online_retail_analysis_big_pandas.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Install necessary modules"
      ],
      "metadata": {
        "id": "_g2yPKWXF1n4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openpyxl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEpghL2nAUOl",
        "outputId": "c8abc9f2-74e6-4e17-cb60-5aefd5089fa9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openpyxl\n",
            "  Downloading openpyxl-3.1.5-py2.py3-none-any.whl.metadata (2.5 kB)\n",
            "Collecting et-xmlfile (from openpyxl)\n",
            "  Downloading et_xmlfile-2.0.0-py3-none-any.whl.metadata (2.7 kB)\n",
            "Downloading openpyxl-3.1.5-py2.py3-none-any.whl (250 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.9/250.9 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading et_xmlfile-2.0.0-py3-none-any.whl (18 kB)\n",
            "Installing collected packages: et-xmlfile, openpyxl\n",
            "Successfully installed et-xmlfile-2.0.0 openpyxl-3.1.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "Mee11EbdAm9g"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Load"
      ],
      "metadata": {
        "id": "5D7tCBP1GBLx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://archive.ics.uci.edu/static/public/352/online+retail.zip"
      ],
      "metadata": {
        "id": "wvAX404bAxS0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c8b160b-ce45-4928-fb8a-9adb27e94f81"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-02-13 06:32:00--  https://archive.ics.uci.edu/static/public/352/online+retail.zip\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified\n",
            "Saving to: ‘online+retail.zip.1’\n",
            "\n",
            "online+retail.zip.1     [            <=>     ]  22.62M  9.43MB/s    in 2.4s    \n",
            "\n",
            "2025-02-13 06:32:03 (9.43 MB/s) - ‘online+retail.zip.1’ saved [23715478]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip online+retail.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVWlAG5IGKo-",
        "outputId": "0e1e461c-f500-474b-d586-3b554638ec25"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  online+retail.zip\n",
            "replace Online Retail.xlsx? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            " extracting: Online Retail.xlsx      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "stime = time.time()"
      ],
      "metadata": {
        "id": "quIjOWmW56sG"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_excel(\"Online Retail.xlsx\", dtype={'InvoiceNo': 'string', 'StockCode': 'string', 'Description': 'string', 'Country': 'string'})\n",
        "df1 = pd.concat([df1] * 60)\n",
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ks_-gO3KGeDP",
        "outputId": "9200c877-7758-4411-d2b7-f435c6d31a8a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32514540, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Mr2Oe2cb_uLF",
        "outputId": "91b97ff4-be27-46dc-c417-447e42c3bce1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                         Description  Quantity  \\\n",
              "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                 WHITE METAL LANTERN         6   \n",
              "2    536365    84406B      CREAM CUPID HEARTS COAT HANGER         8   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  "
            ],
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              "      <th>InvoiceNo</th>\n",
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              "      <td>6</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "df1[df1.InvoiceNo==\"C536379\"]"
      ],
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          "height": 1000
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        "id": "-DcH7yJ6Gswh",
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      "execution_count": 13,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "    InvoiceNo StockCode Description  Quantity         InvoiceDate  UnitPrice  \\\n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "\n",
              "     CustomerID         Country  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
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              "141     14527.0  United Kingdom  \n",
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              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
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              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
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              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  \n",
              "141     14527.0  United Kingdom  "
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              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
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              "      <td>27.5</td>\n",
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              "    <tr>\n",
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              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
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              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
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              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
              "      <th>141</th>\n",
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              "      <td>27.5</td>\n",
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              "      <td>United Kingdom</td>\n",
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              "      <td>C536379</td>\n",
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              "      <td>United Kingdom</td>\n",
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              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.5</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7ae0229-5ec7-4c71-ad7b-55f8da2b86ab')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d7ae0229-5ec7-4c71-ad7b-55f8da2b86ab button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d7ae0229-5ec7-4c71-ad7b-55f8da2b86ab');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2854b157-5420-4a01-911a-d221257517ea\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2854b157-5420-4a01-911a-d221257517ea')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2854b157-5420-4a01-911a-d221257517ea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1[df1\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"InvoiceNo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"C536379\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Discount\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": -1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-12-01 09:41:00\",\n        \"max\": \"2010-12-01 09:41:00\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2010-12-01 09:41:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 27.5,\n        \"max\": 27.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          27.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 14527.0,\n        \"max\": 14527.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          14527.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United Kingdom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "execution_count": 13
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        "df1.info()"
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      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 32514540 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Dtype         \n",
            "---  ------       -----         \n",
            " 0   InvoiceNo    string        \n",
            " 1   StockCode    string        \n",
            " 2   Description  string        \n",
            " 3   Quantity     int64         \n",
            " 4   InvoiceDate  datetime64[ns]\n",
            " 5   UnitPrice    float64       \n",
            " 6   CustomerID   float64       \n",
            " 7   Country      string        \n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), string(4)\n",
            "memory usage: 2.2 GB\n"
          ]
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning: Handle Missing Values"
      ],
      "metadata": {
        "id": "mcLbO7yZA3_l"
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      "execution_count": 15,
      "outputs": [
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              "InvoiceNo            0\n",
              "StockCode            0\n",
              "Description      87240\n",
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        "df1[df1['Description'].isnull()].head()"
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        "outputId": "0a0517c0-29e0-4750-8992-29c6fa9e9039"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     InvoiceNo StockCode Description  Quantity         InvoiceDate  UnitPrice  \\\n",
              "622     536414     22139        <NA>        56 2010-12-01 11:52:00        0.0   \n",
              "1970    536545     21134        <NA>         1 2010-12-01 14:32:00        0.0   \n",
              "1971    536546     22145        <NA>         1 2010-12-01 14:33:00        0.0   \n",
              "1972    536547     37509        <NA>         1 2010-12-01 14:33:00        0.0   \n",
              "1987    536549    85226A        <NA>         1 2010-12-01 14:34:00        0.0   \n",
              "\n",
              "      CustomerID         Country  \n",
