online_retail_analysis.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Install necessary modules"
      ],
      "metadata": {
        "id": "_g2yPKWXF1n4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openpyxl"
      ],
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      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.11/dist-packages (3.1.5)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.11/dist-packages (from openpyxl) (2.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "Mee11EbdAm9g"
      },
      "execution_count": 15,
      "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",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "949f8176-1e3b-4261-fc92-d62324804db5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-02-13 07:28:02--  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.2’\n",
            "\n",
            "online+retail.zip.2     [    <=>             ]  22.62M  30.9MB/s    in 0.7s    \n",
            "\n",
            "2025-02-13 07:28:03 (30.9 MB/s) - ‘online+retail.zip.2’ saved [23715478]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip online+retail.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVWlAG5IGKo-",
        "outputId": "207608a8-3b1f-4c5e-ef8c-70e93f826ba2"
      },
      "execution_count": 17,
      "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": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_excel(\"Online Retail.xlsx\", dtype={'InvoiceNo': 'string', 'StockCode': 'string', 'Description': 'string', 'Country': 'string'})\n",
        "df1.head(3)"
      ],
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          "height": 143
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        "id": "ks_-gO3KGeDP",
        "outputId": "51422e37-118a-4675-be59-99018169f8df"
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      "execution_count": 19,
      "outputs": [
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              "  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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            "text/plain": [
              "(541909, 8)"
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        "df1[df1.InvoiceNo==\"C536379\"]"
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      "execution_count": 21,
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            "text/plain": [
              "    InvoiceNo StockCode Description  Quantity         InvoiceDate  UnitPrice  \\\n",
              "141   C536379         D    Discount        -1 2010-12-01 09:41:00       27.5   \n",
              "\n",
              "     CustomerID         Country  \n",
              "141     14527.0  United Kingdom  "
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9ac628bc-5d3d-409c-9b95-5941f75c135e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9ac628bc-5d3d-409c-9b95-5941f75c135e');\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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              "\n",
              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1[df1\",\n  \"rows\": 1,\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\": null,\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\": null,\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\": null,\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}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iG_WkB8QIN2K",
        "outputId": "ab15fe93-2cb1-4986-f84e-04a615037724"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   InvoiceNo    541909 non-null  string        \n",
            " 1   StockCode    541909 non-null  string        \n",
            " 2   Description  540455 non-null  string        \n",
            " 3   Quantity     541909 non-null  int64         \n",
            " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
            " 5   UnitPrice    541909 non-null  float64       \n",
            " 6   CustomerID   406829 non-null  float64       \n",
            " 7   Country      541909 non-null  string        \n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), string(4)\n",
            "memory usage: 33.1 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning: Handle Missing Values"
      ],
      "metadata": {
        "id": "mcLbO7yZA3_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isnull().sum()"
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          "height": 335
        },
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        "outputId": "8871d050-4e96-4c77-ab50-f7cf1e073aa0"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo           0\n",
              "StockCode           0\n",
              "Description      1454\n",
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              "InvoiceDate         0\n",
              "UnitPrice           0\n",
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    {
      "cell_type": "code",
      "source": [
        "df1[df1['Description'].isnull()].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4Q_AguZYL7hp",
        "outputId": "7cc26d54-9aea-405e-e5ba-9fe2a1764a91"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     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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              "        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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-26803b7d-f583-4870-b59f-8e1d14ff0944 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",
              "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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1[df1.StockCode==\"22139\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "E9549C_aL7Wh",
        "outputId": "10c82675-a611-4809-c5f2-b2c4005fc3a5"
      },
      "execution_count": 25,
      "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",
              "[993 rows x 8 columns]"
            ],
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              "      <th>InvoiceNo</th>\n",
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              "      <td>536381</td>\n",
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              "      <td>15311.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>6885</th>\n",
