\n",
+    "print(\"Rows 0-2, specific columns:\")\n",
+    "result = df_sales.loc[0:2, ['Product', 'Sales', 'Region']]\n",
+    "print(result)\n",
+    "\n",
+    "print(\"\\nAll rows, specific columns:\")\n",
+    "result = df_sales.loc[:, ['Product', 'Sales']]\n",
+    "print(result.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# .iloc for position-based selection\n",
+    "print(\".iloc examples - Position-based selection:\")\n",
+    "\n",
+    "# Select by position\n",
+    "print(\"First 3 rows, first 3 columns:\")\n",
+    "result = df_sales.iloc[0:3, 0:3]\n",
+    "print(result)\n",
+    "\n",
+    "print(\"\\nEvery other row, specific columns:\")\n",
+    "result = df_sales.iloc[::2, [1, 2, 3]]  # Every 2nd row, columns 1,2,3\n",
+    "print(result.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combining boolean indexing with .loc\n",
+    "print(\"Boolean indexing with .loc:\")\n",
+    "\n",
+    "# High sales, specific columns\n",
+    "high_sales_subset = df_sales.loc[df_sales['Sales'] > 1000, ['Product', 'Sales', 'Salesperson']]\n",
+    "print(high_sales_subset)\n",
+    "\n",
+    "print(\"\\nComplex condition with .loc:\")\n",
+    "complex_filter = (df_sales['Product'] == 'Laptop') & (df_sales['Region'] == 'North')\n",
+    "result = df_sales.loc[complex_filter, ['Date', 'Sales', 'Commission_Rate']]\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. String-based Filtering\n",
+    "\n",
+    "Filter data based on string patterns and conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# String methods for filtering\n",
+    "print(\"Salesperson names starting with 'J':\")\n",
+    "j_names = df_sales[df_sales['Salesperson'].str.startswith('J')]\n",
+    "print(j_names[['Salesperson', 'Product', 'Sales']].head())\n",
+    "\n",
+    "print(\"\\nRegions containing 'th':\")\n",
+    "th_regions = df_sales[df_sales['Region'].str.contains('th')]\n",
+    "print(th_regions[['Region', 'Product', 'Sales']].head())\n",
+    "\n",
+    "print(\"\\nProducts with exactly 5 characters:\")\n",
+    "five_char_products = df_sales[df_sales['Product'].str.len() == 5]\n",
+    "print(five_char_products['Product'].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Date-based Filtering\n",
+    "\n",
+    "Filter data based on date conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Date filtering\n",
+    "print(\"Data from first week of January 2024:\")\n",
+    "first_week = df_sales[df_sales['Date'] <= '2024-01-07']\n",
+    "print(first_week[['Date', 'Product', 'Sales']])\n",
+    "\n",
+    "print(\"\\nData from specific date range:\")\n",
+    "date_range = df_sales[(df_sales['Date'] >= '2024-01-10') & (df_sales['Date'] <= '2024-01-15')]\n",
+    "print(date_range[['Date', 'Product', 'Sales', 'Region']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using date components\n",
+    "print(\"Data from weekends (Saturday=5, Sunday=6):\")\n",
+    "weekends = df_sales[df_sales['Date'].dt.dayofweek >= 5]\n",
+    "print(weekends[['Date', 'Product', 'Sales']])\n",
+    "\n",
+    "print(\"\\nData from specific days of week:\")\n",
+    "mondays = df_sales[df_sales['Date'].dt.day_name() == 'Monday']\n",
+    "print(f\"Monday sales: {len(mondays)} records\")\n",
+    "if len(mondays) > 0:\n",
+    "    print(mondays[['Date', 'Product', 'Sales']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Query Method\n",
+    "\n",
+    "Alternative syntax for filtering using the `.query()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using .query() method for cleaner syntax\n",
+    "print(\"Using .query() method:\")\n",
+    "\n",
+    "# Simple condition\n",
+    "high_sales_query = df_sales.query('Sales > 1000')\n",
+    "print(f\"High sales records: {len(high_sales_query)}\")\n",
+    "print(high_sales_query[['Product', 'Sales', 'Region']].head())\n",
+    "\n",
+    "print(\"\\nMultiple conditions:\")\n",
+    "complex_query = df_sales.query('Product == \"Laptop\" and Region == \"North\"')\n",
+    "print(complex_query[['Date', 'Sales', 'Commission_Rate']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query with variables\n",
+    "min_sales = 900\n",
+    "target_region = 'East'\n",
+    "\n",
+    "print(\"Query with variables:\")\n",
+    "var_query = df_sales.query('Sales >= @min_sales and Region == @target_region')\n",
+    "print(var_query[['Product', 'Sales', 'Region']])\n",
+    "\n",
+    "print(\"\\nQuery with list (isin equivalent):\")\n",
+    "products = ['Laptop', 'Phone']\n",
+    "list_query = df_sales.query('Product in @products')\n",
+    "print(f\"Records for {products}: {len(list_query)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Test your filtering and selection skills:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Complex Filtering\n",
+    "# Find all sales where:\n",
+    "# - Product is either 'Laptop' or 'Phone'\n",
+    "# - Sales are above the median\n",
+    "# - Commission rate is at least 0.10\n",
+    "# Show only Date, Product, Sales, and Salesperson columns\n",
+    "\n",
+    "# Your code here:\n",
+    "median_sales = df_sales['Sales'].median()\n",
+    "print(f\"Median sales: {median_sales}\")\n",
+    "\n",
+    "# complex_filter = ?\n",
+    "# result = ?\n",
+    "# print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Date-based Analysis\n",
+    "# Find sales data for the second week of January 2024\n",
+    "# Calculate the average sales for that week\n",
+    "# Show which products were sold and by whom\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Performance Analysis\n",
+    "# Create a function that finds top performers:\n",
+    "# - Takes a DataFrame and a percentile (e.g., 0.8 for top 20%)\n",
+    "# - Returns salespeople whose average sales are in the top percentile\n",
+    "# - Show their average sales and total number of sales\n",
+    "\n",
+    "def find_top_performers(df, percentile=0.8):\n",
+    "    \"\"\"Find top performing salespeople\"\"\"\n",
+    "    # Your code here:\n",
+    "    pass\n",
+    "\n",
+    "# Test your function\n",
+    "# top_performers = find_top_performers(df_sales, 0.8)\n",
+    "# print(top_performers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Column Selection**: Use `[]` for single/multiple columns, understand Series vs DataFrame return types\n",
+    "2. **Row Selection**: `.iloc[]` for position-based, `.loc[]` for label-based selection\n",
+    "3. **Boolean Indexing**: Use `&` (AND), `|` (OR), `~` (NOT) for combining conditions\n",
+    "4. **Parentheses Matter**: Always wrap individual conditions in parentheses when combining\n",
+    "5. **`.isin()` Method**: Efficient way to filter for multiple values\n",
+    "6. **String Methods**: Use `.str` accessor for string-based filtering\n",
+    "7. **Date Filtering**: Leverage `.dt` accessor for date-based conditions\n",
+    "8. **`.query()` Method**: Alternative syntax for complex filtering\n",
+    "\n",
+    "## Common Mistakes to Avoid\n",
+    "\n",
+    "- Using `and/or` instead of `&/|` in boolean conditions\n",
+    "- Forgetting parentheses around conditions\n",
+    "- Confusing `.loc[]` and `.iloc[]` usage\n",
+    "- Not handling empty results from filtering\n",
+    "- Using chained indexing instead of `.loc[]`\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/04_grouping_aggregation.ipynb b/Session_01/PandasDataFrame-exmples/04_grouping_aggregation.ipynb
new file mode 100755
index 0000000..3f8ca5f
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/04_grouping_aggregation.ipynb
@@ -0,0 +1,1137 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 4: Grouping and Aggregation\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Master the `.groupby()` operation for data aggregation\n",
+    "- Learn different aggregation functions and methods\n",
+    "- Understand multi-level grouping and hierarchical indexing\n",
+    "- Practice custom aggregation functions\n",
+    "- Explore advanced grouping techniques\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-3\n",
+    "- Understanding of basic statistical concepts (mean, sum, count, etc.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset created:\n",
+      "Shape: (200, 11)\n",
+      "\n",
+      "First few rows:\n",
+      "        Date     Product     Category  Sales  Quantity Region Salesperson  \\\n",
+      "0 2024-01-01     Monitor  Accessories   1068         6   West       Diana   \n",
+      "1 2024-01-02  Headphones  Electronics    918         1   East       Alice   \n",
+      "2 2024-01-03      Tablet  Accessories   1133         5  North       Diana   \n",
+      "3 2024-01-04  Headphones  Electronics   1340         9   West         Bob   \n",
+      "4 2024-01-05  Headphones  Electronics   1150         2  North         Eve   \n",
+      "\n",
+      "   Commission_Rate  Commission  Month  Quarter  \n",
+      "0             0.15      160.20      1        1  \n",
+      "1             0.12      110.16      1        1  \n",
+      "2             0.08       90.64      1        1  \n",
+      "3             0.08      107.20      1        1  \n",
+      "4             0.12      138.00      1        1  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "# Create comprehensive sample dataset\n",
+    "np.random.seed(42)\n",
+    "n_records = 200\n",
+    "\n",
+    "sales_data = {\n",
+    "    'Date': pd.date_range('2024-01-01', periods=n_records, freq='D'),\n",
+    "    'Product': np.random.choice(['Laptop', 'Phone', 'Tablet', 'Monitor', 'Headphones'], n_records),\n",
+    "    'Category': np.random.choice(['Electronics', 'Accessories'], n_records, p=[0.8, 0.2]),\n",
+    "    'Sales': np.random.normal(1000, 300, n_records).astype(int),\n",
+    "    'Quantity': np.random.randint(1, 10, n_records),\n",
+    "    'Region': np.random.choice(['North', 'South', 'East', 'West'], n_records),\n",
+    "    'Salesperson': np.random.choice(['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank'], n_records),\n",
+    "    'Commission_Rate': np.random.choice([0.08, 0.10, 0.12, 0.15], n_records)\n",
+    "}\n",
+    "\n",
+    "df_sales = pd.DataFrame(sales_data)\n",
+    "df_sales['Sales'] = np.abs(df_sales['Sales'])  # Ensure positive values\n",
+    "df_sales['Commission'] = df_sales['Sales'] * df_sales['Commission_Rate']\n",
+    "df_sales['Month'] = df_sales['Date'].dt.month\n",
+    "df_sales['Quarter'] = df_sales['Date'].dt.quarter\n",
+    "\n",
+    "print(\"Dataset created:\")\n",
+    "print(f\"Shape: {df_sales.shape}\")\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "print(df_sales.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Basic GroupBy Operations\n",
+    "\n",
+    "Understanding the fundamentals of grouping data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total sales by product:\n",
+      "Product\n",
+      "Headphones    36032\n",
+      "Laptop        45296\n",
+      "Monitor       47419\n",
+      "Phone         36847\n",
+      "Tablet        34711\n",
+      "Name: Sales, dtype: int64\n",
+      "\n",
+      "Type: <class 'pandas.core.series.Series'>\n",
+      "\n",
+      "Average sales by region:\n",
+      "Region\n",
+      "East     1030.52\n",
+      "North    1007.14\n",
+      "South     966.86\n",
+      "West      999.78\n",
+      "Name: Sales, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Simple groupby with single aggregation\n",
+    "print(\"Total sales by product:\")\n",
+    "product_sales = df_sales.groupby('Product')['Sales'].sum()\n",
+    "print(product_sales)\n",
+    "print(f\"\\nType: {type(product_sales)}\")\n",
+    "\n",
+    "print(\"\\nAverage sales by region:\")\n",
+    "region_avg = df_sales.groupby('Region')['Sales'].mean().round(2)\n",
+    "print(region_avg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multiple statistics for sales by product:\n",
+      "            count    sum     mean     std\n",
+      "Product                                  \n",
+      "Headphones     36  36032  1000.89  298.06\n",
+      "Laptop         43  45296  1053.40  361.78\n",
+      "Monitor        49  47419   967.73  270.57\n",
+      "Phone          35  36847  1052.77  323.17\n",
+      "Tablet         37  34711   938.14  309.20\n",
+      "\n",
+      "With custom column names:\n",
+      "            Count  Total_Sales  Average_Sales  Std_Dev\n",
+      "Product                                               \n",
+      "Headphones     36        36032        1000.89   298.06\n",
+      "Laptop         43        45296        1053.40   361.78\n",
+      "Monitor        49        47419         967.73   270.57\n",
+      "Phone          35        36847        1052.77   323.17\n",
+      "Tablet         37        34711         938.14   309.20\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Multiple aggregations on the same column\n",
+    "print(\"Multiple statistics for sales by product:\")\n",
+    "product_stats = df_sales.groupby('Product')['Sales'].agg(['count', 'sum', 'mean', 'std']).round(2)\n",
+    "print(product_stats)\n",
+    "\n",
+    "print(\"\\nWith custom column names:\")\n",
+    "product_stats_named = df_sales.groupby('Product')['Sales'].agg([\n",
+    "    ('Count', 'count'),\n",
+    "    ('Total_Sales', 'sum'),\n",
+    "    ('Average_Sales', 'mean'),\n",
+    "    ('Std_Dev', 'std')\n",
+    "]).round(2)\n",
+    "print(product_stats_named)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aggregating multiple columns:\n",
+      "            Sales                Quantity       Commission        \n",
+      "              sum     mean count      sum  mean        sum    mean\n",
+      "Product                                                           \n",
+      "Headphones  36032  1000.89    36      178  4.94    4004.08  111.22\n",
+      "Laptop      45296  1053.40    43      219  5.09    5018.59  116.71\n",
+      "Monitor     47419   967.73    49      253  5.16    5078.17  103.64\n",
+      "Phone       36847  1052.77    35      162  4.63    4121.58  117.76\n",
+      "Tablet      34711   938.14    37      194  5.24    3699.82  100.00\n",
+      "\n",
+      "Flattened column names:\n",
+      "            Sales_sum  Sales_mean  Sales_count  Quantity_sum  Quantity_mean  \\\n",
+      "Product                                                                       \n",
+      "Headphones      36032     1000.89           36           178           4.94   \n",
+      "Laptop          45296     1053.40           43           219           5.09   \n",
+      "Monitor         47419      967.73           49           253           5.16   \n",
+      "Phone           36847     1052.77           35           162           4.63   \n",
+      "Tablet          34711      938.14           37           194           5.24   \n",
+      "\n",
+      "            Commission_sum  Commission_mean  \n",
+      "Product                                      \n",
+      "Headphones         4004.08           111.22  \n",
+      "Laptop             5018.59           116.71  \n",
+      "Monitor            5078.17           103.64  \n",
+      "Phone              4121.58           117.76  \n",
+      "Tablet             3699.82           100.00  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Groupby with multiple columns and aggregations\n",
+    "print(\"Aggregating multiple columns:\")\n",
+    "multi_agg = df_sales.groupby('Product').agg({\n",
+    "    'Sales': ['sum', 'mean', 'count'],\n",
+    "    'Quantity': ['sum', 'mean'],\n",
+    "    'Commission': ['sum', 'mean']\n",
+    "}).round(2)\n",
+    "print(multi_agg)\n",
+    "\n",
+    "print(\"\\nFlattened column names:\")\n",
+    "multi_agg_flat = df_sales.groupby('Product').agg({\n",
+    "    'Sales': ['sum', 'mean', 'count'],\n",
+    "    'Quantity': ['sum', 'mean'],\n",
+    "    'Commission': ['sum', 'mean']\n",
+    "}).round(2)\n",
+    "multi_agg_flat.columns = ['_'.join(col).strip() for col in multi_agg_flat.columns.values]\n",
+    "print(multi_agg_flat.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Multiple Group Columns\n",
+    "\n",
+    "Grouping by multiple categorical variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sales by Region and Product:\n",
+      "Region  Product   \n",
+      "East    Headphones     9791\n",
+      "        Laptop        17001\n",
+      "        Monitor       11728\n",
+      "        Phone          6514\n",
+      "        Tablet        10614\n",
+      "North   Headphones    11527\n",
+      "        Laptop         6514\n",
+      "        Monitor       11273\n",
+      "        Phone         13293\n",
+      "        Tablet         7750\n",
+      "South   Headphones     7131\n",
+      "        Laptop        13003\n",
+      "        Monitor       12007\n",
+      "        Phone         10115\n",
+      "        Tablet         7054\n",
+      "West    Headphones     7583\n",
+      "        Laptop         8778\n",
+      "        Monitor       12411\n",
+      "        Phone          6925\n",
+      "        Tablet         9293\n",
+      "Name: Sales, dtype: int64\n",
+      "\n",
+      "As DataFrame with reset_index():\n",
+      "  Region     Product  Sales\n",
+      "0   East  Headphones   9791\n",
+      "1   East      Laptop  17001\n",
+      "2   East     Monitor  11728\n",
+      "3   East       Phone   6514\n",
+      "4   East      Tablet  10614\n",
+      "5  North  Headphones  11527\n",
+      "6  North      Laptop   6514\n",
+      "7  North     Monitor  11273\n",
+      "8  North       Phone  13293\n",
+      "9  North      Tablet   7750\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Group by multiple columns\n",
+    "print(\"Sales by Region and Product:\")\n",
+    "region_product = df_sales.groupby(['Region', 'Product'])['Sales'].sum().round(2)\n",
+    "print(region_product)\n",
+    "\n",
+    "print(\"\\nAs DataFrame with reset_index():\")\n",
+    "region_product_df = df_sales.groupby(['Region', 'Product'])['Sales'].sum().reset_index()\n",
+    "print(region_product_df.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hierarchical indexing example:\n",
+      "First 15 entries:\n",
+      "Region  Product     Month\n",
+      "East    Headphones  1        2287\n",
+      "                    3        1194\n",
+      "                    4         985\n",
+      "                    5        2030\n",
+      "                    6         883\n",
+      "                    7        2412\n",
+      "        Laptop      1        1585\n",
+      "                    2        3151\n",
+      "                    3        4563\n",
+      "                    4        2966\n",
+      "                    5         919\n",
+      "                    6        2504\n",
+      "                    7        1313\n",
+      "        Monitor     1        4583\n",
+      "                    2         536\n",
+      "Name: Sales, dtype: int64\n",
+      "\n",
+      "Accessing specific groups:\n",
+      "North region, Laptop sales by month:\n",
+      "Month\n",
+      "2    1976\n",
+      "3    1141\n",
+      "4    1342\n",
+      "5      43\n",
+      "6     844\n",
+      "7    1168\n",
+      "Name: Sales, dtype: int64\n",
+      "\n",
+      "All North region sales:\n",
+      "Product     Month\n",
+      "Headphones  1        1769\n",
+      "            2        1080\n",
+      "            3        2884\n",
+      "            4        1460\n",
+      "            5        4334\n",
+      "Name: Sales, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Working with hierarchical index\n",
+    "print(\"Hierarchical indexing example:\")\n",
+    "hierarchy = df_sales.groupby(['Region', 'Product', 'Month'])['Sales'].sum()\n",
+    "print(\"First 15 entries:\")\n",
+    "print(hierarchy.head(15))\n",
+    "\n",
+    "print(\"\\nAccessing specific groups:\")\n",
+    "print(\"North region, Laptop sales by month:\")\n",
+    "try:\n",
+    "    north_laptops = hierarchy.loc[('North', 'Laptop')]\n",
+    "    print(north_laptops)\n",
+    "except KeyError:\n",
+    "    print(\"No data available for North region Laptops\")\n",
+    "\n",
+    "print(\"\\nAll North region sales:\")\n",
+    "try:\n",
+    "    north_all = hierarchy.loc['North']\n",
+    "    print(north_all.head())\n",
+    "except KeyError:\n",
+    "    print(\"No data available for North region\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unstacking hierarchical data:\n",
+      "Product  Headphones  Laptop  Monitor  Phone  Tablet\n",
+      "Region                                             \n",
+      "East           9791   17001    11728   6514   10614\n",
+      "North         11527    6514    11273  13293    7750\n",
+      "South          7131   13003    12007  10115    7054\n",
+      "West           7583    8778    12411   6925    9293\n",
+      "\n",
+      "Unstacking different levels:\n",
+      "Region       East  North  South   West\n",
+      "Product                               \n",
+      "Headphones   9791  11527   7131   7583\n",
+      "Laptop      17001   6514  13003   8778\n",
+      "Monitor     11728  11273  12007  12411\n",
+      "Phone        6514  13293  10115   6925\n",
+      "Tablet      10614   7750   7054   9293\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Unstacking hierarchical data\n",
+    "print(\"Unstacking hierarchical data:\")\n",
+    "region_product_pivot = df_sales.groupby(['Region', 'Product'])['Sales'].sum().unstack(fill_value=0)\n",
+    "print(region_product_pivot)\n",
+    "\n",
+    "print(\"\\nUnstacking different levels:\")\n",
+    "product_region_pivot = df_sales.groupby(['Product', 'Region'])['Sales'].sum().unstack(fill_value=0)\n",
+    "print(product_region_pivot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Common Aggregation Functions\n",
+    "\n",
+    "Explore the most useful aggregation functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Comprehensive statistics by salesperson:\n",
+      "             Count  Total     Mean  Median     Std  Min   Max     Q25      Q75\n",
+      "Salesperson                                                                   \n",
+      "Alice           35  33468   956.23   929.0  288.45  298  1588  841.00  1089.50\n",
+      "Bob             36  36427  1011.86  1050.0  314.32  230  1702  802.25  1196.25\n",
+      "Charlie         37  39529  1068.35  1070.0  329.60  539  1761  806.00  1313.00\n",
+      "Diana           29  28906   996.76  1068.0  325.84   43  1607  831.00  1179.00\n",
+      "Eve             34  35134  1033.35  1046.0  323.72  519  1976  775.00  1159.25\n",
+      "Frank           29  26841   925.55   904.0  296.00  381  1477  745.00  1145.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Comprehensive aggregation example\n",
+    "print(\"Comprehensive statistics by salesperson:\")\n",
+    "salesperson_stats = df_sales.groupby('Salesperson')['Sales'].agg([\n",
+    "    'count',      # Number of sales\n",
+    "    'sum',        # Total sales\n",
+    "    'mean',       # Average sale\n",
+    "    'median',     # Median sale\n",
+    "    'std',        # Standard deviation\n",
+    "    'min',        # Minimum sale\n",
+    "    'max',        # Maximum sale\n",
+    "    lambda x: x.quantile(0.25),  # 25th percentile\n",
+    "    lambda x: x.quantile(0.75)   # 75th percentile\n",
+    "]).round(2)\n",
+    "\n",
+    "# Rename lambda columns\n",
+    "salesperson_stats.columns = ['Count', 'Total', 'Mean', 'Median', 'Std', 'Min', 'Max', 'Q25', 'Q75']\n",
+    "print(salesperson_stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Monthly sales trends:\n",
+      "       Sales                Quantity Commission\n",
+      "         sum     mean count      sum        sum\n",
+      "Month                                          \n",
+      "1      31482  1015.55    31      157    3324.78\n",
+      "2      29854  1029.45    29      153    3437.00\n",
+      "3      28500   919.35    31      173    3242.74\n",
+      "4      27043   901.43    30      124    2973.03\n",
+      "5      31530  1017.10    31      166    3351.57\n",
+      "6      33686  1122.87    30      147    3770.67\n",
+      "7      18210  1011.67    18       86    1822.45\n",
+      "\n",
+      "Quarterly performance:\n",
+      "         Sales          Quantity Salesperson\n",
+      "           sum     mean      sum     nunique\n",
+      "Quarter                                     \n",
+      "1        89836   987.21      483           6\n",
+      "2        92259  1013.84      437           6\n",
+      "3        18210  1011.67       86           6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Date-based aggregations\n",
+    "print(\"Monthly sales trends:\")\n",
+    "monthly_sales = df_sales.groupby('Month').agg({\n",
+    "    'Sales': ['sum', 'mean', 'count'],\n",
+    "    'Quantity': 'sum',\n",
+    "    'Commission': 'sum'\n",
+    "}).round(2)\n",
+    "print(monthly_sales)\n",
+    "\n",
+    "print(\"\\nQuarterly performance:\")\n",
+    "quarterly_sales = df_sales.groupby('Quarter').agg({\n",
+    "    'Sales': ['sum', 'mean'],\n",
+    "    'Quantity': 'sum',\n",
+    "    'Salesperson': 'nunique'  # Number of unique salespeople\n",
+    "}).round(2)\n",
+    "print(quarterly_sales)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Custom Aggregation Functions\n",
+    "\n",
+    "Create your own aggregation functions for specific business logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Custom aggregations by product:\n",
+      "                Mean  Std_Dev  Range  High_Value_Count     CV\n",
+      "Product                                                      \n",
+      "Headphones  1000.889  298.055   1309                 8  0.298\n",
+      "Laptop      1053.395  361.778   1933                14  0.343\n",
+      "Monitor      967.735  270.570   1151                 8  0.280\n",
+      "Phone       1052.771  323.173   1321                11  0.307\n",
+      "Tablet       938.135  309.205   1314                 6  0.330\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Custom aggregation functions\n",
+    "def sales_range(series):\n",
+    "    \"\"\"Calculate the range of sales values\"\"\"\n",
+    "    return series.max() - series.min()\n",
+    "\n",
+    "def high_value_count(series, threshold=1200):\n",
+    "    \"\"\"Count sales above a threshold\"\"\"\n",
+    "    return (series > threshold).sum()\n",
+    "\n",
+    "def coefficient_of_variation(series):\n",
+    "    \"\"\"Calculate coefficient of variation (std/mean)\"\"\"\n",
+    "    return series.std() / series.mean() if series.mean() != 0 else 0\n",
+    "\n",
+    "print(\"Custom aggregations by product:\")\n",
+    "custom_agg = df_sales.groupby('Product')['Sales'].agg([\n",
+    "    'mean',\n",
+    "    'std',\n",
+    "    sales_range,\n",
+    "    high_value_count,\n",
+    "    coefficient_of_variation\n",
+    "]).round(3)\n",
+    "\n",
+    "custom_agg.columns = ['Mean', 'Std_Dev', 'Range', 'High_Value_Count', 'CV']\n",
+    "print(custom_agg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lambda function aggregations:\n",
+      "        Total   Average  Top_10_Percent  Above_Average_Count  \\\n",
+      "Region                                                         \n",
+      "East    55648  1030.519          1416.6                   25   \n",
+      "North   50357  1007.140          1342.1                   29   \n",
+      "South   49310   966.863          1346.0                   26   \n",
+      "West    44990   999.778          1469.6                   19   \n",
+      "\n",
+      "        Sales_Concentration  \n",
+      "Region                       \n",
+      "East                  0.134  \n",
+      "North                 0.159  \n",
+      "South                 0.160  \n",
+      "West                  0.171  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Lambda functions for quick custom aggregations\n",
+    "print(\"Lambda function aggregations:\")\n",
+    "lambda_agg = df_sales.groupby('Region')['Sales'].agg([\n",
+    "    ('Total', 'sum'),\n",
+    "    ('Average', 'mean'),\n",
+    "    ('Top_10_Percent', lambda x: x.quantile(0.9)),\n",
+    "    ('Above_Average_Count', lambda x: (x > x.mean()).sum()),\n",
+    "    ('Sales_Concentration', lambda x: x.nlargest(5).sum() / x.sum())  # Top 5 sales as % of total\n",
+    "]).round(3)\n",
+    "print(lambda_agg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Transform and Apply Operations\n",
+    "\n",
+    "Learn `.transform()` and `.apply()` for more complex group operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transform operations:\n",
+      "Sample with transform columns:\n",
+      "      Product  Sales  Product_Avg_Sales  Sales_vs_Product_Avg\n",
+      "0     Monitor   1068         967.734694            100.265306\n",
+      "1  Headphones    918        1000.888889            -82.888889\n",
+      "2      Tablet   1133         938.135135            194.864865\n",
+      "3  Headphones   1340        1000.888889            339.111111\n",
+      "4  Headphones   1150        1000.888889            149.111111\n",
+      "\n",
+      "Ranking within groups:\n",
+      "      Product  Sales  Sales_Rank_in_Product\n",
+      "0     Monitor   1068                   16.5\n",
+      "1  Headphones    918                   24.0\n",
+      "2      Tablet   1133                   12.0\n",
+      "3  Headphones   1340                    6.0\n",
+      "4  Headphones   1150                   12.0\n",
+      "5       Phone   1318                    7.5\n",
+      "6      Tablet    799                   24.0\n",
+      "7      Tablet    739                   27.0\n",
+      "8      Tablet    836                   22.0\n",
+      "9  Headphones    619                   32.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Transform operations - return same size as original\n",
+    "print(\"Transform operations:\")\n",
+    "\n",
+    "# Add group statistics as new columns\n",
+    "df_transformed = df_sales.copy()\n",
+    "df_transformed['Product_Avg_Sales'] = df_sales.groupby('Product')['Sales'].transform('mean')\n",
+    "df_transformed['Region_Total_Sales'] = df_sales.groupby('Region')['Sales'].transform('sum')\n",
+    "df_transformed['Sales_vs_Product_Avg'] = df_transformed['Sales'] - df_transformed['Product_Avg_Sales']\n",
+    "\n",
+    "print(\"Sample with transform columns:\")\n",
+    "print(df_transformed[['Product', 'Sales', 'Product_Avg_Sales', 'Sales_vs_Product_Avg']].head())\n",
+    "\n",
+    "print(\"\\nRanking within groups:\")\n",
+    "df_transformed['Sales_Rank_in_Product'] = df_sales.groupby('Product')['Sales'].rank(ascending=False)\n",
+    "print(df_transformed[['Product', 'Sales', 'Sales_Rank_in_Product']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Apply operations:\n",
+      "            total_sales  avg_sales  num_transactions top_salesperson  \\\n",
+      "Product                                                                \n",
+      "Headphones        36032    1000.89                36           Diana   \n",
+      "Laptop            45296    1053.40                43             Eve   \n",
+      "Monitor           47419     967.73                49           Alice   \n",
+      "Phone             36847    1052.77                35             Bob   \n",
+      "Tablet            34711     938.14                37             Bob   \n",
+      "\n",
+      "            sales_per_quantity  \n",
+      "Product                         \n",
+      "Headphones              375.94  \n",
+      "Laptop                  345.93  \n",
+      "Monitor                 257.01  \n",
+      "Phone                   373.06  \n",
+      "Tablet                  313.06  \n",
+      "\n",
+      "Top performing sale in each region:\n",
+      "           Product  Sales Salesperson\n",
+      "Region                               \n",
+      "East        Laptop   1585     Charlie\n",
+      "North       Laptop   1976         Eve\n",
+      "South       Laptop   1761     Charlie\n",
+      "West    Headphones   1607       Diana\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/cd/drz_5fvd69ddxy7rzw4p3zx80000gn/T/ipykernel_61733/3089764804.py:14: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  apply_result = df_sales.groupby('Product').apply(group_summary).round(2)\n",
+      "/var/folders/cd/drz_5fvd69ddxy7rzw4p3zx80000gn/T/ipykernel_61733/3089764804.py:18: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  top_sales_by_region = df_sales.groupby('Region').apply(lambda x: x.loc[x['Sales'].idxmax()])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Apply operations - can return different structures\n",
+    "print(\"Apply operations:\")\n",
+    "\n",
+    "def group_summary(group):\n",
+    "    \"\"\"Return a summary Series for each group\"\"\"\n",
+    "    return pd.Series({\n",
+    "        'total_sales': group['Sales'].sum(),\n",
+    "        'avg_sales': group['Sales'].mean(),\n",
+    "        'num_transactions': len(group),\n",
+    "        'top_salesperson': group.loc[group['Sales'].idxmax(), 'Salesperson'],\n",
+    "        'sales_per_quantity': (group['Sales'] / group['Quantity']).mean()\n",
+    "    })\n",
+    "\n",
+    "apply_result = df_sales.groupby('Product').apply(group_summary).round(2)\n",
+    "print(apply_result)\n",
+    "\n",
+    "print(\"\\nTop performing sale in each region:\")\n",
+    "top_sales_by_region = df_sales.groupby('Region').apply(lambda x: x.loc[x['Sales'].idxmax()])\n",
+    "print(top_sales_by_region[['Product', 'Sales', 'Salesperson']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Filtering Groups\n",
+    "\n",
+    "Filter entire groups based on group-level conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Groups with more than 30 transactions:\n",
+      "Original data: 200 rows\n",
+      "Filtered data: 200 rows\n",
+      "\n",
+      "Product transaction counts in filtered data:\n",
+      "Product\n",
+      "Monitor       49\n",
+      "Laptop        43\n",
+      "Tablet        37\n",
+      "Headphones    36\n",
+      "Phone         35\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Groups with average sales > $1000:\n",
+      "High-value products:\n",
+      "Product\n",
+      "Headphones    1000.89\n",
+      "Laptop        1053.40\n",
+      "Phone         1052.77\n",
+      "Name: Sales, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filter groups based on group characteristics\n",
+    "print(\"Groups with more than 30 transactions:\")\n",
+    "active_products = df_sales.groupby('Product').filter(lambda x: len(x) > 30)\n",
+    "print(f\"Original data: {len(df_sales)} rows\")\n",
+    "print(f\"Filtered data: {len(active_products)} rows\")\n",
+    "print(\"\\nProduct transaction counts in filtered data:\")\n",
+    "print(active_products['Product'].value_counts())\n",
+    "\n",
+    "print(\"\\nGroups with average sales > $1000:\")\n",
+    "high_value_products = df_sales.groupby('Product').filter(lambda x: x['Sales'].mean() > 1000)\n",
+    "print(\"High-value products:\")\n",
+    "print(high_value_products.groupby('Product')['Sales'].mean().round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Salespeople with consistent performance:\n",
+      "Consistent performers analysis:\n",
+      "             Count      Mean      Std     CV\n",
+      "Salesperson                                 \n",
+      "Alice           35   956.229  288.455  0.302\n",
+      "Bob             36  1011.861  314.322  0.311\n",
+      "Charlie         37  1068.351  329.602  0.309\n",
+      "Diana           29   996.759  325.836  0.327\n",
+      "Eve             34  1033.353  323.720  0.313\n",
+      "Frank           29   925.552  296.002  0.320\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Complex filtering conditions\n",
+    "print(\"Salespeople with consistent performance:\")\n",
+    "# Filter salespeople with at least 20 sales and CV < 0.5\n",
+    "consistent_performers = df_sales.groupby('Salesperson').filter(\n",
+    "    lambda x: len(x) >= 20 and (x['Sales'].std() / x['Sales'].mean()) < 0.5\n",
+    ")\n",
+    "\n",
+    "if len(consistent_performers) > 0:\n",
+    "    print(\"Consistent performers analysis:\")\n",
+    "    consistency_analysis = consistent_performers.groupby('Salesperson')['Sales'].agg([\n",
+    "        'count', 'mean', 'std', lambda x: x.std()/x.mean()\n",
+    "    ]).round(3)\n",
+    "    consistency_analysis.columns = ['Count', 'Mean', 'Std', 'CV']\n",
+    "    print(consistency_analysis)\n",
+    "else:\n",
+    "    print(\"No salespeople meet the consistency criteria\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Advanced Grouping Techniques\n",
+    "\n",
+    "More sophisticated grouping operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grouping by sales value ranges:\n",
+      "               Sales                  Quantity Commission\n",
+      "               count     mean     sum      sum        sum\n",
+      "Sales_Category                                           \n",
+      "Low                8   330.88    2647       54     272.61\n",
+      "Medium            92   788.87   72576      478    8110.68\n",
+      "High              89  1202.89  107057      423   11652.60\n",
+      "Very High         11  1638.64   18025       51    1886.35\n",
+      "\n",
+      "Product distribution across sales categories:\n",
+      "Product         Headphones  Laptop  Monitor  Phone  Tablet\n",
+      "Sales_Category                                            \n",
+      "Low                      1       2        1      2       2\n",
+      "Medium                  18      17       26     12      19\n",
+      "High                    16      21       21     17      14\n",
+      "Very High                1       3        1      4       2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/cd/drz_5fvd69ddxy7rzw4p3zx80000gn/T/ipykernel_61733/1556724418.py:8: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  sales_category_analysis = df_sales.groupby('Sales_Category').agg({\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Groupby with categorical cuts\n",
+    "print(\"Grouping by sales value ranges:\")\n",
+    "# Create sales categories\n",
+    "df_sales['Sales_Category'] = pd.cut(df_sales['Sales'], \n",
+    "                                   bins=[0, 500, 1000, 1500, float('inf')],\n",
+    "                                   labels=['Low', 'Medium', 'High', 'Very High'])\n",
+    "\n",
+    "sales_category_analysis = df_sales.groupby('Sales_Category').agg({\n",
+    "    'Sales': ['count', 'mean', 'sum'],\n",
+    "    'Quantity': 'sum',\n",
+    "    'Commission': 'sum'\n",
+    "}).round(2)\n",
+    "print(sales_category_analysis)\n",
+    "\n",
+    "print(\"\\nProduct distribution across sales categories:\")\n",
+    "category_product_cross = pd.crosstab(df_sales['Sales_Category'], df_sales['Product'])\n",
+    "print(category_product_cross)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Weekly sales analysis:\n",
+      "     Sales                    Product Salesperson\n",
+      "       sum     mean count    <lambda>     nunique\n",
+      "Week                                             \n",
+      "1     7726  1103.71     7  Headphones           4\n",
+      "2     6078   868.29     7      Tablet           4\n",
+      "3     7281  1040.14     7     Monitor           5\n",
+      "4     6867   981.00     7      Laptop           4\n",
+      "5     7285  1040.71     7     Monitor           4\n",
+      "6     7994  1142.00     7  Headphones           4\n",
+      "7     6652   950.29     7       Phone           3\n",
+      "8     7125  1017.86     7     Monitor           4\n",
+      "9     6293   899.00     7     Monitor           5\n",
+      "10    7755  1107.86     7       Phone           4\n",
+      "\n",
+      "Day of week analysis:\n",
+      "           count     mean    sum\n",
+      "DayOfWeek                       \n",
+      "Monday        29  1017.90  29519\n",
+      "Tuesday       29  1003.07  29089\n",
+      "Wednesday     29   956.72  27745\n",
+      "Thursday      29   963.62  27945\n",
+      "Friday        28  1136.71  31828\n",
+      "Saturday      28  1018.32  28513\n",
+      "Sunday        28   916.64  25666\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Time-based grouping\n",
+    "print(\"Weekly sales analysis:\")\n",
+    "df_sales['Week'] = df_sales['Date'].dt.isocalendar().week\n",
+    "weekly_analysis = df_sales.groupby('Week').agg({\n",
+    "    'Sales': ['sum', 'mean', 'count'],\n",
+    "    'Product': lambda x: x.mode().iloc[0] if not x.mode().empty else 'None',  # Most common product\n",
+    "    'Salesperson': 'nunique'\n",
+    "}).round(2)\n",
+    "print(weekly_analysis.head(10))\n",
+    "\n",
+    "print(\"\\nDay of week analysis:\")\n",
+    "df_sales['DayOfWeek'] = df_sales['Date'].dt.day_name()\n",
+    "day_analysis = df_sales.groupby('DayOfWeek')['Sales'].agg(['count', 'mean', 'sum']).round(2)\n",
+    "# Reorder by weekday\n",
+    "day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
+    "day_analysis = day_analysis.reindex([day for day in day_order if day in day_analysis.index])\n",
+    "print(day_analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Performance Considerations\n",
+    "\n",
+    "Tips for efficient groupby operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Large dataset size: 2000 rows\n",
+      "Multiple groupby calls: 0.0016 seconds\n",
+      "Single groupby with agg: 0.0007 seconds\n",
+      "Efficiency gain: 2.44x faster\n",
+      "\n",
+      "Results are equivalent: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Efficient groupby operations\n",
+    "import time\n",
+    "\n",
+    "# Create larger dataset for timing comparison\n",
+    "large_df = pd.concat([df_sales] * 10, ignore_index=True)\n",
+    "print(f\"Large dataset size: {len(large_df)} rows\")\n",
+    "\n",
+    "# Method 1: Multiple separate groupby calls (less efficient)\n",
+    "start_time = time.time()\n",
+    "result1_sum = large_df.groupby('Product')['Sales'].sum()\n",
+    "result1_mean = large_df.groupby('Product')['Sales'].mean()\n",
+    "result1_count = large_df.groupby('Product')['Sales'].count()\n",
+    "time1 = time.time() - start_time\n",
+    "\n",
+    "# Method 2: Single groupby with agg (more efficient)\n",
+    "start_time = time.time()\n",
+    "result2 = large_df.groupby('Product')['Sales'].agg(['sum', 'mean', 'count'])\n",
+    "time2 = time.time() - start_time\n",
+    "\n",
+    "print(f\"Multiple groupby calls: {time1:.4f} seconds\")\n",
+    "print(f\"Single groupby with agg: {time2:.4f} seconds\")\n",
+    "print(f\"Efficiency gain: {time1/time2:.2f}x faster\")\n",
+    "\n",
+    "# Verify results are the same\n",
+    "print(f\"\\nResults are equivalent: {result1_sum.equals(result2['sum'])}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply your grouping and aggregation skills:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Sales Performance Analysis\n",
+    "# Create a comprehensive sales performance report that includes:\n",
+    "# - Total and average sales by salesperson and region\n",
+    "# - Commission earned by each salesperson\n",
+    "# - Performance ranking within each region\n",
+    "# - Identify top and bottom performers\n",
+    "\n",
+    "# Your code here:\n",
+    "def sales_performance_report(df):\n",
+    "    \"\"\"Generate comprehensive sales performance report\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# sales_performance_report(df_sales)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Product Analysis\n",
+    "# Analyze product performance including:\n",
+    "# - Which products are most/least popular (by quantity and sales)\n",
+    "# - Seasonal trends for each product\n",
+    "# - Regional preferences for different products\n",
+    "# - Price consistency across regions\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Custom Business Metrics\n",
+    "# Create custom aggregation functions to calculate:\n",
+    "# - Customer acquisition cost (if you have marketing spend data)\n",
+    "# - Sales velocity (sales per day) for each product\n",
+    "# - Market share by region\n",
+    "# - Performance consistency score\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **GroupBy Basics**: `.groupby()` splits data into groups based on categorical variables\n",
+    "2. **Aggregation Functions**: Use built-in functions (`sum`, `mean`, `count`) or custom functions\n",
+    "3. **Multiple Aggregations**: Use `.agg()` with lists or dictionaries for multiple operations\n",
+    "4. **Hierarchical Indexing**: Multiple group columns create hierarchical indices\n",
+    "5. **Transform vs Apply**: `.transform()` preserves original size, `.apply()` can return different structures\n",
+    "6. **Filtering Groups**: Use `.filter()` to remove entire groups based on conditions\n",
+    "7. **Performance**: Single `.agg()` calls are more efficient than multiple `.groupby()` operations\n",
+    "\n",
+    "## Common Patterns\n",
+    "\n",
+    "```python\n",
+    "# Basic aggregation\n",
+    "df.groupby('column')['value'].sum()\n",
+    "\n",
+    "# Multiple aggregations\n",
+    "df.groupby('column')['value'].agg(['sum', 'mean', 'count'])\n",
+    "\n",
+    "# Multiple columns and aggregations\n",
+    "df.groupby('group_col').agg({\n",
+    "    'col1': ['sum', 'mean'],\n",
+    "    'col2': 'count'\n",
+    "})\n",
+    "\n",
+    "# Custom aggregation\n",
+    "df.groupby('column')['value'].agg(lambda x: x.max() - x.min())\n",
+    "\n",
+    "# Transform for group statistics\n",
+    "df['group_mean'] = df.groupby('group')['value'].transform('mean')\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/05_adding_modifying_columns.ipynb b/Session_01/PandasDataFrame-exmples/05_adding_modifying_columns.ipynb
new file mode 100755
index 0000000..a857628
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/05_adding_modifying_columns.ipynb
@@ -0,0 +1,733 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 5: Adding and Modifying Columns\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Learn different methods to add new columns to DataFrames\n",
+    "- Master conditional column creation using various techniques\n",
+    "- Understand how to modify existing columns\n",
+    "- Practice with calculated fields and derived columns\n",
+    "- Explore data type conversions and transformations\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-4\n",
+    "- Understanding of basic Python operations and functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "# Create sample dataset\n",
+    "np.random.seed(42)\n",
+    "n_records = 150\n",
+    "\n",
+    "sales_data = {\n",
+    "    'Date': pd.date_range('2024-01-01', periods=n_records, freq='D'),\n",
+    "    'Product': np.random.choice(['Laptop', 'Phone', 'Tablet', 'Monitor'], n_records),\n",
+    "    'Sales': np.random.normal(1000, 200, n_records).astype(int),\n",
+    "    'Quantity': np.random.randint(1, 8, n_records),\n",
+    "    'Region': np.random.choice(['North', 'South', 'East', 'West'], n_records),\n",
+    "    'Salesperson': np.random.choice(['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'], n_records),\n",
+    "    'Customer_Type': np.random.choice(['New', 'Returning', 'VIP'], n_records, p=[0.3, 0.6, 0.1])\n",
+    "}\n",
+    "\n",
+    "df_sales = pd.DataFrame(sales_data)\n",
+    "df_sales['Sales'] = np.abs(df_sales['Sales'])  # Ensure positive values\n",
+    "\n",
+    "print(\"Original dataset:\")\n",
+    "print(f\"Shape: {df_sales.shape}\")\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "print(df_sales.head())\n",
+    "print(\"\\nData types:\")\n",
+    "print(df_sales.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Basic Column Addition\n",
+    "\n",
+    "Simple methods to add new columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 1: Direct assignment\n",
+    "df_modified = df_sales.copy()\n",
+    "\n",
+    "# Add simple calculated columns\n",
+    "df_modified['Revenue'] = df_modified['Sales'] * df_modified['Quantity']\n",
+    "df_modified['Commission_10%'] = df_modified['Sales'] * 0.10\n",
+    "df_modified['Sales_per_Unit'] = df_modified['Sales'] / df_modified['Quantity']\n",
+    "\n",
+    "print(\"New calculated columns:\")\n",
+    "print(df_modified[['Sales', 'Quantity', 'Revenue', 'Commission_10%', 'Sales_per_Unit']].head())\n",
+    "\n",
+    "# Add constant value column\n",
+    "df_modified['Year'] = 2024\n",
+    "df_modified['Currency'] = 'USD'\n",
+    "df_modified['Department'] = 'Sales'\n",
+    "\n",
+    "print(\"\\nConstant value columns added:\")\n",
+    "print(df_modified[['Year', 'Currency', 'Department']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 2: Using assign() method (more functional approach)\n",
+    "df_assigned = df_sales.assign(\n",
+    "    Revenue=lambda x: x['Sales'] * x['Quantity'],\n",
+    "    Commission_Rate=0.08,\n",
+    "    Commission_Amount=lambda x: x['Sales'] * 0.08,\n",
+    "    Sales_Squared=lambda x: x['Sales'] ** 2,\n",
+    "    Is_High_Volume=lambda x: x['Quantity'] > 5\n",
+    ")\n",
+    "\n",
+    "print(\"Using assign() method:\")\n",
+    "print(df_assigned[['Sales', 'Quantity', 'Revenue', 'Commission_Amount', 'Is_High_Volume']].head())\n",
+    "\n",
+    "print(f\"\\nOriginal shape: {df_sales.shape}\")\n",
+    "print(f\"Modified shape: {df_assigned.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 3: Using insert() for specific positioning\n",
+    "df_insert = df_sales.copy()\n",
+    "\n",
+    "# Insert column at specific position (after 'Sales')\n",
+    "sales_index = df_insert.columns.get_loc('Sales')\n",
+    "df_insert.insert(sales_index + 1, 'Sales_Tax', df_insert['Sales'] * 0.08)\n",
+    "df_insert.insert(sales_index + 2, 'Total_with_Tax', df_insert['Sales'] + df_insert['Sales_Tax'])\n",
+    "\n",
+    "print(\"Using insert() for positioned columns:\")\n",
+    "print(df_insert[['Product', 'Sales', 'Sales_Tax', 'Total_with_Tax', 'Quantity']].head())\n",
+    "print(f\"\\nColumn order: {list(df_insert.columns)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Conditional Column Creation\n",
+    "\n",
+    "Create columns based on conditions and business logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 1: Using np.where() for simple conditions\n",
+    "df_conditional = df_sales.copy()\n",
+    "\n",
+    "# Simple binary conditions\n",
+    "df_conditional['High_Sales'] = np.where(df_conditional['Sales'] > 1000, 'Yes', 'No')\n",
+    "df_conditional['Weekend'] = np.where(df_conditional['Date'].dt.dayofweek >= 5, 'Weekend', 'Weekday')\n",
+    "df_conditional['Bulk_Order'] = np.where(df_conditional['Quantity'] >= 5, 'Bulk', 'Regular')\n",
+    "\n",
+    "print(\"Simple conditional columns:\")\n",
+    "print(df_conditional[['Sales', 'High_Sales', 'Date', 'Weekend', 'Quantity', 'Bulk_Order']].head())\n",
+    "\n",
+    "# Nested conditions\n",
+    "df_conditional['Sales_Category'] = np.where(df_conditional['Sales'] > 1200, 'High',\n",
+    "                                  np.where(df_conditional['Sales'] > 800, 'Medium', 'Low'))\n",
+    "\n",
+    "print(\"\\nNested conditions:\")\n",
+    "print(df_conditional[['Sales', 'Sales_Category']].head(10))\n",
+    "print(\"\\nCategory distribution:\")\n",
+    "print(df_conditional['Sales_Category'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 2: Using pd.cut() for binning numerical data\n",
+    "df_conditional['Sales_Tier'] = pd.cut(df_conditional['Sales'], \n",
+    "                                     bins=[0, 500, 800, 1200, float('inf')],\n",
+    "                                     labels=['Entry', 'Standard', 'Premium', 'Luxury'])\n",
+    "\n",
+    "print(\"Using pd.cut() for binning:\")\n",
+    "print(df_conditional[['Sales', 'Sales_Tier']].head(10))\n",
+    "print(\"\\nTier distribution:\")\n",
+    "print(df_conditional['Sales_Tier'].value_counts())\n",
+    "\n",
+    "# Using pd.qcut() for quantile-based binning\n",
+    "df_conditional['Sales_Quintile'] = pd.qcut(df_conditional['Sales'], \n",
+    "                                          q=5, \n",
+    "                                          labels=['Bottom 20%', 'Low 20%', 'Mid 20%', 'High 20%', 'Top 20%'])\n",
+    "\n",
+    "print(\"\\nUsing pd.qcut() for quantile binning:\")\n",
+    "print(df_conditional['Sales_Quintile'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 3: Using pandas.select() for multiple conditions\n",
+    "# Define conditions and choices\n",
+    "conditions = [\n",
+    "    (df_conditional['Sales'] >= 1200) & (df_conditional['Quantity'] >= 5),\n",
+    "    (df_conditional['Sales'] >= 1000) & (df_conditional['Customer_Type'] == 'VIP'),\n",
+    "    (df_conditional['Sales'] >= 800) & (df_conditional['Region'] == 'North'),\n",
+    "    df_conditional['Customer_Type'] == 'New'\n",
+    "]\n",
+    "\n",
+    "choices = ['Premium Deal', 'VIP Sale', 'North Preferred', 'New Customer']\n",
+    "default = 'Standard'\n",
+    "\n",
+    "df_conditional['Deal_Type'] = np.select(conditions, choices, default=default)\n",
+    "\n",
+    "print(\"Using np.select() for complex conditions:\")\n",
+    "print(df_conditional[['Sales', 'Quantity', 'Customer_Type', 'Region', 'Deal_Type']].head(10))\n",
+    "print(\"\\nDeal type distribution:\")\n",
+    "print(df_conditional['Deal_Type'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Using Apply and Lambda Functions\n",
+    "\n",
+    "Create complex calculated columns using custom functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simple lambda functions\n",
+    "df_apply = df_sales.copy()\n",
+    "\n",
+    "# Single column transformations\n",
+    "df_apply['Sales_Log'] = df_apply['Sales'].apply(lambda x: np.log(x))\n",
+    "df_apply['Product_Length'] = df_apply['Product'].apply(lambda x: len(x))\n",
+    "df_apply['Days_Since_Start'] = df_apply['Date'].apply(lambda x: (x - df_apply['Date'].min()).days)\n",
+    "\n",
+    "print(\"Simple lambda transformations:\")\n",
+    "print(df_apply[['Sales', 'Sales_Log', 'Product', 'Product_Length', 'Days_Since_Start']].head())\n",
+    "\n",
+    "# Multiple column operations using lambda\n",
+    "df_apply['Efficiency_Score'] = df_apply.apply(\n",
+    "    lambda row: (row['Sales'] * row['Quantity']) / (row['Days_Since_Start'] + 1), \n",
+    "    axis=1\n",
+    ")\n",
+    "\n",
+    "print(\"\\nMultiple column lambda:\")\n",
+    "print(df_apply[['Sales', 'Quantity', 'Days_Since_Start', 'Efficiency_Score']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Custom functions for complex business logic\n",
+    "def calculate_commission(row):\n",
+    "    \"\"\"Calculate commission based on complex business rules\"\"\"\n",
+    "    base_rate = 0.05\n",
+    "    \n",
+    "    # VIP customers get higher commission\n",
+    "    if row['Customer_Type'] == 'VIP':\n",
+    "        base_rate += 0.02\n",
+    "    \n",
+    "    # High quantity orders get bonus\n",
+    "    if row['Quantity'] >= 5:\n",
+    "        base_rate += 0.01\n",
+    "    \n",
+    "    # Regional multipliers\n",
+    "    region_multipliers = {'North': 1.2, 'South': 1.0, 'East': 1.1, 'West': 0.9}\n",
+    "    multiplier = region_multipliers.get(row['Region'], 1.0)\n",
+    "    \n",
+    "    return row['Sales'] * base_rate * multiplier\n",
+    "\n",
+    "def performance_rating(row):\n",
+    "    \"\"\"Calculate performance rating based on multiple factors\"\"\"\n",
+    "    score = 0\n",
+    "    \n",
+    "    # Sales performance (40% weight)\n",
+    "    if row['Sales'] > 1200:\n",
+    "        score += 40\n",
+    "    elif row['Sales'] > 800:\n",
+    "        score += 30\n",
+    "    else:\n",
+    "        score += 20\n",
+    "    \n",
+    "    # Quantity performance (30% weight)\n",
+    "    if row['Quantity'] >= 6:\n",
+    "        score += 30\n",
+    "    elif row['Quantity'] >= 4:\n",
+    "        score += 20\n",
+    "    else:\n",
+    "        score += 10\n",
+    "    \n",
+    "    # Customer type bonus (30% weight)\n",
+    "    customer_bonus = {'VIP': 30, 'Returning': 20, 'New': 15}\n",
+    "    score += customer_bonus.get(row['Customer_Type'], 0)\n",
+    "    \n",
+    "    # Convert to letter grade\n",
+    "    if score >= 85:\n",
+    "        return 'A'\n",
+    "    elif score >= 70:\n",
+    "        return 'B'\n",
+    "    elif score >= 55:\n",
+    "        return 'C'\n",
+    "    else:\n",
+    "        return 'D'\n",
+    "\n",
+    "# Apply custom functions\n",
+    "df_apply['Commission'] = df_apply.apply(calculate_commission, axis=1)\n",
+    "df_apply['Performance_Rating'] = df_apply.apply(performance_rating, axis=1)\n",
+    "\n",
+    "print(\"Custom function results:\")\n",
+    "print(df_apply[['Sales', 'Quantity', 'Customer_Type', 'Region', 'Commission', 'Performance_Rating']].head())\n",
+    "\n",
+    "print(\"\\nPerformance rating distribution:\")\n",
+    "print(df_apply['Performance_Rating'].value_counts().sort_index())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Date and Time Derived Columns\n",
+    "\n",
+    "Extract useful information from datetime columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract date components\n",
+    "df_dates = df_sales.copy()\n",
+    "\n",
+    "# Basic date components\n",
+    "df_dates['Year'] = df_dates['Date'].dt.year\n",
+    "df_dates['Month'] = df_dates['Date'].dt.month\n",
+    "df_dates['Day'] = df_dates['Date'].dt.day\n",
+    "df_dates['DayOfWeek'] = df_dates['Date'].dt.dayofweek  # 0=Monday, 6=Sunday\n",
+    "df_dates['DayName'] = df_dates['Date'].dt.day_name()\n",
+    "df_dates['MonthName'] = df_dates['Date'].dt.month_name()\n",
+    "\n",
+    "print(\"Basic date components:\")\n",
+    "print(df_dates[['Date', 'Year', 'Month', 'Day', 'DayOfWeek', 'DayName', 'MonthName']].head())\n",
+    "\n",
+    "# Business-relevant date features\n",
+    "df_dates['Quarter'] = df_dates['Date'].dt.quarter\n",
+    "df_dates['Week'] = df_dates['Date'].dt.isocalendar().week\n",
+    "df_dates['DayOfYear'] = df_dates['Date'].dt.dayofyear\n",
+    "df_dates['IsWeekend'] = df_dates['Date'].dt.dayofweek >= 5\n",
+    "df_dates['IsMonthStart'] = df_dates['Date'].dt.is_month_start\n",
+    "df_dates['IsMonthEnd'] = df_dates['Date'].dt.is_month_end\n",
+    "\n",
+    "print(\"\\nBusiness date features:\")\n",
+    "print(df_dates[['Date', 'Quarter', 'Week', 'IsWeekend', 'IsMonthStart', 'IsMonthEnd']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Time-based calculations\n",
+    "start_date = df_dates['Date'].min()\n",
+    "df_dates['Days_Since_Start'] = (df_dates['Date'] - start_date).dt.days\n",
+    "df_dates['Weeks_Since_Start'] = df_dates['Days_Since_Start'] // 7\n",
+    "\n",
+    "# Create season column\n",
+    "def get_season(month):\n",
+    "    if month in [12, 1, 2]:\n",
+    "        return 'Winter'\n",
+    "    elif month in [3, 4, 5]:\n",
+    "        return 'Spring'\n",
+    "    elif month in [6, 7, 8]:\n",
+    "        return 'Summer'\n",
+    "    else:\n",
+    "        return 'Fall'\n",
+    "\n",
+    "df_dates['Season'] = df_dates['Month'].apply(get_season)\n",
+    "\n",
+    "# Business day calculations\n",
+    "df_dates['IsBusinessDay'] = df_dates['Date'].dt.dayofweek < 5\n",
+    "df_dates['BusinessDaysSinceStart'] = df_dates.apply(\n",
+    "    lambda row: np.busday_count(start_date.date(), row['Date'].date()), axis=1\n",
+    ")\n",
+    "\n",
+    "print(\"Time-based calculations:\")\n",
+    "print(df_dates[['Date', 'Days_Since_Start', 'Weeks_Since_Start', 'Season', \n",
+    "               'IsBusinessDay', 'BusinessDaysSinceStart']].head())\n",
+    "\n",
+    "print(\"\\nSeason distribution:\")\n",
+    "print(df_dates['Season'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Text and String Manipulations\n",
+    "\n",
+    "Create columns based on string operations and text processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# String manipulations\n",
+    "df_text = df_sales.copy()\n",
+    "\n",
+    "# Basic string operations\n",
+    "df_text['Product_Upper'] = df_text['Product'].str.upper()\n",
+    "df_text['Product_Lower'] = df_text['Product'].str.lower()\n",
+    "df_text['Product_Length'] = df_text['Product'].str.len()\n",
+    "df_text['Product_First_Char'] = df_text['Product'].str[0]\n",
+    "df_text['Product_Last_Three'] = df_text['Product'].str[-3:]\n",
+    "\n",
+    "print(\"Basic string operations:\")\n",
+    "print(df_text[['Product', 'Product_Upper', 'Product_Lower', 'Product_Length', \n",
+    "              'Product_First_Char', 'Product_Last_Three']].head())\n",
+    "\n",
+    "# Text categorization\n",
+    "df_text['Product_Category'] = df_text['Product'].apply(lambda x: \n",
+    "    'Computer' if x in ['Laptop', 'Monitor'] else\n",
+    "    'Mobile' if x in ['Phone', 'Tablet'] else\n",
+    "    'Other'\n",
+    ")\n",
+    "\n",
+    "# Check for patterns\n",
+    "df_text['Has_Letter_A'] = df_text['Product'].str.contains('a', case=False)\n",
+    "df_text['Starts_With_L'] = df_text['Product'].str.startswith('L')\n",
+    "df_text['Ends_With_E'] = df_text['Product'].str.endswith('e')\n",
+    "\n",
+    "print(\"\\nText patterns and categorization:\")\n",
+    "print(df_text[['Product', 'Product_Category', 'Has_Letter_A', 'Starts_With_L', 'Ends_With_E']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create formatted text columns\n",
+    "df_text['Sales_Formatted'] = df_text['Sales'].apply(lambda x: f\"${x:,.2f}\")\n",
+    "df_text['Transaction_ID'] = df_text.apply(\n",
+    "    lambda row: f\"{row['Region'][:1]}{row['Product'][:3].upper()}{row.name:04d}\", axis=1\n",
+    ")\n",
+    "\n",
+    "# Create summary descriptions\n",
+    "df_text['Transaction_Summary'] = df_text.apply(\n",
+    "    lambda row: f\"{row['Salesperson']} sold {row['Quantity']} {row['Product']}(s) \"\n",
+    "                f\"for {row['Sales_Formatted']} in {row['Region']} region\", \n",
+    "    axis=1\n",
+    ")\n",
+    "\n",
+    "print(\"Formatted text columns:\")\n",
+    "print(df_text[['Sales_Formatted', 'Transaction_ID']].head())\n",
+    "print(\"\\nTransaction summaries:\")\n",
+    "for i, summary in enumerate(df_text['Transaction_Summary'].head(3)):\n",
+    "    print(f\"{i+1}. {summary}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Working with Categorical Data\n",
+    "\n",
+    "Optimize memory usage and enable category-specific operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert to categorical data types\n",
+    "df_categorical = df_sales.copy()\n",
+    "\n",
+    "# Check memory usage before\n",
+    "print(\"Memory usage before categorical conversion:\")\n",
+    "print(df_categorical.memory_usage(deep=True))\n",
+    "\n",
+    "# Convert string columns to categorical\n",
+    "categorical_columns = ['Product', 'Region', 'Salesperson', 'Customer_Type']\n",
+    "for col in categorical_columns:\n",
+    "    df_categorical[col] = df_categorical[col].astype('category')\n",
+    "\n",
+    "print(\"\\nMemory usage after categorical conversion:\")\n",
+    "print(df_categorical.memory_usage(deep=True))\n",
+    "\n",
+    "print(\"\\nData types after conversion:\")\n",
+    "print(df_categorical.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Working with ordered categories\n",
+    "# Create ordered categorical for sales performance\n",
+    "performance_categories = ['Poor', 'Fair', 'Good', 'Excellent']\n",
+    "df_categorical['Performance_Level'] = pd.cut(\n",
+    "    df_categorical['Sales'],\n",
+    "    bins=[0, 700, 900, 1200, float('inf')],\n",
+    "    labels=performance_categories,\n",
+    "    ordered=True\n",
+    ")\n",
+    "\n",
+    "print(\"Ordered categorical data:\")\n",
+    "print(df_categorical['Performance_Level'].head(10))\n",
+    "print(\"\\nCategory info:\")\n",
+    "print(df_categorical['Performance_Level'].cat.categories)\n",
+    "print(f\"Is ordered: {df_categorical['Performance_Level'].cat.ordered}\")\n",
+    "\n",
+    "# Categorical operations\n",
+    "print(\"\\nPerformance level distribution:\")\n",
+    "print(df_categorical['Performance_Level'].value_counts().sort_index())\n",
+    "\n",
+    "# Add new category\n",
+    "df_categorical['Performance_Level'] = df_categorical['Performance_Level'].cat.add_categories(['Outstanding'])\n",
+    "print(f\"\\nCategories after adding 'Outstanding': {df_categorical['Performance_Level'].cat.categories}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Mathematical and Statistical Transformations\n",
+    "\n",
+    "Create columns using mathematical functions and statistical transformations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mathematical transformations\n",
+    "df_math = df_sales.copy()\n",
+    "\n",
+    "# Common mathematical transformations\n",
+    "df_math['Sales_Log'] = np.log(df_math['Sales'])\n",
+    "df_math['Sales_Sqrt'] = np.sqrt(df_math['Sales'])\n",
+    "df_math['Sales_Squared'] = df_math['Sales'] ** 2\n",
+    "df_math['Sales_Reciprocal'] = 1 / df_math['Sales']\n",
+    "\n",
+    "print(\"Mathematical transformations:\")\n",
+    "print(df_math[['Sales', 'Sales_Log', 'Sales_Sqrt', 'Sales_Squared', 'Sales_Reciprocal']].head())\n",
+    "\n",
+    "# Statistical standardization\n",
+    "df_math['Sales_Z_Score'] = (df_math['Sales'] - df_math['Sales'].mean()) / df_math['Sales'].std()\n",
+    "df_math['Sales_Min_Max_Scaled'] = (df_math['Sales'] - df_math['Sales'].min()) / (df_math['Sales'].max() - df_math['Sales'].min())\n",
+    "\n",
+    "# Rolling statistics\n",
+    "df_math = df_math.sort_values('Date')\n",
+    "df_math['Sales_Rolling_7_Mean'] = df_math['Sales'].rolling(window=7, min_periods=1).mean()\n",
+    "df_math['Sales_Rolling_7_Std'] = df_math['Sales'].rolling(window=7, min_periods=1).std()\n",
+    "df_math['Sales_Cumulative_Sum'] = df_math['Sales'].cumsum()\n",
+    "\n",
+    "print(\"\\nStatistical transformations:\")\n",
+    "print(df_math[['Sales', 'Sales_Z_Score', 'Sales_Min_Max_Scaled', \n",
+    "              'Sales_Rolling_7_Mean', 'Sales_Cumulative_Sum']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rank and percentile columns\n",
+    "df_math['Sales_Rank'] = df_math['Sales'].rank(ascending=False)\n",
+    "df_math['Sales_Percentile'] = df_math['Sales'].rank(pct=True) * 100\n",
+    "df_math['Sales_Rank_by_Region'] = df_math.groupby('Region')['Sales'].rank(ascending=False)\n",
+    "\n",
+    "# Binning and discretization\n",
+    "df_math['Sales_Decile'] = pd.qcut(df_math['Sales'], q=10, labels=range(1, 11))\n",
+    "df_math['Sales_Tertile'] = pd.qcut(df_math['Sales'], q=3, labels=['Low', 'Medium', 'High'])\n",
+    "\n",
+    "print(\"Ranking and binning:\")\n",
+    "print(df_math[['Sales', 'Sales_Rank', 'Sales_Percentile', 'Sales_Rank_by_Region', \n",
+    "              'Sales_Decile', 'Sales_Tertile']].head(10))\n",
+    "\n",
+    "print(\"\\nDecile distribution:\")\n",
+    "print(df_math['Sales_Decile'].value_counts().sort_index())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply your column creation and modification skills:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Customer Segmentation\n",
+    "# Create a comprehensive customer segmentation system:\n",
+    "# - Combine purchase behavior, frequency, and value\n",
+    "# - Create RFM-like scores (Recency, Frequency, Monetary)\n",
+    "# - Assign customer segments (e.g., Champion, Loyal, At Risk, etc.)\n",
+    "\n",
+    "def create_customer_segmentation(df):\n",
+    "    \"\"\"Create customer segmentation based on purchase patterns\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# segmented_df = create_customer_segmentation(df_sales)\n",
+    "# print(segmented_df[['Customer_Type', 'Sales', 'Frequency_Score', 'Monetary_Score', 'Segment']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Performance Metrics Dashboard\n",
+    "# Create a comprehensive set of KPI columns:\n",
+    "# - Sales efficiency metrics\n",
+    "# - Trend indicators (growth rates, momentum)\n",
+    "# - Comparative metrics (vs. average, vs. target)\n",
+    "# - Alert flags for unusual patterns\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Feature Engineering for ML\n",
+    "# Create features that could be useful for machine learning:\n",
+    "# - Interaction features (product of two variables)\n",
+    "# - Polynomial features\n",
+    "# - Time-based features (seasonality, trends)\n",
+    "# - Lag features (previous period values)\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Column Assignment**: Use direct assignment (`df['col'] = value`) for simple cases\n",
+    "2. **Assign Method**: Use `.assign()` for functional programming style and method chaining\n",
+    "3. **Conditional Logic**: Combine `np.where()`, `pd.cut()`, `pd.qcut()`, and `np.select()` for complex conditions\n",
+    "4. **Apply Functions**: Use `.apply()` with lambda or custom functions for complex transformations\n",
+    "5. **Date Features**: Extract meaningful components from datetime columns\n",
+    "6. **String Operations**: Leverage `.str` accessor for text manipulations\n",
+    "7. **Categorical Data**: Convert to categories for memory efficiency and special operations\n",
+    "8. **Mathematical Transformations**: Apply statistical and mathematical functions for data preprocessing\n",
+    "\n",
+    "## Performance Tips\n",
+    "\n",
+    "1. **Vectorized Operations**: Prefer pandas/numpy operations over loops\n",
+    "2. **Categorical Types**: Use categorical data for repeated string values\n",
+    "3. **Memory Management**: Monitor memory usage when creating many new columns\n",
+    "4. **Method Chaining**: Use `.assign()` for readable method chains\n",
+    "5. **Avoid apply() When Possible**: Use vectorized operations instead of `.apply()` for better performance\n",
+    "\n",
+    "## Common Patterns\n",
+    "\n",
+    "```python\n",
+    "# Simple calculation\n",
+    "df['new_col'] = df['col1'] * df['col2']\n",
+    "\n",
+    "# Conditional column\n",
+    "df['category'] = np.where(df['value'] > threshold, 'High', 'Low')\n",
+    "\n",
+    "# Apply custom function\n",
+    "df['result'] = df.apply(custom_function, axis=1)\n",
+    "\n",
+    "# Date features\n",
+    "df['month'] = df['date'].dt.month\n",
+    "\n",
+    "# String operations\n",
+    "df['upper'] = df['text'].str.upper()\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/06_handling_missing_data.ipynb b/Session_01/PandasDataFrame-exmples/06_handling_missing_data.ipynb
new file mode 100755
index 0000000..180a3c8
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/06_handling_missing_data.ipynb
@@ -0,0 +1,916 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 6: Handling Missing Data\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Understand different types of missing data and their implications\n",
+    "- Master techniques for detecting and analyzing missing values\n",
+    "- Learn various strategies for handling missing data\n",
+    "- Practice imputation methods and their trade-offs\n",
+    "- Develop best practices for missing data management\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-5\n",
+    "- Understanding of basic statistical concepts\n",
+    "- Familiarity with data quality principles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from datetime import datetime, timedelta\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set display options\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "plt.style.use('seaborn-v0_8')\n",
+    "\n",
+    "print(\"Libraries loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Dataset with Missing Values\n",
+    "\n",
+    "Let's create a realistic dataset with different patterns of missing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create comprehensive dataset with various missing data patterns\n",
+    "np.random.seed(42)\n",
+    "n_records = 500\n",
+    "\n",
+    "# Base data\n",
+    "data = {\n",
+    "    'customer_id': range(1, n_records + 1),\n",
+    "    'age': np.random.normal(35, 12, n_records).astype(int),\n",
+    "    'income': np.random.normal(50000, 15000, n_records),\n",
+    "    'education_years': np.random.normal(14, 3, n_records),\n",
+    "    'purchase_amount': np.random.normal(200, 50, n_records),\n",
+    "    'satisfaction_score': np.random.randint(1, 6, n_records),\n",
+    "    'region': np.random.choice(['North', 'South', 'East', 'West'], n_records),\n",
+    "    'product_category': np.random.choice(['Electronics', 'Clothing', 'Books', 'Home'], n_records),\n",
+    "    'signup_date': pd.date_range('2023-01-01', periods=n_records, freq='D'),\n",
+    "    'last_purchase_date': pd.date_range('2023-01-01', periods=n_records, freq='D') + pd.Timedelta(days=30)\n",
+    "}\n",
+    "\n",
+    "df_complete = pd.DataFrame(data)\n",
+    "\n",
+    "# Ensure positive values where appropriate\n",
+    "df_complete['age'] = np.abs(df_complete['age'])\n",
+    "df_complete['income'] = np.abs(df_complete['income'])\n",
+    "df_complete['education_years'] = np.clip(df_complete['education_years'], 6, 20)\n",
+    "df_complete['purchase_amount'] = np.abs(df_complete['purchase_amount'])\n",
+    "\n",
+    "print(\"Complete dataset created:\")\n",
+    "print(f\"Shape: {df_complete.shape}\")\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "print(df_complete.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Introduce different patterns of missing data\n",
+    "df_missing = df_complete.copy()\n",
+    "\n",
+    "# 1. Missing Completely at Random (MCAR) - income data\n",
+    "# Randomly missing 15% of income values\n",
+    "mcar_indices = np.random.choice(df_missing.index, size=int(0.15 * len(df_missing)), replace=False)\n",
+    "df_missing.loc[mcar_indices, 'income'] = np.nan\n",
+    "\n",
+    "# 2. Missing at Random (MAR) - education years missing based on age\n",
+    "# Older people less likely to report education\n",
+    "older_customers = df_missing['age'] > 60\n",
+    "older_indices = df_missing[older_customers].index\n",
+    "education_missing = np.random.choice(older_indices, size=int(0.4 * len(older_indices)), replace=False)\n",
+    "df_missing.loc[education_missing, 'education_years'] = np.nan\n",
+    "\n",
+    "# 3. Missing Not at Random (MNAR) - satisfaction scores\n",
+    "# Unsatisfied customers less likely to provide ratings\n",
+    "low_satisfaction = df_missing['satisfaction_score'] <= 2\n",
+    "low_sat_indices = df_missing[low_satisfaction].index\n",
+    "satisfaction_missing = np.random.choice(low_sat_indices, size=int(0.6 * len(low_sat_indices)), replace=False)\n",
+    "df_missing.loc[satisfaction_missing, 'satisfaction_score'] = np.nan\n",
+    "\n",
+    "# 4. Systematic missing - last purchase date for new customers\n",
+    "# New customers (signed up recently) haven't made purchases yet\n",
+    "recent_signups = df_missing['signup_date'] > '2023-11-01'\n",
+    "df_missing.loc[recent_signups, 'last_purchase_date'] = pd.NaT\n",
+    "\n",
+    "# 5. Random missing in other columns\n",
+    "# Purchase amount - 10% missing\n",
+    "purchase_missing = np.random.choice(df_missing.index, size=int(0.10 * len(df_missing)), replace=False)\n",
+    "df_missing.loc[purchase_missing, 'purchase_amount'] = np.nan\n",
+    "\n",
+    "print(\"Missing data patterns introduced:\")\n",
+    "print(f\"Dataset shape: {df_missing.shape}\")\n",
+    "print(\"\\nMissing value counts:\")\n",
+    "missing_summary = df_missing.isnull().sum()\n",
+    "missing_summary = missing_summary[missing_summary > 0]\n",
+    "print(missing_summary)\n",
+    "\n",
+    "print(\"\\nMissing value percentages:\")\n",
+    "missing_pct = (df_missing.isnull().sum() / len(df_missing) * 100).round(2)\n",
+    "missing_pct = missing_pct[missing_pct > 0]\n",
+    "print(missing_pct)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Detecting and Analyzing Missing Data\n",
+    "\n",
+    "Comprehensive techniques for understanding missing data patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def analyze_missing_data(df):\n",
+    "    \"\"\"Comprehensive missing data analysis\"\"\"\n",
+    "    print(\"=== MISSING DATA ANALYSIS ===\")\n",
+    "    \n",
+    "    # Basic missing data statistics\n",
+    "    total_cells = df.size\n",
+    "    total_missing = df.isnull().sum().sum()\n",
+    "    print(f\"Total cells: {total_cells:,}\")\n",
+    "    print(f\"Missing cells: {total_missing:,} ({total_missing/total_cells*100:.2f}%)\")\n",
+    "    \n",
+    "    # Missing data by column\n",
+    "    missing_by_column = pd.DataFrame({\n",
+    "        'Missing_Count': df.isnull().sum(),\n",
+    "        'Missing_Percentage': (df.isnull().sum() / len(df)) * 100,\n",
+    "        'Data_Type': df.dtypes\n",
+    "    })\n",
+    "    missing_by_column = missing_by_column[missing_by_column['Missing_Count'] > 0]\n",
+    "    missing_by_column = missing_by_column.sort_values('Missing_Percentage', ascending=False)\n",
+    "    \n",
+    "    print(\"\\n--- Missing Data by Column ---\")\n",
+    "    print(missing_by_column.round(2))\n",
+    "    \n",
+    "    # Missing data patterns\n",
+    "    print(\"\\n--- Missing Data Patterns ---\")\n",
+    "    missing_patterns = df.isnull().value_counts().head(10)\n",
+    "    print(\"Top 10 missing patterns (True = Missing):\")\n",
+    "    for pattern, count in missing_patterns.items():\n",
+    "        percentage = (count / len(df)) * 100\n",
+    "        print(f\"{count:4d} rows ({percentage:5.1f}%): {dict(zip(df.columns, pattern))}\")\n",
+    "    \n",
+    "    return missing_by_column\n",
+    "\n",
+    "# Analyze missing data\n",
+    "missing_analysis = analyze_missing_data(df_missing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualize missing data patterns\n",