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              "summary": "{\n  \"name\": \"df1[df1['Description']\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"InvoiceNo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"536545\",\n          \"536549\",\n          \"536546\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"21134\",\n          \"85226A\",\n          \"22145\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 1,\n        \"max\": 56,\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-12-01 11:52:00\",\n        \"max\": \"2010-12-01 14:34:00\",\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1[df1.StockCode==\"22139\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "E9549C_aL7Wh",
        "outputId": "14074553-d5dd-40c6-fdba-c8d26b07931d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       InvoiceNo StockCode                       Description  Quantity  \\\n",
              "106       536381     22139  RETROSPOT TEA SET CERAMIC 11 PC         23   \n",
              "622       536414     22139                              <NA>        56   \n",
              "6392      536942     22139                            amazon        15   \n",
              "6885      536982     22139  RETROSPOT TEA SET CERAMIC 11 PC         10   \n",
              "7203      537011     22139                              <NA>        -5   \n",
              "...          ...       ...                               ...       ...   \n",
              "538411    581405     22139  RETROSPOT TEA SET CERAMIC 11 PC          1   \n",
              "539531    581439     22139  RETROSPOT TEA SET CERAMIC 11 PC          1   \n",
              "540441    581486     22139  RETROSPOT TEA SET CERAMIC 11 PC          6   \n",
              "541387    581498     22139  RETROSPOT TEA SET CERAMIC 11 PC          2   \n",
              "541726    581570     22139  RETROSPOT TEA SET CERAMIC 11 PC          3   \n",
              "\n",
              "               InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "106    2010-12-01 09:41:00       4.25     15311.0  United Kingdom  \n",
              "622    2010-12-01 11:52:00       0.00         NaN  United Kingdom  \n",
              "6392   2010-12-03 12:08:00       0.00         NaN  United Kingdom  \n",
              "6885   2010-12-03 14:27:00      11.02         NaN  United Kingdom  \n",
              "7203   2010-12-03 15:38:00       0.00         NaN  United Kingdom  \n",
              "...                    ...        ...         ...             ...  \n",
              "538411 2011-12-08 13:50:00       4.95     13521.0  United Kingdom  \n",
              "539531 2011-12-08 16:30:00      10.79         NaN  United Kingdom  \n",
              "540441 2011-12-09 09:38:00       4.95     17001.0  United Kingdom  \n",
              "541387 2011-12-09 10:26:00      10.79         NaN  United Kingdom  \n",
              "541726 2011-12-09 11:59:00       4.95     12662.0         Germany  \n",
              "\n",
              "[59580 rows x 8 columns]"
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              "summary": "{\n  \"name\": \"df1[df1\",\n  \"rows\": 59580,\n  \"fields\": [\n    {\n      \"column\": \"InvoiceNo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 983,\n        \"samples\": [\n          \"576089\",\n          \"575919\",\n          \"576269\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"22139\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"amazon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": -14,\n        \"max\": 96,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-12-01 09:41:00\",\n        \"max\": \"2011-12-09 11:59:00\",\n        \"num_unique_values\": 978,\n        \"samples\": [\n          \"2011-04-12 12:40:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.055664818101253,\n        \"min\": 0.0,\n        \"max\": 11.02,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1757.7540403469216,\n        \"min\": 12360.0,\n        \"max\": 18283.0,\n        \"num_unique_values\": 489,\n        \"samples\": [\n          13471.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"United Kingdom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 17
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        "df1[df1.StockCode==\"22139\"].Description.mode()"
      ],
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        "id": "7Hh25G-TMhwR",
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      "execution_count": 18,
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            "text/plain": [
              "0    RETROSPOT TEA SET CERAMIC 11 PC \n",
              "Name: Description, dtype: string"
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        "most_freq = df1[[\"StockCode\",\"Description\"]].value_counts().reset_index()\n",
        "most_freq"
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      "execution_count": 19,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     StockCode                         Description   count\n",
              "0       85123A  WHITE HANGING HEART T-LIGHT HOLDER  138120\n",
              "1        22423            REGENCY CAKESTAND 3 TIER  132000\n",
              "2       85099B             JUMBO BAG RED RETROSPOT  129540\n",
              "3        47566                       PARTY BUNTING  103620\n",
              "4        20725             LUNCH BAG RED RETROSPOT   98280\n",
              "...        ...                                 ...     ...\n",
              "4787    35833P                               check      60\n",
              "4788     21410     COUNTRY COTTAGE  DOORSTOP GREEN      60\n",
              "4789     21412            VINTAGE GOLD TINSEL REEL      60\n",
              "4790     21414             SCALLOP SHELL SOAP DISH      60\n",
              "4791     35972                               check      60\n",
              "\n",
              "[4792 rows x 3 columns]"
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              "summary": "{\n  \"name\": \"most_freq\",\n  \"rows\": 4792,\n  \"fields\": [\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3958,\n        \"samples\": [\n          \"21843\",\n          \"22586\",\n          \"84509C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4223,\n        \"samples\": [\n          \"mixed up\",\n          \"SMALL SILVER FLOWER CANDLE POT\",\n          \"5 HOOK HANGER MAGIC TOADSTOOL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11214,\n        \"min\": 60,\n        \"max\": 138120,\n        \"num_unique_values\": 617,\n        \"samples\": [\n          55140,\n          2160,\n          44520\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "most_freq[most_freq.StockCode==\"85123A\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "JQVwyMxnNzsg",
        "outputId": "5053a957-760b-4f34-fdea-9ac2a236ba85"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     StockCode                         Description   count\n",
              "0       85123A  WHITE HANGING HEART T-LIGHT HOLDER  138120\n",
              "3300    85123A  CREAM HANGING HEART T-LIGHT HOLDER     540\n",
              "4620    85123A         wrongly marked carton 22804      60\n",
              "4658    85123A                                   ?      60"
            ],
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"most_freq[most_freq\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"85123A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"CREAM HANGING HEART T-LIGHT HOLDER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68950,\n        \"min\": 60,\n        \"max\": 138120,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          138120\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "most_freq = most_freq.groupby(\"StockCode\").head(1)\n",
        "most_freq.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "blmtZpyBQMb9",
        "outputId": "fd094646-9ab2-4b78-b832-2a72746ea423"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  StockCode                         Description   count\n",
              "0    85123A  WHITE HANGING HEART T-LIGHT HOLDER  138120\n",
              "1     22423            REGENCY CAKESTAND 3 TIER  132000\n",
              "2    85099B             JUMBO BAG RED RETROSPOT  129540\n",
              "3     47566                       PARTY BUNTING  103620\n",
              "4     20725             LUNCH BAG RED RETROSPOT   98280"
            ],
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              "      background-color: #434B5C;\n",
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              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a925b0fd-4bcf-4fd9-ab31-239ec2b5b08f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "most_freq",
              "summary": "{\n  \"name\": \"most_freq\",\n  \"rows\": 3958,\n  \"fields\": [\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3958,\n        \"samples\": [\n          \"21843\",\n          \"22586\",\n          \"84509C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3821,\n        \"samples\": [\n          \"ROUND PINK HEART MIRROR\",\n          \"BLACK MINI TAPE MEASURE \",\n          \"IVORY WIRE SWEETHEART LETTER TRAY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11880,\n        \"min\": 60,\n        \"max\": 138120,\n        \"num_unique_values\": 616,\n        \"samples\": [\n          45840,\n          26700,\n          2760\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "most_freq[most_freq.StockCode==\"85123A\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "xHu0-7_sQjVT",
        "outputId": "3b88cead-e83d-4446-9cf5-87c08ba0356e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  StockCode                         Description   count\n",