              "      <td>536982</td>\n",
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              "      <td>10</td>\n",
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              "      <td>NaN</td>\n",
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              "    </tr>\n",
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              "      <th>7203</th>\n",
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              "      <td>-5</td>\n",
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              "      <th>539531</th>\n",
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              "      <td>22139</td>\n",
              "      <td>RETROSPOT TEA SET CERAMIC 11 PC</td>\n",
              "      <td>1</td>\n",
              "      <td>2011-12-08 16:30:00</td>\n",
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              "      <td>22139</td>\n",
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              "      <td>3</td>\n",
              "      <td>2011-12-09 11:59:00</td>\n",
              "      <td>4.95</td>\n",
              "      <td>12662.0</td>\n",
              "      <td>Germany</td>\n",
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              "<p>993 rows × 8 columns</p>\n",
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              "summary": "{\n  \"name\": \"df1[df1\",\n  \"rows\": 993,\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\": 2.056683418428884,\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\": 1758.7728831262707,\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": 25
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      "source": [
        "df1[df1.StockCode==\"22139\"].Description.mode()"
      ],
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        "id": "7Hh25G-TMhwR",
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      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
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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": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     StockCode                         Description  count\n",
              "0       85123A  WHITE HANGING HEART T-LIGHT HOLDER   2302\n",
              "1        22423            REGENCY CAKESTAND 3 TIER   2200\n",
              "2       85099B             JUMBO BAG RED RETROSPOT   2159\n",
              "3        47566                       PARTY BUNTING   1727\n",
              "4        20725             LUNCH BAG RED RETROSPOT   1638\n",
              "...        ...                                 ...    ...\n",
              "4787    35833P                               check      1\n",
              "4788     21410     COUNTRY COTTAGE  DOORSTOP GREEN      1\n",
              "4789     21412            VINTAGE GOLD TINSEL REEL      1\n",
              "4790     21414             SCALLOP SHELL SOAP DISH      1\n",
              "4791     35972                               check      1\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\": 186,\n        \"min\": 1,\n        \"max\": 2302,\n        \"num_unique_values\": 617,\n        \"samples\": [\n          919,\n          36,\n          742\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "most_freq[most_freq.StockCode==\"85123A\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "JQVwyMxnNzsg",
        "outputId": "9ff886fc-e5d6-4f08-9db7-d91ff80e18fd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     StockCode                         Description  count\n",
              "0       85123A  WHITE HANGING HEART T-LIGHT HOLDER   2302\n",
              "3300    85123A  CREAM HANGING HEART T-LIGHT HOLDER      9\n",
              "4620    85123A         wrongly marked carton 22804      1\n",
              "4658    85123A                                   ?      1"
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              "\n",
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            ],
            "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\": 1149,\n        \"min\": 1,\n        \"max\": 2302,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2302\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "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": "854bf44d-2b6d-469e-c487-722171849dcd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  StockCode                         Description  count\n",
              "0    85123A  WHITE HANGING HEART T-LIGHT HOLDER   2302\n",
              "1     22423            REGENCY CAKESTAND 3 TIER   2200\n",
              "2    85099B             JUMBO BAG RED RETROSPOT   2159\n",
              "3     47566                       PARTY BUNTING   1727\n",
              "4     20725             LUNCH BAG RED RETROSPOT   1638"
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      --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-4e8580d6-46fd-4c99-a96c-dbb05541800e 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\": 198,\n        \"min\": 1,\n        \"max\": 2302,\n        \"num_unique_values\": 616,\n        \"samples\": [\n          764,\n          445,\n          46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "most_freq[most_freq.StockCode==\"85123A\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "xHu0-7_sQjVT",
        "outputId": "17643f2c-0138-4f83-8fdb-ce6b23809879"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  StockCode                         Description  count\n",
              "0    85123A  WHITE HANGING HEART T-LIGHT HOLDER   2302"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83fdea5f-53b0-480a-b0d5-e7d1094b7bdb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Description</th>\n",
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              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>85123A</td>\n",
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              "      <td>2302</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\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-83fdea5f-53b0-480a-b0d5-e7d1094b7bdb 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-83fdea5f-53b0-480a-b0d5-e7d1094b7bdb');\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\": 2302,\n        \"max\": 2302,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2302\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "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": "de69f2d1-58a7-4248-bab7-75c6c9c57fe1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  StockCode                    freq_Description  count\n",
