391 lines
11 KiB
Text
Executable file
391 lines
11 KiB
Text
Executable file
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Session 1 - DataFrames - Lesson 1: Creating DataFrames\n",
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"\n",
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"## Learning Objectives\n",
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"- Understand different methods to create pandas DataFrames\n",
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"- Learn to create DataFrames from dictionaries, lists, and NumPy arrays\n",
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"- Practice with various data types and structures\n",
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"\n",
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"## Prerequisites\n",
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"- Basic Python knowledge\n",
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"- Understanding of lists and dictionaries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Pandas version: 2.2.3\n",
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"NumPy version: 2.2.6\n"
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]
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}
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],
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"source": [
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"# Import required libraries\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from datetime import datetime, timedelta\n",
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"\n",
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"print(f\"Pandas version: {pd.__version__}\")\n",
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"print(f\"NumPy version: {np.__version__}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Method 1: Creating DataFrame from Dictionary\n",
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"\n",
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"This is the most common and intuitive way to create a DataFrame."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Student DataFrame:\n",
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" Name Age Grade Score\n",
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"0 Alice 23 A 95\n",
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"1 Bob 25 B 87\n",
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"2 Charlie 22 A 92\n",
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"3 Diana 24 C 78\n",
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"4 Eve 23 B 89\n",
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"\n",
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"Shape: (5, 4)\n",
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"Data types:\n",
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"Name object\n",
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"Age int64\n",
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"Grade object\n",
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"Score int64\n",
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"dtype: object\n"
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]
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}
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],
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"source": [
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"# Creating DataFrame from dictionary\n",
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"student_data = {\n",
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" 'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],\n",
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" 'Age': [23, 25, 22, 24, 23],\n",
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" 'Grade': ['A', 'B', 'A', 'C', 'B'],\n",
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" 'Score': [95, 87, 92, 78, 89]\n",
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"}\n",
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"\n",
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"df_students = pd.DataFrame(student_data)\n",
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"print(\"Student DataFrame:\")\n",
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"print(df_students)\n",
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"print(f\"\\nShape: {df_students.shape}\")\n",
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"print(f\"Data types:\\n{df_students.dtypes}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Method 2: Creating DataFrame from Lists\n",
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"\n",
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"You can create DataFrames from separate lists by combining them in a dictionary."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Cities DataFrame:\n",
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" City Population_Million Country\n",
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"0 New York 8.4 USA\n",
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"1 London 8.9 UK\n",
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"2 Tokyo 13.9 Japan\n",
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"3 Paris 2.1 France\n",
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"4 Sydney 5.3 Australia\n",
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"\n",
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"Index: [0, 1, 2, 3, 4]\n",
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"Columns: ['City', 'Population_Million', 'Country']\n"
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]
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}
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],
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"source": [
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"# Creating DataFrame from separate lists\n",
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"cities = ['New York', 'London', 'Tokyo', 'Paris', 'Sydney']\n",
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"populations = [8.4, 8.9, 13.9, 2.1, 5.3]\n",
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"countries = ['USA', 'UK', 'Japan', 'France', 'Australia']\n",
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"\n",
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"df_cities = pd.DataFrame({\n",
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" 'City': cities,\n",
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" 'Population_Million': populations,\n",
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" 'Country': countries\n",
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"})\n",
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"\n",
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"print(\"Cities DataFrame:\")\n",
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"print(df_cities)\n",
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"print(f\"\\nIndex: {df_cities.index.tolist()}\")\n",
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"print(f\"Columns: {df_cities.columns.tolist()}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Method 3: Creating DataFrame from NumPy Array\n",
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"\n",
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"This method is useful when working with numerical data or when you need random data for testing."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Random DataFrame:\n",
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" Column_A Column_B Column_C\n",
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"Row1 52 93 15\n",
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"Row2 72 61 21\n",
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"Row3 83 87 75\n",
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"Row4 75 88 24\n",
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"Row5 3 22 53\n",
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"\n",
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"Summary statistics:\n",
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" Column_A Column_B Column_C\n",
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"count 5.000000 5.000000 5.000000\n",
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"mean 57.000000 70.200000 37.600000\n",
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"std 32.272279 29.693434 25.530374\n",
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"min 3.000000 22.000000 15.000000\n",
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"25% 52.000000 61.000000 21.000000\n",
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"50% 72.000000 87.000000 24.000000\n",
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"75% 75.000000 88.000000 53.000000\n",
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"max 83.000000 93.000000 75.000000\n"
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]
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}
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],
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"source": [
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"# Creating DataFrame from NumPy array\n",
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"np.random.seed(42) # For reproducible results\n",
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"random_data = np.random.randint(1, 100, size=(5, 3))\n",
