eda/pivot-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Click [here](https://morihosseini.medium.com/simplify-data-analysis-with-pandas-pivot-tables-73143c8f5b12) to access the associated Medium article."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: textract 1.6.5 has a non-standard dependency specifier extract-msg<=0.29.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of textract or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -q pandas matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date Region Product  Sales\n",
      "0  2023-01-01  North       A    100\n",
      "1  2023-01-01  South       A    150\n",
      "2  2023-01-02  North       B    200\n",
      "3  2023-01-02  South       B    250\n",
      "4  2023-01-03  North       A    300\n",
      "5  2023-01-03  South       A    350\n"
     ]
    }
   ],
   "source": [
    "# Sample DataFrame\n",
    "data = {\n",
    "    \"Date\": [\n",
    "        \"2023-01-01\",\n",
    "        \"2023-01-01\",\n",
    "        \"2023-01-02\",\n",
    "        \"2023-01-02\",\n",
    "        \"2023-01-03\",\n",
    "        \"2023-01-03\",\n",
    "    ],\n",
    "    \"Region\": [\"North\", \"South\", \"North\", \"South\", \"North\", \"South\"],\n",
    "    \"Product\": [\"A\", \"A\", \"B\", \"B\", \"A\", \"A\"],\n",
    "    \"Sales\": [100, 150, 200, 250, 300, 350],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Your First Pivot Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product    A    B\n",
      "Region           \n",
      "North    400  200\n",
      "South    500  250\n"
     ]
    }
   ],
   "source": [
    "pivot_table = pd.pivot_table(\n",
    "    df, values=\"Sales\", index=\"Region\", columns=\"Product\", aggfunc=\"sum\"\n",
    ")\n",
    "print(pivot_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing Aggregation Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product    A    B\n",
      "Region           \n",
      "North    200  200\n",
      "South    250  250\n"
     ]
    }
   ],
   "source": [
    "pivot_table_avg = pd.pivot_table(\n",
    "    df, values=\"Sales\", index=\"Region\", columns=\"Product\", aggfunc=\"mean\"\n",
    ")\n",
    "print(pivot_table_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tips for Working with Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 sum  mean\n",
      "               Sales Sales\n",
      "Region Product            \n",
      "North  A         400   200\n",
      "       B         200   200\n",
      "South  A         500   250\n",
      "       B         250   250\n"
     ]
    }
   ],
   "source": [
    "pivot_table_multi = pd.pivot_table(\n",
    "    df, values=\"Sales\", index=[\"Region\", \"Product\"], aggfunc=[\"sum\", \"mean\"]\n",
    ")\n",
    "print(pivot_table_multi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Pivot Table Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date Region Product     Category  Sales\n",
      "0  2023-01-01  North       A  Electronics    100\n",
      "1  2023-01-01  South       A  Electronics    150\n",
      "2  2023-01-02  North       B    Furniture    200\n",
      "3  2023-01-02  South       B    Furniture    250\n",
      "4  2023-01-03  North       A  Electronics    300\n",
      "5  2023-01-03  South       A  Electronics    350\n"
     ]
    }
   ],
   "source": [
    "# Expanded sample DataFrame\n",
    "data = {\n",
    "    \"Date\": [\n",
    "        \"2023-01-01\",\n",
    "        \"2023-01-01\",\n",
    "        \"2023-01-02\",\n",
    "        \"2023-01-02\",\n",
    "        \"2023-01-03\",\n",
    "        \"2023-01-03\",\n",
    "    ],\n",
    "    \"Region\": [\"North\", \"South\", \"North\", \"South\", \"North\", \"South\"],\n",
    "    \"Product\": [\"A\", \"A\", \"B\", \"B\", \"A\", \"A\"],\n",
    "    \"Category\": [\n",
    "        \"Electronics\",\n",
    "        \"Electronics\",\n",
    "        \"Furniture\",\n",
    "        \"Furniture\",\n",
    "        \"Electronics\",\n",
    "        \"Electronics\",\n",
    "    ],\n",
    "    \"Sales\": [100, 150, 200, 250, 300, 350],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Level Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product                 A      B\n",
      "Region Category                 \n",
      "North  Electronics  400.0    NaN\n",
      "       Furniture      NaN  200.0\n",
      "South  Electronics  500.0    NaN\n",
      "       Furniture      NaN  250.0\n"
     ]
    }
   ],