              "0    85123A  WHITE HANGING HEART T-LIGHT HOLDER   2302\n",
              "1     22423            REGENCY CAKESTAND 3 TIER   2200\n",
              "2    85099B             JUMBO BAG RED RETROSPOT   2159"
            ],
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              "\n",
              "  <div id=\"df-983654e6-9bc4-48fa-bd02-897b429cedb6\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <td>2302</td>\n",
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              "      <td>2200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>85099B</td>\n",
              "      <td>JUMBO BAG RED RETROSPOT</td>\n",
              "      <td>2159</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-983654e6-9bc4-48fa-bd02-897b429cedb6 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-983654e6-9bc4-48fa-bd02-897b429cedb6');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
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              "\n",
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              "    border-radius: 50%;\n",
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              "    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",
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              "\n",
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              "    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",
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              "    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\": 198,\n        \"min\": 1,\n        \"max\": 2302,\n        \"num_unique_values\": 616,\n        \"samples\": [\n          764,\n          445,\n          46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "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": "89b98fb4-92d0-484b-c3b5-e54eaf960104"
      },
      "execution_count": 32,
      "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  2302.0  \n",
              "1                 WHITE METAL LANTERN   328.0  \n",
              "2      CREAM CUPID HEARTS COAT HANGER   293.0  "
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              "        const element = document.querySelector('#df-58de6d74-c367-4755-81b6-a44876e9ded3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\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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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\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": 32
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['Description'].isnull()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "h9RYvk43UlRG",
        "outputId": "b9b21f72-15c2-4e69-f003-b3a98da4f981"
      },
      "execution_count": 33,
      "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",
              "535322    581199     84581        <NA>        -2 2011-12-07 18:26:00   \n",
              "535326    581203     23406        <NA>        15 2011-12-07 18:31:00   \n",
              "535332    581209     21620        <NA>         6 2011-12-07 18:35:00   \n",
              "536981    581234     72817        <NA>        27 2011-12-08 10:33:00   \n",
              "538554    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",
              "535322        0.0         NaN  United Kingdom   \n",
              "535326        0.0         NaN  United Kingdom   \n",
              "535332        0.0         NaN  United Kingdom   \n",
              "536981        0.0         NaN  United Kingdom   \n",
              "538554        0.0         NaN  United Kingdom   \n",
              "\n",
              "                           freq_Description  count  \n",
              "622        RETROSPOT TEA SET CERAMIC 11 PC   988.0  \n",
              "1970                                   <NA>    NaN  \n",
              "1971        CHRISTMAS CRAFT HEART STOCKING     1.0  \n",
              "1972             NEW ENGLAND MUG W GIFT BOX    2.0  \n",
              "1987                                   <NA>    NaN  \n",
              "...                                     ...    ...  \n",
              "535322      DOG TOY WITH PINK CROCHET SKIRT   91.0  \n",
              "535326           HOME SWEET HOME KEY HOLDER  114.0  \n",
              "535332      SET OF 4 ROSE BOTANICAL CANDLES  172.0  \n",
              "536981  SET OF 2 CHRISTMAS DECOUPAGE CANDLE  164.0  \n",
              "538554                CACTI T-LIGHT CANDLES  148.0  \n",
              "\n",
              "[1454 rows x 10 columns]"
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              "      <th>InvoiceNo</th>\n",
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              "      <td>988.0</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>1.0</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>2.0</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>535322</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>91.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>535326</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>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>535332</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>172.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536981</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>164.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>538554</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>148.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1454 rows × 10 columns</p>\n",
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              "summary": "{\n  \"name\": \"df2[df2['Description']\",\n  \"rows\": 1454,\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\": 221.96487804114625,\n        \"min\": 1.0,\n        \"max\": 1473.0,\n        \"num_unique_values\": 253,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['Description'] = df2['Description'].mask(df2['Description'].isnull(), df2['freq_Description'])\n",