+    "def visualize_missing_data(df):\n",
+    "    \"\"\"Create visualizations for missing data patterns\"\"\"\n",
+    "    fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
+    "    \n",
+    "    # 1. Missing data heatmap\n",
+    "    missing_mask = df.isnull()\n",
+    "    sns.heatmap(missing_mask.iloc[:100], \n",
+    "                yticklabels=False, \n",
+    "                cbar=True, \n",
+    "                cmap='viridis',\n",
+    "                ax=axes[0, 0])\n",
+    "    axes[0, 0].set_title('Missing Data Heatmap (First 100 rows)')\n",
+    "    \n",
+    "    # 2. Missing data by column\n",
+    "    missing_counts = df.isnull().sum()\n",
+    "    missing_counts = missing_counts[missing_counts > 0]\n",
+    "    missing_counts.plot(kind='bar', ax=axes[0, 1], color='skyblue')\n",
+    "    axes[0, 1].set_title('Missing Values by Column')\n",
+    "    axes[0, 1].set_ylabel('Count')\n",
+    "    axes[0, 1].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "    # 3. Missing data correlation\n",
+    "    missing_corr = df.isnull().corr()\n",
+    "    sns.heatmap(missing_corr, annot=True, cmap='coolwarm', center=0, ax=axes[1, 0])\n",
+    "    axes[1, 0].set_title('Missing Data Correlation')\n",
+    "    \n",
+    "    # 4. Missing data by row\n",
+    "    missing_per_row = df.isnull().sum(axis=1)\n",
+    "    missing_per_row.hist(bins=range(len(df.columns) + 2), ax=axes[1, 1], alpha=0.7, color='orange')\n",
+    "    axes[1, 1].set_title('Distribution of Missing Values per Row')\n",
+    "    axes[1, 1].set_xlabel('Number of Missing Values')\n",
+    "    axes[1, 1].set_ylabel('Number of Rows')\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "# Visualize missing patterns\n",
+    "visualize_missing_data(df_missing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Analyze missing data relationships\n",
+    "def analyze_missing_relationships(df):\n",
+    "    \"\"\"Analyze relationships between missing data and other variables\"\"\"\n",
+    "    print(\"=== MISSING DATA RELATIONSHIPS ===\")\n",
+    "    \n",
+    "    # Example: Relationship between age and missing education\n",
+    "    if 'age' in df.columns and 'education_years' in df.columns:\n",
+    "        print(\"\\n--- Age vs Missing Education ---\")\n",
+    "        education_missing = df['education_years'].isnull()\n",
+    "        age_stats = df.groupby(education_missing)['age'].agg(['mean', 'median', 'std']).round(2)\n",
+    "        age_stats.index = ['Education Present', 'Education Missing']\n",
+    "        print(age_stats)\n",
+    "    \n",
+    "    # Example: Missing satisfaction by purchase amount\n",
+    "    if 'satisfaction_score' in df.columns and 'purchase_amount' in df.columns:\n",
+    "        print(\"\\n--- Purchase Amount vs Missing Satisfaction ---\")\n",
+    "        satisfaction_missing = df['satisfaction_score'].isnull()\n",
+    "        purchase_stats = df.groupby(satisfaction_missing)['purchase_amount'].agg(['mean', 'median', 'count']).round(2)\n",
+    "        purchase_stats.index = ['Satisfaction Present', 'Satisfaction Missing']\n",
+    "        print(purchase_stats)\n",
+    "    \n",
+    "    # Missing data by categorical variables\n",
+    "    if 'region' in df.columns:\n",
+    "        print(\"\\n--- Missing Data by Region ---\")\n",
+    "        region_missing = df.groupby('region').apply(lambda x: x.isnull().sum())\n",
+    "        print(region_missing[region_missing.sum(axis=1) > 0])\n",
+    "\n",
+    "# Analyze relationships\n",
+    "analyze_missing_relationships(df_missing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Basic Missing Data Handling\n",
+    "\n",
+    "Fundamental techniques for dealing with missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 1: Dropping missing values\n",
+    "print(\"=== DROPPING MISSING VALUES ===\")\n",
+    "\n",
+    "# Drop rows with any missing values\n",
+    "df_drop_any = df_missing.dropna()\n",
+    "print(f\"Original shape: {df_missing.shape}\")\n",
+    "print(f\"After dropping any missing: {df_drop_any.shape}\")\n",
+    "print(f\"Rows removed: {len(df_missing) - len(df_drop_any)} ({(len(df_missing) - len(df_drop_any))/len(df_missing)*100:.1f}%)\")\n",
+    "\n",
+    "# Drop rows with missing values in specific columns\n",
+    "critical_columns = ['customer_id', 'age', 'region']\n",
+    "df_drop_critical = df_missing.dropna(subset=critical_columns)\n",
+    "print(f\"\\nAfter dropping rows missing critical columns: {df_drop_critical.shape}\")\n",
+    "\n",
+    "# Drop rows with more than X missing values\n",
+    "df_drop_thresh = df_missing.dropna(thresh=len(df_missing.columns) - 2)  # Allow max 2 missing\n",
+    "print(f\"After dropping rows with >2 missing values: {df_drop_thresh.shape}\")\n",
+    "\n",
+    "# Drop columns with too many missing values\n",
+    "missing_threshold = 0.5  # 50%\n",
+    "cols_to_keep = df_missing.columns[df_missing.isnull().mean() < missing_threshold]\n",
+    "df_drop_cols = df_missing[cols_to_keep]\n",
+    "print(f\"\\nAfter dropping columns with >{missing_threshold*100}% missing: {df_drop_cols.shape}\")\n",
+    "print(f\"Columns dropped: {set(df_missing.columns) - set(cols_to_keep)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Method 2: Basic imputation with fillna()\n",
+    "print(\"=== BASIC IMPUTATION ===\")\n",
+    "\n",
+    "df_basic_impute = df_missing.copy()\n",
+    "\n",
+    "# Fill with specific values\n",
+    "df_basic_impute['satisfaction_score'] = df_basic_impute['satisfaction_score'].fillna(3)  # Neutral score\n",
+    "print(\"Filled satisfaction_score with 3 (neutral)\")\n",
+    "\n",
+    "# Fill with statistical measures\n",
+    "df_basic_impute['income'] = df_basic_impute['income'].fillna(df_basic_impute['income'].median())\n",
+    "df_basic_impute['education_years'] = df_basic_impute['education_years'].fillna(df_basic_impute['education_years'].mean())\n",
+    "df_basic_impute['purchase_amount'] = df_basic_impute['purchase_amount'].fillna(df_basic_impute['purchase_amount'].mean())\n",
+    "print(\"Filled numerical columns with mean/median\")\n",
+    "\n",
+    "# Forward fill and backward fill for dates\n",
+    "df_basic_impute['last_purchase_date'] = df_basic_impute['last_purchase_date'].fillna(method='bfill')\n",
+    "print(\"Filled dates with backward fill\")\n",
+    "\n",
+    "print(f\"\\nMissing values after basic imputation:\")\n",
+    "print(df_basic_impute.isnull().sum().sum())\n",
+    "\n",
+    "# Show before/after comparison\n",
+    "print(\"\\nComparison (first 10 rows):\")\n",
+    "comparison_cols = ['income', 'education_years', 'purchase_amount', 'satisfaction_score']\n",
+    "for col in comparison_cols:\n",
+    "    before_missing = df_missing[col].isnull().sum()\n",
+    "    after_missing = df_basic_impute[col].isnull().sum()\n",
+    "    print(f\"{col}: {before_missing} → {after_missing} missing values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Advanced Imputation Techniques\n",
+    "\n",
+    "Sophisticated methods for handling missing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Group-based imputation\n",
+    "def group_based_imputation(df):\n",
+    "    \"\"\"Impute missing values based on group statistics\"\"\"\n",
+    "    df_group_impute = df.copy()\n",
+    "    \n",
+    "    print(\"=== GROUP-BASED IMPUTATION ===\")\n",
+    "    \n",
+    "    # Impute income based on region and education level\n",
+    "    # First, create education level categories\n",
+    "    df_group_impute['education_level'] = pd.cut(\n",
+    "        df_group_impute['education_years'].fillna(df_group_impute['education_years'].median()),\n",
+    "        bins=[0, 12, 16, 20],\n",
+    "        labels=['High School', 'Bachelor', 'Advanced']\n",
+    "    )\n",
+    "    \n",
+    "    # Calculate group-based statistics\n",
+    "    income_by_group = df_group_impute.groupby(['region', 'education_level'])['income'].median()\n",
+    "    \n",
+    "    # Fill missing income values\n",
+    "    def fill_income(row):\n",
+    "        if pd.isna(row['income']):\n",
+    "            try:\n",
+    "                return income_by_group.loc[(row['region'], row['education_level'])]\n",
+    "            except KeyError:\n",
+    "                return df_group_impute['income'].median()\n",
+    "        return row['income']\n",
+    "    \n",
+    "    df_group_impute['income'] = df_group_impute.apply(fill_income, axis=1)\n",
+    "    \n",
+    "    print(\"Income imputed based on region and education level\")\n",
+    "    print(\"Group-based median income:\")\n",
+    "    print(income_by_group.round(0))\n",
+    "    \n",
+    "    return df_group_impute\n",
+    "\n",
+    "# Apply group-based imputation\n",
+    "df_group_imputed = group_based_imputation(df_missing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Comparison of Imputation Methods\n",
+    "\n",
+    "Compare different imputation approaches and their impact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_imputation_methods(original_complete, original_missing, *imputed_dfs, methods_names):\n",
+    "    \"\"\"Compare different imputation methods\"\"\"\n",
+    "    print(\"=== IMPUTATION METHODS COMPARISON ===\")\n",
+    "    \n",
+    "    # Focus on a specific column for comparison\n",
+    "    column = 'income'\n",
+    "    \n",
+    "    if column not in original_complete.columns:\n",
+    "        print(f\"Column {column} not found\")\n",
+    "        return\n",
+    "    \n",
+    "    # Get original values that were made missing\n",
+    "    missing_mask = original_missing[column].isnull()\n",
+    "    true_values = original_complete.loc[missing_mask, column]\n",
+    "    \n",
+    "    print(f\"Comparing imputation for '{column}' column\")\n",
+    "    print(f\"Number of missing values: {len(true_values)}\")\n",
+    "    \n",
+    "    # Calculate errors for each method\n",
+    "    results = {}\n",
+    "    \n",
+    "    for df_imputed, method_name in zip(imputed_dfs, methods_names):\n",
+    "        if column in df_imputed.columns:\n",
+    "            imputed_values = df_imputed.loc[missing_mask, column]\n",
+    "            \n",
+    "            # Calculate metrics\n",
+    "            mae = np.mean(np.abs(true_values - imputed_values))\n",
+    "            rmse = np.sqrt(np.mean((true_values - imputed_values) ** 2))\n",
+    "            bias = np.mean(imputed_values - true_values)\n",
+    "            \n",
+    "            results[method_name] = {\n",
+    "                'MAE': mae,\n",
+    "                'RMSE': rmse,\n",
+    "                'Bias': bias,\n",
+    "                'Mean_Imputed': np.mean(imputed_values),\n",
+    "                'Std_Imputed': np.std(imputed_values)\n",
+    "            }\n",
+    "    \n",
+    "    # True statistics\n",
+    "    print(f\"\\nTrue statistics for missing values:\")\n",
+    "    print(f\"Mean: {np.mean(true_values):.2f}\")\n",
+    "    print(f\"Std: {np.std(true_values):.2f}\")\n",
+    "    \n",
+    "    # Results comparison\n",
+    "    results_df = pd.DataFrame(results).T\n",
+    "    print(f\"\\nImputation comparison results:\")\n",
+    "    print(results_df.round(2))\n",
+    "    \n",
+    "    # Visualize comparison\n",
+    "    fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
+    "    \n",
+    "    # Distribution comparison\n",
+    "    axes[0, 0].hist(true_values, alpha=0.7, label='True Values', bins=20)\n",
+    "    for df_imputed, method_name in zip(imputed_dfs, methods_names):\n",
+    "        if column in df_imputed.columns:\n",
+    "            imputed_values = df_imputed.loc[missing_mask, column]\n",
+    "            axes[0, 0].hist(imputed_values, alpha=0.7, label=f'{method_name}', bins=20)\n",
+    "    axes[0, 0].set_title('Distribution Comparison')\n",
+    "    axes[0, 0].legend()\n",
+    "    \n",
+    "    # Error metrics\n",
+    "    metrics = ['MAE', 'RMSE']\n",
+    "    for i, metric in enumerate(metrics):\n",
+    "        values = [results[method][metric] for method in results.keys()]\n",
+    "        axes[0, 1].bar(range(len(values)), values, alpha=0.7)\n",
+    "        axes[0, 1].set_xticks(range(len(results)))\n",
+    "        axes[0, 1].set_xticklabels(list(results.keys()), rotation=45)\n",
+    "        axes[0, 1].set_title(f'{metric} Comparison')\n",
+    "        break  # Show only MAE for now\n",
+    "    \n",
+    "    # Scatter plot: True vs Imputed\n",
+    "    for i, (df_imputed, method_name) in enumerate(zip(imputed_dfs[:2], methods_names[:2])):\n",
+    "        if column in df_imputed.columns:\n",
+    "            imputed_values = df_imputed.loc[missing_mask, column]\n",
+    "            ax = axes[1, i]\n",
+    "            ax.scatter(true_values, imputed_values, alpha=0.6)\n",
+    "            ax.plot([true_values.min(), true_values.max()], \n",
+    "                   [true_values.min(), true_values.max()], 'r--', label='Perfect Prediction')\n",
+    "            ax.set_xlabel('True Values')\n",
+    "            ax.set_ylabel('Imputed Values')\n",
+    "            ax.set_title(f'{method_name}: True vs Imputed')\n",
+    "            ax.legend()\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return results_df\n",
+    "\n",
+    "# Compare methods\n",
+    "comparison_results = compare_imputation_methods(\n",
+    "    df_complete, \n",
+    "    df_missing,\n",
+    "    df_basic_impute,\n",
+    "    methods_names=['Basic Fill', 'KNN', 'Iterative']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Domain-Specific Imputation Strategies\n",
+    "\n",
+    "Business logic-driven approaches to missing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def business_logic_imputation(df):\n",
+    "    \"\"\"Apply business logic for missing value imputation\"\"\"\n",
+    "    print(\"=== BUSINESS LOGIC IMPUTATION ===\")\n",
+    "    \n",
+    "    df_business = df.copy()\n",
+    "    \n",
+    "    # 1. Income imputation based on age and education\n",
+    "    def estimate_income(row):\n",
+    "        if pd.notna(row['income']):\n",
+    "            return row['income']\n",
+    "        \n",
+    "        # Base income estimation\n",
+    "        base_income = 30000\n",
+    "        \n",
+    "        # Age factor (experience premium)\n",
+    "        if pd.notna(row['age']):\n",
+    "            if row['age'] > 40:\n",
+    "                base_income *= 1.5\n",
+    "            elif row['age'] > 30:\n",
+    "                base_income *= 1.2\n",
+    "        \n",
+    "        # Education factor\n",
+    "        if pd.notna(row['education_years']):\n",
+    "            if row['education_years'] > 16:  # Graduate degree\n",
+    "                base_income *= 1.8\n",
+    "            elif row['education_years'] > 12:  # Bachelor's\n",
+    "                base_income *= 1.4\n",
+    "        \n",
+    "        # Regional adjustment\n",
+    "        regional_multipliers = {\n",
+    "            'North': 1.2,  # Higher cost of living\n",
+    "            'South': 0.9,\n",
+    "            'East': 1.1,\n",
+    "            'West': 1.0\n",
+    "        }\n",
+    "        base_income *= regional_multipliers.get(row['region'], 1.0)\n",
+    "        \n",
+    "        return base_income\n",
+    "    \n",
+    "    # Apply income estimation\n",
+    "    df_business['income'] = df_business.apply(estimate_income, axis=1)\n",
+    "    \n",
+    "    # 2. Satisfaction score based on purchase behavior\n",
+    "    def estimate_satisfaction(row):\n",
+    "        if pd.notna(row['satisfaction_score']):\n",
+    "            return row['satisfaction_score']\n",
+    "        \n",
+    "        # Base satisfaction\n",
+    "        base_satisfaction = 3  # Neutral\n",
+    "        \n",
+    "        # Purchase amount influence\n",
+    "        if pd.notna(row['purchase_amount']):\n",
+    "            if row['purchase_amount'] > 250:  # High value purchase\n",
+    "                base_satisfaction = 4\n",
+    "            elif row['purchase_amount'] < 100:  # Low value might indicate dissatisfaction\n",
+    "                base_satisfaction = 2\n",
+    "        \n",
+    "        return base_satisfaction\n",
+    "    \n",
+    "    # Apply satisfaction estimation\n",
+    "    df_business['satisfaction_score'] = df_business.apply(estimate_satisfaction, axis=1)\n",
+    "    \n",
+    "    # 3. Education years based on income and age\n",
+    "    def estimate_education(row):\n",
+    "        if pd.notna(row['education_years']):\n",
+    "            return row['education_years']\n",
+    "        \n",
+    "        # Base education\n",
+    "        base_education = 12  # High school\n",
+    "        \n",
+    "        # Income-based estimation\n",
+    "        if pd.notna(row['income']):\n",
+    "            if row['income'] > 70000:\n",
+    "                base_education = 18  # Graduate level\n",
+    "            elif row['income'] > 45000:\n",
+    "                base_education = 16  # Bachelor's\n",
+    "            elif row['income'] > 35000:\n",
+    "                base_education = 14  # Some college\n",
+    "        \n",
+    "        # Age adjustment (older people might have different education patterns)\n",
+    "        if pd.notna(row['age']) and row['age'] > 55:\n",
+    "            base_education = max(12, base_education - 2)  # Lower average for older generation\n",
+    "        \n",
+    "        return base_education\n",
+    "    \n",
+    "    # Apply education estimation\n",
+    "    df_business['education_years'] = df_business.apply(estimate_education, axis=1)\n",
+    "    \n",
+    "    print(\"Business logic imputation completed\")\n",
+    "    print(f\"Missing values remaining: {df_business.isnull().sum().sum()}\")\n",
+    "    \n",
+    "    return df_business\n",
+    "\n",
+    "# Apply business logic imputation\n",
+    "df_business_imputed = business_logic_imputation(df_missing)\n",
+    "\n",
+    "print(\"\\nBusiness logic imputation summary:\")\n",
+    "for col in ['income', 'satisfaction_score', 'education_years']:\n",
+    "    before = df_missing[col].isnull().sum()\n",
+    "    after = df_business_imputed[col].isnull().sum()\n",
+    "    print(f\"{col}: {before} → {after} missing values\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Missing Data Flags and Indicators\n",
+    "\n",
+    "Track which values were imputed for transparency and analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_missing_indicators(df_original, df_imputed):\n",
+    "    \"\"\"Create indicator variables for missing data\"\"\"\n",
+    "    print(\"=== CREATING MISSING DATA INDICATORS ===\")\n",
+    "    \n",
+    "    df_with_indicators = df_imputed.copy()\n",
+    "    \n",
+    "    # Create indicator columns for each column that had missing data\n",
+    "    columns_with_missing = df_original.columns[df_original.isnull().any()].tolist()\n",
+    "    \n",
+    "    for col in columns_with_missing:\n",
+    "        indicator_col = f'{col}_was_missing'\n",
+    "        df_with_indicators[indicator_col] = df_original[col].isnull().astype(int)\n",
+    "    \n",
+    "    print(f\"Created {len(columns_with_missing)} missing data indicators\")\n",
+    "    print(f\"Indicator columns: {[f'{col}_was_missing' for col in columns_with_missing]}\")\n",
+    "    \n",
+    "    # Summary of missing patterns\n",
+    "    indicator_cols = [f'{col}_was_missing' for col in columns_with_missing]\n",
+    "    missing_patterns = df_with_indicators[indicator_cols].sum()\n",
+    "    \n",
+    "    print(\"\\nMissing data summary by column:\")\n",
+    "    for col, count in missing_patterns.items():\n",
+    "        original_col = col.replace('_was_missing', '')\n",
+    "        percentage = (count / len(df_with_indicators)) * 100\n",
+    "        print(f\"{original_col}: {count} values imputed ({percentage:.1f}%)\")\n",
+    "    \n",
+    "    # Create composite missing indicator\n",
+    "    df_with_indicators['total_missing_count'] = df_with_indicators[indicator_cols].sum(axis=1)\n",
+    "    df_with_indicators['has_any_missing'] = (df_with_indicators['total_missing_count'] > 0).astype(int)\n",
+    "    \n",
+    "    return df_with_indicators, indicator_cols\n",
+    "\n",
+    "# Create missing indicators\n",
+    "df_with_indicators, indicator_columns = create_missing_indicators(df_missing, df_business_imputed)\n",
+    "\n",
+    "print(\"\\nDataset with missing indicators:\")\n",
+    "sample_cols = ['income', 'income_was_missing', 'education_years', 'education_years_was_missing', \n",
+    "               'satisfaction_score', 'satisfaction_score_was_missing', 'total_missing_count']\n",
+    "available_cols = [col for col in sample_cols if col in df_with_indicators.columns]\n",
+    "print(df_with_indicators[available_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Validation and Quality Assessment\n",
+    "\n",
+    "Validate the quality of imputation results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def validate_imputation_quality(df_original, df_missing, df_imputed):\n",
+    "    \"\"\"Validate the quality of imputation\"\"\"\n",
+    "    print(\"=== IMPUTATION QUALITY VALIDATION ===\")\n",
+    "    \n",
+    "    validation_results = {}\n",
+    "    \n",
+    "    # Check each column that had missing data\n",
+    "    for col in df_missing.columns:\n",
+    "        if df_missing[col].isnull().any() and col in df_imputed.columns:\n",
+    "            print(f\"\\n--- Validating {col} ---\")\n",
+    "            \n",
+    "            # Get missing mask\n",
+    "            missing_mask = df_missing[col].isnull()\n",
+    "            \n",
+    "            # Original statistics (complete data)\n",
+    "            original_stats = df_original[col].describe()\n",
+    "            \n",
+    "            # Imputed statistics (only imputed values)\n",
+    "            if missing_mask.any():\n",
+    "                imputed_values = df_imputed.loc[missing_mask, col]\n",
+    "                \n",
+    "                if pd.api.types.is_numeric_dtype(df_original[col]):\n",
+    "                    imputed_stats = imputed_values.describe()\n",
+    "                    \n",
+    "                    # Statistical tests\n",
+    "                    mean_diff = abs(original_stats['mean'] - imputed_stats['mean'])\n",
+    "                    std_diff = abs(original_stats['std'] - imputed_stats['std'])\n",
+    "                    \n",
+    "                    validation_results[col] = {\n",
+    "                        'original_mean': original_stats['mean'],\n",
+    "                        'imputed_mean': imputed_stats['mean'],\n",
+    "                        'mean_difference': mean_diff,\n",
+    "                        'original_std': original_stats['std'],\n",
+    "                        'imputed_std': imputed_stats['std'],\n",
+    "                        'std_difference': std_diff,\n",
+    "                        'values_imputed': len(imputed_values)\n",
+    "                    }\n",
+    "                    \n",
+    "                    print(f\"Original mean: {original_stats['mean']:.2f}, Imputed mean: {imputed_stats['mean']:.2f}\")\n",
+    "                    print(f\"Mean difference: {mean_diff:.2f} ({mean_diff/original_stats['mean']*100:.1f}%)\")\n",
+    "                    print(f\"Original std: {original_stats['std']:.2f}, Imputed std: {imputed_stats['std']:.2f}\")\n",
+    "                    \n",
+    "                else:\n",
+    "                    # Categorical data\n",
+    "                    original_dist = df_original[col].value_counts(normalize=True)\n",
+    "                    imputed_dist = imputed_values.value_counts(normalize=True)\n",
+    "                    print(f\"Original distribution: {original_dist.to_dict()}\")\n",
+    "                    print(f\"Imputed distribution: {imputed_dist.to_dict()}\")\n",
+    "    \n",
+    "    # Overall validation summary\n",
+    "    if validation_results:\n",
+    "        validation_df = pd.DataFrame(validation_results).T\n",
+    "        print(\"\\n=== VALIDATION SUMMARY ===\")\n",
+    "        print(validation_df.round(3))\n",
+    "        \n",
+    "        # Flag potential issues\n",
+    "        print(\"\\n--- Potential Issues ---\")\n",
+    "        for col, stats in validation_results.items():\n",
+    "            mean_change = abs(stats['mean_difference'] / stats['original_mean']) * 100\n",
+    "            if mean_change > 10:  # More than 10% change in mean\n",
+    "                print(f\"⚠️  {col}: Large mean change ({mean_change:.1f}%)\")\n",
+    "            \n",
+    "            std_change = abs(stats['std_difference'] / stats['original_std']) * 100\n",
+    "            if std_change > 20:  # More than 20% change in std\n",
+    "                print(f\"⚠️  {col}: Large variance change ({std_change:.1f}%)\")\n",
+    "    \n",
+    "    return validation_results\n",
+    "\n",
+    "# Validate imputation quality\n",
+    "validation_results = validate_imputation_quality(df_complete, df_missing, df_business_imputed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply missing data handling techniques to challenging scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Multi-step imputation strategy\n",
+    "# Create a sophisticated imputation pipeline that:\n",
+    "# 1. Handles different types of missing data appropriately\n",
+    "# 2. Uses multiple imputation methods in sequence\n",
+    "# 3. Validates results at each step\n",
+    "# 4. Creates comprehensive documentation\n",
+    "\n",
+    "def comprehensive_imputation_pipeline(df):\n",
+    "    \"\"\"Comprehensive missing data handling pipeline\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# result_df = comprehensive_imputation_pipeline(df_missing)\n",
+    "# print(\"Comprehensive pipeline results:\")\n",
+    "# print(result_df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Missing data pattern analysis\n",
+    "# Analyze if missing data follows specific patterns:\n",
+    "# - Time-based patterns\n",
+    "# - User behavior patterns\n",
+    "# - System/technical patterns\n",
+    "# Create insights and recommendations\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Impact assessment\n",
+    "# Assess how different missing data handling approaches\n",
+    "# affect downstream analysis:\n",
+    "# - Statistical analysis results\n",
+    "# - Machine learning model performance\n",
+    "# - Business insights and decisions\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Understanding Missing Data Types**:\n",
+    "   - **MCAR**: Missing Completely at Random\n",
+    "   - **MAR**: Missing at Random (depends on observed data)\n",
+    "   - **MNAR**: Missing Not at Random (depends on unobserved data)\n",
+    "\n",
+    "2. **Detection and Analysis**:\n",
+    "   - Always analyze missing patterns before imputation\n",
+    "   - Use visualizations to understand missing data structure\n",
+    "   - Look for relationships between missing values and other variables\n",
+    "\n",
+    "3. **Handling Strategies**:\n",
+    "   - **Deletion**: Simple but can lose valuable information\n",
+    "   - **Simple Imputation**: Fast but may not preserve relationships\n",
+    "   - **Advanced Methods**: KNN, MICE preserve more complex relationships\n",
+    "   - **Business Logic**: Domain knowledge often provides best results\n",
+    "\n",
+    "4. **Best Practices**:\n",
+    "   - Create missing data indicators for transparency\n",
+    "   - Validate imputation quality against original data when possible\n",
+    "   - Consider the impact on downstream analysis\n",
+    "   - Document all imputation decisions and methods\n",
+    "\n",
+    "## Method Selection Guide\n",
+    "\n",
+    "| Scenario | Recommended Method | Rationale |\n",
+    "|----------|-------------------|----------|\n",
+    "| < 5% missing, MCAR | Simple imputation | Low impact, efficiency |\n",
+    "| 5-20% missing, MAR | KNN or Group-based | Preserve relationships |\n",
+    "| > 20% missing, complex patterns | MICE or Multiple imputation | Handle complex dependencies |\n",
+    "| Business-critical decisions | Domain knowledge + validation | Accuracy and explainability |\n",
+    "| Machine learning features | Advanced methods + indicators | Preserve predictive power |\n",
+    "\n",
+    "## Common Pitfalls to Avoid\n",
+    "\n",
+    "1. **Data Leakage**: Don't use future information to impute past values\n",
+    "2. **Ignoring Patterns**: Missing data often has meaningful patterns\n",
+    "3. **Over-imputation**: Sometimes missing data is informative itself\n",
+    "4. **One-size-fits-all**: Different columns may need different strategies\n",
+    "5. **No Validation**: Always check if imputation preserved data characteristics"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/07_merging_joining.ipynb b/Session_01/PandasDataFrame-exmples/07_merging_joining.ipynb
new file mode 100755
index 0000000..0af7c16
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/07_merging_joining.ipynb
@@ -0,0 +1,937 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 7: Merging and Joining DataFrames\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Master different types of joins (inner, outer, left, right)\n",
+    "- Understand when to use merge vs join vs concat\n",
+    "- Handle duplicate keys and join conflicts\n",
+    "- Learn advanced merging techniques and best practices\n",
+    "- Practice with real-world data integration scenarios\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-6\n",
+    "- Understanding of relational database concepts (helpful)\n",
+    "- Basic knowledge of SQL joins (helpful but not required)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set display options\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', 50)\n",
+    "\n",
+    "print(\"Libraries loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Sample Datasets\n",
+    "\n",
+    "Let's create realistic datasets that represent common business scenarios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create sample datasets for merging examples\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Customer dataset\n",
+    "customers_data = {\n",
+    "    'customer_id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+    "    'customer_name': ['Alice Johnson', 'Bob Smith', 'Charlie Brown', 'Diana Prince', 'Eve Wilson',\n",
+    "                     'Frank Miller', 'Grace Lee', 'Henry Davis', 'Ivy Chen', 'Jack Robinson'],\n",
+    "    'email': ['alice@email.com', 'bob@email.com', 'charlie@email.com', 'diana@email.com', 'eve@email.com',\n",
+    "             'frank@email.com', 'grace@email.com', 'henry@email.com', 'ivy@email.com', 'jack@email.com'],\n",
+    "    'age': [28, 35, 42, 31, 29, 45, 38, 33, 27, 41],\n",
+    "    'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix',\n",
+    "            'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'San Jose'],\n",
+    "    'signup_date': pd.date_range('2023-01-01', periods=10, freq='M')\n",
+    "}\n",
+    "\n",
+    "df_customers = pd.DataFrame(customers_data)\n",
+    "\n",
+    "# Orders dataset\n",
+    "orders_data = {\n",
+    "    'order_id': [101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112],\n",
+    "    'customer_id': [1, 2, 1, 3, 4, 2, 5, 1, 6, 11, 3, 2],  # Note: customer_id 11 doesn't exist in customers\n",
+    "    'order_date': pd.date_range('2023-06-01', periods=12, freq='W'),\n",
+    "    'product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Monitor', 'Phone', \n",
+    "               'Headphones', 'Mouse', 'Keyboard', 'Laptop', 'Tablet', 'Monitor'],\n",
+    "    'quantity': [1, 2, 1, 1, 1, 1, 3, 2, 1, 1, 2, 1],\n",
+    "    'amount': [1200, 800, 400, 1200, 300, 800, 150, 50, 75, 1200, 800, 300]\n",
+    "}\n",
+    "\n",
+    "df_orders = pd.DataFrame(orders_data)\n",
+    "\n",
+    "# Product information dataset\n",
+    "products_data = {\n",
+    "    'product': ['Laptop', 'Phone', 'Tablet', 'Monitor', 'Headphones', 'Mouse', 'Keyboard', 'Webcam'],\n",
+    "    'category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', \n",
+    "                'Audio', 'Accessories', 'Accessories', 'Electronics'],\n",
+    "    'price': [1200, 800, 400, 300, 150, 50, 75, 100],\n",
+    "    'supplier': ['TechCorp', 'MobileCorp', 'TechCorp', 'DisplayCorp', \n",
+    "                'AudioCorp', 'AccessoryCorp', 'AccessoryCorp', 'TechCorp']\n",
+    "}\n",
+    "\n",
+    "df_products = pd.DataFrame(products_data)\n",
+    "\n",
+    "# Customer segments dataset\n",
+    "segments_data = {\n",
+    "    'customer_id': [1, 2, 3, 4, 5, 6, 7, 8, 12, 13],  # Some customers not in main customer table\n",
+    "    'segment': ['Premium', 'Standard', 'Premium', 'Standard', 'Basic', \n",
+    "               'Premium', 'Standard', 'Basic', 'Premium', 'Standard'],\n",
+    "    'loyalty_points': [1500, 800, 1200, 600, 200, 1800, 750, 300, 2000, 900]\n",
+    "}\n",
+    "\n",
+    "df_segments = pd.DataFrame(segments_data)\n",
+    "\n",
+    "print(\"Sample datasets created:\")\n",
+    "print(f\"Customers: {df_customers.shape}\")\n",
+    "print(f\"Orders: {df_orders.shape}\")\n",
+    "print(f\"Products: {df_products.shape}\")\n",
+    "print(f\"Segments: {df_segments.shape}\")\n",
+    "\n",
+    "print(\"\\nCustomers dataset:\")\n",
+    "print(df_customers.head())\n",
+    "\n",
+    "print(\"\\nOrders dataset:\")\n",
+    "print(df_orders.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Basic Merge Operations\n",
+    "\n",
+    "Understanding the fundamental merge operations and join types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inner Join - only matching records\n",
+    "print(\"=== INNER JOIN ===\")\n",
+    "inner_join = pd.merge(df_customers, df_orders, on='customer_id', how='inner')\n",
+    "print(f\"Result shape: {inner_join.shape}\")\n",
+    "print(\"Sample results:\")\n",
+    "print(inner_join[['customer_name', 'order_id', 'product', 'amount']].head())\n",
+    "\n",
+    "print(f\"\\nUnique customers in result: {inner_join['customer_id'].nunique()}\")\n",
+    "print(f\"Total orders: {len(inner_join)}\")\n",
+    "\n",
+    "# Check which customers have orders\n",
+    "customers_with_orders = inner_join['customer_id'].unique()\n",
+    "print(f\"Customers with orders: {sorted(customers_with_orders)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Left Join - all records from left table\n",
+    "print(\"=== LEFT JOIN ===\")\n",
+    "left_join = pd.merge(df_customers, df_orders, on='customer_id', how='left')\n",
+    "print(f\"Result shape: {left_join.shape}\")\n",
+    "print(\"Sample results:\")\n",
+    "print(left_join[['customer_name', 'order_id', 'product', 'amount']].head(10))\n",
+    "\n",
+    "# Check customers without orders\n",
+    "customers_without_orders = left_join[left_join['order_id'].isnull()]['customer_name'].tolist()\n",
+    "print(f\"\\nCustomers without orders: {customers_without_orders}\")\n",
+    "\n",
+    "# Summary statistics\n",
+    "print(f\"\\nTotal records: {len(left_join)}\")\n",
+    "print(f\"Records with orders: {left_join['order_id'].notna().sum()}\")\n",
+    "print(f\"Records without orders: {left_join['order_id'].isnull().sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Right Join - all records from right table\n",
+    "print(\"=== RIGHT JOIN ===\")\n",
+    "right_join = pd.merge(df_customers, df_orders, on='customer_id', how='right')\n",
+    "print(f\"Result shape: {right_join.shape}\")\n",
+    "print(\"Sample results:\")\n",
+    "print(right_join[['customer_name', 'order_id', 'product', 'amount']].head())\n",
+    "\n",
+    "# Check orders without customer information\n",
+    "orders_without_customers = right_join[right_join['customer_name'].isnull()]\n",
+    "print(f\"\\nOrders without customer info: {len(orders_without_customers)}\")\n",
+    "if len(orders_without_customers) > 0:\n",
+    "    print(orders_without_customers[['customer_id', 'order_id', 'product', 'amount']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Outer Join - all records from both tables\n",
+    "print(\"=== OUTER JOIN ===\")\n",
+    "outer_join = pd.merge(df_customers, df_orders, on='customer_id', how='outer')\n",
+    "print(f\"Result shape: {outer_join.shape}\")\n",
+    "\n",
+    "# Analyze the result\n",
+    "print(\"\\nData quality analysis:\")\n",
+    "print(f\"Records with complete customer info: {outer_join['customer_name'].notna().sum()}\")\n",
+    "print(f\"Records with complete order info: {outer_join['order_id'].notna().sum()}\")\n",
+    "print(f\"Records with both customer and order info: {(outer_join['customer_name'].notna() & outer_join['order_id'].notna()).sum()}\")\n",
+    "\n",
+    "# Show different categories of records\n",
+    "print(\"\\nCustomers without orders:\")\n",
+    "customers_only = outer_join[(outer_join['customer_name'].notna()) & (outer_join['order_id'].isnull())]\n",
+    "print(customers_only[['customer_name', 'city']].drop_duplicates())\n",
+    "\n",
+    "print(\"\\nOrders without customer data:\")\n",
+    "orders_only = outer_join[(outer_join['customer_name'].isnull()) & (outer_join['order_id'].notna())]\n",
+    "print(orders_only[['customer_id', 'order_id', 'product', 'amount']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Multiple Table Joins\n",
+    "\n",
+    "Combining data from multiple sources in sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Three-way join: Customers + Orders + Products\n",
+    "print(\"=== THREE-WAY JOIN ===\")\n",
+    "\n",
+    "# Step 1: Join customers and orders\n",
+    "customer_orders = pd.merge(df_customers, df_orders, on='customer_id', how='inner')\n",
+    "print(f\"After joining customers and orders: {customer_orders.shape}\")\n",
+    "\n",
+    "# Step 2: Join with products\n",
+    "complete_data = pd.merge(customer_orders, df_products, on='product', how='left')\n",
+    "print(f\"After joining with products: {complete_data.shape}\")\n",
+    "\n",
+    "# Display comprehensive view\n",
+    "print(\"\\nComplete order information:\")\n",
+    "display_cols = ['customer_name', 'order_id', 'product', 'category', 'quantity', 'amount', 'price', 'supplier']\n",
+    "print(complete_data[display_cols].head())\n",
+    "\n",
+    "# Verify data consistency\n",
+    "print(\"\\nData consistency check:\")\n",
+    "# Check if order amount matches product price * quantity\n",
+    "complete_data['calculated_amount'] = complete_data['price'] * complete_data['quantity']\n",
+    "amount_matches = (complete_data['amount'] == complete_data['calculated_amount']).all()\n",
+    "print(f\"Order amounts match calculated amounts: {amount_matches}\")\n",
+    "\n",
+    "if not amount_matches:\n",
+    "    mismatched = complete_data[complete_data['amount'] != complete_data['calculated_amount']]\n",
+    "    print(f\"\\nMismatched records: {len(mismatched)}\")\n",
+    "    print(mismatched[['order_id', 'product', 'amount', 'calculated_amount']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add customer segment information\n",
+    "print(\"=== ADDING CUSTOMER SEGMENTS ===\")\n",
+    "\n",
+    "# Join with segments (left join to keep all customers)\n",
+    "customers_with_segments = pd.merge(df_customers, df_segments, on='customer_id', how='left')\n",
+    "print(f\"Customers with segments shape: {customers_with_segments.shape}\")\n",
+    "\n",
+    "# Check which customers don't have segment information\n",
+    "missing_segments = customers_with_segments[customers_with_segments['segment'].isnull()]\n",
+    "print(f\"\\nCustomers without segment info: {len(missing_segments)}\")\n",
+    "if len(missing_segments) > 0:\n",
+    "    print(missing_segments[['customer_name', 'city']])\n",
+    "\n",
+    "# Create comprehensive customer profile\n",
+    "full_customer_profile = pd.merge(complete_data, df_segments, on='customer_id', how='left')\n",
+    "print(f\"\\nFull customer profile shape: {full_customer_profile.shape}\")\n",
+    "\n",
+    "# Analyze by segment\n",
+    "segment_analysis = full_customer_profile.groupby('segment').agg({\n",
+    "    'amount': ['sum', 'mean', 'count'],\n",
+    "    'customer_id': 'nunique'\n",
+    "}).round(2)\n",
+    "segment_analysis.columns = ['Total_Revenue', 'Avg_Order_Value', 'Total_Orders', 'Unique_Customers']\n",
+    "print(\"\\nRevenue by customer segment:\")\n",
+    "print(segment_analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Advanced Merge Techniques\n",
+    "\n",
+    "Handling complex merging scenarios and edge cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge with different column names\n",
+    "print(\"=== MERGE WITH DIFFERENT COLUMN NAMES ===\")\n",
+    "\n",
+    "# Create a dataset with different column name\n",
+    "customer_demographics = pd.DataFrame({\n",
+    "    'cust_id': [1, 2, 3, 4, 5],\n",
+    "    'income_range': ['50-75k', '75-100k', '50-75k', '100k+', '25-50k'],\n",
+    "    'education': ['Bachelor', 'Master', 'PhD', 'Master', 'Bachelor'],\n",
+    "    'occupation': ['Engineer', 'Manager', 'Professor', 'Director', 'Analyst']\n",
+    "})\n",
+    "\n",
+    "# Merge using left_on and right_on parameters\n",
+    "customers_with_demographics = pd.merge(\n",
+    "    df_customers, \n",
+    "    customer_demographics, \n",
+    "    left_on='customer_id', \n",
+    "    right_on='cust_id', \n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "print(\"Merge with different column names:\")\n",
+    "print(customers_with_demographics[['customer_name', 'customer_id', 'cust_id', 'income_range', 'education']].head())\n",
+    "\n",
+    "# Clean up duplicate columns\n",
+    "customers_with_demographics = customers_with_demographics.drop('cust_id', axis=1)\n",
+    "print(f\"\\nAfter cleanup: {customers_with_demographics.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge on multiple columns\n",
+    "print(\"=== MERGE ON MULTIPLE COLUMNS ===\")\n",
+    "\n",
+    "# Create time-based pricing data\n",
+    "pricing_data = pd.DataFrame({\n",
+    "    'product': ['Laptop', 'Laptop', 'Phone', 'Phone', 'Tablet', 'Tablet'],\n",
+    "    'date': pd.to_datetime(['2023-06-01', '2023-08-01', '2023-06-01', '2023-08-01', '2023-06-01', '2023-08-01']),\n",
+    "    'price': [1200, 1100, 800, 750, 400, 380],\n",
+    "    'promotion': [False, True, False, True, False, True]\n",
+    "})\n",
+    "\n",
+    "# Add year-month to orders for matching\n",
+    "df_orders_with_period = df_orders.copy()\n",
+    "df_orders_with_period['order_month'] = df_orders_with_period['order_date'].dt.to_period('M').dt.start_time\n",
+    "\n",
+    "# Create matching periods in pricing data\n",
+    "pricing_data['period'] = pricing_data['date'].dt.to_period('M').dt.start_time\n",
+    "\n",
+    "# Merge on product and time period\n",
+    "orders_with_pricing = pd.merge(\n",
+    "    df_orders_with_period,\n",
+    "    pricing_data,\n",
+    "    left_on=['product', 'order_month'],\n",
+    "    right_on=['product', 'period'],\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "print(\"Orders with time-based pricing:\")\n",
+    "print(orders_with_pricing[['order_id', 'product', 'order_date', 'amount', 'price', 'promotion']].head())\n",
+    "\n",
+    "# Check for pricing discrepancies\n",
+    "pricing_discrepancies = orders_with_pricing[\n",
+    "    (orders_with_pricing['amount'] != orders_with_pricing['price'] * orders_with_pricing['quantity']) &\n",
+    "    orders_with_pricing['price'].notna()\n",
+    "]\n",
+    "print(f\"\\nOrders with pricing discrepancies: {len(pricing_discrepancies)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Handling duplicate keys in merge\n",
+    "print(\"=== HANDLING DUPLICATE KEYS ===\")\n",
+    "\n",
+    "# Create data with duplicate keys\n",
+    "customer_contacts = pd.DataFrame({\n",
+    "    'customer_id': [1, 1, 2, 2, 3],\n",
+    "    'contact_type': ['email', 'phone', 'email', 'phone', 'email'],\n",
+    "    'contact_value': ['alice@email.com', '555-0101', 'bob@email.com', '555-0102', 'charlie@email.com'],\n",
+    "    'is_primary': [True, False, True, True, True]\n",
+    "})\n",
+    "\n",
+    "print(\"Customer contacts with duplicates:\")\n",
+    "print(customer_contacts)\n",
+    "\n",
+    "# Merge will create cartesian product for duplicate keys\n",
+    "customers_with_contacts = pd.merge(df_customers, customer_contacts, on='customer_id', how='inner')\n",
+    "print(f\"\\nResult of merge with duplicates: {customers_with_contacts.shape}\")\n",
+    "print(customers_with_contacts[['customer_name', 'contact_type', 'contact_value', 'is_primary']].head())\n",
+    "\n",
+    "# Strategy 1: Filter before merge\n",
+    "primary_contacts = customer_contacts[customer_contacts['is_primary'] == True]\n",
+    "customers_primary_contacts = pd.merge(df_customers, primary_contacts, on='customer_id', how='left')\n",
+    "print(f\"\\nAfter filtering to primary contacts: {customers_primary_contacts.shape}\")\n",
+    "\n",
+    "# Strategy 2: Pivot contacts to columns\n",
+    "contacts_pivoted = customer_contacts.pivot_table(\n",
+    "    index='customer_id',\n",
+    "    columns='contact_type',\n",
+    "    values='contact_value',\n",
+    "    aggfunc='first'\n",
+    ").reset_index()\n",
+    "print(\"\\nPivoted contacts:\")\n",
+    "print(contacts_pivoted)\n",
+    "\n",
+    "customers_with_pivoted_contacts = pd.merge(df_customers, contacts_pivoted, on='customer_id', how='left')\n",
+    "print(f\"\\nAfter merging pivoted contacts: {customers_with_pivoted_contacts.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Index-based Joins\n",
+    "\n",
+    "Using DataFrame indices for joining operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up DataFrames with indices\n",
+    "print(\"=== INDEX-BASED JOINS ===\")\n",
+    "\n",
+    "# Set customer_id as index\n",
+    "customers_indexed = df_customers.set_index('customer_id')\n",
+    "segments_indexed = df_segments.set_index('customer_id')\n",
+    "\n",
+    "print(\"Customers with index:\")\n",
+    "print(customers_indexed.head())\n",
+    "\n",
+    "# Join using indices\n",
+    "joined_by_index = customers_indexed.join(segments_indexed, how='left')\n",
+    "print(f\"\\nJoined by index shape: {joined_by_index.shape}\")\n",
+    "print(joined_by_index[['customer_name', 'city', 'segment', 'loyalty_points']].head())\n",
+    "\n",
+    "# Compare with merge\n",
+    "merged_equivalent = pd.merge(df_customers, df_segments, on='customer_id', how='left')\n",
+    "print(f\"\\nEquivalent merge shape: {merged_equivalent.shape}\")\n",
+    "\n",
+    "# Verify they're the same (after sorting)\n",
+    "joined_sorted = joined_by_index.reset_index().sort_values('customer_id')\n",
+    "merged_sorted = merged_equivalent.sort_values('customer_id')\n",
+    "are_equal = joined_sorted.equals(merged_sorted)\n",
+    "print(f\"Results are identical: {are_equal}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Multi-index joins\n",
+    "print(\"=== MULTI-INDEX JOINS ===\")\n",
+    "\n",
+    "# Create a dataset with multiple index levels\n",
+    "sales_by_region_product = pd.DataFrame({\n",
+    "    'region': ['North', 'North', 'South', 'South', 'East', 'East'],\n",
+    "    'product': ['Laptop', 'Phone', 'Laptop', 'Phone', 'Laptop', 'Phone'],\n",
+    "    'sales_target': [10, 15, 8, 12, 12, 18],\n",
+    "    'commission_rate': [0.05, 0.04, 0.06, 0.05, 0.05, 0.04]\n",
+    "})\n",
+    "\n",
+    "# Set multi-index\n",
+    "sales_targets = sales_by_region_product.set_index(['region', 'product'])\n",
+    "print(\"Sales targets with multi-index:\")\n",
+    "print(sales_targets)\n",
+    "\n",
+    "# Create customer orders with region mapping\n",
+    "customer_regions = {\n",
+    "    1: 'North', 2: 'South', 3: 'East', 4: 'North', 5: 'South', 6: 'East'\n",
+    "}\n",
+    "\n",
+    "orders_with_region = df_orders.copy()\n",
+    "orders_with_region['region'] = orders_with_region['customer_id'].map(customer_regions)\n",
+    "orders_with_region = orders_with_region.dropna(subset=['region'])\n",
+    "\n",
+    "# Merge on multiple columns to match multi-index\n",
+    "orders_with_targets = pd.merge(\n",
+    "    orders_with_region,\n",
+    "    sales_targets.reset_index(),\n",
+    "    on=['region', 'product'],\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "print(\"\\nOrders with sales targets:\")\n",
+    "print(orders_with_targets[['order_id', 'region', 'product', 'amount', 'sales_target', 'commission_rate']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Concatenation Operations\n",
+    "\n",
+    "Combining DataFrames vertically and horizontally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Vertical concatenation (stacking DataFrames)\n",
+    "print(\"=== VERTICAL CONCATENATION ===\")\n",
+    "\n",
+    "# Create additional customer data (new batch)\n",
+    "new_customers = pd.DataFrame({\n",
+    "    'customer_id': [11, 12, 13, 14, 15],\n",
+    "    'customer_name': ['Kate Wilson', 'Liam Brown', 'Mia Garcia', 'Noah Jones', 'Olivia Miller'],\n",
+    "    'email': ['kate@email.com', 'liam@email.com', 'mia@email.com', 'noah@email.com', 'olivia@email.com'],\n",
+    "    'age': [26, 39, 31, 44, 28],\n",
+    "    'city': ['Austin', 'Seattle', 'Denver', 'Boston', 'Miami'],\n",
+    "    'signup_date': pd.date_range('2024-01-01', periods=5, freq='M')\n",
+    "})\n",
+    "\n",
+    "# Concatenate vertically\n",
+    "all_customers = pd.concat([df_customers, new_customers], ignore_index=True)\n",
+    "print(f\"Original customers: {len(df_customers)}\")\n",
+    "print(f\"New customers: {len(new_customers)}\")\n",
+    "print(f\"Combined customers: {len(all_customers)}\")\n",
+    "\n",
+    "print(\"\\nCombined customer data:\")\n",
+    "print(all_customers.tail())\n",
+    "\n",
+    "# Concatenation with different columns\n",
+    "customers_with_extra_info = pd.DataFrame({\n",
+    "    'customer_id': [16, 17],\n",
+    "    'customer_name': ['Paul Davis', 'Quinn Taylor'],\n",
+    "    'email': ['paul@email.com', 'quinn@email.com'],\n",
+    "    'age': [35, 29],\n",
+    "    'city': ['Portland', 'Nashville'],\n",
+    "    'signup_date': pd.date_range('2024-06-01', periods=2, freq='M'),\n",
+    "    'referral_source': ['Google', 'Facebook']  # Extra column\n",
+    "})\n",
+    "\n",
+    "# Concat with different columns (creates NaN for missing columns)\n",
+    "all_customers_extended = pd.concat([all_customers, customers_with_extra_info], ignore_index=True, sort=False)\n",
+    "print(f\"\\nAfter adding customers with extra info: {all_customers_extended.shape}\")\n",
+    "print(\"Missing values in referral_source:\")\n",
+    "print(all_customers_extended['referral_source'].isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Horizontal concatenation\n",
+    "print(\"=== HORIZONTAL CONCATENATION ===\")\n",
+    "\n",
+    "# Split customer data into parts\n",
+    "customer_basic_info = df_customers[['customer_id', 'customer_name', 'email']]\n",
+    "customer_demographics = df_customers[['customer_id', 'age', 'city', 'signup_date']]\n",
+    "\n",
+    "print(\"Customer basic info:\")\n",
+    "print(customer_basic_info.head())\n",
+    "\n",
+    "print(\"\\nCustomer demographics:\")\n",
+    "print(customer_demographics.head())\n",
+    "\n",
+    "# Concatenate horizontally (by index)\n",
+    "customers_recombined = pd.concat([customer_basic_info, customer_demographics.drop('customer_id', axis=1)], axis=1)\n",
+    "print(f\"\\nRecombined shape: {customers_recombined.shape}\")\n",
+    "print(customers_recombined.head())\n",
+    "\n",
+    "# Verify it matches original\n",
+    "columns_match = set(customers_recombined.columns) == set(df_customers.columns)\n",
+    "print(f\"\\nColumns match original: {columns_match}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Concat with keys (creating hierarchical columns)\n",
+    "print(\"=== CONCAT WITH KEYS ===\")\n",
+    "\n",
+    "# Create quarterly sales data\n",
+    "q1_sales = pd.DataFrame({\n",
+    "    'product': ['Laptop', 'Phone', 'Tablet'],\n",
+    "    'units_sold': [50, 75, 30],\n",
+    "    'revenue': [60000, 60000, 12000]\n",
+    "})\n",
+    "\n",
+    "q2_sales = pd.DataFrame({\n",
+    "    'product': ['Laptop', 'Phone', 'Tablet'],\n",
+    "    'units_sold': [45, 80, 35],\n",
+    "    'revenue': [54000, 64000, 14000]\n",
+    "})\n",
+    "\n",
+    "# Concatenate with keys\n",
+    "quarterly_sales = pd.concat([q1_sales, q2_sales], keys=['Q1', 'Q2'])\n",
+    "print(\"Quarterly sales with hierarchical index:\")\n",
+    "print(quarterly_sales)\n",
+    "\n",
+    "# Access specific quarter\n",
+    "print(\"\\nQ1 sales only:\")\n",
+    "print(quarterly_sales.loc['Q1'])\n",
+    "\n",
+    "# Create summary comparison\n",
+    "quarterly_comparison = pd.concat([q1_sales.set_index('product'), q2_sales.set_index('product')], \n",
+    "                                keys=['Q1', 'Q2'], axis=1)\n",
+    "print(\"\\nQuarterly comparison (side by side):\")\n",
+    "print(quarterly_comparison)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Performance and Best Practices\n",
+    "\n",
+    "Optimizing merge operations and avoiding common pitfalls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Performance comparison: merge vs join\n",
+    "import time\n",
+    "\n",
+    "print(\"=== PERFORMANCE COMPARISON ===\")\n",
+    "\n",
+    "# Create larger datasets for performance testing\n",
+    "np.random.seed(42)\n",
+    "large_customers = pd.DataFrame({\n",
+    "    'customer_id': range(1, 10001),\n",
+    "    'customer_name': [f'Customer_{i}' for i in range(1, 10001)],\n",
+    "    'city': np.random.choice(['New York', 'Los Angeles', 'Chicago'], 10000)\n",
+    "})\n",
+    "\n",
+    "large_orders = pd.DataFrame({\n",
+    "    'order_id': range(1, 50001),\n",
+    "    'customer_id': np.random.randint(1, 10001, 50000),\n",
+    "    'amount': np.random.normal(100, 30, 50000)\n",
+    "})\n",
+    "\n",
+    "print(f\"Large customers: {large_customers.shape}\")\n",
+    "print(f\"Large orders: {large_orders.shape}\")\n",
+    "\n",
+    "# Test merge performance\n",
+    "start_time = time.time()\n",
+    "merged_result = pd.merge(large_customers, large_orders, on='customer_id', how='inner')\n",
+    "merge_time = time.time() - start_time\n",
+    "\n",
+    "# Test join performance\n",
+    "customers_indexed = large_customers.set_index('customer_id')\n",
+    "orders_indexed = large_orders.set_index('customer_id')\n",
+    "\n",
+    "start_time = time.time()\n",
+    "joined_result = customers_indexed.join(orders_indexed, how='inner')\n",
+    "join_time = time.time() - start_time\n",
+    "\n",
+    "print(f\"\\nMerge time: {merge_time:.4f} seconds\")\n",
+    "print(f\"Join time: {join_time:.4f} seconds\")\n",
+    "print(f\"Join is {merge_time/join_time:.2f}x faster\")\n",
+    "\n",
+    "print(f\"\\nResults shape - Merge: {merged_result.shape}, Join: {joined_result.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Best practices and common pitfalls\n",
+    "print(\"=== BEST PRACTICES ===\")\n",
+    "\n",
+    "def analyze_merge_keys(df1, df2, key_col):\n",
+    "    \"\"\"Analyze merge keys before joining\"\"\"\n",
+    "    print(f\"\\n--- Analyzing merge on '{key_col}' ---\")\n",
+    "    \n",
+    "    # Check for duplicates\n",
+    "    df1_dups = df1[key_col].duplicated().sum()\n",
+    "    df2_dups = df2[key_col].duplicated().sum()\n",
+    "    \n",
+    "    print(f\"Duplicates in left table: {df1_dups}\")\n",
+    "    print(f\"Duplicates in right table: {df2_dups}\")\n",
+    "    \n",
+    "    # Check for missing values\n",
+    "    df1_missing = df1[key_col].isnull().sum()\n",
+    "    df2_missing = df2[key_col].isnull().sum()\n",
+    "    \n",
+    "    print(f\"Missing values in left table: {df1_missing}\")\n",
+    "    print(f\"Missing values in right table: {df2_missing}\")\n",
+    "    \n",
+    "    # Check overlap\n",
+    "    left_keys = set(df1[key_col].dropna())\n",
+    "    right_keys = set(df2[key_col].dropna())\n",
+    "    \n",
+    "    overlap = left_keys & right_keys\n",
+    "    left_only = left_keys - right_keys\n",
+    "    right_only = right_keys - left_keys\n",
+    "    \n",
+    "    print(f\"Keys in both tables: {len(overlap)}\")\n",
+    "    print(f\"Keys only in left: {len(left_only)}\")\n",
+    "    print(f\"Keys only in right: {len(right_only)}\")\n",
+    "    \n",
+    "    # Predict result sizes\n",
+    "    if df1_dups == 0 and df2_dups == 0:\n",
+    "        inner_size = len(overlap)\n",
+    "        left_size = len(df1)\n",
+    "        right_size = len(df2)\n",
+    "        outer_size = len(left_keys | right_keys)\n",
+    "    else:\n",
+    "        print(\"Warning: Duplicates present, result size may be larger than expected\")\n",
+    "        inner_size = \"Cannot predict (duplicates present)\"\n",
+    "        left_size = \"Cannot predict (duplicates present)\"\n",
+    "        right_size = \"Cannot predict (duplicates present)\"\n",
+    "        outer_size = \"Cannot predict (duplicates present)\"\n",
+    "    \n",
+    "    print(f\"\\nPredicted result sizes:\")\n",
+    "    print(f\"Inner join: {inner_size}\")\n",
+    "    print(f\"Left join: {left_size}\")\n",
+    "    print(f\"Right join: {right_size}\")\n",
+    "    print(f\"Outer join: {outer_size}\")\n",
+    "\n",
+    "# Analyze our sample data\n",
+    "analyze_merge_keys(df_customers, df_orders, 'customer_id')\n",
+    "analyze_merge_keys(df_customers, df_segments, 'customer_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data validation after merge\n",
+    "def validate_merge_result(df, expected_rows=None, key_col=None):\n",
+    "    \"\"\"Validate merge results\"\"\"\n",
+    "    print(\"\\n=== MERGE VALIDATION ===\")\n",
+    "    \n",
+    "    print(f\"Result shape: {df.shape}\")\n",
+    "    \n",
+    "    if expected_rows:\n",
+    "        print(f\"Expected rows: {expected_rows}\")\n",
+    "        if len(df) != expected_rows:\n",
+    "            print(\"⚠️  Row count doesn't match expectation!\")\n",
+    "    \n",
+    "    # Check for unexpected duplicates\n",
+    "    if key_col and key_col in df.columns:\n",
+    "        duplicates = df[key_col].duplicated().sum()\n",
+    "        if duplicates > 0:\n",
+    "            print(f\"⚠️  Found {duplicates} duplicate keys after merge\")\n",
+    "    \n",
+    "    # Check for missing values in key columns\n",
+    "    missing_summary = df.isnull().sum()\n",
+    "    critical_missing = missing_summary[missing_summary > 0]\n",
+    "    \n",
+    "    if len(critical_missing) > 0:\n",
+    "        print(\"Missing values after merge:\")\n",
+    "        print(critical_missing)\n",
+    "    \n",
+    "    # Data type consistency\n",
+    "    print(f\"\\nData types:\")\n",
+    "    print(df.dtypes)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# Example validation\n",
+    "sample_merge = pd.merge(df_customers, df_orders, on='customer_id', how='inner')\n",
+    "validated_result = validate_merge_result(sample_merge, key_col='customer_id')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply merging and joining techniques to real-world scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Customer Lifetime Value Analysis\n",
+    "# Create a comprehensive customer analysis by joining:\n",
+    "# - Customer demographics\n",
+    "# - Order history\n",
+    "# - Product information\n",
+    "# - Customer segments\n",
+    "# Calculate CLV metrics for each customer\n",
+    "\n",
+    "def calculate_customer_lifetime_value(customers, orders, products, segments):\n",
+    "    \"\"\"Calculate comprehensive customer lifetime value metrics\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# clv_analysis = calculate_customer_lifetime_value(df_customers, df_orders, df_products, df_segments)\n",
+    "# print(\"Customer Lifetime Value Analysis:\")\n",
+    "# print(clv_analysis.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Data Quality Assessment\n",
+    "# Create a function that analyzes data quality issues when merging multiple datasets:\n",
+    "# - Identify orphaned records\n",
+    "# - Find data inconsistencies\n",
+    "# - Suggest data cleaning steps\n",
+    "# - Provide merge recommendations\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Time-series Join Challenge\n",
+    "# Create a complex time-based join scenario:\n",
+    "# - Join orders with time-varying product prices\n",
+    "# - Handle seasonal promotions\n",
+    "# - Calculate accurate historical revenue\n",
+    "# - Account for price changes over time\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Join Types**:\n",
+    "   - **Inner**: Only matching records from both tables\n",
+    "   - **Left**: All records from left table + matching from right\n",
+    "   - **Right**: All records from right table + matching from left\n",
+    "   - **Outer**: All records from both tables\n",
+    "\n",
+    "2. **Method Selection**:\n",
+    "   - **`pd.merge()`**: Most flexible, works with any columns\n",
+    "   - **`.join()`**: Faster for index-based joins\n",
+    "   - **`pd.concat()`**: For stacking DataFrames vertically/horizontally\n",
+    "\n",
+    "3. **Best Practices**:\n",
+    "   - Always analyze merge keys before joining\n",
+    "   - Check for duplicates and missing values\n",
+    "   - Validate results after merging\n",
+    "   - Use appropriate join types for your use case\n",
+    "   - Consider performance implications for large datasets\n",
+    "\n",
+    "4. **Common Pitfalls**:\n",
+    "   - Cartesian products from duplicate keys\n",
+    "   - Unexpected result sizes\n",
+    "   - Data type inconsistencies\n",
+    "   - Missing value propagation\n",
+    "\n",
+    "## Join Type Selection Guide\n",
+    "\n",
+    "| Use Case | Recommended Join | Rationale |\n",
+    "|----------|-----------------|----------|\n",
+    "| Customer orders analysis | Inner | Only customers with orders |\n",
+    "| Customer segmentation | Left | Keep all customers, add segment info |\n",
+    "| Order validation | Right | Keep all orders, check customer validity |\n",
+    "| Data completeness analysis | Outer | See all records and identify gaps |\n",
+    "| Performance-critical operations | Index-based join | Faster execution |\n",
+    "\n",
+    "## Performance Tips\n",
+    "\n",
+    "1. **Index Usage**: Set indexes for frequently joined columns\n",
+    "2. **Data Types**: Ensure consistent data types before joining\n",
+    "3. **Memory Management**: Consider chunking for very large datasets\n",
+    "4. **Join Order**: Start with smallest datasets\n",
+    "5. **Validation**: Always validate merge results"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/08_sorting_ranking.ipynb b/Session_01/PandasDataFrame-exmples/08_sorting_ranking.ipynb
new file mode 100755
index 0000000..5e5a9df
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/08_sorting_ranking.ipynb
@@ -0,0 +1,1408 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 7: Merging and Joining DataFrames\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Master different types of joins (inner, outer, left, right)\n",
+    "- Understand when to use merge vs join vs concat\n",
+    "- Handle duplicate keys and join conflicts\n",
+    "- Learn advanced merging techniques and best practices\n",
+    "- Practice with real-world data integration scenarios\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-6\n",
+    "- Understanding of relational database concepts (helpful)\n",
+    "- Basic knowledge of SQL joins (helpful but not required)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Libraries loaded successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set display options\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', 50)\n",
+    "\n",
+    "print(\"Libraries loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Sample Datasets\n",
+    "\n",
+    "Let's create realistic datasets that represent common business scenarios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample datasets created:\n",
+      "Customers: (10, 6)\n",
+      "Orders: (12, 6)\n",
+      "Products: (8, 4)\n",
+      "Segments: (10, 3)\n",
+      "\n",
+      "Customers dataset:\n",
+      "   customer_id  customer_name              email  age         city signup_date\n",
+      "0            1  Alice Johnson    alice@email.com   28     New York  2023-01-31\n",
+      "1            2      Bob Smith      bob@email.com   35  Los Angeles  2023-02-28\n",
+      "2            3  Charlie Brown  charlie@email.com   42      Chicago  2023-03-31\n",
+      "3            4   Diana Prince    diana@email.com   31      Houston  2023-04-30\n",
+      "4            5     Eve Wilson      eve@email.com   29      Phoenix  2023-05-31\n",
+      "\n",
+      "Orders dataset:\n",
+      "   order_id  customer_id order_date  product  quantity  amount\n",
+      "0       101            1 2023-06-04   Laptop         1    1200\n",
+      "1       102            2 2023-06-11    Phone         2     800\n",
+      "2       103            1 2023-06-18   Tablet         1     400\n",
+      "3       104            3 2023-06-25   Laptop         1    1200\n",
+      "4       105            4 2023-07-02  Monitor         1     300\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create sample datasets for merging examples\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Customer dataset\n",
+    "customers_data = {\n",
+    "    'customer_id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+    "    'customer_name': ['Alice Johnson', 'Bob Smith', 'Charlie Brown', 'Diana Prince', 'Eve Wilson',\n",
+    "                     'Frank Miller', 'Grace Lee', 'Henry Davis', 'Ivy Chen', 'Jack Robinson'],\n",
+    "    'email': ['alice@email.com', 'bob@email.com', 'charlie@email.com', 'diana@email.com', 'eve@email.com',\n",
+    "             'frank@email.com', 'grace@email.com', 'henry@email.com', 'ivy@email.com', 'jack@email.com'],\n",
+    "    'age': [28, 35, 42, 31, 29, 45, 38, 33, 27, 41],\n",
+    "    'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix',\n",
+    "            'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'San Jose'],\n",
+    "    'signup_date': pd.date_range('2023-01-01', periods=10, freq='M')\n",
+    "}\n",
+    "\n",
+    "df_customers = pd.DataFrame(customers_data)\n",
+    "\n",
+    "# Orders dataset\n",
+    "orders_data = {\n",
+    "    'order_id': [101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112],\n",
+    "    'customer_id': [1, 2, 1, 3, 4, 2, 5, 1, 6, 11, 3, 2],  # Note: customer_id 11 doesn't exist in customers\n",
+    "    'order_date': pd.date_range('2023-06-01', periods=12, freq='W'),\n",
+    "    'product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Monitor', 'Phone', \n",
+    "               'Headphones', 'Mouse', 'Keyboard', 'Laptop', 'Tablet', 'Monitor'],\n",
+    "    'quantity': [1, 2, 1, 1, 1, 1, 3, 2, 1, 1, 2, 1],\n",
+    "    'amount': [1200, 800, 400, 1200, 300, 800, 150, 50, 75, 1200, 800, 300]\n",
+    "}\n",
+    "\n",
+    "df_orders = pd.DataFrame(orders_data)\n",
+    "\n",
+    "# Product information dataset\n",
+    "products_data = {\n",
+    "    'product': ['Laptop', 'Phone', 'Tablet', 'Monitor', 'Headphones', 'Mouse', 'Keyboard', 'Webcam'],\n",
+    "    'category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', \n",
+    "                'Audio', 'Accessories', 'Accessories', 'Electronics'],\n",
+    "    'price': [1200, 800, 400, 300, 150, 50, 75, 100],\n",
+    "    'supplier': ['TechCorp', 'MobileCorp', 'TechCorp', 'DisplayCorp', \n",
+    "                'AudioCorp', 'AccessoryCorp', 'AccessoryCorp', 'TechCorp']\n",
+    "}\n",
+    "\n",
+    "df_products = pd.DataFrame(products_data)\n",
+    "\n",
+    "# Customer segments dataset\n",
+    "segments_data = {\n",
+    "    'customer_id': [1, 2, 3, 4, 5, 6, 7, 8, 12, 13],  # Some customers not in main customer table\n",
+    "    'segment': ['Premium', 'Standard', 'Premium', 'Standard', 'Basic', \n",
+    "               'Premium', 'Standard', 'Basic', 'Premium', 'Standard'],\n",
+    "    'loyalty_points': [1500, 800, 1200, 600, 200, 1800, 750, 300, 2000, 900]\n",
+    "}\n",
+    "\n",
+    "df_segments = pd.DataFrame(segments_data)\n",
+    "\n",
+    "print(\"Sample datasets created:\")\n",
+    "print(f\"Customers: {df_customers.shape}\")\n",
+    "print(f\"Orders: {df_orders.shape}\")\n",
+    "print(f\"Products: {df_products.shape}\")\n",
+    "print(f\"Segments: {df_segments.shape}\")\n",
+    "\n",
+    "print(\"\\nCustomers dataset:\")\n",
+    "print(df_customers.head())\n",
+    "\n",
+    "print(\"\\nOrders dataset:\")\n",
+    "print(df_orders.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Basic Merge Operations\n",
+    "\n",
+    "Understanding the fundamental merge operations and join types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== INNER JOIN ===\n",
+      "Result shape: (11, 11)\n",
+      "Sample results:\n",
+      "   customer_name  order_id product  amount\n",
+      "0  Alice Johnson       101  Laptop    1200\n",
+      "1  Alice Johnson       103  Tablet     400\n",
+      "2  Alice Johnson       108   Mouse      50\n",
+      "3      Bob Smith       102   Phone     800\n",
+      "4      Bob Smith       106   Phone     800\n",
+      "\n",
+      "Unique customers in result: 6\n",
+      "Total orders: 11\n",
+      "Customers with orders: [np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inner Join - only matching records\n",
+    "print(\"=== INNER JOIN ===\")\n",
+    "inner_join = pd.merge(df_customers, df_orders, on='customer_id', how='inner')\n",
+    "print(f\"Result shape: {inner_join.shape}\")\n",
+    "print(\"Sample results:\")\n",
+    "print(inner_join[['customer_name', 'order_id', 'product', 'amount']].head())\n",
+    "\n",
+    "print(f\"\\nUnique customers in result: {inner_join['customer_id'].nunique()}\")\n",
+    "print(f\"Total orders: {len(inner_join)}\")\n",
+    "\n",
+    "# Check which customers have orders\n",
+    "customers_with_orders = inner_join['customer_id'].unique()\n",
+    "print(f\"Customers with orders: {sorted(customers_with_orders)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== LEFT JOIN ===\n",
+      "Result shape: (15, 11)\n",
+      "Sample results:\n",
+      "   customer_name  order_id     product  amount\n",
+      "0  Alice Johnson     101.0      Laptop  1200.0\n",
+      "1  Alice Johnson     103.0      Tablet   400.0\n",
+      "2  Alice Johnson     108.0       Mouse    50.0\n",
+      "3      Bob Smith     102.0       Phone   800.0\n",
+      "4      Bob Smith     106.0       Phone   800.0\n",
+      "5      Bob Smith     112.0     Monitor   300.0\n",
+      "6  Charlie Brown     104.0      Laptop  1200.0\n",
+      "7  Charlie Brown     111.0      Tablet   800.0\n",
+      "8   Diana Prince     105.0     Monitor   300.0\n",
+      "9     Eve Wilson     107.0  Headphones   150.0\n",
+      "\n",
+      "Customers without orders: ['Grace Lee', 'Henry Davis', 'Ivy Chen', 'Jack Robinson']\n",
+      "\n",
+      "Total records: 15\n",
+      "Records with orders: 11\n",
+      "Records without orders: 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Left Join - all records from left table\n",
+    "print(\"=== LEFT JOIN ===\")\n",
+    "left_join = pd.merge(df_customers, df_orders, on='customer_id', how='left')\n",
+    "print(f\"Result shape: {left_join.shape}\")\n",
+    "print(\"Sample results:\")\n",
+    "print(left_join[['customer_name', 'order_id', 'product', 'amount']].head(10))\n",
+    "\n",
+    "# Check customers without orders\n",
+    "customers_without_orders = left_join[left_join['order_id'].isnull()]['customer_name'].tolist()\n",
+    "print(f\"\\nCustomers without orders: {customers_without_orders}\")\n",
+    "\n",
+    "# Summary statistics\n",
+    "print(f\"\\nTotal records: {len(left_join)}\")\n",
+    "print(f\"Records with orders: {left_join['order_id'].notna().sum()}\")\n",
+    "print(f\"Records without orders: {left_join['order_id'].isnull().sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== RIGHT JOIN ===\n",
+      "Result shape: (12, 11)\n",
+      "Sample results:\n",
+      "   customer_name  order_id  product  amount\n",
+      "0  Alice Johnson       101   Laptop    1200\n",
+      "1      Bob Smith       102    Phone     800\n",
+      "2  Alice Johnson       103   Tablet     400\n",
+      "3  Charlie Brown       104   Laptop    1200\n",
+      "4   Diana Prince       105  Monitor     300\n",
+      "\n",
+      "Orders without customer info: 1\n",
+      "   customer_id  order_id product  amount\n",
+      "9           11       110  Laptop    1200\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Right Join - all records from right table\n",
+    "print(\"=== RIGHT JOIN ===\")\n",
+    "right_join = pd.merge(df_customers, df_orders, on='customer_id', how='right')\n",
+    "print(f\"Result shape: {right_join.shape}\")\n",
+    "print(\"Sample results:\")\n",
+    "print(right_join[['customer_name', 'order_id', 'product', 'amount']].head())\n",
+    "\n",
+    "# Check orders without customer information\n",
+    "orders_without_customers = right_join[right_join['customer_name'].isnull()]\n",
+    "print(f\"\\nOrders without customer info: {len(orders_without_customers)}\")\n",
+    "if len(orders_without_customers) > 0:\n",
+    "    print(orders_without_customers[['customer_id', 'order_id', 'product', 'amount']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== OUTER JOIN ===\n",
+      "Result shape: (16, 11)\n",
+      "\n",
+      "Data quality analysis:\n",
+      "Records with complete customer info: 15\n",
+      "Records with complete order info: 12\n",
+      "Records with both customer and order info: 11\n",
+      "\n",
+      "Customers without orders:\n",
+      "    customer_name         city\n",
+      "11      Grace Lee  San Antonio\n",
+      "12    Henry Davis    San Diego\n",
+      "13       Ivy Chen       Dallas\n",
+      "14  Jack Robinson     San Jose\n",
+      "\n",
+      "Orders without customer data:\n",
+      "    customer_id  order_id product  amount\n",
+      "15           11     110.0  Laptop  1200.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Outer Join - all records from both tables\n",
+    "print(\"=== OUTER JOIN ===\")\n",
+    "outer_join = pd.merge(df_customers, df_orders, on='customer_id', how='outer')\n",
+    "print(f\"Result shape: {outer_join.shape}\")\n",
+    "\n",
+    "# Analyze the result\n",
+    "print(\"\\nData quality analysis:\")\n",
+    "print(f\"Records with complete customer info: {outer_join['customer_name'].notna().sum()}\")\n",
+    "print(f\"Records with complete order info: {outer_join['order_id'].notna().sum()}\")\n",
+    "print(f\"Records with both customer and order info: {(outer_join['customer_name'].notna() & outer_join['order_id'].notna()).sum()}\")\n",
+    "\n",
+    "# Show different categories of records\n",
+    "print(\"\\nCustomers without orders:\")\n",
+    "customers_only = outer_join[(outer_join['customer_name'].notna()) & (outer_join['order_id'].isnull())]\n",
+    "print(customers_only[['customer_name', 'city']].drop_duplicates())\n",
+    "\n",
+    "print(\"\\nOrders without customer data:\")\n",
+    "orders_only = outer_join[(outer_join['customer_name'].isnull()) & (outer_join['order_id'].notna())]\n",
+    "print(orders_only[['customer_id', 'order_id', 'product', 'amount']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Multiple Table Joins\n",
+    "\n",
+    "Combining data from multiple sources in sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== THREE-WAY JOIN ===\n",
+      "After joining customers and orders: (11, 11)\n",
+      "After joining with products: (11, 14)\n",
+      "\n",
+      "Complete order information:\n",
+      "   customer_name  order_id product     category  quantity  amount  price  \\\n",
+      "0  Alice Johnson       101  Laptop  Electronics         1    1200   1200   \n",
+      "1  Alice Johnson       103  Tablet  Electronics         1     400    400   \n",
+      "2  Alice Johnson       108   Mouse  Accessories         2      50     50   \n",
+      "3      Bob Smith       102   Phone  Electronics         2     800    800   \n",
+      "4      Bob Smith       106   Phone  Electronics         1     800    800   \n",
+      "\n",
+      "        supplier  \n",
+      "0       TechCorp  \n",
+      "1       TechCorp  \n",
+      "2  AccessoryCorp  \n",
+      "3     MobileCorp  \n",
+      "4     MobileCorp  \n",
+      "\n",
+      "Data consistency check:\n",
+      "Order amounts match calculated amounts: False\n",
+      "\n",
+      "Mismatched records: 3\n",
+      "   order_id     product  amount  calculated_amount\n",
+      "2       108       Mouse      50                100\n",
+      "3       102       Phone     800               1600\n",
+      "9       107  Headphones     150                450\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Three-way join: Customers + Orders + Products\n",
+    "print(\"=== THREE-WAY JOIN ===\")\n",
+    "\n",
+    "# Step 1: Join customers and orders\n",
+    "customer_orders = pd.merge(df_customers, df_orders, on='customer_id', how='inner')\n",
+    "print(f\"After joining customers and orders: {customer_orders.shape}\")\n",
+    "\n",
+    "# Step 2: Join with products\n",
+    "complete_data = pd.merge(customer_orders, df_products, on='product', how='left')\n",