   "source": [
    "pivot_table_multi = pd.pivot_table(\n",
    "    df,\n",
    "    values=\"Sales\",\n",
    "    index=[\"Region\", \"Category\"],\n",
    "    columns=\"Product\",\n",
    "    aggfunc=\"sum\",\n",
    ")\n",
    "print(pivot_table_multi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product               A    B\n",
      "Region Category             \n",
      "North  Electronics  400    0\n",
      "       Furniture      0  200\n",
      "South  Electronics  500    0\n",
      "       Furniture      0  250\n"
     ]
    }
   ],
   "source": [
    "pivot_table_filled = pd.pivot_table(\n",
    "    df,\n",
    "    values=\"Sales\",\n",
    "    index=[\"Region\", \"Category\"],\n",
    "    columns=\"Product\",\n",
    "    aggfunc=\"sum\",\n",
    "    fill_value=0,\n",
    ")\n",
    "print(pivot_table_filled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Multiple Aggregation Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    sum      mean     \n",
      "Product               A    B    A    B\n",
      "Region Category                       \n",
      "North  Electronics  400    0  200    0\n",
      "       Furniture      0  200    0  200\n",
      "South  Electronics  500    0  250    0\n",
      "       Furniture      0  250    0  250\n"
     ]
    }
   ],
   "source": [
    "pivot_table_multi_agg = pd.pivot_table(\n",
    "    df,\n",
    "    values=\"Sales\",\n",
    "    index=[\"Region\", \"Category\"],\n",
    "    columns=\"Product\",\n",
    "    aggfunc=[\"sum\", \"mean\"],\n",
    "    fill_value=0,\n",
    ")\n",
    "print(pivot_table_multi_agg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product               A    B  Total\n",
      "Region Category                    \n",
      "North  Electronics  400    0    400\n",
      "       Furniture      0  200    200\n",
      "South  Electronics  500    0    500\n",
      "       Furniture      0  250    250\n",
      "Total               900  450   1350\n"
     ]
    }
   ],
   "source": [
    "pivot_table_margins = pd.pivot_table(\n",
    "    df,\n",
    "    values=\"Sales\",\n",
    "    index=[\"Region\", \"Category\"],\n",
    "    columns=\"Product\",\n",
    "    aggfunc=\"sum\",\n",
    "    fill_value=0,\n",
    "    margins=True,\n",
    "    margins_name=\"Total\",\n",
    ")\n",
    "print(pivot_table_margins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tips for Effective Use of Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of visualizing the pivot table\n",
    "pivot_table_margins.plot(kind=\"bar\")\n",
    "plt.title(\"Sales by Region and Product Category\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real-World Examples and Case Studies\n",
    "\n",
    "## Example 1: Sales Analysis by Region and Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date Region Product     Category  Sales\n",
      "0  2023-01-01  North       A  Electronics    100\n",
      "1  2023-01-01  South       A  Electronics    150\n",
      "2  2023-01-02  North       B    Furniture    200\n",
      "3  2023-01-02  South       B    Furniture    250\n",
      "4  2023-01-03  North       A  Electronics    300\n",
      "5  2023-01-03  South       A  Electronics    350\n",
      "6  2023-01-04  North       B    Furniture    400\n",
      "7  2023-01-04  South       B    Furniture    450\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    \"Date\": [\n",
    "        \"2023-01-01\",\n",
    "        \"2023-01-01\",\n",
    "        \"2023-01-02\",\n",
    "        \"2023-01-02\",\n",
    "        \"2023-01-03\",\n",
    "        \"2023-01-03\",\n",
    "        \"2023-01-04\",\n",
    "        \"2023-01-04\",\n",
    "    ],\n",
    "    \"Region\": [\n",
    "        \"North\",\n",
    "        \"South\",\n",
    "        \"North\",\n",
    "        \"South\",\n",
    "        \"North\",\n",
    "        \"South\",\n",
    "        \"North\",\n",
    "        \"South\",\n",
    "    ],\n",
    "    \"Product\": [\"A\", \"A\", \"B\", \"B\", \"A\", \"A\", \"B\", \"B\"],\n",
    "    \"Category\": [\n",
    "        \"Electronics\",\n",
    "        \"Electronics\",\n",
    "        \"Furniture\",\n",
    "        \"Furniture\",\n",
    "        \"Electronics\",\n",
    "        \"Electronics\",\n",
    "        \"Furniture\",\n",
    "        \"Furniture\",\n",
    "    ],\n",
    "    \"Sales\": [100, 150, 200, 250, 300, 350, 400, 450],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Summarize Total Sales by Region and Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product    A    B\n",