              "0    85123A  WHITE HANGING HEART T-LIGHT HOLDER  138120"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c28925f-36f3-43cb-927b-38947be4731a\" class=\"colab-df-container\">\n",
              "    <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",
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              "        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>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>138120</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c28925f-36f3-43cb-927b-38947be4731a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7c28925f-36f3-43cb-927b-38947be4731a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7c28925f-36f3-43cb-927b-38947be4731a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"most_freq[most_freq\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"85123A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"WHITE HANGING HEART T-LIGHT HOLDER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 138120,\n        \"max\": 138120,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          138120\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "most_freq.columns = [\"StockCode\", \"freq_Description\", \"count\"]\n",
        "most_freq.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "GPbFwRpBPaXX",
        "outputId": "fe280aa5-9b7d-40d2-9b74-232ba28d6685"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  StockCode                    freq_Description   count\n",
              "0    85123A  WHITE HANGING HEART T-LIGHT HOLDER  138120\n",
              "1     22423            REGENCY CAKESTAND 3 TIER  132000\n",
              "2    85099B             JUMBO BAG RED RETROSPOT  129540"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-573f60db-e10d-41a2-bc90-4d1f13034ae6\" class=\"colab-df-container\">\n",
              "    <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",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StockCode</th>\n",
              "      <th>freq_Description</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>138120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22423</td>\n",
              "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
              "      <td>132000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>85099B</td>\n",
              "      <td>JUMBO BAG RED RETROSPOT</td>\n",
              "      <td>129540</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-573f60db-e10d-41a2-bc90-4d1f13034ae6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-573f60db-e10d-41a2-bc90-4d1f13034ae6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-573f60db-e10d-41a2-bc90-4d1f13034ae6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4a10d58a-e03c-42a2-be01-50718f5ddaa8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a10d58a-e03c-42a2-be01-50718f5ddaa8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "type": "dataframe",
              "variable_name": "most_freq",
              "summary": "{\n  \"name\": \"most_freq\",\n  \"rows\": 3958,\n  \"fields\": [\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3958,\n        \"samples\": [\n          \"21843\",\n          \"22586\",\n          \"84509C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq_Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3821,\n        \"samples\": [\n          \"ROUND PINK HEART MIRROR\",\n          \"BLACK MINI TAPE MEASURE \",\n          \"IVORY WIRE SWEETHEART LETTER TRAY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11880,\n        \"min\": 60,\n        \"max\": 138120,\n        \"num_unique_values\": 616,\n        \"samples\": [\n          45840,\n          26700,\n          2760\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df1.merge(most_freq, on=\"StockCode\", how=\"left\")\n",
        "df2.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "7rR25X6fPtRj",
        "outputId": "e5e324e4-a510-437e-e104-4d9f2181f29c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                         Description  Quantity  \\\n",
              "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                 WHITE METAL LANTERN         6   \n",
              "2    536365    84406B      CREAM CUPID HEARTS COAT HANGER         8   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  \\\n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom   \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom   \n",
              "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom   \n",
              "\n",
              "                     freq_Description     count  \n",
              "0  WHITE HANGING HEART T-LIGHT HOLDER  138120.0  \n",
              "1                 WHITE METAL LANTERN   19680.0  \n",
              "2      CREAM CUPID HEARTS COAT HANGER   17580.0  "
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-68266719-1116-4932-bc8e-e47ad5af1d2b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2"
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          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['Description'].isnull()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "h9RYvk43UlRG",
        "outputId": "1cf8a701-0e0d-44a9-8c15-0dcc5d539633"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         InvoiceNo StockCode Description  Quantity         InvoiceDate  \\\n",
              "622         536414     22139        <NA>        56 2010-12-01 11:52:00   \n",
              "1970        536545     21134        <NA>         1 2010-12-01 14:32:00   \n",
              "1971        536546     22145        <NA>         1 2010-12-01 14:33:00   \n",
              "1972        536547     37509        <NA>         1 2010-12-01 14:33:00   \n",
              "1987        536549    85226A        <NA>         1 2010-12-01 14:34:00   \n",
              "...            ...       ...         ...       ...                 ...   \n",
              "32507953    581199     84581        <NA>        -2 2011-12-07 18:26:00   \n",
              "32507957    581203     23406        <NA>        15 2011-12-07 18:31:00   \n",
              "32507963    581209     21620        <NA>         6 2011-12-07 18:35:00   \n",
              "32509612    581234     72817        <NA>        27 2011-12-08 10:33:00   \n",
              "32511185    581408     85175        <NA>        20 2011-12-08 14:06:00   \n",
              "\n",
              "          UnitPrice  CustomerID         Country  \\\n",
              "622             0.0         NaN  United Kingdom   \n",
              "1970            0.0         NaN  United Kingdom   \n",
              "1971            0.0         NaN  United Kingdom   \n",
              "1972            0.0         NaN  United Kingdom   \n",
              "1987            0.0         NaN  United Kingdom   \n",
              "...             ...         ...             ...   \n",
              "32507953        0.0         NaN  United Kingdom   \n",
              "32507957        0.0         NaN  United Kingdom   \n",
              "32507963        0.0         NaN  United Kingdom   \n",
              "32509612        0.0         NaN  United Kingdom   \n",
              "32511185        0.0         NaN  United Kingdom   \n",
              "\n",
              "                             freq_Description    count  \n",
              "622          RETROSPOT TEA SET CERAMIC 11 PC   59280.0  \n",
              "1970                                     <NA>      NaN  \n",
              "1971          CHRISTMAS CRAFT HEART STOCKING      60.0  \n",
              "1972               NEW ENGLAND MUG W GIFT BOX    120.0  \n",
              "1987                                     <NA>      NaN  \n",
              "...                                       ...      ...  \n",
              "32507953      DOG TOY WITH PINK CROCHET SKIRT   5460.0  \n",
              "32507957           HOME SWEET HOME KEY HOLDER   6840.0  \n",
              "32507963      SET OF 4 ROSE BOTANICAL CANDLES  10320.0  \n",
              "32509612  SET OF 2 CHRISTMAS DECOUPAGE CANDLE   9840.0  \n",
              "32511185                CACTI T-LIGHT CANDLES   8880.0  \n",
              "\n",
              "[87240 rows x 10 columns]"
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              "      <td>RETROSPOT TEA SET CERAMIC 11 PC</td>\n",
              "      <td>59280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1970</th>\n",
              "      <td>536545</td>\n",
              "      <td>21134</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-12-01 14:32:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1971</th>\n",
              "      <td>536546</td>\n",
              "      <td>22145</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-12-01 14:33:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>CHRISTMAS CRAFT HEART STOCKING</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1972</th>\n",
              "      <td>536547</td>\n",
              "      <td>37509</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-12-01 14:33:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NEW ENGLAND MUG W GIFT BOX</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1987</th>\n",
              "      <td>536549</td>\n",
              "      <td>85226A</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-12-01 14:34:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32507953</th>\n",
              "      <td>581199</td>\n",
              "      <td>84581</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>-2</td>\n",
              "      <td>2011-12-07 18:26:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>DOG TOY WITH PINK CROCHET SKIRT</td>\n",