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"\n",
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"df_random = pd.DataFrame(random_data, \n",
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" columns=['Column_A', 'Column_B', 'Column_C'],\n",
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" index=['Row1', 'Row2', 'Row3', 'Row4', 'Row5'])\n",
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"\n",
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"print(\"Random DataFrame:\")\n",
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"print(df_random)\n",
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"print(f\"\\nSummary statistics:\")\n",
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"print(df_random.describe())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Method 4: Creating DataFrame with Custom Index\n",
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"\n",
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"You can specify custom row labels (index) when creating DataFrames."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Products DataFrame with Custom Index:\n",
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" Product Price Stock\n",
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"PROD001 Laptop 1200 15\n",
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"PROD002 Phone 800 50\n",
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"PROD003 Tablet 600 30\n",
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"PROD004 Monitor 300 20\n",
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"\n",
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"Accessing by index label 'PROD002':\n",
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"Product Phone\n",
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"Price 800\n",
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"Stock 50\n",
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"Name: PROD002, dtype: object\n"
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]
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}
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],
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"source": [
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"# Creating DataFrame with custom index\n",
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"product_data = {\n",
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" 'Product': ['Laptop', 'Phone', 'Tablet', 'Monitor'],\n",
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" 'Price': [1200, 800, 600, 300],\n",
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" 'Stock': [15, 50, 30, 20]\n",
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"}\n",
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"\n",
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"# Custom index using product codes\n",
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"custom_index = ['PROD001', 'PROD002', 'PROD003', 'PROD004']\n",
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"df_products = pd.DataFrame(product_data, index=custom_index)\n",
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"\n",
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"print(\"Products DataFrame with Custom Index:\")\n",
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"print(df_products)\n",
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"print(f\"\\nAccessing by index label 'PROD002':\")\n",
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"print(df_products.loc['PROD002'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Method 5: Creating Empty DataFrame and Adding Data\n",
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"\n",
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"Sometimes you need to start with an empty DataFrame and add data incrementally."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Empty DataFrame:\n",
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"Empty DataFrame\n",
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"Columns: [Date, Temperature, Humidity, Pressure]\n",
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"Index: []\n",
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"Shape: (0, 4)\n",
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"\n",
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"DataFrame after adding data:\n",
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" Date Temperature Humidity Pressure\n",
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"0 2024-01-01 22.5 65 1013.2\n",
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"1 2024-01-02 24.1 68 1015.1\n",
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"2 2024-01-03 21.8 72 1012.8\n"
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]
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}
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],
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"source": [
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"# Creating empty DataFrame with specified columns\n",
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"columns = ['Date', 'Temperature', 'Humidity', 'Pressure']\n",
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"df_weather = pd.DataFrame(columns=columns)\n",
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"\n",
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"print(\"Empty DataFrame:\")\n",
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"print(df_weather)\n",
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"print(f\"Shape: {df_weather.shape}\")\n",
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"\n",
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"# Adding data row by row (not recommended for large datasets)\n",
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"weather_data = [\n",
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" ['2024-01-01', 22.5, 65, 1013.2],\n",
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" ['2024-01-02', 24.1, 68, 1015.1],\n",
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" ['2024-01-03', 21.8, 72, 1012.8]\n",
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"]\n",
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"\n",
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"for row in weather_data:\n",
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" df_weather.loc[len(df_weather)] = row\n",
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"\n",
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"print(\"\\nDataFrame after adding data:\")\n",
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"print(df_weather)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Practice Exercises\n",
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"\n",
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"Try these exercises to reinforce your learning:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Exercise 1: Create a DataFrame from dictionary with employee information\n",
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"# Include: Employee ID, Name, Department, Salary, Years of Experience\n",
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"\n",
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"# Your code here:\n",
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"employee_data = {\n",
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" # Add your data here\n",
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"}\n",
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"\n",
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"# df_employees = pd.DataFrame(employee_data)\n",
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"# print(df_employees)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Exercise 2: Create a DataFrame using NumPy with 6 rows and 4 columns\n",
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"# Use column names: 'A', 'B', 'C', 'D'\n",
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"# Use row indices: 'R1', 'R2', 'R3', 'R4', 'R5', 'R6'\n",
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"\n",
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"# Your code here:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Exercise 3: Create a DataFrame with mixed data types\n",
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"# Include at least one string, integer, float, and boolean column\n",
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"\n",
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"# Your code here:\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Key Takeaways\n",
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"\n",
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"1. **Dictionary method** is most intuitive for creating DataFrames\n",
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"2. **NumPy arrays** are useful for numerical data and testing\n",
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"3. **Custom indices** provide meaningful row labels\n",
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"4. **Empty DataFrames** can be useful but avoid adding rows one by one for large datasets\n",
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"5. Always check the **shape** and **data types** of your DataFrame after creation\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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