        "df2.isnull().sum()"
      ],
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      "execution_count": 34,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo                0\n",
              "StockCode                0\n",
              "Description            112\n",
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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": {
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        "outputId": "8b61d09a-469c-4fbe-d37e-a0af7aec9e04"
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      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo                0\n",
              "StockCode                0\n",
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              "      <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>134968</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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.drop(columns = [\"freq_Description\", \"count\"], inplace=True)"
      ],
      "metadata": {
        "id": "14kKUDZlP0e9"
      },
      "execution_count": 36,
      "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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          "height": 300
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        "id": "UBLIWMQhV-fx",
        "outputId": "9af947ed-cd69-482d-e5c7-b657db181026"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity                    InvoiceDate      UnitPrice  \\\n",
              "count  541797.000000                         541797  541797.000000   \n",
              "mean        9.555919  2011-07-04 14:06:48.671255296       4.612067   \n",
              "min    -80995.000000            2010-12-01 08:26:00  -11062.060000   \n",
              "25%         1.000000            2011-03-28 11:36:00       1.250000   \n",
              "50%         3.000000            2011-07-20 08:59:00       2.080000   \n",
              "75%        10.000000            2011-10-19 11:41:00       4.130000   \n",
              "max     80995.000000            2011-12-09 12:50:00   38970.000000   \n",
              "std       218.103428                            NaN      96.769831   \n",
              "\n",
              "          CustomerID  \n",
              "count  406829.000000  \n",
              "mean    15287.690570  \n",
              "min     12346.000000  \n",
              "25%     13953.000000  \n",
              "50%     15152.000000  \n",
              "75%     16791.000000  \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\": 196373.79753242992,\n        \"min\": -80995.0,\n        \"max\": 541797.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.555918545137754,\n          10.0,\n          541797.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000541797\",\n        \"max\": \"2011-12-09 12:50:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"541797\",\n          \"2011-07-04 14:06:48.671255296\",\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\": 190712.59619572028,\n        \"min\": -11062.06,\n        \"max\": 541797.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.612066833149686,\n          4.13,\n          541797.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 139204.1680069419,\n        \"min\": 1713.6003033215986,\n        \"max\": 406829.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15287.690570239585,\n          16791.0,\n          406829.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "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": "f8b2dc1b-6a69-4dd3-beeb-dbaaf79ca506"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10527, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2.UnitPrice<=0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUjd98IZWVqn",
        "outputId": "abf4860b-6c9c-41d8-fd4e-d0ffe885edc5"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2405, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
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    {
      "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": 308
        },
        "id": "istRIA2YWa86",
        "outputId": "a7815a43-3f8a-4fb1-90df-15b93a34d4f1"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity                    InvoiceDate      UnitPrice  \\\n",
              "count  530104.000000                         530104  530104.000000   \n",
              "mean       10.542037  2011-07-04 20:16:05.225087744       3.907625   \n",
              "min         1.000000            2010-12-01 08:26:00       0.001000   \n",
              "25%         1.000000            2011-03-28 12:22:00       1.250000   \n",
              "50%         3.000000            2011-07-20 12:58:00       2.080000   \n",
              "75%        10.000000            2011-10-19 12:39:00       4.130000   \n",
              "max     80995.000000            2011-12-09 12:50:00   13541.330000   \n",
              "std       155.524124                            NaN      35.915681   \n",
              "\n",
              "          CustomerID  \n",
              "count  397884.000000  \n",
              "mean    15294.423453  \n",
              "min     12346.000000  \n",
              "25%     13969.000000  \n",
              "50%     15159.000000  \n",
              "75%     16795.000000  \n",
              "max     18287.000000  \n",
              "std      1713.141560  "
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              "    <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>530104.000000</td>\n",
              "      <td>530104</td>\n",
              "      <td>530104.000000</td>\n",
              "      <td>397884.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.542037</td>\n",
              "      <td>2011-07-04 20:16:05.225087744</td>\n",
              "      <td>3.907625</td>\n",
              "      <td>15294.423453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>12346.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2011-03-28 12:22:00</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>13969.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>2011-07-20 12:58:00</td>\n",
              "      <td>2.080000</td>\n",