              "      <td>5460.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32507957</th>\n",
              "      <td>581203</td>\n",
              "      <td>23406</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>15</td>\n",
              "      <td>2011-12-07 18:31:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>HOME SWEET HOME KEY HOLDER</td>\n",
              "      <td>6840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32507963</th>\n",
              "      <td>581209</td>\n",
              "      <td>21620</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>6</td>\n",
              "      <td>2011-12-07 18:35:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>SET OF 4 ROSE BOTANICAL CANDLES</td>\n",
              "      <td>10320.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32509612</th>\n",
              "      <td>581234</td>\n",
              "      <td>72817</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>27</td>\n",
              "      <td>2011-12-08 10:33:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>SET OF 2 CHRISTMAS DECOUPAGE CANDLE</td>\n",
              "      <td>9840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32511185</th>\n",
              "      <td>581408</td>\n",
              "      <td>85175</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>20</td>\n",
              "      <td>2011-12-08 14:06:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>CACTI T-LIGHT CANDLES</td>\n",
              "      <td>8880.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>87240 rows × 10 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2[df2['Description']\",\n  \"rows\": 87240,\n  \"fields\": [\n    {\n      \"column\": \"InvoiceNo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1454,\n        \"samples\": [\n          \"548997\",\n          \"569347\",\n          \"547957\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 960,\n        \"samples\": [\n          \"48173C\",\n          \"84858C\",\n          \"84678\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243,\n        \"min\": -3667,\n        \"max\": 5568,\n        \"num_unique_values\": 266,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-12-01 11:52:00\",\n        \"max\": \"2011-12-08 14:06:00\",\n        \"num_unique_values\": 1121,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq_Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 846,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13313.012469966656,\n        \"min\": 60.0,\n        \"max\": 88380.0,\n        \"num_unique_values\": 253,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['Description'] = df2['Description'].mask(df2['Description'].isnull(), df2['freq_Description'])\n",
        "df2.isnull().sum()"
      ],
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        "outputId": "7713740b-0a9e-4ecf-988b-cb6fd63f33be"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo                 0\n",
              "StockCode                 0\n",
              "Description            6720\n",
              "Quantity                  0\n",
              "InvoiceDate               0\n",
              "UnitPrice                 0\n",
              "CustomerID          8104800\n",
              "Country                   0\n",
              "freq_Description       6720\n",
              "count                  6720\n",
              "dtype: int64"
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df2.dropna(subset=['Description'], inplace=True)\n",
        "df2.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "8-HYmqLpVKAG",
        "outputId": "8675ab66-947d-41c3-fb87-8452fa3c3782"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo                 0\n",
              "StockCode                 0\n",
              "Description               0\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>InvoiceNo</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StockCode</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Description</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InvoiceDate</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnitPrice</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>8098080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq_Description</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.drop(columns = [\"freq_Description\", \"count\"], inplace=True)"
      ],
      "metadata": {
        "id": "14kKUDZlP0e9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "After talking to a business manager, we confirmed that it is ok to have null values in CustomerID column"
      ],
      "metadata": {
        "id": "N-NOHBbOVh-e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning: Handle Invalid Values"
      ],
      "metadata": {
        "id": "CH-Lk4jUVv1C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "UBLIWMQhV-fx",
        "outputId": "189b468a-38bd-4412-8782-80fa7cee38b8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Quantity                    InvoiceDate     UnitPrice    CustomerID\n",
              "count  3.250782e+07                       32507820  3.250782e+07  2.440974e+07\n",
              "mean   9.555919e+00  2011-07-04 14:06:48.671256832  4.612067e+00  1.528769e+04\n",
              "min   -8.099500e+04            2010-12-01 08:26:00 -1.106206e+04  1.234600e+04\n",
              "25%    1.000000e+00            2011-03-28 11:36:00  1.250000e+00  1.395300e+04\n",
              "50%    3.000000e+00            2011-07-20 08:59:00  2.080000e+00  1.515200e+04\n",
              "75%    1.000000e+01            2011-10-19 11:41:00  4.130000e+00  1.679100e+04\n",
              "max    8.099500e+04            2011-12-09 12:50:00  3.897000e+04  1.828700e+04\n",
              "std    2.181032e+02                            NaN  9.676974e+01  1.713598e+03"
            ],
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              "      <th>count</th>\n",
              "      <td>3.250782e+07</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.555919e+00</td>\n",
              "      <td>2011-07-04 14:06:48.671256832</td>\n",
              "      <td>4.612067e+00</td>\n",
              "      <td>1.528769e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-8.099500e+04</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>-1.106206e+04</td>\n",
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              "    </tr>\n",
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              "      <th>25%</th>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11493319.317112906,\n        \"min\": -80995.0,\n        \"max\": 32507820.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.555918545137754,\n          10.0,\n          32507820.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.032507820\",\n        \"max\": \"2011-12-09 12:50:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"32507820\",\n          \"2011-07-04 14:06:48.671256832\",\n          \"2011-10-19 11:41:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11491844.423672866,\n        \"min\": -11062.06,\n        \"max\": 32507820.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.6120668331496955,\n          4.13,\n          32507820.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8625423.838089814,\n        \"min\": 1713.598232376083,\n        \"max\": 24409740.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15287.690570239585,\n          16791.0,\n          24409740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quanity and UnitPrice contains negative values. We need to fix it"
      ],
      "metadata": {
        "id": "MI0JcAGmWHru"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2.Quantity<=0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nhd6R8aVWNQd",
        "outputId": "9e510069-fe50-48cc-fbda-e49077228f50"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(631620, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2.UnitPrice<=0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUjd98IZWVqn",
        "outputId": "8ebfadb4-4108-483d-c63a-cf0ad837b376"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144300, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove negative or zero quantities and prices\n",
        "df3 = df2[(df2['Quantity'] > 0) & (df2['UnitPrice'] > 0)]\n",
        "df3.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "istRIA2YWa86",
        "outputId": "e3c5f78e-1100-45b0-f014-4e687f6fccce"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Quantity                    InvoiceDate     UnitPrice    CustomerID\n",
              "count  3.180624e+07                       31806240  3.180624e+07  2.387304e+07\n",
              "mean   1.054204e+01  2011-07-04 20:16:05.225086720  3.907625e+00  1.529442e+04\n",
              "min    1.000000e+00            2010-12-01 08:26:00  1.000000e-03  1.234600e+04\n",
              "25%    1.000000e+00            2011-03-28 12:22:00  1.250000e+00  1.396900e+04\n",
              "50%    3.000000e+00            2011-07-20 12:58:00  2.080000e+00  1.515900e+04\n",
              "75%    1.000000e+01            2011-10-19 12:39:00  4.130000e+00  1.679500e+04\n",
              "max    8.099500e+04            2011-12-09 12:50:00  1.354133e+04  1.828700e+04\n",
              "std    1.555240e+02                            NaN  3.591565e+01  1.713139e+03"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.180624e+07</td>\n",
              "      <td>31806240</td>\n",
              "      <td>3.180624e+07</td>\n",
              "      <td>2.387304e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.054204e+01</td>\n",