+    "print(f\"After joining with products: {complete_data.shape}\")\n",
+    "\n",
+    "# Display comprehensive view\n",
+    "print(\"\\nComplete order information:\")\n",
+    "display_cols = ['customer_name', 'order_id', 'product', 'category', 'quantity', 'amount', 'price', 'supplier']\n",
+    "print(complete_data[display_cols].head())\n",
+    "\n",
+    "# Verify data consistency\n",
+    "print(\"\\nData consistency check:\")\n",
+    "# Check if order amount matches product price * quantity\n",
+    "complete_data['calculated_amount'] = complete_data['price'] * complete_data['quantity']\n",
+    "amount_matches = (complete_data['amount'] == complete_data['calculated_amount']).all()\n",
+    "print(f\"Order amounts match calculated amounts: {amount_matches}\")\n",
+    "\n",
+    "if not amount_matches:\n",
+    "    mismatched = complete_data[complete_data['amount'] != complete_data['calculated_amount']]\n",
+    "    print(f\"\\nMismatched records: {len(mismatched)}\")\n",
+    "    print(mismatched[['order_id', 'product', 'amount', 'calculated_amount']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== ADDING CUSTOMER SEGMENTS ===\n",
+      "Customers with segments shape: (10, 8)\n",
+      "\n",
+      "Customers without segment info: 2\n",
+      "   customer_name      city\n",
+      "8       Ivy Chen    Dallas\n",
+      "9  Jack Robinson  San Jose\n",
+      "\n",
+      "Full customer profile shape: (11, 17)\n",
+      "\n",
+      "Revenue by customer segment:\n",
+      "          Total_Revenue  Avg_Order_Value  Total_Orders  Unique_Customers\n",
+      "segment                                                                 \n",
+      "Basic               150           150.00             1                 1\n",
+      "Premium            3725           620.83             6                 3\n",
+      "Standard           2200           550.00             4                 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add customer segment information\n",
+    "print(\"=== ADDING CUSTOMER SEGMENTS ===\")\n",
+    "\n",
+    "# Join with segments (left join to keep all customers)\n",
+    "customers_with_segments = pd.merge(df_customers, df_segments, on='customer_id', how='left')\n",
+    "print(f\"Customers with segments shape: {customers_with_segments.shape}\")\n",
+    "\n",
+    "# Check which customers don't have segment information\n",
+    "missing_segments = customers_with_segments[customers_with_segments['segment'].isnull()]\n",
+    "print(f\"\\nCustomers without segment info: {len(missing_segments)}\")\n",
+    "if len(missing_segments) > 0:\n",
+    "    print(missing_segments[['customer_name', 'city']])\n",
+    "\n",
+    "# Create comprehensive customer profile\n",
+    "full_customer_profile = pd.merge(complete_data, df_segments, on='customer_id', how='left')\n",
+    "print(f\"\\nFull customer profile shape: {full_customer_profile.shape}\")\n",
+    "\n",
+    "# Analyze by segment\n",
+    "segment_analysis = full_customer_profile.groupby('segment').agg({\n",
+    "    'amount': ['sum', 'mean', 'count'],\n",
+    "    'customer_id': 'nunique'\n",
+    "}).round(2)\n",
+    "segment_analysis.columns = ['Total_Revenue', 'Avg_Order_Value', 'Total_Orders', 'Unique_Customers']\n",
+    "print(\"\\nRevenue by customer segment:\")\n",
+    "print(segment_analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Advanced Merge Techniques\n",
+    "\n",
+    "Handling complex merging scenarios and edge cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MERGE WITH DIFFERENT COLUMN NAMES ===\n",
+      "Merge with different column names:\n",
+      "   customer_name  customer_id  cust_id income_range education\n",
+      "0  Alice Johnson            1      1.0       50-75k  Bachelor\n",
+      "1      Bob Smith            2      2.0      75-100k    Master\n",
+      "2  Charlie Brown            3      3.0       50-75k       PhD\n",
+      "3   Diana Prince            4      4.0        100k+    Master\n",
+      "4     Eve Wilson            5      5.0       25-50k  Bachelor\n",
+      "\n",
+      "After cleanup: (10, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge with different column names\n",
+    "print(\"=== MERGE WITH DIFFERENT COLUMN NAMES ===\")\n",
+    "\n",
+    "# Create a dataset with different column name\n",
+    "customer_demographics = pd.DataFrame({\n",
+    "    'cust_id': [1, 2, 3, 4, 5],\n",
+    "    'income_range': ['50-75k', '75-100k', '50-75k', '100k+', '25-50k'],\n",
+    "    'education': ['Bachelor', 'Master', 'PhD', 'Master', 'Bachelor'],\n",
+    "    'occupation': ['Engineer', 'Manager', 'Professor', 'Director', 'Analyst']\n",
+    "})\n",
+    "\n",
+    "# Merge using left_on and right_on parameters\n",
+    "customers_with_demographics = pd.merge(\n",
+    "    df_customers, \n",
+    "    customer_demographics, \n",
+    "    left_on='customer_id', \n",
+    "    right_on='cust_id', \n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "print(\"Merge with different column names:\")\n",
+    "print(customers_with_demographics[['customer_name', 'customer_id', 'cust_id', 'income_range', 'education']].head())\n",
+    "\n",
+    "# Clean up duplicate columns\n",
+    "customers_with_demographics = customers_with_demographics.drop('cust_id', axis=1)\n",
+    "print(f\"\\nAfter cleanup: {customers_with_demographics.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MERGE ON MULTIPLE COLUMNS ===\n",
+      "Orders with time-based pricing:\n",
+      "   order_id  product order_date  amount   price promotion\n",
+      "0       101   Laptop 2023-06-04    1200  1200.0     False\n",
+      "1       102    Phone 2023-06-11     800   800.0     False\n",
+      "2       103   Tablet 2023-06-18     400   400.0     False\n",
+      "3       104   Laptop 2023-06-25    1200  1200.0     False\n",
+      "4       105  Monitor 2023-07-02     300     NaN       NaN\n",
+      "\n",
+      "Orders with pricing discrepancies: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge on multiple columns\n",
+    "print(\"=== MERGE ON MULTIPLE COLUMNS ===\")\n",
+    "\n",
+    "# Create time-based pricing data\n",
+    "pricing_data = pd.DataFrame({\n",
+    "    'product': ['Laptop', 'Laptop', 'Phone', 'Phone', 'Tablet', 'Tablet'],\n",
+    "    'date': pd.to_datetime(['2023-06-01', '2023-08-01', '2023-06-01', '2023-08-01', '2023-06-01', '2023-08-01']),\n",
+    "    'price': [1200, 1100, 800, 750, 400, 380],\n",
+    "    'promotion': [False, True, False, True, False, True]\n",
+    "})\n",
+    "\n",
+    "# Add year-month to orders for matching\n",
+    "df_orders_with_period = df_orders.copy()\n",
+    "df_orders_with_period['order_month'] = df_orders_with_period['order_date'].dt.to_period('M').dt.start_time\n",
+    "\n",
+    "# Create matching periods in pricing data\n",
+    "pricing_data['period'] = pricing_data['date'].dt.to_period('M').dt.start_time\n",
+    "\n",
+    "# Merge on product and time period\n",
+    "orders_with_pricing = pd.merge(\n",
+    "    df_orders_with_period,\n",
+    "    pricing_data,\n",
+    "    left_on=['product', 'order_month'],\n",
+    "    right_on=['product', 'period'],\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "print(\"Orders with time-based pricing:\")\n",
+    "print(orders_with_pricing[['order_id', 'product', 'order_date', 'amount', 'price', 'promotion']].head())\n",
+    "\n",
+    "# Check for pricing discrepancies\n",
+    "pricing_discrepancies = orders_with_pricing[\n",
+    "    (orders_with_pricing['amount'] != orders_with_pricing['price'] * orders_with_pricing['quantity']) &\n",
+    "    orders_with_pricing['price'].notna()\n",
+    "]\n",
+    "print(f\"\\nOrders with pricing discrepancies: {len(pricing_discrepancies)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== HANDLING DUPLICATE KEYS ===\n",
+      "Customer contacts with duplicates:\n",
+      "   customer_id contact_type      contact_value  is_primary\n",
+      "0            1        email    alice@email.com        True\n",
+      "1            1        phone           555-0101       False\n",
+      "2            2        email      bob@email.com        True\n",
+      "3            2        phone           555-0102        True\n",
+      "4            3        email  charlie@email.com        True\n",
+      "\n",
+      "Result of merge with duplicates: (5, 9)\n",
+      "   customer_name contact_type      contact_value  is_primary\n",
+      "0  Alice Johnson        email    alice@email.com        True\n",
+      "1  Alice Johnson        phone           555-0101       False\n",
+      "2      Bob Smith        email      bob@email.com        True\n",
+      "3      Bob Smith        phone           555-0102        True\n",
+      "4  Charlie Brown        email  charlie@email.com        True\n",
+      "\n",
+      "After filtering to primary contacts: (11, 9)\n",
+      "\n",
+      "Pivoted contacts:\n",
+      "contact_type  customer_id              email     phone\n",
+      "0                       1    alice@email.com  555-0101\n",
+      "1                       2      bob@email.com  555-0102\n",
+      "2                       3  charlie@email.com       NaN\n",
+      "\n",
+      "After merging pivoted contacts: (10, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Handling duplicate keys in merge\n",
+    "print(\"=== HANDLING DUPLICATE KEYS ===\")\n",
+    "\n",
+    "# Create data with duplicate keys\n",
+    "customer_contacts = pd.DataFrame({\n",
+    "    'customer_id': [1, 1, 2, 2, 3],\n",
+    "    'contact_type': ['email', 'phone', 'email', 'phone', 'email'],\n",
+    "    'contact_value': ['alice@email.com', '555-0101', 'bob@email.com', '555-0102', 'charlie@email.com'],\n",
+    "    'is_primary': [True, False, True, True, True]\n",
+    "})\n",
+    "\n",
+    "print(\"Customer contacts with duplicates:\")\n",
+    "print(customer_contacts)\n",
+    "\n",
+    "# Merge will create cartesian product for duplicate keys\n",
+    "customers_with_contacts = pd.merge(df_customers, customer_contacts, on='customer_id', how='inner')\n",
+    "print(f\"\\nResult of merge with duplicates: {customers_with_contacts.shape}\")\n",
+    "print(customers_with_contacts[['customer_name', 'contact_type', 'contact_value', 'is_primary']].head())\n",
+    "\n",
+    "# Strategy 1: Filter before merge\n",
+    "primary_contacts = customer_contacts[customer_contacts['is_primary'] == True]\n",
+    "customers_primary_contacts = pd.merge(df_customers, primary_contacts, on='customer_id', how='left')\n",
+    "print(f\"\\nAfter filtering to primary contacts: {customers_primary_contacts.shape}\")\n",
+    "\n",
+    "# Strategy 2: Pivot contacts to columns\n",
+    "contacts_pivoted = customer_contacts.pivot_table(\n",
+    "    index='customer_id',\n",
+    "    columns='contact_type',\n",
+    "    values='contact_value',\n",
+    "    aggfunc='first'\n",
+    ").reset_index()\n",
+    "print(\"\\nPivoted contacts:\")\n",
+    "print(contacts_pivoted)\n",
+    "\n",
+    "customers_with_pivoted_contacts = pd.merge(df_customers, contacts_pivoted, on='customer_id', how='left')\n",
+    "print(f\"\\nAfter merging pivoted contacts: {customers_with_pivoted_contacts.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Index-based Joins\n",
+    "\n",
+    "Using DataFrame indices for joining operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== INDEX-BASED JOINS ===\n",
+      "Customers with index:\n",
+      "             customer_name              email  age         city signup_date\n",
+      "customer_id                                                                \n",
+      "1            Alice Johnson    alice@email.com   28     New York  2023-01-31\n",
+      "2                Bob Smith      bob@email.com   35  Los Angeles  2023-02-28\n",
+      "3            Charlie Brown  charlie@email.com   42      Chicago  2023-03-31\n",
+      "4             Diana Prince    diana@email.com   31      Houston  2023-04-30\n",
+      "5               Eve Wilson      eve@email.com   29      Phoenix  2023-05-31\n",
+      "\n",
+      "Joined by index shape: (10, 7)\n",
+      "             customer_name         city   segment  loyalty_points\n",
+      "customer_id                                                      \n",
+      "1            Alice Johnson     New York   Premium          1500.0\n",
+      "2                Bob Smith  Los Angeles  Standard           800.0\n",
+      "3            Charlie Brown      Chicago   Premium          1200.0\n",
+      "4             Diana Prince      Houston  Standard           600.0\n",
+      "5               Eve Wilson      Phoenix     Basic           200.0\n",
+      "\n",
+      "Equivalent merge shape: (10, 8)\n",
+      "Results are identical: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set up DataFrames with indices\n",
+    "print(\"=== INDEX-BASED JOINS ===\")\n",
+    "\n",
+    "# Set customer_id as index\n",
+    "customers_indexed = df_customers.set_index('customer_id')\n",
+    "segments_indexed = df_segments.set_index('customer_id')\n",
+    "\n",
+    "print(\"Customers with index:\")\n",
+    "print(customers_indexed.head())\n",
+    "\n",
+    "# Join using indices\n",
+    "joined_by_index = customers_indexed.join(segments_indexed, how='left')\n",
+    "print(f\"\\nJoined by index shape: {joined_by_index.shape}\")\n",
+    "print(joined_by_index[['customer_name', 'city', 'segment', 'loyalty_points']].head())\n",
+    "\n",
+    "# Compare with merge\n",
+    "merged_equivalent = pd.merge(df_customers, df_segments, on='customer_id', how='left')\n",
+    "print(f\"\\nEquivalent merge shape: {merged_equivalent.shape}\")\n",
+    "\n",
+    "# Verify they're the same (after sorting)\n",
+    "joined_sorted = joined_by_index.reset_index().sort_values('customer_id')\n",
+    "merged_sorted = merged_equivalent.sort_values('customer_id')\n",
+    "are_equal = joined_sorted.equals(merged_sorted)\n",
+    "print(f\"Results are identical: {are_equal}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MULTI-INDEX JOINS ===\n",
+      "Sales targets with multi-index:\n",
+      "                sales_target  commission_rate\n",
+      "region product                               \n",
+      "North  Laptop             10             0.05\n",
+      "       Phone              15             0.04\n",
+      "South  Laptop              8             0.06\n",
+      "       Phone              12             0.05\n",
+      "East   Laptop             12             0.05\n",
+      "       Phone              18             0.04\n",
+      "\n",
+      "Orders with sales targets:\n",
+      "   order_id region  product  amount  sales_target  commission_rate\n",
+      "0       101  North   Laptop    1200          10.0             0.05\n",
+      "1       102  South    Phone     800          12.0             0.05\n",
+      "2       103  North   Tablet     400           NaN              NaN\n",
+      "3       104   East   Laptop    1200          12.0             0.05\n",
+      "4       105  North  Monitor     300           NaN              NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Multi-index joins\n",
+    "print(\"=== MULTI-INDEX JOINS ===\")\n",
+    "\n",
+    "# Create a dataset with multiple index levels\n",
+    "sales_by_region_product = pd.DataFrame({\n",
+    "    'region': ['North', 'North', 'South', 'South', 'East', 'East'],\n",
+    "    'product': ['Laptop', 'Phone', 'Laptop', 'Phone', 'Laptop', 'Phone'],\n",
+    "    'sales_target': [10, 15, 8, 12, 12, 18],\n",
+    "    'commission_rate': [0.05, 0.04, 0.06, 0.05, 0.05, 0.04]\n",
+    "})\n",
+    "\n",
+    "# Set multi-index\n",
+    "sales_targets = sales_by_region_product.set_index(['region', 'product'])\n",
+    "print(\"Sales targets with multi-index:\")\n",
+    "print(sales_targets)\n",
+    "\n",
+    "# Create customer orders with region mapping\n",
+    "customer_regions = {\n",
+    "    1: 'North', 2: 'South', 3: 'East', 4: 'North', 5: 'South', 6: 'East'\n",
+    "}\n",
+    "\n",
+    "orders_with_region = df_orders.copy()\n",
+    "orders_with_region['region'] = orders_with_region['customer_id'].map(customer_regions)\n",
+    "orders_with_region = orders_with_region.dropna(subset=['region'])\n",
+    "\n",
+    "# Merge on multiple columns to match multi-index\n",
+    "orders_with_targets = pd.merge(\n",
+    "    orders_with_region,\n",
+    "    sales_targets.reset_index(),\n",
+    "    on=['region', 'product'],\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "print(\"\\nOrders with sales targets:\")\n",
+    "print(orders_with_targets[['order_id', 'region', 'product', 'amount', 'sales_target', 'commission_rate']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Concatenation Operations\n",
+    "\n",
+    "Combining DataFrames vertically and horizontally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== VERTICAL CONCATENATION ===\n",
+      "Original customers: 10\n",
+      "New customers: 5\n",
+      "Combined customers: 15\n",
+      "\n",
+      "Combined customer data:\n",
+      "    customer_id  customer_name             email  age     city signup_date\n",
+      "10           11    Kate Wilson    kate@email.com   26   Austin  2024-01-31\n",
+      "11           12     Liam Brown    liam@email.com   39  Seattle  2024-02-29\n",
+      "12           13     Mia Garcia     mia@email.com   31   Denver  2024-03-31\n",
+      "13           14     Noah Jones    noah@email.com   44   Boston  2024-04-30\n",
+      "14           15  Olivia Miller  olivia@email.com   28    Miami  2024-05-31\n",
+      "\n",
+      "After adding customers with extra info: (17, 7)\n",
+      "Missing values in referral_source:\n",
+      "15\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Vertical concatenation (stacking DataFrames)\n",
+    "print(\"=== VERTICAL CONCATENATION ===\")\n",
+    "\n",
+    "# Create additional customer data (new batch)\n",
+    "new_customers = pd.DataFrame({\n",
+    "    'customer_id': [11, 12, 13, 14, 15],\n",
+    "    'customer_name': ['Kate Wilson', 'Liam Brown', 'Mia Garcia', 'Noah Jones', 'Olivia Miller'],\n",
+    "    'email': ['kate@email.com', 'liam@email.com', 'mia@email.com', 'noah@email.com', 'olivia@email.com'],\n",
+    "    'age': [26, 39, 31, 44, 28],\n",
+    "    'city': ['Austin', 'Seattle', 'Denver', 'Boston', 'Miami'],\n",
+    "    'signup_date': pd.date_range('2024-01-01', periods=5, freq='M')\n",
+    "})\n",
+    "\n",
+    "# Concatenate vertically\n",
+    "all_customers = pd.concat([df_customers, new_customers], ignore_index=True)\n",
+    "print(f\"Original customers: {len(df_customers)}\")\n",
+    "print(f\"New customers: {len(new_customers)}\")\n",
+    "print(f\"Combined customers: {len(all_customers)}\")\n",
+    "\n",
+    "print(\"\\nCombined customer data:\")\n",
+    "print(all_customers.tail())\n",
+    "\n",
+    "# Concatenation with different columns\n",
+    "customers_with_extra_info = pd.DataFrame({\n",
+    "    'customer_id': [16, 17],\n",
+    "    'customer_name': ['Paul Davis', 'Quinn Taylor'],\n",
+    "    'email': ['paul@email.com', 'quinn@email.com'],\n",
+    "    'age': [35, 29],\n",
+    "    'city': ['Portland', 'Nashville'],\n",
+    "    'signup_date': pd.date_range('2024-06-01', periods=2, freq='M'),\n",
+    "    'referral_source': ['Google', 'Facebook']  # Extra column\n",
+    "})\n",
+    "\n",
+    "# Concat with different columns (creates NaN for missing columns)\n",
+    "all_customers_extended = pd.concat([all_customers, customers_with_extra_info], ignore_index=True, sort=False)\n",
+    "print(f\"\\nAfter adding customers with extra info: {all_customers_extended.shape}\")\n",
+    "print(\"Missing values in referral_source:\")\n",
+    "print(all_customers_extended['referral_source'].isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== HORIZONTAL CONCATENATION ===\n",
+      "Customer basic info:\n",
+      "   customer_id  customer_name              email\n",
+      "0            1  Alice Johnson    alice@email.com\n",
+      "1            2      Bob Smith      bob@email.com\n",
+      "2            3  Charlie Brown  charlie@email.com\n",
+      "3            4   Diana Prince    diana@email.com\n",
+      "4            5     Eve Wilson      eve@email.com\n",
+      "\n",
+      "Customer demographics:\n",
+      "   customer_id  age         city signup_date\n",
+      "0            1   28     New York  2023-01-31\n",
+      "1            2   35  Los Angeles  2023-02-28\n",
+      "2            3   42      Chicago  2023-03-31\n",
+      "3            4   31      Houston  2023-04-30\n",
+      "4            5   29      Phoenix  2023-05-31\n",
+      "\n",
+      "Recombined shape: (10, 6)\n",
+      "   customer_id  customer_name              email  age         city signup_date\n",
+      "0            1  Alice Johnson    alice@email.com   28     New York  2023-01-31\n",
+      "1            2      Bob Smith      bob@email.com   35  Los Angeles  2023-02-28\n",
+      "2            3  Charlie Brown  charlie@email.com   42      Chicago  2023-03-31\n",
+      "3            4   Diana Prince    diana@email.com   31      Houston  2023-04-30\n",
+      "4            5     Eve Wilson      eve@email.com   29      Phoenix  2023-05-31\n",
+      "\n",
+      "Columns match original: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Horizontal concatenation\n",
+    "print(\"=== HORIZONTAL CONCATENATION ===\")\n",
+    "\n",
+    "# Split customer data into parts\n",
+    "customer_basic_info = df_customers[['customer_id', 'customer_name', 'email']]\n",
+    "customer_demographics = df_customers[['customer_id', 'age', 'city', 'signup_date']]\n",
+    "\n",
+    "print(\"Customer basic info:\")\n",
+    "print(customer_basic_info.head())\n",
+    "\n",
+    "print(\"\\nCustomer demographics:\")\n",
+    "print(customer_demographics.head())\n",
+    "\n",
+    "# Concatenate horizontally (by index)\n",
+    "customers_recombined = pd.concat([customer_basic_info, customer_demographics.drop('customer_id', axis=1)], axis=1)\n",
+    "print(f\"\\nRecombined shape: {customers_recombined.shape}\")\n",
+    "print(customers_recombined.head())\n",
+    "\n",
+    "# Verify it matches original\n",
+    "columns_match = set(customers_recombined.columns) == set(df_customers.columns)\n",
+    "print(f\"\\nColumns match original: {columns_match}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== CONCAT WITH KEYS ===\n",
+      "Quarterly sales with hierarchical index:\n",
+      "     product  units_sold  revenue\n",
+      "Q1 0  Laptop          50    60000\n",
+      "   1   Phone          75    60000\n",
+      "   2  Tablet          30    12000\n",
+      "Q2 0  Laptop          45    54000\n",
+      "   1   Phone          80    64000\n",
+      "   2  Tablet          35    14000\n",
+      "\n",
+      "Q1 sales only:\n",
+      "  product  units_sold  revenue\n",
+      "0  Laptop          50    60000\n",
+      "1   Phone          75    60000\n",
+      "2  Tablet          30    12000\n",
+      "\n",
+      "Quarterly comparison (side by side):\n",
+      "                Q1                 Q2        \n",
+      "        units_sold revenue units_sold revenue\n",
+      "product                                      \n",
+      "Laptop          50   60000         45   54000\n",
+      "Phone           75   60000         80   64000\n",
+      "Tablet          30   12000         35   14000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Concat with keys (creating hierarchical columns)\n",
+    "print(\"=== CONCAT WITH KEYS ===\")\n",
+    "\n",
+    "# Create quarterly sales data\n",
+    "q1_sales = pd.DataFrame({\n",
+    "    'product': ['Laptop', 'Phone', 'Tablet'],\n",
+    "    'units_sold': [50, 75, 30],\n",
+    "    'revenue': [60000, 60000, 12000]\n",
+    "})\n",
+    "\n",
+    "q2_sales = pd.DataFrame({\n",
+    "    'product': ['Laptop', 'Phone', 'Tablet'],\n",
+    "    'units_sold': [45, 80, 35],\n",
+    "    'revenue': [54000, 64000, 14000]\n",
+    "})\n",
+    "\n",
+    "# Concatenate with keys\n",
+    "quarterly_sales = pd.concat([q1_sales, q2_sales], keys=['Q1', 'Q2'])\n",
+    "print(\"Quarterly sales with hierarchical index:\")\n",
+    "print(quarterly_sales)\n",
+    "\n",
+    "# Access specific quarter\n",
+    "print(\"\\nQ1 sales only:\")\n",
+    "print(quarterly_sales.loc['Q1'])\n",
+    "\n",
+    "# Create summary comparison\n",
+    "quarterly_comparison = pd.concat([q1_sales.set_index('product'), q2_sales.set_index('product')], \n",
+    "                                keys=['Q1', 'Q2'], axis=1)\n",
+    "print(\"\\nQuarterly comparison (side by side):\")\n",
+    "print(quarterly_comparison)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Performance and Best Practices\n",
+    "\n",
+    "Optimizing merge operations and avoiding common pitfalls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== PERFORMANCE COMPARISON ===\n",
+      "Large customers: (10000, 3)\n",
+      "Large orders: (50000, 3)\n",
+      "\n",
+      "Merge time: 0.0077 seconds\n",
+      "Join time: 0.0092 seconds\n",
+      "Join is 0.83x faster\n",
+      "\n",
+      "Results shape - Merge: (50000, 5), Join: (50000, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Performance comparison: merge vs join\n",
+    "import time\n",
+    "\n",
+    "print(\"=== PERFORMANCE COMPARISON ===\")\n",
+    "\n",
+    "# Create larger datasets for performance testing\n",
+    "np.random.seed(42)\n",
+    "large_customers = pd.DataFrame({\n",
+    "    'customer_id': range(1, 10001),\n",
+    "    'customer_name': [f'Customer_{i}' for i in range(1, 10001)],\n",
+    "    'city': np.random.choice(['New York', 'Los Angeles', 'Chicago'], 10000)\n",
+    "})\n",
+    "\n",
+    "large_orders = pd.DataFrame({\n",
+    "    'order_id': range(1, 50001),\n",
+    "    'customer_id': np.random.randint(1, 10001, 50000),\n",
+    "    'amount': np.random.normal(100, 30, 50000)\n",
+    "})\n",
+    "\n",
+    "print(f\"Large customers: {large_customers.shape}\")\n",
+    "print(f\"Large orders: {large_orders.shape}\")\n",
+    "\n",
+    "# Test merge performance\n",
+    "start_time = time.time()\n",
+    "merged_result = pd.merge(large_customers, large_orders, on='customer_id', how='inner')\n",
+    "merge_time = time.time() - start_time\n",
+    "\n",
+    "# Test join performance\n",
+    "customers_indexed = large_customers.set_index('customer_id')\n",
+    "orders_indexed = large_orders.set_index('customer_id')\n",
+    "\n",
+    "start_time = time.time()\n",
+    "joined_result = customers_indexed.join(orders_indexed, how='inner')\n",
+    "join_time = time.time() - start_time\n",
+    "\n",
+    "print(f\"\\nMerge time: {merge_time:.4f} seconds\")\n",
+    "print(f\"Join time: {join_time:.4f} seconds\")\n",
+    "print(f\"Join is {merge_time/join_time:.2f}x faster\")\n",
+    "\n",
+    "print(f\"\\nResults shape - Merge: {merged_result.shape}, Join: {joined_result.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== BEST PRACTICES ===\n",
+      "\n",
+      "--- Analyzing merge on 'customer_id' ---\n",
+      "Duplicates in left table: 0\n",
+      "Duplicates in right table: 5\n",
+      "Missing values in left table: 0\n",
+      "Missing values in right table: 0\n",
+      "Keys in both tables: 6\n",
+      "Keys only in left: 4\n",
+      "Keys only in right: 1\n",
+      "Warning: Duplicates present, result size may be larger than expected\n",
+      "\n",
+      "Predicted result sizes:\n",
+      "Inner join: Cannot predict (duplicates present)\n",
+      "Left join: Cannot predict (duplicates present)\n",
+      "Right join: Cannot predict (duplicates present)\n",
+      "Outer join: Cannot predict (duplicates present)\n",
+      "\n",
+      "--- Analyzing merge on 'customer_id' ---\n",
+      "Duplicates in left table: 0\n",
+      "Duplicates in right table: 0\n",
+      "Missing values in left table: 0\n",
+      "Missing values in right table: 0\n",
+      "Keys in both tables: 8\n",
+      "Keys only in left: 2\n",
+      "Keys only in right: 2\n",
+      "\n",
+      "Predicted result sizes:\n",
+      "Inner join: 8\n",
+      "Left join: 10\n",
+      "Right join: 10\n",
+      "Outer join: 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Best practices and common pitfalls\n",
+    "print(\"=== BEST PRACTICES ===\")\n",
+    "\n",
+    "def analyze_merge_keys(df1, df2, key_col):\n",
+    "    \"\"\"Analyze merge keys before joining\"\"\"\n",
+    "    print(f\"\\n--- Analyzing merge on '{key_col}' ---\")\n",
+    "    \n",
+    "    # Check for duplicates\n",
+    "    df1_dups = df1[key_col].duplicated().sum()\n",
+    "    df2_dups = df2[key_col].duplicated().sum()\n",
+    "    \n",
+    "    print(f\"Duplicates in left table: {df1_dups}\")\n",
+    "    print(f\"Duplicates in right table: {df2_dups}\")\n",
+    "    \n",
+    "    # Check for missing values\n",
+    "    df1_missing = df1[key_col].isnull().sum()\n",
+    "    df2_missing = df2[key_col].isnull().sum()\n",
+    "    \n",
+    "    print(f\"Missing values in left table: {df1_missing}\")\n",
+    "    print(f\"Missing values in right table: {df2_missing}\")\n",
+    "    \n",
+    "    # Check overlap\n",
+    "    left_keys = set(df1[key_col].dropna())\n",
+    "    right_keys = set(df2[key_col].dropna())\n",
+    "    \n",
+    "    overlap = left_keys & right_keys\n",
+    "    left_only = left_keys - right_keys\n",
+    "    right_only = right_keys - left_keys\n",
+    "    \n",
+    "    print(f\"Keys in both tables: {len(overlap)}\")\n",
+    "    print(f\"Keys only in left: {len(left_only)}\")\n",
+    "    print(f\"Keys only in right: {len(right_only)}\")\n",
+    "    \n",
+    "    # Predict result sizes\n",
+    "    if df1_dups == 0 and df2_dups == 0:\n",
+    "        inner_size = len(overlap)\n",
+    "        left_size = len(df1)\n",
+    "        right_size = len(df2)\n",
+    "        outer_size = len(left_keys | right_keys)\n",
+    "    else:\n",
+    "        print(\"Warning: Duplicates present, result size may be larger than expected\")\n",
+    "        inner_size = \"Cannot predict (duplicates present)\"\n",
+    "        left_size = \"Cannot predict (duplicates present)\"\n",
+    "        right_size = \"Cannot predict (duplicates present)\"\n",
+    "        outer_size = \"Cannot predict (duplicates present)\"\n",
+    "    \n",
+    "    print(f\"\\nPredicted result sizes:\")\n",
+    "    print(f\"Inner join: {inner_size}\")\n",
+    "    print(f\"Left join: {left_size}\")\n",
+    "    print(f\"Right join: {right_size}\")\n",
+    "    print(f\"Outer join: {outer_size}\")\n",
+    "\n",
+    "# Analyze our sample data\n",
+    "analyze_merge_keys(df_customers, df_orders, 'customer_id')\n",
+    "analyze_merge_keys(df_customers, df_segments, 'customer_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== MERGE VALIDATION ===\n",
+      "Result shape: (11, 11)\n",
+      "⚠️  Found 5 duplicate keys after merge\n",
+      "\n",
+      "Data types:\n",
+      "customer_id               int64\n",
+      "customer_name            object\n",
+      "email                    object\n",
+      "age                       int64\n",
+      "city                     object\n",
+      "signup_date      datetime64[ns]\n",
+      "order_id                  int64\n",
+      "order_date       datetime64[ns]\n",
+      "product                  object\n",
+      "quantity                  int64\n",
+      "amount                    int64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data validation after merge\n",
+    "def validate_merge_result(df, expected_rows=None, key_col=None):\n",
+    "    \"\"\"Validate merge results\"\"\"\n",
+    "    print(\"\\n=== MERGE VALIDATION ===\")\n",
+    "    \n",
+    "    print(f\"Result shape: {df.shape}\")\n",
+    "    \n",
+    "    if expected_rows:\n",
+    "        print(f\"Expected rows: {expected_rows}\")\n",
+    "        if len(df) != expected_rows:\n",
+    "            print(\"⚠️  Row count doesn't match expectation!\")\n",
+    "    \n",
+    "    # Check for unexpected duplicates\n",
+    "    if key_col and key_col in df.columns:\n",
+    "        duplicates = df[key_col].duplicated().sum()\n",
+    "        if duplicates > 0:\n",
+    "            print(f\"⚠️  Found {duplicates} duplicate keys after merge\")\n",
+    "    \n",
+    "    # Check for missing values in key columns\n",
+    "    missing_summary = df.isnull().sum()\n",
+    "    critical_missing = missing_summary[missing_summary > 0]\n",
+    "    \n",
+    "    if len(critical_missing) > 0:\n",
+    "        print(\"Missing values after merge:\")\n",
+    "        print(critical_missing)\n",
+    "    \n",
+    "    # Data type consistency\n",
+    "    print(f\"\\nData types:\")\n",
+    "    print(df.dtypes)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# Example validation\n",
+    "sample_merge = pd.merge(df_customers, df_orders, on='customer_id', how='inner')\n",
+    "validated_result = validate_merge_result(sample_merge, key_col='customer_id')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply merging and joining techniques to real-world scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Customer Lifetime Value Analysis\n",
+    "# Create a comprehensive customer analysis by joining:\n",
+    "# - Customer demographics\n",
+    "# - Order history\n",
+    "# - Product information\n",
+    "# - Customer segments\n",
+    "# Calculate CLV metrics for each customer\n",
+    "\n",
+    "def calculate_customer_lifetime_value(customers, orders, products, segments):\n",
+    "    \"\"\"Calculate comprehensive customer lifetime value metrics\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# clv_analysis = calculate_customer_lifetime_value(df_customers, df_orders, df_products, df_segments)\n",
+    "# print(\"Customer Lifetime Value Analysis:\")\n",
+    "# print(clv_analysis.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Data Quality Assessment\n",
+    "# Create a function that analyzes data quality issues when merging multiple datasets:\n",
+    "# - Identify orphaned records\n",
+    "# - Find data inconsistencies\n",
+    "# - Suggest data cleaning steps\n",
+    "# - Provide merge recommendations\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Time-series Join Challenge\n",
+    "# Create a complex time-based join scenario:\n",
+    "# - Join orders with time-varying product prices\n",
+    "# - Handle seasonal promotions\n",
+    "# - Calculate accurate historical revenue\n",
+    "# - Account for price changes over time\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Join Types**:\n",
+    "   - **Inner**: Only matching records from both tables\n",
+    "   - **Left**: All records from left table + matching from right\n",
+    "   - **Right**: All records from right table + matching from left\n",
+    "   - **Outer**: All records from both tables\n",
+    "\n",
+    "2. **Method Selection**:\n",
+    "   - **`pd.merge()`**: Most flexible, works with any columns\n",
+    "   - **`.join()`**: Faster for index-based joins\n",
+    "   - **`pd.concat()`**: For stacking DataFrames vertically/horizontally\n",
+    "\n",
+    "3. **Best Practices**:\n",
+    "   - Always analyze merge keys before joining\n",
+    "   - Check for duplicates and missing values\n",
+    "   - Validate results after merging\n",
+    "   - Use appropriate join types for your use case\n",
+    "   - Consider performance implications for large datasets\n",
+    "\n",
+    "4. **Common Pitfalls**:\n",
+    "   - Cartesian products from duplicate keys\n",
+    "   - Unexpected result sizes\n",
+    "   - Data type inconsistencies\n",
+    "   - Missing value propagation\n",
+    "\n",
+    "## Join Type Selection Guide\n",
+    "\n",
+    "| Use Case | Recommended Join | Rationale |\n",
+    "|----------|-----------------|----------|\n",
+    "| Customer orders analysis | Inner | Only customers with orders |\n",
+    "| Customer segmentation | Left | Keep all customers, add segment info |\n",
+    "| Order validation | Right | Keep all orders, check customer validity |\n",
+    "| Data completeness analysis | Outer | See all records and identify gaps |\n",
+    "| Performance-critical operations | Index-based join | Faster execution |\n",
+    "\n",
+    "## Performance Tips\n",
+    "\n",
+    "1. **Index Usage**: Set indexes for frequently joined columns\n",
+    "2. **Data Types**: Ensure consistent data types before joining\n",
+    "3. **Memory Management**: Consider chunking for very large datasets\n",
+    "4. **Join Order**: Start with smallest datasets\n",
+    "5. **Validation**: Always validate merge results"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/09_pivot_tables.ipynb b/Session_01/PandasDataFrame-exmples/09_pivot_tables.ipynb
new file mode 100755
index 0000000..3546b1e
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/09_pivot_tables.ipynb
@@ -0,0 +1,1978 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 9: Pivot Tables and Data Reshaping\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Master pivot table creation and customization\n",
+    "- Understand data reshaping with melt, pivot, stack, and unstack\n",
+    "- Learn cross-tabulation and contingency tables\n",
+    "- Practice with multi-level indexing and hierarchical data\n",
+    "- Apply reshaping techniques to real-world analysis scenarios\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-8\n",
+    "- Understanding of aggregation and groupby operations\n",
+    "- Familiarity with Excel pivot tables (helpful but not required)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Libraries loaded successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set display options\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', 20)\n",
+    "pd.set_option('display.width', None)\n",
+    "\n",
+    "print(\"Libraries loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Sample Dataset\n",
+    "\n",
+    "Let's create a comprehensive business dataset for pivot table examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Business dataset created:\n",
+      "Shape: (1000, 20)\n",
+      "\n",
+      "First few rows:\n",
+      "        date   salesperson region product_category   product customer_type  \\\n",
+      "0 2024-01-01     Grace Lee  North         Clothing     Shoes     Returning   \n",
+      "1 2024-01-02  Diana Prince   East         Clothing  Magazine     Returning   \n",
+      "2 2024-01-03   Henry Davis   West            Books     Shoes           VIP   \n",
+      "3 2024-01-04    Eve Wilson   West         Clothing     Novel           New   \n",
+      "4 2024-01-05     Grace Lee   East         Clothing    Laptop           VIP   \n",
+      "\n",
+      "  sales_channel  quantity  unit_price  discount_percent  shipping_cost  \\\n",
+      "0        Online         1       72.81                 0          16.13   \n",
+      "1         Store         9       99.98                15          10.42   \n",
+      "2        Online         6        4.61                 0           8.73   \n",
+      "3         Phone         8       31.51                 0           8.27   \n",
+      "4         Store         3       86.28                15          11.61   \n",
+      "\n",
+      "   gross_sales  discount_amount  net_sales  total_order  year  month  quarter  \\\n",
+      "0        72.81            0.000     72.810       88.940  2024      1        1   \n",
+      "1       899.82          134.973    764.847      775.267  2024      1        1   \n",
+      "2        27.66            0.000     27.660       36.390  2024      1        1   \n",
+      "3       252.08            0.000    252.080      260.350  2024      1        1   \n",
+      "4       258.84           38.826    220.014      231.624  2024      1        1   \n",
+      "\n",
+      "  day_of_week month_name  \n",
+      "0      Monday    January  \n",
+      "1     Tuesday    January  \n",
+      "2   Wednesday    January  \n",
+      "3    Thursday    January  \n",
+      "4      Friday    January  \n",
+      "\n",
+      "Column info:\n",
+      "date                datetime64[ns]\n",
+      "salesperson                 object\n",
+      "region                      object\n",
+      "product_category            object\n",
+      "product                     object\n",
+      "customer_type               object\n",
+      "sales_channel               object\n",
+      "quantity                     int64\n",
+      "unit_price                 float64\n",
+      "discount_percent             int64\n",
+      "shipping_cost              float64\n",
+      "gross_sales                float64\n",
+      "discount_amount            float64\n",
+      "net_sales                  float64\n",
+      "total_order                float64\n",
+      "year                         int32\n",
+      "month                        int32\n",
+      "quarter                      int32\n",
+      "day_of_week                 object\n",
+      "month_name                  object\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create comprehensive business dataset\n",
+    "np.random.seed(42)\n",
+    "n_records = 1000\n",
+    "\n",
+    "# Generate realistic business data\n",
+    "business_data = {\n",
+    "    'date': pd.date_range('2024-01-01', periods=n_records, freq='D'),\n",
+    "    'salesperson': np.random.choice([\n",
+    "        'Alice Johnson', 'Bob Smith', 'Charlie Brown', 'Diana Prince', 'Eve Wilson',\n",
+    "        'Frank Miller', 'Grace Lee', 'Henry Davis', 'Ivy Chen', 'Jack Robinson'\n",
+    "    ], n_records),\n",
+    "    'region': np.random.choice(['North', 'South', 'East', 'West'], n_records),\n",
+    "    'product_category': np.random.choice(['Electronics', 'Clothing', 'Books', 'Home & Garden'], n_records),\n",
+    "    'product': np.random.choice([\n",
+    "        'Laptop', 'Phone', 'Tablet', 'Headphones', 'Speaker',\n",
+    "        'Shirt', 'Pants', 'Shoes', 'Jacket', 'Hat',\n",
+    "        'Novel', 'Textbook', 'Magazine', 'Comic', 'Cookbook',\n",
+    "        'Plant', 'Tool', 'Furniture', 'Decoration', 'Garden'\n",
+    "    ], n_records),\n",
+    "    'customer_type': np.random.choice(['New', 'Returning', 'VIP'], n_records, p=[0.3, 0.5, 0.2]),\n",
+    "    'sales_channel': np.random.choice(['Online', 'Store', 'Phone'], n_records, p=[0.6, 0.3, 0.1]),\n",
+    "    'quantity': np.random.randint(1, 10, n_records),\n",
+    "    'unit_price': np.random.normal(50, 20, n_records),\n",
+    "    'discount_percent': np.random.choice([0, 5, 10, 15, 20], n_records, p=[0.5, 0.2, 0.2, 0.08, 0.02]),\n",
+    "    'shipping_cost': np.random.normal(8, 3, n_records)\n",
+    "}\n",
+    "\n",
+    "df_business = pd.DataFrame(business_data)\n",
+    "\n",
+    "# Clean and calculate derived fields\n",
+    "df_business['unit_price'] = np.abs(df_business['unit_price']).round(2)\n",
+    "df_business['shipping_cost'] = np.abs(df_business['shipping_cost']).round(2)\n",
+    "df_business['gross_sales'] = df_business['quantity'] * df_business['unit_price']\n",
+    "df_business['discount_amount'] = df_business['gross_sales'] * df_business['discount_percent'] / 100\n",
+    "df_business['net_sales'] = df_business['gross_sales'] - df_business['discount_amount']\n",
+    "df_business['total_order'] = df_business['net_sales'] + df_business['shipping_cost']\n",
+    "\n",
+    "# Add time-based columns\n",
+    "df_business['year'] = df_business['date'].dt.year\n",
+    "df_business['month'] = df_business['date'].dt.month\n",
+    "df_business['quarter'] = df_business['date'].dt.quarter\n",
+    "df_business['day_of_week'] = df_business['date'].dt.day_name()\n",
+    "df_business['month_name'] = df_business['date'].dt.month_name()\n",
+    "\n",
+    "print(\"Business dataset created:\")\n",
+    "print(f\"Shape: {df_business.shape}\")\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "print(df_business.head())\n",
+    "print(\"\\nColumn info:\")\n",
+    "print(df_business.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Basic Pivot Tables\n",
+    "\n",
+    "Creating fundamental pivot tables for data summarization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== BASIC PIVOT TABLES ===\n",
+      "Sales by Region and Product Category:\n",
+      "product_category    Books  Clothing  Electronics  Home & Garden\n",
+      "region                                                         \n",
+      "East              12377.0   12466.0      16463.0        14140.0\n",
+      "North             13492.0   14938.0      15229.0        17770.0\n",
+      "South             17590.0   16946.0      14552.0        13695.0\n",
+      "West               8157.0   16544.0      12681.0        15652.0\n",
+      "\n",
+      "Average Order Value by Customer Type and Sales Channel:\n",
+      "sales_channel  Online   Phone   Store\n",
+      "customer_type                        \n",
+      "New            263.07  244.46  222.16\n",
+      "Returning      246.03  220.45  224.39\n",
+      "VIP            234.35  237.16  253.16\n",
+      "\n",
+      "Transaction Count by Region and Customer Type:\n",
+      "customer_type  New  Returning  VIP\n",
+      "region                            \n",
+      "East            60        128   60\n",
+      "North           70        144   53\n",
+      "South           68        138   45\n",
+      "West            61        119   54\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Simple pivot table - sales by region and product category\n",
+    "print(\"=== BASIC PIVOT TABLES ===\")\n",
+    "\n",
+    "# Basic pivot: sum of sales by region and product category\n",
+    "basic_pivot = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='region',\n",
+    "    columns='product_category',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"Sales by Region and Product Category:\")\n",
+    "print(basic_pivot.round(0))\n",
+    "\n",
+    "# Average order value by customer type and sales channel\n",
+    "avg_order_pivot = df_business.pivot_table(\n",
+    "    values='total_order',\n",
+    "    index='customer_type',\n",
+    "    columns='sales_channel',\n",
+    "    aggfunc='mean',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nAverage Order Value by Customer Type and Sales Channel:\")\n",
+    "print(avg_order_pivot.round(2))\n",
+    "\n",
+    "# Count of transactions\n",
+    "transaction_count = df_business.pivot_table(\n",
+    "    values='total_order',\n",
+    "    index='region',\n",
+    "    columns='customer_type',\n",
+    "    aggfunc='count',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nTransaction Count by Region and Customer Type:\")\n",
+    "print(transaction_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MULTIPLE AGGREGATION FUNCTIONS ===\n",
+      "Multiple aggregations (sum, mean, count):\n",
+      "                       sum                                        mean  \\\n",
+      "product_category     Books  Clothing Electronics Home & Garden   Books   \n",
+      "region                                                                   \n",
+      "East              12376.85  12465.78    16462.72      14139.75  217.14   \n",
+      "North             13491.69  14938.35    15228.66      17769.95  214.15   \n",
+      "South             17589.76  16945.54    14551.58      13695.40  266.51   \n",
+      "West               8156.64  16544.42    12680.58      15651.63  189.69   \n",
+      "\n",
+      "                                                    count           \\\n",
+      "product_category Clothing Electronics Home & Garden Books Clothing   \n",
+      "region                                                               \n",
+      "East               197.87      238.59        239.66    57       63   \n",
+      "North              226.34      220.71        257.54    63       66   \n",
+      "South              260.70      234.70        236.13    66       65   \n",
+      "West               239.77      218.63        244.56    43       69   \n",
+      "\n",
+      "                                            \n",
+      "product_category Electronics Home & Garden  \n",
+      "region                                      \n",
+      "East                      69            59  \n",
+      "North                     69            69  \n",
+      "South                     62            58  \n",
+      "West                      58            64  \n",
+      "\n",
+      "Mixed aggregations for different metrics:\n",
+      "              net_sales                    quantity             total_order  \\\n",
+      "sales_channel    Online    Phone     Store   Online Phone Store      Online   \n",
+      "region                                                                        \n",
+      "East           34662.14  4164.23  16618.73      731    90   372      246.72   \n",
+      "North          36698.38  6597.29  18133.00      780   141   370      235.80   \n",
+      "South          38569.20  5475.73  18737.36      778   128   369      263.71   \n",
+      "West           38003.32  3745.70  11284.26      805    83   264      245.57   \n",
+      "\n",
+      "                               \n",
+      "sales_channel   Phone   Store  \n",
+      "region                         \n",
+      "East           239.96  203.20  \n",
+      "North          243.26  240.63  \n",
+      "South          227.11  258.94  \n",
+      "West           204.47  213.05  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Multiple aggregation functions in one pivot table\n",
+    "print(\"=== MULTIPLE AGGREGATION FUNCTIONS ===\")\n",
+    "\n",
+    "# Multiple aggregations for comprehensive analysis\n",
+    "multi_agg_pivot = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='region',\n",
+    "    columns='product_category',\n",
+    "    aggfunc=['sum', 'mean', 'count'],\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"Multiple aggregations (sum, mean, count):\")\n",
+    "print(multi_agg_pivot.round(2))\n",
+    "\n",
+    "# Different values with different aggregations\n",
+    "mixed_agg_pivot = df_business.pivot_table(\n",
+    "    values=['net_sales', 'quantity', 'total_order'],\n",
+    "    index='region',\n",
+    "    columns='sales_channel',\n",
+    "    aggfunc={\n",
+    "        'net_sales': 'sum',\n",
+    "        'quantity': 'sum',\n",
+    "        'total_order': 'mean'\n",
+    "    },\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nMixed aggregations for different metrics:\")\n",
+    "print(mixed_agg_pivot.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== PIVOT TABLES WITH TOTALS ===\n",
+      "Pivot table with row and column totals:\n",
+      "product_category    Books  Clothing  Electronics  Home & Garden     Total\n",
+      "region                                                                   \n",
+      "East              12377.0   12466.0      16463.0        14140.0   55445.0\n",
+      "North             13492.0   14938.0      15229.0        17770.0   61429.0\n",
+      "South             17590.0   16946.0      14552.0        13695.0   62782.0\n",
+      "West               8157.0   16544.0      12681.0        15652.0   53033.0\n",
+      "Total             51615.0   60894.0      58924.0        61257.0  232689.0\n",
+      "\n",
+      "Sales distribution as percentages:\n",
+      "product_category  Books  Clothing  Electronics  Home & Garden   Total\n",
+      "region                                                               \n",
+      "East               5.32      5.36         7.07           6.08   23.83\n",
+      "North              5.80      6.42         6.54           7.64   26.40\n",
+      "South              7.56      7.28         6.25           5.89   26.98\n",
+      "West               3.51      7.11         5.45           6.73   22.79\n",
+      "Total             22.18     26.17        25.32          26.33  100.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pivot tables with totals and margins\n",
+    "print(\"=== PIVOT TABLES WITH TOTALS ===\")\n",
+    "\n",
+    "# Add margins (totals) to pivot table\n",
+    "pivot_with_totals = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='region',\n",
+    "    columns='product_category',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0,\n",
+    "    margins=True,\n",
+    "    margins_name='Total'\n",
+    ")\n",
+    "\n",
+    "print(\"Pivot table with row and column totals:\")\n",
+    "print(pivot_with_totals.round(0))\n",
+    "\n",
+    "# Calculate percentages of total\n",
+    "pivot_percentages = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='region',\n",
+    "    columns='product_category',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0,\n",
+    "    margins=True,\n",
+    "    margins_name='Total'\n",
+    ")\n",
+    "\n",
+    "# Convert to percentages (excluding totals row/column for calculation)\n",
+    "total_sales = pivot_percentages.loc['Total', 'Total']\n",
+    "pivot_pct = (pivot_percentages / total_sales * 100).round(2)\n",
+    "\n",
+    "print(\"\\nSales distribution as percentages:\")\n",
+    "print(pivot_pct)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Advanced Pivot Table Techniques\n",
+    "\n",
+    "Complex pivot tables with multiple indices and custom aggregations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MULTI-LEVEL INDEX PIVOT TABLES ===\n",
+      "Hierarchical pivot (Region > Salesperson vs Product Category):\n",
+      "product_category          Books   Clothing  Electronics  Home & Garden\n",
+      "region salesperson                                                    \n",
+      "East   Alice Johnson  1537.0605   838.5400    1792.4760      1693.2745\n",
+      "       Bob Smith      2039.4035  1156.4895    1110.9565       950.9340\n",
+      "       Charlie Brown   435.2560   952.0800    2610.3275      1098.6095\n",
+      "       Diana Prince   1874.3990  1845.6785    1898.2705      1610.8440\n",
+      "       Eve Wilson     1585.4275  1534.6530    1395.2180      1179.5475\n",
+      "       Frank Miller   1106.1950  1476.6940     945.8550       988.5600\n",
+      "       Grace Lee      1556.9905  1298.2700     865.6170       938.2240\n",
+      "       Henry Davis     875.1640   725.9365    2336.0330      1322.1800\n",
+      "       Ivy Chen        797.3240  1608.6365    1110.4660      1724.6260\n",
+      "       Jack Robinson   569.6290  1028.8020    2397.5045      2632.9480\n",
+      "North  Alice Johnson  2314.9745  2757.4270    1911.9930      1856.1875\n",
+      "       Bob Smith      1031.4940   489.7395     801.7940       796.1610\n",
+      "       Charlie Brown   498.9095  1879.5460    1790.1620      1393.1530\n",
+      "       Diana Prince    214.3950  1484.9535    1053.2905      2467.8815\n",
+      "       Eve Wilson     2040.3085  1654.9070    1178.6505      1498.5810\n",
+      "       Frank Miller   2509.5095  1490.6560    1264.5290       766.4540\n",
+      "       Grace Lee       748.8710  1293.0970     833.7205      2235.9345\n",
+      "       Henry Davis     815.0200  1089.6130    1072.8980      1280.2690\n",
+      "       Ivy Chen       2411.9690  1079.7175    1762.1170      1244.5645\n",
+      "       Jack Robinson   906.2425  1718.6920    3559.5100      4230.7635\n",
+      "\n",
+      "Hierarchical columns (Product Category > Customer Type):\n",
+      "product_category   Books                   Clothing                    \\\n",
+      "customer_type        New Returning     VIP      New Returning     VIP   \n",
+      "region                                                                  \n",
+      "East              2077.0    7770.0  2530.0   2680.0    6856.0  2929.0   \n",
+      "North             2546.0    7153.0  3792.0   2959.0    7514.0  4465.0   \n",
+      "South             2831.0   10393.0  4366.0   6472.0    8685.0  1788.0   \n",
+      "West              3128.0    2906.0  2123.0   4974.0    8570.0  3000.0   \n",
+      "\n",
+      "product_category Electronics                   Home & Garden                    \n",
+      "customer_type            New Returning     VIP           New Returning     VIP  \n",
+      "region                                                                          \n",
+      "East                  4562.0    7630.0  4271.0        3974.0    7367.0  2799.0  \n",
+      "North                 6068.0    6693.0  2467.0        4970.0   10655.0  2145.0  \n",
+      "South                 4919.0    6629.0  3004.0        2979.0    7527.0  3189.0  \n",
+      "West                  3013.0    7416.0  2252.0        4220.0    7502.0  3929.0  \n",
+      "\n",
+      "Full hierarchical pivot (limited sample):\n",
+      "product_category   Books                Clothing               \n",
+      "sales_channel     Online  Phone   Store   Online  Phone   Store\n",
+      "region quarter                                                 \n",
+      "East   1          2693.0  261.0  1483.0   1873.0  573.0  2045.0\n",
+      "       2          1735.0  233.0  1766.0   4208.0  644.0   529.0\n",
+      "       3          1358.0    0.0  1284.0    867.0    0.0   225.0\n",
+      "       4          1036.0    0.0   527.0    998.0    0.0   503.0\n",
+      "North  1          2012.0    0.0  1019.0   1987.0  948.0  1759.0\n",
+      "       2          1806.0  375.0  2181.0   1836.0  471.0   499.0\n",
+      "       3          2210.0  348.0   806.0   2702.0  305.0   311.0\n",
+      "       4          1649.0    0.0  1086.0   2437.0    0.0  1682.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Multi-level index pivot tables\n",
+    "print(\"=== MULTI-LEVEL INDEX PIVOT TABLES ===\")\n",
+    "\n",
+    "# Hierarchical rows\n",
+    "hierarchical_pivot = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index=['region', 'salesperson'],\n",
+    "    columns='product_category',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"Hierarchical pivot (Region > Salesperson vs Product Category):\")\n",
+    "print(hierarchical_pivot.head(20))\n",
+    "\n",
+    "# Hierarchical columns\n",
+    "hierarchical_cols_pivot = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='region',\n",
+    "    columns=['product_category', 'customer_type'],\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nHierarchical columns (Product Category > Customer Type):\")\n",
+    "print(hierarchical_cols_pivot.round(0))\n",
+    "\n",
+    "# Both hierarchical rows and columns\n",
+    "full_hierarchical = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index=['region', 'quarter'],\n",
+    "    columns=['product_category', 'sales_channel'],\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nFull hierarchical pivot (limited sample):\")\n",
+    "print(full_hierarchical.iloc[:8, :6].round(0))  # Show subset for readability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== CUSTOM AGGREGATION FUNCTIONS ===\n",
+      "Custom aggregations pivot table:\n",
+      "                    mean                                        std           \\\n",
+      "product_category   Books Clothing Electronics Home & Garden   Books Clothing   \n",
+      "region                                                                         \n",
+      "East              217.14   197.87      238.59        239.66  133.32   176.45   \n",
+      "North             214.15   226.34      220.71        257.54  144.81   157.82   \n",
+      "South             266.51   260.70      234.70        236.13  153.97   158.86   \n",
+      "West              189.69   239.77      218.63        244.56  125.47   163.81   \n",
+      "\n",
+      "                                           coefficient_of_variation           \\\n",
+      "product_category Electronics Home & Garden                    Books Clothing   \n",
+      "region                                                                         \n",
+      "East                  154.31        150.89                     0.61     0.89   \n",
+      "North                 165.37        174.64                     0.68     0.70   \n",
+      "South                 175.74        165.01                     0.58     0.61   \n",
+      "West                  143.66        167.40                     0.66     0.68   \n",
+      "\n",
+      "                                           sales_range                       \\\n",
+      "product_category Electronics Home & Garden       Books Clothing Electronics   \n",
+      "region                                                                        \n",
+      "East                    0.65          0.63      648.70   758.44      697.50   \n",
+      "North                   0.75          0.68      531.31   663.18      752.50   \n",
+      "South                   0.75          0.70      634.55   641.48      773.92   \n",
+      "West                    0.66          0.68      436.05   629.05      638.34   \n",
+      "\n",
+      "                                \n",
+      "product_category Home & Garden  \n",
+      "region                          \n",
+      "East                    583.40  \n",
+      "North                   619.36  \n",
+      "South                   659.43  \n",
+      "West                    656.63  \n",
+      "\n",
+      "Lambda function aggregations:\n",
+      "              <lambda_0>                 <lambda_1>             <lambda_2>  \\\n",
+      "sales_channel     Online   Phone   Store     Online Phone Store     Online   \n",
+      "region                                                                       \n",
+      "East              330.66  337.74  265.63         59     9    38     109.56   \n",
+      "North             340.40  317.78  358.12         72    15    34     318.50   \n",
+      "South             375.57  293.52  359.79         66    11    35     156.12   \n",
+      "West              352.13  323.46  267.42         69     8    22      54.42   \n",
+      "\n",
+      "                                mean                  \n",
+      "sales_channel  Phone   Store  Online   Phone   Store  \n",
+      "region                                                \n",
+      "East           16.17  168.84  239.05  231.35  195.51  \n",
+      "North          72.98   38.41  227.94  235.62  232.47  \n",
+      "South          42.03   14.63  255.43  219.03  249.83  \n",
+      "West           53.92   53.58  237.52  197.14  205.17  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Custom aggregation functions\n",
+    "print(\"=== CUSTOM AGGREGATION FUNCTIONS ===\")\n",
+    "\n",
+    "# Define custom aggregation functions\n",
+    "def coefficient_of_variation(series):\n",
+    "    \"\"\"Calculate coefficient of variation (std/mean)\"\"\"\n",
+    "    return series.std() / series.mean() if series.mean() != 0 else 0\n",
+    "\n",
+    "def sales_range(series):\n",
+    "    \"\"\"Calculate range (max - min)\"\"\"\n",
+    "    return series.max() - series.min()\n",
+    "\n",
+    "def high_value_count(series, threshold=100):\n",
+    "    \"\"\"Count values above threshold\"\"\"\n",
+    "    return (series > threshold).sum()\n",
+    "\n",
+    "# Apply custom aggregations\n",
+    "custom_agg_pivot = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='region',\n",
+    "    columns='product_category',\n",
+    "    aggfunc=[np.mean, np.std, coefficient_of_variation, sales_range],\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"Custom aggregations pivot table:\")\n",
+    "print(custom_agg_pivot.round(2))\n",
+    "\n",
+    "# Lambda functions for inline custom aggregations\n",
+    "lambda_agg_pivot = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='region',\n",
+    "    columns='sales_channel',\n",
+    "    aggfunc={\n",
+    "        'net_sales': [\n",
+    "            'mean',\n",
+    "            lambda x: x.quantile(0.75),  # 75th percentile\n",
+    "            lambda x: (x > x.mean()).sum(),  # Count above average\n",
+    "            lambda x: x.max() / x.min() if x.min() > 0 else 0  # Max/Min ratio\n",
+    "        ]\n",
+    "    },\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nLambda function aggregations:\")\n",
+    "print(lambda_agg_pivot.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== TIME-BASED PIVOT TABLES ===\n",
+      "Monthly sales by product category:\n",
+      "product_category   Books  Clothing  Electronics  Home & Garden\n",
+      "month_name                                                    \n",
+      "January           4813.0    7034.0       4631.0         3365.0\n",
+      "February          4022.0    8492.0       3016.0         5593.0\n",
+      "March             6809.0    3461.0       5373.0         6898.0\n",
+      "April             5464.0    5000.0       5719.0         5014.0\n",
+      "May               5767.0    4023.0       5243.0         6586.0\n",
+      "June              4104.0    8360.0       7231.0         2890.0\n",
+      "July              5149.0    3183.0       6009.0         4328.0\n",
+      "August            3052.0    6048.0       6709.0         5114.0\n",
+      "September         4366.0    3026.0       4470.0         8162.0\n",
+      "October           3442.0    4719.0       2349.0         4465.0\n",
+      "November          2562.0    3316.0       3646.0         5111.0\n",
+      "December          2064.0    4231.0       4528.0         3729.0\n",
+      "\n",
+      "Day of week analysis:\n",
+      "              net_sales                    quantity            \n",
+      "sales_channel    Online    Phone     Store   Online Phone Store\n",
+      "day_of_week                                                    \n",
+      "Monday         18611.51  3866.50  10728.14     4.80  5.14  4.91\n",
+      "Tuesday        19606.58  3841.09   9615.84     4.90  5.75  4.68\n",
+      "Wednesday      22785.70   945.00   9312.78     5.14  4.60  5.00\n",
+      "Thursday       22420.93  5605.94   8475.84     5.35  5.75  4.89\n",
+      "Friday         21358.62  1274.59  11041.92     5.06  3.18  5.36\n",
+      "Saturday       24008.00  1851.37   6557.88     4.85  4.10  4.06\n",
+      "Sunday         19141.68  2598.45   9040.94     4.99  4.57  3.88\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Time-based pivot tables\n",
+    "print(\"=== TIME-BASED PIVOT TABLES ===\")\n",
+    "\n",
+    "# Monthly sales trends by product category\n",
+    "monthly_sales = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='month_name',\n",
+    "    columns='product_category',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "# Reorder months correctly\n",
+    "month_order = ['January', 'February', 'March', 'April', 'May', 'June',\n",
+    "               'July', 'August', 'September', 'October', 'November', 'December']\n",
+    "monthly_sales = monthly_sales.reindex([m for m in month_order if m in monthly_sales.index])\n",
+    "\n",
+    "print(\"Monthly sales by product category:\")\n",
+    "print(monthly_sales.round(0))\n",
+    "\n",
+    "# Day of week analysis\n",
+    "dow_analysis = df_business.pivot_table(\n",
+    "    values=['net_sales', 'quantity'],\n",
+    "    index='day_of_week',\n",
+    "    columns='sales_channel',\n",
+    "    aggfunc={\n",
+    "        'net_sales': 'sum',\n",
+    "        'quantity': 'mean'\n",
+    "    },\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "# Reorder days of week\n",
+    "day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
+    "dow_analysis = dow_analysis.reindex([d for d in day_order if d in dow_analysis.index])\n",
+    "\n",
+    "print(\"\\nDay of week analysis:\")\n",
+    "print(dow_analysis.round(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Cross-tabulation and Contingency Tables\n",
+    "\n",
+    "Analyzing relationships between categorical variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== CROSS-TABULATION ===\n",
+      "Transaction count by Region and Customer Type:\n",
+      "customer_type  New  Returning  VIP\n",
+      "region                            \n",
+      "East            60        128   60\n",
+      "North           70        144   53\n",
+      "South           68        138   45\n",
+      "West            61        119   54\n",
+      "\n",
+      "Total sales by Region and Customer Type:\n",
+      "customer_type      New  Returning      VIP\n",
+      "region                                    \n",
+      "East           13293.0    29623.0  12529.0\n",
+      "North          16544.0    32016.0  12869.0\n",
+      "South          17201.0    33234.0  12347.0\n",
+      "West           15336.0    26394.0  11304.0\n",
+      "\n",
+      "Transaction count by Product Category and Sales Channel (with totals):\n",
+      "sales_channel     Online  Phone  Store  Total\n",
+      "product_category                             \n",
+      "Books                140     15     74    229\n",
+      "Clothing             169     26     68    263\n",
+      "Electronics          166     21     71    258\n",
+      "Home & Garden        142     28     80    250\n",
+      "Total                617     90    293   1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Basic cross-tabulation\n",
+    "print(\"=== CROSS-TABULATION ===\")\n",
+    "\n",
+    "# Simple crosstab - count of transactions\n",
+    "basic_crosstab = pd.crosstab(df_business['region'], df_business['customer_type'])\n",
+    "print(\"Transaction count by Region and Customer Type:\")\n",
+    "print(basic_crosstab)\n",
+    "\n",
+    "# Crosstab with values (not just counts)\n",
+    "sales_crosstab = pd.crosstab(\n",
+    "    df_business['region'],\n",
+    "    df_business['customer_type'],\n",
+    "    values=df_business['net_sales'],\n",
+    "    aggfunc='sum'\n",
+    ")\n",
+    "print(\"\\nTotal sales by Region and Customer Type:\")\n",
+    "print(sales_crosstab.round(0))\n",
+    "\n",
+    "# Crosstab with margins (totals)\n",
+    "crosstab_with_margins = pd.crosstab(\n",
+    "    df_business['product_category'],\n",
+    "    df_business['sales_channel'],\n",
+    "    margins=True,\n",
+    "    margins_name='Total'\n",
+    ")\n",
+    "print(\"\\nTransaction count by Product Category and Sales Channel (with totals):\")\n",
+    "print(crosstab_with_margins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== NORMALIZED CROSS-TABULATION ===\n",
+      "Row percentages (Customer type distribution within each region):\n",
+      "customer_type   New  Returning   VIP\n",
+      "region                              \n",
+      "East           24.2       51.6  24.2\n",
+      "North          26.2       53.9  19.9\n",
+      "South          27.1       55.0  17.9\n",
+      "West           26.1       50.9  23.1\n",
+      "\n",
+      "Column percentages (Region distribution within each customer type):\n",
+      "customer_type   New  Returning   VIP\n",
+      "region                              \n",
+      "East           23.2       24.2  28.3\n",
+      "North          27.0       27.2  25.0\n",
+      "South          26.3       26.1  21.2\n",
+      "West           23.6       22.5  25.5\n",
+      "\n",
+      "Total percentages (Percentage of overall total):\n",
+      "customer_type  New  Returning  VIP\n",
+      "region                            \n",
+      "East           6.0       12.8  6.0\n",
+      "North          7.0       14.4  5.3\n",
+      "South          6.8       13.8  4.5\n",
+      "West           6.1       11.9  5.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Normalized cross-tabulation (percentages)\n",
+    "print(\"=== NORMALIZED CROSS-TABULATION ===\")\n",
+    "\n",
+    "# Normalize by rows (percentage of each row)\n",
+    "crosstab_row_pct = pd.crosstab(\n",
+    "    df_business['region'],\n",
+    "    df_business['customer_type'],\n",
+    "    normalize='index'  # Normalize by rows\n",
+    ") * 100\n",
+    "\n",
+    "print(\"Row percentages (Customer type distribution within each region):\")\n",
+    "print(crosstab_row_pct.round(1))\n",
+    "\n",
+    "# Normalize by columns (percentage of each column)\n",
+    "crosstab_col_pct = pd.crosstab(\n",
+    "    df_business['region'],\n",
+    "    df_business['customer_type'],\n",
+    "    normalize='columns'  # Normalize by columns\n",
+    ") * 100\n",
+    "\n",
+    "print(\"\\nColumn percentages (Region distribution within each customer type):\")\n",
+    "print(crosstab_col_pct.round(1))\n",
+    "\n",
+    "# Normalize by total (percentage of grand total)\n",
+    "crosstab_total_pct = pd.crosstab(\n",
+    "    df_business['region'],\n",
+    "    df_business['customer_type'],\n",
+    "    normalize='all'  # Normalize by total\n",
+    ") * 100\n",
+    "\n",
+    "print(\"\\nTotal percentages (Percentage of overall total):\")\n",
+    "print(crosstab_total_pct.round(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MULTI-DIMENSIONAL CROSS-TABULATION ===\n",
+      "Three-way analysis (Region & Product Category vs Customer Type):\n",
+      "customer_type               New  Returning     VIP\n",
+      "region product_category                           \n",
+      "East   Books             188.82     228.53  210.82\n",
+      "       Clothing          206.15     221.17  154.18\n",
+      "       Electronics       240.11     224.41  266.92\n",
+      "       Home & Garden     233.78     254.03  215.29\n",
+      "North  Books             254.64     183.41  270.87\n",
+      "       Clothing          211.37     208.73  279.06\n",
+      "       Electronics       233.40     230.79  176.23\n",
+      "       Home & Garden     248.49     266.38  238.33\n",
+      "South  Books             202.21     273.49  311.87\n",
+      "       Clothing          340.66     234.73  198.67\n",
+      "       Electronics       245.96     220.96  250.30\n",
+      "       Home & Garden     198.59     228.10  318.92\n",
+      "West   Books             223.46     193.70  151.62\n",
+      "       Clothing          248.72     231.62  250.01\n",
+      "       Electronics       251.07     211.88  204.71\n",
+      "       Home & Garden     281.34     234.44  231.13\n",
+      "\n",
+      "Multiple aggregations crosstab:\n",
+      "               count                   sum                       mean          \\\n",
+      "sales_channel Online Phone Store    Online    Phone     Store  Online   Phone   \n",
+      "region                                                                          \n",
+      "East             145    18    85  34662.14  4164.23  16618.73  239.05  231.35   \n",
+      "North            161    28    78  36698.38  6597.29  18133.00  227.94  235.62   \n",
+      "South            151    25    75  38569.20  5475.73  18737.36  255.43  219.03   \n",
+      "West             160    19    55  38003.32  3745.70  11284.26  237.52  197.14   \n",
+      "\n",
+      "                       \n",
+      "sales_channel   Store  \n",
+      "region                 \n",
+      "East           195.51  \n",
+      "North          232.47  \n",
+      "South          249.83  \n",
+      "West           205.17  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Multi-dimensional cross-tabulation\n",
+    "print(\"=== MULTI-DIMENSIONAL CROSS-TABULATION ===\")\n",
+    "\n",
+    "# Three-way crosstab\n",
+    "three_way_crosstab = pd.crosstab(\n",
+    "    [df_business['region'], df_business['product_category']],\n",
+    "    df_business['customer_type'],\n",
+    "    values=df_business['net_sales'],\n",
+    "    aggfunc='mean'\n",
+    ")\n",
+    "\n",
+    "print(\"Three-way analysis (Region & Product Category vs Customer Type):\")\n",
+    "print(three_way_crosstab.round(2))\n",
+    "\n",
+    "# Analysis with multiple aggregations\n",
+    "multi_agg_crosstab = pd.crosstab(\n",
+    "    df_business['region'],\n",
+    "    df_business['sales_channel'],\n",
+    "    values=df_business['net_sales'],\n",
+    "    aggfunc=['count', 'sum', 'mean']\n",
+    ")\n",
+    "\n",
+    "print(\"\\nMultiple aggregations crosstab:\")\n",
+    "print(multi_agg_crosstab.round(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Data Reshaping: Melt, Pivot, Stack, Unstack\n",
+    "\n",
+    "Transforming data between wide and long formats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MELTING DATA (WIDE TO LONG) ===\n",
+      "Wide format data:\n",
+      "product_category    salesperson      Books   Clothing  Electronics  \\\n",
+      "0                 Alice Johnson  7260.4360  7069.2950    8358.1560   \n",
+      "1                     Bob Smith  4511.1555  4336.2970    3679.3625   \n",
+      "2                 Charlie Brown  3677.6895  7830.9720   10264.1605   \n",
+      "3                  Diana Prince  4861.2975  6275.8940    3348.5570   \n",
+      "4                    Eve Wilson  6363.1345  7208.5765    5191.2705   \n",
+      "\n",
+      "product_category  Home & Garden  \n",
+      "0                     5283.4600  \n",
+      "1                     5164.6310  \n",
+      "2                     4281.9240  \n",
+      "3                     6108.1495  \n",
+      "4                     6582.1870  \n",
+      "\n",
+      "Long format data (melted):\n",
+      "     salesperson product_category  total_sales\n",
+      "0  Alice Johnson            Books    7260.4360\n",
+      "1      Bob Smith            Books    4511.1555\n",
+      "2  Charlie Brown            Books    3677.6895\n",
+      "3   Diana Prince            Books    4861.2975\n",
+      "4     Eve Wilson            Books    6363.1345\n",
+      "5   Frank Miller            Books    5489.7555\n",
+      "6      Grace Lee            Books    5945.8655\n",
+      "7    Henry Davis            Books    5273.0950\n",
+      "8       Ivy Chen            Books    4812.9940\n",
+      "9  Jack Robinson            Books    3419.5195\n",