              "      <td>15159.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>2011-10-19 12:39:00</td>\n",
              "      <td>4.130000</td>\n",
              "      <td>16795.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80995.000000</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>13541.330000</td>\n",
              "      <td>18287.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>155.524124</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.915681</td>\n",
              "      <td>1713.141560</td>\n",
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              "\n",
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            "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\": 185496.45754809,\n        \"min\": 1.0,\n        \"max\": 530104.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          530104.0,\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 00:00:00.000530104\",\n        \"max\": \"2011-12-09 12:50:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"530104\",\n          \"2011-07-04 20:16:05.225087744\",\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\": 186793.77949489455,\n        \"min\": 0.001,\n        \"max\": 530104.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.90762524712132,\n          4.13,\n          530104.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 136042.15806984305,\n        \"min\": 1713.141560439856,\n        \"max\": 397884.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15294.423452564064,\n          16795.0,\n          397884.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "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": "c8d86b85-5cf3-449d-be92-afa3257a98ce"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1439.8763999990188"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "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": "8bb4b2ee-1e1b-4374-9e64-24ae48403f79"
      },
      "execution_count": 42,
      "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",
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              "\n",
              "<div id=\"df-3c0eda06-def1-47bf-9c7f-100d2d162c09\">\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
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              "      const quickchartButtonEl =\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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3c0eda06-def1-47bf-9c7f-100d2d162c09 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"
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          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "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": "18aa3221-6168-4b7e-f74e-5b8993af41ee"
      },
      "execution_count": 43,
      "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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              "        const element = document.querySelector('#df-e30e2921-1f2c-43a3-b5c0-efc9100333f6');\n",
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              "                                                    [key], {});\n",
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              "\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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              "\n",
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              "    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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\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": 43
        }
      ]
    },
    {
      "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": "0b49d8a0-d677-438b-f3c7-d875e6eb8a4f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       InvoiceNo StockCode                         Description  Quantity  \\\n",
              "176674    552013     22434       BALLOON PUMP WITH 10 BALLOONS        24   \n",
              "509588    579387     90094  NECKLACE+BRACELET SET FRUIT SALAD          1   \n",
              "\n",
              "               InvoiceDate  UnitPrice  CustomerID         Country  TotalSales  \\\n",
              "176674 2011-05-05 17:34:00       1.95     12451.0     Switzerland       46.80   \n",
              "509588 2011-11-29 12:06:00       2.55     14452.0  United Kingdom        2.55   \n",
              "\n",
              "        Month  \n",
              "176674      5  \n",
              "509588     11  "
            ],
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              "\n",
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              "      <td>24</td>\n",
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              "      <td>1.95</td>\n",
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              "      <td>Switzerland</td>\n",
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              "      <td>United Kingdom</td>\n",
              "      <td>2.55</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-460ed410-9514-4191-b9ae-bc301a4967cd 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-460ed410-9514-4191-b9ae-bc301a4967cd');\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",
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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",
              "      --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",
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              "      --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-284ae9b5-7ed4-4193-b2c6-e9f06d9ee658 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",