              "      <td>2011-07-04 20:16:05.225086720</td>\n",
              "      <td>3.907625e+00</td>\n",
              "      <td>1.529442e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>1.000000e-03</td>\n",
              "      <td>1.234600e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2011-03-28 12:22:00</td>\n",
              "      <td>1.250000e+00</td>\n",
              "      <td>1.396900e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2011-07-20 12:58:00</td>\n",
              "      <td>2.080000e+00</td>\n",
              "      <td>1.515900e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>2011-10-19 12:39:00</td>\n",
              "      <td>4.130000e+00</td>\n",
              "      <td>1.679500e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.099500e+04</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>1.354133e+04</td>\n",
              "      <td>1.828700e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.555240e+02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.591565e+01</td>\n",
              "      <td>1.713139e+03</td>\n",
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              "    0% {\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df3\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11241139.686953012,\n        \"min\": 1.0,\n        \"max\": 31806240.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          31806240.0,\n          10.542037034242338,\n          80995.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.031806240\",\n        \"max\": \"2011-12-09 12:50:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"31806240\",\n          \"2011-07-04 20:16:05.225086720\",\n          \"2011-10-19 12:39:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11244518.662608987,\n        \"min\": 0.001,\n        \"max\": 31806240.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.907625247121316,\n          4.13,\n          31806240.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8435670.087208414,\n        \"min\": 1713.1394435033712,\n        \"max\": 23873040.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15294.423452564064,\n          16795.0,\n          23873040.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can notice HUGE values in both Quantity and UnitPrice. Let us use quantile to remove those big values (outliers)"
      ],
      "metadata": {
        "id": "X0KL3SecXbQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3.Quantity.quantile(0.9999)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cl0UNm0GXhjG",
        "outputId": "a9c3ca01-5cc1-4329-efd7-85c72a58504f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1440.0"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For now, we are not going to worry about extreme values because these can be legitimate values"
      ],
      "metadata": {
        "id": "tGcOCg4VX-CT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Engineering: Create New Columns"
      ],
      "metadata": {
        "id": "dC2oj1pcl0hj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "BA4PKRgImEc0",
        "outputId": "65164053-9976-46cf-cb11-76fa22292f84"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                         Description  Quantity  \\\n",
              "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                 WHITE METAL LANTERN         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
            ],
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1dc33cc4-a0e2-41fa-9cce-b6798bb517f1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df3"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4 = df3.copy()\n",
        "\n",
        "df4['TotalSales'] = df4['Quantity'] * df4['UnitPrice']\n",
        "df4.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "v-myKNwjl3J1",
        "outputId": "bea01232-70a6-4b84-b1e3-8d98707971e7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                         Description  Quantity  \\\n",
              "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                 WHITE METAL LANTERN         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  TotalSales  \n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30  \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  "
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-fc612480-f511-4fc1-bb31-1529de45a4ee button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fc612480-f511-4fc1-bb31-1529de45a4ee');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df4"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4['Month'] = df4['InvoiceDate'].dt.month\n",
        "df4.sample(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "GtXLcs7TmMdD",
        "outputId": "64798df8-f843-412a-94cf-2036397a5a00"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         InvoiceNo StockCode                      Description  Quantity  \\\n",
              "17668667    565682     21166       COOK WITH WINE METAL SIGN         10   \n",
              "17072928    560858     22457  NATURAL SLATE HEART CHALKBOARD         96   \n",
              "\n",
              "                 InvoiceDate  UnitPrice  CustomerID         Country  \\\n",
              "17668667 2011-09-06 10:42:00       2.08     14796.0  United Kingdom   \n",
              "17072928 2011-07-21 13:25:00       2.55     14403.0  United Kingdom   \n",
              "\n",
              "          TotalSales  Month  \n",
              "17668667        20.8      9  \n",
              "17072928       244.8      7  "
            ],
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              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>17072928</th>\n",
              "      <td>560858</td>\n",
              "      <td>22457</td>\n",
              "      <td>NATURAL SLATE HEART CHALKBOARD</td>\n",
              "      <td>96</td>\n",
              "      <td>2011-07-21 13:25:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>14403.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>244.8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </button>\n",
              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"InvoiceNo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"560858\",\n          \"565682\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"22457\",\n          \"21166\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NATURAL SLATE HEART CHALKBOARD \",\n          \"COOK WITH WINE METAL SIGN \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60,\n        \"min\": 10,\n        \"max\": 96,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          96,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-07-21 13:25:00\",\n        \"max\": \"2011-09-06 10:42:00\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2011-07-21 13:25:00\",\n          \"2011-09-06 10:42:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33234018715767716,\n        \"min\": 2.08,\n        \"max\": 2.55,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.55,\n          2.08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 277.8929650063132,\n        \"min\": 14403.0,\n        \"max\": 14796.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          14403.0,\n          14796.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United Kingdom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalSales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 158.39191898578665,\n        \"min\": 20.8,\n        \"max\": 244.79999999999998,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          244.79999999999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Visualization and Insights"
      ],
      "metadata": {
        "id": "VWIWt3lZmi_A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Plot Monthly Total Sales Trend"
      ],
      "metadata": {
        "id": "4XYzmN46mpYw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_sales = df4.groupby('Month')['TotalSales'].sum()\n",
        "monthly_sales"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "KJQbRvjrmvBe",
        "outputId": "ae79c989-c15d-478c-f784-ffaf5ac8b435"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "1     41481873.60\n",
              "2     31417913.40\n",
              "3     43058361.60\n",
              "4     32268517.26\n",
              "5     46232161.20\n",
              "6     45704394.00\n",
              "7     43153271.46\n",
              "8     45548302.80\n",
              "9     63515410.32\n",
              "10    69298758.00\n",
              "11    90569779.80\n",
              "12    87752329.20\n",
              "Name: TotalSales, dtype: float64"
            ],
            "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>TotalSales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41481873.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31417913.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43058361.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32268517.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>46232161.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>45704394.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>43153271.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>45548302.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>63515410.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>69298758.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>90569779.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>87752329.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_sales.plot(kind='line', title='Monthly Total Sales Trend', marker='o')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Total Sales')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "sJ92A4DFm9EA",
        "outputId": "9bb1407f-ef81-4abc-b05b-da465e638193"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights**\n",
        "\n",
        "Total sales started rising up in August having a peek in November. This is likely due to the holiday season at the end of the year"
      ],
      "metadata": {
        "id": "guAqu-y2nSuh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Top 5 countries based on total sales"
      ],
      "metadata": {
        "id": "8CUOLwEdnZ03"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4.groupby('Country')['TotalSales'].sum().sort_values(ascending=False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "eTe9KWrjnaIH",
        "outputId": "9cf09a93-7251-4611-d09d-485ebaa8cdbf"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "United Kingdom    5.415133e+08\n",
              "Netherlands       1.712678e+07\n",
              "EIRE              1.700724e+07\n",
              "Germany           1.373203e+07\n",
              "France            1.258291e+07\n",
              "Name: TotalSales, dtype: float64"
            ],
            "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>TotalSales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>5.415133e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>1.712678e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EIRE</th>\n",
              "      <td>1.700724e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>1.373203e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>1.258291e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: plot horizontal bar chart for country wise monthly sales for top 5 countries\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "top_5_countries = df4.groupby('Country')['TotalSales'].sum().sort_values(ascending=False).head(5)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(top_5_countries.index, top_5_countries.values)\n",
        "plt.xlabel('Total Sales')\n",
        "plt.ylabel('Country')\n",
        "plt.title('Top 5 Countries by Total Sales')\n",
        "plt.grid(axis='x')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "DyEV9m5Mnnr9",
        "outputId": "ece5020a-0128-436c-a446-62a77ef0080c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights**\n",
        "\n",
        "1. UK has the highest sales (around 9 million)\n",
        "1. Netherlands, EIRE, Germany and France are the next 4 countries each having a sales of more than 2 million\n",
        "\n",
        "Since these countries cover the major sales revenues, we need to pay special attention to customers in these countries and make sure our product quality and service are the best. Also to break dependancy of sales from a single country we can focus on expanding sales in other countries as well"
      ],
      "metadata": {
        "id": "Y8ZU_oVPoE5B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Plot same chart as above but this time use percentage contribution. Show % on the bar\n",
        "\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "country_wise_sales = df4.groupby('Country')['TotalSales'].sum()\n",
        "total_sales = country_wise_sales.sum()\n",
        "\n",
        "top_5_countries = country_wise_sales.sort_values(ascending=False).head(5)\n",
        "percentages = (top_5_countries / total_sales) * 100\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.barh(top_5_countries.index, percentages)\n",
        "plt.xlabel('Percentage Contribution to Total Sales')\n",
        "plt.ylabel('Country')\n",
        "plt.title('Top 5 Countries by Percentage Contribution to Total Sales')\n",
        "plt.grid(axis='x')\n",
        "\n",
        "# Add percentage labels to the bars\n",
        "for bar, percentage in zip(bars, percentages):\n",
        "    plt.text(bar.get_width() + 0.5, bar.get_y() + bar.get_height()/2, f'{percentage:.1f}%', va='center')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "vEthlNFbxlV4",
        "outputId": "428a4c89-e1a1-4d3e-eaab-e32490bb22b5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Top 5 products based on total sales"
      ],
      "metadata": {
        "id": "3SgCx7RtoLg5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "product_wise_sales = df4.groupby('StockCode')['TotalSales'].sum()\n",
        "\n",
        "top_5_products = product_wise_sales.sort_values(ascending=False).head(5)\n",
        "top_5_products.plot(kind='barh', color='skyblue')\n",
        "plt.title('Product-wise Sales')\n",
        "plt.xlabel('Total Sales')\n",
        "plt.ylabel('Product Stock Code')\n",
        "plt.gca().invert_yaxis()  # To show the highest sales at the top\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7_CjhWCHoRv9",
        "outputId": "72ba91a7-2e93-4757-fb92-a29ea6589850"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product_wise_sales.sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "o7byrK_Mz5aT",
        "outputId": "5436ff6c-c608-461c-f10b-c997533de6e3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StockCode\n",
              "DOT       12374926.20\n",
              "22423     10469084.40\n",
              "23843     10108176.00\n",
              "85123A     6271128.00\n",
              "47566      5970259.80\n",
              "             ...     \n",
              "90084           51.00\n",
              "21268           50.40\n",
              "51014c          49.80\n",
              "84227           25.20\n",
              "PADS             0.18\n",
              "Name: TotalSales, Length: 3922, dtype: float64"
            ],
            "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>TotalSales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StockCode</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>DOT</th>\n",
              "      <td>12374926.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22423</th>\n",
              "      <td>10469084.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23843</th>\n",
              "      <td>10108176.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85123A</th>\n",
              "      <td>6271128.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47566</th>\n",
              "      <td>5970259.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90084</th>\n",
              "      <td>51.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21268</th>\n",
              "      <td>50.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51014c</th>\n",
              "      <td>49.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84227</th>\n",
              "      <td>25.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PADS</th>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3922 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product_wise_sales.sum()"
      ],
      "metadata": {
        "id": "y155ddQU0DN4",
        "outputId": "c8270598-813b-4fa4-d03d-03af7ad5cd1b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "640001072.6400001"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4[df4.StockCode==\"DOT\"].Description.iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ExxLHmLAohhw",
        "outputId": "93cd9d31-0bf3-432d-ce90-9f29bc5f9636"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'DOTCOM POSTAGE'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for stock_code in top_5_products.index:\n",
        "    description = df4[df4.StockCode==stock_code].Description.iloc[0]\n",
        "    print(f\"{stock_code} ==> {description}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjH1zSEbolBj",
        "outputId": "e9aa1a30-7cc6-4b7d-cb4e-779e926d174d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DOT ==> DOTCOM POSTAGE\n",
            "22423 ==> REGENCY CAKESTAND 3 TIER\n",
            "23843 ==> PAPER CRAFT , LITTLE BIRDIE\n",
            "85123A ==> WHITE HANGING HEART T-LIGHT HOLDER\n",
            "47566 ==> PARTY BUNTING\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Plot same chart as above for product sales but use percentage this time. Show % on the bar\n",
        "\n",
        "# Assuming df4 is already created from the previous code\n",
        "total_sales = product_wise_sales.sum()\n",
        "percentages = (top_5_products / total_sales) * 100\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.barh(top_5_products.index, percentages)\n",
        "plt.xlabel('Percentage Contribution to Total Sales')\n",
        "plt.ylabel('Product Stock Code')\n",
        "plt.title('Top 5 Products by Percentage Contribution to Total Sales')\n",
        "plt.grid(axis='x')\n",
        "\n",
        "# Add percentage labels to the bars\n",
        "for bar, percentage in zip(bars, percentages):\n",
        "    plt.text(bar.get_width() + 0.5, bar.get_y() + bar.get_height()/2, f'{percentage:.1f}%', va='center')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0PJhiZ-dyDZd",
        "outputId": "c1f3014b-8cd4-4290-8962-339ca4d4911e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4. RFM Analysis (Recency, Frequency, Monetary)"
      ],
      "metadata": {
        "id": "Ng0H7MhOo1JG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4['InvoiceDate'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-v0ufQISo4F2",
        "outputId": "aabe27ff-adf7-447d-a751-4f64aa6b982b"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2011-12-09 12:50:00')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "current_date = df4['InvoiceDate'].max() + pd.Timedelta(days=1)\n",
        "rfm = df4.groupby('CustomerID').agg({\n",
        "    'InvoiceDate': lambda x: (current_date - x.max()).days,\n",