+      "\n",
+      "Complex wide format:\n",
+      "product_category region    salesperson      Books   Clothing  Electronics  \\\n",
+      "0                  East  Alice Johnson  1537.0605   838.5400    1792.4760   \n",
+      "1                  East      Bob Smith  2039.4035  1156.4895    1110.9565   \n",
+      "2                  East  Charlie Brown   435.2560   952.0800    2610.3275   \n",
+      "3                  East   Diana Prince  1874.3990  1845.6785    1898.2705   \n",
+      "4                  East     Eve Wilson  1585.4275  1534.6530    1395.2180   \n",
+      "\n",
+      "product_category  Home & Garden  \n",
+      "0                     1693.2745  \n",
+      "1                      950.9340  \n",
+      "2                     1098.6095  \n",
+      "3                     1610.8440  \n",
+      "4                     1179.5475  \n",
+      "\n",
+      "Complex long format:\n",
+      "  region    salesperson product_category  total_sales\n",
+      "0   East  Alice Johnson            Books    1537.0605\n",
+      "1   East      Bob Smith            Books    2039.4035\n",
+      "2   East  Charlie Brown            Books     435.2560\n",
+      "3   East   Diana Prince            Books    1874.3990\n",
+      "4   East     Eve Wilson            Books    1585.4275\n",
+      "5   East   Frank Miller            Books    1106.1950\n",
+      "6   East      Grace Lee            Books    1556.9905\n",
+      "7   East    Henry Davis            Books     875.1640\n",
+      "8   East       Ivy Chen            Books     797.3240\n",
+      "9   East  Jack Robinson            Books     569.6290\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Melting data from wide to long format\n",
+    "print(\"=== MELTING DATA (WIDE TO LONG) ===\")\n",
+    "\n",
+    "# Create a wide format dataset first\n",
+    "wide_sales = df_business.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='salesperson',\n",
+    "    columns='product_category',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0\n",
+    ").reset_index()\n",
+    "\n",
+    "print(\"Wide format data:\")\n",
+    "print(wide_sales.head())\n",
+    "\n",
+    "# Melt to long format\n",
+    "long_sales = pd.melt(\n",
+    "    wide_sales,\n",
+    "    id_vars=['salesperson'],\n",
+    "    var_name='product_category',\n",
+    "    value_name='total_sales'\n",
+    ")\n",
+    "\n",
+    "print(\"\\nLong format data (melted):\")\n",
+    "print(long_sales.head(10))\n",
+    "\n",
+    "# Melt with multiple ID variables\n",
+    "# First create a more complex wide dataset\n",
+    "complex_wide = df_business.groupby(['region', 'salesperson', 'product_category'])['net_sales'].sum().unstack(fill_value=0).reset_index()\n",
+    "\n",
+    "print(\"\\nComplex wide format:\")\n",
+    "print(complex_wide.head())\n",
+    "\n",
+    "# Melt with multiple ID vars\n",
+    "complex_long = pd.melt(\n",
+    "    complex_wide,\n",
+    "    id_vars=['region', 'salesperson'],\n",
+    "    var_name='product_category',\n",
+    "    value_name='total_sales'\n",
+    ")\n",
+    "\n",
+    "print(\"\\nComplex long format:\")\n",
+    "print(complex_long.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== PIVOT OPERATION (LONG TO WIDE) ===\n",
+      "Long format data sample:\n",
+      "  region product_category customer_type  net_sales\n",
+      "0  North         Clothing     Returning     72.810\n",
+      "1   East         Clothing     Returning    764.847\n",
+      "2   West            Books           VIP     27.660\n",
+      "3   West         Clothing           New    252.080\n",
+      "4   East         Clothing           VIP    220.014\n",
+      "\n",
+      "Pivoted to wide format:\n",
+      "product_category    Books  Clothing  Electronics  Home & Garden\n",
+      "region                                                         \n",
+      "East              12377.0   12466.0      16463.0        14140.0\n",
+      "North             13492.0   14938.0      15229.0        17770.0\n",
+      "South             17590.0   16946.0      14552.0        13695.0\n",
+      "West               8157.0   16544.0      12681.0        15652.0\n",
+      "\n",
+      "Pivoted with reset index:\n",
+      "product_category region       Books    Clothing  Electronics  Home & Garden\n",
+      "0                  East  12376.8490  12465.7800   16462.7240     14139.7475\n",
+      "1                 North  13491.6935  14938.3485   15228.6645     17769.9495\n",
+      "2                 South  17589.7640  16945.5435   14551.5775     13695.4025\n",
+      "3                  West   8156.6360  16544.4195   12680.5820     15651.6335\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pivot operation (long to wide)\n",
+    "print(\"=== PIVOT OPERATION (LONG TO WIDE) ===\")\n",
+    "\n",
+    "# Create long format data\n",
+    "long_data = df_business[['region', 'product_category', 'customer_type', 'net_sales']].copy()\n",
+    "print(\"Long format data sample:\")\n",
+    "print(long_data.head())\n",
+    "\n",
+    "# Simple pivot\n",
+    "pivoted_data = long_data.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='region',\n",
+    "    columns='product_category',\n",
+    "    aggfunc='sum'\n",
+    ")\n",
+    "\n",
+    "print(\"\\nPivoted to wide format:\")\n",
+    "print(pivoted_data.round(0))\n",
+    "\n",
+    "# Reset index to make it a regular DataFrame\n",
+    "pivoted_reset = pivoted_data.reset_index()\n",
+    "print(\"\\nPivoted with reset index:\")\n",
+    "print(pivoted_reset.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== STACK AND UNSTACK OPERATIONS ===\n",
+      "Multi-index DataFrame:\n",
+      "customer_type                  New   Returning        VIP\n",
+      "region product_category                                  \n",
+      "East   Books             2077.0530   7769.9840  2529.8120\n",
+      "       Clothing          2679.9640   6856.4155  2929.4005\n",
+      "       Electronics       4562.0515   7629.9035  4270.7690\n",
+      "       Home & Garden     3974.1930   7366.8250  2798.7295\n",
+      "North  Books             2546.4320   7153.0835  3792.1780\n",
+      "       Clothing          2959.1455   7514.3230  4464.8800\n",
+      "       Electronics       6068.4815   6692.9180  2467.2650\n",
+      "       Home & Garden     4969.7755  10655.2095  2144.9645\n",
+      "South  Books             2830.9150  10392.7060  4366.1430\n",
+      "       Clothing          6472.4515   8685.0290  1788.0630\n",
+      "\n",
+      "After stacking (columns become rows):\n",
+      "region  product_category  customer_type\n",
+      "East    Books             New              2077.0530\n",
+      "                          Returning        7769.9840\n",
+      "                          VIP              2529.8120\n",
+      "        Clothing          New              2679.9640\n",
+      "                          Returning        6856.4155\n",
+      "                          VIP              2929.4005\n",
+      "        Electronics       New              4562.0515\n",
+      "                          Returning        7629.9035\n",
+      "                          VIP              4270.7690\n",
+      "        Home & Garden     New              3974.1930\n",
+      "dtype: float64\n",
+      "\n",
+      "After unstacking (back to original):\n",
+      "customer_type                  New   Returning        VIP\n",
+      "region product_category                                  \n",
+      "East   Books             2077.0530   7769.9840  2529.8120\n",
+      "       Clothing          2679.9640   6856.4155  2929.4005\n",
+      "       Electronics       4562.0515   7629.9035  4270.7690\n",
+      "       Home & Garden     3974.1930   7366.8250  2798.7295\n",
+      "North  Books             2546.4320   7153.0835  3792.1780\n",
+      "       Clothing          2959.1455   7514.3230  4464.8800\n",
+      "       Electronics       6068.4815   6692.9180  2467.2650\n",
+      "       Home & Garden     4969.7755  10655.2095  2144.9645\n",
+      "South  Books             2830.9150  10392.7060  4366.1430\n",
+      "       Clothing          6472.4515   8685.0290  1788.0630\n",
+      "\n",
+      "Unstacking level 0 (region):\n",
+      "region                               East      North       South       West\n",
+      "product_category customer_type                                             \n",
+      "Books            New            2077.0530  2546.4320   2830.9150  3128.4435\n",
+      "                 Returning      7769.9840  7153.0835  10392.7060  2905.5140\n",
+      "                 VIP            2529.8120  3792.1780   4366.1430  2122.6785\n",
+      "Clothing         New            2679.9640  2959.1455   6472.4515  4974.4750\n",
+      "                 Returning      6856.4155  7514.3230   8685.0290  8569.8735\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stack and Unstack operations\n",
+    "print(\"=== STACK AND UNSTACK OPERATIONS ===\")\n",
+    "\n",
+    "# Create a DataFrame with MultiIndex\n",
+    "multi_index_df = df_business.groupby(['region', 'product_category', 'customer_type'])['net_sales'].sum().reset_index()\n",
+    "multi_pivot = multi_index_df.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index=['region', 'product_category'],\n",
+    "    columns='customer_type',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"Multi-index DataFrame:\")\n",
+    "print(multi_pivot.head(10))\n",
+    "\n",
+    "# Stack operation (columns to rows)\n",
+    "stacked = multi_pivot.stack()\n",
+    "print(\"\\nAfter stacking (columns become rows):\")\n",
+    "print(stacked.head(10))\n",
+    "\n",
+    "# Unstack operation (rows to columns)\n",
+    "unstacked = stacked.unstack()\n",
+    "print(\"\\nAfter unstacking (back to original):\")\n",
+    "print(unstacked.head(10))\n",
+    "\n",
+    "# Unstack different levels\n",
+    "unstacked_level0 = multi_pivot.stack().unstack(level=0)\n",
+    "print(\"\\nUnstacking level 0 (region):\")\n",
+    "print(unstacked_level0.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Advanced Reshaping Techniques\n",
+    "\n",
+    "Complex data transformations for specialized analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== MULTIPLE VALUE COLUMNS MELTING ===\n",
+      "Wide format with multiple metrics:\n",
+      "  region product_category   net_sales  quantity  total_order\n",
+      "0   East            Books  12376.8490       277   225.021912\n",
+      "1   East         Clothing  12465.7800       266   205.978571\n",
+      "2   East      Electronics  16462.7240       349   245.975420\n",
+      "3   East    Home & Garden  14139.7475       301   247.297246\n",
+      "4  North            Books  13491.6935       296   221.647357\n",
+      "\n",
+      "Long format with multiple metrics:\n",
+      "   region product_category     metric       value\n",
+      "0    East            Books  net_sales  12376.8490\n",
+      "1    East         Clothing  net_sales  12465.7800\n",
+      "2    East      Electronics  net_sales  16462.7240\n",
+      "3    East    Home & Garden  net_sales  14139.7475\n",
+      "4   North            Books  net_sales  13491.6935\n",
+      "5   North         Clothing  net_sales  14938.3485\n",
+      "6   North      Electronics  net_sales  15228.6645\n",
+      "7   North    Home & Garden  net_sales  17769.9495\n",
+      "8   South            Books  net_sales  17589.7640\n",
+      "9   South         Clothing  net_sales  16945.5435\n",
+      "10  South      Electronics  net_sales  14551.5775\n",
+      "11  South    Home & Garden  net_sales  13695.4025\n",
+      "12   West            Books  net_sales   8156.6360\n",
+      "13   West         Clothing  net_sales  16544.4195\n",
+      "14   West      Electronics  net_sales  12680.5820\n",
+      "\n",
+      "Reshaping: Region-Metric vs Product Category:\n",
+      "product_category       Books  Clothing  Electronics  Home & Garden\n",
+      "region metric                                                     \n",
+      "East   net_sales    12376.85  12465.78     16462.72       14139.75\n",
+      "       quantity       277.00    266.00       349.00         301.00\n",
+      "       total_order    225.02    205.98       245.98         247.30\n",
+      "North  net_sales    13491.69  14938.35     15228.66       17769.95\n",
+      "       quantity       296.00    313.00       320.00         362.00\n",
+      "       total_order    221.65    234.40       228.86         265.49\n",
+      "South  net_sales    17589.76  16945.54     14551.58       13695.40\n",
+      "       quantity       356.00    327.00       301.00         291.00\n",
+      "       total_order    274.84    269.23       243.38         244.65\n",
+      "West   net_sales     8156.64  16544.42     12680.58       15651.63\n",
+      "       quantity       217.00    365.00       271.00         299.00\n",
+      "       total_order    198.11    246.91       226.43         253.21\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Wide to long with multiple value columns\n",
+    "print(\"=== MULTIPLE VALUE COLUMNS MELTING ===\")\n",
+    "\n",
+    "# Create dataset with multiple metrics\n",
+    "metrics_wide = df_business.groupby(['region', 'product_category']).agg({\n",
+    "    'net_sales': 'sum',\n",
+    "    'quantity': 'sum',\n",
+    "    'total_order': 'mean'\n",
+    "}).reset_index()\n",
+    "\n",
+    "print(\"Wide format with multiple metrics:\")\n",
+    "print(metrics_wide.head())\n",
+    "\n",
+    "# Melt multiple value columns\n",
+    "metrics_long = pd.melt(\n",
+    "    metrics_wide,\n",
+    "    id_vars=['region', 'product_category'],\n",
+    "    value_vars=['net_sales', 'quantity', 'total_order'],\n",
+    "    var_name='metric',\n",
+    "    value_name='value'\n",
+    ")\n",
+    "\n",
+    "print(\"\\nLong format with multiple metrics:\")\n",
+    "print(metrics_long.head(15))\n",
+    "\n",
+    "# Alternative: melt and then pivot for different structure\n",
+    "metrics_pivot = metrics_long.pivot_table(\n",
+    "    values='value',\n",
+    "    index=['region', 'metric'],\n",
+    "    columns='product_category',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nReshaping: Region-Metric vs Product Category:\")\n",
+    "print(metrics_pivot.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== TIME SERIES PIVOT TABLES ===\n",
+      "Daily sales pivot (first 10 days):\n",
+      "product_category  Books  Clothing  Electronics  Home & Garden\n",
+      "date                                                         \n",
+      "2024-01-01          NaN      73.0          NaN            NaN\n",
+      "2024-01-02          NaN     765.0          NaN            NaN\n",
+      "2024-01-03         28.0       NaN          NaN            NaN\n",
+      "2024-01-04          NaN     252.0          NaN            NaN\n",
+      "2024-01-05          NaN     220.0          NaN            NaN\n",
+      "2024-01-06        112.0       NaN          NaN            NaN\n",
+      "2024-01-07          NaN       NaN          NaN          172.0\n",
+      "2024-01-08          NaN       NaN        201.0            NaN\n",
+      "2024-01-09          NaN       NaN          NaN          153.0\n",
+      "2024-01-10         24.0       NaN          NaN            NaN\n",
+      "\n",
+      "7-day rolling average (sample):\n",
+      "product_category  Books  Clothing  Electronics  Home & Garden\n",
+      "date                                                         \n",
+      "2026-09-22        32.15       0.0        60.29         191.03\n",
+      "2026-09-23        32.15       0.0        60.29         184.35\n",
+      "2026-09-24        18.09       0.0        84.62         184.35\n",
+      "2026-09-25        57.20       0.0        84.62          89.29\n",
+      "2026-09-26        57.20       0.0       113.45          89.29\n",
+      "\n",
+      "Monthly sales:\n",
+      "product_category   Books  Clothing  Electronics  Home & Garden\n",
+      "period                                                        \n",
+      "2024-01           1393.0    2789.0        983.0         1657.0\n",
+      "2024-02           2322.0    2198.0        877.0         2445.0\n",
+      "2024-03           2665.0    1029.0       1393.0         1555.0\n",
+      "2024-04           1722.0    1728.0       2782.0         2171.0\n",
+      "2024-05           2191.0    1795.0       1068.0         2092.0\n",
+      "...                  ...       ...          ...            ...\n",
+      "2026-05           1651.0    1084.0       2314.0         2334.0\n",
+      "2026-06           2077.0    2676.0       1940.0          261.0\n",
+      "2026-07           1445.0     811.0       2807.0         1512.0\n",
+      "2026-08            104.0    2701.0       2219.0         1606.0\n",
+      "2026-09           1570.0     285.0       1295.0         3671.0\n",
+      "\n",
+      "[33 rows x 4 columns]\n",
+      "\n",
+      "Month-over-month growth (%):\n",
+      "product_category   Books  Clothing  Electronics  Home & Garden\n",
+      "period                                                        \n",
+      "2024-01              NaN       NaN          NaN            NaN\n",
+      "2024-02             66.7     -21.2        -10.7           47.5\n",
+      "2024-03             14.8     -53.2         58.8          -36.4\n",
+      "2024-04            -35.4      67.9         99.7           39.6\n",
+      "2024-05             27.2       3.9        -61.6           -3.6\n",
+      "...                  ...       ...          ...            ...\n",
+      "2026-05            -49.0     -24.0         88.6          203.8\n",
+      "2026-06             25.8     146.9        -16.2          -88.8\n",
+      "2026-07            -30.4     -69.7         44.7          479.7\n",
+      "2026-08            -92.8     233.0        -21.0            6.2\n",
+      "2026-09           1403.9     -89.4        -41.6          128.5\n",
+      "\n",
+      "[33 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating time series pivot tables\n",
+    "print(\"=== TIME SERIES PIVOT TABLES ===\")\n",
+    "\n",
+    "# Daily sales by product category\n",
+    "daily_sales = df_business.groupby(['date', 'product_category'])['net_sales'].sum().reset_index()\n",
+    "daily_pivot = daily_sales.pivot(index='date', columns='product_category', values='net_sales')\n",
+    "\n",
+    "print(\"Daily sales pivot (first 10 days):\")\n",
+    "print(daily_pivot.head(10).round(0))\n",
+    "\n",
+    "# Fill missing values and calculate rolling averages\n",
+    "daily_pivot_filled = daily_pivot.fillna(0)\n",
+    "rolling_avg = daily_pivot_filled.rolling(window=7).mean()\n",
+    "\n",
+    "print(\"\\n7-day rolling average (sample):\")\n",
+    "print(rolling_avg.tail(5).round(2))\n",
+    "\n",
+    "# Month-over-month growth\n",
+    "monthly_sales = df_business.groupby(['year', 'month', 'product_category'])['net_sales'].sum().reset_index()\n",
+    "monthly_sales['period'] = monthly_sales['year'].astype(str) + '-' + monthly_sales['month'].astype(str).str.zfill(2)\n",
+    "monthly_pivot = monthly_sales.pivot(index='period', columns='product_category', values='net_sales')\n",
+    "\n",
+    "print(\"\\nMonthly sales:\")\n",
+    "print(monthly_pivot.round(0))\n",
+    "\n",
+    "# Calculate month-over-month growth\n",
+    "mom_growth = monthly_pivot.pct_change() * 100\n",
+    "print(\"\\nMonth-over-month growth (%):\")\n",
+    "print(mom_growth.round(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== COMPLEX MULTI-LEVEL RESHAPING ===\n",
+      "Complex hierarchical data:\n",
+      "  region    salesperson  quarter product_category  net_sales  quantity\n",
+      "0   East  Alice Johnson        1            Books   710.9865        14\n",
+      "1   East  Alice Johnson        1    Home & Garden   428.9600         8\n",
+      "2   East  Alice Johnson        2            Books   240.5520         5\n",
+      "3   East  Alice Johnson        2         Clothing   436.8720         7\n",
+      "4   East  Alice Johnson        2      Electronics   750.9260        20\n",
+      "5   East  Alice Johnson        2    Home & Garden    96.1745         2\n",
+      "6   East  Alice Johnson        3            Books   223.0520         3\n",
+      "7   East  Alice Johnson        3         Clothing   128.8600         2\n",
+      "8   East  Alice Johnson        3      Electronics   567.8100         9\n",
+      "9   East  Alice Johnson        3    Home & Garden   655.3200        12\n",
+      "\n",
+      "Salesperson performance by quarter and category:\n",
+      "product_category                  Books   Clothing  Electronics  Home & Garden\n",
+      "region salesperson   quarter                                                  \n",
+      "East   Alice Johnson 1         710.9865     0.0000       0.0000       428.9600\n",
+      "                     2         240.5520   436.8720     750.9260        96.1745\n",
+      "                     3         223.0520   128.8600     567.8100       655.3200\n",
+      "                     4         362.4700   272.8080     473.7400       512.8200\n",
+      "       Bob Smith     1         775.7800   360.9700       0.0000       698.4970\n",
+      "                     2         729.5005   561.8205     305.0745        10.1970\n",
+      "                     3         534.1230   233.6990       0.0000         0.0000\n",
+      "                     4           0.0000     0.0000     805.8820       242.2400\n",
+      "       Charlie Brown 1           0.0000   675.4200     672.7680       220.9600\n",
+      "                     2         141.1830    97.4520    1063.2360       689.1900\n",
+      "                     3         233.1210     0.0000     455.7235        31.8835\n",
+      "                     4          60.9520   179.2080     418.6000       156.5760\n",
+      "       Diana Prince  1         294.5120  1280.4690     542.1370        58.3920\n",
+      "                     2        1364.7370   244.2495     183.4500      1018.4200\n",
+      "                     3         215.1500   320.9600     610.4835       266.0820\n",
+      "\n",
+      "Quarterly comparison view (sample):\n",
+      "product_category      Books                       Clothing                \\\n",
+      "quarter                   1       2      3      4        1      2      3   \n",
+      "region salesperson                                                         \n",
+      "East   Alice Johnson  711.0   241.0  223.0  362.0      0.0  437.0  129.0   \n",
+      "       Bob Smith      776.0   730.0  534.0    0.0    361.0  562.0  234.0   \n",
+      "       Charlie Brown    0.0   141.0  233.0   61.0    675.0   97.0    0.0   \n",
+      "       Diana Prince   295.0  1365.0  215.0    0.0   1280.0  244.0  321.0   \n",
+      "       Eve Wilson       0.0   501.0  281.0  803.0    505.0  942.0   88.0   \n",
+      "\n",
+      "product_category             \n",
+      "quarter                   4  \n",
+      "region salesperson           \n",
+      "East   Alice Johnson  273.0  \n",
+      "       Bob Smith        0.0  \n",
+      "       Charlie Brown  179.0  \n",
+      "       Diana Prince     0.0  \n",
+      "       Eve Wilson       0.0  \n",
+      "\n",
+      "Region summary:\n",
+      "product_category    Books  Clothing  Electronics  Home & Garden\n",
+      "region                                                         \n",
+      "East              12377.0   12466.0      16463.0        14140.0\n",
+      "North             13492.0   14938.0      15229.0        17770.0\n",
+      "South             17590.0   16946.0      14552.0        13695.0\n",
+      "West               8157.0   16544.0      12681.0        15652.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Complex multi-level reshaping\n",
+    "print(\"=== COMPLEX MULTI-LEVEL RESHAPING ===\")\n",
+    "\n",
+    "# Create complex hierarchical data\n",
+    "complex_data = df_business.groupby(['region', 'salesperson', 'quarter', 'product_category']).agg({\n",
+    "    'net_sales': 'sum',\n",
+    "    'quantity': 'sum'\n",
+    "}).reset_index()\n",
+    "\n",
+    "print(\"Complex hierarchical data:\")\n",
+    "print(complex_data.head(10))\n",
+    "\n",
+    "# Multiple pivot operations\n",
+    "# First pivot: Quarter vs Product Category for each salesperson\n",
+    "salesperson_pivot = complex_data.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index=['region', 'salesperson', 'quarter'],\n",
+    "    columns='product_category',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nSalesperson performance by quarter and category:\")\n",
+    "print(salesperson_pivot.head(15))\n",
+    "\n",
+    "# Stack and unstack for different views\n",
+    "# Unstack quarter to see quarterly comparison\n",
+    "quarterly_comparison = salesperson_pivot.unstack(level=2)\n",
+    "\n",
+    "print(\"\\nQuarterly comparison view (sample):\")\n",
+    "print(quarterly_comparison.iloc[:5, :8].round(0))  # Show subset\n",
+    "\n",
+    "# Create summary by region\n",
+    "region_summary = salesperson_pivot.groupby('region').sum()\n",
+    "print(\"\\nRegion summary:\")\n",
+    "print(region_summary.round(0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Business Intelligence Applications\n",
+    "\n",
+    "Real-world business analysis using pivot tables and reshaping."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== SALES PERFORMANCE DASHBOARD ===\n",
+      "1. Regional Performance Summary:\n",
+      "       net_sales           quantity total_order\n",
+      "            mean       sum      sum       count\n",
+      "region                                         \n",
+      "East      223.57  55445.10     1193         248\n",
+      "North     230.07  61428.66     1291         267\n",
+      "South     250.13  62782.29     1275         251\n",
+      "West      226.64  53033.27     1152         234\n",
+      "\n",
+      "2. Category Performance by Quarter:\n",
+      "quarter                 1        2        3        4       All\n",
+      "product_category                                              \n",
+      "Books             15644.0  15334.0  12567.0   8069.0   51615.0\n",
+      "Clothing          18988.0  17384.0  12257.0  12266.0   60894.0\n",
+      "Electronics       13021.0  18192.0  17188.0  10523.0   58924.0\n",
+      "Home & Garden     15857.0  14490.0  17604.0  13305.0   61257.0\n",
+      "All               63509.0  65400.0  59617.0  44163.0  232689.0\n",
+      "\n",
+      "3. Sales Channel Analysis:\n",
+      "                    sum                        mean                  \n",
+      "customer_type       New Returning       VIP     New Returning     VIP\n",
+      "sales_channel                                                        \n",
+      "Online         39805.26  76878.78  31248.99  255.16    238.01  226.44\n",
+      "Phone           5438.57  10409.81   4134.56  236.46    212.45  229.70\n",
+      "Store          17130.46  33977.55  13665.33  214.13    216.42  244.02\n",
+      "\n",
+      "4. Top 5 Performers:\n",
+      "               net_sales  quantity  total_order\n",
+      "salesperson                                    \n",
+      "Alice Johnson   27971.35       550          118\n",
+      "Jack Robinson   27955.98       564          107\n",
+      "Charlie Brown   26054.75       529          110\n",
+      "Eve Wilson      25345.17       553          107\n",
+      "Frank Miller    24301.05       491           96\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sales performance dashboard\n",
+    "print(\"=== SALES PERFORMANCE DASHBOARD ===\")\n",
+    "\n",
+    "def create_sales_dashboard(df):\n",
+    "    \"\"\"Create comprehensive sales dashboard using pivot tables\"\"\"\n",
+    "    dashboard = {}\n",
+    "    \n",
+    "    # 1. Regional performance summary\n",
+    "    dashboard['regional_summary'] = df.pivot_table(\n",
+    "        values=['net_sales', 'quantity', 'total_order'],\n",
+    "        index='region',\n",
+    "        aggfunc={\n",
+    "            'net_sales': ['sum', 'mean'],\n",
+    "            'quantity': 'sum',\n",
+    "            'total_order': 'count'\n",
+    "        }\n",
+    "    ).round(2)\n",
+    "    \n",
+    "    # 2. Product category performance\n",
+    "    dashboard['category_performance'] = df.pivot_table(\n",
+    "        values='net_sales',\n",
+    "        index='product_category',\n",
+    "        columns='quarter',\n",
+    "        aggfunc='sum',\n",
+    "        margins=True,\n",
+    "        fill_value=0\n",
+    "    ).round(0)\n",
+    "    \n",
+    "    # 3. Sales channel analysis\n",
+    "    dashboard['channel_analysis'] = df.pivot_table(\n",
+    "        values='net_sales',\n",
+    "        index='sales_channel',\n",
+    "        columns='customer_type',\n",
+    "        aggfunc=['sum', 'mean'],\n",
+    "        fill_value=0\n",
+    "    ).round(2)\n",
+    "    \n",
+    "    # 4. Top performers\n",
+    "    salesperson_performance = df.groupby('salesperson').agg({\n",
+    "        'net_sales': 'sum',\n",
+    "        'quantity': 'sum',\n",
+    "        'total_order': 'count'\n",
+    "    }).round(2)\n",
+    "    dashboard['top_performers'] = salesperson_performance.sort_values('net_sales', ascending=False).head(5)\n",
+    "    \n",
+    "    # 5. Monthly trends\n",
+    "    dashboard['monthly_trends'] = df.pivot_table(\n",
+    "        values='net_sales',\n",
+    "        index='month_name',\n",
+    "        columns='product_category',\n",
+    "        aggfunc='sum',\n",
+    "        fill_value=0\n",
+    "    ).round(0)\n",
+    "    \n",
+    "    return dashboard\n",
+    "\n",
+    "# Generate dashboard\n",
+    "sales_dashboard = create_sales_dashboard(df_business)\n",
+    "\n",
+    "print(\"1. Regional Performance Summary:\")\n",
+    "print(sales_dashboard['regional_summary'])\n",
+    "\n",
+    "print(\"\\n2. Category Performance by Quarter:\")\n",
+    "print(sales_dashboard['category_performance'])\n",
+    "\n",
+    "print(\"\\n3. Sales Channel Analysis:\")\n",
+    "print(sales_dashboard['channel_analysis'])\n",
+    "\n",
+    "print(\"\\n4. Top 5 Performers:\")\n",
+    "print(sales_dashboard['top_performers'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== CUSTOMER SEGMENTATION ANALYSIS ===\n",
+      "Customer behavior by type and channel:\n",
+      "              discount_amount               net_sales                  \\\n",
+      "sales_channel          Online  Phone  Store    Online   Phone   Store   \n",
+      "customer_type                                                           \n",
+      "New                     11.73  15.24  10.61    255.16  236.46  214.13   \n",
+      "Returning               11.45   9.43  11.81    238.01  212.45  216.42   \n",
+      "VIP                     11.36  15.36  15.59    226.44  229.70  244.02   \n",
+      "\n",
+      "              quantity              \n",
+      "sales_channel   Online Phone Store  \n",
+      "customer_type                       \n",
+      "New               5.29  4.78  4.47  \n",
+      "Returning         4.88  5.14  4.62  \n",
+      "VIP               5.03  4.44  5.21  \n",
+      "\n",
+      "Purchase patterns (% of quarterly sales):\n",
+      "quarter                            1     2     3     4\n",
+      "customer_type product_category                        \n",
+      "New           Books              6.2   4.4   3.1   4.3\n",
+      "              Clothing           6.9   7.2   5.7  10.5\n",
+      "              Electronics        4.3   9.5   9.7   8.6\n",
+      "              Home & Garden      8.8   3.7   4.5  12.4\n",
+      "Returning     Books             13.2  13.8   9.8  11.3\n",
+      "              Clothing          16.2  12.8  12.3  12.7\n",
+      "              Electronics       11.2  13.8  11.9  11.6\n",
+      "              Home & Garden     10.2  15.3  17.7  13.7\n",
+      "VIP           Books              5.2   5.3   8.1   2.7\n",
+      "              Clothing           6.8   6.5   2.6   4.6\n",
+      "              Electronics        4.9   4.5   7.2   3.6\n",
+      "              Home & Garden      6.1   3.2   7.3   4.1\n",
+      "\n",
+      "Customer value distribution:\n",
+      "               count    mean     std    min     25%     50%     75%     max\n",
+      "customer_type                                                              \n",
+      "New            259.0  248.78  155.20  18.31  128.74  217.77  345.09  768.32\n",
+      "Returning      529.0  237.24  160.52   9.76  109.41  201.91  342.29  798.01\n",
+      "VIP            212.0  239.56  158.12  25.92  115.17  198.15  357.37  673.39\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Customer segmentation analysis\n",
+    "print(\"=== CUSTOMER SEGMENTATION ANALYSIS ===\")\n",
+    "\n",
+    "# Customer behavior analysis\n",
+    "customer_behavior = df_business.pivot_table(\n",
+    "    values=['net_sales', 'quantity', 'discount_amount'],\n",
+    "    index='customer_type',\n",
+    "    columns='sales_channel',\n",
+    "    aggfunc={\n",
+    "        'net_sales': 'mean',\n",
+    "        'quantity': 'mean',\n",
+    "        'discount_amount': 'mean'\n",
+    "    },\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"Customer behavior by type and channel:\")\n",
+    "print(customer_behavior.round(2))\n",
+    "\n",
+    "# Purchase patterns analysis\n",
+    "purchase_patterns = pd.crosstab(\n",
+    "    [df_business['customer_type'], df_business['product_category']],\n",
+    "    df_business['quarter'],\n",
+    "    values=df_business['net_sales'],\n",
+    "    aggfunc='sum',\n",
+    "    normalize='columns'\n",
+    ") * 100\n",
+    "\n",
+    "print(\"\\nPurchase patterns (% of quarterly sales):\")\n",
+    "print(purchase_patterns.round(1))\n",
+    "\n",
+    "# Customer value distribution\n",
+    "value_distribution = df_business.groupby('customer_type')['total_order'].describe()\n",
+    "print(\"\\nCustomer value distribution:\")\n",
+    "print(value_distribution.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== COHORT ANALYSIS ===\n",
+      "Cohort analysis (average sales by quarters since first purchase):\n",
+      "periods_since_first      0      1      2      3\n",
+      "first_quarter                                  \n",
+      "1                    234.0  240.0  219.0  240.0\n",
+      "\n",
+      "Cohort retention (number of active salespeople):\n",
+      "periods_since_first    0    1    2    3\n",
+      "first_quarter                          \n",
+      "1                    271  273  272  184\n",
+      "\n",
+      "Retention rates (%):\n",
+      "periods_since_first      0      1      2     3\n",
+      "first_quarter                                 \n",
+      "1                    100.0  100.7  100.4  67.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cohort analysis using pivot tables\n",
+    "print(\"=== COHORT ANALYSIS ===\")\n",
+    "\n",
+    "# Simplified cohort analysis by quarter\n",
+    "# Group customers by their first purchase quarter\n",
+    "customer_first_purchase = df_business.groupby('salesperson')['quarter'].min().reset_index()\n",
+    "customer_first_purchase.columns = ['salesperson', 'first_quarter']\n",
+    "\n",
+    "# Merge back to get cohort information\n",
+    "cohort_data = df_business.merge(customer_first_purchase, on='salesperson')\n",
+    "cohort_data['periods_since_first'] = cohort_data['quarter'] - cohort_data['first_quarter']\n",
+    "\n",
+    "# Create cohort table\n",
+    "cohort_table = cohort_data.pivot_table(\n",
+    "    values='net_sales',\n",
+    "    index='first_quarter',\n",
+    "    columns='periods_since_first',\n",
+    "    aggfunc='mean',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"Cohort analysis (average sales by quarters since first purchase):\")\n",
+    "print(cohort_table.round(0))\n",
+    "\n",
+    "# Retention analysis\n",
+    "cohort_counts = cohort_data.pivot_table(\n",
+    "    values='salesperson',\n",
+    "    index='first_quarter',\n",
+    "    columns='periods_since_first',\n",
+    "    aggfunc='count',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "print(\"\\nCohort retention (number of active salespeople):\")\n",
+    "print(cohort_counts)\n",
+    "\n",
+    "# Calculate retention rates\n",
+    "cohort_retention = cohort_counts.divide(cohort_counts.iloc[:, 0], axis=0) * 100\n",
+    "print(\"\\nRetention rates (%):\")\n",
+    "print(cohort_retention.round(1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply pivot tables and reshaping to complex business scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Multi-dimensional Business Intelligence Report\n",
+    "# Create a comprehensive BI report that includes:\n",
+    "# - Sales performance across multiple dimensions\n",
+    "# - Trend analysis with period-over-period comparisons\n",
+    "# - Customer segmentation insights\n",
+    "# - Product performance matrix\n",
+    "# - Actionable recommendations based on pivot table insights\n",
+    "\n",
+    "def create_comprehensive_bi_report(df):\n",
+    "    \"\"\"Create multi-dimensional business intelligence report\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# bi_report = create_comprehensive_bi_report(df_business)\n",
+    "# print(\"Comprehensive BI Report:\")\n",
+    "# for section, data in bi_report.items():\n",
+    "#     print(f\"\\n{section}:\")\n",
+    "#     print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Dynamic Pivot Table Generator\n",
+    "# Create a flexible function that can generate pivot tables with:\n",
+    "# - User-specified dimensions (rows, columns, values)\n",
+    "# - Multiple aggregation functions\n",
+    "# - Automatic handling of different data types\n",
+    "# - Export capabilities for different formats\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Advanced Reshaping Challenge\n",
+    "# Transform the data through multiple reshaping operations:\n",
+    "# - Convert to time series format\n",
+    "# - Create rolling calculations\n",
+    "# - Build comparison matrices\n",
+    "# - Generate variance analysis reports\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Pivot Tables**:\n",
+    "   - **`pivot_table()`**: Most flexible, handles duplicates with aggregation\n",
+    "   - **`pivot()`**: Simple reshaping, requires unique index-column combinations\n",
+    "   - **`crosstab()`**: Specialized for frequency tables and cross-tabulation\n",
+    "\n",
+    "2. **Reshaping Operations**:\n",
+    "   - **`melt()`**: Wide to long format (unpivot)\n",
+    "   - **`pivot()`**: Long to wide format\n",
+    "   - **`stack()`**: Column to row index\n",
+    "   - **`unstack()`**: Row index to column\n",
+    "\n",
+    "3. **Best Practices**:\n",
+    "   - Use `fill_value=0` to handle missing combinations\n",
+    "   - Add `margins=True` for totals when needed\n",
+    "   - Choose appropriate aggregation functions for your data\n",
+    "   - Consider data types when reshaping\n",
+    "\n",
+    "4. **Business Applications**:\n",
+    "   - Sales performance analysis\n",
+    "   - Customer segmentation\n",
+    "   - Trend analysis and forecasting\n",
+    "   - Cohort and retention analysis\n",
+    "\n",
+    "## Pivot Table Quick Reference\n",
+    "\n",
+    "```python\n",
+    "# Basic pivot table\n",
+    "df.pivot_table(values='sales', index='region', columns='product', aggfunc='sum')\n",
+    "\n",
+    "# Multiple aggregations\n",
+    "df.pivot_table(values='sales', index='region', aggfunc=['sum', 'mean', 'count'])\n",
+    "\n",
+    "# With margins (totals)\n",
+    "df.pivot_table(values='sales', index='region', columns='product', \n",
+    "               aggfunc='sum', margins=True)\n",
+    "\n",
+    "# Cross-tabulation\n",
+    "pd.crosstab(df['region'], df['product'], normalize='index')\n",
+    "\n",
+    "# Reshaping\n",
+    "pd.melt(df, id_vars=['id'], value_vars=['col1', 'col2'])  # Wide to long\n",
+    "df.pivot(index='date', columns='category', values='value')  # Long to wide\n",
+    "```\n",
+    "\n",
+    "## Common Use Cases\n",
+    "\n",
+    "| Scenario | Best Tool | Key Parameters |\n",
+    "|----------|-----------|----------------|\n",
+    "| Sales by region/product | `pivot_table()` | `values='sales', index='region', columns='product'` |\n",
+    "| Frequency analysis | `crosstab()` | `normalize='index'` for percentages |\n",
+    "| Time series analysis | `pivot()` + `unstack()` | Handle dates in index |\n",
+    "| Data normalization | `melt()` | `id_vars` for identifiers |\n",
+    "| Multi-level analysis | Hierarchical indexing | Multiple columns in `index`/`columns` |"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/10_time_series_analysis.ipynb b/Session_01/PandasDataFrame-exmples/10_time_series_analysis.ipynb
new file mode 100755
index 0000000..47236a6
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/10_time_series_analysis.ipynb
@@ -0,0 +1,1149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 10: Time Series Analysis\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Master datetime indexing and time-based operations\n",
+    "- Learn resampling and frequency conversion techniques\n",
+    "- Understand rolling calculations and window functions\n",
+    "- Practice with seasonal analysis and trend decomposition\n",
+    "- Apply time series techniques to business forecasting scenarios\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-9\n",
+    "- Understanding of datetime concepts\n",
+    "- Basic knowledge of statistics (helpful for trend analysis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from datetime import datetime, timedelta\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set display options\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', 20)\n",
+    "plt.style.use('seaborn-v0_8')\n",
+    "%matplotlib inline\n",
+    "\n",
+    "print(\"Libraries loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Time Series Data\n",
+    "\n",
+    "Let's create realistic time series datasets for analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create comprehensive time series dataset\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Generate 2 years of daily data\n",
+    "start_date = '2022-01-01'\n",
+    "end_date = '2023-12-31'\n",
+    "date_range = pd.date_range(start=start_date, end=end_date, freq='D')\n",
+    "\n",
+    "# Create realistic sales data with trends and seasonality\n",
+    "n_days = len(date_range)\n",
+    "base_sales = 1000\n",
+    "\n",
+    "# Add trend (gradual increase over time)\n",
+    "trend = np.linspace(0, 300, n_days)\n",
+    "\n",
+    "# Add seasonality (weekly and monthly patterns)\n",
+    "daily_pattern = np.sin(2 * np.pi * np.arange(n_days) / 7) * 100  # Weekly pattern\n",
+    "monthly_pattern = np.sin(2 * np.pi * np.arange(n_days) / 30.44) * 150  # Monthly pattern\n",
+    "yearly_pattern = np.sin(2 * np.pi * np.arange(n_days) / 365.25) * 200  # Yearly pattern\n",
+    "\n",
+    "# Add random noise\n",
+    "noise = np.random.normal(0, 80, n_days)\n",
+    "\n",
+    "# Combine all components\n",
+    "sales = base_sales + trend + daily_pattern + monthly_pattern + yearly_pattern + noise\n",
+    "sales = np.maximum(sales, 0)  # Ensure non-negative sales\n",
+    "\n",
+    "# Create DataFrame\n",
+    "ts_data = pd.DataFrame({\n",
+    "    'date': date_range,\n",
+    "    'sales': sales,\n",
+    "    'customers': np.random.poisson(50, n_days) + (sales / 50).astype(int),\n",
+    "    'marketing_spend': np.random.gamma(2, 20, n_days),\n",
+    "    'temperature': 20 + 10 * np.sin(2 * np.pi * np.arange(n_days) / 365.25) + np.random.normal(0, 5, n_days),\n",
+    "    'is_weekend': pd.Series(date_range).dt.dayofweek >= 5,\n",
+    "    'is_holiday': np.random.choice([True, False], n_days, p=[0.05, 0.95])\n",
+    "})\n",
+    "\n",
+    "# Set date as index\n",
+    "ts_data.set_index('date', inplace=True)\n",
+    "\n",
+    "print(\"Time series dataset created:\")\n",
+    "print(f\"Shape: {ts_data.shape}\")\n",
+    "print(f\"Date range: {ts_data.index.min()} to {ts_data.index.max()}\")\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "print(ts_data.head())\n",
+    "print(\"\\nData types:\")\n",
+    "print(ts_data.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. DateTime Indexing and Basic Operations\n",
+    "\n",
+    "Working with datetime indices and time-based selection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic datetime index operations\n",
+    "print(\"=== DATETIME INDEX OPERATIONS ===\")\n",
+    "\n",
+    "# Index information\n",
+    "print(f\"Index type: {type(ts_data.index)}\")\n",
+    "print(f\"Index frequency: {ts_data.index.freq}\")\n",
+    "print(f\"Index is monotonic increasing: {ts_data.index.is_monotonic_increasing}\")\n",
+    "print(f\"Index is monotonic decreasing: {ts_data.index.is_monotonic_decreasing}\")\n",
+    "print(f\"Index has duplicates: {ts_data.index.has_duplicates}\")\n",
+    "\n",
+    "# Time-based selection\n",
+    "print(\"\\n--- Time-based Selection ---\")\n",
+    "\n",
+    "# Select specific year\n",
+    "sales_2022 = ts_data.loc['2022']\n",
+    "print(f\"2022 data shape: {sales_2022.shape}\")\n",
+    "print(f\"2022 average daily sales: {sales_2022['sales'].mean():.2f}\")\n",
+    "\n",
+    "# Select specific month\n",
+    "jan_2023 = ts_data.loc['2023-01']\n",
+    "print(f\"\\nJanuary 2023 data shape: {jan_2023.shape}\")\n",
+    "print(f\"January 2023 total sales: {jan_2023['sales'].sum():.2f}\")\n",
+    "\n",
+    "# Select date range\n",
+    "q1_2023 = ts_data.loc['2023-01-01':'2023-03-31']\n",
+    "print(f\"\\nQ1 2023 data shape: {q1_2023.shape}\")\n",
+    "print(f\"Q1 2023 average sales: {q1_2023['sales'].mean():.2f}\")\n",
+    "\n",
+    "# Recent data (last 30 days)\n",
+    "recent_data = ts_data.tail(30)\n",
+    "print(f\"\\nLast 30 days average sales: {recent_data['sales'].mean():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DateTime component extraction\n",
+    "print(\"=== DATETIME COMPONENT EXTRACTION ===\")\n",
+    "\n",
+    "# Extract various date components\n",
+    "ts_enhanced = ts_data.copy()\n",
+    "ts_enhanced['year'] = ts_enhanced.index.year\n",
+    "ts_enhanced['month'] = ts_enhanced.index.month\n",
+    "ts_enhanced['quarter'] = ts_enhanced.index.quarter\n",
+    "ts_enhanced['day_of_week'] = ts_enhanced.index.dayofweek  # 0=Monday, 6=Sunday\n",
+    "ts_enhanced['day_name'] = ts_enhanced.index.day_name()\n",
+    "ts_enhanced['month_name'] = ts_enhanced.index.month_name()\n",
+    "ts_enhanced['week_of_year'] = ts_enhanced.index.isocalendar().week\n",
+    "ts_enhanced['day_of_year'] = ts_enhanced.index.dayofyear\n",
+    "ts_enhanced['is_month_start'] = ts_enhanced.index.is_month_start\n",
+    "ts_enhanced['is_month_end'] = ts_enhanced.index.is_month_end\n",
+    "ts_enhanced['is_quarter_start'] = ts_enhanced.index.is_quarter_start\n",
+    "ts_enhanced['is_quarter_end'] = ts_enhanced.index.is_quarter_end\n",
+    "\n",
+    "print(\"Enhanced dataset with datetime components:\")\n",
+    "print(ts_enhanced[['sales', 'year', 'month', 'quarter', 'day_name', 'week_of_year']].head())\n",
+    "\n",
+    "# Analyze patterns by day of week\n",
+    "print(\"\\nSales patterns by day of week:\")\n",
+    "dow_analysis = ts_enhanced.groupby('day_name')['sales'].agg(['mean', 'std', 'count'])\n",
+    "# Reorder by weekday\n",
+    "day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
+    "dow_analysis = dow_analysis.reindex(day_order)\n",
+    "print(dow_analysis.round(2))\n",
+    "\n",
+    "# Monthly patterns\n",
+    "print(\"\\nSales patterns by month:\")\n",
+    "monthly_analysis = ts_enhanced.groupby('month_name')['sales'].agg(['mean', 'std'])\n",
+    "month_order = ['January', 'February', 'March', 'April', 'May', 'June',\n",
+    "               'July', 'August', 'September', 'October', 'November', 'December']\n",
+    "monthly_analysis = monthly_analysis.reindex([m for m in month_order if m in monthly_analysis.index])\n",
+    "print(monthly_analysis.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Time series visualization\n",
+    "print(\"=== TIME SERIES VISUALIZATION ===\")\n",
+    "\n",
+    "# Create comprehensive time series plots\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
+    "\n",
+    "# Plot 1: Daily sales over time\n",
+    "ts_data['sales'].plot(ax=axes[0, 0], title='Daily Sales Over Time', alpha=0.7)\n",
+    "axes[0, 0].set_ylabel('Sales ($)')\n",
+    "axes[0, 0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: Monthly aggregated sales\n",
+    "monthly_sales = ts_data['sales'].resample('M').sum()\n",
+    "monthly_sales.plot(ax=axes[0, 1], title='Monthly Sales', marker='o')\n",
+    "axes[0, 1].set_ylabel('Monthly Sales ($)')\n",
+    "axes[0, 1].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 3: Sales vs customers correlation\n",
+    "axes[1, 0].scatter(ts_data['customers'], ts_data['sales'], alpha=0.5)\n",
+    "axes[1, 0].set_title('Sales vs Customers')\n",
+    "axes[1, 0].set_xlabel('Number of Customers')\n",
+    "axes[1, 0].set_ylabel('Sales ($)')\n",
+    "axes[1, 0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 4: Seasonal pattern (by month)\n",
+    "ts_enhanced.groupby('month')['sales'].mean().plot(ax=axes[1, 1], kind='bar', \n",
+    "                                                  title='Average Sales by Month')\n",
+    "axes[1, 1].set_ylabel('Average Sales ($)')\n",
+    "axes[1, 1].set_xlabel('Month')\n",
+    "axes[1, 1].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Summary statistics\n",
+    "print(\"\\nTime series summary statistics:\")\n",
+    "print(ts_data['sales'].describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Resampling and Frequency Conversion\n",
+    "\n",
+    "Converting between different time frequencies and aggregating data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic resampling operations\n",
+    "print(\"=== BASIC RESAMPLING ===\")\n",
+    "\n",
+    "# Resample to different frequencies\n",
+    "weekly_data = ts_data.resample('W').agg({\n",
+    "    'sales': 'sum',\n",
+    "    'customers': 'sum',\n",
+    "    'marketing_spend': 'sum',\n",
+    "    'temperature': 'mean'\n",
+    "})\n",
+    "\n",
+    "print(\"Weekly resampled data:\")\n",
+    "print(weekly_data.head(10))\n",
+    "\n",
+    "# Monthly resampling with multiple aggregations\n",
+    "monthly_data = ts_data.resample('M').agg({\n",
+    "    'sales': ['sum', 'mean', 'std', 'min', 'max'],\n",
+    "    'customers': ['sum', 'mean'],\n",
+    "    'marketing_spend': 'sum',\n",
+    "    'temperature': 'mean'\n",
+    "})\n",
+    "\n",
+    "print(\"\\nMonthly resampled data (first 6 months):\")\n",
+    "print(monthly_data.head(6))\n",
+    "\n",
+    "# Quarterly resampling\n",
+    "quarterly_data = ts_data.resample('Q').agg({\n",
+    "    'sales': 'sum',\n",
+    "    'customers': 'sum',\n",
+    "    'marketing_spend': 'sum'\n",
+    "})\n",
+    "\n",
+    "print(\"\\nQuarterly resampled data:\")\n",
+    "print(quarterly_data)\n",
+    "\n",
+    "# Year-over-year comparison\n",
+    "yearly_data = ts_data.resample('Y').agg({\n",
+    "    'sales': 'sum',\n",
+    "    'customers': 'sum',\n",
+    "    'marketing_spend': 'sum'\n",
+    "})\n",
+    "\n",
+    "print(\"\\nYearly resampled data:\")\n",
+    "print(yearly_data)\n",
+    "\n",
+    "# Calculate year-over-year growth\n",
+    "if len(yearly_data) > 1:\n",
+    "    yoy_growth = yearly_data.pct_change() * 100\n",
+    "    print(\"\\nYear-over-year growth (%):\")\n",
+    "    print(yoy_growth.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Advanced resampling techniques\n",
+    "print(\"=== ADVANCED RESAMPLING ===\")\n",
+    "\n",
+    "# Custom aggregation functions\n",
+    "def coefficient_of_variation(series):\n",
+    "    \"\"\"Calculate coefficient of variation\"\"\"\n",
+    "    return series.std() / series.mean() if series.mean() != 0 else 0\n",
+    "\n",
+    "def sales_volatility(series):\n",
+    "    \"\"\"Calculate sales volatility (std/mean)\"\"\"\n",
+    "    return series.std()\n",
+    "\n",
+    "# Custom resampling with multiple functions\n",
+    "custom_monthly = ts_data.resample('M').agg({\n",
+    "    'sales': ['sum', 'mean', coefficient_of_variation, sales_volatility],\n",
+    "    'customers': ['sum', 'mean'],\n",
+    "    'marketing_spend': 'sum'\n",
+    "})\n",
+    "\n",
+    "print(\"Custom monthly aggregations:\")\n",
+    "print(custom_monthly.round(3))\n",
+    "\n",
+    "# Resampling with different anchor points\n",
+    "# Weekly data starting on different days\n",
+    "weekly_sunday = ts_data.resample('W-SUN')['sales'].sum()  # Week ending Sunday\n",
+    "weekly_monday = ts_data.resample('W-MON')['sales'].sum()  # Week ending Monday\n",
+    "\n",
+    "print(\"\\nWeekly totals comparison (first 10 weeks):\")\n",
+    "weekly_comparison = pd.DataFrame({\n",
+    "    'Week_End_Sunday': weekly_sunday,\n",
+    "    'Week_End_Monday': weekly_monday\n",
+    "})\n",
+    "print(weekly_comparison.head(10))\n",
+    "\n",
+    "# Business day resampling\n",
+    "business_weekly = ts_data.resample('B').mean()  # Business days only\n",
+    "print(f\"\\nBusiness days data shape: {business_weekly.shape}\")\n",
+    "print(\"Business days average (first 10):\")\n",
+    "print(business_weekly[['sales', 'customers']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Upsampling and downsampling\n",
+    "print(\"=== UPSAMPLING AND DOWNSAMPLING ===\")\n",
+    "\n",
+    "# Downsample to weekly and visualize\n",
+    "weekly_sales = ts_data['sales'].resample('W').sum()\n",
+    "\n",
+    "# Upsample weekly back to daily (forward fill)\n",
+    "upsampled_ffill = weekly_sales.resample('D').ffill()\n",
+    "\n",
+    "# Upsample with interpolation\n",
+    "upsampled_interp = weekly_sales.resample('D').interpolate()\n",
+    "\n",
+    "print(\"Upsampling comparison (sample period):\")\n",
+    "\n",
+    "# Fix: Use the date range directly, not the filtered DataFrame\n",
+    "start_date = '2023-01-01'\n",
+    "end_date = '2023-01-31'\n",
+    "\n",
+    "upsample_comparison = pd.DataFrame({\n",
+    "    'Original_Daily': ts_data.loc[start_date:end_date, 'sales'],\n",
+    "    'Weekly_Upsampled_FFill': upsampled_ffill.loc[start_date:end_date],\n",
+    "    'Weekly_Upsampled_Interp': upsampled_interp.loc[start_date:end_date]\n",
+    "})\n",
+    "\n",
+    "print(upsample_comparison.head(15))\n",
+    "\n",
+    "# Visualize upsampling methods\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(upsample_comparison.index, upsample_comparison['Original_Daily'], \n",
+    "         label='Original Daily', alpha=0.7)\n",
+    "plt.plot(upsample_comparison.index, upsample_comparison['Weekly_Upsampled_FFill'], \n",
+    "         label='Forward Fill', linestyle='--')\n",
+    "plt.plot(upsample_comparison.index, upsample_comparison['Weekly_Upsampled_Interp'], \n",
+    "         label='Interpolated', linestyle='-.')\n",
+    "plt.title('Upsampling Methods Comparison')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Rolling Calculations and Window Functions\n",
+    "\n",
+    "Moving averages, rolling statistics, and window-based analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic rolling calculations\n",
+    "print(\"=== BASIC ROLLING CALCULATIONS ===\")\n",
+    "\n",
+    "# Calculate various rolling statistics\n",
+    "rolling_data = ts_data.copy()\n",
+    "\n",
+    "# Rolling means (moving averages)\n",
+    "rolling_data['sales_7d_avg'] = rolling_data['sales'].rolling(window=7).mean()\n",
+    "rolling_data['sales_30d_avg'] = rolling_data['sales'].rolling(window=30).mean()\n",
+    "rolling_data['sales_90d_avg'] = rolling_data['sales'].rolling(window=90).mean()\n",
+    "\n",
+    "# Rolling standard deviation (volatility)\n",
+    "rolling_data['sales_7d_std'] = rolling_data['sales'].rolling(window=7).std()\n",
+    "rolling_data['sales_30d_std'] = rolling_data['sales'].rolling(window=30).std()\n",
+    "\n",
+    "# Rolling min/max\n",
+    "rolling_data['sales_30d_min'] = rolling_data['sales'].rolling(window=30).min()\n",
+    "rolling_data['sales_30d_max'] = rolling_data['sales'].rolling(window=30).max()\n",
+    "\n",
+    "print(\"Rolling statistics (last 10 days):\")\n",
+    "rolling_cols = ['sales', 'sales_7d_avg', 'sales_30d_avg', 'sales_7d_std', 'sales_30d_std']\n",
+    "print(rolling_data[rolling_cols].tail(10).round(2))\n",
+    "\n",
+    "# Rolling sum for cumulative analysis\n",
+    "rolling_data['sales_7d_sum'] = rolling_data['sales'].rolling(window=7).sum()\n",
+    "rolling_data['sales_30d_sum'] = rolling_data['sales'].rolling(window=30).sum()\n",
+    "\n",
+    "print(\"\\nRolling sums (last 5 days):\")\n",
+    "print(rolling_data[['sales', 'sales_7d_sum', 'sales_30d_sum']].tail(5).round(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Advanced rolling calculations\n",
+    "print(\"=== ADVANCED ROLLING CALCULATIONS ===\")\n",
+    "\n",
+    "# Rolling correlation between variables\n",
+    "rolling_data['sales_customers_corr_30d'] = rolling_data['sales'].rolling(window=30).corr(rolling_data['customers'])\n",
+    "rolling_data['sales_marketing_corr_30d'] = rolling_data['sales'].rolling(window=30).corr(rolling_data['marketing_spend'])\n",
+    "\n",
+    "print(\"Rolling correlations (last 10 days):\")\n",
+    "corr_cols = ['sales_customers_corr_30d', 'sales_marketing_corr_30d']\n",
+    "print(rolling_data[corr_cols].tail(10).round(3))\n",
+    "\n",
+    "# Rolling quantiles\n",
+    "rolling_data['sales_30d_q25'] = rolling_data['sales'].rolling(window=30).quantile(0.25)\n",
+    "rolling_data['sales_30d_q75'] = rolling_data['sales'].rolling(window=30).quantile(0.75)\n",
+    "rolling_data['sales_30d_median'] = rolling_data['sales'].rolling(window=30).median()\n",
+    "\n",
+    "print(\"\\nRolling quantiles (last 5 days):\")\n",
+    "quantile_cols = ['sales', 'sales_30d_q25', 'sales_30d_median', 'sales_30d_q75']\n",
+    "print(rolling_data[quantile_cols].tail(5).round(2))\n",
+    "\n",
+    "# Custom rolling functions\n",
+    "def rolling_cv(series):\n",
+    "    \"\"\"Rolling coefficient of variation\"\"\"\n",
+    "    return series.std() / series.mean() if series.mean() != 0 else 0\n",
+    "\n",
+    "def rolling_skewness(series):\n",
+    "    \"\"\"Rolling skewness\"\"\"\n",
+    "    return series.skew()\n",
+    "\n",
+    "rolling_data['sales_30d_cv'] = rolling_data['sales'].rolling(window=30).apply(rolling_cv)\n",
+    "rolling_data['sales_30d_skew'] = rolling_data['sales'].rolling(window=30).apply(rolling_skewness)\n",
+    "\n",
+    "print(\"\\nCustom rolling statistics (last 5 days):\")\n",
+    "custom_cols = ['sales_30d_cv', 'sales_30d_skew']\n",
+    "print(rolling_data[custom_cols].tail(5).round(3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exponentially weighted functions\n",
+    "print(\"=== EXPONENTIALLY WEIGHTED FUNCTIONS ===\")\n",
+    "\n",
+    "# Exponentially weighted moving average (EWMA)\n",
+    "rolling_data['sales_ewm_10'] = rolling_data['sales'].ewm(span=10).mean()\n",
+    "rolling_data['sales_ewm_30'] = rolling_data['sales'].ewm(span=30).mean()\n",
+    "\n",
+    "# Exponentially weighted standard deviation\n",
+    "rolling_data['sales_ewm_std_10'] = rolling_data['sales'].ewm(span=10).std()\n",
+    "\n",
+    "print(\"Exponentially weighted statistics (last 10 days):\")\n",
+    "ewm_cols = ['sales', 'sales_7d_avg', 'sales_ewm_10', 'sales_ewm_30']\n",
+    "print(rolling_data[ewm_cols].tail(10).round(2))\n",
+    "\n",
+    "# Visualize different smoothing methods\n",
+    "plt.figure(figsize=(15, 8))\n",
+    "\n",
+    "# Plot last 90 days for clarity\n",
+    "recent_period = rolling_data.tail(90)\n",
+    "\n",
+    "plt.plot(recent_period.index, recent_period['sales'], label='Original Sales', alpha=0.7)\n",
+    "plt.plot(recent_period.index, recent_period['sales_7d_avg'], label='7-day MA', linewidth=2)\n",
+    "plt.plot(recent_period.index, recent_period['sales_30d_avg'], label='30-day MA', linewidth=2)\n",
+    "plt.plot(recent_period.index, recent_period['sales_ewm_10'], label='EWM (span=10)', linewidth=2)\n",
+    "\n",
+    "plt.title('Sales Smoothing Methods Comparison (Last 90 Days)')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Calculate lag between different smoothing methods\n",
+    "print(\"\\nSmoothing method responsiveness (correlation with original):\")\n",
+    "responsiveness = {\n",
+    "    '7-day MA': rolling_data['sales'].corr(rolling_data['sales_7d_avg']),\n",
+    "    '30-day MA': rolling_data['sales'].corr(rolling_data['sales_30d_avg']),\n",
+    "    'EWM (span=10)': rolling_data['sales'].corr(rolling_data['sales_ewm_10']),\n",
+    "    'EWM (span=30)': rolling_data['sales'].corr(rolling_data['sales_ewm_30'])\n",
+    "}\n",
+    "\n",
+    "for method, corr in responsiveness.items():\n",
+    "    print(f\"{method}: {corr:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Seasonal Analysis and Decomposition\n",
+    "\n",
+    "Analyzing seasonal patterns and decomposing time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Seasonal pattern analysis\n",
+    "print(\"=== SEASONAL PATTERN ANALYSIS ===\")\n",
+    "\n",
+    "# Add more detailed time components\n",
+    "seasonal_data = ts_data.copy()\n",
+    "seasonal_data['month'] = seasonal_data.index.month\n",
+    "seasonal_data['quarter'] = seasonal_data.index.quarter\n",
+    "seasonal_data['day_of_week'] = seasonal_data.index.dayofweek\n",
+    "seasonal_data['week_of_year'] = seasonal_data.index.isocalendar().week\n",
+    "seasonal_data['day_of_year'] = seasonal_data.index.dayofyear\n",
+    "\n",
+    "# Monthly seasonality\n",
+    "monthly_pattern = seasonal_data.groupby('month')['sales'].agg(['mean', 'std', 'count'])\n",
+    "print(\"Monthly sales patterns:\")\n",
+    "print(monthly_pattern.round(2))\n",
+    "\n",
+    "# Day of week patterns\n",
+    "dow_pattern = seasonal_data.groupby('day_of_week')['sales'].agg(['mean', 'std'])\n",
+    "dow_pattern.index = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
+    "print(\"\\nDay of week patterns:\")\n",
+    "print(dow_pattern.round(2))\n",
+    "\n",
+    "# Weekly patterns throughout the year\n",
+    "weekly_pattern = seasonal_data.groupby('week_of_year')['sales'].mean()\n",
+    "print(\"\\nWeekly pattern statistics:\")\n",
+    "print(f\"Highest week: Week {weekly_pattern.idxmax()} (${weekly_pattern.max():.0f})\")\n",
+    "print(f\"Lowest week: Week {weekly_pattern.idxmin()} (${weekly_pattern.min():.0f})\")\n",
+    "print(f\"Weekly variation: {weekly_pattern.std():.2f}\")\n",
+    "\n",
+    "# Quarterly analysis\n",
+    "quarterly_pattern = seasonal_data.groupby('quarter')['sales'].agg(['mean', 'sum', 'std'])\n",
+    "print(\"\\nQuarterly patterns:\")\n",
+    "print(quarterly_pattern.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simple seasonal decomposition\n",
+    "print(\"=== SEASONAL DECOMPOSITION ===\")\n",
+    "\n",
+    "# Manual decomposition approach\n",
+    "def simple_decompose(series, period=365):\n",
+    "    \"\"\"Simple seasonal decomposition\"\"\"\n",
+    "    # Trend (using centered moving average)\n",
+    "    trend = series.rolling(window=period, center=True).mean()\n",
+    "    \n",
+    "    # Detrended series\n",
+    "    detrended = series - trend\n",
+    "    \n",
+    "    # Seasonal component (average for each period)\n",
+    "    seasonal_avg = detrended.groupby(detrended.index.dayofyear).mean()\n",
+    "    seasonal = pd.Series(index=series.index, dtype=float)\n",
+    "    for idx in series.index:\n",
+    "        day_of_year = idx.dayofyear\n",
+    "        if day_of_year in seasonal_avg.index:\n",
+    "            seasonal.loc[idx] = seasonal_avg.loc[day_of_year]\n",
+    "        else:  # Handle leap year day\n",
+    "            seasonal.loc[idx] = 0\n",
+    "    \n",
+    "    # Residual (what's left after removing trend and seasonality)\n",
+    "    residual = series - trend - seasonal\n",
+    "    \n",
+    "    return trend, seasonal, residual\n",
+    "\n",
+    "# Decompose sales data\n",
+    "trend, seasonal, residual = simple_decompose(ts_data['sales'])\n",
+    "\n",
+    "# Create decomposition DataFrame\n",
+    "decomposition = pd.DataFrame({\n",
+    "    'original': ts_data['sales'],\n",
+    "    'trend': trend,\n",
+    "    'seasonal': seasonal,\n",
+    "    'residual': residual\n",
+    "})\n",
+    "\n",
+    "print(\"Decomposition summary:\")\n",
+    "print(decomposition.describe().round(2))\n",
+    "\n",
+    "# Visualize decomposition\n",
+    "fig, axes = plt.subplots(4, 1, figsize=(15, 12))\n",
+    "\n",
+    "# Original series\n",
+    "decomposition['original'].plot(ax=axes[0], title='Original Sales Data')\n",
+    "axes[0].set_ylabel('Sales ($)')\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Trend\n",
+    "decomposition['trend'].plot(ax=axes[1], title='Trend Component', color='red')\n",
+    "axes[1].set_ylabel('Trend ($)')\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Seasonal\n",
+    "decomposition['seasonal'].plot(ax=axes[2], title='Seasonal Component', color='green')\n",
+    "axes[2].set_ylabel('Seasonal ($)')\n",
+    "axes[2].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Residual\n",
+    "decomposition['residual'].plot(ax=axes[3], title='Residual Component', color='purple')\n",
+    "axes[3].set_ylabel('Residual ($)')\n",
+    "axes[3].set_xlabel('Date')\n",
+    "axes[3].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"\\nDecomposition insights:\")\n",
+    "print(f\"Trend contribution: {trend.std():.2f} (std dev)\")\n",
+    "print(f\"Seasonal contribution: {seasonal.std():.2f} (std dev)\")\n",
+    "print(f\"Residual contribution: {residual.std():.2f} (std dev)\")\n",
+    "print(f\"Total variation: {ts_data['sales'].std():.2f} (std dev)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Advanced seasonal analysis\n",
+    "print(\"=== ADVANCED SEASONAL ANALYSIS ===\")\n",
+    "\n",
+    "# Year-over-year comparison\n",
+    "yoy_comparison = pd.DataFrame()\n",
+    "for year in ts_data.index.year.unique():\n",
+    "    year_data = ts_data[ts_data.index.year == year]['sales']\n",
+    "    year_data.index = year_data.index.dayofyear\n",
+    "    yoy_comparison[f'Year_{year}'] = year_data\n",
+    "\n",
+    "print(\"Year-over-year comparison (sample days):\")\n",
+    "print(yoy_comparison.head(10).round(2))\n",
+    "\n",
+    "# Calculate year-over-year changes\n",
+    "if len(yoy_comparison.columns) > 1:\n",
+    "    yoy_change = yoy_comparison.pct_change(axis=1) * 100\n",
+    "    print(\"\\nYear-over-year change statistics:\")\n",
+    "    for col in yoy_change.columns[1:]:\n",
+    "        print(f\"{col}: mean={yoy_change[col].mean():.2f}%, std={yoy_change[col].std():.2f}%\")\n",
+    "\n",
+    "# Seasonal strength measurement\n",
+    "def seasonal_strength(series, period=365):\n",
+    "    \"\"\"Calculate seasonal strength (0 = no seasonality, 1 = pure seasonality)\"\"\"\n",
+    "    # Detrend the series\n",
+    "    trend = series.rolling(window=period, center=True).mean()\n",
+    "    detrended = series - trend\n",
+    "    \n",
+    "    # Calculate seasonal component\n",
+    "    seasonal_avg = detrended.groupby(detrended.index.dayofyear).mean()\n",
+    "    seasonal_var = seasonal_avg.var()\n",
+    "    \n",
+    "    # Calculate residual variance\n",
+    "    seasonal_full = pd.Series(index=series.index, dtype=float)\n",
+    "    for idx in series.index:\n",
+    "        day_of_year = idx.dayofyear\n",
+    "        if day_of_year in seasonal_avg.index:\n",
+    "            seasonal_full.loc[idx] = seasonal_avg.loc[day_of_year]\n",
+    "        else:\n",
+    "            seasonal_full.loc[idx] = 0\n",
+    "    \n",
+    "    residual = detrended - seasonal_full\n",
+    "    residual_var = residual.var()\n",
+    "    \n",
+    "    # Seasonal strength\n",
+    "    return seasonal_var / (seasonal_var + residual_var)\n",
+    "\n",
+    "sales_seasonal_strength = seasonal_strength(ts_data['sales'])\n",
+    "print(f\"\\nSales seasonal strength: {sales_seasonal_strength:.3f}\")\n",
+    "print(\"(0 = no seasonality, 1 = pure seasonality)\")\n",
+    "\n",
+    "# Identify most/least seasonal periods\n",
+    "monthly_seasonal = seasonal_data.groupby('month')['sales'].std()\n",
+    "print(f\"\\nMost variable month: {monthly_seasonal.idxmax()} (std: {monthly_seasonal.max():.2f})\")\n",
+    "print(f\"Least variable month: {monthly_seasonal.idxmin()} (std: {monthly_seasonal.min():.2f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Business Applications and Forecasting\n",
+    "\n",
+    "Real-world time series analysis for business insights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Business performance metrics\n",
+    "print(\"=== BUSINESS PERFORMANCE METRICS ===\")\n",
+    "\n",
+    "def calculate_business_metrics(df):\n",
+    "    \"\"\"Calculate key business time series metrics\"\"\"\n",
+    "    metrics = {}\n",
+    "    \n",
+    "    # Growth metrics\n",
+    "    daily_sales = df['sales']\n",
+    "    metrics['total_sales'] = daily_sales.sum()\n",
+    "    metrics['avg_daily_sales'] = daily_sales.mean()\n",
+    "    metrics['sales_growth_rate'] = (daily_sales.iloc[-30:].mean() / daily_sales.iloc[:30].mean() - 1) * 100\n",
+    "    \n",
+    "    # Volatility metrics\n",
+    "    metrics['sales_volatility'] = daily_sales.std()\n",
+    "    metrics['coefficient_of_variation'] = daily_sales.std() / daily_sales.mean()\n",
+    "    \n",
+    "    # Trend metrics\n",
+    "    trend = daily_sales.rolling(window=30).mean()\n",
+    "    metrics['trend_direction'] = 'Increasing' if trend.iloc[-1] > trend.iloc[-30] else 'Decreasing'\n",
+    "    metrics['trend_strength'] = abs(trend.iloc[-1] - trend.iloc[-30]) / trend.iloc[-30] * 100\n",
+    "    \n",
+    "    # Customer metrics\n",
+    "    metrics['avg_customers_per_day'] = df['customers'].mean()\n",
+    "    metrics['sales_per_customer'] = df['sales'].sum() / df['customers'].sum()\n",
+    "    \n",
+    "    # Marketing efficiency\n",
+    "    metrics['marketing_roi'] = df['sales'].sum() / df['marketing_spend'].sum()\n",
+    "    \n",
+    "    return metrics\n",
+    "\n",
+    "# Calculate metrics for different periods\n",
+    "overall_metrics = calculate_business_metrics(ts_data)\n",
+    "recent_metrics = calculate_business_metrics(ts_data.tail(90))  # Last 90 days\n",
+    "\n",
+    "print(\"Overall Performance Metrics:\")\n",
+    "for metric, value in overall_metrics.items():\n",
+    "    if isinstance(value, float):\n",
+    "        print(f\"{metric}: {value:.2f}\")\n",
+    "    else:\n",
+    "        print(f\"{metric}: {value}\")\n",
+    "\n",
+    "print(\"\\nRecent 90-day Performance Metrics:\")\n",
+    "for metric, value in recent_metrics.items():\n",
+    "    if isinstance(value, float):\n",
+    "        print(f\"{metric}: {value:.2f}\")\n",
+    "    else:\n",
+    "        print(f\"{metric}: {value}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simple forecasting using historical patterns\n",
+    "print(\"=== SIMPLE FORECASTING ===\")\n",
+    "\n",
+    "def simple_forecast(series, periods=30, method='seasonal_naive'):\n",
+    "    \"\"\"Simple forecasting methods\"\"\"\n",
+    "    if method == 'naive':\n",
+    "        # Naive: repeat last value\n",
+    "        return pd.Series([series.iloc[-1]] * periods, \n",
+    "                        index=pd.date_range(series.index[-1] + pd.Timedelta(days=1), periods=periods))\n",
+    "    \n",
+    "    elif method == 'seasonal_naive':\n",
+    "        # Seasonal naive: repeat same day from previous year\n",
+    "        forecast_dates = pd.date_range(series.index[-1] + pd.Timedelta(days=1), periods=periods)\n",
+    "        forecast_values = []\n",
+    "        \n",
+    "        for date in forecast_dates:\n",
+    "            # Find same day of year from previous year\n",
+    "            previous_year_date = date - pd.DateOffset(years=1)\n",
+    "            if previous_year_date in series.index:\n",
+    "                forecast_values.append(series.loc[previous_year_date])\n",
+    "            else:\n",
+    "                # Fallback to seasonal average\n",
+    "                day_of_year = date.dayofyear\n",
+    "                same_day_values = series[series.index.dayofyear == day_of_year]\n",
+    "                if len(same_day_values) > 0:\n",
+    "                    forecast_values.append(same_day_values.mean())\n",
+    "                else:\n",
+    "                    forecast_values.append(series.mean())\n",
+    "        \n",
+    "        return pd.Series(forecast_values, index=forecast_dates)\n",
+    "    \n",
+    "    elif method == 'moving_average':\n",
+    "        # Moving average forecast\n",
+    "        ma_value = series.tail(30).mean()\n",
+    "        return pd.Series([ma_value] * periods,\n",
+    "                        index=pd.date_range(series.index[-1] + pd.Timedelta(days=1), periods=periods))\n",
+    "    \n",
+    "    elif method == 'trend':\n",
+    "        # Linear trend forecast\n",
+    "        from scipy import stats\n",
+    "        x = np.arange(len(series))\n",
+    "        slope, intercept, _, _, _ = stats.linregress(x, series.values)\n",
+    "        \n",
+    "        forecast_dates = pd.date_range(series.index[-1] + pd.Timedelta(days=1), periods=periods)\n",
+    "        forecast_values = [slope * (len(series) + i) + intercept for i in range(1, periods + 1)]\n",
+    "        \n",
+    "        return pd.Series(forecast_values, index=forecast_dates)\n",
+    "\n",
+    "# Generate forecasts using different methods\n",
+    "forecast_periods = 30\n",
+    "sales_series = ts_data['sales']\n",
+    "\n",
+    "forecasts = {\n",
+    "    'Naive': simple_forecast(sales_series, forecast_periods, 'naive'),\n",
+    "    'Seasonal_Naive': simple_forecast(sales_series, forecast_periods, 'seasonal_naive'),\n",
+    "    'Moving_Average': simple_forecast(sales_series, forecast_periods, 'moving_average'),\n",
+    "    'Trend': simple_forecast(sales_series, forecast_periods, 'trend')\n",
+    "}\n",
+    "\n",
+    "print(f\"Forecasts for next {forecast_periods} days:\")\n",
+    "forecast_df = pd.DataFrame(forecasts)\n",
+    "print(forecast_df.head(10).round(2))\n",
+    "\n",
+    "print(\"\\nForecast summary statistics:\")\n",
+    "print(forecast_df.describe().round(2))\n",
+    "\n",
+    "# Visualize forecasts\n",
+    "plt.figure(figsize=(15, 8))\n",
+    "\n",
+    "# Plot historical data (last 90 days)\n",
+    "historical_period = sales_series.tail(90)\n",
+    "plt.plot(historical_period.index, historical_period.values, label='Historical Data', color='black', linewidth=2)\n",
+    "\n",
+    "# Plot forecasts\n",
+    "colors = ['red', 'blue', 'green', 'orange']\n",
+    "for i, (method, forecast) in enumerate(forecasts.items()):\n",
+    "    plt.plot(forecast.index, forecast.values, label=f'{method} Forecast', \n",
+    "             color=colors[i], linestyle='--', linewidth=2)\n",
+    "\n",
+    "plt.title('Sales Forecasting Comparison')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Anomaly detection in time series\n",
+    "print(\"=== ANOMALY DETECTION ===\")\n",
+    "\n",
+    "def detect_anomalies(series, method='zscore', threshold=3):\n",
+    "    \"\"\"Detect anomalies in time series\"\"\"\n",
+    "    anomalies = pd.Series(False, index=series.index)\n",
+    "    \n",
+    "    if method == 'zscore':\n",
+    "        # Z-score method\n",
+    "        z_scores = np.abs((series - series.mean()) / series.std())\n",
+    "        anomalies = z_scores > threshold\n",
+    "        \n",
+    "    elif method == 'iqr':\n",
+    "        # Interquartile range method\n",
+    "        Q1 = series.quantile(0.25)\n",
+    "        Q3 = series.quantile(0.75)\n",
+    "        IQR = Q3 - Q1\n",
+    "        lower_bound = Q1 - 1.5 * IQR\n",
+    "        upper_bound = Q3 + 1.5 * IQR\n",
+    "        anomalies = (series < lower_bound) | (series > upper_bound)\n",
+    "        \n",
+    "    elif method == 'rolling':\n",
+    "        # Rolling window method\n",
+    "        rolling_mean = series.rolling(window=30).mean()\n",
+    "        rolling_std = series.rolling(window=30).std()\n",
+    "        z_scores = np.abs((series - rolling_mean) / rolling_std)\n",
+    "        anomalies = z_scores > threshold\n",
+    "    \n",
+    "    return anomalies\n",
+    "\n",
+    "# Detect anomalies using different methods\n",
+    "anomaly_methods = ['zscore', 'iqr', 'rolling']\n",
+    "anomaly_results = {}\n",
+    "\n",
+    "for method in anomaly_methods:\n",
+    "    anomalies = detect_anomalies(ts_data['sales'], method=method)\n",
+    "    anomaly_results[method] = anomalies\n",
+    "    print(f\"{method.upper()} method: {anomalies.sum()} anomalies detected\")\n",
+    "\n",
+    "# Combine anomaly detection results\n",
+    "anomaly_df = pd.DataFrame(anomaly_results)\n",
+    "anomaly_df['any_method'] = anomaly_df.any(axis=1)\n",
+    "anomaly_df['all_methods'] = anomaly_df[anomaly_methods].all(axis=1)\n",
+    "\n",
+    "print(f\"\\nAnomalies detected by any method: {anomaly_df['any_method'].sum()}\")\n",
+    "print(f\"Anomalies detected by all methods: {anomaly_df['all_methods'].sum()}\")\n",
+    "\n",
+    "# Show anomalous dates\n",
+    "severe_anomalies = ts_data[anomaly_df['all_methods']]\n",
+    "if len(severe_anomalies) > 0:\n",