              "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          \"579387\",\n          \"552013\"\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          \"90094\",\n          \"22434\"\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          \"NECKLACE+BRACELET SET FRUIT SALAD \",\n          \"BALLOON PUMP WITH 10 BALLOONS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 1,\n        \"max\": 24,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-05-05 17:34:00\",\n        \"max\": \"2011-11-29 12:06:00\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2011-11-29 12:06:00\",\n          \"2011-05-05 17:34:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.42426406871192845,\n        \"min\": 1.95,\n        \"max\": 2.55,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.55,\n          1.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1414.9206691542815,\n        \"min\": 12451.0,\n        \"max\": 14452.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          14452.0,\n          12451.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"United Kingdom\",\n          \"Switzerland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalSales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.289475067504725,\n        \"min\": 2.55,\n        \"max\": 46.8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.55,\n          46.8\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          11,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "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": "9a1e82ff-6034-491a-cdf9-5fb0098b9961"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "1      691364.560\n",
              "2      523631.890\n",
              "3      717639.360\n",
              "4      537808.621\n",
              "5      770536.020\n",
              "6      761739.900\n",
              "7      719221.191\n",
              "8      759138.380\n",
              "9     1058590.172\n",
              "10    1154979.300\n",
              "11    1509496.330\n",
              "12    1462538.820\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>691364.560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>523631.890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>717639.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>537808.621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>770536.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>761739.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>719221.191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>759138.380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1058590.172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1154979.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1509496.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1462538.820</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "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": "fdb7de53-6422-479e-c48c-797a192dea14"
      },
      "execution_count": 46,
      "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": "3e6a6ebf-82d1-47be-a16c-08c21fab5963"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "United Kingdom    9025222.084\n",
              "Netherlands        285446.340\n",
              "EIRE               283453.960\n",
              "Germany            228867.140\n",
              "France             209715.110\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>9025222.084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>285446.340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EIRE</th>\n",
              "      <td>283453.960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>228867.140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>209715.110</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "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": "dc154f79-06b5-456c-b000-ba74f84ff095"
      },
      "execution_count": 48,
      "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": "68e43961-3dff-4649-8373-d9f500633279"
      },
      "execution_count": 49,
      "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": "3826012f-7677-4c2d-ffa8-5b798f41fabd"
      },
      "execution_count": 50,
      "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": "f506960d-a41b-48d1-e742-febe961e57c2"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StockCode\n",
              "DOT       206248.770\n",
              "22423     174484.740\n",
              "23843     168469.600\n",
              "85123A    104518.800\n",
              "47566      99504.330\n",
              "             ...    \n",
              "90084          0.850\n",
              "21268          0.840\n",
              "51014c         0.830\n",
              "84227          0.420\n",
              "PADS           0.003\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>206248.770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22423</th>\n",
              "      <td>174484.740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23843</th>\n",
              "      <td>168469.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85123A</th>\n",
              "      <td>104518.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47566</th>\n",
              "      <td>99504.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90084</th>\n",
              "      <td>0.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21268</th>\n",
              "      <td>0.840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51014c</th>\n",
              "      <td>0.830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84227</th>\n",
              "      <td>0.420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PADS</th>\n",
              "      <td>0.003</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": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product_wise_sales.sum()"
      ],
      "metadata": {
        "id": "y155ddQU0DN4",
        "outputId": "5ddbbdb1-e6d7-418c-f84c-a072b6e37938",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10666684.544"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4[df4.StockCode==\"DOT\"].Description.iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ExxLHmLAohhw",
        "outputId": "822bb5db-1a82-41ba-8603-99f1170fa858"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'DOTCOM POSTAGE'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "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": "4080e8eb-86c7-4667-8b45-15b1b8e93e80"
      },
      "execution_count": 54,
      "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": "967663b7-dd2a-4d28-be3a-2ec7deba53ff"
      },
      "execution_count": 55,
