{
 "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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": {
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       "    }\n",
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       "    }\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",
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       "\n",
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       "    }\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.*"
   ]
  }
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