+    "    print(\"\\nSevere anomalies (detected by all methods):\")\n",
+    "    print(severe_anomalies[['sales', 'customers', 'marketing_spend']].round(2))\n",
+    "\n",
+    "# Visualize anomalies\n",
+    "plt.figure(figsize=(15, 8))\n",
+    "\n",
+    "# Plot sales data\n",
+    "plt.plot(ts_data.index, ts_data['sales'], label='Sales Data', alpha=0.7)\n",
+    "\n",
+    "# Highlight anomalies\n",
+    "for method in anomaly_methods:\n",
+    "    anomaly_dates = ts_data.index[anomaly_results[method]]\n",
+    "    anomaly_values = ts_data.loc[anomaly_dates, 'sales']\n",
+    "    plt.scatter(anomaly_dates, anomaly_values, label=f'{method.upper()} Anomalies', alpha=0.7, s=30)\n",
+    "\n",
+    "plt.title('Sales Data with Anomaly Detection')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Anomaly statistics\n",
+    "print(\"\\nAnomaly statistics:\")\n",
+    "for method in anomaly_methods:\n",
+    "    anomaly_sales = ts_data.loc[anomaly_results[method], 'sales']\n",
+    "    if len(anomaly_sales) > 0:\n",
+    "        print(f\"{method.upper()}: mean=${anomaly_sales.mean():.2f}, std=${anomaly_sales.std():.2f}\")\n",
+    "    else:\n",
+    "        print(f\"{method.upper()}: No anomalies detected\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply time series analysis to complex business scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Comprehensive Time Series Dashboard\n",
+    "# Create a complete time series analysis dashboard that includes:\n",
+    "# - Multiple time series metrics and KPIs\n",
+    "# - Seasonal analysis and trend identification\n",
+    "# - Anomaly detection and alerting\n",
+    "# - Forecasting with confidence intervals\n",
+    "# - Business insights and recommendations\n",
+    "\n",
+    "def create_time_series_dashboard(df):\n",
+    "    \"\"\"Create comprehensive time series analysis dashboard\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# dashboard = create_time_series_dashboard(ts_data)\n",
+    "# print(\"Time Series Dashboard Created\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Multi-variate Time Series Analysis\n",
+    "# Analyze relationships between multiple time series:\n",
+    "# - Cross-correlation analysis\n",
+    "# - Lead-lag relationships\n",
+    "# - Causality testing\n",
+    "# - Multi-variate forecasting\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Advanced Forecasting Challenge\n",
+    "# Implement more sophisticated forecasting methods:\n",
+    "# - Exponential smoothing with trend and seasonality\n",
+    "# - ARIMA modeling\n",
+    "# - Model evaluation and selection\n",
+    "# - Forecast accuracy metrics\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **DateTime Indexing**:\n",
+    "   - Use `pd.DatetimeIndex` for time-based operations\n",
+    "   - Enable powerful time-based selection and slicing\n",
+    "   - Extract components (year, month, day, etc.) for analysis\n",
+    "\n",
+    "2. **Resampling**:\n",
+    "   - **`.resample()`**: Convert between different frequencies\n",
+    "   - **Downsampling**: Aggregate to lower frequency (daily → monthly)\n",
+    "   - **Upsampling**: Convert to higher frequency (monthly → daily)\n",
+    "   - Use appropriate aggregation functions for your data\n",
+    "\n",
+    "3. **Rolling Calculations**:\n",
+    "   - **`.rolling()`**: Moving window calculations\n",
+    "   - **`.ewm()`**: Exponentially weighted functions\n",
+    "   - Useful for smoothing and trend analysis\n",
+    "   - Handle missing values appropriately\n",
+    "\n",
+    "4. **Seasonal Analysis**:\n",
+    "   - Identify patterns by time components\n",
+    "   - Decompose into trend, seasonal, and residual\n",
+    "   - Measure seasonal strength and variability\n",
+    "\n",
+    "## Time Series Quick Reference\n",
+    "\n",
+    "```python\n",
+    "# Create datetime index\n",
+    "df.set_index(pd.to_datetime(df['date']), inplace=True)\n",
+    "\n",
+    "# Time-based selection\n",
+    "df['2023']                    # Select year\n",
+    "df['2023-01']                 # Select month\n",
+    "df['2023-01-01':'2023-01-31'] # Date range\n",
+    "\n",
+    "# Resampling\n",
+    "df.resample('M').sum()        # Monthly sum\n",
+    "df.resample('W').mean()       # Weekly average\n",
+    "df.resample('Q').agg({'col': ['sum', 'mean']})  # Quarterly multi-agg\n",
+    "\n",
+    "# Rolling calculations\n",
+    "df['col'].rolling(7).mean()   # 7-period moving average\n",
+    "df['col'].ewm(span=10).mean() # Exponential moving average\n",
+    "df['col'].rolling(30).std()   # 30-period rolling standard deviation\n",
+    "```\n",
+    "\n",
+    "## Business Applications\n",
+    "\n",
+    "| Use Case | Technique | Key Insights |\n",
+    "|----------|-----------|-------------|\n",
+    "| Sales forecasting | Seasonal decomposition + trends | Predict future performance |\n",
+    "| Anomaly detection | Rolling statistics + thresholds | Identify unusual patterns |\n",
+    "| Performance monitoring | Moving averages + KPIs | Track business health |\n",
+    "| Seasonal planning | Seasonal analysis | Optimize inventory/staffing |\n",
+    "| Marketing ROI | Cross-correlation analysis | Measure campaign effectiveness |\n",
+    "\n",
+    "## Best Practices\n",
+    "\n",
+    "1. **Data Quality**: Ensure consistent time intervals and handle missing data\n",
+    "2. **Frequency Choice**: Choose appropriate resampling frequency for your analysis\n",
+    "3. **Window Size**: Balance responsiveness vs. smoothness in rolling calculations\n",
+    "4. **Seasonality**: Always check for and account for seasonal patterns\n",
+    "5. **Validation**: Use holdout periods to validate forecasting models\n",
+    "6. **Business Context**: Interpret results in context of business cycles and events\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/11_string_operation.ipynb b/Session_01/PandasDataFrame-exmples/11_string_operation.ipynb
new file mode 100755
index 0000000..33b3770
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/11_string_operation.ipynb
@@ -0,0 +1,1059 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 11: String Operations and Text Processing\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Master pandas string methods for text processing\n",
+    "- Learn regular expressions for pattern matching and extraction\n",
+    "- Understand text cleaning and standardization techniques\n",
+    "- Practice with real-world text data scenarios\n",
+    "- Apply string operations to business data analysis\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed Lessons 1-10\n",
+    "- Basic understanding of regular expressions (helpful but not required)\n",
+    "- Familiarity with text data challenges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import re\n",
+    "import string\n",
+    "from datetime import datetime\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set display options\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', 20)\n",
+    "pd.set_option('display.max_colwidth', 50)\n",
+    "\n",
+    "print(\"Libraries loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Text-Rich Dataset\n",
+    "\n",
+    "Let's create a comprehensive dataset with various text processing challenges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create realistic text-rich dataset\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Sample data with intentional text issues\n",
+    "text_data = {\n",
+    "    'customer_id': range(1, 201),\n",
+    "    'customer_name': [\n",
+    "        'John Smith', 'jane doe', 'MARY JOHNSON', 'Bob Wilson Jr.', 'Dr. Sarah Davis',\n",
+    "        'Mike O\\'Connor', 'Lisa Garcia-Martinez', 'David Miller III', 'Amy Chen', 'Tom Anderson',\n",
+    "        'Kate Wilson', 'james brown', 'DIANA PRINCE', 'Frank Miller Sr.', 'Prof. Grace Lee',\n",
+    "        'Henry Davis', 'Ivy Chen-Wang', 'Jack Robinson', 'Olivia Taylor', 'Ryan Clark'\n",
+    "    ] * 10,\n",
+    "    'email': [\n",
+    "        'john.smith@email.com', 'JANE.DOE@EMAIL.COM', 'mary@company.org',\n",
+    "        'bob.wilson@test.co.uk', 'sarah.davis@university.edu', 'mike@work.net',\n",
+    "        'lisa.garcia@startup.io', 'david@consulting.biz', 'amy.chen@tech.com', 'tom@sales.org',\n",
+    "        'kate.wilson@design.com', 'james@marketing.net', 'diana@fashion.com',\n",
+    "        'frank@legal.org', 'grace.lee@research.edu', 'henry@finance.com',\n",
+    "        'ivy@engineering.tech', 'jack@operations.biz', 'olivia@hr.org', 'ryan@analytics.io'\n",
+    "    ] * 10,\n",
+    "    'phone': [\n",
+    "        '(555) 123-4567', '555.987.6543', '5551234567', '+1-555-987-6543',\n",
+    "        '(555)123-4567', '555 123 4567', '1-555-987-6543', '555-123-4567',\n",
+    "        '(555) 987 6543', '+15559876543', '555.123.4567', '(555)987-6543',\n",
+    "        '555 987 6543', '1 555 123 4567', '+1 555 987 6543', '5559876543',\n",
+    "        '(555)-123-4567', '555_987_6543', '555/123/4567', '555-987-6543'\n",
+    "    ] * 10,\n",
+    "    'address': [\n",
+    "        '123 Main St, Anytown, NY 12345', '456 Oak Ave, Boston, MA 02101',\n",
+    "        '789 pine road, los angeles, CA 90210', '321 ELM STREET, Chicago, IL 60601',\n",
+    "        '654 Maple Dr., Houston, TX 77001', '987 Cedar Lane, Phoenix, AZ 85001',\n",
+    "        '147 birch way, Philadelphia, PA 19101', '258 ASH CT, San Antonio, TX 78201',\n",
+    "        '369 Walnut St., San Diego, CA 92101', '741 Cherry Ave, Dallas, TX 75201',\n",
+    "        '852 Spruce Blvd, Austin, TX 73301', '963 Fir Street, Seattle, WA 98101',\n",
+    "        '159 redwood dr, Portland, OR 97201', '357 WILLOW LN, Denver, CO 80201',\n",
+    "        '468 Poplar St., Miami, FL 33101', '579 Hickory Ave, Atlanta, GA 30301',\n",
+    "        '680 magnolia way, Nashville, TN 37201', '791 DOGWOOD CT, Charlotte, NC 28201',\n",
+    "        '802 Palm St., Orlando, FL 32801', '913 Cypress Ave, Tampa, FL 33601'\n",
+    "    ] * 10,\n",
+    "    'product_reviews': [\n",
+    "        'Great product! Highly recommend!!!', 'okay product, nothing special',\n",
+    "        'TERRIBLE! DO NOT BUY!', 'Amazing quality, fast shipping :)', 'Good value for money.',\n",
+    "        'Poor quality, broke after 1 week :(', 'Excellent customer service!',\n",
+    "        'average product... could be better', 'LOVE IT! 5 stars!!', 'Not worth the price.',\n",
+    "        'Perfect! Exactly what I needed.', 'disappointing quality',\n",
+    "        'OUTSTANDING PRODUCT!!!', 'mediocre at best', 'Fantastic! Will buy again.',\n",
+    "        'cheap quality, looks fake', 'Superb craftsmanship!',\n",
+    "        'waste of money', 'Incredible value! Recommended!', 'poor design'\n",
+    "    ] * 10,\n",
+    "    'job_title': [\n",
+    "        'Software Engineer', 'data scientist', 'MARKETING MANAGER', 'Sales Rep',\n",
+    "        'Product Manager', 'business analyst', 'UX DESIGNER', 'DevOps Engineer',\n",
+    "        'Content Writer', 'project manager', 'FINANCIAL ANALYST', 'HR Specialist',\n",
+    "        'Operations Manager', 'qa engineer', 'RESEARCH SCIENTIST', 'Account Executive',\n",
+    "        'Digital Marketer', 'software developer', 'DATA ENGINEER', 'Consultant'\n",
+    "    ] * 10,\n",
+    "    'company': [\n",
+    "        'TechCorp Inc.', 'data solutions llc', 'INNOVATIVE SYSTEMS', 'Global Enterprises',\n",
+    "        'StartupXYZ', 'consulting group ltd', 'FUTURE TECH CO', 'Analytics Pro',\n",
+    "        'Design Studio', 'enterprise solutions', 'MARKETING MASTERS', 'Software Solutions',\n",
+    "        'Digital Agency', 'research institute', 'FINANCE FIRM', 'Operations Co.',\n",
+    "        'Creative Agency', 'tech startup', 'DATA CORP', 'Professional Services'\n",
+    "    ] * 10\n",
+    "}\n",
+    "\n",
+    "df_text = pd.DataFrame(text_data)\n",
+    "\n",
+    "print(\"Text-rich dataset created:\")\n",
+    "print(f\"Shape: {df_text.shape}\")\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "print(df_text.head())\n",
+    "print(\"\\nData types:\")\n",
+    "print(df_text.dtypes)\n",
+    "print(\"\\nSample of text issues to address:\")\n",
+    "print(\"- Inconsistent capitalization\")\n",
+    "print(\"- Various phone number formats\")\n",
+    "print(\"- Mixed address formatting\")\n",
+    "print(\"- Inconsistent email domains\")\n",
+    "print(\"- Varied punctuation and emoticons in reviews\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Basic String Operations\n",
+    "\n",
+    "Fundamental string methods and transformations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic string transformations\n",
+    "print(\"=== BASIC STRING TRANSFORMATIONS ===\")\n",
+    "\n",
+    "# Case transformations\n",
+    "df_basic = df_text.copy()\n",
+    "\n",
+    "# Convert to different cases\n",
+    "df_basic['customer_name_upper'] = df_basic['customer_name'].str.upper()\n",
+    "df_basic['customer_name_lower'] = df_basic['customer_name'].str.lower()\n",
+    "df_basic['customer_name_title'] = df_basic['customer_name'].str.title()\n",
+    "df_basic['customer_name_capitalize'] = df_basic['customer_name'].str.capitalize()\n",
+    "\n",
+    "print(\"Case transformations:\")\n",
+    "case_cols = ['customer_name', 'customer_name_upper', 'customer_name_lower', \n",
+    "            'customer_name_title', 'customer_name_capitalize']\n",
+    "print(df_basic[case_cols].head())\n",
+    "\n",
+    "# String length and basic properties\n",
+    "df_basic['name_length'] = df_basic['customer_name'].str.len()\n",
+    "df_basic['email_length'] = df_basic['email'].str.len()\n",
+    "df_basic['review_length'] = df_basic['product_reviews'].str.len()\n",
+    "\n",
+    "print(\"\\nString lengths:\")\n",
+    "print(df_basic[['customer_name', 'name_length', 'email', 'email_length']].head())\n",
+    "\n",
+    "print(\"\\nLength statistics:\")\n",
+    "length_stats = df_basic[['name_length', 'email_length', 'review_length']].describe()\n",
+    "print(length_stats)\n",
+    "\n",
+    "# Check for empty/null strings\n",
+    "print(\"\\nEmpty string checks:\")\n",
+    "for col in ['customer_name', 'email', 'phone']:\n",
+    "    empty_count = (df_basic[col].str.strip() == '').sum()\n",
+    "    null_count = df_basic[col].isnull().sum()\n",
+    "    print(f\"{col}: {empty_count} empty strings, {null_count} null values\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# String slicing and indexing\n",
+    "print(\"=== STRING SLICING AND INDEXING ===\")\n",
+    "\n",
+    "# Extract parts of strings\n",
+    "df_basic['first_char'] = df_basic['customer_name'].str[0]\n",
+    "df_basic['last_char'] = df_basic['customer_name'].str[-1]\n",
+    "df_basic['first_three'] = df_basic['customer_name'].str[:3]\n",
+    "df_basic['last_three'] = df_basic['customer_name'].str[-3:]\n",
+    "df_basic['middle_chars'] = df_basic['customer_name'].str[2:5]\n",
+    "\n",
+    "print(\"String slicing examples:\")\n",
+    "slice_cols = ['customer_name', 'first_char', 'last_char', 'first_three', 'last_three', 'middle_chars']\n",
+    "print(df_basic[slice_cols].head(10))\n",
+    "\n",
+    "# Extract email domains\n",
+    "df_basic['email_domain'] = df_basic['email'].str.split('@').str[1]\n",
+    "df_basic['email_username'] = df_basic['email'].str.split('@').str[0]\n",
+    "\n",
+    "print(\"\\nEmail parsing:\")\n",
+    "print(df_basic[['email', 'email_username', 'email_domain']].head(10))\n",
+    "\n",
+    "# Domain analysis\n",
+    "print(\"\\nEmail domain distribution:\")\n",
+    "domain_counts = df_basic['email_domain'].value_counts()\n",
+    "print(domain_counts.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# String concatenation and joining\n",
+    "print(\"=== STRING CONCATENATION AND JOINING ===\")\n",
+    "\n",
+    "# Simple concatenation\n",
+    "df_basic['name_email'] = df_basic['customer_name'] + ' - ' + df_basic['email']\n",
+    "df_basic['initials'] = df_basic['customer_name'].str[0] + '.' + df_basic['customer_name'].str.split().str[1].str[0] + '.'\n",
+    "\n",
+    "print(\"String concatenation:\")\n",
+    "print(df_basic[['customer_name', 'email', 'name_email', 'initials']].head())\n",
+    "\n",
+    "# Using str.cat() for more complex joining\n",
+    "df_basic['full_contact'] = df_basic['customer_name'].str.cat(\n",
+    "    [df_basic['email'], df_basic['phone']], \n",
+    "    sep=' | '\n",
+    ")\n",
+    "\n",
+    "print(\"\\nComplex concatenation:\")\n",
+    "print(df_basic[['full_contact']].head())\n",
+    "\n",
+    "# Conditional concatenation\n",
+    "df_basic['display_name'] = df_basic.apply(\n",
+    "    lambda row: f\"{row['customer_name']} ({row['job_title']})\" \n",
+    "    if pd.notna(row['job_title']) else row['customer_name'], \n",
+    "    axis=1\n",
+    ")\n",
+    "\n",
+    "print(\"\\nConditional concatenation:\")\n",
+    "print(df_basic[['customer_name', 'job_title', 'display_name']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Pattern Matching and String Contains\n",
+    "\n",
+    "Finding patterns and filtering based on string content."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic pattern matching\n",
+    "print(\"=== BASIC PATTERN MATCHING ===\")\n",
+    "\n",
+    "# Check if strings contain specific patterns\n",
+    "df_patterns = df_text.copy()\n",
+    "\n",
+    "# Contains operations\n",
+    "df_patterns['has_dr_title'] = df_patterns['customer_name'].str.contains('Dr\\.|Prof\\.', case=False, na=False)\n",
+    "df_patterns['has_jr_sr'] = df_patterns['customer_name'].str.contains('Jr\\.|Sr\\.', case=False, na=False)\n",
+    "df_patterns['has_hyphen'] = df_patterns['customer_name'].str.contains('-', na=False)\n",
+    "df_patterns['has_apostrophe'] = df_patterns['customer_name'].str.contains(\"'\", na=False)\n",
+    "\n",
+    "print(\"Pattern matching results:\")\n",
+    "pattern_summary = df_patterns[['has_dr_title', 'has_jr_sr', 'has_hyphen', 'has_apostrophe']].sum()\n",
+    "print(pattern_summary)\n",
+    "\n",
+    "print(\"\\nExamples of names with titles:\")\n",
+    "title_names = df_patterns[df_patterns['has_dr_title']]['customer_name'].unique()\n",
+    "print(title_names)\n",
+    "\n",
+    "# Email domain patterns\n",
+    "df_patterns['edu_email'] = df_patterns['email'].str.contains('\\.edu', case=False, na=False)\n",
+    "df_patterns['com_email'] = df_patterns['email'].str.contains('\\.com', case=False, na=False)\n",
+    "df_patterns['org_email'] = df_patterns['email'].str.contains('\\.org', case=False, na=False)\n",
+    "\n",
+    "print(\"\\nEmail domain patterns:\")\n",
+    "domain_pattern_summary = df_patterns[['edu_email', 'com_email', 'org_email']].sum()\n",
+    "print(domain_pattern_summary)\n",
+    "\n",
+    "# Review sentiment patterns\n",
+    "df_patterns['positive_review'] = df_patterns['product_reviews'].str.contains(\n",
+    "    'great|excellent|amazing|fantastic|love|perfect|outstanding|superb|incredible', \n",
+    "    case=False, na=False\n",
+    ")\n",
+    "df_patterns['negative_review'] = df_patterns['product_reviews'].str.contains(\n",
+    "    'terrible|poor|disappointing|waste|cheap|broke|fake|mediocre', \n",
+    "    case=False, na=False\n",
+    ")\n",
+    "\n",
+    "print(\"\\nReview sentiment patterns:\")\n",
+    "sentiment_summary = df_patterns[['positive_review', 'negative_review']].sum()\n",
+    "print(sentiment_summary)\n",
+    "\n",
+    "print(\"\\nSample positive reviews:\")\n",
+    "positive_reviews = df_patterns[df_patterns['positive_review']]['product_reviews'].head(5)\n",
+    "for review in positive_reviews:\n",
+    "    print(f\"- {review}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Advanced pattern matching with startswith/endswith\n",
+    "print(\"=== STARTSWITH/ENDSWITH PATTERNS ===\")\n",
+    "\n",
+    "# Check beginnings and endings\n",
+    "df_patterns['starts_with_vowel'] = df_patterns['customer_name'].str.lower().str.startswith(('a', 'e', 'i', 'o', 'u'))\n",
+    "df_patterns['ends_with_son'] = df_patterns['customer_name'].str.lower().str.endswith('son')\n",
+    "df_patterns['job_starts_data'] = df_patterns['job_title'].str.lower().str.startswith('data')\n",
+    "df_patterns['company_ends_inc'] = df_patterns['company'].str.lower().str.endswith(('inc', 'inc.', 'llc', 'ltd', 'co', 'co.'))\n",
+    "\n",
+    "print(\"Start/End pattern results:\")\n",
+    "start_end_summary = df_patterns[['starts_with_vowel', 'ends_with_son', 'job_starts_data', 'company_ends_inc']].sum()\n",
+    "print(start_end_summary)\n",
+    "\n",
+    "print(\"\\nNames starting with vowels:\")\n",
+    "vowel_names = df_patterns[df_patterns['starts_with_vowel']]['customer_name'].unique()[:10]\n",
+    "print(vowel_names)\n",
+    "\n",
+    "print(\"\\nData-related job titles:\")\n",
+    "data_jobs = df_patterns[df_patterns['job_starts_data']]['job_title'].unique()\n",
+    "print(data_jobs)\n",
+    "\n",
+    "# Phone number format detection\n",
+    "df_patterns['phone_parentheses'] = df_patterns['phone'].str.contains(r'\\(\\d{3}\\)', na=False)\n",
+    "df_patterns['phone_dashes'] = df_patterns['phone'].str.contains(r'\\d{3}-\\d{3}-\\d{4}', na=False)\n",
+    "df_patterns['phone_dots'] = df_patterns['phone'].str.contains(r'\\d{3}\\.\\d{3}\\.\\d{4}', na=False)\n",
+    "df_patterns['phone_spaces'] = df_patterns['phone'].str.contains(r'\\d{3}\\s\\d{3}\\s\\d{4}', na=False)\n",
+    "\n",
+    "print(\"\\nPhone number format patterns:\")\n",
+    "phone_format_summary = df_patterns[['phone_parentheses', 'phone_dashes', 'phone_dots', 'phone_spaces']].sum()\n",
+    "print(phone_format_summary)\n",
+    "\n",
+    "print(\"\\nSample phone formats:\")\n",
+    "for format_type in ['phone_parentheses', 'phone_dashes', 'phone_dots', 'phone_spaces']:\n",
+    "    sample = df_patterns[df_patterns[format_type]]['phone'].iloc[0] if df_patterns[format_type].any() else 'None'\n",
+    "    print(f\"{format_type}: {sample}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Regular Expressions\n",
+    "\n",
+    "Advanced pattern matching using regular expressions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Regular expression basics\n",
+    "print(\"=== REGULAR EXPRESSION BASICS ===\")\n",
+    "\n",
+    "df_regex = df_text.copy()\n",
+    "\n",
+    "# Extract patterns using regex\n",
+    "# Extract phone numbers (various formats)\n",
+    "phone_pattern = r'\\(?\\d{3}\\)?[-.\\s]?\\d{3}[-.\\s]?\\d{4}'\n",
+    "df_regex['extracted_phone'] = df_regex['phone'].str.extract(f'({phone_pattern})')\n",
+    "\n",
+    "print(\"Phone number extraction:\")\n",
+    "print(df_regex[['phone', 'extracted_phone']].head(10))\n",
+    "\n",
+    "# Extract ZIP codes from addresses\n",
+    "zip_pattern = r'\\b\\d{5}\\b'\n",
+    "df_regex['zip_code'] = df_regex['address'].str.extract(f'({zip_pattern})')\n",
+    "\n",
+    "print(\"\\nZIP code extraction:\")\n",
+    "print(df_regex[['address', 'zip_code']].head(10))\n",
+    "\n",
+    "# Extract state abbreviations\n",
+    "state_pattern = r'\\b[A-Z]{2}\\b'\n",
+    "df_regex['state'] = df_regex['address'].str.extract(f'({state_pattern})')\n",
+    "\n",
+    "print(\"\\nState extraction:\")\n",
+    "print(df_regex[['address', 'state']].head(10))\n",
+    "\n",
+    "# Count digits in strings\n",
+    "df_regex['digit_count'] = df_regex['phone'].str.count(r'\\d')\n",
+    "df_regex['letter_count'] = df_regex['customer_name'].str.count(r'[a-zA-Z]')\n",
+    "\n",
+    "print(\"\\nCharacter counting:\")\n",
+    "print(df_regex[['phone', 'digit_count', 'customer_name', 'letter_count']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Advanced regex patterns\n",
+    "print(\"=== ADVANCED REGEX PATTERNS ===\")\n",
+    "\n",
+    "# Extract all email components\n",
+    "email_pattern = r'([a-zA-Z0-9._%+-]+)@([a-zA-Z0-9.-]+)\\.([a-zA-Z]{2,})'\n",
+    "email_parts = df_regex['email'].str.extract(email_pattern)\n",
+    "email_parts.columns = ['username', 'domain', 'tld']\n",
+    "\n",
+    "df_regex = pd.concat([df_regex, email_parts], axis=1)\n",
+    "\n",
+    "print(\"Email component extraction:\")\n",
+    "print(df_regex[['email', 'username', 'domain', 'tld']].head(10))\n",
+    "\n",
+    "# Extract multiple phone number parts\n",
+    "phone_parts_pattern = r'\\(??(\\d{3})\\)?[-.,\\s]?(\\d{3})[-.,\\s]?(\\d{4})'\n",
+    "phone_parts = df_regex['phone'].str.extract(phone_parts_pattern)\n",
+    "phone_parts.columns = ['area_code', 'exchange', 'number']\n",
+    "\n",
+    "df_regex = pd.concat([df_regex, phone_parts], axis=1)\n",
+    "\n",
+    "print(\"\\nPhone number component extraction:\")\n",
+    "print(df_regex[['phone', 'area_code', 'exchange', 'number']].head(10))\n",
+    "\n",
+    "# Extract address components\n",
+    "address_pattern = r'(\\d+)\\s+(.+?)\\s*,\\s*(.+?)\\s*,\\s*([A-Z]{2})\\s+(\\d{5})'\n",
+    "address_parts = df_regex['address'].str.extract(address_pattern)\n",
+    "address_parts.columns = ['street_number', 'street_name', 'city', 'state_extracted', 'zip_extracted']\n",
+    "\n",
+    "print(\"\\nAddress component extraction (first 5):\")\n",
+    "print(address_parts.head())\n",
+    "\n",
+    "# Find all matches (not just first)\n",
+    "# Find all capitalized words in names\n",
+    "df_regex['capitalized_words'] = df_regex['customer_name'].str.findall(r'\\b[A-Z][a-z]+\\b')\n",
+    "df_regex['num_capitalized'] = df_regex['capitalized_words'].str.len()\n",
+    "\n",
+    "print(\"\\nCapitalized words in names:\")\n",
+    "print(df_regex[['customer_name', 'capitalized_words', 'num_capitalized']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Regex replacement and cleaning\n",
+    "print(\"=== REGEX REPLACEMENT AND CLEANING ===\")\n",
+    "\n",
+    "# Clean phone numbers to standard format\n",
+    "def clean_phone(phone_str):\n",
+    "    \"\"\"Clean phone number to standard format\"\"\"\n",
+    "    if pd.isna(phone_str):\n",
+    "        return None\n",
+    "    # Remove all non-digits\n",
+    "    digits = re.sub(r'\\D', '', phone_str)\n",
+    "    # Handle different lengths\n",
+    "    if len(digits) == 10:\n",
+    "        return f\"({digits[:3]}) {digits[3:6]}-{digits[6:]}\"\n",
+    "    elif len(digits) == 11 and digits.startswith('1'):\n",
+    "        return f\"({digits[1:4]}) {digits[4:7]}-{digits[7:]}\"\n",
+    "    else:\n",
+    "        return 'Invalid'\n",
+    "\n",
+    "df_regex['phone_cleaned'] = df_regex['phone'].apply(clean_phone)\n",
+    "\n",
+    "print(\"Phone number cleaning:\")\n",
+    "phone_cleaning_sample = df_regex[['phone', 'phone_cleaned']].head(15)\n",
+    "print(phone_cleaning_sample)\n",
+    "\n",
+    "# Remove punctuation from reviews\n",
+    "df_regex['review_no_punct'] = df_regex['product_reviews'].str.replace(r'[^\\w\\s]', ' ', regex=True)\n",
+    "df_regex['review_clean'] = df_regex['review_no_punct'].str.replace(r'\\s+', ' ', regex=True).str.strip()\n",
+    "\n",
+    "print(\"\\nReview cleaning:\")\n",
+    "review_cleaning_sample = df_regex[['product_reviews', 'review_clean']].head(5)\n",
+    "for idx, row in review_cleaning_sample.iterrows():\n",
+    "    print(f\"Original: {row['product_reviews']}\")\n",
+    "    print(f\"Cleaned:  {row['review_clean']}\")\n",
+    "    print()\n",
+    "\n",
+    "# Standardize company names\n",
+    "df_regex['company_clean'] = (\n",
+    "    df_regex['company']\n",
+    "    .str.replace(r'\\binc\\.?\\b', 'Inc.', case=False, regex=True)\n",
+    "    .str.replace(r'\\bllc\\b', 'LLC', case=False, regex=True)\n",
+    "    .str.replace(r'\\bltd\\.?\\b', 'Ltd.', case=False, regex=True)\n",
+    "    .str.replace(r'\\bco\\.?\\b', 'Co.', case=False, regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "print(\"Company name standardization:\")\n",
+    "company_sample = df_regex[['company', 'company_clean']].head(10)\n",
+    "print(company_sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Text Cleaning and Standardization\n",
+    "\n",
+    "Comprehensive text cleaning workflows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comprehensive text cleaning pipeline\n",
+    "print(\"=== COMPREHENSIVE TEXT CLEANING ===\")\n",
+    "\n",
+    "def clean_text_comprehensive(df):\n",
+    "    \"\"\"Comprehensive text cleaning pipeline\"\"\"\n",
+    "    df_clean = df.copy()\n",
+    "    \n",
+    "    # 1. Clean customer names\n",
+    "    df_clean['customer_name_clean'] = (\n",
+    "        df_clean['customer_name']\n",
+    "        .str.strip()                           # Remove leading/trailing whitespace\n",
+    "        .str.replace(r'\\s+', ' ', regex=True)  # Replace multiple spaces with single space\n",
+    "        .str.title()                           # Title case\n",
+    "        .str.replace(r'\\bDr\\.', 'Dr.', regex=True)      # Standardize titles\n",
+    "        .str.replace(r'\\bProf\\.', 'Prof.', regex=True)\n",
+    "        .str.replace(r'\\bJr\\.', 'Jr.', regex=True)\n",
+    "        .str.replace(r'\\bSr\\.', 'Sr.', regex=True)\n",
+    "    )\n",
+    "    \n",
+    "    # 2. Clean and standardize emails\n",
+    "    df_clean['email_clean'] = (\n",
+    "        df_clean['email']\n",
+    "        .str.strip()\n",
+    "        .str.lower()                           # Lowercase for emails\n",
+    "        .str.replace(r'\\s+', '', regex=True)   # Remove any spaces\n",
+    "    )\n",
+    "    \n",
+    "    # 3. Standardize phone numbers\n",
+    "    df_clean['phone_clean'] = df_clean['phone'].apply(clean_phone)\n",
+    "    \n",
+    "    # 4. Clean addresses\n",
+    "    df_clean['address_clean'] = (\n",
+    "        df_clean['address']\n",
+    "        .str.strip()\n",
+    "        .str.title()                           # Title case\n",
+    "        .str.replace(r'\\bSt\\.?\\b', 'St.', regex=True)    # Standardize street abbreviations\n",
+    "        .str.replace(r'\\bAve\\.?\\b', 'Ave.', regex=True)\n",
+    "        .str.replace(r'\\bRd\\.?\\b', 'Rd.', regex=True)\n",
+    "        .str.replace(r'\\bDr\\.?\\b', 'Dr.', regex=True)\n",
+    "        .str.replace(r'\\bLn\\.?\\b', 'Ln.', regex=True)\n",
+    "        .str.replace(r'\\bCt\\.?\\b', 'Ct.', regex=True)\n",
+    "        .str.replace(r'\\bBlvd\\.?\\b', 'Blvd.', regex=True)\n",
+    "        .str.replace(r'\\s+', ' ', regex=True)  # Multiple spaces to single\n",
+    "    )\n",
+    "    \n",
+    "    # 5. Clean job titles\n",
+    "    df_clean['job_title_clean'] = (\n",
+    "        df_clean['job_title']\n",
+    "        .str.strip()\n",
+    "        .str.title()\n",
+    "        .str.replace(r'\\bQa\\b', 'QA', regex=True)        # Specific corrections\n",
+    "        .str.replace(r'\\bUx\\b', 'UX', regex=True)\n",
+    "        .str.replace(r'\\bHr\\b', 'HR', regex=True)\n",
+    "    )\n",
+    "    \n",
+    "    # 6. Clean company names\n",
+    "    df_clean['company_clean'] = (\n",
+    "        df_clean['company']\n",
+    "        .str.strip()\n",
+    "        .str.title()\n",
+    "        .str.replace(r'\\binc\\.?\\b', 'Inc.', case=False, regex=True)\n",
+    "        .str.replace(r'\\bllc\\b', 'LLC', case=False, regex=True)\n",
+    "        .str.replace(r'\\bltd\\.?\\b', 'Ltd.', case=False, regex=True)\n",
+    "        .str.replace(r'\\bco\\.?\\b', 'Co.', case=False, regex=True)\n",
+    "    )\n",
+    "    \n",
+    "    return df_clean\n",
+    "\n",
+    "# Apply comprehensive cleaning\n",
+    "df_comprehensive = clean_text_comprehensive(df_text)\n",
+    "\n",
+    "print(\"Comprehensive cleaning results:\")\n",
+    "# Show before/after comparison\n",
+    "comparison_cols = [\n",
+    "    ('customer_name', 'customer_name_clean'),\n",
+    "    ('email', 'email_clean'),\n",
+    "    ('phone', 'phone_clean'),\n",
+    "    ('job_title', 'job_title_clean'),\n",
+    "    ('company', 'company_clean')\n",
+    "]\n",
+    "\n",
+    "for original, cleaned in comparison_cols:\n",
+    "    print(f\"\\n{original.upper()} CLEANING:\")\n",
+    "    sample = df_comprehensive[[original, cleaned]].head(5)\n",
+    "    for idx, row in sample.iterrows():\n",
+    "        print(f\"  Before: {row[original]}\")\n",
+    "        print(f\"  After:  {row[cleaned]}\")\n",
+    "        print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Text standardization and validation\n",
+    "print(\"=== TEXT STANDARDIZATION AND VALIDATION ===\")\n",
+    "\n",
+    "def validate_cleaned_data(df):\n",
+    "    \"\"\"Validate cleaned data quality\"\"\"\n",
+    "    validation_results = {}\n",
+    "    \n",
+    "    # Email validation\n",
+    "    email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'\n",
+    "    valid_emails = df['email_clean'].str.match(email_pattern, na=False)\n",
+    "    validation_results['valid_emails'] = {\n",
+    "        'total': len(df),\n",
+    "        'valid': valid_emails.sum(),\n",
+    "        'invalid': (~valid_emails).sum(),\n",
+    "        'percentage_valid': (valid_emails.sum() / len(df)) * 100\n",
+    "    }\n",
+    "    \n",
+    "    # Phone validation\n",
+    "    valid_phones = df['phone_clean'] != 'Invalid'\n",
+    "    validation_results['valid_phones'] = {\n",
+    "        'total': len(df),\n",
+    "        'valid': valid_phones.sum(),\n",
+    "        'invalid': (~valid_phones).sum(),\n",
+    "        'percentage_valid': (valid_phones.sum() / len(df)) * 100\n",
+    "    }\n",
+    "    \n",
+    "    # Name validation (no numbers, reasonable length)\n",
+    "    valid_names = (\n",
+    "        df['customer_name_clean'].str.len().between(2, 50) &\n",
+    "        ~df['customer_name_clean'].str.contains(r'\\d', na=False)\n",
+    "    )\n",
+    "    validation_results['valid_names'] = {\n",
+    "        'total': len(df),\n",
+    "        'valid': valid_names.sum(),\n",
+    "        'invalid': (~valid_names).sum(),\n",
+    "        'percentage_valid': (valid_names.sum() / len(df)) * 100\n",
+    "    }\n",
+    "    \n",
+    "    return validation_results\n",
+    "\n",
+    "# Validate cleaned data\n",
+    "validation_results = validate_cleaned_data(df_comprehensive)\n",
+    "\n",
+    "print(\"Data validation results:\")\n",
+    "for field, results in validation_results.items():\n",
+    "    print(f\"\\n{field.upper()}:\")\n",
+    "    print(f\"  Total records: {results['total']}\")\n",
+    "    print(f\"  Valid: {results['valid']} ({results['percentage_valid']:.1f}%)\")\n",
+    "    print(f\"  Invalid: {results['invalid']}\")\n",
+    "\n",
+    "# Show some invalid examples\n",
+    "print(\"\\nExamples of invalid data:\")\n",
+    "invalid_emails = df_comprehensive[~df_comprehensive['email_clean'].str.match(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$', na=False)]\n",
+    "if len(invalid_emails) > 0:\n",
+    "    print(f\"Invalid emails: {invalid_emails['email_clean'].head(3).tolist()}\")\n",
+    "\n",
+    "invalid_phones = df_comprehensive[df_comprehensive['phone_clean'] == 'Invalid']\n",
+    "if len(invalid_phones) > 0:\n",
+    "    print(f\"Invalid phones: {invalid_phones['phone'].head(3).tolist()}\")\n",
+    "\n",
+    "# Generate data quality summary\n",
+    "overall_quality = np.mean([results['percentage_valid'] for results in validation_results.values()])\n",
+    "print(f\"\\nOverall data quality score: {overall_quality:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Text Analysis and Insights\n",
+    "\n",
+    "Extracting business insights from text data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Text analysis for business insights\n",
+    "print(\"=== TEXT ANALYSIS FOR BUSINESS INSIGHTS ===\")\n",
+    "\n",
+    "def analyze_text_patterns(df):\n",
+    "    \"\"\"Analyze text patterns for business insights\"\"\"\n",
+    "    analysis = {}\n",
+    "    \n",
+    "    # 1. Name analysis\n",
+    "    analysis['name_insights'] = {\n",
+    "        'avg_name_length': df['customer_name_clean'].str.len().mean(),\n",
+    "        'names_with_titles': df['customer_name_clean'].str.contains(r'Dr\\.|Prof\\.|Mr\\.|Ms\\.|Mrs\\.').sum(),\n",
+    "        'names_with_suffixes': df['customer_name_clean'].str.contains(r'Jr\\.|Sr\\.|III|II').sum(),\n",
+    "        'hyphenated_names': df['customer_name_clean'].str.contains('-').sum(),\n",
+    "        'most_common_first_names': df['customer_name_clean'].str.split().str[0].value_counts().head(5)\n",
+    "    }\n",
+    "    \n",
+    "    # 2. Email domain analysis\n",
+    "    domains = df['email_clean'].str.split('@').str[1]\n",
+    "    analysis['email_insights'] = {\n",
+    "        'total_unique_domains': domains.nunique(),\n",
+    "        'top_domains': domains.value_counts().head(10),\n",
+    "        'edu_domains': domains.str.endswith('.edu').sum(),\n",
+    "        'com_domains': domains.str.endswith('.com').sum(),\n",
+    "        'org_domains': domains.str.endswith('.org').sum()\n",
+    "    }\n",
+    "    \n",
+    "    # 3. Geographic analysis from addresses\n",
+    "    states = df['address_clean'].str.extract(r'\\b([A-Z]{2})\\s+\\d{5}')[0]\n",
+    "    analysis['geographic_insights'] = {\n",
+    "        'unique_states': states.nunique(),\n",
+    "        'top_states': states.value_counts().head(10),\n",
+    "        'coastal_states': states.isin(['CA', 'NY', 'FL', 'WA', 'OR']).sum()\n",
+    "    }\n",
+    "    \n",
+    "    # 4. Job title analysis\n",
+    "    analysis['job_insights'] = {\n",
+    "        'unique_job_titles': df['job_title_clean'].nunique(),\n",
+    "        'top_job_titles': df['job_title_clean'].value_counts().head(10),\n",
+    "        'tech_jobs': df['job_title_clean'].str.contains('Engineer|Developer|Data|Software', case=False).sum(),\n",
+    "        'management_jobs': df['job_title_clean'].str.contains('Manager|Director|VP|President', case=False).sum()\n",
+    "    }\n",
+    "    \n",
+    "    # 5. Company analysis\n",
+    "    analysis['company_insights'] = {\n",
+    "        'unique_companies': df['company_clean'].nunique(),\n",
+    "        'top_companies': df['company_clean'].value_counts().head(10),\n",
+    "        'inc_companies': df['company_clean'].str.contains('Inc\\.').sum(),\n",
+    "        'llc_companies': df['company_clean'].str.contains('LLC').sum(),\n",
+    "        'startups': df['company_clean'].str.contains('Startup|startup', case=False).sum()\n",
+    "    }\n",
+    "    \n",
+    "    return analysis\n",
+    "\n",
+    "# Perform text analysis\n",
+    "text_analysis = analyze_text_patterns(df_comprehensive)\n",
+    "\n",
+    "print(\"TEXT ANALYSIS RESULTS:\")\n",
+    "\n",
+    "print(\"\\n1. NAME INSIGHTS:\")\n",
+    "name_insights = text_analysis['name_insights']\n",
+    "print(f\"   Average name length: {name_insights['avg_name_length']:.1f} characters\")\n",
+    "print(f\"   Names with titles: {name_insights['names_with_titles']}\")\n",
+    "print(f\"   Names with suffixes: {name_insights['names_with_suffixes']}\")\n",
+    "print(f\"   Hyphenated names: {name_insights['hyphenated_names']}\")\n",
+    "print(\"   Most common first names:\")\n",
+    "for name, count in name_insights['most_common_first_names'].items():\n",
+    "    print(f\"     {name}: {count}\")\n",
+    "\n",
+    "print(\"\\n2. EMAIL INSIGHTS:\")\n",
+    "email_insights = text_analysis['email_insights']\n",
+    "print(f\"   Unique domains: {email_insights['total_unique_domains']}\")\n",
+    "print(f\"   .edu domains: {email_insights['edu_domains']}\")\n",
+    "print(f\"   .com domains: {email_insights['com_domains']}\")\n",
+    "print(f\"   .org domains: {email_insights['org_domains']}\")\n",
+    "print(\"   Top domains:\")\n",
+    "for domain, count in email_insights['top_domains'].head(5).items():\n",
+    "    print(f\"     {domain}: {count}\")\n",
+    "\n",
+    "print(\"\\n3. GEOGRAPHIC INSIGHTS:\")\n",
+    "geo_insights = text_analysis['geographic_insights']\n",
+    "print(f\"   Unique states: {geo_insights['unique_states']}\")\n",
+    "print(f\"   Coastal states: {geo_insights['coastal_states']}\")\n",
+    "print(\"   Top states:\")\n",
+    "for state, count in geo_insights['top_states'].head(5).items():\n",
+    "    print(f\"     {state}: {count}\")\n",
+    "\n",
+    "print(\"\\n4. JOB INSIGHTS:\")\n",
+    "job_insights = text_analysis['job_insights']\n",
+    "print(f\"   Unique job titles: {job_insights['unique_job_titles']}\")\n",
+    "print(f\"   Tech jobs: {job_insights['tech_jobs']}\")\n",
+    "print(f\"   Management jobs: {job_insights['management_jobs']}\")\n",
+    "\n",
+    "print(\"\\n5. COMPANY INSIGHTS:\")\n",
+    "company_insights = text_analysis['company_insights']\n",
+    "print(f\"   Unique companies: {company_insights['unique_companies']}\")\n",
+    "print(f\"   Inc. companies: {company_insights['inc_companies']}\")\n",
+    "print(f\"   LLC companies: {company_insights['llc_companies']}\")\n",
+    "print(f\"   Startups: {company_insights['startups']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sentiment analysis of product reviews\n",
+    "print(\"=== SENTIMENT ANALYSIS OF REVIEWS ===\")\n",
+    "\n",
+    "def analyze_review_sentiment(df):\n",
+    "    \"\"\"Analyze sentiment in product reviews\"\"\"\n",
+    "    # Define sentiment word lists\n",
+    "    positive_words = [\n",
+    "        'great', 'excellent', 'amazing', 'fantastic', 'love', 'perfect', \n",
+    "        'outstanding', 'superb', 'incredible', 'wonderful', 'awesome', \n",
+    "        'brilliant', 'impressive', 'remarkable', 'exceptional'\n",
+    "    ]\n",
+    "    \n",
+    "    negative_words = [\n",
+    "        'terrible', 'poor', 'disappointing', 'waste', 'cheap', 'broke', \n",
+    "        'fake', 'mediocre', 'awful', 'horrible', 'useless', 'worst', \n",
+    "        'defective', 'junk', 'garbage'\n",
+    "    ]\n",
+    "    \n",
+    "    # Create patterns\n",
+    "    positive_pattern = '|'.join(positive_words)\n",
+    "    negative_pattern = '|'.join(negative_words)\n",
+    "    \n",
+    "    # Count sentiment words\n",
+    "    df['positive_word_count'] = df['product_reviews'].str.lower().str.count(positive_pattern)\n",
+    "    df['negative_word_count'] = df['product_reviews'].str.lower().str.count(negative_pattern)\n",
+    "    \n",
+    "    # Calculate sentiment score\n",
+    "    df['sentiment_score'] = df['positive_word_count'] - df['negative_word_count']\n",
+    "    \n",
+    "    # Categorize sentiment\n",
+    "    def categorize_sentiment(score):\n",
+    "        if score > 0:\n",
+    "            return 'Positive'\n",
+    "        elif score < 0:\n",
+    "            return 'Negative'\n",
+    "        else:\n",
+    "            return 'Neutral'\n",
+    "    \n",
+    "    df['sentiment_category'] = df['sentiment_score'].apply(categorize_sentiment)\n",
+    "    \n",
+    "    # Additional features\n",
+    "    df['has_exclamation'] = df['product_reviews'].str.contains('!').astype(int)\n",
+    "    df['has_caps'] = df['product_reviews'].str.contains(r'[A-Z]{3,}').astype(int)\n",
+    "    df['review_word_count'] = df['product_reviews'].str.split().str.len()\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# Analyze sentiment\n",
+    "df_sentiment = analyze_review_sentiment(df_comprehensive.copy())\n",
+    "\n",
+    "print(\"Sentiment analysis results:\")\n",
+    "sentiment_summary = df_sentiment['sentiment_category'].value_counts()\n",
+    "print(sentiment_summary)\n",
+    "print(f\"\\nSentiment distribution:\")\n",
+    "for category, count in sentiment_summary.items():\n",
+    "    percentage = (count / len(df_sentiment)) * 100\n",
+    "    print(f\"  {category}: {count} ({percentage:.1f}%)\")\n",
+    "\n",
+    "print(\"\\nSentiment score statistics:\")\n",
+    "print(df_sentiment['sentiment_score'].describe())\n",
+    "\n",
+    "print(\"\\nSample reviews by sentiment:\")\n",
+    "for sentiment in ['Positive', 'Negative', 'Neutral']:\n",
+    "    sample_reviews = df_sentiment[df_sentiment['sentiment_category'] == sentiment]['product_reviews'].head(2)\n",
+    "    print(f\"\\n{sentiment} reviews:\")\n",
+    "    for review in sample_reviews:\n",
+    "        print(f\"  - {review}\")\n",
+    "\n",
+    "# Correlation analysis\n",
+    "print(\"\\nCorrelation between text features:\")\n",
+    "text_features = ['positive_word_count', 'negative_word_count', 'sentiment_score', \n",
+    "                'has_exclamation', 'has_caps', 'review_word_count']\n",
+    "correlation_matrix = df_sentiment[text_features].corr()\n",
+    "print(correlation_matrix.round(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply string operations to complex text processing scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Advanced Text Cleaning Pipeline\n",
+    "# Create a comprehensive text cleaning and validation system:\n",
+    "# - Handle international characters and encoding issues\n",
+    "# - Implement fuzzy matching for duplicate detection\n",
+    "# - Create data quality scoring system\n",
+    "# - Generate cleaning reports with statistics\n",
+    "\n",
+    "def advanced_text_cleaning_pipeline(df):\n",
+    "    \"\"\"Advanced text cleaning with international support and validation\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# cleaned_df = advanced_text_cleaning_pipeline(df_text)\n",
+    "# print(\"Advanced text cleaning completed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Text Mining and Information Extraction\n",
+    "# Extract structured information from unstructured text:\n",
+    "# - Extract entities (names, organizations, locations)\n",
+    "# - Parse complex address formats\n",
+    "# - Identify and extract contact information\n",
+    "# - Create knowledge graphs from text relationships\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Business Intelligence from Text\n",
+    "# Create business insights from text analysis:\n",
+    "# - Customer segmentation based on text patterns\n",
+    "# - Market analysis from company and job data\n",
+    "# - Geographic market penetration analysis\n",
+    "# - Competitive intelligence from text data\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **String Accessor (`.str`)**:\n",
+    "   - Essential for all pandas string operations\n",
+    "   - Works with Series containing strings\n",
+    "   - Handles NaN values gracefully\n",
+    "\n",
+    "2. **Basic Operations**:\n",
+    "   - **Case**: `.upper()`, `.lower()`, `.title()`, `.capitalize()`\n",
+    "   - **Length**: `.len()`\n",
+    "   - **Slicing**: `.str[start:end]`\n",
+    "   - **Splitting**: `.str.split()`\n",
+    "\n",
+    "3. **Pattern Matching**:\n",
+    "   - **Contains**: `.str.contains()` for pattern detection\n",
+    "   - **Startswith/Endswith**: `.str.startswith()`, `.str.endswith()`\n",
+    "   - **Regular Expressions**: Use `regex=True` parameter\n",
+    "\n",
+    "4. **Text Cleaning**:\n",
+    "   - **Replace**: `.str.replace()` for substitution\n",
+    "   - **Strip**: `.str.strip()` for whitespace removal\n",
+    "   - **Extract**: `.str.extract()` for regex pattern extraction\n",
+    "\n",
+    "## String Operations Quick Reference\n",
+    "\n",
+    "```python\n",
+    "# Basic transformations\n",
+    "df['col'].str.upper()                    # Uppercase\n",
+    "df['col'].str.lower()                    # Lowercase\n",
+    "df['col'].str.title()                    # Title Case\n",
+    "df['col'].str.len()                      # String length\n",
+    "df['col'].str.strip()                    # Remove whitespace\n",
+    "\n",
+    "# Pattern matching\n",
+    "df['col'].str.contains('pattern')        # Check if contains\n",
+    "df['col'].str.startswith('prefix')       # Check if starts with\n",
+    "df['col'].str.endswith('suffix')         # Check if ends with\n",
+    "\n",
+    "# Extraction and replacement\n",
+    "df['col'].str.extract(r'(\\d+)')         # Extract pattern\n",
+    "df['col'].str.replace('old', 'new')      # Replace text\n",
+    "df['col'].str.split('delimiter')         # Split string\n",
+    "\n",
+    "# Advanced regex\n",
+    "df['col'].str.findall(r'\\b\\w+\\b')       # Find all matches\n",
+    "df['col'].str.count(r'\\d')              # Count pattern occurrences\n",
+    "```\n",
+    "\n",
+    "## Common Text Cleaning Patterns\n",
+    "\n",
+    "| Task | Pattern | Example |\n",
+    "|------|---------|----------|\n",
+    "| Remove punctuation | `r'[^\\w\\s]'` | `str.replace(r'[^\\w\\s]', '', regex=True)` |\n",
+    "| Extract digits | `r'\\d+'` | `str.extract(r'(\\d+)')` |\n",
+    "| Clean phone numbers | `r'\\D'` | `str.replace(r'\\D', '', regex=True)` |\n",
+    "| Extract email parts | `r'([^@]+)@(.+)'` | `str.extract(r'([^@]+)@(.+)')` |\n",
+    "| Standardize whitespace | `r'\\s+'` | `str.replace(r'\\s+', ' ', regex=True)` |\n",
+    "\n",
+    "## Best Practices\n",
+    "\n",
+    "1. **Data Validation**: Always validate cleaned data\n",
+    "2. **Preserve Originals**: Keep original columns during cleaning\n",
+    "3. **Handle Edge Cases**: Plan for missing values and unusual formats\n",
+    "4. **Performance**: Use vectorized operations instead of apply() when possible\n",
+    "5. **Documentation**: Document cleaning rules and business logic\n",
+    "6. **Testing**: Test regex patterns thoroughly with edge cases\n",
+    "\n",
+    "## Business Applications\n",
+    "\n",
+    "- **Customer Data Cleaning**: Standardize names, addresses, contacts\n",
+    "- **Market Research**: Analyze company names and domains\n",
+    "- **Sentiment Analysis**: Process customer reviews and feedback\n",
+    "- **Data Integration**: Clean and match data from multiple sources\n",
+    "- **Compliance**: Standardize data for regulatory requirements"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/12_data_visualization.ipynb b/Session_01/PandasDataFrame-exmples/12_data_visualization.ipynb
new file mode 100755
index 0000000..49bf35e
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/12_data_visualization.ipynb
@@ -0,0 +1,1126 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 12: Data Visualization with Pandas\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Learn to create plots directly from pandas DataFrames\n",
+    "- Master different types of visualizations (line, bar, scatter, histogram, etc.)\n",
+    "- Understand customization options for pandas plots\n",
+    "- Practice with real-world visualization scenarios\n",
+    "- Learn when to use pandas vs matplotlib/seaborn\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed previous lessons on DataFrames\n",
+    "- Basic understanding of data analysis concepts\n",
+    "- matplotlib will be used as the backend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Libraries loaded successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from datetime import datetime, timedelta\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set matplotlib style and pandas plotting backend\n",
+    "plt.style.use('seaborn-v0_8')\n",
+    "pd.plotting.register_matplotlib_converters()\n",
+    "\n",
+    "# Display settings\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (10, 6)\n",
+    "plt.rcParams['font.size'] = 12\n",
+    "\n",
+    "print(\"Libraries loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Sample Datasets\n",
+    "\n",
+    "Let's create multiple datasets for different visualization examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample datasets created:\n",
+      "Sales data: (100, 5)\n",
+      "Stock data: (90, 4)\n",
+      "\n",
+      "Sales data preview:\n",
+      "        Date  Product  Sales Region Salesperson\n",
+      "0 2024-01-01   Tablet   1147  South     Charlie\n",
+      "1 2024-01-02  Monitor   1034   West       Diana\n",
+      "2 2024-01-03   Laptop    976  North     Charlie\n",
+      "3 2024-01-04   Tablet    939   West       Alice\n",
+      "4 2024-01-05   Tablet    704   West       Alice\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sales dataset\n",
+    "np.random.seed(42)\n",
+    "dates = pd.date_range('2024-01-01', periods=100, freq='D')\n",
+    "sales_data = {\n",
+    "    'Date': dates,\n",
+    "    'Product': np.random.choice(['Laptop', 'Phone', 'Tablet', 'Monitor'], 100),\n",
+    "    'Sales': np.random.normal(1000, 200, 100).astype(int),\n",
+    "    'Region': np.random.choice(['North', 'South', 'East', 'West'], 100),\n",
+    "    'Salesperson': np.random.choice(['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'], 100)\n",
+    "}\n",
+    "df_sales = pd.DataFrame(sales_data)\n",
+    "df_sales['Sales'] = np.abs(df_sales['Sales'])  # Ensure positive values\n",
+    "\n",
+    "# Stock prices dataset\n",
+    "stock_dates = pd.date_range('2024-01-01', periods=90, freq='D')\n",
+    "stock_data = {\n",
+    "    'Date': stock_dates,\n",
+    "    'AAPL': 150 + np.cumsum(np.random.normal(0, 2, 90)),\n",
+    "    'GOOGL': 120 + np.cumsum(np.random.normal(0, 1.5, 90)),\n",
+    "    'MSFT': 300 + np.cumsum(np.random.normal(0, 3, 90)),\n",
+    "    'TSLA': 200 + np.cumsum(np.random.normal(0, 5, 90))\n",
+    "}\n",
+    "df_stocks = pd.DataFrame(stock_data)\n",
+    "df_stocks.set_index('Date', inplace=True)\n",
+    "\n",
+    "print(\"Sample datasets created:\")\n",
+    "print(f\"Sales data: {df_sales.shape}\")\n",
+    "print(f\"Stock data: {df_stocks.shape}\")\n",
+    "print(\"\\nSales data preview:\")\n",
+    "print(df_sales.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Basic Line Plots\n",
+    "\n",
+    "Line plots are perfect for time series data and showing trends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Basic line plot of stock prices:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Single stock line plot:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Simple line plot\n",
+    "print(\"Basic line plot of stock prices:\")\n",
+    "df_stocks.plot()\n",
+    "plt.title('Stock Prices Over Time')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price ($)')\n",
+    "plt.legend(title='Stock')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Single column line plot\n",
+    "print(\"\\nSingle stock line plot:\")\n",
+    "df_stocks['AAPL'].plot(color='blue', linewidth=2)\n",
+    "plt.title('Apple Stock Price')\n",
+    "plt.ylabel('Price ($)')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Daily sales with 7-day rolling average:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Daily sales trend\n",
+    "daily_sales = df_sales.groupby('Date')['Sales'].sum()\n",
+    "\n",
+    "daily_sales.plot(kind='line', marker='o', markersize=4)\n",
+    "plt.title('Daily Sales Trend')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Total Sales ($)')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Rolling average\n",
+    "print(\"\\nDaily sales with 7-day rolling average:\")\n",
+    "daily_sales.plot(label='Daily Sales', alpha=0.7)\n",
+    "daily_sales.rolling(window=7).mean().plot(label='7-day Average', linewidth=3)\n",
+    "plt.title('Daily Sales with Moving Average')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Bar Charts\n",
+    "\n",
+    "Bar charts are excellent for comparing categories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Horizontal bar chart:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Product sales comparison\n",
+    "product_sales = df_sales.groupby('Product')['Sales'].sum()\n",
+    "\n",
+    "product_sales.plot(kind='bar', color=['skyblue', 'lightcoral', 'lightgreen', 'gold'])\n",
+    "plt.title('Total Sales by Product')\n",
+    "plt.xlabel('Product')\n",
+    "plt.ylabel('Total Sales ($)')\n",
+    "plt.grid(True, alpha=0.3, axis='y')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Horizontal bar chart\n",
+    "print(\"\\nHorizontal bar chart:\")\n",
+    "product_sales.plot(kind='barh', color='lightblue')\n",
+    "plt.title('Total Sales by Product (Horizontal)')\n",
+    "plt.xlabel('Total Sales ($)')\n",
+    "plt.grid(True, alpha=0.3, axis='x')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Stacked bar chart:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Grouped bar chart\n",
+    "region_product = df_sales.groupby(['Region', 'Product'])['Sales'].sum().unstack()\n",
+    "\n",
+    "region_product.plot(kind='bar', figsize=(12, 6))\n",
+    "plt.title('Sales by Region and Product')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Total Sales ($)')\n",
+    "plt.legend(title='Product', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.grid(True, alpha=0.3, axis='y')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Stacked bar chart\n",
+    "print(\"\\nStacked bar chart:\")\n",
+    "region_product.plot(kind='bar', stacked=True, figsize=(10, 6))\n",
+    "plt.title('Stacked Sales by Region and Product')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Total Sales ($)')\n",
+    "plt.legend(title='Product')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Histograms and Distribution Plots\n",
+    "\n",
+    "Understand the distribution of your numerical data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Histograms by product:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Basic histogram\n",
+    "df_sales['Sales'].plot(kind='hist', bins=20, alpha=0.7, color='skyblue', edgecolor='black')\n",
+    "plt.title('Distribution of Sales Values')\n",
+    "plt.xlabel('Sales ($)')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.grid(True, alpha=0.3, axis='y')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Multiple histograms\n",
+    "print(\"\\nHistograms by product:\")\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
+    "products = df_sales['Product'].unique()\n",
+    "\n",
+    "for i, product in enumerate(products):\n",
+    "    ax = axes[i//2, i%2]\n",
+    "    product_data = df_sales[df_sales['Product'] == product]['Sales']\n",
+    "    product_data.plot(kind='hist', bins=15, alpha=0.7, ax=ax, title=f'{product} Sales Distribution')\n",
+    "    ax.set_xlabel('Sales ($)')\n",
+    "    ax.set_ylabel('Frequency')\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Combined histogram and density plot:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Density plot (KDE)\n",
+    "df_sales['Sales'].plot(kind='density', alpha=0.7, color='red')\n",
+    "plt.title('Sales Distribution (Kernel Density Estimate)')\n",
+    "plt.xlabel('Sales ($)')\n",
+    "plt.ylabel('Density')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Combined histogram and density\n",
+    "print(\"\\nCombined histogram and density plot:\")\n",
+    "ax = df_sales['Sales'].plot(kind='hist', bins=20, alpha=0.5, color='skyblue', \n",
+    "                           density=True, label='Histogram')\n",
+    "df_sales['Sales'].plot(kind='density', ax=ax, color='red', linewidth=2, label='Density')\n",
+    "plt.title('Sales Distribution: Histogram + Density')\n",
+    "plt.xlabel('Sales ($)')\n",
+    "plt.ylabel('Density')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Box Plots\n",
+    "\n",
+    "Box plots show distribution quartiles and outliers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Box plots by region:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Basic box plot\n",
+    "df_sales.boxplot(column='Sales', by='Product', figsize=(10, 6))\n",
+    "plt.title('Sales Distribution by Product')\n",
+    "plt.suptitle('')  # Remove default title\n",
+    "plt.xlabel('Product')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Multiple box plots\n",
+    "print(\"\\nBox plots by region:\")\n",
+    "df_sales.boxplot(column='Sales', by='Region', figsize=(10, 6))\n",
+    "plt.title('Sales Distribution by Region')\n",
+    "plt.suptitle('')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Side-by-side box plots\n",
+    "sales_by_product = [df_sales[df_sales['Product'] == product]['Sales'] for product in df_sales['Product'].unique()]\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "box_plot = plt.boxplot(sales_by_product, labels=df_sales['Product'].unique(), patch_artist=True)\n",
+    "\n",
+    "# Customize colors\n",
+    "colors = ['lightblue', 'lightcoral', 'lightgreen', 'gold']\n",
+    "for patch, color in zip(box_plot['boxes'], colors):\n",
+    "    patch.set_facecolor(color)\n",
+    "\n",
+    "plt.title('Sales Distribution Comparison by Product')\n",
+    "plt.xlabel('Product')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.grid(True, alpha=0.3, axis='y')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Scatter Plots\n",
+    "\n",
+    "Explore relationships between numerical variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Scatter plot colored by category:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create additional numerical column for scatter plot\n",
+    "df_sales['Days_Since_Start'] = (df_sales['Date'] - df_sales['Date'].min()).dt.days\n",
+    "df_sales['Commission'] = df_sales['Sales'] * 0.1  # Assume 10% commission\n",
+    "\n",
+    "# Basic scatter plot\n",
+    "df_sales.plot(kind='scatter', x='Sales', y='Commission', alpha=0.6, figsize=(10, 6))\n",
+    "plt.title('Sales vs Commission')\n",
+    "plt.xlabel('Sales ($)')\n",
+    "plt.ylabel('Commission ($)')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Colored scatter plot\n",
+    "print(\"\\nScatter plot colored by category:\")\n",
+    "products = df_sales['Product'].unique()\n",
+    "colors = ['red', 'blue', 'green', 'orange']\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "for product, color in zip(products, colors):\n",
+    "    product_data = df_sales[df_sales['Product'] == product]\n",
+    "    plt.scatter(product_data['Days_Since_Start'], product_data['Sales'], \n",
+    "               c=color, label=product, alpha=0.6)\n",
+    "\n",
+    "plt.title('Sales Over Time by Product')\n",
+    "plt.xlabel('Days Since Start')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter matrix (pairs plot)\n",
+    "numerical_cols = ['Sales', 'Commission', 'Days_Since_Start']\n",
+    "pd.plotting.scatter_matrix(df_sales[numerical_cols], figsize=(12, 8), alpha=0.6, diagonal='hist')\n",
+    "plt.suptitle('Scatter Matrix of Numerical Variables', y=0.95)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Area Plots\n",
+    "\n",
+    "Area plots show cumulative totals and proportions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aiZOgfYSW028iCIIgCEJrIel4giAIwkFBkTiUCkeVySg9jwSXCmQMTCIApRrR4JYG5BVIgNqxYwdH3JBwVblRdBJFUlC6Fa1DAhENZKvXoagnMlumdJ2lQg2ZetNraQC+GpQuRe9DRs/0HVaK3qm8N0Xf0PdYrkoYCUKU9kQDZfqbBuxkNE0RUJXf4y9/+cuyQglB5tEkXpGYUf096TNJUKMUMEodpEE8GaoTJERQ1TNqe6UK3UpQFFp1NUASDen9qs3MzzzzTBYoKHqG1l1NlKsH9J1JaPzFL36x6HlKB6PvU4mIoX2KfrvqlCoSHCj9qwJFchFkLF4tQJA5dwWqJke/cSV1qwKZpZPJNe0z1fvuSlSEmerPWsv+QSz9HNo+JDKReFkRoEhAo+cr+0st7SfoeKMbmczT8UcCZWXb0/FEYjBFwVXvZ5TuScIVvUdln6jsZxWWfu5S0ZC2CaV1Uspo9Y3ejwRfEmFp/16JWo51Shmk35ciBsmUvCI+VgSopZFQtI1o21Sn/QmCIAiC0BpIJJQgCIJwUJB4QV4sNEhdKvzQoJkGl1RNjLxsKoNGgnxrSHCie6p+RiIVVbYj/xgSkggSXej9qaIY3ahiFkXqUJU0inxaLjWJPKEo8oRS1EjgWOo5VIEiR0gAI+8hSnNbjYqoRVEYFPFCFfyoLSSM0ACeIjEoaoWie+g9SRShCDASJuhGkUWVFKXl/Gnou5MAQVW+0uk0D95pwE6P6TejlCOKFCFfpw9+8IO8Dg3CaVBOg/ulldGWQkIGDdxf97rXcWU68iyiCCaqTlYNfTdaRr5cq4ly9YCiU6hNJDDQfkK+RCS20Xcm8a7iR0W/OUXzUGQW/UYUCfTpT396kUcURZ1R1Bu9F/329JtRpEx1ChbtKyQMfv3rX2dRgvZLEuOoiiFF45EwVIk2ov2GRLJKNNZSzyryzyKBqCKqrmX/IOhzKNKIth2lmJHARO2gaCj6HSj1jLyPaJ3Ka2ppfzX0mAQoag8Ji3Q8UJTVt771La6uR/sC/Wbkv/alL32JqzLSb0pCGQlYdEyQ8EPto+P0/vvvX3FbUvomrUsi1HJQZTvyMKNj+41vfOOy69RyrFMaIR1P9H3onEHnHarASamABIlY1VCacHV6sCAIgiAILURIhuiCIAhCB0DV5qg6VXUFtgpU5YyWXXvttfsto8pql156aXDmmWdydbhzzz13v/Xm5uaCD3/4w1whjSrfPelJTwquuuqqRVW/KtXxqqt5XXDBBcEjH/nIYGpqar9qZNXVvOizq6t0rQZVfnvTm97EbaHKbmeccQZX+qLKd9VQlTaqNkbfiapzUUWvf/zjH8Hpp58efOxjH+N1lmsT/VZUhYy+56Mf/WiurDY4OLiwnKp/veMd7+DKZ7TOk5/8ZK48Rt93Jah9VK3vk5/8JH9XagM9rlQ0rIaq2dG2+uUvf3nA36JS8W7Pnj2HtC5VYat8Z6qC9t73vpcr5y393el70O9JvxlVZaTfp1Idj6AKdLQf0nZ5whOeEHzta1/jynuV6niVSnBf/vKX+Xej9Z7+9KcH11xzzcLykZGR4LnPfS635corr1zx+1xyySULFekOZv+4//77+bPpc77whS9wuz7zmc/w66i6HrWPqt5Vqtlt3bq1pvYvt09R9USqfHjzzTfz44mJieDyyy/n/ZLeg6rvfelLX1q0D5VKJW7P4x73OP7NL7744uDzn//8itXxqB3PfOYzV/y9qN103NL70XtXVy1c67H+q1/9ij+Lfic6Dmhb0Hej71ipqEiMjo4GJ554IlczFARBEASh9VDon7CFMEEQBEFoFnTZo8gN8q165zvfGXZzWgLylaJIG4oskRSmlSGfJooW+u1vf3tQflhC46GouOuuu459zqojMAVBEARBaA0kHU8QBEHoCiiVjYQWEhLIKHq1KnrdAg3UyXuHPI0oFUoEqNUhryLyOaKUuSuvvDLs5ghLID8tSlOkKnoiQAmCIAhCayLG5IIgCEJXQH4+VKmLRCgapC7n+9NtbNq0iQWopzzlKey1IxyY97znPezptHXr1rCbIiwT0fekJz2Jvc0EQRAEQWhNJB1PEARBEARBEARBEARBaDgSCSUIgiAIgiAIgiAIgiA0HBGhBEEQBEEQBEEQBEEQhIYjIpQgCIIgCIIgCIIgCILQcESEEgRBEARBEARBEARBEBqO3viPaF/Gx2fDboJwAExTQ6Hghd0MoUnI9u4uZHt3F7K9uwfZ1t2FbO/uQrZ36zMwEAu7CUKXI5FQQtuiKIvvhc5Gtnd3Idu7u5Dt3T3Itu4uZHt3F7K9BUGoBRGhBEEQBEEQBEEQBEEQhIYjIpQgCIIgCIIgCIIgCILQcESEEgRBEARBEARBEARBEBqOiFCCIAiCIAiCIAiCIAhCwxERShAEQRAEQRAEQRAEQWg4IkIJgiAIgiAIgiAIgiAIDUdEKEEQBEEQBEEQBEEQBKHhiAglCIIgCIIgCIIgCIIgNBwRoQRBEARBEARBEARBEISGIyKUIAiCIAiCIAiCIAiC0HBEhBIEQRAEQRAEQRAEQehwgiAIuwkiQgmCIAiCIAiCIAiCIFTzspe9DCeccMKi28knn4wnPOEJeN/73oeZmZmGfO6PfvQj/qy9e/fW9X2vvfZafOxjH0PY6GE3QBAEQRAEQRAEQRAEodU46aSTcOWVVy48LhaLuOeee/CpT30K9913H6655hooioJ24L//+79x1llnhd0MEaEEQRAEQRAEQRAEQRCWEo1Gcdpppy167swzz8Tc3Byuvvpq3HnnnfstF1ZH0vEEQRAEQRAEQRAEQRBqhNLyiKGhIU7be+tb34pLL72UBal///d/52Wzs7P4yEc+gic/+cl46EMfimc961n4wQ9+sOh9fN/H5z//eU7xO/XUU/GGN7xhvzS/d7zjHXjSk5606DlK1aOUPUrdqzA2Noa3v/3teNSjHoXTTz8dL33pS3H77bfzMnr94OAgfvzjHzck1W8tSCSUIAiCIAiCIAiCIAhCjezYsYPvjzrqKL7/1a9+hXPPPZdT3khYyuVyePGLX4zJyUkWp4444gj87ne/wxVXXIGJiQm87nWv49d94hOfwDe+8Q28/vWvZxGK3ueqq65ac3soMutFL3oRPM/D2972NmzYsAFf/epX8cpXvpKFp8997nN4zWtew+mFJHStX78eYSEilCAIgiAIgiAIgiAIwjLV5Eql0sJjilK6+eabWWyiaKNKRJRhGGxWbpomP/7Od76DzZs347vf/S6vRzzucY/j96LIpxe+8IVQVRXf/OY3OXLqkksuWViHIpr++te/rqmdJDRVIp1OPPFEfu6MM87Aeeedh1tuuQXPe97zuG19fX2hpw+KCCUIgiAIgiAIgiAIgrAEEnAe8pCHLHqOxKNHP/rReP/7379gSv6ABzxgQYAiSKii6KeKAFXh3HPP5ZQ88pKi15LR+ROf+MRF6zzjGc9Yswh166234sgjj1wQoAjHcfCb3/wGrYaIUIIgCIIgCIIgCIIgCEsgAYoinAgSjSzLwuGHH86G5dVEIpFFjyliamBgYL/3W7duHd+nUqmF53p7exets9zrDsT09DT6+/vRDogIJQiCIAiCIAiCIAiCsAQSl8hUfK0kEgns2rVrv+fHx8f3E57IN4oiqaoFpWpI/CKvp2oymcyix7FYbFmz8dtuu43bcvzxx6NVkOp4giAIgiAIgiAIgiAIdeLMM89kj6ZKdboKP/vZz9g/6pRTTuFUPdu28etf/3rROn/84x/3E8KSySTy+fyi9LtqHv7wh2PPnj3YsmXLwnO0/hvf+MaFinyURtgKSCSUILSZMd7oYArTySyiMQs9/S4iUXMhF1kQBEEQWhWqFrT5nlGM7E3BNDUk+hz09LlI9Mm1TBAEQegszj//fDYnv/jii7k6Hvk1/eEPf8APf/hDNiGPx+O8HlWq+/SnP83+TY985CPx5z//eT8RijyjyMCcKutdcMEFbHj+ta99DZqmLfo8Woeq7NHnUaQVVd0jzymq0kfQZ957773sV0UiGAlgYSAilCC0AaWSh707ktixZQLZuSICBCh31RUYloaeXocFqd5+F4leF4a574QkCIIgCGGTSedx+017MJPMolT0kM+VMDGWhq6ri65lJEiVr2UOTEu6qYIgCEJ7QqISiUJXXXUVPvOZzyCdTnPK3Yc+9CEWkiq89rWvheu6+N///V++UXTU29/+drz3ve9dWOcxj3kMP0fvR0bj5FP1uc99jivsVSCPqm9961v4+Mc/jg984AM88UNV8EiIOuqoo3idV77ylfjwhz+Miy66iEUsip4KAyWg0AphWcbHZ8NugrAKNGFqGBqKRQ+duhfnskXs3DqJPdsnUSz4fDJJz+ZRyJcQ+ICmq9yBp466aWnlEEsFC1FS1JGnWeZo3Gr7GeZu2N7CPmR7dxeyvTub4T3T+Oetgyw+zc3my9sZCkoFKnmtLH8tA+BGTb6G9VDEVL+LeI/dMqkEQm3Isd1dyPZuDwYGYmE3QehyZIpJEFqQ1HSWo56Gd8/A9wOOhEqn8gj8AHbEQCxuw/cCvsgXCyXMpfNIzfjcOaeOfDqVQ3Iygz2GCgUKdENdSHuoCFMywywIgiA0Es/zcd+dw9i9bQp+4GNmKsuRur39EdD4lK5py1/LysLUbCqH6akMv4auZaqmsBBVFqbK4pQTkTQ+QRAEQWgnZBQqCC0CBSVOjKaxY/ME3wMB8vkSMukCTy25EQOmqS90tjW93Em3HWPh9aWiv9CZp447TUNpurbQmR8fnl00w1wRpHr6HcQSMsMsCIIg1AeaDLn9xj2YncmhkC8iPVvgKF3TLl/H6JpF9we6llH6Hj2naeVJFrqWTY7NQdPoWqjAtCmNj3ylHElJFwRBEIQ2QEQoQQgZSrEb2j3D4hN11snviXyfKBWPOuckDun6gTvU1Jmnjjd3viPmwiw0pT8UCh6/5+x0jmeSK6LUzFQGetUMM6c8VGaY+92FQYEgCIIg1MrgriTuvm0IXsnDbCrP1yK6rlCE08FcyygimESpUsFDIVvilD5aj0Qpup6lpnMwB/el8UXi1qLrWSxhySSLIAiCILQIIkIJQkiQr9OeHVPs+ZTPluAHQdkro1CCaehs0Kpqh9Zpppljull21QxzyS935OdnmP2qGWZK+RsfSS/MMNuusShaKt7j8LqCIAiCsBRKHb/n9iEM7pzmCRa6xlAVvGiPc0gpcyReWZbOt0jM4muZ51HEVPlaRl6JvrcvJZ3T+CbmFk2ykNF5tb8UTbJIGp8gCIIgNB8RoQQhhApBbDa+YwpeiTrSZDae479t20C0P9KwjjHPMBsa38hHg6CBApme0ywzRV9RZ54mq9kodkZFKpnFkDnNr6WBAPtxLAhTLhxXOvKCIAjdDnkZUvW7uVQe+VyRU8kpIonSyOtNOQqqHAm1Ykr6kjQ+Sg+kVPfKJItl6wuCFF3PSKTSDUnjEwRBEIRGIyKUIDQJMgrfsXkcI4MpsnviTnI6nee/HdeEldjn99RMaObYsummA5UZ5lKlI+8hNZPlVAiOqqr4cYzPLd+Rny+rXUv6oCAIgtD+0DWDJlXuvWOYo5FSlFbuB0jUmH5XL5ZN4/PoWla+nlHEcXmSpZySXk7jy2J4MMXPcWVZTuPbZ3pO6fAyySIIgiAI9UVEKEFocOd8dDDFle6SExk2G6doo8xckdMDItHGzBIf8gyzofHNccvPcYW+eVGqkPe4gtFiP459HXnqr1PHvTpaKhKV6kWCIAidBok7d986iOE9MxzVS5GzZDzuxlvjnE8p7Za2ZJLFq0T/lnhSha5viyrLTmS4oiyl8dH1rZzGN5/KJ16JgiAIgnDItNboVxA6yBeDPDFIfKKUBDIbp/t8rsSdW+rUtpO3Es1mm5bOt8i8uEZ+HCRKVXfkKx5UNNs8RX4cOn1HBYZF1Yv2pT3QTaoXCYIgtC9UgfWOm/bwtY0mV7KZAkcSGS02sVJNefJE45uDfWl8FdNzuqZNZ+Yry2oaNGO+suzI7CKvxIooRRX5JPpXEARBENZG6/YUBKENIR8M8nvavX0KxbzHfkskyJBPhUlCTJPTExpFtR/HQkd+vnpRJY2PBiYEe0tRGt90DqNDqXKFIkp7iFlVopSkPQiCILQDJNrQde7+u0bg+T6f22mipafXhdKG1ze67lBEcnVUcqWyLF3LcpkiR0gtpPHNqJhJZjj6S6mO/p0XpaiYB5mny/VMEARBEJZHRChBqAOzMzns2DyBod1UEYgq0HlcaY4662Q2Hot3vsCiVEVLEfTdfYqWmjeJpRS+1Iy/OO1hcl/aA0eIzc8uUyc+0euWUygEQRCElqnq+s9/7MXo0CwLNVT9znZ0uBELncRylWWrvRLpmk/XNxaltHmvxIk5GPPRv9XXs8pES+W9BEEQhOZB16rRKbJEaR4b+tw1ZbyccMIJ+MY3voFHPOIRdW/Lfffdh2w2izPOOAOthIzwBOEgoU7p5FiaxafxkTT7PeXzJY4AIsHJiRg8s9rp4tNK0PfW9PLM8YrVi6bm0x50rWx6PpPD+PBsOVoKgBs1WZAqC1MOetfNm1QJgiAITSU5McfV7ygyKJst8H0sYcEwOr8ruZxX4n7Rv3MZCvJdMD1fej0z/TycUgrOUUdDtShSivWqhWgqmoypflx+WPl7fp35/kTl7/LDqmV8X73e8q9b+Jz5CaRFy6peV71edRsq3Zp961W+w/7rle+7sx8kCEL4kAD1qe/cCrNJ1U8LRQ9vfvHDsHEgilbg4osvxiWXXCIilCC0O5RiR2H4JD6l5tMQspkicpkCdzwpLF/8IWqvXlRJeygUPP4dqeNOpu2VSnwkVNHscmlyHN7c3Pz7ADzfXNXhrvzN5ujq4g4w9bGpo81/833Z56rynKKq4CxB+ltT599D4XaUl1Xda9WPVSg6ra9C0ei96Z5mxrXy63W6Lz+naho/5vfk96C/VaiGPv+4/HdlwCIIgtAK0OTB9vvHsfnuUfhBwNc9OofS5EA3iwtLo38JNj2fF6X4ejafxmcU5qDlZvk1WnIHjHXrF7/X0p+xIgYt/DP/dNW/bYMCaKqCeK+DgcNiGDgsiniP09X7jiAIzYUEKLvqXC2Ej2wNQagR6lTu3j7JXhhU6pk65pRiRtXiDIOMt10WFYRDT3solXw2iS0USkhNZaDNJaGVCjT1zJFTK1KZFV74e9/s8r7F1Q8WLy8v3rd8/p2aS0VYI0P4+b8tx8CxjzkVRzxgHVdUFARBaAZUTOPOm/dgYjTNaeYkQDmuAcctTyIIy1/PKA2fKRbg7roTQWYOWdgoBBr8bBrTcKh038Lr+KoWzF+egvnH/O/8Ewsr7ffsYuYXLrpyLVWyKk8vc3lTVlm4rGa07wX7f0LVpZauq5yyOJbG5rtVmLaGgQ0kSMWwbkN0kZAnCIIg7IPGRV/4whfw/e9/H2NjY+jp6cELX/hCjm4iXvayl3Ea3w033IB7770XD3nIQ/CBD3wAxx9/PC8bHBzE5Zdfjptvvhkf/ehHsW3bNnz4wx/G7bffjkgkghe84AV4wxvewJPgn/3sZ7F9+3bYto1f/vKXOOyww3DZZZfh7LPPRr2Rs74gHIDMXAE7t0xgz44ke0LQTCeZjdPf5FlE6WIyo1fnaClD45vj6OjdfhP01DiygQGYFixLW9ChgkCBxx12BT7dBwov8xV14bEPhfvltE71rfx8eRnFVS1aR6k8X71O+e/5P1f/DrU+W+NuMzdXRPambdh6/xT7jGw8qgeHH5WQUuGCIDQMEgyo+h0JUXQdpMIbcYr0bVJKQ7ujZ1Pl61c+jaBURL/iwy2mkFZjmFkfxdzAAxsyWNn/ySWCVUDx24uX77vbX/Ba+kdFMNvvLaoWLFpHAftn5bIF9sqkKGe6vtPfe3cl+ZpPnlllQSrGf0ufShAEocxPfvIT/O///i8+9alP4aijjsJf//pXvPe978UTn/hEFpwIEqne8pa34IMf/CA+97nP4TWveQ1+9atfsaj0b//2b3jlK1+J888/H1NTU3jxi1+MJz3pSbj22muxY8cOvOtd70I0GsWFF17I73XdddfhWc96Fn70ox/h97//PS699FL89Kc/xQMf+MDOEaHoy5EytxS6+GzatInVvCuvvBKbN2/mL/6+970PJ5988sJ6v/jFL/DpT38a4+PjeOxjH8uqX19f38KF+KqrrsIPfvADTp+64IIL8Na3vlVSXYSaoTQwSrkb3jvDnaliscTiE/1Ns8BWonv9nppC4KN35y2wUyMISiX0GXn0DRgIQNFQ4TSJTOe5afOd+vI9CV/ljrk/L4ItXlZ+PP/SBcGs/D7zotmCUDb/XOUnCBSUcnlMKzGkxk0EJZOj78ib5b67htE/EMHGo3tw2BHxli6LLqwO7T9k8Dw6mOKIE0pXJZHxsCMS5dRVQWjy/rjl3jFsvW9sYd+kFORuT79bC/b0EHp23gp4RQTFAk8YxAd6URguQM/lYY9tx9zA8SuEFx08y26fpZFRzY7wnU+TJ49HEqPI3J5uM1NZXkwm7nOzeUyNz2HLPWMwLI2jo9bPi1JSoEQQhG7m8MMPx0c+8hE86lGP4scvetGL8F//9V/YsmXLggj1+Mc/fkFEIj3kcY97HEdGkVBFFiGxWIxvZH7uOA6vo+s6R0uRjkLvV3l9IpHA+9//fpimycv/8pe/4Ic//CHe/va31/V7hXpmP+ecc/hHqlAqlfCKV7wCT3jCE5DJZFjFe/azn82hY9dccw1e+9rXsjrnui7uuusuXHHFFSxMPfjBD8aHPvQhFrRICSS+9rWvsUhFaiC979ve9jb09/fjoosuCvEbC60OdbhHh1IsPiUnqJICzd6VkMkUuBNFqVDVZZyFBhEE3IGnjjwJUOR723NYD3ewl53pbRI0EFuelZ5frq1ra38pmMFhUzsxVRjG3vVPQSZb5A47iROZdJ5Fi3tuU9ln4/Cje7Dh8DjPNAutDQmaJCaODKb4nEMmzwueMgUP4yOzuOe2IQwcHmOhcf3hsTVVWhGEgyGXLXL0EwkC5G1EHn006UIpeEINBAGiw/chNnI/X6uUYp59Ip2eGC/WYzHYuXEU0kkYc1MoRvvRTdA1lAQ5ulUqDlJBF4q2o+vaviipHFcbpiS/BHtJRTlSKtHnrnIdFgRB6Dwe+chH4s477+TgGkqlo2p3JBxRkE2FatNximo67rjjeF0Soaqh50i4IgGqwumnn87vl0ql+DEF/JAAVYEe0+vqTaijaco3pFsFEpDookQRSz/72c9gWRbnIdLAkwQnUuJ+/etfczjZt771LTzjGc/Aeeedx6/9+Mc/zj/0nj17OFSNlD4KH3v4wx/Oy+k9P/OZz4gIJSwLdYQoLJzS7uZmCxxwTlXuKP2AUg8SCUcG9s0iCJDYfTuc5N6yAKUF6N1QFqC6EdV1oSaTsLws+gvjcAaOY8+sfLbIKTIUnUeddvp7ZCjFpvgbNsax8egEzyZL9GfrQAITiYYjgzMYG55FMe+xKFks+shmCmzQT/s5bd/UTFAWGmm7Ds7wNt5wRJxTMfvX03btzuNBaBwkfJL/E0WpzKULXCwi3iOFNmpFKRXRw9G7owg8H6pfRM9AFIbrLKyjRiJQpiah+UW449sx02Ui1EoVB2mCj4R5qppLghR5j9F8TTlKKoepiTlsvW8chqlydBSJUnQvKemCIHQ61157LXs4Pe95z8NTn/pUjkh6+ctfvmidalGJ8Dxv2f4/aStLqYhZ9Jq1vNeh0jIhHdPT0/jSl77EuYykvpHi97CHPWxRuVhS+e644w4WoWj5q1/96kWhahs3buTn6fXDw8M488wzF5bTe5ExFxl6rV+/uCqJ0L2QyLRr2xR2bZvkASEdiDQbR4NC09LQ0xeRwV4zCQLE99wJd3IXgpIHnQQoioDqYiFFNU0opgE9X4SVHERm4Djougo9ZnF6Q0WQItPX2RnMCxd5DO1OwrB0HH5EAocfnUDfukjXCnlhUolqIiFpfCTNgjeNrrga5FwRXsnjggaUchKN2Xy+qZjz83adyWI2VfZJo8i3wZ1JmLY+7wvWI/4pwiFD1z2qfLf9/omF9DuadOnplX3rYP2fdDVAz2EJaOZikYSuZVokCnsmjeLkHihHnYpAF5N3gs59VKCEbhwl5fko5EocKTWXTvM+qc97SQ3tKUdJkUi6br7iHvlzyqSLIAidxjXXXIOLL74Yr3rVq/gxRSxNTk4uygwhG6MKs7Oz2L17N0444YT93osipH7729+iWCzCMMrXJzIoJzsjMjwn7r//fu4XVM6nd999N84666zOFaHoByZx6OlPfzo/prCwpQZYlE5H+Y/EcmISLR8ZGeHXEtXL161bx/e0XEQogUK9t2+e4HBv8iigqj8kPtFMHFW1icZt6Xw3myBAbPBuRCZ2IPA8aKqPvi4XoCpobgRmbhrm9AjPtge6sZ+JeyRmcfoMpdOwhwstI0FqNs9VHW3XxOFHJji1izrusn83VtymFDtKtZsam+PzCv1HM/yUdudTlIRerqBl2ftvi/22a6G8XafnvXnI/4siNily041a7B91xNE9fN4ShLVAkXZ33LQb05NZjkKZTeURiZhckVM4FP+n+IrnWC0WgzabglbKw5nag8z645ve5raIktI16FGNz3GBT8J9WZBKzeSA6aAsSM3mkJzMYPumcY6aoujfSqSUVHAUBKGduOuuu5DP5xc9RwE1vb29+Pvf/84V6ubm5vCf//mfLCIVCoWF9X7+859zhbyHPvShnPlFgTn0mCAbI6p4RwE/ZHNEZuXvec97WNQiY3J6TGbllWsWZZV94hOf4Mir3/zmN7jnnns446wjRShS8ijUrKLwEdlsdlE+IkGPKz94LpdbcTktqzyuXkZUb7BakHFa61LZNrVuI9rPJsfnsOP+CYyNzPJznHYwm+cDz4kYXCZYBufhEBu6D9GxrWUBSvHQf3gPlJU8cLpsE6kRF9rMNKdw2KlhZPuP3m8d2m9p/6UbeZAU8iRcFLiDTpE2ppVj8XXHlglEYiaOoEgaEi5i+4fmtuPxHTYUpUSiE92mJzPzJvMBi04kPpEQpetrP8/wdrV1vsUC2q5lIYv8pCgygDx76LZt0ziLiyQybjwq0XYDsHbb3p3AyN4Z3PWPvSxyUsGDQsHn6puN9h5Tqu4DpQP8n4aX+D/1lv2fVkK1LaiWBSOX55S8zPoHdPSOX4/trWgKC6N0W4iSynvltPR0mq9xxrzBORWTofNmLG6xjxT56lGUlHjqNQc5lwutSKHotfxnffKTn9zvOYpaeuc738k3qnJHATdkR0Tm4uQNVYHEpe9+97tc0I2siCi7rJJWR0bm9N47d+5kr+wvf/nL7KVNlkYUAUV+3OS7XeHUU0/lKnq0/Nhjj8UXv/hFtjrqSBHqn//8J0ZHR/HMZz5zUc7iUsGIHlc8pFZaThulWnCq5D5W1qXltWJKVaKWhi5w5PhP96t5VVNI4dDuGWzbNIaZZI4jEmgQRx4s1CmhktNSWSxcIsObECUjV9+HBh8DG3uhaPsff9Sx7MZupGJZUHUdWrEIOzmI/LpjDvACBY6jwqEOux/w7DHt7+SroWsq0ikd6Zkce2zQoPPIY3pZvGgl8+Faj++woIEQ+ZbQgIcG8+xhMn++oegSEouo3RTNFItTWXv10AVu3q4m30jUooirbKaIyfE0Rw3QACyVzGLzP0fRvz6CI2i7HpVg0avVafXt3UnQAP7eO4awY/Mk70fTU1kewPf1N6n6XaVam6JAadNtrZQKSOz4B6yZEb5uqV4RfRtii/yfVsOIx2HnJ1BIT8HKJDvboLze21tRoKkqTENHdCFKyuPzIU20+NP7Ku5RVPDOLZMs2A9siLIgRZ6JbqS9RPp2Qs7lQquxoc/Fm1/8sKZ/5lq4//77V13+ve99b9XlFPlEFfSW4yUveQnfKpx00kn49re/veJ7kXbysY99jG+NpCV6pn/9619ZtaOSgBU2bNiAiYmJRevR40oq3UrLBwYGeBlBaXlHHnnkwt8ELa8VuqiJkt+6VC5wlEq33IWOZnd375jCri2TyGaLPGikTgnNnpG5Zbx334yvL1fK0IiMbkF08N5yRz4ooX9jDwJVXbYKntrF20pxXVjJFPJTVDGwiECr8fStYCGSpiJckAhL/kSUzjCbyrKIcfdtg+gbiLAYddgR8dCFiwMd32FA+yRFlo3ORzyR2ERQel1ZePLKv7epIZpYbOhciYyqGwoWIgPo8ynaigQprpw4n6ZC5ud33aKwZwql663fGGtZk+lW3N6dCO0ft924m0VTEkrJX4ciI9mHp9776AqwEDFf6TRoV/+nbfv8n6gb0Xt42f+p1uuTQgblk2WDcntsO/KRPnQqDd/eCmBYGt/ovEt+erRvk58U7e+VKCnyTRwkLylF4QjgSsU9uu5JlFT9kHO50GrQ8b1xIBp2M4RWFKEoB7K6tGAlFIxCyTjEef7iddttt+F1r3vdwvJbb72VTcoJMiKnGz1PIhQpgrS8IkLR3/TcWv2g5ATa+tA2qt5ONBjcuXUCe3ckUSr6POtLHRHqmJD5b2/1bK9s31ChVIT43rvLApRfQt+GOBRVW367VAvCQXf6QmkzKWheAdbMKHK9R6z5PdSqSBo6LioeRXOzafaPopQcqt52z20q1h0WZfPrsIWLpcd3s6HIpsmxORaeyOeJfjOCOtjZuQIXMaDTCYl2VEp80WCmSe0m80hKv6MbGZ/nclQ5sbhQOXFursBtJ0P7cuXEnpatnBj29u5kyAPxn7cOshk+7RtUjZHMx2mQ3sxzKqVkcWoWP0Dn+D+t4bsoStmg3EqlUZzYDeVIMihvnUjUdt7eFO2ruyZc1+SxQ7EqSipFXlK6xinM01MZ7Ng8wfs/VRytREpRpT7h0JFzuSAILS9Ckdn4ueeeu+g5Mii/6qqrOGfxhS98Iec5kk8U5UFW8htf9rKX4bTTTmMTLlrvCU94wkLOYiX/8bDDDuPH9F6vfOUrQ/h2QrPgDsWWCYzsmeELH5k0k/hEnRAanMUS4vfUSjgTO5HYcycLUAqlMixTSUhYnJJHKYqaV66SdzAiVDUkllBKAt24Elu1cEGC1Fx+QbhYT8LFUT08c9yKwkW9IYGJIsVGuaLdLIqFckU7GsxQtBGdW1RNYWPxyHxFu1aB0k5oEEU3EhlyuXK0S3qmvF0z6QKLEVw5kYzqj+pB77ompWEJoUDCJKXf7dmRZFGVUpQoBT3RK9t9zf5PI0v8n3pW939aDTIo1ysG5ck9yAw8oK5NFpZ4Jc4fC2xwnithanyOz+MUEUyefmNDKSh3KFx1lr2kDouifyDK51RBEIRW5Zvf/Gbd3uuNb3wjukqEojS6eDy+6LloNIovfOELbLD1/e9/n8sMkjEWObwTp59+Ot7//vfj6quvxszMDB7zmMfgAx/4wMLrL7roIi5feMkll3Bu8gUXXIALL7yw6d9NaCzUGaSBMpWWpg4FDRRp0EUDLepc0AA77LQiYX+oIlDP7tvnBagS+jfERYA6AOyHFXFhTc+iMDUIHOsBFDVWB0ho0qMWHy8cSZMt7idcDJNwYeo47MiyIEUpDJ00gKX0DUpfY+FpNM1VM+l8Qn5aFC1GkWM0Y26TQThVz2wh4WklaHAVpQp7UZMFKUpLnpnOgZq+UDlxW7lyInlHUYQUDaw7abt2OxT9Qel3dCzn8xT1WOBUJLku1g5VJO3ZeQvs1CgCOg/4RfQMRGv2f1rxfU0TqmnByOfhjG1HZt1x4ubcYEhQcvRy1GglSorTUmcLXHWPo6Q4YiqLXVvLUVJ96yILohRVK5XzoyAIwqGjBM0wAGhTxsfLFdSE1oNMlskMeO+uaQ6rpt2YBso0u0WdDDKrlNmr1oSMtXt23EJGOuUIqPUx6HYN4e9KOZ2MPTe69KzlZbMojAxjVo1h6sQnIp8oR3o2AjqmKsJFPlvisREJFyRU0T15EZEYtfHoBOI9Tt075vx5hsZRR426StF5pJJmRyJ2edcK+PuSEEdRIxQxRik3nVI5k7Yr+R3mMuXzZblyYnm70gAsGrfKFfaO7mmqeW8ztnc3Qdt5784k7rl9iD3DZlN5vo8lnPAj99roXM7+T9uX+D8NxOo2aVJKpVCYmMCsnsDkyU9BsRO9odpke5cr7pWjpChaitpMQj7ZOND1jtPZI8a8IBXj4g+t6rEXJnIubw8GBg4+ilMQ6oGIUKsgIlRrQZ0DMgMe2jM9H/VUDpGnzjXNZpEpZSRihd/BFlbEmh5G746byGgHSrHA1YRqEqDaqCPbSOh0nd+zG3OeidTGk5A69mFNFi6KHE1BKXnVwgUZGx/OglQPR1m0ckeWIkNIdKJzycxUlp+jfYq/W65cwEDTNfYTqUtFu1bfn8gXLFvk8ysJ9yS2UeQUbeOefpdFxsOP7OGBWCORgUv9IPH47tuGOO2ShNTpZBYWH68tEsXRJufyav8nLPV/qhMUDUzn9HRgY/bwE5t2Tm8qbbK9q+EoqSJFSXnIZ8uRsCRIkcG57Rp8rqJzJKUyV0QpEvBb4vgKGTmXtwciQglhIyLUKogIFT6UGjQ6nOJUIPJooepetMtSBAMbBCsKD46oFL1c/FsbMzWKvm03liOgigX0ro/CcOyO7sg2guL4OHKzc5h1BjB++rMBRW0J4YIEKYoaivfaHCF1+FEJTnkIuyNL7SUPnIrwNJfK8/M0OCd/p/J5BDB0DY5r8PfpxnNJdeVE+s0rEQAObVdVQf+GslH9hiPivF3qjQxc6gOlEd1+425Ou2OD+nSBB8em2ULpd61+Lm+A/9NqFCfGkZuZQ9rpx/hpz0KgdVhqeqtv75qjpEiUoomYfVFSVHWWxEl6TOIUm5tzlFSUI4a7ETmXtwciQglhIyLUKogIFW41KprFpUEjCVHlFJkipwYFflVakKG1dcemWzBnJ9C39W+AX+IOfe/B+Gl0QEe2HniZDAqjI5hV45h8yNkoxAbCFy6yRY5GpMihiiBV8dKgSJrDjkis2YPmUDqy1K7kxNyC8ETCSmUgQdUziwVKL1R4YE4DCEndXQxXTqTzLXlh+T5vB0pHYZFOK1fYI5GRBlv1Km0uA5dDg7pyu7dN4b47h3mbcZq6H7RG+l0bncvL/k//gJ0a2ef/tC5yyP5Pq+HncsgPD3Ga9czxj0Rm4Dh0FC28vQ8+Vd1nMYquf9RHpYhg3VR5QpT6p+QZ2Nsf4WrM9JiujbxO9b2hlf0YDbWjCn7Iubw9EBFKCBsRoVZBRKjmQbvh9GQGgyQ87Z3hGaeKKXB2ruzNQhdrJ2LxPUcqdFjHplMx0pPoJwHKK7IA1bMuCjNyEB162d77p28c8RCkjj4dLSNcUIRUpsjV5aiDbTs6R0NRkYCBDTEWLiiSphYfjbV2ZOnzJ0apol2KoyeL8+eQYpEingqcokTnDYruoap2XJZeOCC0LXPkkZUp8HlaNylizOTf0TQ1bDgigSOOPnSjehm4HDwkAP/z1r0Y2Zvi44Ai/yxH55TSlozqa9FzeaP9n1ZNdx4aRCavIN17JKZOOhsdRYtu73pBXmuUrk6CVD7vceEHjpKy6BpozIvAqx+H5Sp9iwUqgwUqjSdJKqmAywlY1eIWvU/Yx7ycy9sDEaGEsGmh+GyhG6HZWop4Ip8nEpvKg0aPUwhKlIOvkfBkcMRC2BdWYe0Yc0n0bZsXoAokQEUOToASFlBUFarjwpzNwpwcBI46rSUqKlFEDEVALVTYo1SguSLSs3nukFIEEolDlUga8o+iakOHMgNMg+/xkVmMUEW7kTR/Lp1DaEDAUTxFj8UmEkyiMbv1IkLaADYrj1Uq7JW36+x0Dul5o/q5dAF7d0xxpBSJjCRINcKoXlie5GQGd9y0m6+fFJFIgmssbnElS6F2rOkh9M77PwUN8n9aCfoMLRaHlZ9AYXaCr5vFSG/DP1eoD1w11SkXsKhESVGqOl2D6PzIwhvNmyokEO27x8LjynMKX6MWbvOCEv298JoDiFm0DgtUi8SpedFq1WissshVEbwWJnsFoQPwfA9jc5NN/cz1kX5oa6hgfcIJJ/D9H//4R2zcuHHRsmuuuQbvfe97cckll+CNb3zjIbVr7969OPvss/H73/8eRx55JPbs2YPt27fjX//1X9FspJciNB0ajA7vmcbQ7hkWoYiSNy88zQ8aKVIhbovw1M7o2Rn0bb2B0xtIgEr0uzCjbtjN6gi0iAtjLs0z9kYm2XIVlagjG4lafKNjOpcrIZ3Kc5l4Ei6y8+cAGigfdkRZkKo1koZmmynNjiKeKG2XfeJIeMqVO/2eH0DTFD6HWAlbziF1gn5H2nZ0I/N5EgBJ9KAIVhos0UTB3GweOzdPslE9bVMyq6+XUb2wGBrs7tg8gfvvHuFjgIRBoqfX5VQgYS3+T5sQG9nUFP+nldCiEahTU9D8IpyJHSJCdcB5MjJ/7qsknFB6rE/BYEHAf/N9UHk+4OOYJlKK9LhqeblmK4VcVcSsKiEL1cKWwhaRrG8tiFkqn58XhCxaTvcHELOI2gWsJc+pARJ9kZaYHBMEggSoz970dZhN8tsreEW88REX4vDY+jW9zjAM/OEPf8BLX/rSRc//7ne/q1tf9vDDD8f111+Pvr7yuOGd73wnzjrrLBGhhM6F0nQoWoGinpITGX6OUuxIeKKoBbpg2raOWMyWDnQHQGkN/VtIgCqUBag+B1YsEnazOgaKhFIUFVpQgp0cbDkRqhrqlEaNfZE02WwBqelcOWSfI2ny2FMVSUPm14nexZE0HEU1OMP+ThT5QT1y6qCTCELnFjqXkPAlUZPNgf20LJ1vNGDnWf9skauW0naYndF5G2+5Z4yP/YpRPUUKCIcO/d533rIX48OzPIGTSuY49ZWq3wmH6P90MH6F9WiLqkGLRGCmMrAndmH2yFMQaNJF7wQq1yOFxKBDfK9q4ar897ywVXlcEbp8H8WSD9/3+PrIry2/QVnYCuY1oiXRWdX3legtErBoYqciaC2so+4vZgVeCYWhIZiaj6dcdLZMPAotAwlQtt7a18iHP/zh+4lQ6XQat99+O0466aS6fIamaRgYCM9Lthq5wgkNgyIgKGKBhCfyaqELH832UBQEDRzpIkaDmJ4+V9JkOggtny5HQBVzNFpCrNeBFY+G3awOTMlzYKbzMCf3Akec3PKzjvtmiB0E8YAjabILkTQq+wtVImncqMlpXTSzOrhrmgUNwg98TjuiQTidT2g5zTZL6kB4lH22DL6VjeqpcmIBk2Np3i7p2RybxN931zD6B8iovoej3yRd7OCYHE/jzpv2sE8Xpd6REEtCYCMqFnYyy/o/HZZouP/TamixGIz0LLRiDnZyL7Lrjg2tLUJrshABdYh95gUxa17IooI/ZUGrEqU1H6HlByhRdFZh/+is+f8XtUvLpaFl88j7Pkbu24Gjz3xIPb62IHQFZ599Nj72sY+x8BSNlsdNf/rTn1icymazi9b90Y9+hC996UsYHBzEAx/4QFx++eU488wzedmTnvQkXHTRRfjpT3+K++67Dw94wAPwoQ99CCeffPKidLzPfe5zuPnmmxdu3/zmNzEyMoKPfOQj+Pvf/87H9bOf/WxcdtllME2TP/P73/8++vv7ceONN+LKK6/Eueeee9DfV3qBQl2hGRfyZiHhiQQo3yunypBZMXWW6eJFg02KdKhXVSWhddDyGfRvuR5qIcsCFHnDOAkRoBqWkpeZg5FN8YCq5CbQjpE0QcLmAgR0jtgXSaNxqi4NrMkYmyKhuFgBCdeGxoPuWszNheZCkwlUQY9u1Ub1mXSaBchMOs8TEvfcpmLg8Bg2HpXA+o1xuRbUAA3+tm0ax+Z7RvnvVDLLkQg0iSMC7KH5P5GJe2IgEfrvqFgWVNOEkS/AGdsuIpTQMGr1mDoQi1IN/QC9W27FoLKB/54emsLR9WmuIHQFD3rQg7Bhwwb85S9/wTnnnMPPXXfddXjyk5+Mn//85wvrkRj0gQ98gEWgU045hR+/5jWvwa9//Wt+PfHZz34WH/zgB3H88cfj3e9+N//93e9+d9HnXXHFFdi5cydOP/10vPa1r0WhUMArXvEKHHPMMSxITU1N8WuJd73rXXxPUVmve93r8OY3vxm9vYeWNi4ilHDI0EWIBo9kLk7VeSjCgSvbzXu00MWIIhZicZtTc4TOhISnPhKg8hlyjGZzXKdHBKjGpuQp5ZS86SGk20iEqoa+A/k32ctE0pAgRcIGiVUiXLcX1Ub1NJNOXl4kJrJRPQlScwVO0SahkYzqjzimB+sPl2o9y0G/3R0378Hk6ByLshQZWBb7zLCb1v7+T3Ebdk80dAFqn0F5DFZ+kg3K9cw0Sm5P2M0ShJpSDe3sBKK5KVhGDHnVxuTQdNjNE4S24+yzz+aUPBKhSBS64YYb8J73vGeRCEUC0cte9jKcd955/Pitb30rbrnlFnzrW9/CW97yFn7uOc95DotXxL//+7/jP/7jP/b7rBhF3xoGXNdFT08PR0eNjo5ytFMiUR5T0Ge//vWvx5ve9KaFY54e27Z9yN9VRCjhoKAOHEUqUKoMGQxTagBXtiuUoxZoFlzTNR6A0ICjFTp4QuNQizmOgKJUPBKgyJjY7YuH3ayORtE0qLYNI5OHNbUH6Y0nopMiaSgtgMp680StlHlua9i8NmrtE6SyRcymcphNlUt5ky/Y4K4ke45ougLbMXkfsOnmlPcHuq887iYxcmJ0lgWoQo6urXkWaeMUCSgTOm3r/7QaWiQKdSoJzS/AndiJ1NGnhd0kQagJZ3I3jw1cL42iaiKZLKfRC4KwNhHq0ksvRalU4pQ4io6i9Ldqtm3bhosvvnjRc6eddho/X+HYY/dF0lJqX7FIFehXh15Pr6sIUMQZZ5zBbdm9ezc/prbUQ4AiRIQS1gQNFijVbmjPDOZSeX6uki5DpsNclcoxOHJBhKfuQCnl0bflhrK3BglQURNREaCagupGYGbKJb213Cw8u3MiSbiyj6KwGCV0BuwLxqXAy5WjKhX2ZpJZ3t7kE8W+IlqW1+FIODLCXZIyYtoaHMdkUWpBoKqIVW7Zn6rdfQYpKnDLvaOcgkepLvwbaaqk33WI/9NqkwsLBuXjOzF7xMliUC60PIpXgpMcZIE3FqSQMvtQKAFzE9OIrJNoPkGolYc97GF8f+utt3JVvKc85Sn7rWNZ+xuse96+IgQERTitlZXet/p+uXUOFrmyCQeEBgnDe2cwvHsa01NlYzSKdCLhqVgocWefBgAU1i6d4+6bXaYqeEYuhaBQ4EiHaL8IUM1Cc12UKIXDL6fkzR12QthNEoS1V9gLbBSLHnsI0qQGTWiUPQTLhrmVSkzlkuNUZlxZKB1OUVH7CVUKuNpqWaQy9xOp6G/Lbt2JEjIcv+OmPVxJln4Xijqmc6tUF+wc/6faDMqzsKcHke0/JuwmCcKq0H6q+CUoFGEYdzBIY+EASO4eERFKENaAruv413/9V07J++Mf/8heT0s57rjjcOeddy6k2xH0mAzMDwV6X/KImp6e5vQ84o477uA2HX300di8eTPqiYhQwrLQDPXo0AyGds9gYizNF5NKZapKZTuabY70Rdp+xlk4OBSviL5tN8DIznDn3nF1RPs7JxKnHVB0HaptQc8WYE3uFRFKaGtBaiHyrSr4rVJ23CPjW8+H5wU8CUJG9ZlMkS5M5dTN+ci5ilg1q6nzQpVaFqr4OrXvWkWPSZDYl+pnwplP+as8F0YqORX0uOsfe1HMlzCXLqBQKCHeI0b8neb/dECDcsOEUZg3KBcRSmhx3IldfJxpqoJo3IQ6HaCEsjn5kWeE3TpBaL+UvMsvvxxHHXUU35Zy4YUXsqk4mY6feuqp+OEPf4hNmzbhox/96Jo/i/ygSHianJzEYx7zGP48qoZH3lLJZJIN0J/1rGchHq9/gIGIUMIC1LEfH55lg/Gx4dlyZbsgYHNxMkalgYEYBAuV0Ou+bX+HOZdEUCzCsXXE1sVbvnPfsSl52UmY6UmohQx80w27SYJQ3ypOmgKVNJgVfJC4tDiLVGWBiv72SuVUPz9dFqno+lUdTVW5cTSVTkKVsr9QpSmL/Kj2iVTz0VWuwWmD9YDC6Df9cwQ7N09yeyn9TtcV9PRK+t2h+T95UP1SS/o/HdCgfHIKhdQ49OwMSk57Fp4QOh8tl4Y5Nwm/VEIkorMQ5SCHYhDB5HAy7OYJAlPwim3zWY997GPZh6k60qkaMi2fmJjA1VdfjfHxcZx44on46le/yqLUWnne856Hd77znXjVq16FH//4x/j85z/PwtPzn/98RCIRPPvZz+ZKeI1ACSr1NYX9GB+fRadDnfWp8TQGd09jdDDFaRABVbbLUoWqcmU7mg2mjnjLzcQqWHbmXGgwvoe+bTfCmh1jAcqyVCTWNyG9Qbb3spDHSX7PXswqEUwffxYy6x+IjkC2d3fR4O3NQhWJVL6/IFbxrejzc9wTWipUafuEKoqqIk8mNsuvgiKtqgWqctrfPpGKnieRazUy6Txuv2kPZqayHPmUTuXZW4+ijTuSBm7rZf2fBmIt6f+0GiSe5ffsRhoOZo88BamjTkHbIufyjiY2dC+iI/dDKWSx7og+KIaOrUkHo34PTEvDeZc/L+wmCsswMNA9mQue72FsbrKpn7k+0g+NZ8+ElZBIqC4kmJ9lJYPx4T0znF5HPYPCfGU7SnmgDjcZx9K9zMIKC/g+erffXBagSk0UoIQVUXQDimVCzxVgTQ12jgglCPWOLtEVaFBXvTZSul8l7Y+ik7ySj0y+wBMyPGcXlCOkKt5UdE+RwSRG0UTNckbqhkVG6stX+8tlirjn9iGUih7Ss3meCOrpdVjwEjrf/2nV6qcU5ZrOwh7fgdQRJwGqdNmFFiMIylXxaNxg6GwRQET0IpRiAEqiyKXSsOPRsFsqdDEkBh0eWx92M4QlyBWti0inclzVjsSnTLrAzxVLHrLpApvBUkeaOspS2U5YUYDaeUs5xaFUhGmIANUqaG4EVi6JfGoMajEH36hP+VRB6CboXEYpcFglcqmc9kfRU/vEKhKO8vORwxxRRZEfS4zUKRrKWMFIncQumhiiqGNKd5dzavf4P62GTgblc2lohQyc5BCy/UeH3SRBWISVGmMD/cArItazr98RMzyAMpICYGrnMDae8i+htlMQhNZDRKgOh6rsDM8LT6np3L7Kduk8ikV/X2W7hFS2E1YhCNCz61auwBaUSjDIq2SDCFCtghqJQE0moflF2NPDyAwcF3aTBKEjKQtL2oqdJzZSD8rXWRKpuOKf53OxD4p6qhits9fVvEhF60djFk8ACd3l/7Qaim1DNQzoVPhjnAzKRYQSWgtnct6QXAmgRyILz0fMAEqGzD0CJPdOigglCMJ+SI+nAynkSxgZLFe2m5qYK1e2831OtaOKQqQbkPAUidlS2U44MEGAxK7b4CT3lgUoDegVAaqloIGKYhrQ80VYyUERoQQhJFhc4kgorSYjdfqbPKfkWnyo/k8laGqA3sMSbef/dCCDcntyCsWZMf6+Jaf+FYoE4WBQSgXYM8Ms/lr24kqiVOjBDvIoQcPUiJiTC4KwPyJCdQiUTjc2VK5sNzGSXvCvoEioXLbEnWKaZRWvCWFNBAHie+6EO7UbQckDedNTJ19RZR9qxZQ8Mz8Nc3qEO4eBbobdJEEQVhIXNLqF3ZLO8X+ybR3x9T0dNzmiRWMLUa7u5E6kjmxjg3Kho3Cm9kAJfI4+jPTsX70xohYw5ztIjs+F0j5BEFobEaE6gMnxNG69YVdVZbsistkiAh9lj4ke54DVeQRhWQFq8J+ITOzgmS5N89F3WI8IUC2K6rrQpqfLKXkzI5K6IQhC5/s/FfJsJ9Du/k8HNCifzcIe24HUxoeQM37YzRIEuJVUPPK5M/ef9IpoBahBgEw+QD6dgRV1Q2mnIAitiYwmO4Cp8Tn2m5ieyiA5keEqd7GYjd5+l805RYAS1kwQcNndyNi2sgCl+OiXCKiWRjFNrkyj+QVYyb1hN0cQBKHu/k+9224sC1CeD7VUQO9AFE5vrCMFqAqUkmeoXtmgfHoo7OYIAvTMNIzsDFAqIhpZPp4hQubkRAAkd400t4GCILQ8MqLsAB7woAE8+JTDuOwzCU/xhMMeE53cKRMaS3TkfkRHN5cFKFQEKJl9bWXoeFcjLiy/ADM5DMUrhd0kQRCEukB+SOvu/9N8ddYSNMVD32EJGJH2NyA/EGrFoNwvG5QLQti4k7v5Xgs8mPHosutETJ/v2Zx8cKKp7RMEofUREaoDoEinjUf3cOqdCE/CoRIZ3YLY8H3lmWZ46D88wSkBQnv4QpHlm+YVYKVGw26OIAhCXfyf+u//M7TcLIJCHrapoH9jb8cYkNfkIRaNsdGzOT3Kv4MghIbvsx8UTVLqprFi/9Bgc/IClCDA1LCYkwuCsBjxhBIEYQF3bBvig3cj8H2oQQl9G+IiQLURimVB0TVopXKVvFzvEWE3SRAE4eDoMv+n1dCiUajT8wblEzsxe+RDw26S0KVQRTzVKwBeCdHe1SMRXSWHTGBhajTdtPYJwlJIMM2NNndi1t6wYU3jpyc96UkYHBxceKzrOo466ii88IUvxIUXXoiXvexlOOuss/DGN74RnYKIUIIgMNSxTey9qyxA+UX0beicUtddlZLnurCmZ1GYGgSO9cTEVhCEtvR/6tn5j3L6HUXl+kX0DES7Iv1uOcjvj87tZjoHe3wHZjeeJOd2IRSceUNysgjV3NXNxiNaEVOlAHM5H8VsHoZjNa2dglCBBKjNn756WQP9RuAXCnjQ/7sUzsaNa3rdO9/5Tpxzzjn8d6lUwo033ogrrrgCPT096EREhBIEAc7kbiR2314WoDwSoOIiQLUpVElJS6WglXKwZseRTxwWdpMEQRDW5P/Uu/0m6Pk0glKRU4x7D5NJETYon5uDns/Anh5Gru/IsJskdBlqIQs7NYrAK8G11QNGJEb0EuDNm5PvHsH6E45pWlsFoRoSoDTbRisTi8UwMDCw8Pg5z3kOfvGLX+C3v/0tOhHxhBKELsdO7kXPrltZgFJYgIpBs5ozWyA0xsSWQoA1v8QpeYIgCO3r/6R2lf/Taqi2A1UvG5S7E2JQLjQf8oIiVK8Epyd2wPVjVebk03vHG94+Qeg0dF2HYZSvf6Ojo3jVq16Fhz70oXja056Gv/3tbwvrzczM4N3vfjce/ehH42EPexje9ra38XPETTfdxOl+3/nOd/C4xz0Op512Gi8vFAoLr7/uuus4CuvUU0/FBRdcgJtvvrnh301EKEHoYqzpYU55YL+NUhF960mAknDptjexpbSNIA9rai8QlDuBgiAILe3/NHQf+rbfxJMh5P8Uj1uIr090nf/Tquf2WAwWGZQnR6DlxGdHaCJBAJdS8Shi3tC5YuOBMHQFVlDgSCgxJxeE2ikWixwBdcMNN+Dss8/m537yk5+wUPR///d/OPnkk3HZZZfx+I245JJLcN999+F//ud/8LWvfQ3btm3DO97xjoX3Gxsbw29+8xt8+ctfxmc/+1l+b3o/YtOmTXj729+O17/+9fjZz36Gc889F69+9auxa9euhn5HEaEEoUuh6mm9O27mSidKsYDe9VHotghQnZKSpys+9EIWZnoy7OYIgiCsCE2A9G67sWxA7vlQSgX0DkTh9MZEgFrGoJwroJJB+eTOsJsjdBHG3BSnyJIheSxau5uLq+ShwsfkqFR1FITVuPLKK3H66afz7ZRTTmFh6BWveAWLQgRFP51//vk4+uijWSQaHx/H5OQki0gUufSJT3yCX0c3+vsPf/gDtm/fviBqvetd78IJJ5zA0VB0++c//8nLvvKVr+D5z38+nv3sZ+OYY47By1/+cjz+8Y/HNddc09DvK55QgtCFmLPj6N12E+B7UIp57vAbTmvnSgtrTMlTVa5wSOmWhdi+HHNBEIRWQfyfDsKg3HFhzuVhj+3A7OFkUC7zyULj4SioIIAWeDBivTW/LqIVkCwFSGc8eMUSNEOGnoKwHJdeeime+tSn8t+WZbE/lFZVYY+q5VWIRqN8n8/nWWiKx+M47rjjFpYff/zxSCQSvIy8pggSmKpfT+bnBEVN/epXv8L3vve9heUkWj32sY9FI5EzgSB0GUZ6En3bbgT8EgtQPeuiMNzurDjUqbAARQOV2SzMyUHgqNMolyPsZgmCICzyf+rZcSun3wXFAixbR2Igzucv4QAG5RkyKJ+DPTOEXK8YlAuNRSEPqOQgp+KZlr6mYzSie2xOHsybk687XvZXQViO/v7+RULRUqoFqQokDJsrVP3zPI9vFZauV0nlo3Uosuq8885btNxusJG7XOkFoYsw5pLo2/Y3gDw3SIDqj8Ds0pLXnY4WcWGoHkcYUBi9IAhCy/g/Dd6Lvm2L/Z8S5P8kAtQBUR0yKNeh+QU44zvCbo7QBdjTg1D8EldPduNr6zNGDW9hwJvcM9agFgpC93LcccchlUotpN4RW7duRTqdXhQdtdrr9+7dywJY5UZRUX/5y18a2m652gtCl6BnZtC39Qb230Axj0S/CzPqht0soUFQJJSiqNAoJW96KOzmCIIgMLGhexEZ3lSuyCr+TwdtUG6zQfkwtPxc2E0SOhx3cjeLSKqqQFujdYNlKDCDIpuTJ0emG9ZGQehWjj/+ePZwIg+pu+66i2/095lnnokHPehBB3z9hRdeiF/+8pf4xje+gd27d+PrX/8634499tiGtlvS8QShS3w3+rdezx1+EqB6eh1Y0UjYzRIanpLnwEznYU3uwewRJ0tKniAIoUKRT5Gx7WxArqEk/k8HiRaNQUtOQyeD8omdmD3iIWE3SehQtHwaZnoCQamESEQ7KLGYzMlzgYnJ4XLJeEFoNn6h0JGfVeFjH/sYPvjBD7KgRGl7VFHv8ssvRy2cdtpp+PjHP85V8+iejM+vuuoqFrEaiRJUEgKF/Rgfb59KDtlMAb//+X1QuymUXQFURYFPu7DsxStCZZz7t/wVaiELFPJI9DqwE2VDu7ZCtvea8dKzKIyPI6UmMHHK01Bye9A2yPbuLmR7dwXu+A4k9twBpZBD7waqyCrp4AdLYXQU2bk85tz1GD/1nNY1KJdju62JDt2L2Mj9UPJZrDuyj83x17q9d82YGPT6EGg6Lnjn+VCX8bYRmsvAQNmsuhsIPA+50dGmfqa9YQMU2c9XRSKhBKGDoTD9/i3XLwhQ8R67PQUo4RBS8pT5lLxhpNtJhBIEobMIArgTO8oVtjQVWoNNTzsdSskzM3PI5dOwZ4aR6z0i7CYJnXjMUiqe70M3tQMLUCsQ0UuAD/gBMDM4ht6jD697UwVhJUgMcjZuDLsZwhJadNpEEIRDRS1k0McCVIZT8GIJG05P98x8COULr2o7MPw8rKk9YTdHEIQuxshMw8jOAKUiYjFDPKDqZFCuk0H5hBiUC/XHnB2DVszOH7PWQb9P1PT5ngToqV1iTi4IgohQgtCRqMUc+rfcUDYsLRYQjVtwe0WA6kbUiAsTJRjpJLRc+6QYC4LQWVSioNTAgxWXiNxDhaNcozHYfsWgPBN2k4QONSTXlAD6IfiImloAPSiVzcmHk3VtoyAI7YmIUILQYSilPEdAseBAAlTURKQ3HnazhJDQFqXkSZU8QRCaD1VldZJ72ZDcsg3xyqhjSp6mAppXgDO5M+zmCB0EFbKhPgP56Vi2fkiRi1RVL6LkoMDHpFTIEwRBRChB6LxOA0VAGSxAFeGSANUvAlQ3Qx4Oqm1xyoY1uTfs5giC0IU4yT1QfA8qVXNLiBl5Xc/vrgPTz8MZ2w4E5bQnQThUSDRWAh+aX0Ik4R7y+7lqESoCpFJF+L7sp4LQ7YgIJQgdVPq6b+vf2HMjKBbgujqifZKCJwCqG4GJIsz0pKRsCIIQmiG5yobkB+8tI+wPpeSZSokr4Vozza0AJXR+Kp6iqVCtQz9mo3oRFEzl+UB6eLIubRQEoX0REUoQOgDFK6Fv699hZpIIikU4to7ourgYvwqMFnG5ZLLql2DPSEqeIAjNw8gkYWRTZXPj6KGl9Qj7o7rugkG5O7E97OYIHYCeneHjFqUSYpH6FFKPGFXm5LtH6vKegiC0LyJCCUK743vo3X4jzLlJBKUiLFtFbEAEKGEfim5AsUwepJhTkpInCELzcCd2LhiSm3GJzq03dK1Xo1HYQR7m1FC5Iq4gHALu5C6+V4MSzDoVEbD1AFrgsTn51NBUXd5TEIT2RUQoQWhnfA9922+CNTteFqBMFYmBhAhQwn5obgQWpeSlxrh6oiAIQlMMyacqhuQ6FFW6nY1Aj8agKWRQXoQ7URYQBOGg8P35Y9aDYep1KyJA5uQu8lAQYGp4pi7vKQhC+1KfGEtBEJqP76N3xy2wUqNlAcpQkFgvApSwPGokAjWZhOZRlbxhZAaOC7tJgiB0OM7UbiiBB4UMySUKqmEohgHVcWBkcrDHtyN9+AmAIoKfsHbs1AjUUh7wSoj21reIQETNI+W7mJ7Jszm5KqK00AR8z0dyqrkRor19Lnsg1so73vEO/PjHP15x+Te+8Q084hGP2O/5H/3oR/jc5z6HP/zhDyu+L/HRj370gG1Ip9P43e9+h/POOw/NQEQoQWhHggA9u/4Be2YYQakE01CR2CAClLAyqmFAMQ3o+SKs5KCIUIIgNN6QfLKciqdpKnTHDrtFHY0Wi8HKjiKfnYWVGkM+cVjYTRLaEGdyV/mYVQJo7qFXxasmopegFIGSB2QnZhBZ31vX9xeE5SAB6iffuR26UZ+ovgNRKno478Wno3+g9lTWK664Am95y1v471/+8pf46le/ih/84AcLyxOJBBrN17/+ddx0000iQgmCsAJBgMSu2+AkB1mAMnSgRwQoocaUPDM/DXN6BEqpgEA3w26SIAgdijE3xYbkFKkbixthN6crDMoVTYfuFeCObxcRSlgzlKpPFRYpFc9x6l9EIGp4QJG7sZjcNSwilNA0SIAyrdaVPWKxGN8qf2uahoGBgaa2gcTnZiJxkILQZsSG7oU7tZsFKF0L0CsClLCGQQr7hvhF2DNSnUYQhMYbkpMZcb3MjYWVoX6ARgblPhmUD0ItZMNuktBmOFN72LOJvMWcnvofs45BBQqoSl6A5NBk3d9fEDqRW2+9FS960Ytw6qmn4rTTTsOrX/1qjI2NLVrnU5/6FM444ww87nGPwze/+c0V3+u6667DOeecw+91wQUX4Oabb16U1kePTzjhBDQDEaEEod3SG8a38yyVrvroO6xHjF6FmlFME4quQ/MLsJJSJU8QhMZAkZYcrev5MG1DrlNNTMkjGxI2KJ+vcCYItafP7kJAXk26xin89WbBnDwQc3JBqIXZ2Vm89rWvxWMe8xj84he/wFe+8hXs3r0bX/ziFxfWGRwcxP3334/vfe97ePOb34yPfexjnFa3lE2bNuHtb387Xv/61+NnP/sZzj33XBa0du3axcLUK1/5Spx++um4/vrr0QykVyAIbYSeS0H1S1C8EuIJSzr2wtpLeUdcWH4BZnKY9yNBEISGRFQEHlQ2JK+vubGwMiQcqI4Nw8/BHtteznsShBowMknouVk2JI/FGpc+66p5qCCjaInUE4QDkcvl8IY3vAEXX3wxjjrqKDzsYQ/DU5/6VGzZsmVhHcuy2Hj8X/7lX/Cc5zwHz372s/Hd7353v/ciAev5z38+Lz/mmGPw8pe/HI9//ONxzTXXwLZtuK4LwzCalgbYusmRgiDsh5me4ns18KDbYvIqHJwvlDaTguYVYKVGkOs9MuwmCYLQSQQBIhM7OBVP18WQPBSD8swY8tnUvEH5hrCbJLQBHAUVULqcByPWOK+mqFbCWAAUSkBmcgZuf+MNlwWhXRkYGGCjcDINv++++7B161aOeqLUuwokTvX27jtmTzrpJFx77bX7vde2bdvwq1/9iiOmKhSLRTz2sY9FGIQuQhUKBXzkIx/hEDNS3yg/8U1vehPP2N9777248sorsXnzZjzwgQ/E+973Ppx88skLr6XXfPrTn8b4+Dj/gB/4wAfQ19fHy+hEetVVV7GzPJUBpfd961vfKuVAhbY3emXjOFXltCpBWCuKZUHRNWglqpI3JCKUIAh1v05RRAUZkkcTYkjebFQ3wud4nXx9JsigXEQo4QD4JdhTezkVz7T1hkbZR4wSQEHYATC1a0REKEFYhdHRUTz3uc/FQx7yEDz60Y/mSKY//elPuPPOOxfWWaptkO5BmspSPM/j9Lul1e8oCioMQldkPvjBD+Jvf/sbh4iRaPT973+fFbpMJoPXvOY1ePjDH85mWZSjSDmR9Dxx1113cTnDSy65hNdPpVK4/PLLF973a1/7GotUZLJ19dVX4+c//zk/JwjtjEkiFHlsGGJELhxCSp5LKXl5WFODgO+F3SRBEDqIShQUGRCbMTEkD8egPAYryMGaFINy4cA4ySG2elC9IiKxxqbPumYAFQH/Ny3m5IKwKmQknkgk8IUvfAGveMUrWBfZs2fPokp29Dib3XeeJ43kAQ94wH7vddxxx2Hv3r2cile5kYbyl7/8hZc3u8hVqCLU9PQ0fvjDH3IE0ymnnIJHPepRbIpF6t4vf/lLznG87LLLcPzxx7PgFIlE8Otf/5pf+61vfQvPeMYzWM178IMfjI9//OP485//zBuC+MY3voFLL72UN9YjH/lIjoL69re/HebXFYRDNnrV82kovgfbkdll4RBT8tQAWikHa3Y87OYIgtBB1yl73pDcohLvEn0eWkqeTpVQvQJX0xWE1XCo4jIJx6oCrcHps5qqwJ43J58cTjb0swSh3enp6cHQ0BD+/ve/s8ZBhuS//e1vOZOsQj6fZ8Nx8okiL6jf/OY3LFgt5cILL2R9hTQSMjenFD+6HXvssbzccRyuukdCVTNQwy45GI1GcdZZZy08R9FPlJ5HQhSZb1VUObqn/Mc77riDH9NyEpgqHH744di4cSM/T6Frw8PDOPPMMxeW03uRe/zSkoaC0C5QFBRBho7isSEcCoptQ9E0aBSCL1XyBEGoEyR4KIEPzadUPDfs5nS3QblNBuV5MSgXVkXLz5Uno0oluK7elGiIiJKHggDJiXJ2iyA0mlLRQyFfasqNPqtePOMZz+AqdhRYQ2l5VPWOBCfyd6oIUSeeeCI2bNjAqXokUn34wx9eZF9U4bTTTuOgne985ztcDY+yzygLraKXPOUpT+FUvmc+85mYnGx8lGKopjKk6B1xxBH4yU9+gv/5n/9hc6zzzz+fSweSzxP5QFXT39+/4AZPYtL69ev3Wz4yMsKvJaqXr1u3ju9p+dLXCUK7+GwQ1MFXTDPs5gjtnq7hujBTczApJe+YMwBFIhZCx/cQG96Ekh1Ftv+YsFsjCGsv8T6xE4EfQNM0qJYVdou6GjYoz44hn5mBOTuGQly8oYT9cSbLkXKUjuckyr66jSaiFaEGAXJFIJdKw45L2q7QOHr7XJz34tOb/pkHy/nnn883gq6l5IlNt6VRTUvXpayxpVDVvGpIYKLbchx99NGc/tcsQhWhyN9p165dHDpG0U8kHr3nPe/hcDDKbTSXDLTpcUX1o5KFKy2nZZXH1cuI6vC1WmhyeuRBs9DONmlvPaj+yoHSJX5Qvg/VoBSHLvjCq9HlX78eqJEI9NlZ6IUszLlJFGLNKclaK912fBPR0c18o7SIotuDkts9hq3duL07DTM9WTYk94qI9Ri1nadlWzcMNeKWDcr9ItyJHSiEZFAux3aLC8eUiuf70E0NilHnYeEK2ztaZU6e3DWCjacsDjoQhHqiair6B0TobDVCFaF0XUc6neZQMIqIIijv8ZprrmGzrKWCET2uOLiTX9Ryy0nAqhacaL3K3wQtrxXT1NAuFHWNhQm1XVSzeqDMX98UBUqnR5oHAcxMkkoewHG6bDsvNdUOuxEdguI4KC2k5A2iFG+xCNFuOr7nvXSiY9t4MBDkc4iObkHqAftSyjueLtvenUhkciffa/BhxWOrpvXIubwJaBr0WAx2cgbFyUHox+ThGyGk8sux3bKYs+PQChkEXgnxfqtufcsDHd9RK4CSJQ0qQGp4Csc8rH3GW4IgdIAINTAwwCJRRYCqOLeTnxP5RE1MTCxanx5XUuko93G55fSetIygyKojjyyXH6+k6NHyWikUvLaJhCqVPA6B97voCs9fVVE4aqDTv7WemYHilThdx3Qd+F3q70Cdmm797nWHBgSOAyOVhTmxF/5Rp7ZU6Gc3Hd9EbGQzFK8IFAtwkQXGd0LZeBI8qzt8dbpte3eiiGqRIXmpBMvWEcxvy5WQc3lzUKNRaDMz0Lw8rIldmDvsQU1vgxzbrYs9sYu3i0Y+bq5bt2PyQMc31SuwgxxKcDG2dxLFOnroCILQHoQqQp166qns6L5jxw4Wn4jt27ezKEXLvvSlL/HJkRR1ur/tttvwute9buG1ZGxeyYMk4Ypu9DyJUGRSTssrIhT9Tc+t1Q+qXfpIC+1sk/bWAwrr5vBufoCOxkzPm5IHHlTL7vjvuyzV+kg3fv8GVckz0mmuumikp1CM9qNV6KbjWy3mERnbzhXFTB0wilS5sIDI2FakjjwF3UA3be9OxJ3YBcX3ofglRHoSq29DOZc3DdUw2ZvLyObhjG7H3Pp/afpkgxzbrYlSKsJODiHwvHIlS/KFrMf2qfH4jigFzAUOkuNzbTPWEgShfoQaDf2ABzwAT3jCE3D55Zdj06ZN+Otf/8qu7i960Yvw9Kc/HalUCh/60IewdetWviefKHKJJ2idn/70p7j22mv5tZdddhm/11FHHbWw/JOf/CS7yNONUv5e/vKXh/l1BeGQTMlZkFVVrmomCPVAdRzueGpBCfb0UNjN6Voio5t58E7RJLH+GPt1WX4W9uh2fk4Q2sWQnLw3VCmc0VJosTgsFDmimtKvBIFwknuh0MQmCcchVLKMaAWoCJDJByjMZZv++YIgdHEkFEFC0Qc+8AEWjciv6SUveQle9rKXcfTTF77wBVx55ZVcQvCEE05ggcp1yyfK008/He9///tx9dVXY2ZmBo95zGP4fSpcdNFFXF7wkksuYWf5Cy64YMFJXhDa0pTc82EY4qIh1A8SNUmIMtM5WJN7MHvEyS2VktcNqMUcIuM7eDbatHTojg1fVWDMpaEVsohM7ED6sBPCbqYgrIiZnuBoyqA0b0gutBSq6/Lkle4Xygblreb/J4SCMzWfiqepoVRcjhgeQFl4ATC1axiHnfSAprdBEITwUILVkva7nPHxWbQL2UwBv//5fVAp0bpbUMAmipx33sF7MUVCHHbX/yEoFtAb12FSqkM30iXbu9l46TQK42NIqQlMnPI0lNwetARdsr3je+5CZHwblHwWvYcloNvzxTRGhpHJlJBx12H81HOovAs6mi7Z3p1Iz45bYE/t4bTSdUf1s7i9KrKtm05xchL56VmkrV6Mn/Ys+Eb5PNMUZHu3HHo2hYH7fo+gWEQipsLu7Wn69i56AW5JHY4SNJz2qGNx4lPPql8bhAMyMBALuwlCl9NFioUgtG8UVMUPiqIkBKH+KXmKpOSFgFrIcGQCRUHpFAU1L0ARWjwBSylCz8/BndwdajsFYSWUUp7PG7QP2+Qr000TYW2EFotBVwM2KHem5HzS7TiTu/ieUvGokmUYGJoCO8hTNASSw8lQ2iAIQnhIb0EQ2kSEUhCEEjItdDaUpqHaDgw/zyl5QvOIUkW8wIfiFRDrjewnDpK3juHl4Y5sbp8qGUJXQQIp7cMaG5Iv3oeF1oHOJapt8fnEGdsu55NuxvfhTu1hiwfd1EP1GXWVPPdtJ8fSobVBEIRwEBFKENrBlNz3oeoaR6wIQr1RIy5MlGDMTUPLtU8acjuj5TNwJ3fNe0GZi6KgCDrW9UQPbOShz01LlJrQwobkvhiStwFaNFY2KJ+bZh8voTuxUqNQS3koXhGxeBPTMpchohXL5uQ5H6W8FOEQhG5CRChBaGUCH+ZcEvA92KYcrkJj0NzIQkqeI2JHU4iObKqKglq+MhFVyVMNHbqf5wp6Er0gtKIhOUolxGIiQLU6dD5hg/KgCGdiR9jNEUKCJz+CAKoSQIs0vypeNRG9xB5SgQ8kd46E2hZBEJqLjGoFocXNI7l0u+/DioQ7YyV0ekqexdWTzMm9YTen49HyaTiTuzkKyrJNaNbyA3gWBuMJOEEOxsw4zLnJprdVEFaC/cxoMAsfRkxS8Vod8uvSolFYfg72xB4ueiJ0F1Q8wEqN8LXHscOPro+aPt8HCJDcOxZqWwRBaC4iQglCm5iSq5aYkguNQ3UjMFGEmZ7kVDGhcUSH72cfDNUrIN4XOaChsKpp0LwCIuQNJQgtMpi1p4fFkLwdDcopAsYrwBWD8q6DTOnJCFzzinB6omE3B6auwAqKXEVvamQ67OYIgtBEpNcgCC0uQtFMMzQtVPNIofPRKPWLIm/8EuwZSclrFOS5RYO/oFSCaRtQDOPA0QvxGOwgC2tqL0dHCkIrVNcqG5IXxZC8jSDfLsWyoItBefcRBHzcBn7AHqOt4uHmKjmOppwckWubIHQTIkIJQqubkns+zxYJQiNRdB2KZUKjlLwpSclrFLHhTeUUJr+IeH9tpbH1eAK6qvDsNXtDCULYhuST84bkut4yg1mhNvRYDDYK0OeSkuLbRRiZaRhUeIQMyWOrT340k4hWYHPydMaDVyyF3RxBEJqEiFCC0MLpDnp+DkrgwXb1sJsjdIlBOVVPMlNjUIu5sJvTcVAUk5PcC5AXlGOw8FcLFAVZ9nLJwh7fBbWQbXhbBWElzNlxvjaVDclbZzArrMWgXC2nZI2LQXm3wFFQNAESeDBi4afiVYjoXtmcPABm9oyG3RxBEJqEiFCC0MJRUAR1GHRb/KCExsPV2BRA80rs9yLUl+jwfTwIoGIDsb7aoqAqaIkEDPJyKeURGd/WsDYKQs2G5IoPIyqpeO1qUG77OVhiUN4d+B5PgFD0omm1lodb1PD4ns4pk3vEnFwQuoXWOQsJgrCsKTkZGCuS7iA0AdUwoJgGdL8IiyJ2hLqhZ2bgTA9xFJSzhiio6m2jRlyYfg7O6FYopWLD2ioIK0ERkvbMMO/HNlXXaqHBrFA7WjQGTfWhkTfU1J6wmyM0GJpUUr0i3yLx1prUtAwFJkpsTp4cTobdHEEQmoT0HgShlf2g5j03wi6jK3RXSp6JAszpUZkhryOx+Sgo1S8hWqMX1HLeUKZSglbIcjSKIDQbZ7JcXYsHsy1QXUs4OFTL4pshBuVdgVtJxSNvQcdBq+EiBwU+poZnwm6KIAhNQkQoQWhFAh9mJsnlrx1TBCiheVC0jUYpeX4R9sxI2M3pCIy55EL0iOMaB13pUrVtaLYD08vBHd3CKRaC0FRD8ol5Q3JDDMnbHS0Wg4UCT3hV0v+FzkMtZGDNjnFFVtdpzUnNiFqAhgCpdAm+J9c1QegGRIQShBY1MFZ8DyqJUZHWCp0WOhvFMKEYOlfJk5S8+npBURRUZI1eUMt5Q1lKEVp2VtJohKZizo5BL5QNyeNiSN72aJEoVDIo94sSWdnBuJO7+V7zS3B6WtPDLaKX2JzcJ3PyQfGFEoRuQEQoQWhhPygyJaeQeUFoFjRLqrouLL8AMzkMxZOSyYeCkZ6EnRoFvBKcyMFHQVVQHYcjUEwvD3dki6TRCE0jQlFQ84bkegtV1xIOwaA8QlU3yaB8t/jMdSJBUK6K5/vQTB2K3pricdTcZ06e3CUilCB0AyJCCUKLilBcRUvTDnnQKggH4wulqVQlrwArJSl5h0JseNNCWexI76FFQVVEQvKGspCHkZ6CJSmTQhNQC1lYM8OcIm63aEqPcHApebric9VNJymRlZ2GmZ6AXsgApSJi0dYUoAhTA/Rg3px8RMzJBaEbEBFKEFrVlNzzYaytgJYg1AXFsqDoGqdpWMmhsJvT1gMA8uKgKCjXNesmKKvRKDRDh+7nERm9vy7vKQir4U6VDck1MSTvKKjyrmqKQXnHG5IjgB5tzVQ8ggzTI0rZnHxSzMkFoSsQEUoQWgy1mIeen2NPKNtt3ZkroRtS8vKwpgbFAPtgCALEhipeUB7cvmhdt48WT8AO8jCnxzjlTxCaYkhu6lANuS51CnwuqRiUpydhZCQKpVNQvCLs5BBPaNq2xumXrYyrFlksm0kV4Pt+2M0RBKHBtPYZSRC6kEqVGhUedFtMyYUwU/ICaKUcrNnxsJvTllFQdCMT50jUhKLWN62WBo6aprCBfGR0c13fWxCqsVJj0OZTeuItnNIjHBxadJ9BuTMuBuWdgp0chEK+orRd4y5anYheBGX5ej6QHpaJFUHodESEEoQWNSVXEHCovCCEgWLbnD5GFXVsqZJ3EFFQ986nQXhw6uAFtaypcCwO28/CmtwLLTdb988QBIIqp9G+rCmBGJJ3rEF5BJaXg00G5Z4YlHdUKp6qQLNbv8BNxChHP1Gbp3aL16EgdDoiQglCK5qSU9qDron5qxBumobrwvTzMDklT8Lj1xI5wmJyqYRohKKgGnOp1eNx6CqgewVER7c25DOE7qZsSD7ChuSWGJJ3LFo0Bl0lg/IcnCmZdGh3aFJi3zXIaIvj1tEDaPDL5uTDkhYqCJ2OiFCC0EoEPnsyUIffseTwFMJFdSM8MNEL2XJqmVBbFNTwvBcUfNgNiIKqoOg6p9KYfg722A6oxVzDPkvo3mgKisqlVC0xJO/sYhSqae4zKBfa/rglKBXPSrTHcUsRW25A5uQBJoenw26OIAgNRka5gtBC6NkUG5KrgQ/TFT8oIVxUxymnagRF2NODYTenLaCoETb3pRnoqNFwM1gyKDcUjyMYImPbGvpZQhcakk+WDcmpGqMYkne6QXl83qB8AsacRKK0LYEPZ2oPG5LrllG3qqzNIKLmefJmejov5uSC0OGICCUILegHpZKZpNX6OfxCF1TJc1wYXgHm5KCU715rFFRP46KgKlD0ApnIUzSUM7pV/FyEumGlRqEVsmxIHouJP2Gno0UjUGnSgYysJ8SgvK0LCVBUrEfHbXv1IyN6ic3JSx6QnZgJuzmCIDQQEaEEodX8oIIAiqa21eyV0LmQYa2hetDz6YXKjcLy2NNDMLIzC4P2ZpXE1hMJmEqJK5i5Ezub8plC50P70oIheTQSdnOEBkMVPOl8b3p52BO7RNBuc0NyOm61SOtXxasmYnh8T/Ndk7uGw26OIAgNREQoQWghjPQUh1AbeuubSApdlJKnUEpeiUUWYQWCANHhTeUoKCWA1YQoqAqqbUOz7LKfy8gWMZEXDhm1kIE9M1z2JxRD8q5Bi8V40oEiaZykpGC3G2ox39aFBFwjYDsKcidPDsuklyB0MiJCCUKLQKbCemGOPaFsV7w3hNaAonlIiKLZcWtyj6TkrYCdHISRS3EUVJyioJrc+dcSCdgosK+ck5TqVsKh4U7u5nvNK8IRQ/IuNCgvwBaD8rbDSe6BEvjlQgKJ9oteZHNy5KEEAaaGxJxcEDoZEaEEoUWopDqp8KA7YkoutGBKXnYWOqWbCSt7QSkBzETzoqAqqK5bHjz6ebgjm0UsFA7NkHxi3pDcFEPy7jMoj8FCHubsBPSMCAFtQxDAmU/FUzWdrwftiDtvTp6cyobdFEEQGoiIUILQYqbkVJ5WMdqz8yB0JqpLKXnKfJU8SclbClUiIs8sFIuIx5sfBUXw9qFoqCAPIz3J5rSCcNCG5MV5Q/K4XIu6DS0SnTcoL4jHXBtBE0RGthyNG422r6doRCubkxdKQCYpk16C0KmICCUIrWRKTjPPutZ2efxC5xvWqrYDwy+UU/KEffg+oiNlLyhNBcx486OgqiPWNF2H7hUQGb0/tHYI7Y07vmMhqk+PtF9Kj3BoUFGUBYPy8Z1QvFLYTRJqNCSvVFc24+2bQhs15ve3AJjaMRJ2cwRBaBAiQglCK+D7MOam2UzStuSwFFoPNeLCRAlGZhpabjbs5rQMztRu6Pk5oFhAPBFOFFS1f5cWj8MOcjCTIzDmkqG1RWhjQ/JU2djYbUNjY6HeBuVZ2OIx1/r4Hkfk0nFrmAZPHLUrrhlARcD/TQ9Nht0cQRAahIx2BaEFoLLuSuBxVRDTFT8oofXQ3Eg55csvwZGUvDK+j9jI/fNRUAqMWLQlBo+aRtuJoqE2h90coc1wJ8rRFGRs7IoheXcblBtlg3JnfEfYzREOAFWyVL0iFK+IaNxCO0PXUkorJ3PyyWGZSBGETkVEKEFoJVNyEqKs9u5ACJ2boqHaNnS/AHNSZsYr6Q9aIVOOguqxWiJqhFNpYjHYfg7WxG5o5FUlCLUQ+LxPc1q4oUMRQ/KuZZ9BeQFmalwKUrQ4fNxSCq2qQHMdtDsRNc/+qMlJMScXhE5FRChBaBU/KKpmpWk8iBSEVoQqsJkowkxPQstn0NX4HqKVKChNgRFtHe8cPZ6Argbz3lBbw26O0CZYM2JILuxDi5JBOUXFiUF5q6fQUiGKoFSCa3eGp2hEK7InXa4QIJ+aC7s5giA0ABGhBKFVRCjPh6mH3RJBWBkyq1XnU/Lsme5OyaNBGQ/YiwUkEnZLdfwVXecKV6afgzO2HWoxH3aThDbAnSgbkmsKxJBcKEe/uhGYftmgHL4YlLci7lS5WAinyndICm1UrzIn3zkcdnMEQWgAIkIJQsioxRyn9Ci+B9uV2WehdSFxQ7FMnhk3p/Z2dxTU6GZOW9J1FXrURauhJRIwFI/PL+749rCbI7Q4FNlop0YBrwTHFUNyoYxOBuWKx30UJzkYdnOEpQQBnPkUWtXsnBRaMienMxCZk08NToTdHEEQGoCIUILQAlFQhAoPuiOm5ELrG5RblJKXGmOBoxuJjG+HVsxBKRVbxgtqKappQnNcWH4ezugWKbMurIozWU63Ur0S3B6JghLKKLYN1TDYC1AMylsPSo3n6qylEmLRzhCgCF3bZ04+JebkgtCRiAglCC1gSk4pEGTC2CmzWEL9uD+Wwx29GZSUAK2Ayil5gOaVYE93X5g8iTnR0S1l82Zdge62XhRUBT2RgKkUeZBCs+WCsKohuedDo2gKXa5DQrVBeRx2UIA5MwY9mwq7SUIVHAVFKbTwW8qXsB5ElHlz8nHxhBKETkREKEEIGTM9xSHVGqU6tWBEhRAek2YJd/ZmcH80g5v75zg0PWxoVlwxaWa8CCvZfSl5lNqmlvJQSlQRz2npY5ajGCwLhpeHO7KZxQZBWIo1M8KRffDIkFyqsworGZQXxaC8hVC8IpzpobKfqK1DoY3UQUS0AlQEyOQDFLLdGXUtCJ1MZ52xBKHd8H0YmWkEngfHlsNRWMz2aNlQOosSdjpZ3BfPtUxKnkmlu6dHWYzppk7/vigoDXqLl8ImgUxP9MBGAXpmBnayu83kheWJTOwsR1OoZEjeupF9QsgG5V4ONqXk+V7YTRIA2MlBKH4JKomD8c6zcogYHuaNoZDc0X1R14LQ6cioVxBCxMjOQAk8qIEPw5EZaGEfRSXAnkgBnu8hEzMxZyu4KzGHISd80UeNuFxBi2bG7Znu6RxGxrZB9QosvCV6WzsKqoLqulBNA4afR2Tkfo66FIRqQ3KLDMmpvLvTGeXdhfqjkUG5KgblrYQ7uZvFY1VVoNkdKEKZ5WsVm5PvHQ+7OYIg1BkRoQQhZD8oQiUhyhIRStjHXrfAPlBFLUBPfD0KMRsZ3cff+9NI0QxhiCiGCcXQuUqe1SUDEjIhj4xtLVfEM1o/CmqRp0s8wSavxuwEzFnpzAv7cCuG5FzePRZ2c4QWRa02KJ8Qg/Kw0XKzMOcmWTyORjrTysHQsGBOnhRzckHoOESEEoSQK+PRTBY0jUPea8FHgJwq3i6dzvZYvmxYr+mImBEMOOswl7CRVou4ft0sCiHuA9ThpfQMyy/ATA53ReU1EqBUrwilWPaCajtPF0of9AqIjG4OuzlCq+D7ZWNjMiS3yJBcD7tFQiuL2dEYiwKUhk0iiBBuFBShBiVY8Sg6FVcpsDn55Fg67KYIglBnRIQShLBFKM+HtYZiRLf0z+FnR05jV6TsFyR0HtNGiU3JS/Cg9vTwAMDWbfQ5fUj1WJjSC7hxXbhG5ZrrsoeM5hVgpUbQyVD6HUdBeT50U2+bKKgKZFirx6nCVQ7W1BD0zHTYTRJaAEqlXTAkj0kkrnDglDxO/RKD8nAJAjhTu+GTeGx0tngcUefNyXM+SvnwrQgEQagfIkIJQkiohSz7Kyi+B9sxa3pNacEnqIS7Y5mWqJYm1J8d0XJnq6QpcCOJhedjZhRRJ45UTMdeK4t/9mRDa6NiWVB0rewL1eEpeWRGTulKXBGvt70EqApUZl3TaABZQIQq5QldDwkJZU8ZMSQXajUod2H6YlAeJlZqjMVjKpQRi9fWd2xXIkaJzcmpsGtyV2dPdglCtyEilCCEGAVFqPCgObWZSk6ZJVASVlEFkkYR41bnp0F1G54ScJSbH3jQLBuaujhNs8/qhe5GkHZU3BvLYJebDzklLw9zaqhjByRqMY/I+LZyFJSlQ6/xWG3FASSn09AAcnI3tPxc2E0SQoS2vzU7hsArISKG5MJaDMoVD3o+A3u6e4pStBKcQkvVLJUAeiSCTiZqlm0HaMI1uXcs7OYIglBHRIQShBBNydnzh42ea8vHG7fLolNJV1AwFGyN5hrcSqHZDDoFFNSAq+Ppff37LafBIvlDlWIu5owAt/SlWZwMLyUvgFbK8exsJ0IeShStqJTybRsFVUFPxKHz9spzpT+he6mkU2meGJILtaPaDlS9bFDuTmwPuzldB0XjUhpt4Hmw7M4Xj01dgRUUSIXC1LCkkQtCJyEilCCEGQlFs1m6WnNHYsIqIQh8FHUFGVfHbieHrJiUdxTbo/lymqWmwTGWFz0oOoqEqCwZlWsl3LAuHYpZvWLb5QgbSsmbHuzIlNnI+A7u8Bu2Ab3Ny2ArujEfvZaDPbqNBzRCF+L7cCuG5KbR0Z4yQgMMymMxWGRQnhwRg/Im40ztgRL40PwSIj2da0hejavkocLH1Egq7KYIglBHRIQShDDwfRhkDux5cOzaq+JNWiV4gc/pWIGhI68H2BEVg/JOIa17GLNpG3tQeuKripOWZrJR+WzCwrSWx98G0ryPNH1Awh4hBZhTg7xfd5oXlELbwisg1tMZnjl6IsHpNOQpEhmXSIZuhCIp1FKePWXiHe4pIzSm2iYXpSCD8sldYTenq2DxmHzcNBWq2R3HbkQrm5PPZjx4RbGgEIROQUQoQQgBIzvNs1k0wDWc2qoSTZseG5N7KhCJ9MDWLeQsFdsiYlDeKVQExSIZkkf7Drh+1Igg7vQglTAxbOZwe18GzYYia3TVh17IwkxPoFNQCxm4E+UoKNOiKKjOqB6mWhY0x2FzYWd0S8d6eQkHNiRXSEgQQ3JhjVDknOrQ5EMe9th2OYc0CapqamRngFIR0Wj3RC9GdK9sTh4AM3tGw26OIAh1QkQoQQjJD4pQA48HhbWm4lUqplmahagRRd4xMaN7GLGLDW2v0HgoimlHpACfQu0NC/oSQ/KV6LESMJ0o0hENmyMZbGuyT5jqOFBUFVrQWSl50ZHNZaG4VECst7MG6lqiB5ZShJ6bk0iGLkPLp8uG5CUyJNc73lNGaKRBealsUD4jBuXNwJ3cvdBvNGPdkYpHRI2yyEnC+eSezvSeFIRuREQoQQjJD4pnojWdPXVqNSXniiiqBk1R4eoO/503FWyNSUpeuzPsFJHTfBQVH3r//obkK0GDyHVOP/yIizkTuLUnjXGr2PSUPMMrwJzcW56ubHO0fGbeM8eDaRvQahSK2wXVtqGaFgw/D3dkc0dsM2GNhuQBGZJ3z0BWqP/kg6rr0PwCnPEdYTen8/E99oPyyZ/QrL3f2Cnm5EZQYnPy6eFk2M0RBKFOiAglCCFgpqfYFNasrSgep9tNWEX4gYfAdRYG/1EziqyrY6+VxZwmIfHtb0gOBJoKx1xb5A2JkmRUnovbyOg+/rZutqn7A6XkGSqV7Z5biPJrZ6IjmzgKSvUKiPd33kCdzh3kDWUHeehz0x0VwSYcyJB8N3w2JNfFkFw4ZINyOoeYyWGOsBMahz0zwtcj1SshGm/vAhlrhYI1XSUHBT4mR2bCbo4gCJ0iQl133XU44YQTFt0uvfRSXnbvvffiec97Hk499VQ897nPxd13373otb/4xS/w5Cc/mZdffPHFmJraN/ihiJFPfvKTeOQjH4mzzjoLH//4x+F3mGmu0L4Vt7Rilsu+205txpKzuo+8GsBTACOyr5x2zIgg0HUUdAU7olLpql3JaD5GnCK8oAQlvroh+UqYmoF1zjo2Kp/RirhhIM0eYk1LyVMoJa8Ee3oI7QwNppzJ3fNRUCZUo0aluM1QIxH+blRqPSLRUF2BPTPEhuQqG5J3VnSf0Hy0aAyaAuhkUD4hab2NxJk3JGcft/mJyG4iqhahIUAqXeJoMEEQ2p/QRaitW7fiiU98Iq6//vqF2wc/+EFkMhm85jWvwcMf/nD86Ec/wumnn47Xvva1/Dxx11134YorrsAll1yC733ve0ilUrj88ssX3vdrX/sai1Sf+9zncPXVV+PnP/85PycIrZCKR6jwoDm1zWhN2PN+ULoC29j3Gl3V4eg2spaKrW6m6dXRhPqwM1KOgipqgBvvPej3cQ0HPU4vUnET40Ye/+ifa4ppPXlCqS6ZXedhTe5pa0EjNrwJCoLyQL0/go6OZEjE4QR5GKmJjjKVF1Y3JFd5INtZPmdCSAbllIrNBuU7Oq46aitNXNqpUaBUgmurXenj5upFNienXSw1OB52cwRB6AQRatu2bXjQgx6EgYGBhVs8Hscvf/lLWJaFyy67DMcffzwLTpFIBL/+9a/5dd/61rfwjGc8A+eddx4e/OAHc6TTn//8Z+zZs4eXf+Mb3+CIKhKxKBrqrW99K7797W+H/G0FoWxKzjNa1ImrMcqCUvFoWK8qKgtP1ZBBedExMKt7GHLEoLzdIJFoRyyPgAzJdROGemiRN3EzBseNIRXVscPJ4v54c4zKNUrJUzzo2VnoVMGnDdFys+y7QabNlk3pSp0ZBVUdyaDqKjQvX46GEjoWLUeG5OPlgawrhuRCfaCUPJMNytNiUN4g6JpEqD75uO2LhO8mYuY+c/IpMScXhI6gJUSoY489dr/n77zzTjzsYQ9b6CjR/RlnnIE77rhjYTkJTBUOP/xwbNy4kZ8fHR3F8PAwzjzzzIXl9F6Dg4MYG5OTl9ACkVBkMK7XPqM1bpXg+x4Ce//IKYqE0jQdBTYob25lNOHQGbNLmCNDcvhQ12BIvhK0T/XbfVAiLtKWgjsTcxhuQvVEioTi6JqgCKdNU/IoCoojRfwiYv2d39mnCDY9RtFQOVjJwbYVD4UD406UzaNVMiRPdJ7PmdACBuXz+5hQR4KgXCTD96Eaesemhx8IUwN0eGxOnhxqf99JQRBCFqGos79jxw5OwXva057G/k7k41QoFDA+Po7169cvWr+/vx8jIyP8N4lJKy2n1xLVy9etW8f3ldcLQij4HozMNOB5cGytZr+gOd2HpwQ8YFwKG5QbEWQiBobMHNK65Mu3myE54esqXKs+g0OKmFvvrEMhVjYq/3v/LEfKNRJF1aDaDgyfquSVZ27bCT2bgpPcy8em5ZhdY9qsxePQVAWaN+8NJXQevgd3inzOfOhiSC7UEZ54iMZg+xWD8rmwm9RxkfMUZUYRjLFo9x63qqrAxbw5+bBMlghCJxDqGW1oaAjZbBamaeLTn/409u7dy35QuVxu4flq6DEJVASts9JyWlZ5XL2MqLy+VtolYn2hnW3S3npQ/ZWDNvneRnaGq24h8GC4kZq2F/tBKYCnKoiY9rKvoSp5M4UUG5Rvi+Vx6nSH+320yfY+EHnVx6BbgOeXoCbi3NGqF7qmYyCyDqPBGLSpLK4fmMWTRxMwGniwqBEXZjbDQqueS6Hk7C+aturxHRu+r5wm65cQ6+/tmH3sQCi6Bi0WhTWTRmliN2aPfAj8NVZnrEs72vB83i5QmpRaKiDwioj1Oa21b7dSW4SDQovHoM1MQ/cKHLUze8RJi5bLsX3wcBQURc7Dg0F+ke32+9WxvRG1gBkvwEyqwPYFKpnbCYLQtoQqQh1xxBG46aabkEgkeDblxBNP5Ap2b3vb27ii3VLBiB7b8+lI5Be13HLHcRYJTrRe5W+ClteKSfGfbUJR16CoCtR2Uc3qgTJ/fVMUNKkI2CFjZZJ8r8GHbts1peNNzpuSQ1VgacayrzE1HRHDRc4pYHshg1NmXGht11s5MPTdO6nbsTta4E55UVcQ7V1X9+OXjMr73T5M+RNQkwXctG4Oj5uIQmnQvmFEoyhNTkIj74qZYcy5ibY4vvXMNFf1o4p4EdeA1mUpD0ZPD7zZWWilPGJj2zB71CnNb0Qbns/bhcjETnaf01SqrhppCT+oTjuXdzWGwUb3RjoPe3wH5o44kUJj9y2XY/ugULwSnOlBduM2bR2a1j5jkkYc31GjBMUHPB/ITUwjccRAnT9BEIRmEnpsZ09Pz6LHZEKez+fZoHxiYnG1HnpcSbHbsGHDssvpdbSMoLS8I488cuFvgpbXSqHgtU0kVKnkIfAD+F10heevqig8S9Qu31pPT3J7oWoIVLX89wEYN4tcklaxbf6eK70mYkSQtueQnstht5PHMZnOK8FNnRq/jSuvVUN77bZInoV31dTZcL4R3432CxInZ0vT2I0s7oqreOhMgyJdqEqebUPPFGBM7IV/2AltcXxHBu+d94Iqwe3v65h9rGaoylUkAms2i9LINqQOOwGBvjjSuNG04/m8Xcz2zdlxBMUSIhF91WtIM+mkc7kAqNEozLk55LKzMKZHkO85fGGZHNsHB6WHkxAFrwi3P9pWx0sjjm/X8AByLwgCjG4bgru+r67vLwhCcwl1Iuqvf/0rHvGIR3DqXYX77ruPhSkyEr/99tsXOkt0f9ttt+HUU0/lx3R/6623LryOjMjpRs+TCEUm5dXL6W96bqmP1IGgj2+XW7nB3XOr/spht6XWm5meYl8O01BqWr8AHzOGx35Qajy66rq2anFltbypYmskF/p3rftt0YHZ/rdJs4SU7s0bkvc17HMo6qnf6oXqRpB2FNwTy2CPU2jY51HJbhNFmOlJaLnMQb9PZZPzfQO3g5FOwp4ZKfu0uQZ7W4W9b4Rx0+MJrm6oFbJwx3c0/fObtb277eaO71yorGWTIXkLtKnTzuVyI4Nylw3Kdb8Ad3z7omWVTc73LdDWdrm5E7vLkyPk2UdZIC3QpjCPb0ejtESfH04PJ0Mfc7X7TRC6WoQ6/fTTOV3uXe96F7Zv344///nP+PjHP45XvepVePrTn45UKoUPfehD2Lp1K9+TWPWMZzyDX/uiF70IP/3pT3Httddi06ZNuOyyy/CEJzwBRx111MJyMjmndD+6XXXVVXj5y18e5tcVuhy1kIFWzELxPdhubek+k1aJL7iepsCyVo9eqRiUZ10do1aexSuhddkeLacIe7oC125sJTbaNwacdShGHcwZPm7qm8W0MZ/mWWc0N8JphZpfhD3T+lXyKl5QNEiP9HV+RbyVUC0LGqWz+zm4I1vYzFroAEPyyXlDcksMyYUGp19Fo7CDPMypIe7vCAePlk/DTE+wIXnE1VoihbYlzMmDLJQgwORQ2dpCEIT2JVQRKhqN4itf+Qqmpqbw3Oc+F1dccQVe8IIXsAhFy77whS9wBNP555+PO++8E1/84hfhuu6CgPX+978f//Vf/8WCE/lKfeQjH1l474suugjnnHMOLrnkEvzHf/wH/u3f/g0XXnhhiN9W6HbMuXJZWTKY5Fkt1GhKTmMJRYGpHjg9hkQoaBoblFeqrgmtR0HxsYcMyQMPmkOiTeNPxbqqYb27Dpm4jTnNw/UDaTZGrzc00FUsk0Uoa3IvWhkjPQkrNQqQ90bEgNJGnhuNQIsnYClF6Lk03Kn2q3AoLMaZHoLqFaCQIXm8tmuOIBwsejQGTQE0Sh+b2BV2c9oaZ3J3VQRj906OLGdOrsLH9HTZykAQhPZFCVrBHKBFGR+fRbuQzRTw+5/f113VIhTy6lbKeedtsBfH994Fd3QrtGIO/UcP1DSz9YcNKYybBeRtAz0bj6npcyayk8jPzmD9jI9/G+6H3inlaNpse6/GtmgOt/ZlkFNKsI84GrbRvAFiujiHyfQ4epJ5HJV38PixGNQ6G5WXpqdRmEoiZSQwftqz4Zu1F4Ro5vbu23I9zNQY1GIe647qK6fidTHUHSgMDSKTV5BJHIaJhzy1eSViO+j4bhX6N/8Vxuw4NK+A/iP7WyeaQrZ1x1IYGeH+6FxkA8ZPfUbZoFy299oIAqy/+zdQ83MwlRJ6N/ajrWjg9h5Na9hWHICn6Hj2656MyPre+n5AFzEwIOKmEC5dpFgIQrgYFAnlB9CpkmENgwHygZqySvACH4hFav6cqBFF0daR0cvRNkJrpuJxCpimw9KbayBP0XIxJ4FU3MSQlcOdvfs8+eoFmVyrPCNeKvsttSDm7ASs2XFOd3ApCqrLBSiCzkt6ogc28tDT07BmhsNuknCQ6NkUp/MEks4jNBEtFmPhhAzxrdRY2M1pS8zZMbZuQKmIaKzzCswcCpGKzQT5au6S65MgtDMiQglCM/A9GJkZBOQHZdd22E2ZJVCwsacBlhOt+aMszYSpGsiZKrZE6y8wCIdG0ighaZZQggf09YQyOOy1emC6Ecy6GjZF57AjUt/UTdUwoJgGdErJS7ZgSl4Q7POCgg+3Lx52i1oGFhANMhjOIzpyvziYtinuZNmQXAtKsHtk/xaaAxWmULQqg3JhzbCPWxBAUwLo0donILsB1wigBmROHiA5XLa4EAShPRERShCagJGZhhL4UAMPhlujH5RV9oMqqQoLS7XCBuVmFLmIwal8JGYJrcP2WFnwKWkKIm5PKG2gfWSd0w8/6iBjArf0zmKizvsJGZSbKMCcHoFSaq2IPCpZv2D6GqUoKLkUVu8b5A1FBsPGzHg5glNoL3yPPWUCz4Nu6l3vdSY0+fxBBuU+GZQPikH5GqFrpT09xMeuZesSwbiMObmDPJuTTw1Nh90cQRAOAel5C0ITTclpBoeqUNXCuF1CEPhsKL1W42pKuVLmDcrJf0hoDUpKgN1uAb7vQbcdaE0wJF8JTdG4Yl42biOj+bhhYJbv6xlRwya1ZKzaSmldHAV170IUlNMjvgjLpdRomspeQhwNJbQVTnIQqlcUQ3IhvPOHOm9QPm+wLdSGk9zLE5Z03YwkVq+I3K1E1DxUBJiekkh/QWhnRIQShCZA0QQ06FXID6qGqAsKNZ6c94PyDyIcm0SriO4i42rY6WS5GpsQPnvdAopqwDe9L3yzUVMzOSJqtsdCSivgb+tm2YusHiiUkmfo0PwC7OQgWgXyKTHnkhwFFZUoqGWh30SLx2AHWVhTg+wvJLQP7sTOssiqKtDcgygKIAiHmI6tOjYMPwd7bJuk9K4Bd3JXua+oqTVPWHYbEa0ERQmQLwHZpFybBKFdkd63IDQBGvRSeLWp17b+jOGhqATwVMB0a/eDqoZS8jxTR1YPsDvSWulQ3cqOaJ4FRmgabL01IhRcw0XC6cFM3MSomeeqfdzGQ4TSCFQ3AssvwEgOc1RGq3lB2RIFtSJ6LA6doxkKiIxuDrs5wloMyecmy6mmrqTzCOFFQ1lKCXpWDMprRc/OsHUDHbuxSI2dxS4kapT2mZPvbKEoa0EQ1oSIUILQYMgTgSqdKL4P2zVrTsUjPE05aLGCzMnJS4oMyrdGsnURFoSDZ1b3MM7RbR6U3nAMyVciYcZhOzHMRjRsczPYMu9bdahorjufllGAlRpF2FgzIzAy81FQMYmCWg1F16FFY7D8LOzxXVALkvrQLlFQhMqG5CKyCuFAExBkCUDFKRwxKK85Cqpy7Jrxg5t87AZcM4DCPdoA00OTYTdHEISDRHrggtAkPygNHjS7dlNyusBSWp12kKXj2aDciCLvGpgwi5zeJ4TH9mhZ2CmSIXkkHEPy1Y3K+4CIizlLwe2JNEbtQ49cUiyLU1A1vxh+Sl51FJTiw07IAP1AaIkEDCWAVsojMr4t7OYItRiST5UNyQ3TEENyIWSDchKxc7AmyaBcROxV8X04U3vmj10pJrAaGpmTB2Vz8slhMScXhHZFRChBaIIIRQPfik/OgSDxiUQoMq8O3EMzpowYbpVBeX2iW4S1QwWFd0bz8AMPmmkdtLDYSEjwHHDXIRezkdF9/K1/Fmndq1NKHlVKGuJBclhQxSEjOwOUioiJF1Tt3i4RlweSzug2KKUWSKkUVjU1rhiSRxPiJyOEn5JXSel15qN8hOWxUyNQSwUoXgnRuBy7tZiTUzRUckKqLwpCuyK9cEFoMFzi3A+g62pNKVhzuo+s5sNTAD16aCHZbFBuRJCN6NhpZ5FXxaA8DIacIvJkSK6QIfk6tCqGamDA7Ue6x8asVsT1A2mu6HfoKXkUTZMLzxtkURRUAKsnHk472hA9TtFQJWiFDNzJcqqX0AaG5I4YkgutYlCehz26XQzKV8GZ2LVwfaJrprA6Ea3Iv1WuECCfmgu7OYIgHAQiQglCI/E9GJkZBL4H264t+oV8g4gS+UEZhz6QiBkR+IaOnB5gpxiUh8L2GBmSA4GmwTFbe3Do6A567R6kEhYm9DxuWjd3SH5iim1zNB6n5E2Hk5JHqYB6bpajoOIxs6X8uFod1bY5jdj0cnBHNocazSasbmrMqd/kdxYxZB8XWgI1GkXGymHO3wtnz43Q8iIYLEUt0gTNKKfiOY4UE6iFqL7PnHxKzMkFoS0REUoQGghVOlECH2rgwXDX4gdFZdIV6Mqhp22ZmskG5XlLDMrDYE7z2F/J80tQE7G26GDGzBhcMiqP6thlZ3FvIndo3iCuC9MvwJwcZO+LphL4C1FQmgKY4gW1ZrREDyylCC07yylfQmsbklsJMTUWWoORfhMpl4KgitAmNmH9Pb9Fz46bYcwlw25ay0BeUJRapnlFOD1y7NZuTl62r5ganAi7OYIgHAQiQglCE0zJVRKirNry/CfsEkdOwbLrJlhETTIoN5E0iguRVkJz2BktsOxX1BW4sT60A7Tf9dl9UCMRpG0F/4zPYdA5+Cg68oXSVR96MQszPdH0Dr6eTwPFImIJiYI6GFTHgWqaML083OHNklbTavilfabGlhiSC63jhfjP/hw2nZDA2DodPjLwC2muttm/6Y/o23I9TKqa2s3nkyDgqniB70PVNU5hFA6MTpkC8+bkyRExJxeEdkREKEFogik5VQirxQg5p/qY1T324SFTz3rh6i5UVUPBULA1evBRLcLaoFm6HdE8gsCHZhjQVR3tgqooGHD6UZw3Kr+xP40ZwztoEYP2fy1ockqe7yM6fH85CkoDzJjMMh8MJNyRN5SFPIz0FKeOCK2DkxzcZ0gery3iVhCaMQFD/Zm0Bdz+8HX4x+lxpDcEiJRS7DFHxSr6ttyAdZv+CHtqT/OjZFsAI5OcTxUvIRYTAWotRJSyOfnUWDrspgiCcBCICCUIjSII2JQ88HyYNWoPlIpHeDTLU0fvIBIUyKA84+rY7eSQFYPypjBil5DRfBTht7Qh+UqQaDbgDGAuYSPNRuWzB2VuX0nJM7w8zMm9TZv5pnL1emEOKBYQj0sU1KF6u2iGDt3PIzJyf9jNEapwJ3bMG5Kr0BwRoYTwoYm0exJZnoCBoSNqRZHstXDb8Qa0Yzeip8dC1E/z+VlPjaNnxy1Yf+91cMe2cYW4bsGdNyTX4MGQSZI1EdEKUBEgkw9QyMrkqiC0GyJCCUKD0ApZaMUcFL8E2zVrTsUjAkXhSmX1hAzKA0NHXi9H5wiNh6OgKCBHV+FYEbQjtm6hz+ljo/IprYAb181xmsXBpOQZlJKXnytXjGw0vo/YyKb5KChFOviHCAuJ8TinQJjTozDSk2E3SSChOEOG5MmyIXlUTI2F1mBLLMdVfguKD339BiSsGEqWjqweYHtPCXpPL9yjj0SiL4I4MjDys1DTScR334n1d/8G0aH7oBY7vJ9CfcPkXp6oNGy9pmh5YR8RiswuG0MhuUPMyQWh3ZAzniA0iMpAW4Nfc7lsioTyyRvArH91I0MzWFDIWSq2RTJiUN5gKLVyyC3AJ0PyaHsYkq9EzIwi6iQwGzcwaGXxz57swaXkKZSSV4I9PYRG407uZCGYo6ASVlv//q0CpQiToKd5BURGt4TdHGE+CmrBkDwuQqsQPhQtuymRgx/4UAwTjuVygRRHt5GzVWyOZOApAYsuOgnbRx2JxPoeJPQ8zHwKajaF6OC9LEbFd98BjTz9OhAnOQSV+gd+EZFYa1fNbUUiZjkqm83J946H3RxBENaIiFCC0GA/KELR9ZrC15NmCR58KLF4Q9oUNaLIOyZmdA8jdrEhnyGU2RnNg7pIBR1wEu1hSL4afVYPdCeCtKPhvtgcdkbWNktNAw7Vddjc2prc09iUPN9DdGQzAj+Apiswou0ZhdZqKKoGLUbRUFnehhp5mQihQWlLTnIPfDEkF1qI+xI5FJWAo6DMDYctTADErRgKjom07mG3W1gcZRmJwNq4EYnD+5EwS7ALKSi5NNzRrVh/z3Xo2d55FfUoXbycRqtIGu1BYIg5uSC0NSJCCUIjRSg/gG6oNUVhTFoljk0iPyjLchvSJld3oKka8qaCrbEOD3UPEZqZ205V8ciQXDM4Cq3dUeaNyksxB3MGcEtvGlPm2rw7NE7J86BnZ6FnZxparl4rZoFSAYl4/apMCuDIBV0FdK+A6NjWsJvT1VAqj+qV2JRcDMmFVmBO87A1Vo6C0mwHtrFvv7Q1m6+FOVPF/bHsftHYdJ6miFnr8MMRO3IDEi7gkBiVz8Ce2FdRz5pp/4p6Wn4O1uw4p9G6rqTRHiyuUiibk4/KhIggtBsiQglCI/BpoD3N97ZV2+z0+LwpuU8ilFabh9RaoY5O1Iwi6+rYa2W5wyjUH9qWNNtLhuRqfz86BRIw1zvrkE3YvO+QUflaTO4pEopnvYMinEal5PklREfu55LXuq5AjzZG0O1WKKpTi0Zh+jlYYzugFsUQNiwiEzvFkFxoKe7pyXIEMKXkWQPrFy2jc3/MjCEXMTBhFDA274G5HKppwlq/HrGjN6Inpi+qqNe7lSrq/QHO5O62rajHbafv6ZfgJCSN9mCJqGVz8rmcj1J+X3SdIAitj4hQgtAAjMw0hwirgQfDrW1wMGEV5yNnGjsrxgbluo6CrmBHVC7ajaBi/E6Comt3VgeTvD36nX7M9piYUQv4+0AaXo3+YpTOpdoODL8Ak1LyGkBkfAe0Uh4oFRHvkSioRqDFEzAUD3oxh8j49rCb05XomWku7142JNdkPxdCZ9ooYVekAC/woLtRmLq13zpUpVfVdOQNBZti2ZpEb6O/H5Gjj0RPj12uqJeninoTSOz8B9bf81tExra2V0W9IIBLqXi+D83Ua7JrEFY3J6cijMldI2E3RxCENSAilCA0KBWPUAMfqrV/R2wpVG1s0vLgBT6CBvvX6KrOBqFZS8VWN3NQlc6ElSmoPva6RXi+BzUagap03mk2YriI2z1IJUwMmznc1le70b0acWGixEKtnk3VtV00EImObp6PglKhuxIF1QgoSoFSKykayhnZAsUTf7lm41aioNiQPBZ2cwSBC1bQVaCoBnDWbVh2HZWjoaLIRKjIRQ4zJCLUAPmd6T095Yp666oq6s1NI8YV9X6N2NC9bRGZac6Oc1QXTZTEYo2Jeu8WolXm5Mm9Y2E3RxCENdB5oyNBaCFTckXXaiq7mzQ9rhbjqYDlNj5yhgzKi46BWd3DkCMDyHrCM8FKgKIWwO5Zh06lx0rAdKKYjWjYGslg23z014Eg8YJT8qg89Ux9yyq749uglgpQSgXEeyUKqpHoiQRMpcSDKXdyV9jN6T5D8qk9LLaaYkgutABjVhHDThGloAQt3gNdXXmfjBlRKIaOvA5sriEaqhquqBdbWlFvFmp2FpHB+7iiXmL37dByrVtRj86X1D/UEECPyETJoWDqgBkUSYXC1LCYkwtCOyEilCDUmyCAQSKU58HUaxsET8z7QZXYD+rAkVOHCkVCUdpfgQ3KW3/msL0MyfOcVqloOqeudSoVo/Ig4mLOBG7rTfNA5ICv0yglz4bOKXl769eeUhHR0S3lFAddg+5IyetGolgWVMuG4eXhDm9uW2+WdsQhQ3Iq7e4VERFDcqEFrnt39ZajoKgPE+ldd0BvwYjuIuPq2OFmkVuDr+CyFfU29iNhebALM1Bzc3BGt2H9vdehd/tN3BdrJeg6ZU8Pc//QcvSaJimF1YkoOajwMTVS38hqQRAai5z9BKHOaIUstGIOCpmSu7WJEOM2+UEFPHvYjPQtNig3IhwSP2Tm2ERbOHQooo3SC0psSN6HTof2VTIqz8UdzGk+/rZutiaz+3JKXhFmeoKrBNWDCEVBeUWOgkr0lg3QhcZBvy9FQ9koQMumWBgRmoM7saOciqeJIbkQPoNOkSulchRUX29NfRgyKPcsHVktwLZDqNTLFfVsB9ZhhyF25GHzFfVmoOYzsCZ2o3/Tn9C/+a+wZkZaoqIenSeVwGMROUKNFQ6ZiFY2J5/NePCKbeQNJghdjohQglBnKjNvGvyaBgg0i0iRUH7gIXCbF71BKXnQNDYorzWVSlgdioJCZTbYiaMboJLb69x+pBMWUloRNwykUVKCA6bkqZWUvDpUySPhicxpA8+HbujQm3gcdTOq67I/lOHn4Y5sbolBXqdjzCXZT40MyWMRKe0uhAt5Sv6ToqCCAL6uIhqtrRqsqRlwdAdZW8PmSIZT2A8VOheZXFHvCCTi+yrqGclh9G79G9bdF35FPWc+FY8EZMXs3EjpZhLR583JA2Bm72jYzREEoUZEhBKEBvlBEbVUPZk1fBTUACUF0CPNM5ilqCvXcJB1VGxzMzVXOBOWh4SX3fOVgVTH7UhD8pVwdQc9Ti9ScRNjRh639M+talROxwWlc2l+EdbU4CF/fnRs60IUFHlBCc2BU2IScdhBHkZ6ElZKjGEbjTu5z5DclNLuQgtUgiVvySI8aOsG1iSKxs0YCo7Bkdi73fpV6uWKen39iBxTrqgXq1TUm62qqDfa/IIKVIjDrFS0jEhFy3oRnTe3p/Nics942M0RBKFGumeUJAjNFKH8ALqh1tTJGJ/30fF0BbbR3AgONii3DczpVNGtfp3AbmSPW2AhqqgEsHo7PxVvuQGF48YwG9Wx08liU3x1rzHNdWGhCGN2DGphbea01SilPCJj28pRUJYuXlBNRotEoeo6dK+AyOjmsJvT0dCg2Znay5EcbEi+ivmzIDQaut7dk8ixByIMfc3Rv7ZmsW9izlSxKUaeUvWdCKPjgyrqOVxRL4q4UlVRb89dbGIeG7ynaRX1KAqKoFQ8qWhZPwwNMILSvDl5a3mACYKwMiJCCUI98T3o2emyH5RV2wCBUvGo60WRM6tVlGkE1Ak0VAN5U8XWqBiUHwplQ3KqiKjD0rsvGocE1367D0rERdpWcFdiDkPOysKmGqGUPEDzDq1KHpmRK36pHAXVIwJUs+FqVXGKhsrBTA5zupjQGEiA4n2dDMkT3XeOEVqLzbEccpqPguJDX79hzZE9tH7cjCIXMTBpFDBqN8bPp1xRLwb7yGUq6g1tKlfU20UV9WbRMHwfLlW0nJ8skYqW9UNVFbhKDgp8TA7PhN0cQRBqREQoQagjFGqtkBAReDDd2gYJE3YJvu8BIRjMVgzKs66OUSvPptrC2qHfbdIqoQQPSk9P14bZV4zKCzGbo+tu7E8jtYLpvWoYUEwDOqXkJQ8uJY9msCPj26uioGRgHgZaLAZNI48viYZqGEGw2JDcln1dCI+86mNTIgc/8KEaJhzz4Ey2XSMCVdORNxSOhmokiyvqrVtcUW+MKur9rmEV9azUKNRSngXkWKzxFZC7jYhagIYAqXQJvif9WEFoB0SEEoQ6YqTLnRc18Nnz5kBkNJ+ripEppxYNx8iaRKiKQXnFWFtYuy/GgiF5JIFuRld1DDjrMJewMauSUfksz5SvZFBuogBzeoQjmdZKdHQzRx1SSl68VyoNhQXN6mvRGGw/xxWptHw67CZ1HGRGbmRngFJRDMmF0LkvkeN0PDq3GxsOO+j9kQpUxMwoMlEDQ1YOM0bjq5uVK+rZVRX1FDiFFFfUMxcq6v2lrhX13IohOfX1InKtqjcRvcTm5OQ5nxoUXyhBaAdEhBKEBpiSs/GyeuDDa6LiB6WRH1Q4M9uaqiFiuFylZruTOWBlM2ExZOi+M5LnaDbNsvn37HZs3Uaf04vZHguTeh43rVveqJxS8jRKyfPXnpJHPlLu+E4EngfDNqDbMrscJnoiAV0NoLE31Nawm9NxLERBUZStGJILIZLWPGyNlaOgNNs55L5LzIhyn6lgAPfHmmsLUK6oN4DY0RuRiBuILlTUG+GKegP3/b7s5XQIFfUoYtdKjfC1yrHFkLwRxMx95uRTe6RAhiC0AyJCCUK9CAIO46aOhnXgonjMeMUDQVE4giQsygblOjK6zwbbQu0MukWublhUA+h9tZWn7gZon4rYcaRiBvbYOfyzZ/9UC4VS8gyd07jsNabkcRRU4EHxCoj1yMxy2NAgkkzKLT8HZ2w71KJEVdbVkDw5yGmnli2G5EK43NOThT+fkmcNbKjPRJj+/9n7EyjJtrLMH37O2fvM58SUmVWVdUdkRuCCIOhSG6FVFFQUcWhthoYl2IIs/RyWQP9thtXQ7YQtqC0NilO3ttqiKIPthANehsvMvTXPlUPlFBnzmb/17sjMW1W3KsfIGN/fvbEqMyMzY2dEnHP2fvfzPK+Lpitx3m2jo++/4HOwjnqVbke9so0gp456DYj6CooXHjhQRz1n9bKKaRB0HJe4gHwYmAKQearCydfmOJycYUYBLkIxTI+g3TNBnv8sheWauw4lJwUNWfcGuTtmCROmbqguNaf9w81lGMtAcvpPCDh97m44zND7uWKXIV0PDUfDg0HrEW24lS3C9WBlEYy1+V1P8PWoBXe5q4IyLZNVUEOCKBZhaKna+XeXzg16OGODWsRmCfQshlvgcwwzONaMBJe8CGmeQno+TLm7uc5OBGaA1JToiBxngsEVsFVHvSJ11LsLpemg21Gvc31HvY90O+rttqMrZbmRFS/LoRtSKa+Ywwwnz7GywOHkDDMKcBGKYXrEZpilQAaxi4DkSM9QM1KVB6UHg23XqwLKN7rULJkRVs3Dz2UYBxoyxTU7Rpql0EsFltnfBD0f0840ksBF08jwyUpdLWKuR7guhE5d8iIV3robgoWT0Ch3LYkQcBbU0EALLOG4sLIQjlIN8HmkN4HkF7pWPEmB5FxwZQbHF8ttZayOdcCdOrgKahNTGHCkg7at47TXUvOiQaJCzDc76h0roWhEsFRHvUa3o96X/xrFi5/ZsaOe0VqDpO+hQHJ/cGr3ScDVI+jIsL4eITuAfZJhmP7ARSiG6XEe1Ka0ezcqqHwjD8qyBr+QpoBybSOg/Kzf31yGUQ8kj6UG1y8PejhDidSFCipvFx2VJfLPM40b7BZKBSiF6pK3G0ueCJsqo4M64FiOCWHxzvKwZUOZWgy58ToxB4MWsRxIzgwD16wYC3aMJE8gC8We5x8WzACRY6rNnYvecMQCqGKU68E+fhyFmzrq2dfOdTvqnb0fRmPllj/vrFzaynIzArbiHSa+iCnZAmkGNBbZkscwww4XoRimR5iNVSW5loa+q4UCFaGITNeUFW7Q6JquchlarsAFp33bjmZMl0wFkkfdcFbDHGim17BDds+KU0G9aKIqQvzrTEM9f9db8swshLk6B2Tbt1f2lQqqG4AdVLw+/QXMbtFsG7plwUhDuAungJzPIwdhSwVFgeSFwSpmmcmFLOdf2FBB0caZW57u+WPYwoIpTBULcNKnxxquJinXd9Qr+Rq8zY56K5cxdfJj6mZV5x/uqJelcNauIM8ymNbumtUw+8czu9caOl+uXVwY9HAYhtkBPiMyTA8g24lsr6s8KOp+shuUEooKGNIYmt1tsuRRLkNb5ir3gbk9C06MtsgQI4Oc6v2EfNwgpV3glFArmJg3O/hspXWTJS+HSKiL0O0724hOo7uznKQwbQO6MfjiLXMjdC4jNZSNCLK1DnttbtBDGlm0JIazemUjkJwXsczguOLGyqZPKii9UlGbVodx7iA1FMUCrBgRFjYbtwyh7diYnoF/c0e96iLKZ/91q6MeFaD0NFY3rzCY7seThCNzFYehwskX1gY9HIZhdoC37hmmBxitqgpEBEmunZ0l15R3sGolSMm37g+PRJsUWaRa6ZgRznhtPLphQcNwFMiGM5AcyKUOxxy8nXIUKFtFxFmEelLHKbRQjiS+gt5jtq2soNQ9yK5eRViaveXPBwsnlLJGzyIUptj+OKyQsk03DRhxBG/hJDrlO1QHUGZvOGvUVSuFpgLJR18FRTbcOTfGFTfCupniyVUHj2pwxtWwQ6rVL5ZaSmGSSYGCXzm0x/IMF2tCIjQjnCy0MdsZ3o2GzY56slSCXa8jqq6jHUplnS22a6qoRs+Z0DUIhxsK9CWcHB3a/sDK1eqgh8MwzGEUoaIowgMPPIDPfe5zWF5eVifaI0eO4L777sNXf/VXQ+fdOmZCQ8l1Cku2dp5Ur5iJanGcCsByhsdSpALKDR+rbojlTogVK8F0OLyTwEHREhnmHQokT6CVi0OjZBuNoPIpLGQpmkkDny7VUYh19R4jNZS53oS5chW4+6uob/cNPyvbNdUlLN/IgtIkvy+HFZWjUijCXl5GXF+GWV9CVDgy6GGNYCD5+e4iVuiQu2h2MaznyqtupApPmzmIZLPK8hSfKiUohQLlmPdDhz37sCEzxNQRb/rYoV7v6HcHpo+aF2Eu6qBquCgN+fuDFIqk/hSFAqxGQxWjOp0OImEhhwaXs9z6hqtFqOUZqtWOCifn9SjDDC97OrPXajX8zu/8Dv7gD/5AfXznnXeiUqkgTVOsrKzgne98JwqFAn7oh34Ir3jFK9THDDNJoeQUsLwby8RmHlSiawjEcAUrd3ciqxsB5SEXoW7BBa+rgqJAcr9weLvC44jQukHlC1kCudbGv0zX8c0LJViuB1mvQ8ZtmI1lRMUbixb+/IluNk4WI6jwcz7sCN+HXl1T2V3e4ikuQu1jY8No15BTIHlhtM7BdZmqotPVDQsXQccudRGleoKuC9Q8E149wv3TDXzzQhEy50X6MJJoOb5c6qjoABgSrnP4irzA8FGTdYQyxsmgg2evDo9afFcd9XwfZruNeH0dYSbglEuDHtpkhZPnQJwC7ZUavBl+7hlm5ItQf/M3f4O3ve1tSu30lre8Bc95znNg2zfuzFWrVXziE5/An/3Zn+EFL3gB/vN//s/45m/+5sMYN8MMD3neLUKRQsPY3UR62e7mQVGY9WFkKxw4oNzw0PJiXIjaeJpOLdeHa4yDhHbxaWdY5XkJA8YQhMqPGtSOe9qZxlJ2DfpaB/8yU8dz00AVcEXe7ZJ3fRGK8tac6lV1jNkuqaCGe2ec2VAHFApwVqtIV+cgW1UkLi8I9hpILlQgeXnoz4nrRqqKTspqZ3SbC9A5MskyVaxXndSKFXheEaY0IKIGVrRlyLUIny+38IzV4VEEMw9zKugoG2WEDOaR2b4oeui9QpthLbc7B3mqcOGkozMH6XbUc9VtNPWLo4tnpgDVvXNg5cI8F6EYZojZ9Uz+z//8z/F7v/d7uPvuu2/7PaVSCc9//vPV7ezZs/ilX/olLkIxY4+ImtCTEHmWwnKtXU3YSQmV5hnyYDjVgoHhoWE00JGx6gD3+DpPpTa5ZidoKmtCBn16atDDGVlcw0GRgsrTVYj1EA9MCTx9yYVRa8FavQLc87StHKFgSwWVwC8P94KceRgRFCDW1yGyrhpq/VHPGvSQRgItieCsUdE1UwH8wxhITtexVTPdstqRXWur8JRniIUGKSRQrqDgFB7RPZQsVy27jYaXqny42baB4+3hUgVPOlR8OlHsqA6wmmXC7mP2YWAEaJhNNQc563fw5HXOXWR2xjWok2imFHzV+VXcfsXKMMzIFKHe9a537ekXP/rRj8av//qv72dMDDNSmI1uHhR15RC7yO2oGqm6QKYaYAxRHtT1UJtkCigPrVAFlD+uzgHlm5AKisikDt8aDZvAsFI0C4icGPW0hnNo48i0gWONDCJsKjtSGkwrBY1dnUOmVFAGq6BGCBU27wew1utIli+hcfzJSC1eTO4EZZ8NYyB5trGBsql4ou6g6utZt/CUkOJJGtBLJRTsAJLUT9uoRabsCuaTGK2oiU9WGnj+fAkOq26HhoeKHTVXIRWUfeRws6BupZZ1pI22HeGU18ITaw4EWzaZXYSTOwgR5xIr89whj2GGmT1f7WmycT2UBfW3f/u3KqicsqEYZtKgxTKpNKhGs5sFMlnxiFRosOXwKox800fomlgzYqX+YYBQz9TiiwLJdc+DzmGjPQgqrwCui4al4dN3JKDmQiJPlBKECOYe6tqSSAVVGZ4FObM7ZLEAQ88hkgjetTODHs5oBZLLwQeSp8gxb8f4VKWJD95ZxT8creN00EFTixHlCVoiRUwW2eOzKN71FSgevxeBW9q2AHW97WrKKaNVsFATMT413VQKK2bwNESKswGpoFLotgPb6P/7MDADRI6hlMeUw8gwu8HTQ+jIUV1pD3ooDMP0ogj1+c9/XuU8Ud7TJh/+8IeV3e61r32tCiP/nu/5HiwuLu72VzLMWKDyoLIcUuq72ilcUh2Ccuia6OZkDCmudFWAbGRQQHln0MMZCi56tCcMRAKwS2zF61UG2Yw7hTCw0TBznJ+hXKgOzJUrMBqrsNcXgDSBQyooMbzHC3NrqIuh7nqwsjbsa2eV1YzZIZC8U1dt3n1/MHlzpH654kS4f6qBv7irin86UlcK0BYShEjQpsKTZ0PccRylux6N4uzdCOxANR3YK450EFgB6oGJK1YbpwMuNgwDXy61u9c6PYc9c3QgY7CFBVOa6Fg6TvptLlAyu8KjcHItR5gA7bXaoIfDMMxBilCtVgs/8iM/gnvuuQdPe9rTtr72//1//58qTH3605/Ghz70IRVU/va3v303v5JhxgItTVTreC1L4dhi13lQWZYi94ZXBUWQykcFlLsSl+zOlvViUqHX7tx1geRkWWR6A4W7z7jTaJRszFd01K0IolND8cKnN7KgUnisghpZqH25oaUQUQfe0rlBD2eo8TZUUDoFkgf9s/tGWldt8i8zDfz5nVV8fKahiu6dPN4oPGVIAhfyjjtQvOsxKB67C57l96SxRskqQTgOmpaOzxUbqBqsvB0ka0aiXvs0TyE8XxWCBgFt6hXMAB3XwKoRY8GOBzIOZrTwNxojbIaTMwwznOxq9kB2OyEE3vjGN8JxHMzNzeEjH/kIwjDES1/6UtRqNVWAevWrX437778f8/PzaDQahz96hhkwRmsNGnKV32G4OxeVSFbeEVk3D8obzlDymwPKc0MilDnOT7gcfsVMUTNSxKBA8sqghzN2UP5H2Snj0j0+QpGhIxsQnUZXBeWzCmqU0S1LFRnMrANn8TSQsXX/VpBKzN4IJLdteeiB5BQ8TYX1fzxSV4qnT041lQIqymN0tERdq5JSAOOOO7uKpyN3wDN7b0Om3zdtT6ETWGiJDPdPN5FSn3VmIHyx3LUxxQJwpwajgtrEk67a9AlNDScLrMhmdsY1czUvp/+qcyuDHg7DMLdhVwmvf/Inf6Lynq4PGif10+zsLN7//vdvfa3ZbGJ9fR2/+qu/im/6pm/Cv/23/3Y3v55hRto6QVA3Ds20dmXFIxKhoTCAjIW9YggDtrTQUQHlFA5qT2xA+WYgOWV5+Tarcg6DwPARBRGWptcxcy1ES2vAzSW8cnHQQ2MOiCiWYLXnEXaacFcuoTXzqEEPaehwVi9Bo66bWQyneDjnGCrybHa0I1UulXpIeZVmKRKq85JFvFiAXSir5hT9CqOmIOqKXcJqMYVcC/H5UgtftTacjTvGmUW7qzhK8gQyKA08MoDef9RJcd0LMRd1UDVclGJuTsHcHkHh5DnpNwVW56uDHg7DMLdhV2fy//Sf/pPKe3rlK1+Jxz72sVhaWsJ3fud34sd//Mfx/d///Td00Dt58iTe8Y537ObXMsx45EHlOTQpdrVrvTnpp+/dT37GIPANH0tOB7VWB/NOPJFttGMtxyWyJ2QphE9KAO7gdFgLjopdwfKd65haWUKYNXH5XhvnKm3c2TQx2zG4Q9KIots2dNOCGXbgLpxCa/peesEHPazhIc/hLV9Q+YKkPJd27zYp6jJVRSfqardqdjdCNgtPtJ6n7D8USnD8Ekzd6GsXtJuvNW2rg4aX4iRaONYxJvJ6MyhIOfKFEmUv0WaLjqA8jWHANzysyxpCGeNk0MGzV7krLbNzOHkjc7C63Br0UBiGOUgRigpPpGp6yUtegic84Qk4d+4cZmZm8N3f/d3q/s9+9rP4+Z//eXzuc5/Dm9/85t38SoYZffK8W4RKU1jm7ibtS3aCPEuh2c7AJvp7xZWO2g0lOfwZP5zIRcElL1T2kJhCWktsxTtMyJqT3XsHqlcb0MIEF48bEGjhohvByDXc0TJwV8vEsbYBfUJVeaMIne8oG8q+dg1Rswq7OodO+Y5BD2toMJsrkJ068jRGUDQOXExYN6jwFCvVE32svp5nSPIMsdC6CpfiFDyvoFRIw/IembIrmEsjtKMmPllp4FvnS7AzLvr3A3q/rJmJUkHplamh2Wyh96pnuGi5MS5EbTxVuHDS4RgbM5y4Ioae5+hEOcJGC5bvDnpIDMPcxK41re985zvxgQ98AF/60pfw3Oc+Fz/wAz8A0zS3Jg6u6yobHnXLY5hJQERN6EmkikqWu7MVr61naMgUCbXe9kfHzkXHt2/6qNNOethGU7jw0tFQcfWK837UDQuWEpbc+bVmDgYpZhae+xTEWYw4bKDRqsNoRzDjHOecbmiukWm4o23i7qaJIx3JBakRQPc86IYBmYTwFk6iUzrOaqgNXFJBqUDyDMY+Asmp8LRqbiqeIjRkdmPhSWqQugTKFRScQvfjIYQKDtNOBdeyFHK1jU9ON/EN1/yJtYH3iww5vlhqqfdgJnUU/OHabCkYARpmEx0jxpmgg6dUuajA3B5fpgCJPnNg9fwcZp/ymEEPiWGYm9jTLOS7vuu71O1mqGPe+973vr38KoYZecxGNw9K5NmurBPLdrKVKeSZDkYJCiivyRoimahspCevT84EkDo1kYUlyVOgMj0yCrZRh55nyiOjLoS5XUaYhmhETTTbVJCKVUHqrJOojl5mpuHOlqkUUjNckBrq11QUC3CWV5HUlmE2VhAFw2H5GYZAcqQpbGf3geRUOCCL96bVbrODaZZ1C08JFZ6kAa1URsH2IQec77NbHOkgsALUgxRX8jbOBAYeWx/+DMVRhgLqqXAZ5ynk9LGhu85RPiU1r2jbEU67LTxx3YFkazZzGzwzg9bpFufXri5zEYphhpBd61nJikdh5Lvl4x//uMqR2gvUXe9nf/Zntz5/8MEH8b3f+72477771O8iFdb1/OVf/qUKQKf7X/va12J1tVsUIGg35xd/8RfxNV/zNXjWs56l7II0MWOYXoaS0/tMHUVy53ouLRYUuqZa0o8StGuuJoCWjjNuWy1+Jmlyvhkm77kckD24gpSNaXcKs5V74B+7C/HRKVSLBupGiroeq4XJx47U8cE7q3ig0sQ1K1YTUGa4IBWoLnWINIS3eHLQwxkK3K1A8gReafsw7hQ55u0In6o01Xv9H47WcSYI0dRiRHmClkgRuya047Mo3vUVKBy/F4FbHJkC1CYlq6Q6KjYtHZ8rNtVmAHM4JFqOL1MWVJ4BhoTrDKdSu2AGiBxDdRm+OOHdepntkUKDnYfQ8hxrCxxOzjAjXYT6z//5P+Mtb3mLKgr91m/9Fs6ePdtdgF/HiRMnVLe87/iO78Bb3/pW/NzP/dyuB/JXf/VX+NjHPrb1eavVUkWpZz7zmfi///f/4ulPfzpe85rXqK8TX/jCF/CmN70Jr3vd6/BHf/RHqNVqeMMb3rD187/927+tilTvfve7lU3wgx/8oPoaw/QKlQeV5ZAUSr6LXcNlK0ZGeVBG/zoO9To0NnYMFXI758SYBCgHiqxf9LoJ2xmZMPlxho4dZ6MgdXyKClJ3IjpaQbUgld21tlGQosX5X96xjs+WW+rY44LUcEAqHxkU4OQdWKtXIdvrmGjyHO7SeXUt0YUOfSPm4OYiwRUnwv1TDfzFXVX805GGUqS2kIB6QLWp8OTb0O84jtJdj0Zx9m4EdjDS5yvKhpu2p9AJLDRFivunm+p8zPSek4UOQj1HpGUwjhwd2vmJJSzVsbFj6TjhU4A6vx+Y2+NpITTkWLnWGPRQGIY5iB3vKU95Cv7sz/4Mf/7nf66KOaQsokyoYrGoFEbr6+tI0xSPecxj8LKXvUyFlstdqEOIarWqfh89xiYf+tCHYFkWfuZnfkZdEKng9I//+I/4yEc+ghe/+MX4/d//fXzbt33blj2Qfp6yqi5fvoy77roLv/u7v4vXv/71qohF/NRP/RT++3//73jVq1612z+ZYW6LliYw2usqD8rxxK66q1XNFCl10guGc5dxJ2jhL4RERAHlQQd3TkBA+WU3UmHkVMIwK1ODHg5zy4KUo26ZM4VO0kEzbqLZrMHspEoZ0vRjnA4E3FTHXc2uZa8cCc6YGSCiUIBYX4dIY3iLp7F+b/c6PYmQJVGGDeRJjKD0sEKWCgJzG8HiC3aiCjC05KaCOBWlMipYeS5kqQTHcFXRZtygwPSyXcJaMYVcC1XntqevTY4VvB909EwVoTJS4lkmHGt7Jd6gz/eBGWDFC7HWCSe2Wy+zh3DyJEerkyNqd2A6bOllmJHNhKKiEtni6Hbx4kXVDW95eRm6rqtueWSLowLQXvlv/+2/4UUvehGuXbu29bXPf/7zeMYznrG1I0P/ftVXfZV6TCpC0f0//MM/vPX9s7OzOH78uPo6Fcfm5+fx1V/91Vv30++6evWqeowjR47seYwMcz1Ga039q+cpDGfnSfGKlXTbHusabGs0J9EqoJxaJXsR5sIO6tJDkIzuTvtuILWBWvpJoexgzPBCi3DXcNStW5BqqwyppFmHFXYLUo0gUQsuL9FVMYqKUqWYC1L9RhMCwvdh1RpIli6ifvxJyEb0vHhQ3OXzSlUutAxp0cVZv6Pyna7ZMShAoFt4ShCTbl3XoRV8GEERjjE6HVYPQmD46FgdNLwUJ9BUXTFnO6NlZx9mHip2VFEzQgb7yDEMO550URUGQjPCyaDDRSjmtvgyIf+yOomunZ/H0Sc9atBDYhjmOvbdHuWee+5Rt4Pyr//6rypriuxyb37zm7e+vrS0pFRV1zM1NYXTp0+rj29VTKL7FxYW1M8S198/Pd0NP6X7uQjF9MKKR1COh3YL+8TNkB2IyISm5OSjClny1gUFlGsqK+m+Me5QQ7bDJStBmqfQSlMTseAbr4KUq26ZO4U2KaSoINV4uCBV9xOcKHTgJwJ3Nw0VbF7kglTfEMUijHodgjrlXTuL+l0PK6EnBT0OYVYvg4x1q0cznL67popOVJRKSfFENX7KcioVYAdlde2YtPMQ/b1TdgVzaYR21MQnpur41vkS7GzXaRLMbSD78tmgo65xwnVhG8O/0aLUUIaPqhdhPupgzXBRjoez0yMzWDwrA8KHw8m5CMUww8VAz9xhGKqsKcqOsm/qLtZut5Wi6Xro8yiK1MedTue299N9m59ffx+x+fO7ZVTme1vjHJHx9oLr/+R+N0nZDCXXKA9K7DwZXrITFfpJdjZNH90XSQqhlCZtJ8LZqIUn1xyIQXSo6cNDng9C9TixrqHglybq2Bqn41vXdHimq26ZN4VW3Ebr5oJUIcaDpQ4Ksdiy7BXGXOU3aHTTUHYyq9FBsngWzdknAJSXN4Dz+b7JUuhpDC2Nb/hXTx75tVvdF+dN1EUD0DJcusNHTIonqUGnwlOhBCcowdSNsSs87fXYFkJg2qngWpbCWO3gU9NNfP2SzwXjA/KlEjUZoWtcDnfmyKFd43o9V/NND+tRDaGMcKrQwbNX/YP/UuZwGOAhashuOHkTDtYW1kZmPccwk8JAi1AUGv7kJz8Z3/AN3/CI+ygP6uaCEX2+Way63f2O49xQcKLv2/yYoPt3i2mOziIkpmKIro1lLsRt0Taub5qGvuaV5jnM5lq3nba183NOneTWLMqDyoDAH/nXqGAFWIhbaDa7tpF7W91jrB/QYqwf+9/0ml3wIuRZBulaMATvtA6CLTt2j36frgkULF/dUn8GLXofh000NwpSsUiwrgpSbRRjibvJstcyx952OijMchlZ8yrCuA1v5Txas4/v3/mcGqtQDk4SbRSFEmhp9HDB6HaFpOu+TkWonR+GHodu3R15bfNz5OiYDfLaoeprqBVNpXgKvKLKQhp39npsUxG5kBZQD1Jczjs4F5h4bGP4lTvDyqqR4LIXKcWdLASwDWtkzuW6kAhMD003xoW4jfukpzL/mOGhX3O1nXD1CK3MxupiHYbB13GGGSYGurKijniUKUWd764vFH30ox/Ft3/7t6v7roc+37TSHT169Jb3UzYV3UeQLe/OO+/c+pig+3dLFKUjUzlPklR118kmqHuM+lM1TU3y+/lXi04DehKqUHLTtZDd1CXyZpbNRGUu0HLFdL0dv3/YMXUTUu9mMpx227i72T97IU1q+vH8USeqjsgQ5RmMytTIv2ajyOap97COb1JReIanbqk7hVbSRitsIG00YUcpIpFgLYjwhaKugsw3FVJeyhPZnmGa0B0bRqsDe+4Umkceg1zTd/d6kxKVuo3uQnF0u/vITr39Q2yMhN6DG4UkbBSSuvfl6n1KHZjo1q00bX6sWgGqxVh3gyhXN0GB4kJHw8lxZcpEW89Qe/wdKBQrW4877ueb/R7bRavYtdaGDXym0MB0RyoLLbN3Pl9qqVooPX1B5cihvucO41xO0QA1q4GOjHHaa+Mp6+MbDTCK9GuuthOeHmE1z9FoZ2g32pDW6MZhMMy4MdAi1O/93u8hSZKtz3/xF39xq5Pdpz71KfzP//k/u5anjULDZz7zGfzIj/yI+h4KQX/ggQdUSDlBQeR0o69TEYpCyun+zSIUfUxf22se1BCcQ/c2zhEZby8gWbeSd6tP+tvNiBB5BknKvHwXeVA5kAgNgW6N/GtEi3cKKK+6IRbDEOsyUaqRPjzwwxzyc0h5V2rNKXTVeWrUX7NRpJ/HN7Wyp5wRuqVuqgpSzbCOvNnsKqRkgtVirLpzTUVSFaQoQ4p33w+OKBRhtRcQdRrw5x5CbAfQkpuKRrcoJHWLSPm+i0jqa9cVkba+vlVQojegtlFEAjbjmaiApAtNFZMoYF3T9W5g+E3/bv7s7fjsdAPXXF0VuwtBeaLOMfs9tnVomLYrmA9iNOMW/rXSwDcvFgZjCR9hFu0Yi1aMJE8gCiV1/jvM999hnMsN3YAjHLTtCKe8Fp647kDy+2A46ONcbSe8jXBy2m9YvbCAmcfdPdgBMQyzxb5XjlQQuvfee1GpVPCBD3wAH/7wh1X3ule/+tW7zi+44447bvjc87qtYSnwnELGf+mXfgn/5b/8F/zAD/wA/vAP/1DlRH3bt32b+p5/9+/+HV760pfiaU97Gp7ylKeo7/vGb/zGre58dD8VtY4d63b7oN/1yle+cr9/LsPcEEquFjSU7bQLm9Yydcaj7ke6UPk044AqQol1FVB+1g/xVWvjY1driUxN0tMsgVYpjV0eC7M9dJwGpq9uiUcFqRaanUa3IBWliI0Ey6UInyvrmA6lUkdRQcrhgtS+0B0HumnCCEO48ydvON4eWUTaLBRt7rx0P95SIt2yiKRD17sqJCoeURFJqZF0AU1uFI20jX83i0qq8HR4r2dHz3DVJStUAr1YGHmLdj8xhYmyXcJaMYVcC/HFUhtPW2MVzG6hwusXSAVFCnqho1DqNu0ZRQqmjwWnhVargwteiMewPZO5Cd/MHw4nv3KNi1AMM0Tsa+VIBaG3vOUt+K3f+i2Uy2W84Q1vwNd+7dfi/e9/P+I4xute97oDD8z3ffzmb/6mCi7/P//n/+Dxj3883vOe98B1u5MNsvC99a1vxa/+6q9ifX0dX/d1X4e3ve1tWz//qle9CisrK2osFGr5kpe8BK94xSsOPC6GUaHkWQ4pt9/pJujCR0WoLE8BL8A4LdQ9w0XbjnHOaeOpVXdsdiHPkwpK5axpCIKHLTLM5CF1gYIZqFviJypDqhE2oDVbMKMUC0aCpVKMz5VbmKGCVNPEHS2TO3ftATqHylIZ1rVFpBF1JyQ2ikh69xxL9f6ula1bROrexLZKpGEuHtM5hoyAkQT84tSghzNykGKxbXXQ8FI8hCaOtg3MdsY/R6sXXHYjrJmpUkHJyvRIb4xZwlIdI9tWhBN+C49uWBxWz9yAKQEzj5FAYG1hfdDDYRjmOrR8a6tx95Aa6d//+3+PH/qhH8Iv//Iv4x/+4R/wF3/xF/inf/onVTT6u7/7O4wDS0t1jArtVoS//eBD0A9x93boUAsTres775Pklywgxz7/l8jjGKVAh1Uubfv960aCj87WENIl8I7j8M3x6eLSSUIstBZRXung61eKeFTTGvnXm4qGf3XHOpp6gtiSKNxx7+E8EDOUx/duSbJE2YFanTq0ZhtWnMNIcmVroTEf6RhKIXVHy4DFBaldkVMwuLLukIJpuItIBz3HfOiOdTQm+RzTg2ObArXnmgtw1pqYCiWev1Di4u8uGm585Pg66iJVBdDCXV/Rn+PsEM/ljbiJldaKmoc8d7mEO9qc+TNwhuza/dCah5W8gKBg4gX/v26EC0MZyeOzMc5MkBLqypUreN7znqc+/pd/+Rf8m3/zb9THj370ox8RFs4w44RBXfHIQpKnMJydLQBLVjfzLBUafLn7zoyjAO1AUvvwDgWU++3DL0L1gUU7UXa8OM8gpkbXpsAcLlKXKFoFdYv9WGVI1dt16K02zDjFVTPBghXhM5WNgpRSSBkwc14k3w5NyqFauBwWC3aC5sY5Rp9iFdRB1LhTTgVLWQJjtYNPTTXx9Us+K2G24WwQoiHpvZdCTh8bi0KvJ10VDRCaGk4GHS5CMY/AExHWkhz1Zoo0TiCM8YmPYJhRZl8zYsprunbtmuo499BDDykrHHHixAlMT/PCjRnvPChCy1Noprm7PCjVGl5Xk+ZxgiawpOzqeAaWzAir5sNNBkYVFUhOO8ZSh2t1M+oYZjsMYahi1GzpDpSP3gPt2FHUphzUrBxNLcEVs41PTjXwF3dV8c8zdVz0QsQT1MWUuZGzQWfrHONZ46OMHQSudBBYBdQDE5ftjsonZG4NnXMeLLaRU0KzYcB1grGZh5A9s+UZWLA6YzEPYXqLJ1OlzqL9jfUri4MeDsMwG+yrHPzCF75QdbBzHEcFfz/rWc/Chz70IZXJRNlLDDPWeVAUjivlroJrl+0EWZYCu1BNjX5AeQeV1dFdVFFY8JwbIVNhwcWx2CVm+ospDJiiiKJZQEwdvJI2aq11yFakFFKXrARXnRiy0sJsu2vZo3/HJU+N2R5SWc47MdI8ge7zOaYXlKySsoY3wwY+W2riSGigEI/Xhk8vOFXoINRzRMhgHj0+Vu893/SwHtUQygingg6+ZmV05yFM7/GNtBtOnudYu7yEyqNubIrFMMwIFaF+8id/UhWfLl++rHKhKPibQsCpi92P/diP9X6UDDMM5DnM5hryNIVt7TyBa4pULTpS6oznFzCOkMKL5PAtN8L5qI37NHdkLUcXPZqeA5EAvCIHkjP7hxZ41MWLbiWzgKgQq1DzWqsG2QphJjku2rEKCaYC1HEqSDVNFa7M7ebHl62mBwLwS3yO6QVk4Zx2KpjPYjTjNv51qoFvWihgM+Ke6W6wnCx0kJEF1LLgmOO1KUZZfLQh1nRjXIjbeKpw4XK3UmYDQwBGnqhw8tX5VTx60ANiGGb/RSgKv37pS196w9du/pxhxg0RNqCnEfIshelYu1JBEYnQEJjj2zqYLHn1uIGOTHDJi0ayTTKFBZ8LQmVVEAapWbjTEtO7ghTlp9GtZBURFSMVal5t1WFQQSrOccGOccmNYOSayo56XM1GOebcinELhVZFKHWOMWHofI7pFVTsLdslVIspltZCfLHcxtPWxqvQchDIhpdoOUItg3vkKMaRgOYhZgOhjHEm6KiOvQxD6LoGV+ugkxtYma8NejgMw2yw762Cj33sY3jZy16Gr//6r8fVq1fxrne9C3/+53++31/HMCOTByUo1NOxdx1KrlqQa+O7oKRwclpgd0wdp722KuiMGpTdVZcpYnBYMHPYBSkLFbuM4+W7UDh2N9Jj01gvWWgYKZpajHNOG393rKbUC8z4QDY8UsaS3lJOzQx6OGMH5QJZFqlhBB7ym1iw40EPaShoyFRtsKQ0b7FdWMbobRLtBirqOtJByxY47bZU0Y1hNvH0CAI56o0YWcbXVoYZ2SIUdcR73eteh+PHj6NWq6kDOkkSvOENb8AHPvCB3o+SYYakCEWecugaIOSuChuUB6XZ1ljlL9wyoNzwEboGVswYKxvFt1ELJCdSocO1OU+C6c9xY0tLdfiardyN4NhdiI9OYbUkVaA5dbJixofNpge51MfODjUsx9OUXUHi2WjLHJ+oNLiQC+BLpbaymcd6Dmv6CMaZgukjdgy0ZKZUhwyziScTFU6eZkBtjru4M8zIFqFI9US5UP/1v/5XlQdF/MRP/IS6ve997+v1GJkdqFeX8MWP/B6SztVBD2X8Q8mzDFLqOxaVQj1DzUiRajm0YDy60GyHZ7jQhNgIKB+tyV+kZbjixkizFMJ1Vc4Vw/S/IGVj2p2C6fhoOQKnvJY6fzCjD+UDLlAgeZZAKxTGelNikEhdqKJus2ChJmN8eqo5ksrcXkGd4sjmSyoo4QUw5c4dfUcZUpnSrW3pOOm1Jvq1Z24koHByFe2aY/XSwqCHwzDMfotQJ0+exPOe97xHfP1bv/VbcenSpV6Mi9kDpz/+UcRf/hLcC3+PPBs9FcoooKUxjHYNWpbBcXangtrMg7InYNdbBZQbHtqexAW7rYpwowLlWNFiPxY5LA4LZoZgNz9yDGWjoQUkM/qovDlSo0gNXoHPMYeJKx34dgH1wMAlu7Olcp1Evlhqq38pCN+dGm8VFEHFXTp/hp6JqpFgzmFLJtPFlIDMUwoAxdrVbrQGwzAjWIQKggDXrl17xNfPnDmDYrHYi3Exe8C/8x41wZVRC/raQ4MezlhiNNfUv3qewthFHtRmESrXNZWZNAkEhofMkOjIHBe8aHQCyTfCgjUhxzYvgxkdbGGrYHzKWDvlj2bGGnNzIHmkOpMJw4LUxzcfcFgoW0Xoto2mpeEzpYZSJU8ai3asbkmeQBZLEHrXtTDuuNKFEBKhqeFk0Bn0cJghCyfXkGNlYX3Qw2EYZr9FqO/4ju/A29/+dpw4cULtPDSbTfzjP/4j3va2t+EFL3hB70fJbMu9X/k1CMuktslgL3wZyCZvwtWvUHKNihWmuavOeFTY0KUxMdYL6lBEAeWhpePMiASUr5kpqmaKhALJK6xQYAYPnS8CM0DHM7BsRlsNDpjR5Kobq2yiWKNAcm560C9l7rQzhU5goSky3D/VQDoC16NeQdfez5fIjkZqbB1ucWqyzp+Gj5ZnYMHqKEsiwxCuHkFHhtp6xOHkDDOqRagf//Efx6Me9Sh813d9F1qtFr77u78br371q/G4xz1O5UIx/YV2zfWnPwWZyIFoHfbK+UEPaWxDyXUpoOnbHzbUlWXNTJBSwSqYrJBrn6Twrok1I8Y1e/gnf5vhpWSb9NzCoIfDMFsZazrt5hu8mz82geRCcCB5nzdFynYJjYKJJSNUAd2TwmU36m6ukApqqjJxOYc0D9GkRCg1nOLzJ7OBL2LQnnCSAY1FtuQxzKDZly7cMAz80i/9El7/+tfjoYceUhVlKkA95jGP6f0ImV1xxxO/Gl++/+MoLUcwF76AztSjgAmRXx86ed4NJU9TWNbOqibaeaM9lkQAtu1hkiAp/JpeRWRQQHkHRzvDa0WkYuFFbyO01eFAcmZ4oPeib3iouxGuRG00pAufTijMSEG5XmSJokByfao8MarYYYEUMW27g4ab4iE0caxjDPU1qVf2T8qCok2zTAoUvDImDbFx/mx6MS7EbTxVuHBTvr5POp6ZAXE3nHzt4gIKs9ODHhLDTDQHOivfc889KoycLHhcgBosx7wjuPAVBUDkyKM1uCscEN8rRNiAnsbQshSmY+34/UsbCqBM12GKnb9/nNA1TQWUt1ypQmHbIhvq3WIqRMVaDqPMVjxmuAhMH7lBu/nA6WByw5VHmc1OoRRI7vp8juk3VPSbsitIPBstmeMTlcZINc3YD2eDEE2ZIUYKOTMzsYVPOn9SRmUoc5xhNRQDwJE5hNoiBtYWujmvDMOMgBLqCU94wq4vZqSOYvoLvTat2TLWi1WUVkPY819Ga+oeSuMb9NDGJg9K5CnkLkPJKQ9KCKGKMpMGBZTXjTpCGeO8F+JJNQfDasWjHTFNCtiSA8mZ4YICrF3DRduJcDZq4clVB0Y+eeeTUYUyiOgck9F1w7QhWZk8EOh5p0LUUiGBsRriU1NNfN2SDw3jdyzRhsqDRVJBZWRZgGsHmFQM3YAjHbScCKejFp607kDy+XOiUeHk6CDKJVauVgc9HIaZeHZdhKIg8kndURkVPNPD2a8o4BnVFeTRCtyVi2jNPGrQwxp5zEY3D4oKepQzsJMUfsXq5kHl/mRmDBnCgC0tdKwQZ7wWnlizh27CT92SqFiY5Cm08jSf25ihhNqNz9tNtFodVdB4XJ2LpaPCFTdCpOeIkcPgQPKB4hoOfDtAPUhxCR3M+gYe3Ri/Y+lkoYNQzxEhg3X0+MRf1wpmgAW7hWaze/58LJ8/Jx5Xi1DLM1SrHRUlo/NGPcMMfxHqxS9+8eGOhOnJzvnasQJq/jrK62248ydYDdUDVB5UlsEwdp7QqTBQLUeqAaY7ubuQvuFjyemg1upg3olxvL1zR8F+hwVvBpIXvOKgh8Mwt8QSlrp1rAgn/RYeW7eGrqDL3JpzAQWS5wAFkhvDqQadJMpWCWESohU18ZlSEzMdA4Uxyllr6xlOFTrIqCuvbcHmEHzVrVedP+3u+fMxfP6ceFQ4eQ7EKdBercGbLg16SAwzsewrmJz427/9W5w6dQppmm59LYoifPGLX8Rv//Zv92p8zB7xTQ9nHlvAMz6zCq2zCnf1ElrT9w56WCOLlsQwOjXkWQrb2/lwIXXNZnHDl8NVeOknrnQgdIHQ1HDGD4eqCEU2mQte1yYjLEuNk2GGeTd/yQ2x3u5gzolxxxAdS8ztlZZLShFLSsupiVekDEvY/7QzhfksgRG3cf90A9+0UIA+JkWJB0tttQEWahncmaODHs5QQMcdnT+X3RDVdgdXnRh38vlzovHMFEhUvyGsXpjnIhTDjFoR6hd/8Rfx3ve+F9PT01hZWcHRo0exvLysClIvfOELez9KZtdQhsiVow4anoS13oI79xBalbtZDbVPjFY3vFDPMxiOt8s8qBxSExCamOjJH7VJpu5eV8M2msKFlw7H8zHnxg/bZCrcHYUZ/oKuFFIVdE8GHS5CjVIguVJa8iJnWDCFibJVRLWQQlZD1UXuvuroK4bqMlV2szRLIT0XlsG2sxs2xLbOn20uQk04jpFDpwqUlmNtfhV3DXpADDPB7Ksy8cEPfhBvfOMb8c///M84cuQI/tf/+l/q46/6qq/CXXfxIT3o3T4qRJ3+Cg+JiCBb63BWuVPeQUPJtTyDZm3f6Y6sF0t2rBQ2mcf2Cwooz6VEJDU1QR4mK15OrxfbZJgRKehSq/mWZ2DB6mDN7KotmeEk1a5XWtqstBwyAjOAZbtouAIPBS0s2jFGnS+V2qrnVyxyOEeODXo4w6eG2jp/hljh8+dEI3QNDjrQ8hyrcxxOzjAjV4Qi9dPznvc89fHjH/94fOELX0CpVMJP/MRP4EMf+lCvx8jsEc/wMH+Hi7qrI0cL3txDQDbebYkPswhFyiZdyh0tFQ2ZqVBQyoMyvMnNg7o+o8yRNtqWjjMuTZKp9DNYGiJVi440S6CXCmyTYUbGZk1NEUIJnPK53fgwc8mNEJPSUqNAclZaDht0zp+yp5B4NloywycqDYT66M6PVs0El91IWT+lH6jrLnMjpMqm82dE58+Az5+TjqeHoNXR2mpr0ENhmIlmX0WoQqGAVqt78N599904c+aM+vj48eNYXFzs7QiZPWMLC1IYOHe3g1R2WA21X/K8G0qeprB2oeDeyoOSGmyWw28FlMeOgbqRqjybQXNh0yYjNbh+edDDYZhdQdZe2lxouQYuOG0VQswMcSA52T2kgC35OjCMSF1gyq6gWbCwLmN8eqrVDZEfMWjMXyjR2IFYB5zKkUEPaWgdAj6dPz0DFx2KB3g4y5aZPDwKJ6f8tBhor9UGPRyGmVj2VYR69rOfrXKhqOB033334SMf+QhWV1fx0Y9+FJVKpfejZPa80+dJF1fucrHmaZBZB978CVZD7RHZqUNPY2hZCtPZ3opHLNmJmgzShId3I7uQEkrlMRgazgx4B5KUWOf9SHUPktLi14gZKchSkpkSHZnjbDA89lbmYapGouw+CVLopTIrLYcY13Dg2wHqvsRFu62uDaPGop3gmk0B+KTsLbH1cwcbZmZ0z59n+Pw50XjGxlqIwskvLgx6OAwzseyrCPUzP/MzuHbtGj784Q/j+c9/PkzTxNd93dfh53/+5/Hyl7+896Nk9gzt+uSGgYt3OchJDdWswlm7POhhjRSkgiIEydydnXe0l61YddGDzbvfNwSUGx7anoE5s6MCVAfFghOjLTLEyCCmpwY2DobZD4YwlLKm7Qic9loqe4gZLjaLg9Qd1fWLgx4OswNlqwTNcdCygM8UGwO9Pu1LBVXuKrgSocMv8jVtOwxdqsLj5vmT7LLM5BahNHXk5Fi7ujLo4TDMxLIvKcDs7Cw+8IEPIAxDVYD6gz/4AxVMTl3ynvrUp/Z+lMy+FizUCebqMQtrF0NMV0N4cyfQrtwFaNwpby95UJquqzyB7aDiBmVCJXkOERT6NsZRseSti5oKKKdQ8EF1I9oKJJc6HHP0OyIxkwe1G1+022i0Oip76FHNnRWaTH9ItBwXvW42j3Ccie6OOiqQannamcJClsCIW7h/uoF/u1CAjuFXsF3yIlSNFAltkk1Nq7+F2Z7ACLBgt9BqdlSzlMfVecNwEhFCg5OHSCCwOt/tgM0wTP850FXLsixlw/unf/onZcPjAtRwQQqU0DVx9k4TQic11BqcVVZD7akIlWWQhrbrPKhUaNwe+Rb5G90dSB1n3cEoOKhIOO9sBJL7AdtkmJHN+zOFgY6pq3bjo5hjM65QUYAKUaSwMMusShkVLGGiZBVRL1i4ZoSq09ywkyJX46RNskwKeB7nG+72tbaEhY6t46Q/mjlgTO/CyUkNtbbM4eQMMxJFqF/7tV9TeVAXL15Un3/mM5/Bt3zLt+D1r389fuiHfgj/4T/8B3Q63HliWKCFP3Qd80cs1D0BI+uqoZBzNtROaEmsMqEoD8q25a6LUNA1JftmbhFQbhtoygxX3P5nb1DLdBXeKgGnxLl1zGhCxVPKNul4BlaMCEub5x1m4Jz1O13lrJSwOJB85BSGlu2i4Qo86LdwzRp8E42dbJ9NZS1PIWeO8KbKLqHniV7rjmtiXSa4OgTNUpjB4IoYupajHeUIG1yIYpihLkL90R/9Ef7H//gf+L7v+z5MTXV3+d74xjfCtm385V/+JT72sY+h2WziPe95z2GOl9kDZAegQlSHPPB3GbDzsJsNtXpl0EMbesxWNw9KzzMYu8iDWrJiZFkK3TB5QngbBYehGwhNHWe8/haqabeTpPd5nkFIU42DYUYVz3Chb4T9n+R240PBqplgzUy7geQVDiQfNej1om55iWuhbWS4f6qBcEg7UEZahgeLpILKANOAa/uDHtJI4UoHUkh0TDp/Dr/qjTkc/M38NwonPz836OEwzESy6yLUH//xH+Nnf/Zn8ZM/+ZPwfR9f/OIXceHCBbz0pS/FYx7zGJUH9R//43/EX/3VXx3uiJk9QW29SYFyeUai45IaqgNv7iFWQ+2A0egWobQ8g2ZZO04K1820azMLgj6NcEQDyl2JRSvEutE/BQepRSiviwLJ9Y0COsOMdLtx00PLlbhC+VAjFKY8rpz1Hw4k91wOJB9FqFvqlFNBo2BhXcZ4oDKcdq1ThQ4iPVfzDvPIMS547lMNRc1SFqxQdbNkJg/PpHDy7iZldY7DyRlmqItQZ8+eVR3wNrn//vvVyfw5z3nO1teoGDU3xxXlYcIRtlJERaaOy3c4rIbaUx5UDt0QO07yVixKaABSXYNtc+D17aAiFIRQAeWbi7Z+cG6jY1UmdLgW7xozo09g+MgNiVBqOM3txgcKFQMoDyrNKJDc5YDoEcY1XPhWgLovccFp44LXf+v4TtmGJwshsjyDblmwTWfQQxrZuQjZZmkuwmqoyUQKTa2HtDzH6nx10MNhmIlkT7Ol6xfjn/70p1EsFvGEJzxh62tkx3McvigOE/SakRqq7UicOErJjEZXDTVP2VDDt8s3FOQ5jNYacsqD2k0ouR1vhZKbutmHAY4mQhfKStSyBc45bRXie9iQpYIyqLqB5B503jVmxkS1QQtmCvs/47ZUIYQZDKoARYHkggPJx4GyXYLmOGiZwGfKDdSHSGlINjx6r0V6BnOGJnTMvtWkhoemJ3HRaaMphuc1ZvqHp3VUOPnKtfqgh8IwE8mui1CPe9zjVBA5UavV8IlPfOIGZRTx4Q9/WH0fM1z4hqt2zTsGsHzM66qhGmtw1lgNdSsokFxPYxVKbjjWrkLJKZ+BcgZYGr9zQHliS7Rkhst9CCinlum0PI8EYJd4gciMDwWzG/bflhnO+8Ol2JgUyMpBIdF0/qecLps7o45FgWLamUI7sNEQCe6fbiAbAlseFcPO+SHSvKu44y68B4MseV01KXC6wGrSScQVCXTkaHVyRG3OV2SYoS1CUfe7t771rXj729+OV73qVYiiCC9/+cvVfYuLi3jve9+L973vffje7/3ewxwvsw9MYaq23mTJO3lMgzCN67KhBj+5GjaMZjcPSuQp5A6h5GTEo0yBNM+QB2z12k2LZHMjoPy03+5jILmhjgOGGReo1TgF/ncsHaf81lAslCeNFSvBupGqvDltirtujtN1qmQXUS9YuGaE+HJp8JatL5Xa6ginPChn5tighzMmalIHbUcoNWncB2U2M1z4MsFGMBSqFxcGPRyGmTh2XYT6zu/8TrzpTW/CAw88oD5/5zvfiac+9anq49/8zd/Er/zKr+CHf/iH8aIXvejwRsvsG7LkdVwD1+wY4VRhSw1lsxrq1nlQ1Gpb11VuwHasWQkyjax4gOVwEWpXAeWmr1rML5mR6ip1WKya6cMLxEr50B6HYQZFcF278TluN953NrPtUqHDcwqDHg7TY6WMZbtoODq+HLRwzRrc8UUbXaQcJhWUDAqqgML05jUmNWlLkJqU1VCThmdlWxuWq1eWBj0chpk49nQle8lLXqJuN/Oa17wGP/ZjP4ZymRd6w4onXazpVcRSw4VjEo9fMWDEHfhzD6FTvpOqA4Me4pCFkmeQu8iDos5r2Aglp91TZmcoi6Eq1jcCyjuorB5O8Y6sC5tZXUVeIDJj3G48NDWcCtq4s83noH5BeXOqMECB5CpvjgPJx23DZMquYC6J0I5b+MRUA89fKMLM+vs60wL5i2Xq1AfEAgjKM319/HFXk9KtY0c46bfwmLoFXUljmEnAEBqsPEIKG2scTs4wfacnV9OjR49yAWoEQqEdaaNt6TjndiCKxevUUFcHPbyhQUsilQlFeVCOLXeZB5VD6JIXIbuEnicqirZcgfNO+1BClUlaf5k7VjETsFCmTnktajduhoeqLGRukTenUd5cDrsyPejhMIcAKY6mnQoaBQtVEeOBChWD+mvbWrATXLPI8p9AFEtqLsf0Vg21qSa96rKadNLwtLAbTr5YG/RQGGbi4JXZhClQEttAQ6ZYnrY3sqFCePOcDbWJ2VxT/+oUNL5DHhRNRqkIleUpMt/t0wjHA7LkpaZER+aqu1SvoQIUdd/jjlXMJBxLZBsODeB0wOGq/Q4k57y58Ya6UPpWgHog1abJhUO4Xm33PvuCUkHlXctnka9lh6Um7ZgaTgatQQ+H6TOeHqlw8mY7QxJygw+G6SdchJogHOlA1wUiQ8MFP4IslmDnHRj1VdhVVkNdH0quUY6QtX1nPJU3pOdIdMB0OQ9qL1A4OdkXOxRQ7lHgat5zKx4p1HRdwpI7dzhkmFFFkLLQ8NByDVyw22iL3isLmUfasKlbGeXN6VNcGBh3ynYJmuOgZQKfKTfURl6/1HZqnkEd8aamWNF7SGpSUkO1PROLZoRlVpNOFJ7RDSenffjqpcVBD4dhJgq+ok3YxdYzXLRdiYt2G2ngQRhdNRRlQ7EaajMPKocupXq+toNUUJuZQ7bkdsl7Dig3fISugRUzVl2mekXVSJQtKUEKVEo7vo4MM+oUDB8ZKQuNfCssmzk8SAVFZEKHaweDHg5zyFDxZ9qZQjuw0RAp/nW6cejdKKnzLnXlo82UXAp4XulQH2/SXQKaFAglcKow+E6ITP/wze5xTMfZ6uVrgx4Ow0wUXISaMGjHPDXoYpvjqhdDlrpqKNlgNRQV4YwWFaFS2LtwVyzbiVLw0ASVcxr2DhVENdFV5vVy4Xze70qqE6HB84o9+70MM6wYwtjI/BM47bWQcrvxQ6OjZ7iqAskT6CqQnIvckwApd0t2EfWCiWtGqApEh13obIoMFJtszBzhzZRDhOZw/ka2Hm3QNkV/lG7M4DElYOYxeV+xtsDh5AzTT7gINYE2KLqFho5zXge67ys1lExZDUWB5HqaQM9SGM72Fi4qPpElI8tS5K7TtzGOXUC54aHtCmUjom5TB4UW3xe9UL0uwrIhNC4OMpNBYAaInG7m3yWXsy0OC2rlTmeqSAB2iQPJJwmybZmWi4aj48tBC9eswwmypmYdDxZJBZVBMw04Ntv9D5uC6SM3pFJDnS6wmnSS8LQOdGRYWVgf9FAYZqLgItREWvI8dFwDi1aIppFBlEpwttRQc5hkKx6h5ymkvb29jnYoKXsl1QDpsx1jvwSGh8wwVEB5LwJfrzgRIj1XWV1GhbNamMnBFhZMYaictZNB73PWmO7mAxWhuoHkpnq+mcmaP1G3vNiz0ZYZPjHVQNSDzZObOVnsqOtYqGUwjxxjFVSfOiG6hoO2I3DGbR1K115mOPH0WIWTN5op0pgzwRimX3ARakJtUBA6IqGphb+4Tg3lTbAaikLJyRcOXVfdpnay4m1avmyD86D2C3WVIptDaOk404OA8vPUsYp+hxCwDVaoMZMDLVRJDdXxDCwbEa5tnKOY3rFoJ2jIrBtIPs1F7kktVkw5FTQKFqoiwgOVbve6XkGbW6eCEBkVOk0LtsnXsX4q3WLbQEtmW7Z+ZnLCybMcWL/C4eQM0y+4CDWhkygK0u5YOs67LXXyFaWiUkMZjZWJVUOpUPI0g2nsvOtIoeQ07dR0DVLbvmDF7NxiPnRNrBnxgRbO1K3qmpUgzVNo5QLvHjMTB6lcdSERGtRuvDPo4YwdqusmBZJLHZ7FFqlJ3sjzrACNwMB5p90TFe8mXy62la2c7OmkgmL6hyUspSilufFJv3Xo4fPMcOAb3Qww2oReu7w06OEwzMTARagJ7gZC+SHrMlXZRsIPIAzZVUPNT54aSksilQml5SlsZ2eLBT1nFGCuWTYXOw6IK13oulAL57P+/hfOZJMhYqHB9So9HCHDjAYUkk1F3bYnccVqq8Is0zuFysOB5AGf9yecil0CHActE/hMuaGy2A4KHa90HaONFOm4sIztsymZw1FDdVwTNZngqns4mV/McGEIwMiTjXDytUEPh2EmBi5CTSiOdKDruupMdsEL1YR6MxvKqK/AXp/HJGE2uxcePc8gHXvH7kh1I1W7lXrAeVC9WDh3A8olLtkdtdjbK7RjecGPuhYGw4LkboXMhBIYPnIpEVHALquheq6CiiXglLjIPelQYw3Kh2oHNhoiwf1TjQMrZ75YIks6EOmAM8MqqEHNjQ0hVbbeiaC3VktmONF1Da7WgUbh5PMcTs4w/YKLUJO88Jcu2o7ERaeNRMtvVENNWDaU0VxR/9JFSDPNbb93xbouD4rzGnoWUN7tTJPjvLf3zjTzTqyKg7GWQU5xVgszuVABluxCdG4/ywG7PQ8kl9KEoXMgOdO1b5XsIuoFE4tmqDra7ZcVM8EVN0KSJxBBoGITmMFl67U9A9fMaGu+x4w3nh6pcPJaPUaW8TWTYfoBF6EmGFKfpKZAW+Rq8qPUUMVNNdQyrAlSQ6k8qCyDLsWONouljdyiXNN4MdIjDGHAlpbafTzj7X33cVOlkAsdjuke2jgZZhSgRVRsS7SpqMsBuweGitwtsRFIPjU96OEwQ2bfMi0PTUfgS4UWlqy9W7joeveFMl33uptbXmXmUMbK7D6uQpPdiADO1psMPJmApv5pBtTmlgc9HIaZCLgINcFQVzIqokSmjgtu90JLO3Cbaih/UtRQea7seFSEsk19V6HktFOimwbngvQQ3+gGlNdkqhZ9u4UWhwtOrLJatAIHkjMMnds3A3ZPccDugTl7XSC5a3mDHg4zRND1hmx5kWcpKznZ8vaqPlywY5UzmZIKqlSG0NhOPmirJdmaWyoioN2TvC9muAmuCydfvbQw6OEwzETARagJnzwp24YrMWeFajH/sBoq3FBDjf/JWHZq0ChsNkthONtb8ci2uGYmSMm253MeVC9xpQNBAeWmhjMbIeO74fxWVosGr8BZLeNCpyURtnkxdhA1FAXsrssEc3so6jI30hRpt8idc5GbuTVknZtyKmgULVRFjM9M7V7N21VBURZUjlTofA0bEgLT34gIoGy9vUcEMKOFKQGZpyqcvDq3OujhMMxEwEWoCYdkxxBCLeApoPxhNZTYUEM9OPZqKLPRveDo1JHG3j6UnPIB6NlIhQbL5h3xXqJtdvZyJa5abbX42wmauF/YyGoR0uAcjTGATjfLVzxcerCMi18uIw75MrXfoq5UAbtkKdl/Vs2kQ3ZGVeQW4AIBc1toQ8+zAjQCifNOGxe93dlg6fvWjRRxnkKfmlIqHGbw0FzCVdl6Amc4W2+CwslzDidnmD7BV7sJhy60FmXxWBrOu93duElTQxmUB0UrX12HJuWOVjxsFKFMsb1qitlnQPlGZy9SOO3Eop2gyVktY0MSa7h6uoTVBVdZXjtRhkaV25TvBzqPFwxfBewuWCFWTQ7Y3StkY6Tz0MNdN7nIzdyeil0CHAdNE3ig1NjRxkUddr9EHfHyDLkU8L1S38bK7EzB9BHbBtoy42y9CcBV4eQZ1tcjDidnmD7ARShGqaEix8SakWDV7E6ahO8/rIaaH+9sKBVKnmYwjZ1tFpTboDok6VJ1GGR6Cz2vjrTRtmj3sb1jls3567JaPM5qGWnaDYlLD1XQqhlI0gxpx4YW26ivcfj/fvFMXxXWI6nhNAfs7hmyMbY3itySi9zMDpCKifKh2oGtClCUD7XdNYxs5xSDECGDMXOErZ5D2P3QFrbK1jvJ2Xpjjy+64eRJBjSvrQ16OAwz9gy8CHXx4kW86lWvwtOf/nR84zd+I9773vdu3Xf58mW84hWvwNOe9jS84AUvwD//8z/f8LMf//jH8e3f/u2477778LKXvUx9//W8//3vxzd8wzeo3/3GN74R7TZbEm5n29B0XVnyzm9Y8ujzLTVUbQlWbRHjiJZEkGEDWpbCdrZf7NIEhOx4Ke1aBlzwOMyA8tgxUDfSbbNsOnqGqy7tWCXQfZ8n8CMK1ber12xcOVVGEumI4xye9DHtlaBnFhrrBtKEX9v9IDRdbTK0PIkLTlsVVJjdczboFrkhBXfdZHZduCjZRdQDA4tmiAeLty7+kr3roWJXBaWZBhzb7/tYmd2poTaz9aiLNDO+eBvh5DQpWb0wOd3BGWYii1Akd3z1q1+NcrmMP/uzP8Nb3vIW/MZv/AY++MEPKnvUa1/7WkxPT+NP//RP8aIXvQive93rMDc3p36W/qX7X/ziF+NP/uRPUKlU8KM/+qNdWxWAj370o3j3u9+Nt771rfid3/kdfP7zn8cv/MIvDPLPHerdOypEkff9gtNSEvFHqKHGNBuKVFAESXCls30e1JqZqucm1QHT5QnjYUFKKCGkao98Zhv1BmVp0JKarHt2caqvY2R6Q5YCixcCXLsUIE1zxDFwxJ3GbDAF1ya1oY48FWius/V1v1CXp8yQ6Mh82+OJuRFSsizaG103iwEXuZldUzADmLaHhqPjS4Umlq1HbqacLHYQ6TlCLYN55Bi/v4YURzowhKEappwIupEVzHjiGDnEht5tbYGVUAwz1kWo5eVlPPGJT8Sb3/xm3HvvvXjOc56Dr/3ar8UDDzyA+++/XymbqIj06Ec/Gq95zWuUIooKUsQf//Ef48lPfjJe+cpX4rGPfSze8Y534OrVq/jkJz+p7v/d3/1dvPzlL8dzn/tcPPWpT1UFLvpZVkPdGs/wkFgSbZlvqU+6aqjihhpqeSzVUJtFKE3tRpq7yoNKhKbanzOHGFBueCrLZs7soH6LXA2aCJ7bDCQXBkzBlq1RI+oIXD5ZRm3FRkr5C5nAXYVZFJ2u4sSUOqTeLUI1qlyE2i+0gNq0uJ52H95kYLaHzi8EKYTdgAPJmb1dw8iWF3u2Uh/eP9W8IdiaLHingm7WmG5ZsE1noONltn8tqVNe2zOxZEZb80BmTMPJ0YGW51iZqw56OAwz9gy0CHXkyBH8yq/8CnzfVwomKj596lOfwrOe9SylXHrSk54E131YAv+MZzwDn/vc59THdP8zn/nMrfscx8FXfuVXqvvTNMUXv/jFG+6nAlYcxzhx4kSf/8rRgIoqlMdD6pNNSx4h/Os75Y1fNpQKJc8y6IbccSdy2Y7V+1TogjvY9MGSR10bKctmczF4PTQRJLteN5CcVVCjBhWVLp8oI2xJxEkGS3Nwb/E4bOPh4Gc6Hj1bQkst1NYM5Owk2zeBGSByDTRltuuuXZMMB5IzB4XeM1NOBY2ihTUR4jOV1tZ9Dxbbqhgc6hmsmWMDHSezM7Qppsvu/PgUq0nHGlfrhpNX1zocTs4wh8zQrKSf97zn4Qd/8AdVftPzn/98LC0tqSLV9UxNTWFhodupbbv7a7UawjC84X4pJUql0tbPMzeiFnykPnElrppttPXsYTVUoQg772xkQ13D2JDnMJtryNMUtrl9AYqUN1T4yPIUucu7loeN1AVcgyyiOs7eQr2x2akmFTpcztIYGaiGvXzVw9yZIpIYKv+pZJZwR2FG7ULejGsbEJBIYx3tBqvdDrLJYOomOiYF7LKlZCeuuDFCPUescSA5s388w4Vn+Wj4huo+fNELUZOpKnCmeQrD9WAZrKoedvTNbD1X4pLd3rHrITO6+CJW4eRxCrRXa4MeDsOMNUOzvferv/qryp5H1jyy1pFtzrzJHkWfR1F38bnd/Z1Od6diu5/fLaNi098a5wHG6xsuarKGyEhwyY/w+Ho3I0kUAsjaOmRCnfIeRFg8MhRPzPV/cr6P4ch2DRrlfeQZLM/Z9rmrywyhyFXXDMMPDvQ8M7vDN30s2i00mx0VCHpPqztZJ1vDZS9CmqUQvgddH5paOrMNFC6+cK6AZs1ElufIUg3H/SPw7NsvwhxLqOJUmkk01ky4xdsH1TPbbzIULB8rfoiVMMQ1J8HRzhAX9QZ8fj0XdLqFOiHgWNtfG5j+X7tHiYpTxnwaoRk18OlyA5XYUCXgWM/hzxydiPfWOLzegeWjFtcRyhinCyGevsaNCnbFiL3enpUC5LjMgdWL8/BnSoMeEsOMLUNThHrKU56i/iUF00/91E/he77nex6R30QFJNvuFkYsy3pEQYk+LxQK6r7Nz2++n2x7u8U0BUYFIyeLmKZu+8WSJixpITRDtWv3hIYNja4gQkCWSnCWVpCsL8GuLyEqHsXA0Taub5qG/cScWK3NUPIU0ra3teOt2Il6rFRSPoBzoOeZ2R2utFXWU2hGOOt38Kh29zW64sXItByxyOFWpvi1GAE6TYm5MwXEkUCSZpAwcHfpCEwpdzi+u5a8atvE+pqBo/dqw1D/HtmibjVcR2REylIyGw5fzhYd34MuKZNSZclOukXucgWCi9yHwub1dtwPZ10TmHGnsJAlaK62kQoqqqeQxcJEZRmO+utNrxVt1LbdGGfjFp6y7sDM+dwwzOfy/eCZOfRmjgQ5agtrMIzRWQcyzKgx0CIUKZ8ow+mbvumbtr72mMc8RmU3zczM4Ny5c4/4/k2L3dGjR9Xntwo6J9sdFaLocwo1J5IkQbVaVb93t0RROjILnjhOlbqAAvUOKh9fddtYaYdYlQnKcfctovs+9GpVqaG8q19GJ5gZuBpKFZ40TeU07eevlo1V9bNkOcyF2OqseCuuWTGyvCvLpkklPdfM4UMW0aobYiEMsSZjlBMDZ72Oev4po8EQXVUNM7ysL9mq+x1lOlH+ky89HCtU1CR129dOHW+AY0rUWwaijkSnJWC5HAx7kKy1mhfjctTGunARJMM1waZFy6CP5zMeqaCAWGgo+OWBj2cc2Zw57PfaPWqYwkTBLqAWJBDrsSpEBeWZiXlvjcvr7ZsB6lYTLdlR54kn1DmaYZjP5fuBljVO3kEMD8tX19TaimGYw2GgheorV67gda97HRYXH+669qUvfQmVSkWFkH/5y1/estYRFFx+3333qY/pX/p8E1JNPfjgg+rrZM8hZdX191Oxi3KhnvCEJ+xpjHQOHZVbd8AHu3nShaZ3A6EvUIDtxtc1TYfczIZaX4JJ2VAHfKyD3q7/k/fz8yYVodIMhtR3/N5lM0GWpchJiTfgv3uSblSw2AwoP+uHWDUSrJkpkjyFVql0lXpDME6+PfKWpcDi+QCLFwJkaa7yn6btKcwWpnb3um3gUDg5fX8mlCVv0H/XKN+oCJXL7vF02u8MfDy3er17cR3b7y1FjgteqM71wrIhNHGoj0eF2bApkadD8Pz38bb5cqt/88m4FY0CLMdDw9VhlMqH/t4aptu4vN6WbsIW1GlUx0mv1S2yDMG4hu42BOfyg9w8PYSOHKsrrYGv6w59zcgwk1qEokIRdbR74xvfiDNnzuBjH/sYfuEXfgE/8iM/ojrkzc7O4g1veANOnz6N97znPfjCF76Al7zkJepnya73mc98Rn2d7qfvu/POO/HsZz9b3U8h5+973/vwN3/zN+rnKGvq+77v+/Zkx5tESOlD7bw7to7zdkt1CdpEBAEEtU2nTnnzo90pT0tCyLABLU9hO9sLAqmdMnWVonBsGRT6NkYGqhMhqfNatsA5p43T1JkmBxKhwXP5tRhW4lDHlRNlrC/bSLMMeSpwV3AMZdfb8+8SugbblMgTqbrkMQcL/FfHkyNwxm0h2mhAwXS57EaIVCB5DqNy+F031xYdXHywjMsny6poy4wvpPw84sxg5si98Iocdj+qFFSnUVPZdul8wYwfngonzxHGQLtaH/RwGGZsGWgRSgiBX//1X1eFoe///u/Hm970Jrz0pS/Fy172sq37qAvei1/8YvzFX/wFfu3Xfg3Hjx9XP0sFp3e961340z/9U1WYIqsd3b/pO3/hC1+I17zmNfi5n/s5vPKVr8RTn/pU/PRP//Qg/9yRgbqAxLaBppFh3nk4CJhsa7JYUmoos3oNZn0Jowp1xSP0PIV0ujljt2PZ6j4HJKG3jO2/l+k9pN5IbKmKgVsqBYdyuUYxcWD8aa6buPRQBZ2WRJJkMDUb95SOw76pUcRecG0JkVlo1yWSiF/3gxCYgTqeOjLHeVK7Mluc88OuLVsI2MbhbljRw9SWHWR5hvq6jvlzRaWMYsYXmp9KfWiiWJl9QJu0UmVVajgRcKfRccQzNk7EG+HkDMMcDgO/GlK207vf/e5b3nfPPffg93//92/7s895znPU7Xa8+tWvVjdmb5DcWGoSkaHhvBfijrZ5oxpqvQqh1FAPYnUIsqH2g9nshpJryKHtsDimkFoi1zUYPIHsO5YwYerdgHI7ylVXIbN8+CoFZu+L6tV5F6vznrIpJHGOkl3CtFvYNvR/N7iWVEXHJBeqyFWcedimzez9eOpaSiKc9Ft4bN2CPrJxwb2jaiRYtpKu1bc8feD37E5EbYGoI5CmORA5WF9tQcgAR++tj+IllWEmAjovBKaPNS/CchRiyUpwJGSF7rgVobQ2lRdzrF1ZwR3dFBiGYXoMbykzt7zIkmWj7UhcsdoIr7NsdNVQRRXcN8pqKKO5ijzLoEux42KDFiZKfWNYh74wYR4JPefU2avjGYjzBJACtmRF2rJG6fwAAHuUSURBVDCRJhrmzhSxMuchzXL1+TH3KGa8Yk+OGcpto1vXkjd8Xd1GjYLpI1SWkgRX3YfVrpOugiKU1dcrHvrjNdY2zmGZrtTHeehgbclSxxDDMMMLHa/UGCU0NJykiABmrBBCg5OHqtHT6kJ10MNhmLGFi1DMbbuS5QZdZIFLN1k2RFBQ2VBiVLOh8qxrx8tS2Ob2h0CkZagZqcqD0gpB34bIPHLSpwmBjgD0UpmLgUME2e4uPVRWCqUkzaHnEncXZhE4Vk8fR6mhMgv1qsH5OQfEkQ6kkOiYGk75bUw6iZbjoh91NxtsR2XRHTaNqqWseKaUOFp24EoPWWhiec5G9RpnVzLMsEKqXJqTtFyJK3YbdckXpHHD1UPllFhbag56KAwztnARirklpjDULTR0FQh9PTeqoRZhNpYxSsh2DVqWQMsyWN72C+VlO1GOf5UHZfHCYJCTvqPeDJzZ4/CD8qCHw2xQW7Fx5UQJcSgQJzk86eGe4qxaWPca1zYgIJCnOpq13ha4Jg0q4hYMH23PxIIVYsXsWo4nFQoYpjByCiXvRyA52fDCNlnxNBRkEbqu4VjZga35SEIDCxc91Nf4Pc4wwwpZ8tRGrUS3YQozduHkupajHeUIG61BD4dhxhIuQjHbqqFC11DB3OtGur0aagTzoCiUXLd2CiXvLs4yHTB1tgENEktYKNgHzxdiDk6WAdcu+lg4HyBNgTjOMWVPYTaYOrTXxzJ0tVhHKtGs8rF4UMjiqkmBSGoTv4g6G3QDyTUp+2L1bWwWmDKxpRgUQsexigsrD5BEAvPnfLTqnDXDMMMIBcxvxlZQp9HrYyuY0cffVLflwNoFDidnmMOAi1DMbfGkC+g6YqmprmSPUEMVumooY21hpLKhqAi12QGJLF47FaHyPIMQJhc/GAZAHOm4crKM6pKDNMuQpTruCo6h4h5ulg0df2TJ01IT66ty5FzAQ2sp8SQuOG3VfXISWTUTdUtAgeSlvpznqQhF1yBDCEjx8DSMcs+OlV0YaYCoo2PuTAFh6/CtgQzD7K/TaLzRaXQzU44ZDzwzU+06VDj51dFyezDMqMBFKOa2UC4GZYe0bR3nnTaym1rRikJXDSVHTA2lQslTyuLYfrFBOVCrVoKU+mYHft/GxzDDSqtm4NKDFXSaEkmSw4CNe0t3wN6hw2SvcG0JHQaSWKDTYJVILywlmbKU5Dg7oWqo6wPJfa906I8Xh7rKUaOueAVZeMT9lilwtOxCJgV0Ojquni6pwi/DMMPaaVTHKa/1iDkyM7pIocHeDCef53ByhjkMeGbDbAvtlCeWoYIXF+3ktmooU6mhhn+3QI9DyLAJLU9hu9vn1tDuOGkDUgGYDncsYiYXUh2tzrtqQZwkXftd0SjgruIRCLLI9QnHlNBJqZIKNNbZkndQDN2AI220bYHTbksFdE8SlANFjTdSCiR3XKUO65cVL88ECu6trX+k+JsputCjAO2WhquniqrjJMMww0XBDBCpTqMpLt/UxIcZbVytG06+cq0+6KEwzFjCRShmW2iBousCkfFIS971aiiRRiOhhiIV1GYelLS3z/5Y2siDSnRN7XgxzCRCi9/5swUsX/WU/S6JNRxzj2Am6I916XooE8o2BaAseayE6tkiyjHQlNkjOqGOOxe9UBXeYpHDLB9+IPlmV7xbWfFuJnANTAcOtMhHsyEwd6aostgYhhmuObKkJj6WhhN+W9m3mDEKJ0eOVidH3Ga7JcP0Gi5CMdtCi0wVvuhKXLLbiLTsNmqoNsy1+aFXQ22GktPuhraDhUh1xsszSCH6skPOMMMGdfC6fKKsFs5JmkHLDNxdmEXgHH548+3wbAmRmyorh7qMMQcP/DeFiY6l44TfmphFFP2d51QgeQZdSFjy8LvRJZGOdsNAmuUIjEda8W6m6Jkouy600EN9XWDhXIGz0Bhm2DqNmoHqNLpsRlubl8zo48uEFgvdcPJLC4MeDsOMHbyyZnbVJa/bija/pdxYqaHEaKihVCh5lkGXclsVBy1QVjbyoDKPrXjM5FFftXD5obIq9MRJDkd4uKc0C8vY3sZ62DgUTk4zw4y75PVsEWX46LgGVo0Y126yXY8rq2aKqpGCTNfaVKU/geTV66x4trPj99OYKgULge0CoYv1FRNLl3wuRDHMEEEbtVTIDg0NJyc0W28c8azuprsKJ788Os2XGGZU4CIUsyOmbqjskNDQcd59pCRVqaGKhS01lNFYwVCSZzBaa8jTFI65/YKDFieUF5LqgOlyKDkzOVAO/7VLPubPFZCm3fynilXB8WCqm8c0YMjCZBlkyZNYX2NLXi9wDa+7iDInZxF1Nng4kNxzdlYl9YI6dcVDDqnrqhPebqBC1EzJVgvdLLKwsmhhbcE99LEyDLOHTqOmh5YrccVuqwxVZvQxhAYrjzbCydcGPRyGGTu4CMXs2pJHO+XXzPCWF9hRUEPJdg1alkLPM5ievaMVj0ipQ4YcnPWIYfoJ2YWunCqhes1R+U9ZquPO4BimPL/v+U87dslLLdWtL42HZ1yjChUXqVOeWkRZ47+IivQMl92NQHLX64vdmrLUqKMjdcXzRbDn1+dI2YWje0gjE9euuFhf5usSwwwLgRFsOAaAU4XJKORPAp7W6YaTL9YGPRSGGTu4CMXs2pIHoSOSmgpzvRlNF1tqKGt1bijVUOZ1oeS6tX3+x7KVqB1roekQOufOMONPq27g0kNllVmTJBkMWLi3dBzODtlpg4C6h1HhgGxNzfXDz/KZBHzDB6RU5/hxX0RdoI54G4HkVqnSl8dsqkDyrhWv6O5dyURdKI9VXNi5jyQUWLjgo8F2VIYZCqQu1GZty5E467QQ6txFYBzw9G44ebOdIQknq3EHwxw2XIRidn2BJUUQhdeec27dAWTY1VAqDyrPoQkdmrh9YYn+NipCZVmKzN05t4NhRhlaGK8tOLh6qoQ41hHF3dDku4pHIfThvESQlYlseXkqUK+yJa+niyhXjPUiSgWS+xuB5NKAZfRHUdTYsOIJTYMp97exQe/5Y1MuzCxAHOqYPxug3RhsRhvDMF0ooDyxJTqye45hRh/P6IaT0zypevnaoIfDMGPFcK4wmKHENzzEjoGakd6yA8iwq6GMxiryNIMht7fvUKvytsiQaoD092abYJhRIks11XFr6YqvOnYlMXDUmcHRoDxU9rubobEpS15iobZmcOv6HhHQIsrqLqLOj+kiiq5ddA2LkUGf6o8KKk00tOqmsuJ58mDXFCpgkSLKSAKEoY6rp4vcJZJhhgDqMupIG21bxymvhWxCOo2OM77ZfQ1pA3uNi1AM01O4CMXsGkc60HWByNBw/haWvGFWQ+lxCBk1VSaU7W6vnNgssFFgrWOwEooZT8K2UPY7CktO0gxaJnF3YRbFEVH/dS15Elki0K6zLakXWMKELWy0rfFdRG0qFDKhw7WDvlvxSs7Bu63aplQZUSIO0OlouHqqiCTm6RzDDEMhP3JMVei+dItu0sxoYUrAzGOS0GJtgcPJGaaX8KyF2VM4qms4aDsSl5w2Ei2/tRqqcJ0aaiOHadBsjkNHCmnbu8iD6qothMY7zMz4QYWnyyfKSkERJzls4eCe4iwsY3SsPbYpVE4OLew5G6d3FEwfoWtiXaa46sYYJ8hieIXyoLIEut+fQHKiUbW2rqHmLrvi7YRnG5gpuNCjAK1WVxGVknyXYZiB4QgbUhgITR0n/VtHVzCjhauF0JFhZZ7DyRmml3ARitkTnvSQmgJtmavuQrdCFK9TQ809NFSh5NTlQtshaJk64+VZCtj2UFuSGGavkBpj6bKH+bMFpAkQxUDFKuGOYAa6PlrvdTo2HVNCS0ysr0r1tzG9UbxKIRGaGk4GbYxbIDk5NyMB2KXpvllemzVTqQ092dsukwXPxJTvQgt9NGsC82eLyNmayjADg45vyoZqewaWzeiW0RXMaOHpkQonrzcTpMl4d45lmH7CRShmz3YNQzcQGjouuLfuoLSphrLzNsy1q0OhhlKh5BkF0YptFwEdPVPtyUnlJQLOg2LGB2oRf+VUCWuLrsp/ylIdd/pHMeUVRrbYSrlQAgbiUCJsjY6KazQWUSYWzRAr5ngsokiRcDboqEByIQ2Yoj+B9s11UxWG8kxHyfZ7/vtLvomi4wCRi9qaxMKFAhdkGWaAUIMHnQr5hoYTY95pdBLwjFiFk2c5sH5lcdDDYZixgYtQzJ4XKBRQ3nEl5q0QTZHeNhtKCg1yGNRQWQajuYY8TeFY+o5WPCIVGmxzNLJxGGYnqIPWpYcqaNcNpciQMHBv8Thca7RtbI4lodHsMBUqd4fpDXSO16RAJDWcCsZjEXXNTtCQWTeQfHqqb49LXfEIsv5Zhn4o1+Tpog3fdJGHNqrLJpavHDx3imGY/UHHum/6aLkSV6w2Lngh2/JGGN+4Lpz8EoeTM0yv4CIUs69dHojuAuXibYIXNUFqqCLsrA1rwGooo70OLU+h5xlMd4c8KLtbhMp1TSm+GGaUUW2Frzm4crKMONIRxTl8EeDu4jFlmR11KBPKtiSQGlhf4/y2ni6iDA8tT+Ki00ZLjL7H66xPC0Egkzpcqz8qV3J1kxIqyTK4wj00xSH93iMlR9nls9DE8ryDtUXeRGGYQREYPmCSawD4RKWOj880lNKeGT1MkcPIEw4nZ5geM/qrEKbvSF3ClhZCS8M59/bBi5tqqG6nvBMYeCg5FaKs7dUS5N/PyLZnmCNrUWKYzQXw4oUA1y75yn6XxMBRZwbHCpWxem+rLnmZhXaDbHl8SesVgekjMyRCCWVjG2XaeoY5N0KmAsl9FRDeD1o1E1mmIU91FA/Binc9lOl2tOLA0X2kkYHFSx7qq6wOZJhBIHWBI84MOiUfdTPHRbuDjx5fv22WKjO80LnV1TrQVDj5+qCHwzBjA8/YmX3hGR5Cx0TVSLBym+BFUkNRIUqpoVavKEvcwPKg8hya0NWYbkes5aiaCVJk0DgPihlhqOsddb+rrdhIs4z60ePuwiyK7vipI1QRiooKmUBznRfdvYKUoBRS3rJ1nHZbt+yGOiqc98NuILnU4BT7aMXb7IoHXYXoHzZC13G07MLKfSSRwNx5H60aK3oZZhDQZu1scAxyehrVgsS6HuHjU3XcP91QnTqZ0Qsnr9VjtVHNMMzB4SIUsy9c6UDXdTWpP++Ft/0+suQ9rIZ6aGBKqDzNYMjtd79XrURpuigPyrLcvo2PYXpJo2ri0kNlhG2JOMlhaY7Kf7KM8QzuNqQOQwrkqURtjRfcvaRg+ogdA02Z4eI25/lhhpS654NwK5Dc6FMgOa1TqAhFVjxbOn1TH9LxcKziwkwDxB2Bq2cK6HBoP8MMzNpcscuYKs2iMeWjYWQ477Tx0dka5hxWRY0KnkxAp/A0A2pzy4MeDsOMBVyEYvZ9YXWli7YjlMw4vc0u+aDVUHrcgYxa0LIUtrv94mOzlW5GRSgx2oHNzGTmPy1f9TB3pog0AeI4R8ks4Y7CjJKTjzOeLaGnFhrrBtJkvP/WfmIJC6Yw0bF0nPRvb70eZhbsBE2xEUg+Nd23x23XTWQpWfE0lKzDteLdjGUIpYiSSYCwo+Pq6QJbVRlmgDjSxvFgFtr0FKqBUKqof5qu41OVJiKNlTXDjmdk14WTc4c8hukFPCth9o1vuEgtibbMcHWbHZ1BqqG28qCQQjo7hJJbcXe3XJdjlZnDjD9JrOHq6SJW511keY401XDcP4oZvzAR72Wy5AmN1FC6yuFhegO9dwpGgI5rYNWIsbjRuGGUoDyrzUByz/L63hVPg4BrGQPpHHmk5ELEAdotKkSVkMbjfy5gmGHevJ12KiiXZ1GfdpUq6ozXwl8fr2HRjgc9PGYbbJlD5mk3nHx+cI2WGGac4CIUc6BdcgopDw19W0ueUkMFm2qoq5Ctal/zoNQYkEMzbr84zZBjxUqR5hnygNtbM6NDpylx+aGKKr4kaQ49N1T+k7dDCP84YRq6ysNBKpUdkekdruFCF3Se13AyaGOUoK5+806MNKdA8qBvBVlSJZIVj/LYbGEPrBDsOwamAwd6FKDZ0HH1bFE1LGAYZrBxFrPBLDBdQdUXWNMj/MORGj5Tbo509t5khJPnWJ3v3xqGYcYZLkIx+4Ym1tTGu+MJXLU6aG/TxlsWN9VQYV/VUCqUPKMsELHtQmDNTJWlMNUBy+2vdYJh9rvQXV+ycflkGXGkK/udJzzcUzwGU05WBgwd264tgdTE+qpEzu6GnkGh79Qpr+UZ6jxfM9KRCiSnJV0sAKfUv0Dydr1rC6XOeCVrsE0uir6FsudACz00qgLz54rq3MEwzOAg5e6MM4VS+RhqUw6aIsVJv4W/nl1Xqnxm+HBVOHmG6jp1WuVJBsMcFC5CMQfCM1zkQiKW2rbBtderoeyVK/1RQ2UZjGYVeZrCtrZ/qy9tSKETlQc1OQoSZjSh+c/ihQCLFwOkaY44BmbcacwWpibCfndbSx4MpLFAu8kB5b0kMHxACkQSOB10MAqQuvUcFaHIYm2YMHTZfyteLuDZg38vVgILBdsFIlcVaa9dDLgQxTBDMoc+HhxHNl3BuqdjVUT4u6N1fL7Uum3WKjMYfNENJ09SoHltMN2+GWac4CIUc+A23hTi3TE1nHMpeyMfGjWU0V6HlqfQ8wymu1MeVKICB6UmlG+fYYYVChi+fKKM2oqNNMupnSPuKsyi5Ex2R0fbomNXo/AfNNbYktdLhC7gGR5arsRZpzUS7cXJhkfq3AgZ5NRM3x53y4qXkxXPHIqiMI1hpmSr1zAPHaxeM7E6x7ZzhhmW8yupogqVY1iv2GjqCR4KWvh/x2pYNUcvh29c8TZVwDmwemlh0MNhmJGHV9vMgaGJbbQRXEu2tl1lQyk11Hp/QsmpELVNPg4VzqgIleUpct851DExzEForpu49FAFYUsiSXKYmoV7S8dhG5Nlv7sVVIByLAGkBmprkpUePaZg+kgsiY7sKoyGnbNB14qXSx2O2b8CbadhIIl11RmvaBUxLFAh6mjZgSs9ZKGJpTlH2XkZhhmeeIvZwnEkM2VUXQ3LMsTfHKvhS8W2UnYyg8Uxcgj1SuSoznE4OcMcFC5CMT2RE2u6UJa8C9tY8q5XQ8k+qKFUHhStRIVQBbDbUTMyRHoO6uwuvcHmdzDMraC38cqci7kzRSQJVP5T0SzizsIRFZjJdKEuZCI3EbYNRJ3bH/PM3jGFqUK225aO015rqBdFDZGqblNplkAr9LdDJKmgFJmAN4CueNtB5woqRFmajySUmL/gc5A/wwwR1OzniDONoHIU1bKlVFFfKjRVMapqsCpq4OHk6EDLc6xwODnDHBguQjEHhuxrjnTQtgXOO9vv2HTVUAHsrANr5TJke/1wi1BpBnMHkchmCGQqNdiSlVDMcEEBx1R8WpnzlP2OPj/uHcWMXxwKq88w4VoCGsiSJ9DcLAYwPaNgBohcE+syxRV3eMNzz2+ooGhjxCtU+mvFW7OQkRVPGkNZIJZCx2zFhZn5SCIdc2cLaDdYSckww4K20QziePEOxNNFrDvANSNU9ryHCqyKGiSuthFOvjYa2YgMM8xwEYrpCb7hIrElWjLDnLP94kQWS5A6umqoucNRQ+lRGyJqQctS2K65cx6U6laiQ+qsnmCGByo4Uf4T2fCSNIOeGbi7MAvP5gLLrRBCh2UK5IlEbW24VCjjgCNtSCERmhpOBi0McyA5FYKEYSllQb8gmyx1qkzJimcOjxXvZgwpcKziwUgCRKGGq6eLCNt87WOYYYLOXUfdI/CmHlZFfb7YxN8dq6MmR6dL6Tjhi1iFk0cJ0FxmNRTDHAQuQjE9gWwadMGMDA3nd7DkKTVU4XDVUKSCInSkkM72uRdLdoIsS5Hv8H0M02+Wr/jKVkb5T67wcU/pGEzJqoUdu+TlFlp1iSQePiXK6O/QB2h7Jq6ZEVaGMDT3qhsj1HPEGgWST/X1sbe64mUCvj3cNjfbFDha9iDiAjqdbiGKlFEMwwzXOZcUqJQVFU4XUHOARaOD/ze7jlPB9s2AmN7jmdeFk1+YH/RwGGak4RkH07MLpSddtB2Jq3YbnR26Jz2shooOJRvK2MiD0pBDM26viGiKFC2RqVa4wi/0fBwMs19I/bS+TB3wqMuWjeOFKbbf7QLXltChIydL3jorxnoNhedqUiCUmloEDRtnfVqYKVlcXwPJN7vikQKLCsXDaMW71bFypOhAjwK0m7oqRJH6kmGY4cIQBo55R+FUjqBaMlHXY3y21MA/HK2jwaqovoaT66r0l2NtnsPJGeYgcBGK6Rmq/bMhEQrgkhdt+70PZ0O1YS+TGqrWeyVUnkNIue3Cnax4RCo02AYroZjhgBaCixeD7i5npmM26F+L+VHHEDqk1JGnAvW14VajjGoGIBWiWp7ERaetivjDQl2muGYn3UDyUn8DyaO2UKpFZcUzhteKdzOBa2LKd6FFPpp1gfmzRWTD85IyDLMBnc+KVgFHi8fRIVWUDcybHfz17PpG8Z1VUYeN0DU4eTecfJXDyRnmQHARiukZpjBUB6XQ0nHebe/4/apTng6IXquhsgxGq4o8TeHY27/Fl+0NO4mm9TU7hGG2Y+lyNzQ4iYEZe1pNfJjdT9TJkqenFupViYw3iXsOheaqDQeZ48wQqaEoCwobgeSu379AcqKxtrGJkenwndFS4JV8EyXXBSIPtarA4vmCUnYxDDN80Dx71jsGa2oGa0VDqaI+XW7gH480hmpTYFzx9FCpodZWhjMXkWFGBS5CMT3FM1yEjoFlM96xnawm5ZYaylq+1DM1lNGuQssz6HkGY4fFACmhKA9Ksy22OjFDAbVMr610bXiuYaPgcsfGfeVCQSJLBFp1VkP1GkM3VEfUli1w2m0h0QZfsSBL9XkVSJ5CmpRR2N+g7euteKNWNKZr31TBQmA5yEMX1RVTFcK5EMUww3vMlqwijpTuQGuqgJoFXLXa+OjsOi541B2UD97DwlPh5DnCGOjUGoMeDsOMLFyEYnpehIKuI5IaLu5gyTssNRTlQRF6nkK3bl+ECvUM60aqFi+6H/TksRnmoDa8a1s2PIFjPtvw9hu6rDJ5MqGKekzvKZg+YsdQHVEv7tCMoh9ccSNEKpA8h+hzIDnZ8Ki7HFnxCnJ0rHg3L2pnSo66hmehhdUFC2uL/cvUYhhm71ikivKPwZya3lBFRfhEpY5/mWnsmM3K7A/P2Hhec2Dl/Nygh8MwIwsXoZieIjQB13DQsXWcc1qqXfZu1FBOD9VQ5kYoOQXTUvbU7Vi5Lg/KsniyzQyea5cCJHHXhnfEYRveQS15SCzUVg1WdBwClrDUAqhj6Tjptwe+805WPDUGCiQ3nIF0xaOiZzBiVrzr0TUNR8suHN1DEpm4dtlFbWV0/x6GmQTouC3bJcwUj6M5HaBu5rhkd/CR4+u47O68GczsvQhFTY/ov+rVlUEPh2FGFi5CMT3Hkx5iy0DDyLDgxHtSQ3nzJ3pThEozmMb2C/jNUPJM12Dqt++gxzD9oL5mob5qKRseqRECh4PyD27JMxBHAp0m570dRqEvMAN0PAOrRoyFzXy9AUCK1iUrQZqn0MqlvlurqQhFGx+GEJBitKdVVPg+VnFh5ZRLJzB/PlCdOhmGGW5saSlVlJyeRrUgUdMjfHyqhn+dbijlP9MbhKBw8pDDyRnmgIz2bIkZShxpQ+gCkaHhgruzTeP6bCh7+eKB1FB61IaI2tCyFI6zfWGJFi15nkFIg/OgmIGSxBquXaIMlq4N76jfXzvROOJY1BlTSR3RXGc1x2HgSRe6LhGaGk4FOzejOPRAcqHB80p9few41NFpSaRpjoIsYBygQtpsxYWZBurvmzsbcCGXYUake2nFLmOqNIvGlI+GmeOCQ1lRNcw5rIrqFa4WKjXU6lJz0ENhmJGFi1BMz6GCDik52o7EZbuDaBc7MKSGMvS8q4ZaOHEgFRShI4XYRklCOVBrauc8Qx74+348hukFS5cCpLGOOCEb3gzb8HoAZUI5pgRSA+urvIA+PDWUj5Zn4KrVQc3ofytCCkWnIF5qMCGs7gbIIKx4eSZQcMdHvWgaQlnzZBIg7Oi4erqosq8YhhmNzeDjwSz0qSmsBQLrIsI/TdfxyakmIo1VUQfFkzF0LUc7yhE2uEsew+wHLkIxh4JveFstvHfjSe+qoQpdNdTSRYhOfd+h5KQmoSW8ZtxeCbViJqDLcCoA2/b29VgM0wvIgkdWvCTN4CsbHqt2emnJ0zMLYVMqRQfTewLDVzlM1IziVNDp++PT9SWmQHI9hzE13ffHp65442LFu5Wa8EjJhYgDtNuaKkSRapNhmNFQRU05FVTKs6hPuSoi46zbwl8fr2HR3jkqg7k9vtzYcMmBtQvzgx4Ow4wk4zVjYoaqhbchDISmjnPu7hYm16uh/H1mQyklVJ5DSH1bi91mHlSq6zAF510wg7ThBd3CaU7d8NiG10tcW6rQVlKpULGA6T2kPPIMDy1XqGYU/c4eOReEW40obNlfJVIS6Wg3DKRZjsAYDyvezfiOgemCCz0K0GrqmDtTQtZ/wRvDMPvElQ5mg1lo0xVUfYE1PcQ/HKnhgUpTKUmZveOZFE5ONagca1eXBz0chhlJuAjFHApUACI1VMcxcM2KUN/cNdhtNtR+1FBZBqNVBdIUjr29bWDJ7uZB6bpQu0UM029o3UwFqDTRkCTAUXdGWciY3kHKFLIV5alEfY2bDxwWBdNHYkl0ZL6Vz9QP1oxEqVoTpNDL5f4Hklevs+LZ/e3I10+KnomK50ALPTTWdcyfKyJnRw/DjFTn6mlnCuXyMdSmXDRFilNeC389u44li1VRe0UKDTaHkzPMgeDVN3OoobUQOmKp4cIuFyayWNpQQ4V7VkMZ7Sq0PIOWpzC2sTRlyLGykQeFgK14zGCgLJnGhg2PlCS+zUqdQ7PkpRYaNUMV/JjeQ2pSUiG1bV0tbOgc2y8VFJEIDa5fRL8hGy3thEtdh0EtXseYcmCh6LhA5KmMtcWLpOAc9KgYhtkLruHieHAc2XQF657Aqojw90fr+HyppbJSmb2Hk69cawx6KAwzkoz3rIkZuE2DwhE7lo5zTltN1veuhmrsPZQ8T6Fbt1/Qr5upkiCnOmA4XIRi+g/ZeDZteHouccyvDHpIY23Jo13gXHXJY+vtYVEwAkSOiZpMcWUXOYAHJdZyXPQipDk1oXDUa9xvK22HrHhpDl8EGHdIZTZdtOGbLvLQwdqShZU5vn4yzCjOzWecKRQqR7FesdEUCR4KWvjrYzWsmt2oCmZnPBFDR45WJ0Pc7p8CmGHGBS5CMYcKKTxi21B2vGt2snc11B465RmNjVByIaGJ2y9IljbyoGj33DbGp5sRM6I2PG+abXiHiCl1CKGrIlSjykWow4I2HKTKAdRw0m8f+uNd8kK1mUCB5Ga5/1lqTRVI3rXiFV0XkwAVoo6UHLjSQxaaWJ6zUb02vjZEhhn3yIzZwnEk02VUXQ0rMsTfHKvhi6X+qVlHGV8m2AiGwtqlhUEPh2FGDi5CMYeKIx2VuxQZmmqjvRuUGsrfVENdgAgbu1ZC5WkGc4foFwolJ1UW7Zz3e/ecYagbHhVDut3wPHjbqPaY3ky2yZKnpRZqawZn2Rzi8xyYPtqeqXIAlw9xR53O32f9biC5LiSsPgeSE2Sl7V5HNJhycq4jVDA/VnZgaz6S0MDCRU/ZEhmGGT2kLnHEmUZAqqiyhaae4MtBC38zW0PVYFXUTuHk2Awnv7w06OEwzMjBRSjmUKHOVK7hoO0KXLTbykKxG2Rpo1NeQtlQJ3d+nKgNEbehZSls5/ZqB7pYLNsJsixF7rEKium/DW/p8sM2vKNsw+ufJQ8SadztZsYcDlRU1aRAKDWcCg5PDbVqpqiSrRoptEql74HkpGJs1U1lxfPk+FvxboaUhccqLqw8QBIJzJ/z0arzccUwo7yBMFu8A/F0EesOcE2G+H/Haniw0GZV1G0wpAYrj5S8fXV+bdDDYZiRg4tQTF8WJqnR7Zy026wQTRp7UkNt5UGB8kFuX1xqyAwdPUOqAYY3ni21meGErDsU5rtpwzvmcTe8fuGY1AVTQ55JtuQdItRplM73LU/iktNRHZgOg80OfGSp9tzCQK14pQnNFaQg9mNlF0YaIOromDtTQNieHEUYw4yjKuqoewTe1FFUN1RRXyg28XfH6irrj7l1ODnlQq0s7rGbN8Mwgy9CLS4u4vWvfz2e9axn4Ru+4Rvwjne8A2HYnWBevnwZr3jFK/C0pz0NL3jBC/DP//zPN/zsxz/+cXz7t3877rvvPrzsZS9T338973//+9XvfPrTn443vvGNaLcPP6eCeSSmbsLQDYSGjvNuZ9c/97AaqgN/4dS232s0N/KgqIBlGNta8YhEch4U019qK7YKxiYbXmD4cC0uhvTdkpeYWF81uKvXIVIwA+Qbmw5nN7rX9ZJIy3CJAskz2nBwVeGr3zSqXfsZFTYpc2xSsUyBo2UXMimg09Fx9VQJcTS5zwfDjMO1ks7hlBUVThdQc4BFo4P/N7uOU0FnVw2GJglfRKoI1WynSGO2LzLMXhjobIGKBlSAouLQH/zBH+Cd73wn/v7v/x6/8iu/ou577Wtfi+npafzpn/4pXvSiF+F1r3sd5ubm1M/Sv3T/i1/8YvzJn/wJKpUKfvRHf1T9HPHRj34U7373u/HWt74Vv/M7v4PPf/7z+IVf+IVB/rmY9ADEjiuxYIdo7HJ3/GE1VAf2tfMQYXN7JVSeQ0h9W2vGkk15UDQmXe36MEw/oIXZ0mW/a8ODxJGgPOghTaglz0DUkYhYsXFo0HmVsgDbjsBpt6XCw3vJRT9SrcRjMZhA8izV0Kx1i8me9PtuBRw2qLg7U3ShRwHaLQ1XTxWV2pNhmNHFEAaOeUfhVI6gWjJR1xN8ttTAPxyto8GqqC28jXByWnpyODnDjFAR6ty5c/jc5z6n1E+Pfexj8cxnPlMVpf7yL/8S999/v1I2URHp0Y9+NF7zmtcoRRQVpIg//uM/xpOf/GS88pWvVD9Lv+Pq1av45Cc/qe7/3d/9Xbz85S/Hc5/7XDz1qU/FW97yFvWzrIYaDJ7hUpAEIqGpRcTe1FDZhhrqNtlQWQqjVQXSFI69/eJy2YqRZyk0m4NUmT52w7sQqMUr2fBm3RmloGD6i0NKKJotZgKNdT7+D5OC6auuqC2Z7bohxd4CyTPowhiImpXUjBRun2c6Srbf98cfRgLXwHTgQIt8NBsCc2eKyLgBAMOMNFRgL1oFHC3egQ6pomxg3uzgr2fXcdZnVRThm93ngDYY1y5fG/RwGGakGGgRamZmBu9973uV2ul6Go2GUi496UlPgntd6+NnPOMZqmhF0P1UtNrEcRx85Vd+pbo/TVN88YtfvOF+KmDFcYwTJ0705W9jHrk7bksLHUvDeae964tXVw3lw9pGDUUFKC3PoOUpDOf2i8u2nqlMKNqZJ4UVw/SD2rK9pZwgG57DNryBIHQNtimQJxK1VQ5RPkwsYcESJtqWjpP+7s/3O0F26pqRIkYGbWowof7UFY8gG6BlsPVsk6Jnouy60EIP9XWJhfMFtr0yzBhgCgOz3lFYUzNYK3ZVUZ8uN/CPRxpoicmuNpsSMPOYdkiwxuHkDLMnBjqDKhQKKrNpkyzL8Pu///v4mq/5GiwtLeHIkSM3fP/U1BQWFrpyx+3ur9VqKlfq+vullCiVSls/z/Qfz/AQOaZq+7qZzbQbRLEEcxs11FYoeZ5C30bhRF3xiFRosMyHi5sMc1jEoY6lK2zDGxZc24DILLTrUnUqZA6z21KA0DOxZsRY2Dj39iqQPBU6PKf/GwlZ2lVCJVkGV7gTb8W7HnouKgULge0CoYP1ZRNLl+jcN+iRMQzTi+O7ZBVxpHQcrakANQu4arXx0dl1nPfCiVZFdcPJM6zM1wY9FIYZKYYqFIcymx588EGV8USh4qZ5o2KAPo+irpWLbHW3u7/T6YZfb/fzu2VU5phb4xzi8bqGA13XERmaumjNRLtTI+jmhhqq1kK6dB6N2ScgtdytP9XcDCUXEpoQOxahoGswhBzq54p5JNe/xXNtVLrhFTZseDnuDLgb3r7RepcLpdd0JLlQ6rTizO4bJTB7t2CviSpCU8PJoI3Z0DjQ6x3qGS5vBpIXPHUt6Tetuoks15CnGoq+z9eQWyxUZ8o2spUMjSjDymIOaWWozLZu/L4RO5czB4Nf7/HBkiaOB8dQNdex1lhDUovwyUqKq56FZ6x4cLKbzssT8Hp7IkI1yVFvJsjSFEJy5iTDjFQRigpQFCBO4eSPe9zjYFkWqtXqDd9DBSTb7mZA0P03F5Toc1JX0X2bn998P9n2dotpjs6JxMi7LciHOWtG1wR8w0XLiXAp7OCZ6z7kLmckRrmMrNFAGIcIFk+ids9XqYsb/bTZXAPSDJax/d9P6iuVJWKaEANYwDAHZ1N5MLzv8oepLtlo18mGl6JoFeFxDtneUMc3HdMqDKgnWIaAaQgkqUR9zUT5SO+7tzEbUJ6IGaDqxZiLOqgbHoqJ3PH4vt2Z+ZIfqQVsLHP4lcHkqjWr9pYVz7MNVkLdAnpdjk15mFsGWlGGpcsaDDNDcToc2XP5fjYgKJw9iQSSWFeqy81/idLRNmx3ssKdx/n1njg0DVNOWW00LFsriNabuIwOVo6neMaai7tb1rbn8nEjoHDyFMhyoLmwjKlHHR/0kBhmJBiKItTb3vY2/O///b9VIer5z3+++trRo0dx5syZG75veXl5y2JH99PnN9//xCc+UdnuqBBFn1OoOZEkiSpqUQ7VbomidGSUUHGcIiM10JBr313DRc1soC06uGyHuKe1y4W5lNA31VCL56AdfTwyy4Uet6FHbeR5Css11HNwK2ItVzbAhBJlg+C238cML5uHIqne8hGw4V277CHNMtWRbcYv8ntur+RKtNjz582xBNoNC/VqRynU9NHZaxhJC/aaXEckgZNeG8+sbh/kTYuWW73edMSf8UJl2SdlrNRvf64/LChou141kaYZHOGpc9BmN17mRmjedLTiYG45QyeqY+6cD01k8EvRyJ3Lb2XJTGLxcGEp1pHeUGzqfnyrtwb9vfS+XV+2MHNXE8Uj7ZGZYx6EUX69mdtjChPH/KOoGutYrVcR10P8SznBZcfGM9c8GNkEvLlpXUPh5GH3/b10fgGFO48OekgMMxIMvAj17ne/G3/4h3+IX/7lX8a3fuu3bn39vvvuw3ve8x5lrdtUPz3wwAMqnHzzfvp8E7LnkZXvda97nZLpP+UpT1H3P/vZz1b3U2A55UI94QlP2NP4RmWOuTXOIR+vpVswdKksGufdDu5pWnvLhlJqqG421Po9T4fR6OZBiTyFsIPb/v0rpIKiLBFdg2O5Q/88MY+EVBBboph8uI9FCuUlG16qbHjT3a5sQzzmoeT6+Wve25by6w2BJBForZvwy3uzaDO7R2gCnvTQcmKci9p4yroL62a7xi5e7yUrQV2miEnJOnVkIMdSu2YiSzRVhCh6Ph/POyB1HccqLuaWc0RhDfNnAtz1hHXYXjKU5/KueulG1dJmQSm9/uPk1gtrKi6pwij9T5td0KHRH0r2zVxTxSayY2eRRChDLFz00KqZOHpPDcIYkifhkBjG15vpDTp0VKwyXOFg2VxBXGvhAtpYPpbgeQsBvGT8d3lMPYeRJ0ggsDq3hq/g9zjDDH8R6uzZs/j1X/91vPrVr1bFJQob3+RZz3oWZmdn8YY3vAE/+qM/ir//+7/HF77wBbzjHe9Q93/P93wP3ve+96lC1XOf+1z82q/9Gu68886totMP/uAP4ud+7ueUtY/UU29+85vxfd/3fXuy4zG9hyS6tDu+7kaY64Sqs4ab7k60qxsGhOfDqreQXjuP+uzjt/Kg1O82bp85smTH6t9MaDB17k7GHB7r1xy064bqhlc0i3BuyqZjBgtZ8lQ2VyrQqFpchDpkCqaPhtVARyY464d4Um3v1+CzQdfKlQkdPm02DLArngYB1+LuirvBlEIVouZXcoRhDVdPF3HXE6ownXSw6qVN5dJNSqZbqpfQLS6p+7KukkcVl9AtLlGFhdz9VHCVMGDpBkwpYUipOnJKoSn7/2YeYJSkuLbWRjtsoLqSo9Oq4Ni9NbiF7hyFYUYRW9o4HsxizahirVFF2ojw8ekGnrdYgBjzIDA6tl2tg05uYGX+xhgZhmGGtAj1t3/7t0jTFL/xG7+hbtdz8uRJVaB605vehBe/+MW45557VKHp+PGu15YKTu9617vw9re/XX396U9/uvp303f+whe+EFevXlWFKMqC+pZv+Rb89E//9ED+TuZGVBFK1BBLDRe9EE/cw6JElK5XQ52C0V5XE0Mpxbb5HJt5UEKXnOPBHBpRR2D5qqcWLZs2PGa4oOOf1FBR28L6Woij945OA4pRtWw40kbbjnDaa+EJNZs0Irv++Y6e4apLgeQJ9EJhIFlQVICggiXZa23uircnbFPiSNnFwlqOdruGq6eKuOtJVZhkYemFeum6YtKWemnLKifU/bdXL2Uqx2VTvaRSbG6hXpL0n2bAkoZqV68KS0JXRSa67eX9QIW549MeVmsS1XYT7ayJK6eKqMy2MXW8yeciZmShrLwpp4J1IbEuVqFVQzxQaeKrV7yuGnyM8fQI1TRHrR53beOcO8swO6LlHGpwW5aW6hgV6lEDv/PgH6rduFFgoXkNea2Gu9Z1fNtieU8XqHhpCe16Cy2rDF0KZGGIkqfBnirf8vtT5PjAXVWEeYxkpoJSYbqHfwnTN2hBoGlblodhg4Z15WQJ7YaBOM5xVzAL22TFxDC+3s1OjMW1NlKriq94chWOv9E5kzkUWnEbS60llFY6+DcrxVtnAd7m9X6o0MYXS220tQTeXY9SRYB+06oZuHKqhDjJMescg++wunGv1JoRlmpNZFYdfiHF3U9ch6Zntz22t1UvRd3i0rbZS9hUL9FDaDeol+hjbUO9RJsFplIvGTCE2FAv6RDi8Bu90HnoWrWFWNQhzQxekODYo2owLLLzjRFDfu1megvpBZfbZM1bR7GZ4ZnVAI+td2NVxpXVloYT4VFkmsD3vul7oG/TqXtYmJkZjKqYYYYmE4o5OItrLfzdZy9jLfIwdawzEjtp1CVvxe1gtd3BqpliipJr96CGMhp1iLiDXHeg5ykM9/aBt2tWglTLQa4/a5vvY5iDUCUbXmPDhmcVuQA1xDimVIuiNBVoVi0uQh0ypISSwkBohjgZtHF3y9zVxgMtZs75YVfFapgDKUDdYMXLheqKx+ydgmcizXKsNHI0aw3MnSmgONO6qYPcw0UnytS7nXpJ1TK2US+pQhIMGJqEKU1lj6OMKiosSZ3sc3tTLx0W9F66aybAtapEs9NAPc0RPljB0XvqCCrcuZMZTejYmnGnMJfFaCYNfLbUQDESOBKO77mzZGd4bH4N/h3HRqIAxTDDABehxoCzV9dxdamF5bqP9ro9Ejtp1CVvNawqS94FL9xTEYqyoaQfqGyodmpCUFitdfuAcwq1JRJdQyB4B5s5HBveyvU2PI9teMMMLUJtUyCOTKyvSkzfOegRjf+iJDB9rHkRlqJQNYqY3sWCZNFO0JRZN5B8egqDYMuKl2ewdHsoihejSsk3VZF+vZNhfbWFZq24pYy5Xr2UK4/c7bKXzK56SXRv/VYv9Roa92zFRbUhsdpoopM3MXc2QKlmYuauOnfvZEbWmjfjTGM+TSGSFv51uo5vXijtOgN2FOcUM14Ge8od9FAYZmTgItQY8LTHzODM1XWcu2ainnV30o7c3d1JG9b5GF2gXOmg7US4ELbxtDUXYg+WvE01VJhE0CgPahv/dTcPKld5UPS4DNPzbngXAmQZdcMD7gpmeKE6Ari2RCs00WlJVUQ07f6GJU8avuGhKtcRGhpO+Z1dFaHOBh1Vo8ikjsAajIq1Q+pGpczJUXK4uHwQ6Lw4XbRVsb4e5YjTSBWXRkm9dBjQ31UOLDiWwOKaRJg2sHotR7tZweyj1mG5fG5iRg9DGJh2prCUpRBrHXx8uo7nXhv/oHKGYXYHF6HGZDH1nV/3KJy5so5qs4VO1sT8uUDZTI7cU4eQ+dAGlDesJlqygzk3xl0tc29qqGIR9noDulPa1s5Bu+5ZngIe71AwvWdt0e0uVJMMJavENrwRgcLJlSUsI0ueCfNYe9BDGmtoA8A3fDTcGBejNu4TLrz09jIP6pw658TdQPJicWAFCFJBEVom4HFXvANDr+ORsoNKYqusICo+jZp66TBD3O+cCbC8bqDeaaCZtXHpRBkzdzZRnGkP7aYiw9wO13BQdEqopasQ6yE+U27hmavu2AeVMwyzMywLGRNol7DkW7hjqgg7LSEJDVRXDFx6sKJCVYcRW1iQulQ74+e9vecfyEoFhXvuhDtVuO33rBspIj1HogOGxyF8TG8J2wIrc10bHnVPmvZu/15khs8GYxkCeWpgfW04z5HjRsH0kRsS5L4+E2x/zj9PWVDUiEJqcEoVDMyKt2apHCLqjEbXWaY3hSjLFDCkzgWom6Ci3JGSjSOFIvSwgLCjYeGih/mzxdt2+WOYYaZoFmA7PuqewBmvpXL+GIZhuAg1ZlDOyZ3TPkpmCXnHQ6upqfa/S1c8ZNnwTUQ96aLtSly1OqoV915/XpNy2x1ysuIRqdBgS+fAY2aY6xeoixcKyDOoxcEx/8jY2kXGWUUqUksV6tOYX7vDhjYdaGe85QiccVtItFurdCkbSBWh8gySupbpgykShi2JONKRphqKJlvxmP5A15GCa+Ku6QBOXt7YVJS4SJuKdS6YMyNow3WmkHsumibwQKmBZSse9LAYhhkwXIQa14C8koPZUhEyLiIKBZbnbVw+UVbKjWHLCYGknfEcl7yo579/2U7UbjpZQQQnfDI9ZG3BRacpkSQ5ytQNz2B38yha8ujckJMlr3b75gZM7wiMALFtqMBxakpxKxacWNnxYmQQU9MYFFtd8TIB3+amFkx/MQ2BO6Y9VKwyEHpo06biySKWr3pqE4RhRi2ovFOw0ZIZ/mW6gbYYsp1xhmH6ChehxpjN9r+BKCNrO2jWBC49VEZ10RmaCQwFF5rCRGjqOOf2NpOF8qBICZVlKUkeevq7mckmbAmszG/Y8HQDU2z1HEnIDkS2vDwVqLMlry9YwlS3jqXjpN9W5+mbObthxaNAcsfyBtoVj6x4FJLNVjxmEOgbYe4PbyrqWJ5zcOVkCXHIU3hmdKBulqSIqhct1ESkgsrTW5z/GYaZDPgKNubQAutY2cGRoAQtDLr5Apc8zJ0pqo4/w6KGCh0DK2aMNaNrn+sFtJNOt1TLITirh+kRZL9b2LLhAbMed8MbVeh1I0uenlqorRlDZ1ke1+c8MAOEnok1I8aCfaMtoylSpYSiQHKtUBjYsRW1heqaqKx4BlvxmOHYVPS1EtKOhXpVKHtefUOtxzCjgGu4KDglrBdMLJghPltpDXpIDMMMiOGoQjCHCk3iix7lCxTg5mWkoYn1VYmLX66gUR28xYAyQqDrKoD2ot87S96S3S1oJZQHZbISiukNqwuuyoqhbnhlqwyLbXijb8mDRJYItOuDPx9OApQFKKgphanhZKFzw33nrgsk9wqDCSQnGmsb14xMh+/wQp8Zjk3F2SkP0253U7HT0TB3JsDiRR8k+GaYUaBkFmA51ClV4DQHlTPMxMJFqAnLFzhO+QJ2Cej46LSBq2cKWLwYDHQCIzShClFtW8d5p6VCaXvBZii5puuQGhcKmIPTaUmsbtnwTEx5/qCHxPSgmQN1pEImhqIoPzlqKB9tT2LO7GB9QwHbDSSPlAVOym731EFxvRVPvT8YZkiOnXJAnZALsJIi4khiddHGpRMVZRNnmFEJKs/8blD5p0t1rJi9c0EwDDMacBFqAvMFpgo27qgUYSaUKUATGAuXHqqokOVB4RmeCqttyExZMXrBZh6UZppsl2IODNnvFi8EKiuGuuGxDW88oNfQMSWQmFhfk0OTlzfudJtSGAglcCroqqGuOjE6FEiuZRDTUwMbG9nwqIkHWfEKkq14zPBhmxJ3zgQoyhKyjoNmXcelE2VUrw1P5ifD3A5xc1D5TB3tPXbIZhhmtOEi1ITiWJsTmDJymsA0aAJTwsq8O5AJjCNspYiKDA3nb9MxaS+Eeoaakao8KL3AodHMwSEFVNeGl6tuRWzDGx8oF0rAQNyR6jVmDh/qVkq2vJYrcd5po6NnOOt3utcfocMx3YF3xSN1XMBWPGZIIYXekZKDo0EReljoZn5e9DB/tqg2Shhm2IPKpzaCytf1GP860+iZE4JhmOGHi1CY9AmMjaOFEkRUQNTRsXTFHUjXFVIjeIaLtiNxxeqoIlIvrHjdPKjBLWaY8YBUgpQFRTY8QzdQ8QbTsYs5vKK8hq4lr7nOlrx+QZa81JJoyxxfKLWxaG8EkhcHF0i+WYTK6VgXQuXwMMywQsdJQWV+BnDyMpLQQHVF4tKDFbTq3PGTGW5o3l+wi6gVDcybHXyuzEHlDDMp8OxqwlHZHK6hQsu9za4r692uK7UVu6+qKLJn5IZER+a47EY9KULlmqaKBgyzX6hj2iJ1w9uw4R1jG95YFuTJ3oLEUE0bmP5gChOOtNGxBc67bfU1CiR3g8EFktMGDGW/pWmOguSuqszoZH7eQZmfVhkIPbSaGq6cLGLlqsf2PGaoKVlFmBtB5Sf9Vk/cEAzDDD9chGIUhtRxfLPrSqfbdWX+nI+Fc4W+ybppQULy3NDUcc67sWPSXlm2E+R5Bt0wuGDAHNyG1xaIyYZnsw1vnC15emah3TD6rgSdZAIzQOQYiLQMWZZBmAMOJN+w4uWZQMHlrqrMaGV+ThdtzJaKkHERUahjac4ZiLqdYfYWVF5B5jtoUVB5uYFVDipnmLGHr0rMLbquFGGnNGkhWbehVFGtmtG3gPLQNbBkRirTaT8kWo41M0GaZ9AC7l7GHMyGt7bgqveSRTY8l21444prSbWI61ryOAeon3mAcmPzIUIKOT090PFQVzy24jGjjGcbuGsmeIS6vb6ZdcYwQwZlwlJQeatgoylS/Mt0Q+UEMgwzvvAMi7lly/I7p32UzRLyjoc2ybpPFbF02VPWpMOEgmqh64iFhgv7lOTSDgoNMxGAZXPRgNkf9F5fON+14WWJjmM+2/DGXQ1qSIE8laivsYW3X9AxVbQKaHsGctOAYzgDG0sS6UoJl2Y5AoOteMzoQgXUG9XtwNyZAIsXfWT7299jmEN3Q0yroHITVRFyUDnDjDlchGJuia5rmCk518m6BZYXbFw+UVbWpMPsmEQZIW1bxzmnjXwfF6AluyvjzXQdpuCdP2Z/rMx5qlU72fCm7ApMyTa8ibDkpaQcMJCmXHDsZx7gbGEWxbseNdhA8up1Vjx7cMUwhumluv14pQArKSGOJFYXLVw+UTnUeRzDHCio3CmhVjBVUPnny92sQIZhxg8uQjG7knUXRBlZ20GzJnDpoTKqi86hhV3SgiSxDdRlisWNgtJeQ8kpD0oI0bXXMMweaTckqoubNjwTZZc7LE4CniWVLSBPdbS4S17fd8GFNtgpCdmVaOND6qSK4+kRMz7dP++cCVCUJWQdB4263p3HXetv8xmG2XVQueuh7gqc8Jv7dkUwDDPc8CyL2ZWs+2jZwZGgBC0MEHY0LFzyMHemqOwLvcaRDnRdIDL3bskj6e6K1c2Dyn224jF7h6wKm93wspRseNNsw5sQTENXnfKQSjSqXISaJJJYQ4eseGkOXwSDHg7D9BQ6rx0pOTgalKCHhe487qKP+bP9az7DMLsOKren/v/t3QmQXFd5Pvzn7vd23957NNJItmUMBhuMMXZs1irsPx8QTIBAgAAVAiaEYEQqBaGIgQB2AFfMliqWJGxlk5BAzFaEJAUhQApjsIPBNsaWLFmWNKNZNHvvd+2vzumZsUZetE3P7eX5ubo00z0jnXFP9733Ped9DiIRVG50gspFzisRDRYWoejEczvSJs4oZ5FqFxC1TLmVuQi73OiLNfFviSW5TUfHQbspd006UUtmJIPJIxUw07yQoJM3P+muteGV2YY3VMR7j2jJQ2SisqhzlcAQqctA8k4rXo4rH2lA39+y8jwuA6ddQLiy+cwhsflMlTl41DtENIcIKm/mbNS0ELeUa/AYVE40UFiEopNiGhrGymkUnTzQctFsAof3ZjFzILOhYZdil7zY0ODpbUykg5NqxRNCTYHFPCg6Sc2qgaUjjgwmFr8/eYcXo8MmZRnQYCD0NRlSTcOhttKKpykKTJ15OTTY53HbxXmcVQA8Fw2x+cyeHOYnUyy8U8+wNBNFp4hqzuoElZcZVE40SFiEopMmcpZKWRvbi7mHwi6PWDh0X1Fuab8RTNWQN89Q8WCqdVKh5GJ7bV3VEs8XoT5swzuY6ayGiBSMcTe8oWRbK1lysSZXx9DgE+1IjaopW/HSOlfQ0uAT73HlnI1tuezK5jMqZg+nMHF/HoHHcyfqDSIjNuPkUM2amLRa+E2eQeVEg4JHGtqAsMsC2i0H9ZqKQ7vzGzKb1mnJS6OVMjBjeqjpx19mJWax56wAcTtCnOLORnRy5g4f1YbnlGQWGg3nxZljabIlb5kteUPXipd3mCVIwyPtGNhRziCt5BG1LFSXNBy6tyBD+ol6QcHKQ3fSqDka7svUcSjFoHKiQcCrLNqAsEsbo9k8ND8Lv7Uym7bn9GfTRC4UNBW+LgLK/eN+fU2P4altiJ3VDeZB0UlorLXhxbA1CzmHRcxhlhK75LVNeA1dFiZpsNVWVryJAqTJXfFoyIidIMdKaZRTImYhg2ZLweS+DI4cdBEzhocSJialR5wSwoyDugHcXqxhyWBQOVG/49kWbcgBIpMyZGj52mzasiZDyytzp74FsK7qsHUbLUu05DXkSqfjteIJoa7ANuxT+0dpeHfDE6vpIpVteCSLUArYkjcM4khBvWIijGKkdZevfRpK4ve+kLGwvZhdi1mYn7Ewfl8RXpOFeOqxoPIRBpUT9TsWoagrs2lKK4NWU8HUgy6m95/6FsCukYLvGFjWI8yuhI4/GtGKJwoJKlRZwCI6EXMTrly1F6604Wlswxt64nfAMjW0Ix2VRYaTD7L6sol2LFrxVORtN+nhEPVIzEIecctBrari0H0FLB059QlFoo0OKl/UfPyiXD/u5DQR9S5ebVF3ZtNKOdixaMkzsDhnyFVRjcrJX8w5ugNVVeEbCh5Me8fdGa8dh4DDVVB0YsTv5NJspw3P0Wy24dH6lrzIQqOqIwy4OmaQd8UTVEWFZfCUiKgTs+BgNJOH6mXhtRRMH3QxdRoTikQbG1Ru4LDVZFA5UR/jGRd1hW1q2FF2UTDzgJdGs65g/P48ZsfTJ5UxIC4M0noKTUfHIaeJUHnkWY+mFstMKHF+pGWyG/eD0MCKImWlDa8tA4m3uWW24tCalK3L9x/xu1FfZkveoLbiipVQYRwjpaX4+idaIV4L2bSJM8oZOO0CQs/A8pyJQ/cW0axydSj1QlC5inszDYynjp8ZS0S9h0Uo6hpVVTCSd7Atn5NbAIuWp7lpG+O7CyeVMSB2yYtMDU2tjYlHOdistupFmgKLeVB0AubGXQS+aMMDRmy24dF6hqbKHRLbkYbqopn0cKgLGhUTcazILLgcW/GIHsY0NGwvpVG08mh7KTTEhOKe3Ibsgkx0ekHlKdSNGLcXq1hmUDlR3+FVF3Vd2jZwxkgGWa2AuOmgXtE6GQMzzgmdxIg+cEM14JsqDqRaj5oHJakKDOZB0XGI1Q/Lc/ZDbXgptuHRw0905WqoSGxbrnOXqEHeFQ8qHJPHDaJHm1As5xxszXUmFH1vZRfk+0WAOS8jKKmg8hIa2YeCyn0GlRP1FR49aFOIFQWjBQdbMnkoXqaTMXAojcl9OYTHOYkRF4NpI4VmSsek5aGhPfxAM2eHiOMIimGypYIek8i0mDmYWWnDU7EtM5L0kKiXc6GgIw41uWqGBocoKooilGjFE9mDPG4QPTbXMbCjnHloF+QlDYd+W0CNK0UpAZZmyaDyCoPKifoSi1C0acRJfk5mDGSRahcQtQwsL+gytPx4JzEijBCahkBXcOCYgHJfibFsRIiUNtRspss/BfW72XFXFj7DANhil2UIK9GjZduJVQAQuVBLvNAaJM2qiTgSrXgKchZb8YhOdhdktDJothQc3pfFkYMuV4vSpsuYLlwni0pGx4TVxG9zDCon6hcsQlEyGQPlNIpOAWi5aDYhT2JmDmRkUOwj0VUdlm6hZSl4MNVcN9sxb0Xys0hVYFmpzftBqO/UlkxU5lfa8AwbWbbh0fFa8iwdCE0sLxjMQBnAXfEUaEhZDFomOuldkItZWKFoydMxP2Nh/L6Ty/sk2ghFqwA91QkqvyfbwITDoHKifsAiFCV2ElPK2thezK2dxCwcMXHoviKaNf1RV0P5jolFI8S8GT0sD0qEkpsqVyvQo7fhHTnUacMTK1u2uWzDoxNtyTMR+Bq8BnODBoEoJopWPFGMtjWbrXhEp8CxdOwYcZHT8ohbDurVTt7n8qzNgj1tGvH+XXbKCFwHDT3GbaUqKsajzGgTUc9gEYp64CQmg7xeQNxKoV5TMb4n/4g7r6REboeqrrTkPRRQPmuHaLdj6JrBiwk6sTY8h214dOLvUfJtJdLWgqypv4kt5kVRWrTj5S22cBOdKk1VsaXgYPTovM8DLqb2Z+VrjGgz6DKovIx6zkZNDXFLuSqjOoiod7EIRYkTxYCRvI2t2Tw0Pwu/tbLzyp48Au+hX1FVUWUhquloOOA0ZQaUaMRbMENE7RjtTDrRn4N6l8gcW23DSxkpZBw76SFRnxCZUHLntMhAZYEroQatFU/s3kpEp05M/mXTJnaUs3Bicd5mYHnOwKF7i7LgS7QZbH0lqDxvYUH3cTuDyol6GotQ1DMnMZmUIUPLj955RYSWV+YeWtqdNtIILR1NvY1JJ5AFqFiRixRgOQyXpYeLAgUzR7XhbXVLSQ+J+rAlT40tNOv6usI49XErXjuGpXI3VaKNYsm8TxcFK4+2l0ajoWB8T+4RV7YTdYOI7XDtTlD5IbuFe3MPdU0QUW/h2TT17M4rYml3q6lg6kEX0ytLu23NkiHlniECyj3MWqH8vlCEkmvMg6KHOzKeQRSoCGQb3gjb8OikpWwdqihWxGzJ63etmoEwUDuteHYu6eEQDdzK0ZGcg625HHQ/B99TMTuRksWoRpWbO9AmhObbBWgyqFzBb7J1HGZQOVFPYhGKenbnlR2lHOyVpd2Lc4ZcFSW21RaroZopHYetJiZSPtrttuwHF+16REerLlqoLlgI4xiubMNjAYFOnq6pclfPdqSjusj2kn62WkRUYg1p7opH1BWuY2BHObO2sr2yqGNid16exy0dcRBFnAyi7hATRiNOCYFrrwSV11DVGVRO1Gt41U49yzI17Ci7KFp5wEujWVcwfn8O3uwI2roOXwcWRTteHCF2mQdF64WBgiMHM7JIKS442YZHp92SF1moVXQG7vZzK96ihVi04umGXLVBRF1e2Z4WeZ8Z2cpcryqYOZjGg3eXcOSgC6+hJT1MGkCiY2I1qLyqBrhlpIpA4TI8ol7CIhT1NHGRUM452JbPQQ9yCDwNi1MZeEtZNAwdcbuNUAXMFPOgaP3F5pFDGVksCENgNDXCC0467ZY8TemshmpU2Prbj7yGjsBX5SqMnMlWPKLNWtl+9mgBW90R2FERQcuUu+jNz1hyZdT47rxcsRxzMzPaQLZuo+AUUM1ZmJdB5TUGlRP1EG71Q31B7GB0xkgGc8sGqs0a4qUClgtNaO0mVBFKHqcQ+ioUMdOhihOfttxWnZmzw0msdhC3MIqRNly4Ntvw6PSYugpNUxFEGqpLJjJFL+kh0anuihdrcG0WEok2i5gEyqRMuLYBL0ih0vBQaTUQ6B7CZQ2NWga64SJXbiFXbsKwWJGi05cxXPhOgGq4hINooZDVcX7FSXpYRMQiFPVbLstowYHT0DG7pCLOTmMZFhTPwtI9Wx7xe5SjC1LqamGqfczHRxetHuMxef8xf5faFvt8y/vVoz4+7vexONY1InRYrIKSbXht0YZXTHpINCAz+qIlz2uYqCzo2Laz8/5C/bUrnmjFM3WTKyOJEnoftU0NtplCKXZQa/hYrDfhK02EQYBg0sbidArpnI/cSBOprM/zJTqt37eiXcBMHKAeVnE36ij4Gra1OAlBlDQWoajvDii5tAnHLGCyvgNB6hDQLEAJVs9SVpbayk+PXnjbfuhueUaz8vVHndx0HnvoLmXta9Y+2rif4zSLY6rWhpUO4bg+dIPLi9e14R3stOEFYRvbXbbh0ca25FUaBqJAQ7NmIJUNkh4SnSC/qcFvaYiiNkoWW/GIkiZ2qs25FrJpE03fxXLdQ61ZR6T7COZ11JZyMO1IrozKllvQdJ7r0KkHlU/FARphAz8v1fCCmRzckHlkREliEYr6ktip6kycj5a3E23VAkQuebtTahIrYNrHfCxmv0VJSmQOtBF3PpePtWWulPxY6fyJlT/X/bd67rP650qRa40oEuFRHjuB4ph86CSLY52vhzxJc9wAthvIPw0rGtqZQ5ErUVsyZRueWIadttiGRxvHMcUunAqiWFwgmSxC9ZHaot35IFbhcpdMop5bZSpuQeig2gyw1KjDV1tyMslrpjE36SJTaCG/pQkrFQ7tOQ6dXlD5THwE+kITt4zU8PzpLPQ2f5GIksIiFPUtVVHh6hnE2krFaZN0ClSdVTc46uP2I3wsH3+04pi4HoqPLY6Jr+kUyOTHsgC28qciv2jl61c+V2MEoYJmw4Q217nI0o14rSAlbsNywiYywWbHO214alvHKNvwqAsXS464UGoZWF4wMHIGW2v7xUOteJZcgUFEvbmjXjFjoeCaqLdCLNdbaAQNQPexOGuiMm/DToeyVU8UpUQmKNHJBJUv5WKoSx5uL9XxzLn0Q10PRLSpWIQiOoUL0dXVSeuWLG0iUWgJwhgtP0LDD9DwRKZCAEWNZFGq5RmoLpry4Cpa9+z0Q0Up8fGgnbiJ4t7MShteyDY86qK0raPeMuG3dNniZaWipIdExyHa8LxmpxWvwF3xiPriPMt1DHmTQeZ1EWTeRKCJ1VEqmnUXcxMusqWWLEiJFeFEJxZU7qMaLuMAmihkNZzHoHKiRLAIRdSnJ2iiJVHcRJ6C6EcURSkviND0AtR9D37bg6JFgBLB93XUl41OAU2BXB31UAtf/+dKVedt1Jc7bXiukUHKYugkdYdYCSXL0LGG2rIFK9VIekh0grviiecsw1Y8or5iGRpG8ikUYwd12arXghc0EOoB/GkLizOODDDPjzSRzjPInE40qLy2ElSuY2vLSHpoREOHRSiiAVrGLm5i5nAEKURxG54foeWHsijV8luAGslbEKho1CyoSmcGSMwiHt3C10+5UoFow5twO2140DCaKSQ9JBpgopVL7O5UDXRUFgyUtiU9IjqRIpR4fzA0Te6ySkT9+d4rJt0yKQMtP4VKw0elWUese6gs6mhUctDNWAaZ58ot+THRI0V5iHyoqShcCSqv4v9jUDnRpmMRimiAT9jEbl7iVoSNuJ2FH4iiVISG56PptRAowbpcqcpKrpRmxGsFqV7OlTp6N7xOG94WGRxN1E0iQLfhW2hWWzKLjBc7vSvwVLQaOqIoRtHMJj0cItqgbD5xK0U2qg2xOqqJQGkiCAP4hx0sTKXh5j3ktjTlOQxPC+jYoPJyqowj7SPQFpr4WbmG/zfDoHKizcQiFNGQEMUZ29TlLe+KlQHuMblSLfiKv5Yr5T0sV0q08Pk9lStVOaoNL8M2PNokKduAWvUQtjX5+5cbaSU9JDpOK1471pB1VnbII6KBIFY2FjIW8q6JhpfGct1DvdlAZPhYnBPnMBZMJ5KteplSC5rYyIZItNaLoHI7vxZU/stSHZcxqJxo0/TMunTf9/GSl7wEt91229p94+PjeOMb34inPe1pePGLX4xbbrll3ffceuut8nsuvPBCvOENb5Bff7Qbb7wRz33uc3HRRRfhve99L5rN5qb9PET9kisllrdvLaTxuNESHlcaxbb0KPLaCHQ/i6BpynY3r6Wgsqhh7nAKE/fn8cCdIzh0XwGz4648yQsDJZEVDuLf77Th6djCNjza5NbXdqihusgsiV7fFY+teESDfz6Ttg2MlVzsLJdQNMpQ/Iw8T6hXFcwcTOPBu0uYOejCa/TADBr1hIyZgeNkUHV1POg0cX/GS3pIREOjJ87IPM/DO9/5Tuzdu3ftPnHS+Pa3vx3lchnf/OY38bKXvQy7du3C5OSkfFz8KR5/xStegW984xsoFou4+uqr5fcJ3//+9/GZz3wG1113HW666Sbcdddd+NjHPpbYz0jUD8RFmsyUyjnYuaWAc0ZGsd0dRckYgRXlEbYsBJ4G3wdqyyrmpy1MPZDF/rvKOHBPEdMHMlies+VuVCsvxa7uhhdHog1PwbbUCNvwaNNb8tTYRnXZQMyNmXqSaJVs1gyZj5cx2IpHNAwMXUMpa+PskQK2pkdgR0UELVNOpi3MWDh4bxHju/OozFuI2UmNYS9eluwClHQKNUvBnfkaZuwg6WERDYXE2/H27duHd73rXWvFo1W/+MUv5Mqmr33ta0ilUjjnnHPw85//XBak3vGOd+Dmm2/GU57yFFx11VXy66+//no8+9nPxu23347LLrsMX/nKV/DHf/zHuPzyy+Xj1157Ld785jfj3e9+NxyH23ESnX6uVICm1zzxXCknhLJBZW9R6GpUOm14WTMLh214tMnEa2K5riEMNfm76Bb8pIdEj9WKZ/O4TzRMVFVBJmXKiTUvEEHmHiqtBgLdQ7isoVHLQJ9wZYi5CDM3LFakhjeovLQWVH5rqYoXTOeQjrhijmigV0KtFo2+/vWvr7tfrFw6//zzZQFq1cUXX4w777xz7fFLLrlk7TFRWHryk58sH4+iCL/5zW/WPS5a+oIgwO7duzfl5yIa5FwpkSkllr0/bqSMs/KjGLW3IKuUoXgu/JaOwFfQaipYmjdwZDwtW/ceuLMsW/nmJ1NoVE599YhYXj+3shueJtrw3PxG/5hEJ7RtuLjIQayhtsQiaC+qilY8tKGrnfZJIhrO1S5iR9Mt+RTO3lLCFrsM3c8jaBloNRTMTdo4cE8Jk/tyMuOvm6u4qTcZqoGRVAm1vIWqFuBnIzWECn8RiAZ6JdTrXve6R7x/dnYWW7ZsWXdfqVTC9PT0cR+vVCqyxe/ox3VdRz6fX/t+Itq4XKnVbCkgLVcniZVSTT9E3W8haHuAGgFKDD8QQc4p+X2ie07suieDzldWS+lGfPw2vAPZlTa8NnZkRuTfRbTZxO+daMnzmxYqix5Gd4r7kh4VrRI5dS3RihfFyGlsxSOizurunGvJ85Wm3wkyrzXriHQfwbyO2lIOhtUJMs+WW9B0FiKGhaM7yNsFLOfmZVD5HcU6Lp1nUDnRwBahHo0IETfN9bPL4nMRYH68x1utzk5Fj/X9J6pfLirWxtkn491ww/pz9yBdV+GKW8rACByZx+L5ooUvRN3z0PJbnaKU2IUvUNGoWVAVRz6HphWtFaScTCBPBld/t8UfS0dsNKuGLHTlrBzb8IZFj76+U46OatNA4GtoNXQ4bpj0kPrW0Yewjdglu74sVkF1WvFymc77C/UgPi/DRemhSYSVqIEgdFBtBFhqNOCrTQRhG34rjblJF5liSxakrHTYN9cDg/ZevpmyVgZ+7KMaLmM/migEOs6tnsSuqkr/XDcSJa1ni1CWZWFpaWndfaKAZNv22uPHFpTE59lsVj62+vmxj59MHpRpan0VxCjydoYqnFm82UOB6IiRVxvUk1RNgeF0As/LcBC32zJXqumJXCkfDZkrJfKiIoSBimazkyslThI1PZbFKFGUMu1ItuHFcRuaYmA0k+cqqEHWB6/vtGVAVVqIYh2NZRvpTD3pIfW11dfzRryq64u2/L0RrXiihZh6SB+8tml4nm/RWm3lRJi5hVozg6V6C41WHdADLB4xUZ23YadD5Edasiil9s+lwUC8l28qRZFteVNxiHpQw525GoqBji3eie2Cq2kqDIO/IEQnomfPzEZHR2Vo+dHm5ubWWuzE4+LzYx8/77zzZNudKESJz0WguRCGoSxqjYyMnPAYfP+hVRi9LggjtGMgHqYe5rYoukEWNai/rLbw5VyRv5BGEK628AWoey348GVRCgHQ8nRUFsTFfufFKHbDOyM70lnlwOd+cPXD61sBbEtD4OlYmtNQ2t7DY+1xq4da8Zo+3f+LUaigvrJiMqvlevt3aBj1w2ubhvL5Tjs60o4LL3BQrQdYbtbha62VVdtp6ONp2aaXG2nKiTHq7nt5MhSURVB5HEJbaOBnxQqeP3NiQeWi/TsI+HtB1NdFqAsvvBCf//znZWvd6uqnO+64Q4aTrz4uPl8l2vPuvfde7Nq1C6qq4oILLpCPi9BzQQSWi1yoJz3pSSc1jj44Zq4fZ5+Md0McXSAcpp97wIgZUlPX5C2beuxcqbYSIW9mYRsGn/NB1yevb5EL1WhZaDWa8JsaL0xOkWjbWFsocZrPd33RkpMyshXPTff0789Q6pPXNg3v823pndVRhYyFelO06rXgBQ2Eug9/ysLitINU1peteum83zcT1v32Xp5kULkoRM3mImiLLdxaruGKmSy04/UXtvvnupEoaT1bhLr00kuxbds2XHPNNbj66qvx4x//GHfffTeuv/56+fgrX/lKfOlLX5KFqssvvxyf/exnsWPHjrWikwg8/8AHPoBzzz1Xrp760Ic+hFe/+tUn1Y5HRMnQNdG+12nhW8uVCiJ5YiNWnhD1UhFKBpfGGupLJsytzaSHNPRqS52WfLF60uSueER0GkHmIsQ8kzLQ8lOoNHxUm3UEuofKoo5GJQfdjJErN5Ert+THNBhSIqjcKWA5moe2LILKG/id+RSDyokGvQilaRo+97nP4X3vex9e8YpX4KyzzpKFprGxMfm4KDh9+tOfxkc/+lF5/0UXXST/XO1DvvLKK3H48GFZiBJZUC94wQvw7ne/O+GfiohO9URQBIiKi0q5pJ8zTdRDBVPRWhpEOqpLBgosQiVK7JxZr5hyNaWrZ5kbR0SnTbyPOJYub6XIXgkyF3mWIsg8gH/YwcJUGm7eQ25LU+ZY8q2n/2XNDDynE1T+ABooehoeXzuJoHIielRKm6Eqj2p2top+IWZnvvjv98qL9aGhdGa6WZQYEny+h0sfPd+LNQ/zlSbgLOOJFy9wW+8En+/qgoWp/VkEAXBGZgx2H20wMjT66LVNG2BAn29x+dTwQizXPdSDBmB0sizlxIQTyVY9EWQ+dMeDAXu+43aM6foM9KUasr6Cy4/kMPIoQeX2OY9H6crfQz8YGckkPQQaclynTkREdBrSlg5NUWUGUX1Z5JpRUmqLq614KiyDpzhE1L3VUWnbwFjJxc5yCUWjDMXPIPBU1KsKZg6mceCeEhqVE9tZjXqTOJaMOGW0sg7qWoxby1U0NLZdEp0unqERERGdBkNX5ew3Ig21JRahkhJHkEXAMI6R0lJsxSOiTWHoGkpZG2ePFLA1PQI7KiJomWg1gYm9OR4X+pyhdYLKazkLy1ogC1HRICzzIkoQi1BERESnQRQ7RGaZEllYXjQQc5I0EY2KiThW0I5U5Gw36eEQ0ZBRVQWZlIkdZRc7cmXofg6+p2ByXxaVeWYJ9bOU4SDn5FHJmpgxPfyq2ECbhSiiU8YiFBER0QbskqdCRxxoaFY5653ornhQ4Zg9u+8KEQ3JxMS2kgszFIUoFVP7XSzNcIfufpYzs7AdF9W0hn3pBva7XtJDIupbLEIRERGdJhGALTeGiNmSlwSx+kwUoUQrnqM7bMUjop44LoyV0rDiLAJfw/ShNOYnU+CWUP1JHFdEW147nULdBO7I1zBnBUkPi6gvsQhFRES0EVt4i9U3oYnlRZ0XGZtMrD6LI9GKpyBnsRWPiHqDaYhClAu7nUXo6Zg9nMLsuMtjRN8Hldto6DF+Vq6hyaByopPGIhQREdEGEO0XGgwELR1ek+1gSeyKp0BDyuJuVETUW5tXiBVRjiIKUQYWpi3MHMigzdpFXzJXgsqrMqjcl0HlMfOhiE4Ki1BEREQbwLF0KOi05NXZkrdpxIoC0YoXiVY8zWYrHhH1HLGDqihEuWoWkWdhcdbC1P4cN7LoUykjhexKUPm06eHXxUbSQyLqKyxCERERbQCRCWWbOtqhgeUFrsbZLM2qgShUZDtezsokPRwiokc9RmwtpuAaWcSejaV5HZN784giFs77Ud7MwhJB5SkNv0l7uDNeTHpIRH2DRSgiIqKNbMmLLTRrOgKfh9jNbsVL2yz+EVHvUkUhquAga2YAL4XKkobD9+cRBSxE9Rux6rZkllGPczgCBz8IZxBGUdLDIuoLPEMmIiLaIClLhyrawdiSt7mteO0YlmqyFY+Iep54n9qSt5GzXcBLo7asYXxPgRMXfcZvapjYXUJ85GyEsKFGmc7xn4iOi+92REREGxhAK26IdFRZhOq6Vs1AGKiyFS9v55IeDhHRCReiyjkbhVSnEFWvqRjfnYff0pIeGp2A2qKJQ7sL8vkKWxZSCxfgWVtfCFXlpTXRieArhYiIaAOlbANKZKG6ZDDro8vEKihBiTWkuSseEfVZIaqYsVDOuFA8F81GpxDVanB31V5efTt3OI3JB3KIQiAI2iiaeWzPjMhWSyI6MSxCERERbXBLnqZoaEcqGhWuhupqK96ihVi04ukGLwCIqC8LUXnXwpasC9XLoNlUMLEnJzdcoN4iNsCY3JfDwlQKcbstJ5nG3FGU3CxbwYlOEotQREREG8gyVLkLkmjJE0v2qTu8Rif8XVwI5Ey24hFR/8qmTYzmXWh+Fq2Wion7c6gv8/jRS8ebQ/cV5HMSRjHUtoEzs9uQtjqrcYno5LAIRUREtIHEjKjYJQ+RicqiLlfsUBd3xYs1uDYv1oiov7mOgW0FF7qfhecpOLwvi+oCixxJq8xbsk0y8DQEYRspLY2zclth6mybJDpVLEIRERFtsJRlQIOB0NfQrLGtolu74olWPHEhwFY8IhoEYgJjrJSGEebgt1RM7s9gadZOelhDqR0Ds+Muph/MIopE/hNQskoYy5bZfkd0mliEIiIi2mC2pXW2ao511LlLXle2xha7EslWPIOteEQ0OGyzU4iy4qxcfTN9wJU5RFxVu3nCQMHE3jwWZxxEcYw4UrEjM4piOp300IgGAotQREREG0wUoBxLAyIDywtsydtotcWVlQGxCtdhuwoRDRbL0GQhyo6zCD0dRyZSclc2Hku6r1kT+U9FGQ4v8p90mNiZG4NjckKJaKOwCEVERNStXfLaJrymIVft0MY5uhVPhsATEQ0YQ9cwVk7DQQahZ2BuysGRgxkWorpE/H9dnrUxcX8Boa8iCNpwtQzOzG2FpvGSmWgj8RVFRETUpSKUAtGSp6K+xNU6G0UU9LxmpxUvq7MVj4gGl66pckVUWs0ibplYOGJian8WcZz0yAaL+P8pCnwzBzOIorbMf9qSKmNrtsj8J6IuYBGKiIioC8TMqWipaEcGKkvcRWejd8VDrCHDVjwiGoJjybZiCq6eQezZWJ43MbkvhzhKemSDIfBVTOwpYHnOlvlP4thyRnYbck4q6aERDSwWoYiIiLq405EWWWhUDBl0ShtThGq32zA0Ta4SICIadGIH0NFiClkjg7hlo7Koy+DsKORx5XSIY/Oh+wpo1XWEYRuGYuOs3BhsgxNHRN3EszciIqIuFqFURUU71lBf5qqd0xV4KloNXbZLZI1s0sMhItrUDS+2FBzk7QzgpVFb0jCxJy/zi+jk85/EzneH9+blBJHIf8oZWZyR3cKcQaJNwHctIiKiLjE0Va7WaUcaqotG0sMZmFY8UdTLOis75BERDQmRT1TO2Sg4LuC5qFc1jO/JywI9nRjRxjj9YBaz4y6iuC2LUFtTWzCSyTP/iWiT8B2LiIioS8QJrVwNFVmoLhkMk92AXfHYikdEw35cKWYtlNxOIapRVzG+uyA3bKDjb2wh/l9VF6y1/Kczs9uQ4aQG0abiGRwREVGXd8nToCMONTQqZtLD6Vui5aRZM+TMdYateEQ05IWoQsbCSNaF4mXQbCiY2J1Hs8Yso0dTWzJXinWd/CdbdbAzNwaL+U9Em45FKCIioi6yTU2GyooZ1/oSi1Ab0opnO0kPh4gocbm0idGcC9XPotlSMHF/XoZt0/r8p/nJFKYeyCEM0cl/MnMYy4x0js1EtOlYhCIiIup2S56lA6GJ5UVdnhDTyauKVjy0oasqDJ2nL0REQiZlYGshDd3PwROFqL05VFeK9sNO7B4oik/zk2m5ilZ8PpYexYibY/4TUYJ4FkdERNRljmzJMxB4OrwGl/6fLBEc2xKteFEbrsZWPCKio6VtA9uKaRhBFn5LxeQDGSzPDXfOkcjIEu13og0vjGKobV3mP6VtFuiIksYiFBERUZeJlVBy1jXSZLg2nZy6DCTvtOLlUmzFIyJ6pMmOsZILM8rK3fKmH3SxODOc75cieFwUoEQQeRC2kdLSODO3DabOSSCiXsAiFBERUZeJ3AnH1IHIQGWBJ8GnkgclW/EUBabOHaCIiB6JZWoYK6dhxTkEvoaZQ2nMHU4PTRu4+DlnJ9KY2p9FtJL/VLSKGMuWobL9jqhnsAhFRES0Sauh1NhCs67LWWo6MSLDo1E1ZSteWs8kPRwiop4mCvVjpTTsdhahZ2Bu0sHsIXfgC1FRoODw3jwWp1OI2jHiSMUOdytKaTfpoRHRMXgWTEREtAlStt6ZiZW75LEl75Ra8Zx00sMhIup5YvMGUYhKKVlELRPzMxamH8yiHWMgteo6Dt1XlDsDhlEbetvEztwYHIs70hL1IhahiIiINoGuqTANDe1IR2WJW2ifKJGhJSbwRQHP5K54REQnfMzZVkrJFaSxZ2NpzsTkAznEEQZKZc7G+J4CAl+V+U+uzH/aCk3j8YKoV/HVSUREtJkteZGF2rIu28zoscWRgnpFtOLFSOsut9QmIjoJmqpgayGFjJFB3LKxvGDg8L78QBx/xKquIwddTB/III7aMv+pbJewNVvisYKox7EIRUREtIkteZoiVkNpaFTYJnA89WVTXmi0YxV5m7keRESnsjHGaMFBzsoAnoPqkoaJ+/MIg/4t1IS+Kn+GpVkHUdzJfzojsxWFFFu2ifoBi1BERESbRLSTyRaBSEdtiUWoE9kVT1AVFZbBUxYiolMhVgaN5G3kHVGIclGvaJgQLWx9uElGs2rg0H0FNGsGwjCGAQs789thmzymEvWL/nvnISIi6uMLAdGSh8jE8oI+sCGxG0HkloiVUGEcI6Wl2F5BRHQaxHtoKWuhKFYLeWnUayrG9+ThNzX0A7FBxdIRR66ACgIVftBGxsjijNyobDskov7BIhQREdFmt+TBQBRociaXHploV4xjBe1IRY6teEREG1KIKmZtlDMuFC+DZr1TiBK7y/X6pMTMgQyOHHIRxW2EATDqjGA0U+AEBVEfYhGKiIhoEzmmJnd6Q8yWvOPtiieoUOGYvX2BRETUT/KuhS25NFQ/i2ZTwcT9OTSqvTkpIloGxe53lXlbFqAQazgzuw25lJP00IjoFLEIRUREtInErK0jW/IMuVORaDGg9eK4U4QSrXiO7nCmm4hog2VTJkbzaehBFq2mCPrO9dzESKMi8p+K8Bo6wrANS7WwMzcGy+DEBFE/YxGKiIhok4lcKK1twm/pfZPHsZmaVRNxJFrxFOQstuIREXWD6xjYWnBhBFn4noLJfVlU5jurUJMkJmcWplI4vDePMASCoI2clcP2zBa52x8R9TcWoYiIiDZZytagQLTkaagvJ3/C36u74inQkLJ6s0WEiGhQJkW2ldIwwxx8T8XU/owMAE+KmICY2p/F3OE0ojhGFCrYmhrFSDrHVbFEA4JFKCIiok2mqSosU0M71FFZYFvBsTPgohVPXHw4ms2LDiKiLrNNHWOlNKw4i8DXMH0wjfnJ1Ka3i/stDYfuK8iJiChqQ4l1nJHZhozDyRqiQcIiFBERUQLSsiXPkmGwoc/D8apm1ZAz32I2PGdlkh4OEdFQMA0NYyUXdjuL0NMxeziFuQl30wpRtUVTFqBEISoQ+U+ajbPyzH8iGkQ86yUiIkpAytblzm/ttmjJ660w2F5pxUvbbMUjItoshq7KFVGOIgpRBuamLMwcyHS1ECX+btF6N/lADtFK/lPRzGN7ZqSzkywRDRwWoYiIiBJg6Jo84Ueoo7rIItS6Vrx2DEs12YpHRLTJdK1TiHLVLGLPwuKshakHcnLX0o0mVr1O7svJEPK43UYUqRhzR1Fys3z/JxpgLEIRERElGAirxBaqyzriKOnRJK9VMxAGqmzFy9u5pIdDRDSUNFXB1mIKrp5F3LKxPG9gcl9evjdvFK+hy/Y7sRI4jGKobQNnZrcibTH/iWjQsQhFRESUYEueBg1xqKFR5WoosQpKUGINae6KR0SUGFVVMFp0kLVctD0HlUUdE/fn5eql01WZtzC+O4/A6+Q/pbQ0zspthakz/4loGLAIRURElBDL0OSJPmJNhrJi2FvxFi3EohVPNzr/X4iIKDEik2lL3kHOdgEvhdqyhvE9hVPeTKMdA7PjLqYfzCKKOvlPJauIsWyZ7XdEQ4RFKCIiooSIk27RkofQQmXR2PTtsHuJaM0IfBWR2BXPZCseEVGvHKfKORuFlChEpVGvqnIVk9jF7mSEgYKJvXkszjiI4hhxpGJHZiuKabdrYyei3sQiFBERUeIteQYCX0OrPrytCNXVXfFiDa493KvCiIh6rRBVzFgoZVwonotGQ8X4nrycPDgRzZrIfyqiWTVk/pMOEztzY3BMvtcTDaOBLkJ5nof3vve9uOSSS/Cc5zwHX/7yl5MeEhER0TqOqXe2oY401FcykYaNWAFWXbAQx7HMBGErHhFR7xWiCq6FLVlRiMqg2VBkIUoUmB7L8qyNifs7LXyi/c7VMjgztxWaNtCXoUT0GAZ6yvWGG27APffcg5tuugmTk5N4z3veg7GxMbzoRS9KemhERESSKLjYpobAN7C8qKO8A0PHb2oyoDaKYpQstuIREfWqbNqUx62ZJQUtVDCxJ4+xx1eQzvnrvi4W+U+HMliesxG32zLQfEuqjJyTSmzsRNQbBrYI1Wg0cPPNN+MLX/gCnvzkJ8vb3r178dWvfpVFKCIi6rmWvHrLQqvelDkbph1hmNQW7Yda8ZzhXA1GRNQvXMeAqriYXlTgtSs4vC+LbWdXkSl58nGR7ze1LydbzEX+E2IdZ2S2wDa56ykRDXA73u7duxGGIS666KK1+y6++GLcddddcrk/ERFRrxDh5LIlL9ZQXx6+jAy5K55sxdOgsRWPiKgvJk+2FdMwwhz8lorJ/RnZeteoGDh0b0EWoMIwhqHY2JkfYwGKiAZ/JdTs7CwKhQLMowLvyuWyzIlaWlpCsVhMdHxERESrdE2FaWgIIh1LRyzE0fAUYtqxAq+lIYrbyHNXPCKivuFYOsZKaUwtAL5XxdSDrpxIEO13YdB5Ty+7OZknRUQ08EWoZrO5rgAlrH7u++t7lh9Lv7xnro2zT8a74Yb15x5WfL6HizI8s8rNqoVmw0PgpzF0T3GsISNa8Ybk+abheW3TCj7fA8kyNYyV05ieV9Dyq4iNAO1YxdbUCDKpTqv1UFD657qRKGkDW4SyLOthxabVz237xN4QR0Yy6Be5MMbzLjkDcdxOeihERHQa/m/qTvhxE8PmDPcMnJnblvQwiIjoFIkVUHtm9+OJ5ccN3S6n5+zIo1zun2tHoiQNbBFqdHQUi4uLMhdK1/W1Fj1RgMpmsxg0hq7ihc/YmfQwiIjoNP0uzk56CERERKfkSjwu6SEQUY8b2GDy8847Txaf7rzzzrX77rjjDlxwwQVQ1YH9sYmIiIiIiIiIetLAVmMcx8HLX/5yfOhDH8Ldd9+NH/7wh/jyl7+MN7zhDUkPjYiIiIiIiIho6Cjtdrs9yOHkogj1gx/8AK7r4s1vfjPe+MY3Jj0sIiIiIiIiIqKhM9BFKCIiIiIiIiIi6g0D245HRERERERERES9g0UoIiIiIiIiIiLqOhahiIiIiIiIiIio61iEor40MzODP//zP8ell16K5z73ubj++uvheV7Sw6JN8Kd/+qf4q7/6q6SHQV3k+z6uvfZa/M7v/A6e9axn4ZOf/CQYXziYpqam8Na3vhVPf/rTccUVV+DGG29MekjUpdf0S17yEtx2221r942Pj8vNYp72tKfhxS9+MW655ZZEx0jdfb7vvPNO/OEf/iEuuugivPCFL8TNN9+c6Bipu8/3qmq1Ks/Tv/WtbyUyNiLqTSxCUd8RF6OiACV2P/zqV7+KT33qU/jxj3+Mv/u7v0t6aNRl//Ef/4H//d//TXoY1GUf/vCHceutt+JLX/oSPvGJT+Df/u3f8PWvfz3pYVEX/MVf/AVSqZS8QHnve98r38f/+7//O+lh0QYSE0TvfOc7sXfv3nXH8be//e0ol8v45je/iZe97GXYtWsXJicnEx0rdef5np2dxVve8hY5cfjtb39bnsP9zd/8DX7yk58kOlbqzvN9tI997GM4cuTIpo+LiHobi1DUd/bv3y9n1MTqpyc84Qm45JJL5AnN9773vaSHRl20tLSEG264ARdccEHSQ6EuP8/iolRcoDz1qU/FM5/5TFx11VW46667kh4abbDl5WX5Xv62t70NO3fuxPOf/3w5Y/7zn/886aHRBtm3bx9e/epX49ChQ+vu/8UvfiFXQl133XU455xz5Go4sSJKvPZp8J7vH/7wh7LgKIoV4rV+5ZVX4uUvfzn+/d//PbGxUvee71W//OUv5Wt9ZGRk08dGRL2NRSjqO+Jg9sUvflGe0BytVqslNibqvr/927+Vs+WPf/zjkx4KddEdd9wB13XljPnRLZii6EyDxbZtOI4jV0EFQSAnGH71q1/hvPPOS3potEFuv/12XHbZZQ9bySiKyueff75cBbfq4osvlkVJGrznezU24Vg8bxvM53u1Re+v//qv8YEPfACmaSYyPiLqXXrSAyA6WdlsVp7QrIrjGP/8z/+MZzzjGYmOi7pHrIwQM2pi1vRDH/pQ0sOhLhKrI7Zv347vfOc7+Id/+AdZnHjFK14hV8uoKudNBollWfICRax6+8pXvoIoiuRz/apXvSrpodEGed3rXveI94v2rC1btqy7r1QqYXp6epNGRpv5fO/YsUPeVs3Pz8v2+ne84x2bODrarOdbEMdvUWh+znOes6ljIqL+wCIU9T3Rb37vvffiG9/4RtJDoS7lDXzwgx+UF6ti5QQNtkajgYMHD+JrX/uanDkXF6viuRcrZkRbHg2WBx54AJdffjne9KY3yUwRUZASLZgvfelLkx4adZHIdDx2dYT4XKyeoMHWarVk8UmsZn/Na16T9HCoS2164hj+3e9+N+mhEFGPYhGK+r4AddNNN8lw8nPPPTfp4VAXfOYzn8FTnvKUdavfaHDpui5bNEQguVgRJYiw4n/9139lEWoAVziKyQOx2YAoMIu8N7Hz6d///d+zCDUEq+BE/tvRRAGKEw2DrV6v4+qrr8aBAwfwL//yL3JygQaL2HTg/e9/v8xqPTY2g4hoFYtQ1LfEjLm4MBWFKLHdLw0msWR/bm5ObussrM6Uf//738evf/3rhEdH3ch8ExeoqwUo4eyzz8bU1FSi46KNd8899+Css85aV3gQ7RuijYMG2+joqFwtcTTxPn9six4NDjG58Cd/8icyxFpMHoqAcho8YtJInJvt2bNHZnmurnwUK9r/8z//U2a6EhGxCEV9uzpGLPX95Cc/iRe96EVJD4e66J/+6Z8QhuHa5x//+Mfln3/5l3+Z4KioWy688ELZgvnggw/K4pMgAquPLkrRYBAFB9F6KQrLq61Z4rk+OjuGBvd1/vnPf162Zq0WIcWmBCKcnAaPyO7ctWsXJiYm5DFd7IhIg1tg/sEPfrDuvj/6oz+SN65wJaJVTHmlvswQ+dznPoe3vOUt8oRVZMas3mjwiOKDWC2xekun0/ImPqbB87jHPQ7Pe97zcM0112D37t346U9/Ki9WX/va1yY9NNpgV1xxBQzDkK0bouj4ox/9SK6CEhcrNNjE7pfbtm2Tr3ORBSZe43fffTf+4A/+IOmhUReIttvbbrsNH/7wh+XmMqvnbMe2ZNJgtNQffc4mbuI+sfGAKFAREQlcCUV953/+53/kLkoiN0TcjiaW/xJRfxOr3US7rSg8icyQ17/+9SxMDKBMJoMbb7wRH/nIR2TxoVgsyl0QGVY8+DRNk5NJ73vf++SOiOJC9bOf/SzGxsaSHhp1gWifF6uh3vrWtz6sGClWRhER0XBR2iJBjoiIiIiIiIiIqIvYjkdERERERERERF3HIhQREREREREREXUdi1BERERERERERNR1LEIREREREREREVHXsQhFRERERERERERdxyIUERERERERERF1HYtQRERERERERETUdSxCERERERERERFR17EIRURERKdk165deNWrXvWw+1/96lfjiU98Im6//fZ193/3u9/Fk570JMzPz5/2v33FFVfg05/+9Gn/PURERES0eViEIiIiolPyzGc+E/fddx9ardbafUtLS/jNb36Dbdu24ac//em6r//lL38pi1ClUimB0RIRERFR0liEIiIiolPyjGc8A0EQyKLTqltvvVUWmV75ylc+YhHqWc96VgIjJSIiIqJewCIUERERnZJzzjkHo6Oj+NWvfrV2nyg8Pec5z5G33bt3Y25uTt6/sLCABx54QN7v+z4+9rGP4bnPfS4uuugi2b53yy23rPu7xd/5+te/Hk996lPxvOc9D9deey1qtdojjqNer+O1r30tXvrSl8p/h4iIiIh6E4tQREREdFoteb/+9a/XPhfFpGc/+9myeJTJZNaKS3fccQds28bFF1+Ma665Bj/72c/w8Y9/HN/+9rfxu7/7u/izP/sz/OQnP5FfK4pXb3rTm2SRSuRIia/77W9/i6uuugrtdnvdv99sNuX3ipbAr3zlKygWi5v8f4CIiIiIThSLUERERHTaRShRHBLFo9nZWVmE0jRNPrbakvd///d/uOSSSzA9PY3vfe97uP7663HZZZdh586dsuB05ZVX4ktf+pL8WvGn+DtEcUk8Lr7vE5/4BO666651Yeee5+Ftb3ubXAl14403Ip/PJ/b/gYiIiIiOTz+BryEiIiJ6RKLQJMLI9+/fL1c9nX/++WurkUQh6TOf+cxaHpQoNN17773y89e97nXr/h6RLZXNZuXH4msOHjwoW/WOJVr6RPFKuOmmm+T3iWyqXC7X9Z+ViIiIiE4Pi1BERER0ykQm1Nlnny1XQ4kWO5H5tEp8/IEPfEC20olVUh/96EdlsUr46le/inQ6ve7vUtXOAu04jvF7v/d7ciXUsY5utzv33HPxnve8R66k+vrXv47XvOY1XfxJiYiIiOh0sR2PiIiITovY8U4EiYtClFj9tGr79u2ynU4UnETx6IlPfCKe8IQnyMdE295ZZ521dvvWt74lb4L4mn379q17PAxD2cI3NTW19veLwPJLL71UFqFuuOGGdY8RERERUe9hEYqIiIhOuyXvv/7rv6AoCp7+9Keve0yEi4vHxNeIx0WB6fLLL8cHP/hB/OhHP8L4+Di+8IUv4B//8R9x5plnyu8RAeSiJU/siCfa70Rx613vehcOHDggi1rH2rVrlyxyvf/979+0n5mIiIiITh6LUERERHRaREaT2J1O/GkYxrrHREteo9FYt0LqU5/6FF7wghfIVr0Xv/jF+M53voOPfOQj+P3f/335+NOe9jR88YtfxH333SfvE+HjouVPhI+bpvmwf1/sunfdddfJTKqbb755E35iIiIiIjoVSvvYvY6JiIiIiIiIiIg2GFdCERERERERERFR17EIRUREREREREREXcciFBERERERERERdR2LUERERERERERE1HUsQhERERERERERUdexCEVERERERERERF3HIhQREREREREREXUdi1BERERERERERNR1LEIREREREREREVHXsQhFRERERERERERdxyIUERERERERERF1HYtQRERERERERESEbvv/ARAGJePtUWxxAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Percentage area plot:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Weekly sales by product (area plot)\n",
+    "df_sales['Week'] = df_sales['Date'].dt.isocalendar().week\n",
+    "weekly_product_sales = df_sales.groupby(['Week', 'Product'])['Sales'].sum().unstack(fill_value=0)\n",
+    "\n",
+    "weekly_product_sales.plot(kind='area', figsize=(12, 6), alpha=0.7)\n",
+    "plt.title('Weekly Sales by Product (Stacked Area)')\n",
+    "plt.xlabel('Week')\n",
+    "plt.ylabel('Sales ($)')\n",
+    "plt.legend(title='Product', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Percentage area plot\n",
+    "print(\"\\nPercentage area plot:\")\n",
+    "weekly_product_pct = weekly_product_sales.div(weekly_product_sales.sum(axis=1), axis=0)\n",
+    "weekly_product_pct.plot(kind='area', figsize=(12, 6), alpha=0.7)\n",
+    "plt.title('Weekly Sales Proportion by Product')\n",
+    "plt.xlabel('Week')\n",
+    "plt.ylabel('Proportion')\n",
+    "plt.legend(title='Product', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Pie Charts\n",
+    "\n",
+    "Show proportions of a whole."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Customized pie chart:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Basic pie chart\n",
+    "product_sales = df_sales.groupby('Product')['Sales'].sum()\n",
+    "\n",
+    "product_sales.plot(kind='pie', figsize=(8, 8), autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Sales Distribution by Product')\n",
+    "plt.ylabel('')  # Remove y-label\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Customized pie chart\n",
+    "print(\"\\nCustomized pie chart:\")\n",
+    "colors = ['gold', 'lightcoral', 'lightskyblue', 'lightgreen']\n",
+    "explode = (0.05, 0.05, 0.05, 0.05)  # Slightly separate all slices\n",
+    "\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.pie(product_sales.values, labels=product_sales.index, autopct='%1.1f%%', \n",
+    "        startangle=90, colors=colors, explode=explode, shadow=True)\n",
+    "plt.title('Sales Distribution by Product (Customized)')\n",
+    "plt.axis('equal')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Subplots and Multiple Visualizations\n",
+    "\n",
+    "Create dashboard-style layouts with multiple plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a dashboard with multiple plots\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
+    "\n",
+    "# Plot 1: Daily sales trend\n",
+    "daily_sales = df_sales.groupby('Date')['Sales'].sum()\n",
+    "daily_sales.plot(ax=axes[0, 0], color='blue')\n",
+    "axes[0, 0].set_title('Daily Sales Trend')\n",
+    "axes[0, 0].set_xlabel('Date')\n",
+    "axes[0, 0].set_ylabel('Sales ($)')\n",
+    "axes[0, 0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: Sales by product (bar chart)\n",
+    "product_sales = df_sales.groupby('Product')['Sales'].sum()\n",
+    "product_sales.plot(kind='bar', ax=axes[0, 1], color=['skyblue', 'lightcoral', 'lightgreen', 'gold'])\n",
+    "axes[0, 1].set_title('Total Sales by Product')\n",
+    "axes[0, 1].set_xlabel('Product')\n",
+    "axes[0, 1].set_ylabel('Sales ($)')\n",
+    "axes[0, 1].tick_params(axis='x', rotation=45)\n",
+    "axes[0, 1].grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "# Plot 3: Sales distribution (histogram)\n",
+    "df_sales['Sales'].plot(kind='hist', bins=20, ax=axes[1, 0], alpha=0.7, color='green')\n",
+    "axes[1, 0].set_title('Sales Distribution')\n",
+    "axes[1, 0].set_xlabel('Sales ($)')\n",
+    "axes[1, 0].set_ylabel('Frequency')\n",
+    "axes[1, 0].grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "# Plot 4: Sales by region (pie chart)\n",
+    "region_sales = df_sales.groupby('Region')['Sales'].sum()\n",
+    "axes[1, 1].pie(region_sales.values, labels=region_sales.index, autopct='%1.1f%%', startangle=90)\n",
+    "axes[1, 1].set_title('Sales by Region')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. Advanced Plotting Techniques\n",
+    "\n",
+    "More sophisticated visualization techniques."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Hexbin plot for dense scatter data\n",
+    "# Create more data points for better hexbin visualization\n",
+    "large_df = pd.concat([df_sales] * 5, ignore_index=True)\n",
+    "large_df['Sales'] += np.random.normal(0, 50, len(large_df))\n",
+    "large_df['Commission'] = large_df['Sales'] * 0.1\n",
+    "\n",
+    "large_df.plot(kind='hexbin', x='Sales', y='Commission', gridsize=20, figsize=(10, 6), cmap='Blues')\n",
+    "plt.title('Sales vs Commission (Hexbin Plot)')\n",
+    "plt.xlabel('Sales ($)')\n",
+    "plt.ylabel('Commission ($)')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10. Customization and Styling\n",
+    "\n",
+    "Make your plots look professional and publication-ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Highly customized plot\n",
+    "fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "\n",
+    "# Create the plot\n",
+    "product_sales = df_sales.groupby('Product')['Sales'].sum()\n",
+    "bars = product_sales.plot(kind='bar', ax=ax, \n",
+    "                         color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4'],\n",
+    "                         edgecolor='black', linewidth=1.2)\n",
+    "\n",
+    "# Customize the plot\n",
+    "ax.set_title('Total Sales by Product', fontsize=18, fontweight='bold', pad=20)\n",
+    "ax.set_xlabel('Product', fontsize=14, fontweight='bold')\n",
+    "ax.set_ylabel('Total Sales ($)', fontsize=14, fontweight='bold')\n",
+    "\n",
+    "# Add value labels on bars\n",
+    "for i, bar in enumerate(ax.patches):\n",
+    "    height = bar.get_height()\n",
+    "    ax.text(bar.get_x() + bar.get_width()/2., height + 1000,\n",
+    "            f'${height:,.0f}', ha='center', va='bottom', fontweight='bold')\n",
+    "\n",
+    "# Styling\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.grid(True, alpha=0.3, axis='y')\n",
+    "ax.set_facecolor('#F8F9FA')\n",
+    "plt.xticks(rotation=0)\n",
+    "\n",
+    "# Format y-axis to show values in thousands\n",
+    "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x/1000:.0f}K'))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply your visualization skills to real scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Create a comprehensive sales dashboard\n",
+    "# Include: time series, comparison charts, distribution, and proportions\n",
+    "# Make it publication-ready with proper titles, labels, and styling\n",
+    "\n",
+    "# Your code here:\n",
+    "def create_sales_dashboard(df):\n",
+    "    \"\"\"Create a comprehensive sales dashboard\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# create_sales_dashboard(df_sales)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Stock performance analysis\n",
+    "# Create visualizations to compare stock performance:\n",
+    "# - Price movements over time\n",
+    "# - Daily returns distribution\n",
+    "# - Correlation analysis\n",
+    "# - Risk vs return scatter plot\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Advanced time series visualization\n",
+    "# Create a plot that shows:\n",
+    "# - Original time series\n",
+    "# - Trend line\n",
+    "# - Seasonal decomposition\n",
+    "# - Confidence intervals\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Pandas Integration**: Direct plotting from DataFrames is convenient and powerful\n",
+    "2. **Plot Types**: Choose the right visualization for your data type and message\n",
+    "3. **Customization**: Always add titles, labels, and legends for clarity\n",
+    "4. **Color and Style**: Use consistent, accessible color schemes\n",
+    "5. **Subplots**: Combine multiple views for comprehensive analysis\n",
+    "6. **Data Preparation**: Clean and prepare data before visualizing\n",
+    "7. **Interactivity**: Consider interactive plots for exploration\n",
+    "\n",
+    "## Plot Type Quick Reference\n",
+    "\n",
+    "| Data Type | Best Plot Type | Pandas Method |\n",
+    "|-----------|---------------|---------------|\n",
+    "| Time Series | Line Plot | `.plot()` or `.plot(kind='line')` |\n",
+    "| Categories | Bar Chart | `.plot(kind='bar')` |\n",
+    "| Distribution | Histogram | `.plot(kind='hist')` |\n",
+    "| Relationships | Scatter Plot | `.plot(kind='scatter')` |\n",
+    "| Proportions | Pie Chart | `.plot(kind='pie')` |\n",
+    "| Comparisons | Box Plot | `.boxplot()` |\n",
+    "| Cumulative | Area Plot | `.plot(kind='area')` |\n",
+    "\n",
+    "## Best Practices\n",
+    "\n",
+    "1. **Start Simple**: Begin with basic plots, then add complexity\n",
+    "2. **Tell a Story**: Each plot should convey a clear message\n",
+    "3. **Consider Your Audience**: Adjust complexity and detail accordingly\n",
+    "4. **Use Consistent Styling**: Maintain visual consistency across plots\n",
+    "5. **Test Different Views**: Try multiple plot types for the same data\n",
+    "6. **Add Context**: Include benchmarks, targets, or reference lines\n",
+    "7. **Make it Accessible**: Use colorblind-friendly palettes"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/PandasDataFrame-exmples/13_advanced_data_cleaning.ipynb b/Session_01/PandasDataFrame-exmples/13_advanced_data_cleaning.ipynb
new file mode 100755
index 0000000..d2636ce
--- /dev/null
+++ b/Session_01/PandasDataFrame-exmples/13_advanced_data_cleaning.ipynb
@@ -0,0 +1,815 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Session 1 - DataFrames - Lesson 13: Advanced Data Cleaning\n",
+    "\n",
+    "## Learning Objectives\n",
+    "- Master advanced techniques for data cleaning and validation\n",
+    "- Learn to detect and handle various types of data quality issues\n",
+    "- Understand data standardization and normalization techniques\n",
+    "- Practice with real-world messy data scenarios\n",
+    "- Develop automated data cleaning pipelines\n",
+    "\n",
+    "## Prerequisites\n",
+    "- Completed previous lessons on DataFrames\n",
+    "- Understanding of basic data cleaning concepts\n",
+    "- Familiarity with regular expressions (helpful but not required)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import re\n",
+    "from datetime import datetime, timedelta\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Display settings\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', 100)\n",
+    "\n",
+    "print(\"Libraries loaded successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Messy Sample Data\n",
+    "\n",
+    "Let's create a realistic messy dataset to practice advanced cleaning techniques."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Create intentionally messy data that mimics real-world issues\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Base data\n",
+    "n_records = 200\n",
+    "messy_data = {\n",
+    "    'customer_id': [f'CUST{i:04d}' if i % 10 != 0 else f'cust{i:04d}' for i in range(1, n_records + 1)],\n",
+    "    'customer_name': [\n",
+    "        'John Smith', 'jane doe', 'MARY JOHNSON', 'bob wilson', 'Sarah Davis',\n",
+    "        'Mike Brown', 'lisa garcia', 'DAVID MILLER', 'Amy Wilson', 'Tom Anderson'\n",
+    "    ] * 20,\n",
+    "    'email': [\n",
+    "        'john.smith@email.com', 'JANE.DOE@EMAIL.COM', 'mary@company.org',\n",
+    "        'bob..wilson@test.com', 'sarah@invalid-email', 'mike@email.com',\n",
+    "        'lisa.garcia@email.com', 'david@company.org', 'amy@email.com', 'tom@test.com'\n",
+    "    ] * 20,\n",
+    "    'phone': [\n",
+    "        '(555) 123-4567', '555.987.6543', '5551234567', '555-987-6543',\n",
+    "        '(555)123-4567', '+1-555-123-4567', '555 123 4567', '5559876543',\n",
+    "        '(555) 987 6543', '555-123-4567'\n",
+    "    ] * 20,\n",
+    "    'address': [\n",
+    "        '123 Main St, Anytown, NY 12345', '456 Oak Ave, Boston, MA 02101',\n",
+    "        '789 Pine Rd, Los Angeles, CA 90210', '321 Elm St, Chicago, IL 60601',\n",
+    "        '654 Maple Dr, Houston, TX 77001', '987 Cedar Ln, Phoenix, AZ 85001',\n",
+    "        '147 Birch Way, Philadelphia, PA 19101', '258 Ash Ct, San Antonio, TX 78201',\n",
+    "        '369 Walnut St, San Diego, CA 92101', '741 Cherry Ave, Dallas, TX 75201'\n",
+    "    ] * 20,\n",
+    "    'purchase_amount': np.random.normal(100, 30, n_records).round(2),\n",
+    "    'purchase_date': [\n",
+    "        '2024-01-15', '01/16/2024', '2024-1-17', '16-01-2024', '2024/01/18',\n",
+    "        'January 19, 2024', '2024-01-20', '01-21-24', '2024.01.22', '23/01/2024'\n",
+    "    ] * 20,\n",
+    "    'category': [\n",
+    "        'Electronics', 'electronics', 'ELECTRONICS', 'Books', 'books',\n",
+    "        'Clothing', 'clothing', 'CLOTHING', 'Home & Garden', 'home&garden'\n",
+    "    ] * 20,\n",
+    "    'satisfaction_score': np.random.choice([1, 2, 3, 4, 5, 99, -1, None], n_records, p=[0.05, 0.1, 0.15, 0.35, 0.3, 0.02, 0.02, 0.01])\n",
+    "}\n",
+    "\n",
+    "# Convert to DataFrame first\n",
+    "df_messy = pd.DataFrame(messy_data)\n",
+    "\n",
+    "# Introduce missing values and anomalies using proper indexing\n",
+    "df_messy.loc[df_messy.index[::25], 'customer_name'] = None  # Some missing names\n",
+    "df_messy.loc[df_messy.index[::30], 'email'] = None  # Some missing emails\n",
+    "df_messy.loc[df_messy.index[::35], 'purchase_amount'] = np.nan  # Some missing amounts\n",
+    "df_messy.loc[df_messy.index[::40], 'purchase_amount'] = -999  # Invalid negative values\n",
+    "\n",
+    "# Add some duplicate records\n",
+    "duplicate_indices = [0, 1, 2, 3, 4]\n",
+    "duplicate_rows = df_messy.iloc[duplicate_indices].copy()\n",
+    "df_messy = pd.concat([df_messy, duplicate_rows], ignore_index=True)\n",
+    "\n",
+    "print(\"Messy dataset created:\")\n",
+    "print(f\"Shape: {df_messy.shape}\")\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "print(df_messy.head(10))\n",
+    "print(\"\\nData types:\")\n",
+    "print(df_messy.dtypes)\n",
+    "print(\"\\nSample of data quality issues:\")\n",
+    "print(\"\\n1. Missing values:\")\n",
+    "print(df_messy.isnull().sum())\n",
+    "print(\"\\n2. Inconsistent formatting examples:\")\n",
+    "print(\"Customer IDs:\", df_messy['customer_id'].head(15).tolist())\n",
+    "print(\"Customer names:\", df_messy['customer_name'].dropna().head(5).tolist())\n",
+    "print(\"Categories:\", df_messy['category'].unique()[:5])\n",
+    "print(\"\\n3. Invalid satisfaction scores:\")\n",
+    "print(\"Unique satisfaction scores:\", sorted(df_messy['satisfaction_score'].dropna().unique()))\n",
+    "print(\"\\n4. Invalid purchase amounts:\")\n",
+    "print(\"Negative amounts:\", df_messy[df_messy['purchase_amount'] < 0]['purchase_amount'].count())\n",
+    "print(\"\\n5. Date format inconsistencies:\")\n",
+    "print(\"Sample dates:\", df_messy['purchase_date'].head(10).tolist())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Data Quality Assessment\n",
+    "\n",
+    "First, let's assess the quality of our messy data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def assess_data_quality(df):\n",
+    "    \"\"\"Comprehensive data quality assessment\"\"\"\n",
+    "    print(\"=== DATA QUALITY ASSESSMENT ===\")\n",
+    "    print(f\"Dataset shape: {df.shape}\")\n",
+    "    print(f\"Total cells: {df.size}\")\n",
+    "    \n",
+    "    # Missing values analysis\n",
+    "    print(\"\\n--- Missing Values ---\")\n",
+    "    missing_stats = pd.DataFrame({\n",
+    "        'Missing_Count': df.isnull().sum(),\n",
+    "        'Missing_Percentage': (df.isnull().sum() / len(df)) * 100\n",
+    "    })\n",
+    "    missing_stats = missing_stats[missing_stats['Missing_Count'] > 0]\n",
+    "    print(missing_stats.round(2))\n",
+    "    \n",
+    "    # Duplicate analysis\n",
+    "    print(\"\\n--- Duplicates ---\")\n",
+    "    total_duplicates = df.duplicated().sum()\n",
+    "    print(f\"Complete duplicate rows: {total_duplicates}\")\n",
+    "    \n",
+    "    # Column-specific analysis\n",
+    "    print(\"\\n--- Column Analysis ---\")\n",
+    "    for col in df.columns:\n",
+    "        unique_count = df[col].nunique()\n",
+    "        unique_percentage = (unique_count / len(df)) * 100\n",
+    "        print(f\"{col}: {unique_count} unique values ({unique_percentage:.1f}%)\")\n",
+    "    \n",
+    "    # Data type issues\n",
+    "    print(\"\\n--- Data Types ---\")\n",
+    "    print(df.dtypes)\n",
+    "    \n",
+    "    return missing_stats, total_duplicates\n",
+    "\n",
+    "# Assess the messy data\n",
+    "missing_stats, duplicate_count = assess_data_quality(df_messy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identify specific data quality issues\n",
+    "def identify_issues(df):\n",
+    "    \"\"\"Identify specific data quality issues\"\"\"\n",
+    "    issues = []\n",
+    "    \n",
+    "    # Check for inconsistent formatting\n",
+    "    print(\"=== SPECIFIC ISSUES IDENTIFIED ===\")\n",
+    "    \n",
+    "    # Customer ID formatting\n",
+    "    id_patterns = df['customer_id'].str.extract(r'(CUST|cust)(\\d+)').fillna('')\n",
+    "    inconsistent_ids = (id_patterns[0] == 'cust').sum()\n",
+    "    print(f\"Inconsistent customer ID format: {inconsistent_ids} records\")\n",
+    "    \n",
+    "    # Email validation\n",
+    "    email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'\n",
+    "    invalid_emails = ~df['email'].str.match(email_pattern, na=False)\n",
+    "    print(f\"Invalid email formats: {invalid_emails.sum()} records\")\n",
+    "    \n",
+    "    # Negative purchase amounts\n",
+    "    negative_amounts = (df['purchase_amount'] < 0).sum()\n",
+    "    print(f\"Negative purchase amounts: {negative_amounts} records\")\n",
+    "    \n",
+    "    # Invalid satisfaction scores\n",
+    "    invalid_scores = ((df['satisfaction_score'] < 1) | (df['satisfaction_score'] > 5)) & df['satisfaction_score'].notna()\n",
+    "    print(f\"Invalid satisfaction scores: {invalid_scores.sum()} records\")\n",
+    "    \n",
+    "    # Category inconsistencies\n",
+    "    category_variations = df['category'].value_counts()\n",
+    "    print(f\"\\nCategory variations: {len(category_variations)} different values\")\n",
+    "    print(category_variations)\n",
+    "    \n",
+    "    return issues\n",
+    "\n",
+    "issues = identify_issues(df_messy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Text Data Standardization\n",
+    "\n",
+    "Clean and standardize text fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Text cleaning functions\n",
+    "def clean_text_data(df):\n",
+    "    \"\"\"Comprehensive text data cleaning\"\"\"\n",
+    "    df_clean = df.copy()\n",
+    "    \n",
+    "    # Standardize customer names\n",
+    "    print(\"Cleaning customer names...\")\n",
+    "    df_clean['customer_name_clean'] = df_clean['customer_name'].str.strip()  # Remove whitespace\n",
+    "    df_clean['customer_name_clean'] = df_clean['customer_name_clean'].str.title()  # Title case\n",
+    "    df_clean['customer_name_clean'] = df_clean['customer_name_clean'].str.replace(r'\\s+', ' ', regex=True)  # Multiple spaces\n",
+    "    \n",
+    "    # Standardize customer IDs\n",
+    "    print(\"Standardizing customer IDs...\")\n",
+    "    df_clean['customer_id_clean'] = df_clean['customer_id'].str.upper()  # All uppercase\n",
+    "    df_clean['customer_id_clean'] = df_clean['customer_id_clean'].str.replace('CUST', 'CUST')  # Ensure consistent prefix\n",
+    "    \n",
+    "    # Clean email addresses\n",
+    "    print(\"Cleaning email addresses...\")\n",
+    "    df_clean['email_clean'] = df_clean['email'].str.lower()  # Lowercase\n",
+    "    df_clean['email_clean'] = df_clean['email_clean'].str.strip()  # Remove whitespace\n",
+    "    df_clean['email_clean'] = df_clean['email_clean'].str.replace(r'\\.{2,}', '.', regex=True)  # Multiple dots\n",
+    "    \n",
+    "    # Standardize categories\n",
+    "    print(\"Standardizing categories...\")\n",
+    "    category_mapping = {\n",
+    "        'electronics': 'Electronics',\n",
+    "        'ELECTRONICS': 'Electronics',\n",
+    "        'books': 'Books',\n",
+    "        'clothing': 'Clothing',\n",
+    "        'CLOTHING': 'Clothing',\n",
+    "        'home&garden': 'Home & Garden',\n",
+    "        'Home & Garden': 'Home & Garden'\n",
+    "    }\n",
+    "    df_clean['category_clean'] = df_clean['category'].map(category_mapping).fillna(df_clean['category'])\n",
+    "    \n",
+    "    return df_clean\n",
+    "\n",
+    "# Apply text cleaning\n",
+    "df_text_clean = clean_text_data(df_messy)\n",
+    "\n",
+    "print(\"\\nText cleaning comparison:\")\n",
+    "comparison_cols = ['customer_name', 'customer_name_clean', 'customer_id', 'customer_id_clean', \n",
+    "                  'email', 'email_clean', 'category', 'category_clean']\n",
+    "print(df_text_clean[comparison_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Advanced text cleaning with regex\n",
+    "def advanced_text_cleaning(df):\n",
+    "    \"\"\"Advanced text cleaning using regular expressions\"\"\"\n",
+    "    df_advanced = df.copy()\n",
+    "    \n",
+    "    # Extract and standardize address components\n",
+    "    print(\"Processing addresses...\")\n",
+    "    # Basic address pattern: number street, city, state zipcode\n",
+    "    address_pattern = r'(\\d+)\\s+([^,]+),\\s*([^,]+),\\s*([A-Z]{2})\\s+(\\d{5})'\n",
+    "    address_parts = df_advanced['address'].str.extract(address_pattern)\n",
+    "    address_parts.columns = ['street_number', 'street_name', 'city', 'state', 'zipcode']\n",
+    "    \n",
+    "    # Clean street names\n",
+    "    address_parts['street_name'] = address_parts['street_name'].str.title()\n",
+    "    address_parts['city'] = address_parts['city'].str.title()\n",
+    "    \n",
+    "    # Combine cleaned parts\n",
+    "    df_advanced['address_clean'] = (\n",
+    "        address_parts['street_number'] + ' ' + address_parts['street_name'] + ', ' +\n",
+    "        address_parts['city'] + ', ' + address_parts['state'] + ' ' + address_parts['zipcode']\n",
+    "    )\n",
+    "    \n",
+    "    # Add individual address components\n",
+    "    for col in address_parts.columns:\n",
+    "        df_advanced[col] = address_parts[col]\n",
+    "    \n",
+    "    return df_advanced\n",
+    "\n",
+    "# Apply advanced cleaning\n",
+    "df_advanced_clean = advanced_text_cleaning(df_text_clean)\n",
+    "\n",
+    "print(\"Address cleaning results:\")\n",
+    "print(df_advanced_clean[['address', 'address_clean', 'city', 'state', 'zipcode']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Phone Number Standardization\n",
+    "\n",
+    "Clean and standardize phone numbers using regex patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize_phone_numbers(df):\n",
+    "    \"\"\"Standardize phone numbers to consistent format\"\"\"\n",
+    "    df_phone = df.copy()\n",
+    "    \n",
+    "    def clean_phone(phone):\n",
+    "        \"\"\"Clean individual phone number\"\"\"\n",
+    "        if pd.isna(phone):\n",
+    "            return None\n",
+    "        \n",
+    "        # Remove all non-digit characters\n",
+    "        digits_only = re.sub(r'\\D', '', str(phone))\n",
+    "        \n",
+    "        # Handle different formats\n",
+    "        if len(digits_only) == 10:\n",
+    "            # Format as (XXX) XXX-XXXX\n",
+    "            return f\"({digits_only[:3]}) {digits_only[3:6]}-{digits_only[6:]}\"\n",
+    "        elif len(digits_only) == 11 and digits_only.startswith('1'):\n",
+    "            # Remove country code and format\n",
+    "            phone_part = digits_only[1:]\n",
+    "            return f\"({phone_part[:3]}) {phone_part[3:6]}-{phone_part[6:]}\"\n",
+    "        else:\n",
+    "            # Invalid phone number\n",
+    "            return 'INVALID'\n",
+    "    \n",
+    "    # Apply phone cleaning\n",
+    "    df_phone['phone_clean'] = df_phone['phone'].apply(clean_phone)\n",
+    "    \n",
+    "    # Extract area code\n",
+    "    df_phone['area_code'] = df_phone['phone_clean'].str.extract(r'\\((\\d{3})\\)')\n",
+    "    \n",
+    "    # Flag invalid phone numbers\n",
+    "    df_phone['phone_is_valid'] = df_phone['phone_clean'] != 'INVALID'\n",
+    "    \n",
+    "    return df_phone\n",
+    "\n",
+    "# Apply phone standardization\n",
+    "df_phone_clean = standardize_phone_numbers(df_advanced_clean)\n",
+    "\n",
+    "print(\"Phone number standardization:\")\n",
+    "print(df_phone_clean[['phone', 'phone_clean', 'area_code', 'phone_is_valid']].head(15))\n",
+    "\n",
+    "print(\"\\nPhone validation summary:\")\n",
+    "print(df_phone_clean['phone_is_valid'].value_counts())\n",
+    "\n",
+    "print(\"\\nArea code distribution:\")\n",
+    "print(df_phone_clean['area_code'].value_counts().head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Date Standardization\n",
+    "\n",
+    "Parse and standardize dates from various formats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize_dates(df):\n",
+    "    \"\"\"Parse and standardize dates from multiple formats\"\"\"\n",
+    "    df_dates = df.copy()\n",
+    "    \n",
+    "    def parse_date(date_str):\n",
+    "        \"\"\"Try to parse date from various formats\"\"\"\n",
+    "        if pd.isna(date_str):\n",
+    "            return None\n",
+    "        \n",
+    "        date_str = str(date_str).strip()\n",
+    "        \n",
+    "        # Common date formats to try\n",
+    "        formats = [\n",
+    "            '%Y-%m-%d',      # 2024-01-15\n",
+    "            '%m/%d/%Y',      # 01/16/2024\n",
+    "            '%Y-%m-%d',      # 2024-1-17 (handled by first format)\n",
+    "            '%d-%m-%Y',      # 16-01-2024\n",
+    "            '%Y/%m/%d',      # 2024/01/18\n",
+    "            '%B %d, %Y',     # January 19, 2024\n",
+    "            '%m-%d-%y',      # 01-21-24\n",
+    "            '%Y.%m.%d',      # 2024.01.22\n",
+    "            '%d/%m/%Y'       # 23/01/2024\n",
+    "        ]\n",
+    "        \n",
+    "        for fmt in formats:\n",
+    "            try:\n",
+    "                return pd.to_datetime(date_str, format=fmt)\n",
+    "            except ValueError:\n",
+    "                continue\n",
+    "        \n",
+    "        # If all else fails, try pandas' flexible parser\n",
+    "        try:\n",
+    "            return pd.to_datetime(date_str, infer_datetime_format=True)\n",
+    "        except:\n",
+    "            return None\n",
+    "    \n",
+    "    # Apply date parsing\n",
+    "    print(\"Parsing dates...\")\n",
+    "    df_dates['purchase_date_clean'] = df_dates['purchase_date'].apply(parse_date)\n",
+    "    \n",
+    "    # Flag unparseable dates\n",
+    "    df_dates['date_is_valid'] = df_dates['purchase_date_clean'].notna()\n",
+    "    \n",
+    "    # Extract date components for valid dates\n",
+    "    df_dates['purchase_year'] = df_dates['purchase_date_clean'].dt.year\n",
+    "    df_dates['purchase_month'] = df_dates['purchase_date_clean'].dt.month\n",
+    "    df_dates['purchase_day'] = df_dates['purchase_date_clean'].dt.day\n",
+    "    df_dates['purchase_day_of_week'] = df_dates['purchase_date_clean'].dt.day_name()\n",
+    "    \n",
+    "    return df_dates\n",
+    "\n",
+    "# Apply date standardization\n",
+    "df_date_clean = standardize_dates(df_phone_clean)\n",
+    "\n",
+    "print(\"Date standardization results:\")\n",
+    "print(df_date_clean[['purchase_date', 'purchase_date_clean', 'date_is_valid', \n",
+    "                    'purchase_year', 'purchase_month', 'purchase_day_of_week']].head(15))\n",
+    "\n",
+    "print(\"\\nDate parsing summary:\")\n",
+    "print(df_date_clean['date_is_valid'].value_counts())\n",
+    "\n",
+    "invalid_dates = df_date_clean[~df_date_clean['date_is_valid']]['purchase_date'].unique()\n",
+    "if len(invalid_dates) > 0:\n",
+    "    print(f\"\\nInvalid date formats found: {invalid_dates}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Numerical Data Cleaning\n",
+    "\n",
+    "Handle outliers, invalid values, and missing numerical data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_numerical_data(df):\n",
+    "    \"\"\"Clean and validate numerical data\"\"\"\n",
+    "    df_numeric = df.copy()\n",
+    "    \n",
+    "    # Clean purchase amounts\n",
+    "    print(\"Cleaning purchase amounts...\")\n",
+    "    \n",
+    "    # Flag invalid values\n",
+    "    df_numeric['amount_is_valid'] = (\n",
+    "        df_numeric['purchase_amount'].notna() & \n",
+    "        (df_numeric['purchase_amount'] >= 0) & \n",
+    "        (df_numeric['purchase_amount'] <= 10000)  # Reasonable upper limit\n",
+    "    )\n",
+    "    \n",
+    "    # Replace invalid values with NaN\n",
+    "    df_numeric['purchase_amount_clean'] = df_numeric['purchase_amount'].where(\n",
+    "        df_numeric['amount_is_valid'], np.nan\n",
+    "    )\n",
+    "    \n",
+    "    # Detect outliers using IQR method\n",
+    "    Q1 = df_numeric['purchase_amount_clean'].quantile(0.25)\n",
+    "    Q3 = df_numeric['purchase_amount_clean'].quantile(0.75)\n",
+    "    IQR = Q3 - Q1\n",
+    "    lower_bound = Q1 - 1.5 * IQR\n",
+    "    upper_bound = Q3 + 1.5 * IQR\n",
+    "    \n",
+    "    df_numeric['amount_is_outlier'] = (\n",
+    "        (df_numeric['purchase_amount_clean'] < lower_bound) |\n",
+    "        (df_numeric['purchase_amount_clean'] > upper_bound)\n",
+    "    )\n",
+    "    \n",
+    "    # Clean satisfaction scores\n",
+    "    print(\"Cleaning satisfaction scores...\")\n",
+    "    \n",
+    "    # Valid satisfaction scores are 1-5\n",
+    "    df_numeric['satisfaction_is_valid'] = (\n",
+    "        df_numeric['satisfaction_score'].notna() &\n",
+    "        (df_numeric['satisfaction_score'].between(1, 5))\n",
+    "    )\n",
+    "    \n",
+    "    df_numeric['satisfaction_score_clean'] = df_numeric['satisfaction_score'].where(\n",
+    "        df_numeric['satisfaction_is_valid'], np.nan\n",
+    "    )\n",
+    "    \n",
+    "    return df_numeric\n",
+    "\n",
+    "# Apply numerical cleaning\n",
+    "df_numeric_clean = clean_numerical_data(df_date_clean)\n",
+    "\n",
+    "print(\"Numerical data cleaning results:\")\n",
+    "print(df_numeric_clean[['purchase_amount', 'purchase_amount_clean', 'amount_is_valid', \n",
+    "                       'amount_is_outlier', 'satisfaction_score', 'satisfaction_score_clean', \n",
+    "                       'satisfaction_is_valid']].head(15))\n",
+    "\n",
+    "print(\"\\nNumerical data quality summary:\")\n",
+    "print(f\"Valid purchase amounts: {df_numeric_clean['amount_is_valid'].sum()}/{len(df_numeric_clean)}\")\n",
+    "print(f\"Outlier amounts: {df_numeric_clean['amount_is_outlier'].sum()}\")\n",
+    "print(f\"Valid satisfaction scores: {df_numeric_clean['satisfaction_is_valid'].sum()}/{len(df_numeric_clean)}\")\n",
+    "\n",
+    "# Show statistics for cleaned data\n",
+    "print(\"\\nCleaned amount statistics:\")\n",
+    "print(df_numeric_clean['purchase_amount_clean'].describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Duplicate Detection and Handling\n",
+    "\n",
+    "Identify and handle duplicate records intelligently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def handle_duplicates(df):\n",
+    "    \"\"\"Comprehensive duplicate detection and handling\"\"\"\n",
+    "    df_dedup = df.copy()\n",
+    "    \n",
+    "    print(\"=== DUPLICATE ANALYSIS ===\")\n",
+    "    \n",
+    "    # 1. Exact duplicates\n",
+    "    exact_duplicates = df_dedup.duplicated()\n",
+    "    print(f\"Exact duplicate rows: {exact_duplicates.sum()}\")\n",
+    "    \n",
+    "    # 2. Duplicates based on key columns (likely same customer)\n",
+    "    key_cols = ['customer_name_clean', 'email_clean']\n",
+    "    key_duplicates = df_dedup.duplicated(subset=key_cols, keep=False)\n",
+    "    print(f\"Duplicate customers (by name/email): {key_duplicates.sum()}\")\n",
+    "    \n",
+    "    # 3. Near duplicates (similar but not exact)\n",
+    "    # For demonstration, we'll check phone numbers\n",
+    "    phone_duplicates = df_dedup.duplicated(subset=['phone_clean'], keep=False)\n",
+    "    print(f\"Duplicate phone numbers: {phone_duplicates.sum()}\")\n",
+    "    \n",
+    "    # Show duplicate examples\n",
+    "    if key_duplicates.any():\n",
+    "        print(\"\\nExample duplicate customers:\")\n",
+    "        duplicate_customers = df_dedup[key_duplicates].sort_values(key_cols)\n",
+    "        print(duplicate_customers[key_cols + ['customer_id_clean', 'purchase_amount_clean']].head(10))\n",
+    "    \n",
+    "    # Remove exact duplicates\n",
+    "    print(f\"\\nRemoving {exact_duplicates.sum()} exact duplicates...\")\n",
+    "    df_no_exact_dups = df_dedup[~exact_duplicates]\n",
+    "    \n",
+    "    # For customer duplicates, keep the one with the highest purchase amount\n",
+    "    print(\"Handling customer duplicates (keeping highest purchase)...\")\n",
+    "    df_final = df_no_exact_dups.sort_values('purchase_amount_clean', ascending=False).drop_duplicates(\n",
+    "        subset=key_cols, keep='first'\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"Final dataset size after deduplication: {len(df_final)} (was {len(df)})\")\n",
+    "    \n",
+    "    return df_final\n",
+    "\n",
+    "# Apply duplicate handling\n",
+    "df_deduplicated = handle_duplicates(df_numeric_clean)\n",
+    "\n",
+    "print(f\"\\nRows removed: {len(df_numeric_clean) - len(df_deduplicated)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Data Validation and Quality Scores\n",
+    "\n",
+    "Create comprehensive data quality metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_quality_scores(df):\n",
+    "    \"\"\"Calculate comprehensive data quality scores\"\"\"\n",
+    "    df_quality = df.copy()\n",
+    "    \n",
+    "    # Define quality checks\n",
+    "    quality_checks = {\n",
+    "        'has_customer_name': df_quality['customer_name_clean'].notna(),\n",
+    "        'has_valid_email': df_quality['email_clean'].notna() & \n",
+    "                          df_quality['email_clean'].str.match(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$', na=False),\n",
+    "        'has_valid_phone': df_quality['phone_is_valid'] == True,\n",
+    "        'has_valid_date': df_quality['date_is_valid'] == True,\n",
+    "        'has_valid_amount': df_quality['amount_is_valid'] == True,\n",
+    "        'has_valid_satisfaction': df_quality['satisfaction_is_valid'] == True,\n",
+    "        'amount_not_outlier': df_quality['amount_is_outlier'] == False,\n",
+    "        'has_complete_address': df_quality['city'].notna() & df_quality['state'].notna() & df_quality['zipcode'].notna()\n",
+    "    }\n",
+    "    \n",
+    "    # Add individual quality flags\n",
+    "    for check_name, check_result in quality_checks.items():\n",
+    "        df_quality[f'quality_{check_name}'] = check_result.astype(int)\n",
+    "    \n",
+    "    # Calculate overall quality score (percentage of passed checks)\n",
+    "    quality_cols = [col for col in df_quality.columns if col.startswith('quality_')]\n",
+    "    df_quality['data_quality_score'] = df_quality[quality_cols].mean(axis=1) * 100\n",
+    "    \n",
+    "    # Categorize quality levels\n",
+    "    def quality_category(score):\n",
+    "        if score >= 90:\n",
+    "            return 'Excellent'\n",
+    "        elif score >= 75:\n",
+    "            return 'Good'\n",
+    "        elif score >= 50:\n",
+    "            return 'Fair'\n",
+    "        else:\n",
+    "            return 'Poor'\n",
+    "    \n",
+    "    df_quality['quality_category'] = df_quality['data_quality_score'].apply(quality_category)\n",
+    "    \n",
+    "    return df_quality, quality_checks\n",
+    "\n",
+    "# Calculate quality scores\n",
+    "df_with_quality, quality_checks = calculate_quality_scores(df_deduplicated)\n",
+    "\n",
+    "print(\"Data quality analysis:\")\n",
+    "print(df_with_quality[['customer_name_clean', 'data_quality_score', 'quality_category']].head(10))\n",
+    "\n",
+    "print(\"\\nQuality category distribution:\")\n",
+    "print(df_with_quality['quality_category'].value_counts())\n",
+    "\n",
+    "print(\"\\nAverage quality scores by check:\")\n",
+    "quality_summary = {}\n",
+    "for check_name in quality_checks.keys():\n",
+    "    col_name = f'quality_{check_name}'\n",
+    "    quality_summary[check_name] = df_with_quality[col_name].mean() * 100\n",
+    "\n",
+    "quality_df = pd.DataFrame(list(quality_summary.items()), columns=['Quality_Check', 'Pass_Rate_%'])\n",
+    "quality_df = quality_df.sort_values('Pass_Rate_%', ascending=False)\n",
+    "print(quality_df.round(1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice Exercises\n",
+    "\n",
+    "Apply advanced data cleaning techniques to challenging scenarios:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1: Create a custom validation function\n",
+    "# Build a function that validates business rules:\n",
+    "# - Email domains should be from approved list\n",
+    "# - Purchase amounts should be within reasonable ranges by category\n",
+    "# - Dates should be within business operating period\n",
+    "# - Customer IDs should follow specific format patterns\n",
+    "\n",
+    "def validate_business_rules(df):\n",
+    "    \"\"\"Validate business-specific rules\"\"\"\n",
+    "    # Your implementation here\n",
+    "    pass\n",
+    "\n",
+    "# validation_results = validate_business_rules(df_final_clean)\n",
+    "# print(validation_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 2: Advanced duplicate detection\n",
+    "# Implement fuzzy matching for near-duplicate detection:\n",
+    "# - Similar names (edit distance)\n",
+    "# - Similar addresses\n",
+    "# - Similar email patterns\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 3: Data cleaning metrics dashboard\n",
+    "# Create a comprehensive data quality dashboard that shows:\n",
+    "# - Data quality trends over time\n",
+    "# - Field-by-field quality scores\n",
+    "# - Impact of cleaning steps\n",
+    "# - Recommendations for further improvement\n",
+    "\n",
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Assessment First**: Always assess data quality before cleaning\n",
+    "2. **Systematic Approach**: Use a structured pipeline for consistent results\n",
+    "3. **Preserve Original Data**: Keep original values while creating cleaned versions\n",
+    "4. **Document Everything**: Log all cleaning steps and decisions\n",
+    "5. **Validation**: Implement business rule validation\n",
+    "6. **Quality Metrics**: Measure and track data quality improvements\n",
+    "7. **Reusable Pipeline**: Create automated, configurable cleaning processes\n",
+    "8. **Context Matters**: Consider domain-specific requirements\n",
+    "\n",
+    "## Common Data Issues and Solutions\n",
+    "\n",
+    "| Issue | Detection Method | Solution |\n",
+    "|-------|-----------------|----------|\n",
+    "| Inconsistent Format | Pattern analysis | Standardization rules |\n",
+    "| Missing Values | `.isnull()` | Imputation or flagging |\n",
+    "| Duplicates | `.duplicated()` | Deduplication logic |\n",
+    "| Outliers | Statistical methods | Capping or flagging |\n",
+    "| Invalid Values | Business rules | Validation and correction |\n",
+    "| Inconsistent Naming | String analysis | Normalization |\n",
+    "| Date Issues | Parsing attempts | Multiple format handling |\n",
+    "| Text Issues | Regex patterns | Cleaning and standardization |\n",
+    "\n",
+    "## Best Practices\n",
+    "\n",
+    "1. **Start with Exploration**: Understand your data before cleaning\n",
+    "2. **Preserve Traceability**: Keep original and cleaned versions\n",
+    "3. **Validate Assumptions**: Test cleaning rules on sample data\n",
+    "4. **Measure Impact**: Quantify improvements from cleaning\n",
+    "5. **Automate When Possible**: Build reusable cleaning pipelines\n",
+    "6. **Handle Edge Cases**: Plan for unusual but valid data\n",
+    "7. **Business Context**: Include domain experts in rule definition\n",
+    "8. **Iterative Process**: Refine cleaning rules based on results\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/ohlcv_analysis.ipynb b/Session_01/ohlcv_analysis.ipynb
new file mode 100755
index 0000000..47ef469
--- /dev/null
+++ b/Session_01/ohlcv_analysis.ipynb
@@ -0,0 +1,1301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 📊 Working with CSV Data: Bitcoin Price Analysis\n",
+    "## A Practical Guide to Data Analysis with Python\n",
+    "\n",
+    "This notebook demonstrates **what you can do with CSV data** using a real Bitcoin price dataset:\n",
+    "\n",
+    "✅ **Load and explore CSV files**  \n",
+    "✅ **Clean and prepare data**  \n",
+    "✅ **Calculate new metrics from existing data**  \n",
+    "✅ **Create visualizations**  \n",
+    "✅ **Find patterns and insights**  \n",
+    "✅ **Export processed data**  \n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📦 Step 1: Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Libraries loaded successfully!\n",
+      "📊 Ready to analyze CSV data!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Essential libraries for data analysis\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "from datetime import datetime\n",
+    "\n",
+    "# Make plots look better\n",
+    "plt.style.use('default')\n",
+    "plt.rcParams['figure.figsize'] = (12, 6)\n",
+    "\n",
+    "print(\"✅ Libraries loaded successfully!\")\n",
+    "print(\"📊 Ready to analyze CSV data!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📁 Step 2: Load CSV Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Successfully loaded: Data/BTCUSD-1h-data.csv\n",
+      "📊 Dataset loaded with 83954 rows and 6 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 🔍 Load CSV file\n",
+    "file_path = os.path.join(\"Data\", \"BTCUSD-1h-data.csv\")\n",
+    "\n",
+    "try:\n",
+    "    # Read the CSV file\n",
+    "    df = pd.read_csv(file_path)\n",
+    "    print(f\"✅ Successfully loaded: {file_path}\")\n",
+    "except FileNotFoundError:\n",
+    "    print(\"❌ File not found. Creating sample data...\")\n",
+    "    \n",
+    "    # Create sample data for demonstration\n",
+    "    dates = pd.date_range('2023-01-01', '2024-01-01', freq='H')\n",
+    "    np.random.seed(42)\n",
+    "    \n",
+    "    # Simulate Bitcoin prices\n",
+    "    price = 30000  # Starting price\n",
+    "    prices = []\n",
+    "    \n",
+    "    for _ in range(len(dates)):\n",
+    "        # Random price movement\n",
+    "        change = np.random.normal(0, 0.01)  # 1% average change\n",
+    "        price = price * (1 + change)\n",
+    "        prices.append(price)\n",
+    "    \n",
+    "    # Create DataFrame\n",
+    "    df = pd.DataFrame({\n",
+    "        'datetime': dates,\n",
+    "        'open': prices,\n",
+    "        'high': [p * 1.01 for p in prices],\n",
+    "        'low': [p * 0.99 for p in prices],\n",
+    "        'close': prices,\n",
+    "        'volume': np.random.uniform(1000, 10000, len(dates))\n",
+    "    })\n",
+    "\n",
+    "print(f\"📊 Dataset loaded with {len(df)} rows and {len(df.columns)} columns\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 👀 Step 3: Explore Your Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📋 FIRST 5 ROWS:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>open</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>close</th>\n",
+       "      <th>volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015-10-03 01:00:00</td>\n",
+       "      <td>237.73</td>\n",
+       "      <td>238.37</td>\n",
+       "      <td>238.20</td>\n",
+       "      <td>238.36</td>\n",
+       "      <td>264.238672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-10-03 00:00:00</td>\n",
+       "      <td>237.87</td>\n",
+       "      <td>238.32</td>\n",
+       "      <td>238.31</td>\n",
+       "      <td>238.25</td>\n",
+       "      <td>192.314590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-10-02 23:00:00</td>\n",
+       "      <td>237.57</td>\n",
+       "      <td>238.38</td>\n",
+       "      <td>237.69</td>\n",
+       "      <td>238.28</td>\n",
+       "      <td>227.700515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-10-02 22:00:00</td>\n",
+       "      <td>236.97</td>\n",
+       "      <td>238.08</td>\n",
+       "      <td>237.96</td>\n",
+       "      <td>237.68</td>\n",
+       "      <td>456.360603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-10-02 21:00:00</td>\n",
+       "      <td>237.76</td>\n",
+       "      <td>238.10</td>\n",
+       "      <td>237.83</td>\n",
+       "      <td>238.03</td>\n",
+       "      <td>243.156239</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              datetime    open    high     low   close      volume\n",
+       "0  2015-10-03 01:00:00  237.73  238.37  238.20  238.36  264.238672\n",
+       "1  2015-10-03 00:00:00  237.87  238.32  238.31  238.25  192.314590\n",
+       "2  2015-10-02 23:00:00  237.57  238.38  237.69  238.28  227.700515\n",
+       "3  2015-10-02 22:00:00  236.97  238.08  237.96  237.68  456.360603\n",
+       "4  2015-10-02 21:00:00  237.76  238.10  237.83  238.03  243.156239"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📋 LAST 5 ROWS:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>open</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>close</th>\n",
+       "      <th>volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>83949</th>\n",
+       "      <td>2025-04-08 06:00:00</td>\n",
+       "      <td>79233.12</td>\n",
+       "      <td>79841.13</td>\n",
+       "      <td>79841.13</td>\n",
+       "      <td>79433.75</td>\n",
+       "      <td>256.821284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83950</th>\n",
+       "      <td>2025-04-08 05:00:00</td>\n",
+       "      <td>79500.03</td>\n",
+       "      <td>80288.77</td>\n",
+       "      <td>80280.84</td>\n",
+       "      <td>79841.14</td>\n",
+       "      <td>217.689942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83951</th>\n",
+       "      <td>2025-04-08 04:00:00</td>\n",
+       "      <td>79827.86</td>\n",
+       "      <td>80388.76</td>\n",
+       "      <td>79882.49</td>\n",
+       "      <td>80280.84</td>\n",
+       "      <td>263.614127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83952</th>\n",
+       "      <td>2025-04-08 03:00:00</td>\n",
+       "      <td>79727.79</td>\n",
+       "      <td>80187.64</td>\n",
+       "      <td>79907.63</td>\n",
+       "      <td>79892.73</td>\n",
+       "      <td>278.713624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83953</th>\n",
+       "      <td>2025-04-08 02:00:00</td>\n",
+       "      <td>79867.46</td>\n",
+       "      <td>80849.98</td>\n",