      "Region           \n",
      "North    400  600\n",
      "South    500  700\n"
     ]
    }
   ],
   "source": [
    "pivot_table_sales = pd.pivot_table(\n",
    "    df, values=\"Sales\", index=\"Region\", columns=\"Product\", aggfunc=\"sum\"\n",
    ")\n",
    "print(pivot_table_sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Analyzing Sales Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product    A     B  Total\n",
      "Region                   \n",
      "North    400   600   1000\n",
      "South    500   700   1200\n",
      "Total    900  1300   2200\n"
     ]
    }
   ],
   "source": [
    "pivot_table_sales_margins = pd.pivot_table(\n",
    "    df,\n",
    "    values=\"Sales\",\n",
    "    index=\"Region\",\n",
    "    columns=\"Product\",\n",
    "    aggfunc=\"sum\",\n",
    "    margins=True,\n",
    "    margins_name=\"Total\",\n",
    ")\n",
    "print(pivot_table_sales_margins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Visualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot_table_sales_margins.plot(kind=\"bar\")\n",
    "plt.title(\"Sales by Region and Product\")\n",
    "plt.xlabel(\"Region\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Customer Purchase Behavior Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CustomerID PurchaseDate     Category  Amount\n",
      "0           1   2023-01-01  Electronics     120\n",
      "1           2   2023-01-01  Electronics     150\n",
      "2           1   2023-01-02    Furniture     200\n",
      "3           3   2023-01-02    Furniture     250\n",
      "4           2   2023-01-03  Electronics     300\n",
      "5           3   2023-01-03  Electronics     350\n",
      "6           1   2023-01-04    Furniture     400\n",
      "7           2   2023-01-04    Furniture     450\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    \"CustomerID\": [1, 2, 1, 3, 2, 3, 1, 2],\n",
    "    \"PurchaseDate\": [\n",
    "        \"2023-01-01\",\n",
    "        \"2023-01-01\",\n",
    "        \"2023-01-02\",\n",
    "        \"2023-01-02\",\n",
    "        \"2023-01-03\",\n",
    "        \"2023-01-03\",\n",
    "        \"2023-01-04\",\n",
    "        \"2023-01-04\",\n",
    "    ],\n",
    "    \"Category\": [\n",
    "        \"Electronics\",\n",
    "        \"Electronics\",\n",
    "        \"Furniture\",\n",
    "        \"Furniture\",\n",
    "        \"Electronics\",\n",
    "        \"Electronics\",\n",
    "        \"Furniture\",\n",
    "        \"Furniture\",\n",
    "    ],\n",
    "    \"Amount\": [120, 150, 200, 250, 300, 350, 400, 450],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Summarize Total Amount Spent by Customer and Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category    Electronics  Furniture\n",
      "CustomerID                        \n",
      "1                   120        600\n",
      "2                   450        450\n",
      "3                   350        250\n"
     ]
    }
   ],
   "source": [
    "pivot_table_customer = pd.pivot_table(\n",
    "    df,\n",
    "    values=\"Amount\",\n",
    "    index=\"CustomerID\",\n",
    "    columns=\"Category\",\n",
    "    aggfunc=\"sum\",\n",
    "    fill_value=0,\n",
    ")\n",
    "print(pivot_table_customer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Analyzing Purchase Patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category    Electronics  Furniture  Total\n",
      "CustomerID                               \n",
      "1                   120        600    720\n",
      "2                   450        450    900\n",
      "3                   350        250    600\n",
      "Total               920       1300   2220\n"
     ]
    }
   ],
   "source": [
    "pivot_table_customer_margins = pd.pivot_table(\n",
    "    df,\n",
    "    values=\"Amount\",\n",
    "    index=\"CustomerID\",\n",
    "    columns=\"Category\",\n",
    "    aggfunc=\"sum\",\n",
    "    fill_value=0,\n",
    "    margins=True,\n",
    "    margins_name=\"Total\",\n",
    ")\n",
    "print(pivot_table_customer_margins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Visualizing Customer Purchase Behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot_table_customer_margins.plot(kind=\"bar\")\n",
    "plt.title(\"Customer Spending by Category\")\n",
    "plt.xlabel(\"Customer ID\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}