        "    'InvoiceNo': 'count',\n",
        "    'TotalSales': 'sum'\n",
        "})\n",
        "rfm.columns = ['Recency', 'Frequency', 'Monetary']\n",
        "rfm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "a4z2cd_xpCDN",
        "outputId": "e54fed46-e8fa-43b9-ffa4-1181f0343078"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency   Monetary\n",
              "CustomerID                               \n",
              "12346.0         326         60  4631016.0\n",
              "12347.0           2      10920   258600.0\n",
              "12348.0          75       1860   107834.4\n",
              "12349.0          19       4380   105453.0\n",
              "12350.0         310       1020    20064.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85e8bf67-5c8f-4150-972e-6cb18934a1a4\" class=\"colab-df-container\">\n",
              "    <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>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12346.0</th>\n",
              "      <td>326</td>\n",
              "      <td>60</td>\n",
              "      <td>4631016.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12347.0</th>\n",
              "      <td>2</td>\n",
              "      <td>10920</td>\n",
              "      <td>258600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12348.0</th>\n",
              "      <td>75</td>\n",
              "      <td>1860</td>\n",
              "      <td>107834.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12349.0</th>\n",
              "      <td>19</td>\n",
              "      <td>4380</td>\n",
              "      <td>105453.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12350.0</th>\n",
              "      <td>310</td>\n",
              "      <td>1020</td>\n",
              "      <td>20064.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85e8bf67-5c8f-4150-972e-6cb18934a1a4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-85e8bf67-5c8f-4150-972e-6cb18934a1a4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-85e8bf67-5c8f-4150-972e-6cb18934a1a4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8cd12dd8-8a71-47d9-85d0-a5378671d3cb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8cd12dd8-8a71-47d9-85d0-a5378671d3cb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8cd12dd8-8a71-47d9-85d0-a5378671d3cb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rfm",
              "summary": "{\n  \"name\": \"rfm\",\n  \"rows\": 4338,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1721.8084917653168,\n        \"min\": 12346.0,\n        \"max\": 18287.0,\n        \"num_unique_values\": 4338,\n        \"samples\": [\n          17785.0,\n          14320.0,\n          15977.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100,\n        \"min\": 1,\n        \"max\": 374,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          187,\n          118,\n          69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13727,\n        \"min\": 60,\n        \"max\": 470820,\n        \"num_unique_values\": 460,\n        \"samples\": [\n          2520,\n          1620,\n          12000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 539353.8264803209,\n        \"min\": 225.0,\n        \"max\": 16812361.2,\n        \"num_unique_values\": 4248,\n        \"samples\": [\n          37085.4,\n          76059.0,\n          14007.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfm.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1FnfdG8ppvw9",
        "outputId": "fbd6fd7a-f1b5-4458-d2c6-9a0557d27c79"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Recency      Frequency      Monetary\n",
              "count  4338.000000    4338.000000  4.338000e+03\n",
              "mean     92.536422    5503.236515  1.232560e+05\n",
              "std     100.014169   13727.105632  5.393538e+05\n",
              "min       1.000000      60.000000  2.250000e+02\n",
              "25%      18.000000    1020.000000  1.844490e+04\n",
              "50%      51.000000    2460.000000  4.046910e+04\n",
              "75%     142.000000    6000.000000  9.970440e+04\n",
              "max     374.000000  470820.000000  1.681236e+07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-296afece-5b52-408e-84a6-2d24699f9485\" class=\"colab-df-container\">\n",
              "    <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>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4338.000000</td>\n",
              "      <td>4338.000000</td>\n",
              "      <td>4.338000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>92.536422</td>\n",
              "      <td>5503.236515</td>\n",
              "      <td>1.232560e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>100.014169</td>\n",
              "      <td>13727.105632</td>\n",
              "      <td>5.393538e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>2.250000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>1020.000000</td>\n",
              "      <td>1.844490e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>2460.000000</td>\n",
              "      <td>4.046910e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>142.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>9.970440e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>374.000000</td>\n",
              "      <td>470820.000000</td>\n",
              "      <td>1.681236e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-296afece-5b52-408e-84a6-2d24699f9485')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-296afece-5b52-408e-84a6-2d24699f9485 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-296afece-5b52-408e-84a6-2d24699f9485');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-004ebfc1-1aea-4c53-bf10-6fd05aba2de5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-004ebfc1-1aea-4c53-bf10-6fd05aba2de5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    animation:\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"rfm\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498.9229266216273,\n        \"min\": 1.0,\n        \"max\": 4338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          92.53642231443061,\n          51.0,\n          4338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 164841.540158511,\n        \"min\": 60.0,\n        \"max\": 470820.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5503.236514522821,\n          2460.0,\n          4338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5905027.748013939,\n        \"min\": 225.0,\n        \"max\": 16812361.2,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          123255.98760719226,\n          40469.1,\n          4338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Segment Customers based on RFM\n",
        "rfm['R_Segment'] = pd.qcut(rfm['Recency'], 4, labels=[4, 3, 2, 1])\n",
        "rfm['F_Segment'] = pd.qcut(rfm['Frequency'], 4, labels=[1, 2, 3, 4])\n",
        "rfm['M_Segment'] = pd.qcut(rfm['Monetary'], 4, labels=[1, 2, 3, 4])\n",
        "rfm['RFM_Score'] = rfm[['R_Segment', 'F_Segment', 'M_Segment']].sum(axis=1)"
      ],
      "metadata": {
        "id": "Onpu4sIbp6ls"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfm.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "rYuVL0LNq5_Z",
        "outputId": "4071649d-6297-442e-d351-b60c28b8437d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary R_Segment F_Segment M_Segment  \\\n",
              "CustomerID                                                               \n",
              "14232.0          43       6900  122884.2         3         4         4   \n",
              "15906.0          20       9000   94061.4         3         4         3   \n",
              "16180.0         100       9720  615250.8         2         4         4   \n",
              "13570.0         243       1620   14369.4         1         2         1   \n",
              "12781.0          64       1500   49604.4         2         2         3   \n",
              "\n",
              "            RFM_Score  \n",
              "CustomerID             \n",
              "14232.0            11  \n",
              "15906.0            10  \n",
              "16180.0            10  \n",
              "13570.0             4  \n",
              "12781.0             7  "
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"rfm\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1473.5339154563087,\n        \"min\": 12781.0,\n        \"max\": 16180.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          15906.0,\n          12781.0,\n          16180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88,\n        \"min\": 20,\n        \"max\": 243,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          20,\n          64,\n          100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3961,\n        \"min\": 1500,\n        \"max\": 9720,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9000,\n          1500,\n          9720\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 247247.08853814236,\n        \"min\": 14369.4,\n        \"max\": 615250.8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          94061.4,\n          49604.4,\n          615250.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R_Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F_Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M_Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RFM_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 4,\n        \"max\": 11,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Customers with highest RFM Scores\n",
        "rfm.sort_values('RFM_Score', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "gyhnkFwdq_pz",