+       "      <td>80577.15</td>\n",
+       "      <td>79907.63</td>\n",
+       "      <td>397.356575</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  datetime      open      high       low     close      volume\n",
+       "83949  2025-04-08 06:00:00  79233.12  79841.13  79841.13  79433.75  256.821284\n",
+       "83950  2025-04-08 05:00:00  79500.03  80288.77  80280.84  79841.14  217.689942\n",
+       "83951  2025-04-08 04:00:00  79827.86  80388.76  79882.49  80280.84  263.614127\n",
+       "83952  2025-04-08 03:00:00  79727.79  80187.64  79907.63  79892.73  278.713624\n",
+       "83953  2025-04-08 02:00:00  79867.46  80849.98  80577.15  79907.63  397.356575"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📊 BASIC INFO:\n",
+      "Shape: (83954, 6) (rows, columns)\n",
+      "Columns: ['datetime', 'open', 'high', 'low', 'close', 'volume']\n",
+      "Data types:\n",
+      "datetime     object\n",
+      "open        float64\n",
+      "high        float64\n",
+      "low         float64\n",
+      "close       float64\n",
+      "volume      float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 🔍 First look at the data\n",
+    "print(\"📋 FIRST 5 ROWS:\")\n",
+    "display(df.head())\n",
+    "\n",
+    "print(\"\\n📋 LAST 5 ROWS:\")\n",
+    "display(df.tail())\n",
+    "\n",
+    "print(\"\\n📊 BASIC INFO:\")\n",
+    "print(f\"Shape: {df.shape} (rows, columns)\")\n",
+    "print(f\"Columns: {list(df.columns)}\")\n",
+    "print(f\"Data types:\\n{df.dtypes}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📈 SUMMARY STATISTICS:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>open</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>close</th>\n",
+       "      <th>volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>83954.000000</td>\n",
+       "      <td>83954.000000</td>\n",
+       "      <td>83954.000000</td>\n",
+       "      <td>83954.000000</td>\n",
+       "      <td>83954.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>23735.567081</td>\n",
+       "      <td>23956.847904</td>\n",
+       "      <td>23849.158711</td>\n",
+       "      <td>23850.217581</td>\n",
+       "      <td>603.636305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>25032.669682</td>\n",
+       "      <td>25246.889352</td>\n",
+       "      <td>25141.442433</td>\n",
+       "      <td>25142.195598</td>\n",
+       "      <td>768.710012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>226.120000</td>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>225.570000</td>\n",
+       "      <td>0.280000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4540.115000</td>\n",
+       "      <td>4589.000000</td>\n",
+       "      <td>4570.027500</td>\n",
+       "      <td>4572.865000</td>\n",
+       "      <td>205.379431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>11311.110000</td>\n",
+       "      <td>11410.585000</td>\n",
+       "      <td>11360.965000</td>\n",
+       "      <td>11361.345000</td>\n",
+       "      <td>374.110380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>38390.207500</td>\n",
+       "      <td>38837.445000</td>\n",
+       "      <td>38621.005000</td>\n",
+       "      <td>38624.597500</td>\n",
+       "      <td>720.276871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>107631.150000</td>\n",
+       "      <td>109358.010000</td>\n",
+       "      <td>108278.800000</td>\n",
+       "      <td>108276.430000</td>\n",
+       "      <td>31505.461253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                open           high            low          close  \\\n",
+       "count   83954.000000   83954.000000   83954.000000   83954.000000   \n",
+       "mean    23735.567081   23956.847904   23849.158711   23850.217581   \n",
+       "std     25032.669682   25246.889352   25141.442433   25142.195598   \n",
+       "min         0.060000     226.120000       0.060000     225.570000   \n",
+       "25%      4540.115000    4589.000000    4570.027500    4572.865000   \n",
+       "50%     11311.110000   11410.585000   11360.965000   11361.345000   \n",
+       "75%     38390.207500   38837.445000   38621.005000   38624.597500   \n",
+       "max    107631.150000  109358.010000  108278.800000  108276.430000   \n",
+       "\n",
+       "             volume  \n",
+       "count  83954.000000  \n",
+       "mean     603.636305  \n",
+       "std      768.710012  \n",
+       "min        0.280000  \n",
+       "25%      205.379431  \n",
+       "50%      374.110380  \n",
+       "75%      720.276871  \n",
+       "max    31505.461253  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "❓ MISSING DATA:\n",
+      "datetime    0\n",
+      "open        0\n",
+      "high        0\n",
+      "low         0\n",
+      "close       0\n",
+      "volume      0\n",
+      "dtype: int64\n",
+      "✅ No missing data found!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 📊 Quick statistics\n",
+    "print(\"📈 SUMMARY STATISTICS:\")\n",
+    "display(df.describe())\n",
+    "\n",
+    "# Check for missing data\n",
+    "print(\"\\n❓ MISSING DATA:\")\n",
+    "missing_data = df.isnull().sum()\n",
+    "print(missing_data)\n",
+    "\n",
+    "if missing_data.sum() == 0:\n",
+    "    print(\"✅ No missing data found!\")\n",
+    "else:\n",
+    "    print(\"⚠️ Found missing data - we'll need to clean this\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🧹 Step 4: Clean and Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Datetime converted and set as index\n",
+      "📅 Date range: 2015-09-20 14:00:00 to 2025-04-20 13:00:00\n",
+      "⏱️ Duration: 3499 days 23:00:00\n",
+      "\n",
+      "🧹 Data cleaning complete:\n",
+      "   Before: 83,954 rows\n",
+      "   After: 83,954 rows\n",
+      "   Removed: 0 rows\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 🔄 Convert datetime and set as index\n",
+    "df['datetime'] = pd.to_datetime(df['datetime'])\n",
+    "df.set_index('datetime', inplace=True)\n",
+    "df.sort_index(inplace=True)\n",
+    "\n",
+    "print(\"✅ Datetime converted and set as index\")\n",
+    "print(f\"📅 Date range: {df.index.min()} to {df.index.max()}\")\n",
+    "print(f\"⏱️ Duration: {df.index.max() - df.index.min()}\")\n",
+    "\n",
+    "# 🧹 Basic data cleaning\n",
+    "original_length = len(df)\n",
+    "\n",
+    "# Remove any rows with missing values\n",
+    "df = df.dropna()\n",
+    "\n",
+    "# Remove any impossible values (negative prices)\n",
+    "df = df[df['close'] > 0]\n",
+    "\n",
+    "print(f\"\\n🧹 Data cleaning complete:\")\n",
+    "print(f\"   Before: {original_length:,} rows\")\n",
+    "print(f\"   After: {len(df):,} rows\")\n",
+    "print(f\"   Removed: {original_length - len(df):,} rows\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🧮 Step 5: Calculate New Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ New metrics calculated:\n",
+      "   📊 Total columns now: 16\n",
+      "   📈 New columns: ['sma_10', 'sma_20', 'sma_50', 'volatility', 'signal_above_sma20', 'signal_golden_cross', 'daily_range', 'daily_range_percent']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 📊 Calculate returns (price changes)\n",
+    "df['price_change'] = df['close'].diff()  # Absolute change\n",
+    "df['returns'] = df['close'].pct_change()  # Percentage change\n",
+    "df['returns_percent'] = df['returns'] * 100  # As percentage\n",
+    "\n",
+    "# 📈 Calculate moving averages\n",
+    "df['sma_10'] = df['close'].rolling(10).mean()   # 10-period moving average\n",
+    "df['sma_20'] = df['close'].rolling(20).mean()   # 20-period moving average\n",
+    "df['sma_50'] = df['close'].rolling(50).mean()   # 50-period moving average\n",
+    "\n",
+    "# 📊 Calculate volatility (how much price moves)\n",
+    "df['volatility'] = df['returns'].rolling(20).std()\n",
+    "\n",
+    "# 🎯 Create trading signals\n",
+    "df['signal_above_sma20'] = df['close'] > df['sma_20']  # True when price above 20-day average\n",
+    "df['signal_golden_cross'] = df['sma_10'] > df['sma_20']  # True when short MA above long MA\n",
+    "\n",
+    "# 📊 Price ranges\n",
+    "df['daily_range'] = df['high'] - df['low']\n",
+    "df['daily_range_percent'] = (df['daily_range'] / df['close']) * 100\n",
+    "\n",
+    "print(\"✅ New metrics calculated:\")\n",
+    "print(f\"   📊 Total columns now: {len(df.columns)}\")\n",
+    "print(f\"   📈 New columns: {list(df.columns[-8:])}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📋 DATA WITH NEW CALCULATIONS:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>close</th>\n",
+       "      <th>returns_percent</th>\n",
+       "      <th>sma_10</th>\n",
+       "      <th>sma_20</th>\n",
+       "      <th>volatility</th>\n",
+       "      <th>signal_above_sma20</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 14:00:00</th>\n",
+       "      <td>232.92</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 15:00:00</th>\n",
+       "      <td>233.24</td>\n",
+       "      <td>0.137386</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 16:00:00</th>\n",
+       "      <td>233.73</td>\n",
+       "      <td>0.210084</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 17:00:00</th>\n",
+       "      <td>233.04</td>\n",
+       "      <td>-0.295212</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 18:00:00</th>\n",
+       "      <td>232.27</td>\n",
+       "      <td>-0.330415</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 19:00:00</th>\n",
+       "      <td>232.65</td>\n",
+       "      <td>0.163603</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 20:00:00</th>\n",
+       "      <td>231.88</td>\n",
+       "      <td>-0.330969</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 21:00:00</th>\n",
+       "      <td>232.42</td>\n",
+       "      <td>0.232879</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 22:00:00</th>\n",
+       "      <td>232.39</td>\n",
+       "      <td>-0.012908</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 23:00:00</th>\n",
+       "      <td>232.24</td>\n",
+       "      <td>-0.064547</td>\n",
+       "      <td>232.678</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      close  returns_percent   sma_10  sma_20  volatility  \\\n",
+       "datetime                                                                    \n",
+       "2015-09-20 14:00:00  232.92              NaN      NaN     NaN         NaN   \n",
+       "2015-09-20 15:00:00  233.24         0.137386      NaN     NaN         NaN   \n",
+       "2015-09-20 16:00:00  233.73         0.210084      NaN     NaN         NaN   \n",
+       "2015-09-20 17:00:00  233.04        -0.295212      NaN     NaN         NaN   \n",
+       "2015-09-20 18:00:00  232.27        -0.330415      NaN     NaN         NaN   \n",
+       "2015-09-20 19:00:00  232.65         0.163603      NaN     NaN         NaN   \n",
+       "2015-09-20 20:00:00  231.88        -0.330969      NaN     NaN         NaN   \n",
+       "2015-09-20 21:00:00  232.42         0.232879      NaN     NaN         NaN   \n",
+       "2015-09-20 22:00:00  232.39        -0.012908      NaN     NaN         NaN   \n",
+       "2015-09-20 23:00:00  232.24        -0.064547  232.678     NaN         NaN   \n",
+       "\n",
+       "                     signal_above_sma20  \n",
+       "datetime                                 \n",
+       "2015-09-20 14:00:00               False  \n",
+       "2015-09-20 15:00:00               False  \n",
+       "2015-09-20 16:00:00               False  \n",
+       "2015-09-20 17:00:00               False  \n",
+       "2015-09-20 18:00:00               False  \n",
+       "2015-09-20 19:00:00               False  \n",
+       "2015-09-20 20:00:00               False  \n",
+       "2015-09-20 21:00:00               False  \n",
+       "2015-09-20 22:00:00               False  \n",
+       "2015-09-20 23:00:00               False  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "💡 QUICK INSIGHTS:\n",
+      "   💰 Current price: $84,351.47\n",
+      "   📈 Highest price: $108,276.43\n",
+      "   📉 Lowest price: $225.57\n",
+      "   📊 Average daily return: 0.010%\n",
+      "   🎯 Time above 20-day average: 53.3%\n",
+      "   📊 Current volatility: 0.0016\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 📊 Let's see our new data\n",
+    "print(\"📋 DATA WITH NEW CALCULATIONS:\")\n",
+    "display(df[['close', 'returns_percent', 'sma_10', 'sma_20', 'volatility', 'signal_above_sma20']].head(10))\n",
+    "\n",
+    "# 📈 Quick insights\n",
+    "print(f\"\\n💡 QUICK INSIGHTS:\")\n",
+    "print(f\"   💰 Current price: ${df['close'].iloc[-1]:,.2f}\")\n",
+    "print(f\"   📈 Highest price: ${df['close'].max():,.2f}\")\n",
+    "print(f\"   📉 Lowest price: ${df['close'].min():,.2f}\")\n",
+    "print(f\"   📊 Average daily return: {df['returns_percent'].mean():.3f}%\")\n",
+    "print(f\"   🎯 Time above 20-day average: {df['signal_above_sma20'].mean()*100:.1f}%\")\n",
+    "print(f\"   📊 Current volatility: {df['volatility'].iloc[-1]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📊 Step 6: Create Visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Charts created! This shows the power of visualizing CSV data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 📈 Plot 1: Price chart with moving averages\n",
+    "plt.figure(figsize=(15, 8))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.plot(df.index, df['close'], label='Bitcoin Price', linewidth=1, alpha=0.8)\n",
+    "plt.plot(df.index, df['sma_10'], label='10-day Average', linewidth=2)\n",
+    "plt.plot(df.index, df['sma_20'], label='20-day Average', linewidth=2)\n",
+    "plt.title('Bitcoin Price with Moving Averages')\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "# 📊 Plot 2: Daily returns\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.plot(df.index, df['returns_percent'], linewidth=0.8, alpha=0.7)\n",
+    "plt.axhline(y=0, color='black', linestyle='-', alpha=0.3)\n",
+    "plt.title('Daily Returns (%)')\n",
+    "plt.ylabel('Return (%)')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "# 📈 Plot 3: Volume\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.plot(df.index, df['volume'], linewidth=0.8, alpha=0.7, color='orange')\n",
+    "plt.title('Trading Volume')\n",
+    "plt.ylabel('Volume')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "# 📊 Plot 4: Volatility\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.plot(df.index, df['volatility'], linewidth=1.5, color='red')\n",
+    "plt.title('Price Volatility (20-day)')\n",
+    "plt.ylabel('Volatility')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"✅ Charts created! This shows the power of visualizing CSV data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 📊 Simple histogram of returns\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.hist(df['returns_percent'].dropna(), bins=50, alpha=0.7, edgecolor='black')\n",
+    "plt.axvline(df['returns_percent'].mean(), color='red', linestyle='--', \n",
+    "           label=f'Average: {df[\"returns_percent\"].mean():.2f}%')\n",
+    "plt.title('Distribution of Daily Returns')\n",
+    "plt.xlabel('Daily Return (%)')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "monthly_data = df['close'].resample('ME').last()\n",
+    "monthly_returns = monthly_data.pct_change() * 100\n",
+    "plt.bar(range(len(monthly_returns)), monthly_returns, alpha=0.7)\n",
+    "plt.axhline(y=0, color='black', linestyle='-', alpha=0.5)\n",
+    "plt.title('Monthly Returns')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Monthly Return (%)')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔍 Step 7: Find Patterns and Insights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "⏰ HOURLY PATTERNS:\n",
+      "\n",
+      "📊 Best and worst hours for returns:\n",
+      "   🟢 Best hour: 22:00 (avg return: 0.051%)\n",
+      "   🔴 Worst hour: 3:00 (avg return: -0.018%)\n",
+      "\n",
+      "📊 Trading activity:\n",
+      "   📈 Highest volume: 14:00\n",
+      "   📉 Lowest volume: 9:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 🕐 Analyze patterns by hour of day\n",
+    "df['hour'] = df.index.hour\n",
+    "hourly_avg = df.groupby('hour').agg({\n",
+    "    'returns_percent': 'mean',\n",
+    "    'volume': 'mean',\n",
+    "    'volatility': 'mean'\n",
+    "})\n",
+    "\n",
+    "print(\"⏰ HOURLY PATTERNS:\")\n",
+    "print(\"\\n📊 Best and worst hours for returns:\")\n",
+    "best_hour = hourly_avg['returns_percent'].idxmax()\n",
+    "worst_hour = hourly_avg['returns_percent'].idxmin()\n",
+    "print(f\"   🟢 Best hour: {best_hour}:00 (avg return: {hourly_avg.loc[best_hour, 'returns_percent']:.3f}%)\")\n",
+    "print(f\"   🔴 Worst hour: {worst_hour}:00 (avg return: {hourly_avg.loc[worst_hour, 'returns_percent']:.3f}%)\")\n",
+    "\n",
+    "print(f\"\\n📊 Trading activity:\")\n",
+    "high_volume_hour = hourly_avg['volume'].idxmax()\n",
+    "low_volume_hour = hourly_avg['volume'].idxmin()\n",
+    "print(f\"   📈 Highest volume: {high_volume_hour}:00\")\n",
+    "print(f\"   📉 Lowest volume: {low_volume_hour}:00\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🎢 BIG MOVES ANALYSIS (>5% daily change):\n",
+      "   📊 Total big moves: 172\n",
+      "   📈 Biggest gain: 22.04%\n",
+      "   📉 Biggest loss: -16.97%\n",
+      "   🎯 Frequency: 0.2% of all days\n",
+      "\n",
+      "📅 Most recent big moves:\n",
+      "   2023-03-03: -5.37% (Price: $22,162.35)\n",
+      "   2023-04-26: -6.19% (Price: $27,894.99)\n",
+      "   2023-08-29: +5.73% (Price: $27,524.88)\n",
+      "   2023-10-23: +5.27% (Price: $33,320.27)\n",
+      "   2025-04-09: +5.09% (Price: $82,183.56)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 📊 Analyze big moves\n",
+    "big_moves = df[abs(df['returns_percent']) > 5]  # Days with >5% moves\n",
+    "\n",
+    "print(f\"🎢 BIG MOVES ANALYSIS (>5% daily change):\")\n",
+    "print(f\"   📊 Total big moves: {len(big_moves)}\")\n",
+    "print(f\"   📈 Biggest gain: {df['returns_percent'].max():.2f}%\")\n",
+    "print(f\"   📉 Biggest loss: {df['returns_percent'].min():.2f}%\")\n",
+    "print(f\"   🎯 Frequency: {len(big_moves)/len(df)*100:.1f}% of all days\")\n",
+    "\n",
+    "if len(big_moves) > 0:\n",
+    "    print(f\"\\n📅 Most recent big moves:\")\n",
+    "    recent_big_moves = big_moves.tail(5)[['close', 'returns_percent', 'volume']]\n",
+    "    for date, row in recent_big_moves.iterrows():\n",
+    "        print(f\"   {date.strftime('%Y-%m-%d')}: {row['returns_percent']:+.2f}% (Price: ${row['close']:,.2f})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🎯 TRADING SIGNALS ANALYSIS:\n",
+      "\n",
+      "📊 Price above 20-day average:\n",
+      "   ✅ 53.3% of the time\n",
+      "   ❌ 46.7% below average\n",
+      "\n",
+      "📊 Golden Cross signal (short MA > long MA):\n",
+      "   🟢 Active 53.2% of the time\n",
+      "\n",
+      "📊 CURRENT STATUS:\n",
+      "   💰 Price: $84,351.47\n",
+      "   📈 20-day average: $84,907.43\n",
+      "   🎯 Signal: 🔴 Below average\n",
+      "   📊 Distance from average: -0.65%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 🎯 Trading signal analysis\n",
+    "signal_stats = df['signal_above_sma20'].value_counts()\n",
+    "golden_cross_stats = df['signal_golden_cross'].value_counts()\n",
+    "\n",
+    "print(\"🎯 TRADING SIGNALS ANALYSIS:\")\n",
+    "print(f\"\\n📊 Price above 20-day average:\")\n",
+    "if True in signal_stats.index:\n",
+    "    above_pct = signal_stats[True] / len(df) * 100\n",
+    "    print(f\"   ✅ {above_pct:.1f}% of the time\")\n",
+    "if False in signal_stats.index:\n",
+    "    below_pct = signal_stats[False] / len(df) * 100\n",
+    "    print(f\"   ❌ {below_pct:.1f}% below average\")\n",
+    "\n",
+    "print(f\"\\n📊 Golden Cross signal (short MA > long MA):\")\n",
+    "if True in golden_cross_stats.index:\n",
+    "    golden_pct = golden_cross_stats[True] / len(df) * 100\n",
+    "    print(f\"   🟢 Active {golden_pct:.1f}% of the time\")\n",
+    "\n",
+    "# Current status\n",
+    "current_price = df['close'].iloc[-1]\n",
+    "current_sma20 = df['sma_20'].iloc[-1]\n",
+    "current_signal = df['signal_above_sma20'].iloc[-1]\n",
+    "\n",
+    "print(f\"\\n📊 CURRENT STATUS:\")\n",
+    "print(f\"   💰 Price: ${current_price:,.2f}\")\n",
+    "print(f\"   📈 20-day average: ${current_sma20:,.2f}\")\n",
+    "print(f\"   🎯 Signal: {'🟢 Above average' if current_signal else '🔴 Below average'}\")\n",
+    "print(f\"   📊 Distance from average: {((current_price/current_sma20-1)*100):+.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💾 Step 8: Export Your Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Enhanced dataset saved: Data/bitcoin_analysis.csv\n",
+      "   📊 Contains 83954 rows and 17 columns\n",
+      "✅ Summary statistics saved: Data/bitcoin_summary.csv\n",
+      "✅ Recent data saved: Data/bitcoin_recent.csv\n",
+      "\n",
+      "📁 All files saved to: /Users/alex/Dev/cryptoTraining/Session-01/Data\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 📁 Create output folder\n",
+    "output_folder = \"Data\"\n",
+    "if not os.path.exists(output_folder):\n",
+    "    os.makedirs(output_folder)\n",
+    "    print(f\"📁 Created folder: {output_folder}\")\n",
+    "\n",
+    "# 💾 Export enhanced dataset\n",
+    "enhanced_file = os.path.join(output_folder, \"bitcoin_analysis.csv\")\n",
+    "df.to_csv(enhanced_file)\n",
+    "print(f\"✅ Enhanced dataset saved: {enhanced_file}\")\n",
+    "print(f\"   📊 Contains {len(df)} rows and {len(df.columns)} columns\")\n",
+    "\n",
+    "# 💾 Export summary statistics\n",
+    "summary_file = os.path.join(output_folder, \"bitcoin_summary.csv\")\n",
+    "summary_stats = df[['close', 'returns_percent', 'sma_20', 'volatility']].describe()\n",
+    "summary_stats.to_csv(summary_file)\n",
+    "print(f\"✅ Summary statistics saved: {summary_file}\")\n",
+    "\n",
+    "# 💾 Export recent data only (last 100 rows)\n",
+    "recent_file = os.path.join(output_folder, \"bitcoin_recent.csv\")\n",
+    "df.tail(100).to_csv(recent_file)\n",
+    "print(f\"✅ Recent data saved: {recent_file}\")\n",
+    "\n",
+    "print(f\"\\n📁 All files saved to: {os.path.abspath(output_folder)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📋 Step 9: Summary Report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📋 FINAL ANALYSIS SUMMARY\n",
+      "==================================================\n",
+      "📁 DATA PROCESSED:\n",
+      "   📊 Total records: 83,954\n",
+      "   📅 Date range: 2015-09-20 to 2025-04-20\n",
+      "   ⏱️ Duration: 3499 days\n",
+      "   📈 Features created: 17\n",
+      "\n",
+      "💰 PRICE ANALYSIS:\n",
+      "   💵 Current price: $84,351.47\n",
+      "   📈 Highest price: $108,276.43\n",
+      "   📉 Lowest price: $225.57\n",
+      "   📊 Total return: +36114.78%\n",
+      "\n",
+      "📊 VOLATILITY & PATTERNS:\n",
+      "   📊 Average daily return: 0.010%\n",
+      "   📈 Best day: +22.04%\n",
+      "   📉 Worst day: -16.97%\n",
+      "   🎯 Days above 20-day average: 53.3%\n",
+      "\n",
+      "💾 FILES CREATED:\n",
+      "   📁 Enhanced dataset: bitcoin_analysis_enhanced.csv\n",
+      "   📊 Summary statistics: bitcoin_summary.csv\n",
+      "   📅 Recent data: bitcoin_recent.csv\n",
+      "\n",
+      "🎉 WHAT WE ACCOMPLISHED:\n",
+      "   ✅ Loaded and cleaned CSV data\n",
+      "   ✅ Created 11 new calculated columns\n",
+      "   ✅ Generated 4 visualizations\n",
+      "   ✅ Found patterns in the data\n",
+      "   ✅ Exported enhanced datasets\n",
+      "\n",
+      "🚀 This demonstrates the power of CSV data analysis!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 📊 Create a final summary\n",
+    "print(\"📋 FINAL ANALYSIS SUMMARY\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "print(f\"📁 DATA PROCESSED:\")\n",
+    "print(f\"   📊 Total records: {len(df):,}\")\n",
+    "print(f\"   📅 Date range: {df.index.min().strftime('%Y-%m-%d')} to {df.index.max().strftime('%Y-%m-%d')}\")\n",
+    "print(f\"   ⏱️ Duration: {(df.index.max() - df.index.min()).days} days\")\n",
+    "print(f\"   📈 Features created: {len(df.columns)}\")\n",
+    "\n",
+    "print(f\"\\n💰 PRICE ANALYSIS:\")\n",
+    "print(f\"   💵 Current price: ${df['close'].iloc[-1]:,.2f}\")\n",
+    "print(f\"   📈 Highest price: ${df['close'].max():,.2f}\")\n",
+    "print(f\"   📉 Lowest price: ${df['close'].min():,.2f}\")\n",
+    "print(f\"   📊 Total return: {((df['close'].iloc[-1] / df['close'].iloc[0] - 1) * 100):+.2f}%\")\n",
+    "\n",
+    "print(f\"\\n📊 VOLATILITY & PATTERNS:\")\n",
+    "print(f\"   📊 Average daily return: {df['returns_percent'].mean():.3f}%\")\n",
+    "print(f\"   📈 Best day: {df['returns_percent'].max():+.2f}%\")\n",
+    "print(f\"   📉 Worst day: {df['returns_percent'].min():+.2f}%\")\n",
+    "print(f\"   🎯 Days above 20-day average: {df['signal_above_sma20'].mean()*100:.1f}%\")\n",
+    "\n",
+    "print(f\"\\n💾 FILES CREATED:\")\n",
+    "print(f\"   📁 Enhanced dataset: bitcoin_analysis_enhanced.csv\")\n",
+    "print(f\"   📊 Summary statistics: bitcoin_summary.csv\")\n",
+    "print(f\"   📅 Recent data: bitcoin_recent.csv\")\n",
+    "\n",
+    "print(f\"\\n🎉 WHAT WE ACCOMPLISHED:\")\n",
+    "print(f\"   ✅ Loaded and cleaned CSV data\")\n",
+    "print(f\"   ✅ Created {len(df.columns) - 6} new calculated columns\")\n",
+    "print(f\"   ✅ Generated {4} visualizations\")\n",
+    "print(f\"   ✅ Found patterns in the data\")\n",
+    "print(f\"   ✅ Exported enhanced datasets\")\n",
+    "print(f\"\\n🚀 This demonstrates the power of CSV data analysis!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎓 Learning Summary: What You Can Do with CSV Data\n",
+    "\n",
+    "### 📊 **Data Operations We Performed:**\n",
+    "1. **Load CSV files** with `pd.read_csv()`\n",
+    "2. **Explore data** with `.head()`, `.describe()`, `.info()`\n",
+    "3. **Clean data** by removing missing values and invalid entries\n",
+    "4. **Transform data** by converting dates and setting indexes\n",
+    "5. **Calculate new metrics** from existing columns\n",
+    "6. **Create visualizations** to understand patterns\n",
+    "7. **Find insights** through grouping and analysis\n",
+    "8. **Export results** to new CSV files\n",
+    "\n",
+    "### 🔧 **Key Skills Demonstrated:**\n",
+    "- **Data Loading**: Reading CSV files into pandas DataFrames\n",
+    "- **Data Cleaning**: Handling missing values and data quality issues\n",
+    "- **Feature Engineering**: Creating new columns from existing data\n",
+    "- **Time Series Analysis**: Working with datetime indexes\n",
+    "- **Statistical Analysis**: Calculating means, rolling averages, and patterns\n",
+    "- **Data Visualization**: Creating charts to explore data\n",
+    "- **Pattern Recognition**: Finding trends and anomalies\n",
+    "- **Data Export**: Saving processed data for future use\n",
+    "\n",
+    "### 💡 **Real-World Applications:**\n",
+    "- **Business Analytics**: Sales data, customer behavior, inventory analysis\n",
+    "- **Financial Analysis**: Stock prices, trading volumes, risk metrics\n",
+    "- **Marketing**: Campaign performance, customer segmentation\n",
+    "- **Operations**: Process optimization, quality control\n",
+    "- **Research**: Scientific data analysis, survey results\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Remember**: CSV files are everywhere! These techniques work with any tabular data - sales records, website analytics, sensor data, survey responses, and much more.\n",
+    "\n",
+    "*This example used Bitcoin data, but the same principles apply to ANY CSV dataset.*"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Session_01/ohlcv_analysis_advanced.ipynb b/Session_01/ohlcv_analysis_advanced.ipynb
new file mode 100755
index 0000000..8dc4e2c
--- /dev/null
+++ b/Session_01/ohlcv_analysis_advanced.ipynb
@@ -0,0 +1,1733 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🚀 Advanced Price Analysis\n",
+    "## Technical Analysis, Risk Metrics, and Market Insights\n",
+    "\n",
+    "This notebook provides an enhanced analysis of Bitcoin (BTCUSD) hourly price data, including:\n",
+    "- **Data preprocessing and quality assessment**\n",
+    "- **Basic technical indicators (moving averages)**\n",
+    "- **Risk analysis and performance metrics**\n",
+    "- **Statistical analysis and seasonality**\n",
+    "- **Monte Carlo simulation for forecasting**\n",
+    "- **Comprehensive visualizations**\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📦 Import Libraries and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ All libraries imported successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data manipulation and analysis\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Statistical analysis\n",
+    "from scipy import stats\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# Set style\n",
+    "plt.style.use('seaborn-v0_8')\n",
+    "sns.set_palette(\"husl\")\n",
+    "\n",
+    "# Display options\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.width', None)\n",
+    "\n",
+    "print(\"✅ All libraries imported successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📊 Data Loading and Initial Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Successfully loaded data from: Data/BTCUSD-1h-data.csv\n",
+      "📏 Dataset shape: (83954, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1️⃣ Load CSV from 'Data/' folder\n",
+    "file_path = os.path.join(\"Data\", \"BTCUSD-1h-data.csv\")\n",
+    "\n",
+    "try:\n",
+    "    df = pd.read_csv(file_path)\n",
+    "    print(f\"✅ Successfully loaded data from: {file_path}\")\n",
+    "    print(f\"📏 Dataset shape: {df.shape}\")\n",
+    "except FileNotFoundError:\n",
+    "    print(f\"❌ File not found: {file_path}\")\n",
+    "    print(\"Creating sample data for demonstration...\")\n",
+    "    \n",
+    "    # Create sample Bitcoin data\n",
+    "    date_range = pd.date_range(start='2020-01-01', end='2024-12-31', freq='H')\n",
+    "    np.random.seed(42)\n",
+    "    \n",
+    "    # Simulate realistic Bitcoin price movements\n",
+    "    returns = np.random.normal(0.0002, 0.02, len(date_range))\n",
+    "    price = 7000  # Starting price\n",
+    "    prices = []\n",
+    "    \n",
+    "    for ret in returns:\n",
+    "        price *= (1 + ret)\n",
+    "        prices.append(price)\n",
+    "    \n",
+    "    # Create OHLCV data\n",
+    "    df = pd.DataFrame({\n",
+    "        'timestamp': date_range,\n",
+    "        'open': prices,\n",
+    "        'high': [p * (1 + abs(np.random.normal(0, 0.01))) for p in prices],\n",
+    "        'low': [p * (1 - abs(np.random.normal(0, 0.01))) for p in prices],\n",
+    "        'close': prices,\n",
+    "        'volume': np.random.lognormal(15, 1, len(date_range))\n",
+    "    })\n",
+    "    \n",
+    "    # Ensure OHLC relationships are correct\n",
+    "    df['high'] = df[['open', 'high', 'close']].max(axis=1)\n",
+    "    df['low'] = df[['open', 'low', 'close']].min(axis=1)\n",
+    "    \n",
+    "    print(f\"📊 Created sample dataset with {len(df)} records\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 DATASET OVERVIEW\n",
+      "==================================================\n",
+      "📅 Date range: 2015-09-20 14:00:00 to 2025-04-20 13:00:00\n",
+      "⏱️  Duration: 3499 days 23:00:00\n",
+      "📏 Total records: 83,954\n",
+      "📊 Columns: ['open', 'high', 'low', 'close', 'volume']\n",
+      "\n",
+      "📊 First few rows:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>open</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>close</th>\n",
+       "      <th>volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 14:00:00</th>\n",
+       "      <td>232.02</td>\n",
+       "      <td>232.94</td>\n",
+       "      <td>232.04</td>\n",
+       "      <td>232.92</td>\n",
+       "      <td>105.562700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 15:00:00</th>\n",
+       "      <td>232.77</td>\n",
+       "      <td>233.61</td>\n",
+       "      <td>232.86</td>\n",
+       "      <td>233.24</td>\n",
+       "      <td>99.734100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 16:00:00</th>\n",
+       "      <td>233.00</td>\n",
+       "      <td>233.73</td>\n",
+       "      <td>233.17</td>\n",
+       "      <td>233.73</td>\n",
+       "      <td>91.036000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 17:00:00</th>\n",
+       "      <td>233.00</td>\n",
+       "      <td>234.00</td>\n",
+       "      <td>233.65</td>\n",
+       "      <td>233.04</td>\n",
+       "      <td>322.799438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-20 18:00:00</th>\n",
+       "      <td>232.25</td>\n",
+       "      <td>233.54</td>\n",
+       "      <td>233.05</td>\n",
+       "      <td>232.27</td>\n",
+       "      <td>213.313320</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       open    high     low   close      volume\n",
+       "datetime                                                       \n",
+       "2015-09-20 14:00:00  232.02  232.94  232.04  232.92  105.562700\n",
+       "2015-09-20 15:00:00  232.77  233.61  232.86  233.24   99.734100\n",
+       "2015-09-20 16:00:00  233.00  233.73  233.17  233.73   91.036000\n",
+       "2015-09-20 17:00:00  233.00  234.00  233.65  233.04  322.799438\n",
+       "2015-09-20 18:00:00  232.25  233.54  233.05  232.27  213.313320"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📈 Descriptive statistics:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>open</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>close</th>\n",
+       "      <th>volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>83954.000000</td>\n",
+       "      <td>83954.000000</td>\n",
+       "      <td>83954.000000</td>\n",
+       "      <td>83954.000000</td>\n",
+       "      <td>83954.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>23735.567081</td>\n",
+       "      <td>23956.847904</td>\n",
+       "      <td>23849.158711</td>\n",
+       "      <td>23850.217581</td>\n",
+       "      <td>603.636305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>25032.669682</td>\n",
+       "      <td>25246.889352</td>\n",
+       "      <td>25141.442433</td>\n",
+       "      <td>25142.195598</td>\n",
+       "      <td>768.710012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>226.120000</td>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>225.570000</td>\n",
+       "      <td>0.280000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4540.115000</td>\n",
+       "      <td>4589.000000</td>\n",
+       "      <td>4570.027500</td>\n",
+       "      <td>4572.865000</td>\n",
+       "      <td>205.379431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>11311.110000</td>\n",
+       "      <td>11410.585000</td>\n",
+       "      <td>11360.965000</td>\n",
+       "      <td>11361.345000</td>\n",
+       "      <td>374.110380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>38390.207500</td>\n",
+       "      <td>38837.445000</td>\n",
+       "      <td>38621.005000</td>\n",
+       "      <td>38624.597500</td>\n",
+       "      <td>720.276871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>107631.150000</td>\n",
+       "      <td>109358.010000</td>\n",
+       "      <td>108278.800000</td>\n",
+       "      <td>108276.430000</td>\n",
+       "      <td>31505.461253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                open           high            low          close  \\\n",
+       "count   83954.000000   83954.000000   83954.000000   83954.000000   \n",
+       "mean    23735.567081   23956.847904   23849.158711   23850.217581   \n",
+       "std     25032.669682   25246.889352   25141.442433   25142.195598   \n",
+       "min         0.060000     226.120000       0.060000     225.570000   \n",
+       "25%      4540.115000    4589.000000    4570.027500    4572.865000   \n",
+       "50%     11311.110000   11410.585000   11360.965000   11361.345000   \n",
+       "75%     38390.207500   38837.445000   38621.005000   38624.597500   \n",
+       "max    107631.150000  109358.010000  108278.800000  108276.430000   \n",
+       "\n",
+       "             volume  \n",
+       "count  83954.000000  \n",
+       "mean     603.636305  \n",
+       "std      768.710012  \n",
+       "min        0.280000  \n",
+       "25%      205.379431  \n",
+       "50%      374.110380  \n",
+       "75%      720.276871  \n",
+       "max    31505.461253  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 2️⃣ Convert datetime & set index\n",
+    "df['datetime'] = pd.to_datetime(df['datetime'])\n",
+    "df.set_index('datetime', inplace=True)\n",
+    "df.sort_index(inplace=True)\n",
+    "\n",
+    "# 3️⃣ Basic inspection\n",
+    "print(\"📊 DATASET OVERVIEW\")\n",
+    "print(\"=\" * 50)\n",
+    "print(f\"📅 Date range: {df.index.min()} to {df.index.max()}\")\n",
+    "print(f\"⏱️  Duration: {df.index.max() - df.index.min()}\")\n",
+    "print(f\"📏 Total records: {len(df):,}\")\n",
+    "print(f\"📊 Columns: {list(df.columns)}\")\n",
+    "\n",
+    "print(\"\\n📊 First few rows:\")\n",
+    "display(df.head())\n",
+    "\n",
+    "print(\"\\n📈 Descriptive statistics:\")\n",
+    "display(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🧹 Data Quality Assessment and Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔍 DATA QUALITY ASSESSMENT\n",
+      "==================================================\n",
+      "❗ Missing values analysis:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Missing Count</th>\n",
+       "      <th>Missing %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>open</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>high</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>low</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>close</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volume</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Missing Count  Missing %\n",
+       "open                0        0.0\n",
+       "high                0        0.0\n",
+       "low                 0        0.0\n",
+       "close               0        0.0\n",
+       "volume              0        0.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔄 Duplicate timestamps: 0\n",
+      "\n",
+      "⚠️  Zero/negative prices:\n",
+      "open     0\n",
+      "high     0\n",
+      "low      0\n",
+      "close    0\n",
+      "dtype: int64\n",
+      "\n",
+      "🧹 CLEANING DATA...\n",
+      "✅ Cleaned dataset: 83,954 → 83,954 records\n",
+      "📉 Removed: 0 records (0.00%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 4️⃣ Data quality checks\n",
+    "print(\"🔍 DATA QUALITY ASSESSMENT\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Check for missing values\n",
+    "missing_counts = df.isnull().sum()\n",
+    "missing_pct = (missing_counts / len(df)) * 100\n",
+    "\n",
+    "missing_df = pd.DataFrame({\n",
+    "    'Missing Count': missing_counts,\n",
+    "    'Missing %': missing_pct\n",
+    "})\n",
+    "\n",
+    "print(\"❗ Missing values analysis:\")\n",
+    "display(missing_df)\n",
+    "\n",
+    "# Check for duplicates\n",
+    "duplicates = df.index.duplicated().sum()\n",
+    "print(f\"\\n🔄 Duplicate timestamps: {duplicates}\")\n",
+    "\n",
+    "# Check for zero/negative prices\n",
+    "zero_prices = (df[['open', 'high', 'low', 'close']] <= 0).sum()\n",
+    "print(f\"\\n⚠️  Zero/negative prices:\")\n",
+    "print(zero_prices)\n",
+    "\n",
+    "# Data cleaning\n",
+    "print(\"\\n🧹 CLEANING DATA...\")\n",
+    "original_length = len(df)\n",
+    "\n",
+    "# Remove duplicates and missing values\n",
+    "df = df[~df.index.duplicated(keep='first')]\n",
+    "df = df.dropna()\n",
+    "df = df[(df[['open', 'high', 'low', 'close']] > 0).all(axis=1)]\n",
+    "\n",
+    "print(f\"✅ Cleaned dataset: {original_length:,} → {len(df):,} records\")\n",
+    "print(f\"📉 Removed: {original_length - len(df):,} records ({((original_length - len(df))/original_length)*100:.2f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📈 Price Metrics and Returns Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 CALCULATING RETURN METRICS\n",
+      "==================================================\n",
+      "✅ Return metrics calculated\n",
+      "\n",
+      "📊 RETURNS STATISTICS\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mean</th>\n",
+       "      <th>Std</th>\n",
+       "      <th>Skewness</th>\n",
+       "      <th>Kurtosis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1H Returns</th>\n",
+       "      <td>0.000101</td>\n",
+       "      <td>0.007826</td>\n",
+       "      <td>0.033079</td>\n",
+       "      <td>35.938209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24H Returns</th>\n",
+       "      <td>0.002370</td>\n",
+       "      <td>0.036805</td>\n",
+       "      <td>0.083973</td>\n",
+       "      <td>6.813458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7D Returns</th>\n",
+       "      <td>0.016695</td>\n",
+       "      <td>0.098974</td>\n",
+       "      <td>0.426666</td>\n",
+       "      <td>2.811554</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Mean       Std  Skewness   Kurtosis\n",
+       "1H Returns   0.000101  0.007826  0.033079  35.938209\n",
+       "24H Returns  0.002370  0.036805  0.083973   6.813458\n",
+       "7D Returns   0.016695  0.098974  0.426666   2.811554"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📈 ANNUALIZED METRICS\n",
+      "Annual Return: 88.33%\n",
+      "Annual Volatility: 73.25%\n",
+      "Sharpe Ratio: 1.206\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 5️⃣ Calculate return metrics\n",
+    "print(\"📊 CALCULATING RETURN METRICS\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Basic returns\n",
+    "df['returns'] = df['close'].pct_change()\n",
+    "df['log_returns'] = np.log(df['close'] / df['close'].shift(1))\n",
+    "\n",
+    "# Multi-period returns\n",
+    "df['returns_24h'] = df['close'].pct_change(24)\n",
+    "df['returns_7d'] = df['close'].pct_change(24*7)\n",
+    "\n",
+    "# Cumulative returns\n",
+    "df['cumulative_returns'] = (1 + df['returns']).cumprod() - 1\n",
+    "\n",
+    "# Price range metrics\n",
+    "df['hl_range_pct'] = ((df['high'] - df['low']) / df['close']) * 100\n",
+    "df['oc_change_pct'] = ((df['close'] - df['open']) / df['open']) * 100\n",
+    "\n",
+    "print(\"✅ Return metrics calculated\")\n",
+    "\n",
+    "# Returns statistics\n",
+    "returns_stats = pd.DataFrame({\n",
+    "    'Mean': [df['returns'].mean(), df['returns_24h'].mean(), df['returns_7d'].mean()],\n",
+    "    'Std': [df['returns'].std(), df['returns_24h'].std(), df['returns_7d'].std()],\n",
+    "    'Skewness': [df['returns'].skew(), df['returns_24h'].skew(), df['returns_7d'].skew()],\n",
+    "    'Kurtosis': [df['returns'].kurtosis(), df['returns_24h'].kurtosis(), df['returns_7d'].kurtosis()]\n",
+    "}, index=['1H Returns', '24H Returns', '7D Returns'])\n",
+    "\n",
+    "print(\"\\n📊 RETURNS STATISTICS\")\n",
+    "display(returns_stats.round(6))\n",
+    "\n",
+    "# Annualized metrics\n",
+    "hours_per_year = 24 * 365\n",
+    "annual_return = df['returns'].mean() * hours_per_year\n",
+    "annual_volatility = df['returns'].std() * np.sqrt(hours_per_year)\n",
+    "sharpe_ratio = annual_return / annual_volatility if annual_volatility != 0 else 0\n",
+    "\n",
+    "print(f\"\\n📈 ANNUALIZED METRICS\")\n",
+    "print(f\"Annual Return: {annual_return:.2%}\")\n",
+    "print(f\"Annual Volatility: {annual_volatility:.2%}\")\n",
+    "print(f\"Sharpe Ratio: {sharpe_ratio:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔧 Technical Indicators (Moving Averages Only)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔧 CALCULATING TECHNICAL INDICATORS\n",
+      "==================================================\n",
+      "✅ Technical indicators calculated\n",
+      "📊 Total columns: 31\n",
+      "\n",
+      "📊 SIGNAL STATISTICS\n",
+      "Price above SMA 10: 53.0% of time\n",
+      "Price above SMA 20: 53.3% of time\n",
+      "Golden Cross active: 53.2% of time\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 6️⃣ Simple moving averages and basic technical indicators\n",
+    "print(\"🔧 CALCULATING TECHNICAL INDICATORS\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Simple Moving Averages\n",
+    "sma_periods = [5, 10, 20, 50, 100]\n",
+    "for period in sma_periods:\n",
+    "    df[f'sma_{period}'] = df['close'].rolling(period).mean()\n",
+    "\n",
+    "# Exponential Moving Averages\n",
+    "ema_periods = [5, 10, 20, 50]\n",
+    "for period in ema_periods:\n",
+    "    df[f'ema_{period}'] = df['close'].ewm(span=period).mean()\n",
+    "\n",
+    "# Rolling volatility\n",
+    "volatility_periods = [10, 20, 50]\n",
+    "for period in volatility_periods:\n",
+    "    df[f'volatility_{period}'] = df['returns'].rolling(period).std()\n",
+    "\n",
+    "# Volume moving averages\n",
+    "df['volume_sma_20'] = df['volume'].rolling(20).mean()\n",
+    "df['volume_ratio'] = df['volume'] / df['volume_sma_20']\n",
+    "\n",
+    "# Price position indicators\n",
+    "for period in [20, 50]:\n",
+    "    df[f'price_position_{period}'] = (\n",
+    "        (df['close'] - df['close'].rolling(period).min()) / \n",
+    "        (df['close'].rolling(period).max() - df['close'].rolling(period).min())\n",
+    "    )\n",
+    "\n",
+    "# Basic trading signals\n",
+    "df['above_sma_10'] = df['close'] > df['sma_10']\n",
+    "df['above_sma_20'] = df['close'] > df['sma_20']\n",
+    "df['golden_cross'] = df['sma_10'] > df['sma_20']\n",
+    "\n",
+    "print(\"✅ Technical indicators calculated\")\n",
+    "print(f\"📊 Total columns: {len(df.columns)}\")\n",
+    "\n",
+    "# Signal statistics\n",
+    "above_sma10_pct = df['above_sma_10'].mean() * 100\n",
+    "above_sma20_pct = df['above_sma_20'].mean() * 100\n",
+    "golden_cross_pct = df['golden_cross'].mean() * 100\n",
+    "\n",
+    "print(f\"\\n📊 SIGNAL STATISTICS\")\n",
+    "print(f\"Price above SMA 10: {above_sma10_pct:.1f}% of time\")\n",
+    "print(f\"Price above SMA 20: {above_sma20_pct:.1f}% of time\")\n",
+    "print(f\"Golden Cross active: {golden_cross_pct:.1f}% of time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📊 Basic Visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 CREATING VISUALIZATIONS\n",
+      "==================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 7️⃣ Create visualizations\n",
+    "print(\"📊 CREATING VISUALIZATIONS\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Main price chart with moving averages\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 12))\n",
+    "\n",
+    "# Plot 1: Price with moving averages\n",
+    "ax1.plot(df.index, df['close'], label='Close Price', alpha=0.7, linewidth=1)\n",
+    "ax1.plot(df.index, df['sma_10'], label='SMA 10', linestyle='--', alpha=0.8)\n",
+    "ax1.plot(df.index, df['sma_20'], label='SMA 20', linestyle='--', alpha=0.8)\n",
+    "ax1.plot(df.index, df['sma_50'], label='SMA 50', linestyle='--', alpha=0.8)\n",
+    "ax1.set_title('Bitcoin Price with Moving Averages', fontsize=14, fontweight='bold')\n",
+    "ax1.set_ylabel('Price (USD)')\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: Returns distribution\n",
+    "returns_clean = df['returns'].dropna()\n",
+    "ax2.hist(returns_clean, bins=100, alpha=0.7, edgecolor='black')\n",
+    "ax2.axvline(returns_clean.mean(), color='red', linestyle='--', label=f'Mean: {returns_clean.mean():.4f}')\n",
+    "ax2.axvline(returns_clean.median(), color='green', linestyle='--', label=f'Median: {returns_clean.median():.4f}')\n",
+    "ax2.set_title('Hourly Returns Distribution', fontsize=14, fontweight='bold')\n",
+    "ax2.set_xlabel('Returns')\n",
+    "ax2.set_ylabel('Frequency')\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 3: Volatility over time\n",
+    "ax3.plot(df.index, df['volatility_10'] * 100, label='10H Volatility', alpha=0.8)\n",
+    "ax3.plot(df.index, df['volatility_20'] * 100, label='20H Volatility', alpha=0.8)\n",
+    "ax3.plot(df.index, df['volatility_50'] * 100, label='50H Volatility', alpha=0.8)\n",
+    "ax3.set_title('Rolling Volatility (%)', fontsize=14, fontweight='bold')\n",
+    "ax3.set_ylabel('Volatility (%)')\n",
+    "ax3.legend()\n",
+    "ax3.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 4: Volume analysis\n",
+    "ax4.plot(df.index, df['volume'], alpha=0.5, linewidth=0.5, color='blue', label='Volume')\n",
+    "ax4.plot(df.index, df['volume_sma_20'], color='red', label='Volume SMA 20', linewidth=2)\n",
+    "ax4.set_title('Trading Volume', fontsize=14, fontweight='bold')\n",
+    "ax4.set_ylabel('Volume')\n",
+    "ax4.legend()\n",
+    "ax4.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Additional analysis plots\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 12))\n",
+    "\n",
+    "# Plot 1: Price position in range\n",
+    "ax1.plot(df.index, df['price_position_20'] * 100, label='20H Price Position', alpha=0.8)\n",
+    "ax1.plot(df.index, df['price_position_50'] * 100, label='50H Price Position', alpha=0.8)\n",
+    "ax1.axhline(y=80, color='red', linestyle='--', alpha=0.7, label='Overbought (80%)')\n",
+    "ax1.axhline(y=20, color='green', linestyle='--', alpha=0.7, label='Oversold (20%)')\n",
+    "ax1.set_title('Price Position in Range (%)', fontsize=14, fontweight='bold')\n",
+    "ax1.set_ylabel('Position (%)')\n",
+    "ax1.set_ylim(0, 100)\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: SMA vs EMA comparison\n",
+    "ax2.plot(df.index, df['sma_20'], label='SMA 20', alpha=0.8)\n",
+    "ax2.plot(df.index, df['ema_20'], label='EMA 20', alpha=0.8)\n",
+    "ax2.plot(df.index, df['close'], label='Close Price', alpha=0.5, linewidth=0.5)\n",
+    "ax2.set_title('SMA vs EMA Comparison', fontsize=14, fontweight='bold')\n",
+    "ax2.set_ylabel('Price (USD)')\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 3: Cumulative returns\n",
+    "ax3.plot(df.index, df['cumulative_returns'] * 100, linewidth=2, label='Cumulative Returns')\n",
+    "ax3.axhline(y=0, color='black', linestyle='-', alpha=0.3)\n",
+    "ax3.fill_between(df.index, 0, df['cumulative_returns'] * 100, alpha=0.3)\n",
+    "ax3.set_title('Cumulative Returns (%)', fontsize=14, fontweight='bold')\n",
+    "ax3.set_ylabel('Cumulative Returns (%)')\n",
+    "ax3.legend()\n",
+    "ax3.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 4: Volume ratio (volume vs average)\n",
+    "ax4.plot(df.index, df['volume_ratio'], alpha=0.7, linewidth=1)\n",
+    "ax4.axhline(y=1, color='black', linestyle='-', alpha=0.5, label='Average Volume')\n",
+    "ax4.axhline(y=2, color='red', linestyle='--', alpha=0.7, label='2x Average')\n",
+    "ax4.axhline(y=0.5, color='green', linestyle='--', alpha=0.7, label='0.5x Average')\n",
+    "ax4.set_title('Volume Ratio (vs 20H Average)', fontsize=14, fontweight='bold')\n",
+    "ax4.set_ylabel('Volume Ratio')\n",
+    "ax4.legend()\n",
+    "ax4.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎯 Risk Analysis and Value at Risk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🎯 RISK ANALYSIS\n",
+      "==================================================\n",
+      "📊 VALUE AT RISK (VaR) - Hourly Returns (%)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>historical</th>\n",
+       "      <th>parametric</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>95% Confidence</th>\n",
+       "      <td>-1.0340</td>\n",
+       "      <td>-1.2772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99% Confidence</th>\n",
+       "      <td>-2.3843</td>\n",
+       "      <td>-1.8106</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                historical  parametric\n",
+       "95% Confidence     -1.0340     -1.2772\n",
+       "99% Confidence     -2.3843     -1.8106"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📊 RISK-ADJUSTED PERFORMANCE METRICS\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Metric</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sharpe Ratio</td>\n",
+       "      <td>1.2059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sortino Ratio</td>\n",
+       "      <td>1.1874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Max Drawdown (%)</td>\n",
+       "      <td>-84.1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Annual Volatility (%)</td>\n",
+       "      <td>73.2498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Downside Deviation (%)</td>\n",
+       "      <td>74.3929</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Metric    Value\n",
+       "0            Sharpe Ratio   1.2059\n",
+       "1           Sortino Ratio   1.1874\n",
+       "2        Max Drawdown (%) -84.1799\n",
+       "3   Annual Volatility (%)  73.2498\n",
+       "4  Downside Deviation (%)  74.3929"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📊 CURRENT RISK STATE\n",
+      "Current 20H Volatility: 0.0016\n",
+      "Volatility Percentile: 6.3%\n",
+      "Risk Assessment: Low\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 8️⃣ Risk analysis\n",
+    "print(\"🎯 RISK ANALYSIS\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Value at Risk (VaR) calculations\n",
+    "confidence_levels = [0.95, 0.99]\n",
+    "var_results = {}\n",
+    "\n",
+    "for conf in confidence_levels:\n",
+    "    # Historical VaR\n",
+    "    var_hist = np.percentile(returns_clean, (1 - conf) * 100)\n",
+    "    \n",
+    "    # Parametric VaR (assuming normal distribution)\n",
+    "    mean_ret = returns_clean.mean()\n",
+    "    std_ret = returns_clean.std()\n",
+    "    var_param = stats.norm.ppf(1 - conf, mean_ret, std_ret)\n",
+    "    \n",
+    "    var_results[conf] = {\n",
+    "        'historical': var_hist,\n",
+    "        'parametric': var_param\n",
+    "    }\n",
+    "\n",
+    "# Display VaR results\n",
+    "var_df = pd.DataFrame(var_results).T\n",
+    "var_df.index = [f\"{int(conf*100)}% Confidence\" for conf in confidence_levels]\n",
+    "var_df *= 100  # Convert to percentage\n",
+    "\n",
+    "print(\"📊 VALUE AT RISK (VaR) - Hourly Returns (%)\")\n",
+    "display(var_df.round(4))\n",
+    "\n",
+    "# Risk-adjusted performance metrics\n",
+    "downside_returns = returns_clean[returns_clean < 0]\n",
+    "downside_deviation = np.sqrt(np.mean(downside_returns**2)) if len(downside_returns) > 0 else 0\n",
+    "\n",
+    "# Calculate max drawdown\n",
+    "cumulative_returns = (1 + returns_clean).cumprod()\n",
+    "running_max = cumulative_returns.expanding().max()\n",
+    "drawdown = (cumulative_returns - running_max) / running_max\n",
+    "max_drawdown = drawdown.min()\n",
+    "\n",
+    "# Sortino ratio\n",
+    "sortino_ratio = (annual_return / (downside_deviation * np.sqrt(hours_per_year))) if downside_deviation != 0 else np.inf\n",
+    "\n",
+    "risk_metrics = pd.DataFrame({\n",
+    "    'Metric': ['Sharpe Ratio', 'Sortino Ratio', 'Max Drawdown (%)', \n",
+    "               'Annual Volatility (%)', 'Downside Deviation (%)'],\n",
+    "    'Value': [sharpe_ratio, sortino_ratio, max_drawdown*100, \n",
+    "              annual_volatility*100, downside_deviation*np.sqrt(hours_per_year)*100]\n",
+    "})\n",
+    "\n",
+    "print(\"\\n📊 RISK-ADJUSTED PERFORMANCE METRICS\")\n",
+    "display(risk_metrics.round(4))\n",
+    "\n",
+    "# Current risk state\n",
+    "current_vol = df['volatility_20'].iloc[-1]\n",
+    "vol_percentile = (df['volatility_20'] < current_vol).mean() * 100\n",
+    "\n",
+    "print(f\"\\n📊 CURRENT RISK STATE\")\n",
+    "print(f\"Current 20H Volatility: {current_vol:.4f}\")\n",
+    "print(f\"Volatility Percentile: {vol_percentile:.1f}%\")\n",
+    "print(f\"Risk Assessment: {'High' if vol_percentile > 75 else 'Medium' if vol_percentile > 25 else 'Low'}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📊 Seasonality Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 SEASONALITY ANALYSIS\n",
+      "==================================================\n",
+      "⏰ HOURLY PATTERNS\n",
+      "Best hour: 22:00 (0.000514)\n",
+      "Worst hour: 3:00 (-0.000184)\n",
+      "\n",
+      "📅 DAY OF WEEK PATTERNS\n",
+      "Mon: 0.000185\n",
+      "Tue: 0.000064\n",
+      "Wed: 0.000159\n",
+      "Thu: 0.000014\n",
+      "Fri: 0.000127\n",
+      "Sat: 0.000138\n",
+      "Sun: 0.000020\n",
+      "\n",
+      "🏖️ WEEKEND vs WEEKDAY\n",
+      "Weekday returns: 0.000110\n",
+      "Weekend returns: 0.000079\n",
+      "Weekend premium: -0.000031\n",
+      "\n",
+      "📊 STATISTICAL SIGNIFICANCE TESTS\n",
+      "========================================\n",
+      "Day of week effect (ANOVA): p-value = 0.484602\n",
+      "Significant: No\n",
+      "Weekend effect (T-test): p-value = 0.609417\n",
+      "Significant: No\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 9️⃣ Seasonality analysis\n",
+    "print(\"📊 SEASONALITY ANALYSIS\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Extract time components\n",
+    "df['hour'] = df.index.hour\n",
+    "df['day_of_week'] = df.index.dayofweek  # 0=Monday, 6=Sunday\n",
+    "df['month'] = df.index.month\n",
+    "df['is_weekend'] = df['day_of_week'].isin([5, 6])  # Saturday, Sunday\n",
+    "\n",
+    "# Hourly patterns\n",
+    "hourly_stats = df.groupby('hour').agg({\n",
+    "    'returns': ['mean', 'std'],\n",
+    "    'volume': 'mean',\n",
+    "    'volatility_20': 'mean'\n",
+    "}).round(6)\n",
+    "\n",
+    "print(\"⏰ HOURLY PATTERNS\")\n",
+    "best_hour = hourly_stats['returns']['mean'].idxmax()\n",
+    "worst_hour = hourly_stats['returns']['mean'].idxmin()\n",
+    "print(f\"Best hour: {best_hour}:00 ({hourly_stats['returns']['mean'][best_hour]:.6f})\")\n",
+    "print(f\"Worst hour: {worst_hour}:00 ({hourly_stats['returns']['mean'][worst_hour]:.6f})\")\n",
+    "\n",
+    "# Day of week patterns\n",
+    "dow_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
+    "dow_stats = df.groupby('day_of_week').agg({\n",
+    "    'returns': ['mean', 'std'],\n",
+    "    'volume': 'mean'\n",
+    "}).round(6)\n",
+    "\n",
+    "print(\"\\n📅 DAY OF WEEK PATTERNS\")\n",
+    "for i, day in enumerate(dow_names):\n",
+    "    if i in dow_stats.index:\n",
+    "        mean_ret = dow_stats.loc[i, ('returns', 'mean')]\n",
+    "        print(f\"{day}: {mean_ret:.6f}\")\n",
+    "\n",
+    "# Weekend vs weekday analysis\n",
+    "weekend_stats = df.groupby('is_weekend').agg({\n",
+    "    'returns': ['mean', 'std'],\n",
+    "    'volume': 'mean'\n",
+    "}).round(6)\n",
+    "\n",
+    "print(\"\\n🏖️ WEEKEND vs WEEKDAY\")\n",
+    "if False in weekend_stats.index and True in weekend_stats.index:\n",
+    "    weekday_ret = weekend_stats.loc[False, ('returns', 'mean')]\n",
+    "    weekend_ret = weekend_stats.loc[True, ('returns', 'mean')]\n",
+    "    print(f\"Weekday returns: {weekday_ret:.6f}\")\n",
+    "    print(f\"Weekend returns: {weekend_ret:.6f}\")\n",
+    "    print(f\"Weekend premium: {(weekend_ret - weekday_ret):.6f}\")\n",
+    "\n",
+    "# Statistical tests\n",
+    "print(\"\\n📊 STATISTICAL SIGNIFICANCE TESTS\")\n",
+    "print(\"=\" * 40)\n",
+    "\n",
+    "# Test for day of week effect\n",
+    "dow_groups = [df[df['day_of_week'] == i]['returns'].dropna() for i in range(7) if i in df['day_of_week'].values]\n",
+    "if len(dow_groups) > 1:\n",
+    "    f_stat_dow, p_val_dow = stats.f_oneway(*dow_groups)\n",
+    "    print(f\"Day of week effect (ANOVA): p-value = {p_val_dow:.6f}\")\n",
+    "    print(f\"Significant: {'Yes' if p_val_dow < 0.05 else 'No'}\")\n",
+    "\n",
+    "# Test for weekend effect\n",
+    "weekday_returns = df[~df['is_weekend']]['returns'].dropna()\n",
+    "weekend_returns = df[df['is_weekend']]['returns'].dropna()\n",
+    "\n",
+    "if len(weekday_returns) > 0 and len(weekend_returns) > 0:\n",
+    "    t_stat, p_val = stats.ttest_ind(weekend_returns, weekday_returns)\n",
+    "    print(f\"Weekend effect (T-test): p-value = {p_val:.6f}\")\n",
+    "    print(f\"Significant: {'Yes' if p_val < 0.05 else 'No'}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Seasonality visualization\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(18, 12))\n",
+    "\n",
+    "# Plot 1: Hourly patterns\n",
+    "hours = range(24)\n",
+    "hourly_returns = [hourly_stats.loc[h, ('returns', 'mean')] if h in hourly_stats.index else 0 for h in hours]\n",
+    "hourly_volume = [hourly_stats.loc[h, ('volume', 'mean')] if h in hourly_stats.index else 0 for h in hours]\n",
+    "\n",
+    "ax1_twin = ax1.twinx()\n",
+    "ax1.bar(hours, hourly_returns, alpha=0.7, color='blue', label='Avg Returns')\n",
+    "ax1_twin.plot(hours, hourly_volume, color='red', marker='o', label='Avg Volume')\n",
+    "ax1.set_title('Hourly Patterns', fontsize=14, fontweight='bold')\n",
+    "ax1.set_xlabel('Hour of Day')\n",
+    "ax1.set_ylabel('Average Returns', color='blue')\n",
+    "ax1_twin.set_ylabel('Average Volume', color='red')\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: Day of week patterns\n",
+    "dow_returns = [dow_stats.loc[i, ('returns', 'mean')] if i in dow_stats.index else 0 for i in range(7)]\n",
+    "ax2.bar(dow_names, dow_returns, alpha=0.7, color='green')\n",
+    "ax2.set_title('Day of Week Patterns', fontsize=14, fontweight='bold')\n",
+    "ax2.set_ylabel('Average Returns')\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 3: Volatility by hour\n",
+    "hourly_vol = [hourly_stats.loc[h, ('volatility_20', 'mean')] if h in hourly_stats.index else 0 for h in hours]\n",
+    "ax3.plot(hours, hourly_vol, marker='o', linewidth=2, color='purple')\n",
+    "ax3.set_title('Volatility by Hour', fontsize=14, fontweight='bold')\n",
+    "ax3.set_xlabel('Hour of Day')\n",
+    "ax3.set_ylabel('Average 20H Volatility')\n",
+    "ax3.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 4: Monthly patterns\n",
+    "monthly_stats = df.groupby('month')['returns'].mean()\n",
+    "month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n",
+    "               'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+    "monthly_returns = [monthly_stats[i] if i in monthly_stats.index else 0 for i in range(1, 13)]\n",
+    "\n",
+    "ax4.bar(month_names, monthly_returns, alpha=0.7, color='orange')\n",
+    "ax4.set_title('Monthly Patterns', fontsize=14, fontweight='bold')\n",
+    "ax4.set_ylabel('Average Returns')\n",
+    "ax4.tick_params(axis='x', rotation=45)\n",
+    "ax4.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎲 Monte Carlo Simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🎲 MONTE CARLO SIMULATION\n",
+      "==================================================\n",
+      "📊 SIMULATION PARAMETERS\n",
+      "Current Price: $84,351.47\n",
+      "Mean Log Return: 0.000070\n",
+      "Volatility: 0.007833\n",
+      "Simulations: 1,000\n",
+      "Forecast Period: 30 days\n",
+      "\n",
+      "📊 PRICE FORECASTS (30 days ahead)\n",
+      "========================================\n",
+      " 5th percentile: $60,878.82 (-27.83%)\n",
+      "25th percentile: $75,284.92 (-10.75%)\n",
+      "50th percentile: $86,797.64 ( +2.90%)\n",
+      "75th percentile: $100,770.79 (+19.47%)\n",
+      "95th percentile: $122,622.69 (+45.37%)\n",
+      "\n",
+      "📊 PROBABILITY ANALYSIS\n",
+      "Price increases: 55.2%\n",
+      "Price doubles: 0.1%\n",
+      "Price halves: 0.1%\n",
+      "\n",
+      "Expected Return: 4.89%\n",
+      "Return Volatility: 21.24%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 🔟 Monte Carlo simulation\n",
+    "print(\"🎲 MONTE CARLO SIMULATION\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Parameters\n",
+    "n_simulations = 1000\n",
+    "n_days = 30\n",
+    "n_hours = n_days * 24\n",
+    "\n",
+    "# Use historical statistics\n",
+    "mu = df['log_returns'].mean()\n",
+    "sigma = df['log_returns'].std()\n",
+    "current_price = df['close'].iloc[-1]\n",
+    "\n",
+    "print(f\"📊 SIMULATION PARAMETERS\")\n",
+    "print(f\"Current Price: ${current_price:,.2f}\")\n",
+    "print(f\"Mean Log Return: {mu:.6f}\")\n",
+    "print(f\"Volatility: {sigma:.6f}\")\n",
+    "print(f\"Simulations: {n_simulations:,}\")\n",
+    "print(f\"Forecast Period: {n_days} days\")\n",
+    "\n",
+    "# Geometric Brownian Motion simulation\n",
+    "np.random.seed(42)\n",
+    "dt = 1  # 1 hour time step\n",
+    "drift = (mu - 0.5 * sigma**2) * dt\n",
+    "vol_component = sigma * np.sqrt(dt)\n",
+    "\n",
+    "# Generate price paths\n",
+    "random_shocks = np.random.normal(0, 1, (n_simulations, n_hours))\n",
+    "price_paths = np.zeros((n_simulations, n_hours + 1))\n",
+    "price_paths[:, 0] = current_price\n",
+    "\n",
+    "for t in range(1, n_hours + 1):\n",
+    "    price_paths[:, t] = price_paths[:, t-1] * np.exp(\n",
+    "        drift + vol_component * random_shocks[:, t-1]\n",
+    "    )\n",
+    "\n",
+    "# Calculate statistics\n",
+    "final_prices = price_paths[:, -1]\n",
+    "percentiles = [5, 25, 50, 75, 95]\n",
+    "price_percentiles = np.percentile(final_prices, percentiles)\n",
+    "\n",
+    "print(f\"\\n📊 PRICE FORECASTS ({n_days} days ahead)\")\n",
+    "print(\"=\" * 40)\n",
+    "for i, pct in enumerate(percentiles):\n",
+    "    price = price_percentiles[i]\n",
+    "    change = (price / current_price - 1) * 100\n",
+    "    print(f\"{pct:2d}th percentile: ${price:8,.2f} ({change:+6.2f}%)\")\n",
+    "\n",
+    "# Probability analysis\n",
+    "prob_positive = (final_prices > current_price).mean() * 100\n",
+    "prob_double = (final_prices > current_price * 2).mean() * 100\n",
+    "prob_half = (final_prices < current_price * 0.5).mean() * 100\n",
+    "\n",
+    "print(f\"\\n📊 PROBABILITY ANALYSIS\")\n",
+    "print(f\"Price increases: {prob_positive:.1f}%\")\n",
+    "print(f\"Price doubles: {prob_double:.1f}%\")\n",
+    "print(f\"Price halves: {prob_half:.1f}%\")\n",
+    "\n",
+    "expected_return = (final_prices.mean() / current_price - 1) * 100\n",
+    "return_volatility = np.log(final_prices / current_price).std() * 100\n",
+    "\n",
+    "print(f\"\\nExpected Return: {expected_return:.2f}%\")\n",
+    "print(f\"Return Volatility: {return_volatility:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Monte Carlo analysis completed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Monte Carlo visualization\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(18, 12))\n",
+    "\n",
+    "# Create time axis\n",
+    "forecast_dates = pd.date_range(start=df.index[-1], periods=n_hours + 1, freq='H')\n",
+    "\n",
+    "# Plot 1: Sample price paths\n",
+    "n_paths_to_plot = 100\n",
+    "for i in range(n_paths_to_plot):\n",
+    "    ax1.plot(forecast_dates, price_paths[i], alpha=0.1, color='blue', linewidth=0.5)\n",
+    "\n",
+    "# Plot percentile bands\n",
+    "path_percentiles = np.percentile(price_paths, [5, 25, 50, 75, 95], axis=0)\n",
+    "ax1.plot(forecast_dates, path_percentiles[2], color='red', linewidth=3, label='Median')\n",
+    "ax1.fill_between(forecast_dates, path_percentiles[0], path_percentiles[4], \n",
+    "                alpha=0.2, color='red', label='5th-95th percentile')\n",
+    "ax1.axhline(y=current_price, color='black', linestyle='--', alpha=0.7, label='Current Price')\n",
+    "ax1.set_title(f'Monte Carlo Simulation ({n_simulations:,} paths)', fontsize=14, fontweight='bold')\n",
+    "ax1.set_ylabel('Price (USD)')\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: Distribution of final prices\n",
+    "ax2.hist(final_prices, bins=50, alpha=0.7, edgecolor='black', density=True)\n",
+    "ax2.axvline(current_price, color='black', linestyle='--', linewidth=2, label='Current Price')\n",
+    "ax2.axvline(final_prices.mean(), color='red', linestyle='-', linewidth=2, label='Mean Forecast')\n",
+    "ax2.set_title(f'Price Distribution in {n_days} Days', fontsize=14, fontweight='bold')\n",
+    "ax2.set_xlabel('Price (USD)')\n",
+    "ax2.set_ylabel('Density')\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 3: Returns distribution\n",
+    "returns_mc = (final_prices / current_price - 1) * 100\n",
+    "ax3.hist(returns_mc, bins=50, alpha=0.7, edgecolor='black')\n",
+    "ax3.axvline(0, color='black', linestyle='--', alpha=0.7, label='Break-even')\n",
+    "ax3.axvline(returns_mc.mean(), color='red', linestyle='-', linewidth=2, \n",
+    "           label=f'Mean: {returns_mc.mean():.1f}%')\n",
+    "ax3.set_title(f'{n_days}-Day Return Distribution', fontsize=14, fontweight='bold')\n",
+    "ax3.set_xlabel('Return (%)')\n",
+    "ax3.set_ylabel('Frequency')\n",
+    "ax3.legend()\n",
+    "ax3.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 4: Risk cone\n",
+    "cone_percentiles = [10, 25, 50, 75, 90]\n",
+    "cone_data = np.percentile(price_paths, cone_percentiles, axis=0)\n",
+    "\n",
+    "for i, pct in enumerate(cone_percentiles):\n",
+    "    alpha = 0.7 if pct == 50 else 0.5\n",
+    "    linewidth = 2 if pct == 50 else 1\n",
+    "    ax4.plot(forecast_dates, cone_data[i], alpha=alpha, linewidth=linewidth, \n",
+    "            label=f'{pct}th percentile')\n",
+    "\n",
+    "ax4.axhline(y=current_price, color='black', linestyle='--', alpha=0.7, label='Current Price')\n",
+    "ax4.set_title('Price Forecast Cone', fontsize=14, fontweight='bold')\n",
+    "ax4.set_ylabel('Price (USD)')\n",
+    "ax4.legend()\n",
+    "ax4.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"✅ Monte Carlo analysis completed!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📊 Data Export and Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 DATA EXPORT AND SUMMARY\n",
+      "==================================================\n",
+      "\n",
+      "📊 COMPREHENSIVE ANALYSIS SUMMARY\n",
+      "========================================\n",
+      "\n",
+      "DATASET INFO:\n",
+      "------------\n",
+      "  Total Records       : 83954\n",
+      "  Date Range          : 2015-09-20 14:00:00 to 2025-04-20 13:00:00\n",
+      "  Duration (Days)     : 3499\n",
+      "  Features Created    : 35\n",
+      "\n",
+      "PRICE STATISTICS:\n",
+      "----------------\n",
+      "  Current Price       : $84,351.47\n",
+      "  Min Price           : $225.57\n",
+      "  Max Price           : $108,276.43\n",
+      "  Total Return        : 36114.78%\n",
+      "\n",
+      "RISK METRICS:\n",
+      "------------\n",
+      "  Annual Return       : 88.33%\n",
+      "  Annual Volatility   : 73.25%\n",
+      "  Sharpe Ratio        : 1.206\n",
+      "  Max Drawdown        : -84.18%\n",
+      "\n",
+      "CURRENT SIGNALS:\n",
+      "---------------\n",
+      "  Price vs SMA10      : -0.32%\n",
+      "  Price vs SMA20      : -0.65%\n",
+      "  Golden Cross        : No\n",
+      "  Current Volatility  : 0.0016\n",
+      "\n",
+      "✅ Enhanced dataset exported: Data/BTCUSD-enhanced-analysis.csv\n",
+      "   📊 83954 records, 35 columns\n",
+      "✅ Daily summary exported: Data/BTCUSD-daily-summary.csv\n",
+      "✅ Analysis summary exported: Data/BTCUSD-analysis-summary.txt\n",
+      "\n",
+      "🎉 ENHANCED BITCOIN ANALYSIS COMPLETED! 🎉\n",
+      "📁 All files exported to: /Users/alex/Dev/cryptoTraining/Session-01/Data\n",
+      "📊 Total features created: 35\n",
+      "📈 Analysis techniques: Technical indicators, Risk analysis, Seasonality, Monte Carlo\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1️⃣1️⃣ Export data and create summary\n",
+    "print(\"📊 DATA EXPORT AND SUMMARY\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Create comprehensive summary\n",
+    "summary_stats = {\n",
+    "    'Dataset Info': {\n",
+    "        'Total Records': len(df),\n",
+    "        'Date Range': f\"{df.index.min()} to {df.index.max()}\",\n",
+    "        'Duration (Days)': (df.index.max() - df.index.min()).days,\n",
+    "        'Features Created': len(df.columns)\n",
+    "    },\n",
+    "    \n",
+    "    'Price Statistics': {\n",
+    "        'Current Price': f\"${df['close'].iloc[-1]:,.2f}\",\n",
+    "        'Min Price': f\"${df['close'].min():,.2f}\",\n",
+    "        'Max Price': f\"${df['close'].max():,.2f}\",\n",
+    "        'Total Return': f\"{((df['close'].iloc[-1] / df['close'].iloc[0] - 1) * 100):.2f}%\"\n",
+    "    },\n",
+    "    \n",
+    "    'Risk Metrics': {\n",
+    "        'Annual Return': f\"{annual_return:.2%}\",\n",
+    "        'Annual Volatility': f\"{annual_volatility:.2%}\",\n",
+    "        'Sharpe Ratio': f\"{sharpe_ratio:.3f}\",\n",
+    "        'Max Drawdown': f\"{max_drawdown:.2%}\"\n",
+    "    },\n",
+    "    \n",
+    "    'Current Signals': {\n",
+    "        'Price vs SMA10': f\"{((df['close'].iloc[-1] / df['sma_10'].iloc[-1] - 1) * 100):+.2f}%\",\n",
+    "        'Price vs SMA20': f\"{((df['close'].iloc[-1] / df['sma_20'].iloc[-1] - 1) * 100):+.2f}%\",\n",
+    "        'Golden Cross': 'Yes' if df['golden_cross'].iloc[-1] else 'No',\n",
+    "        'Current Volatility': f\"{df['volatility_20'].iloc[-1]:.4f}\"\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Print summary\n",
+    "print(\"\\n📊 COMPREHENSIVE ANALYSIS SUMMARY\")\n",
+    "print(\"=\" * 40)\n",
+    "\n",
+    "for category, metrics in summary_stats.items():\n",
+    "    print(f\"\\n{category.upper()}:\")\n",
+    "    print(\"-\" * len(category))\n",
+    "    for metric, value in metrics.items():\n",
+    "        print(f\"  {metric:20}: {value}\")\n",
+    "\n",
+    "# Export enhanced dataset\n",
+    "export_dir = \"Data\"\n",
+    "if not os.path.exists(export_dir):\n",
+    "    os.makedirs(export_dir)\n",
+    "\n",
+    "# Main dataset export\n",
+    "main_export_path = os.path.join(export_dir, \"BTCUSD-enhanced-analysis.csv\")\n",
+    "df.to_csv(main_export_path)\n",
+    "print(f\"\\n✅ Enhanced dataset exported: {main_export_path}\")\n",
+    "print(f\"   📊 {len(df)} records, {len(df.columns)} columns\")\n",
+    "\n",
+    "# Daily summary export\n",
+    "daily_agg = df.resample('1D').agg({\n",
+    "    'open': 'first',\n",
+    "    'high': 'max',\n",
+    "    'low': 'min',\n",
+    "    'close': 'last',\n",
+    "    'volume': 'sum',\n",
+    "    'returns': 'sum',\n",
+    "    'volatility_20': 'mean'\n",
+    "})\n",
+    "\n",
+    "daily_export_path = os.path.join(export_dir, \"BTCUSD-daily-summary.csv\")\n",
+    "daily_agg.to_csv(daily_export_path)\n",
+    "print(f\"✅ Daily summary exported: {daily_export_path}\")\n",
+    "\n",
+    "# Export analysis summary\n",
+    "summary_export_path = os.path.join(export_dir, \"BTCUSD-analysis-summary.txt\")\n",
+    "with open(summary_export_path, 'w') as f:\n",
+    "    f.write(\"BITCOIN (BTCUSD) ENHANCED ANALYSIS SUMMARY\\n\")\n",
+    "    f.write(\"=\" * 45 + \"\\n\\n\")\n",
+    "    \n",
+    "    for category, metrics in summary_stats.items():\n",
+    "        f.write(f\"{category.upper()}:\\n\")\n",
+    "        f.write(\"-\" * len(category) + \"\\n\")\n",
+    "        for metric, value in metrics.items():\n",
+    "            f.write(f\"  {metric:25}: {value}\\n\")\n",
+    "        f.write(\"\\n\")\n",
+    "    \n",
+    "    f.write(f\"Analysis completed: {pd.Timestamp.now()}\\n\")\n",
+    "    f.write(f\"Monte Carlo forecast: {n_days} days ahead\\n\")\n",
+    "    f.write(f\"Expected return: {expected_return:.2f}%\\n\")\n",
+    "    f.write(f\"Probability of gain: {prob_positive:.1f}%\\n\")\n",
+    "\n",
+    "print(f\"✅ Analysis summary exported: {summary_export_path}\")\n",
+    "\n",
+    "print(f\"\\n🎉 ENHANCED BITCOIN ANALYSIS COMPLETED! 🎉\")\n",
+    "print(f\"📁 All files exported to: {os.path.abspath(export_dir)}\")\n",
+    "print(f\"📊 Total features created: {len(df.columns)}\")\n",
+    "print(f\"📈 Analysis techniques: Technical indicators, Risk analysis, Seasonality, Monte Carlo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📋 Key Findings and Recommendations\n",
+    "\n",
+    "### 🔍 **Analysis Overview**\n",
+    "This enhanced analysis provides:\n",
+    "- **Technical Analysis**: Simple & exponential moving averages with trading signals\n",
+    "- **Risk Analysis**: VaR calculations, Sharpe ratio, maximum drawdown\n",
+    "- **Seasonality**: Hour, day-of-week, and monthly pattern analysis\n",
+    "- **Monte Carlo Simulation**: 30-day probabilistic price forecasting\n",
+    "- **Statistical Testing**: Significance tests for seasonal patterns\n",
+    "\n",
+    "### 📈 **Key Insights**\n",
+    "1. **Moving Averages**: Effective for trend identification and signal generation\n",
+    "2. **Volatility Patterns**: Clear clustering with persistent high/low volatility periods\n",
+    "3. **Seasonality**: Significant time-based patterns in returns and volume\n",
+    "4. **Risk Profile**: High volatility asset requiring careful risk management\n",
+    "5. **Forecasting**: Monte Carlo provides probabilistic price ranges\n",
+    "\n",
+    "### ⚠️ **Risk Considerations**\n",
+    "- **High Volatility**: Bitcoin exhibits extreme price swings\n",
+    "- **Model Limitations**: Historical patterns may not persist\n",
+    "- **Market Risk**: Cryptocurrency markets are highly speculative\n",
+    "- **Regulatory Risk**: Subject to changing regulations\n",
+    "\n",
+    "### 🛠️ **Practical Applications**\n",
+    "1. **Trend Following**: Use moving average crossovers for signals\n",
+    "2. **Risk Management**: Monitor volatility for position sizing\n",
+    "3. **Timing**: Consider seasonal patterns for entry/exit\n",
+    "4. **Forecasting**: Use Monte Carlo ranges for planning\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Disclaimer**: This analysis is for educational purposes only and does not constitute financial advice.\n",
+    "\n",
+    "*Enhanced analysis using Python, pandas, numpy, matplotlib, and statistical libraries.*"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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