        "outputId": "3775eda9-e613-4191-fdd8-1b314990d7a8"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary R_Segment F_Segment M_Segment  \\\n",
              "CustomerID                                                               \n",
              "18198.0           4       9540  325533.6         4         4         4   \n",
              "18210.0           2       8040  157282.8         4         4         4   \n",
              "18225.0           3      16260  330547.2         4         4         4   \n",
              "18283.0           4      45360  125692.8         4         4         4   \n",
              "16983.0          13       8880  115875.0         4         4         4   \n",
              "...             ...        ...       ...       ...       ...       ...   \n",
              "12402.0         323        660   13536.0         1         1         1   \n",
              "18185.0         249       1020   18255.0         1         1         1   \n",
              "18190.0         192        900   17067.6         1         1         1   \n",
              "18191.0         262        420   12468.0         1         1         1   \n",
              "18193.0         165        960   14625.6         1         1         1   \n",
              "\n",
              "            RFM_Score  \n",
              "CustomerID             \n",
              "18198.0            12  \n",
              "18210.0            12  \n",
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              "18283.0            12  \n",
              "16983.0            12  \n",
              "...               ...  \n",
              "12402.0             3  \n",
              "18185.0             3  \n",
              "18190.0             3  \n",
              "18191.0             3  \n",
              "18193.0             3  \n",
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              "      <td>165</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"rfm\",\n  \"rows\": 4338,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1721.8084917653168,\n        \"min\": 12346.0,\n        \"max\": 18287.0,\n        \"num_unique_values\": 4338,\n        \"samples\": [\n          16617.0,\n          14306.0,\n          14752.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100,\n        \"min\": 1,\n        \"max\": 374,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          55,\n          160,\n          285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13727,\n        \"min\": 60,\n        \"max\": 470820,\n        \"num_unique_values\": 460,\n        \"samples\": [\n          28560,\n          18600,\n          29280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 539353.8264803209,\n        \"min\": 225.0,\n        \"max\": 16812361.2,\n        \"num_unique_values\": 4248,\n        \"samples\": [\n          158147.4,\n          7575.0,\n          2736.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R_Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F_Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M_Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RFM_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 3,\n        \"max\": 12,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          4,\n          11,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 5. Customer Churn Analysis"
      ],
      "metadata": {
        "id": "lbD78m6Owduf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "RFm-e2hA3dvQ",
        "outputId": "92180ad4-74ff-4e09-d562-cd632df38e9a"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                         Description  Quantity  \\\n",
              "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                 WHITE METAL LANTERN         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  TotalSales  \\\n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30   \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
              "\n",
              "   Month  \n",
              "0     12  \n",
              "1     12  "
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              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df4"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a basket matrix for association rule mining\n",
        "customer_last_purchase = df4.groupby(\"CustomerID\")['InvoiceDate'].max()\n",
        "customer_last_purchase.head(5)"
      ],
      "metadata": {
        "id": "ndCzf6K1whu4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "c5217401-87f2-4940-a1dd-f7bf85b8d41f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID\n",
              "12346.0   2011-01-18 10:01:00\n",
              "12347.0   2011-12-07 15:52:00\n",
              "12348.0   2011-09-25 13:13:00\n",
              "12349.0   2011-11-21 09:51:00\n",
              "12350.0   2011-02-02 16:01:00\n",
              "Name: InvoiceDate, dtype: datetime64[ns]"
            ],
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              "      <td>2011-02-02 16:01:00</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> datetime64[ns]</label>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(customer_last_purchase)"
      ],
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        },
        "id": "YuzJWXHd4V-o",
        "outputId": "e3808b12-c82f-4aa4-91c2-a8952bef1d8f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.series.Series</b><br/>def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool | lib.NoDefault=lib.no_default) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.11/dist-packages/pandas/core/series.py</a>One-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide &lt;basics.dtypes&gt;` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.series&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = [1, 2]\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "[1, 2]\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = np.array([1, 2])\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "array([999,   2])\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.</pre>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "current_date"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSfeTxS54q0d",
        "outputId": "cabac5e3-431c-4d44-8659-f4aae59b7cb7"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2011-12-10 12:50:00')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_last_purchase = (current_date - customer_last_purchase).dt.days\n",
        "customer_last_purchase.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "swUoT3EEzrDH",
        "outputId": "01c507f5-765d-422f-8c02-22660d84b385"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID\n",
              "12346.0    326\n",
              "12347.0      2\n",
              "12348.0     75\n",
              "12349.0     19\n",
              "12350.0    310\n",
              "Name: InvoiceDate, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>InvoiceDate</th>\n",
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              "      <th>12347.0</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12348.0</th>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12349.0</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12350.0</th>\n",
              "      <td>310</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define churn threshold (e.g., 90 days without purchase)\n",
        "churn_threshold = 90\n",
        "churned_customers = customer_last_purchase[customer_last_purchase > churn_threshold]\n",
        "churned_customers.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "0QAXoSiy4iRL",
        "outputId": "d935a104-ba1d-4d02-ddd5-b78e011ad5c4"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID\n",
              "12346.0    326\n",
              "12350.0    310\n",
              "12353.0    204\n",
              "12354.0    232\n",
              "12355.0    214\n",
              "Name: InvoiceDate, dtype: int64"
            ],
            "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>InvoiceDate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12346.0</th>\n",
              "      <td>326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12350.0</th>\n",
              "      <td>310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12353.0</th>\n",
              "      <td>204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12354.0</th>\n",
              "      <td>232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12355.0</th>\n",
              "      <td>214</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of Churned Customers:\", len(churned_customers))\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(customer_last_purchase, bins=50, color='red', alpha=0.7)\n",
        "plt.axvline(churn_threshold, color='black', linestyle='dashed', linewidth=2)\n",
        "plt.title('Customer Churn Distribution')\n",
        "plt.xlabel('Days Since Last Purchase')\n",
        "plt.ylabel('Number of Customers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "9su-QzXh5Kol",
        "outputId": "46669601-ec57-4cbc-b1f4-d6865d0692c2"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Churned Customers: 1449\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "0rQuPeGmvl8t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"[{pd.__name__}] total time taken: {time.time() - stime} sec\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-a3PPo6Z6Cs8",
        "outputId": "18c367ae-3d3a-4878-d630-f97e76182af4"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[pandas] total time taken: 167.82752180099487 sec\n"
          ]
        }
      ]
    }
  ]
}