      "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": "8b852742-70a7-44ae-dfd2-71bdbb2ce145"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2011-12-09 12:50:00')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "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": "88a2d405-b210-408f-8443-facf0dea6c7a"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary\n",
              "CustomerID                              \n",
              "12346.0         326          1  77183.60\n",
              "12347.0           2        182   4310.00\n",
              "12348.0          75         31   1797.24\n",
              "12349.0          19         73   1757.55\n",
              "12350.0         310         17    334.40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-78ed6569-3add-45cb-a302-306722b88a29\" 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>1</td>\n",
              "      <td>77183.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12347.0</th>\n",
              "      <td>2</td>\n",
              "      <td>182</td>\n",
              "      <td>4310.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12348.0</th>\n",
              "      <td>75</td>\n",
              "      <td>31</td>\n",
              "      <td>1797.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12349.0</th>\n",
              "      <td>19</td>\n",
              "      <td>73</td>\n",
              "      <td>1757.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12350.0</th>\n",
              "      <td>310</td>\n",
              "      <td>17</td>\n",
              "      <td>334.40</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-78ed6569-3add-45cb-a302-306722b88a29')\"\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-78ed6569-3add-45cb-a302-306722b88a29 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-78ed6569-3add-45cb-a302-306722b88a29');\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-64326a82-7fee-43c9-bf26-f6d44236fb80\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-64326a82-7fee-43c9-bf26-f6d44236fb80')\"\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-64326a82-7fee-43c9-bf26-f6d44236fb80 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\": 228,\n        \"min\": 1,\n        \"max\": 7847,\n        \"num_unique_values\": 460,\n        \"samples\": [\n          42,\n          27,\n          200\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8989.230441338681,\n        \"min\": 3.75,\n        \"max\": 280206.02,\n        \"num_unique_values\": 4253,\n        \"samples\": [\n          2794.51,\n          379.35,\n          954.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfm.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1FnfdG8ppvw9",
        "outputId": "9c36f995-76ea-4087-9758-6e13d895ee32"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Recency    Frequency       Monetary\n",
              "count  4338.000000  4338.000000    4338.000000\n",
              "mean     92.536422    91.720609    2054.266460\n",
              "std     100.014169   228.785094    8989.230441\n",
              "min       1.000000     1.000000       3.750000\n",
              "25%      18.000000    17.000000     307.415000\n",
              "50%      51.000000    41.000000     674.485000\n",
              "75%     142.000000   100.000000    1661.740000\n",
              "max     374.000000  7847.000000  280206.020000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-48891f78-a025-418d-9cdb-f6fea2cdc850\" 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>4338.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>92.536422</td>\n",
              "      <td>91.720609</td>\n",
              "      <td>2054.266460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>100.014169</td>\n",
              "      <td>228.785094</td>\n",
              "      <td>8989.230441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>307.415000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>674.485000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>142.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1661.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>374.000000</td>\n",
              "      <td>7847.000000</td>\n",
              "      <td>280206.020000</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-48891f78-a025-418d-9cdb-f6fea2cdc850')\"\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-48891f78-a025-418d-9cdb-f6fea2cdc850 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-48891f78-a025-418d-9cdb-f6fea2cdc850');\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-ed90b225-df8b-4149-91e1-a4ae6efbc2af\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed90b225-df8b-4149-91e1-a4ae6efbc2af')\"\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",
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              "            'suggestCharts', [key], {});\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",
              "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\": 2937.8725588254392,\n        \"min\": 1.0,\n        \"max\": 7847.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          91.72060857538035,\n          41.0,\n          4338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98201.36857420603,\n        \"min\": 3.75,\n        \"max\": 280206.02,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2054.2664601198708,\n          674.485,\n          4338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "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": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfm.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "rYuVL0LNq5_Z",
        "outputId": "e2665126-255d-48b7-de52-61fc5708a78e"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary R_Segment F_Segment M_Segment  \\\n",
              "CustomerID                                                               \n",
              "13160.0          22          4     91.80         3         1         1   \n",
              "15539.0           7         41    538.46         4         2         2   \n",
              "15228.0          66        125   3386.71         2         4         4   \n",
              "13366.0          50          1     56.16         3         1         1   \n",
              "17320.0         100         73   1200.62         2         3         3   \n",
              "\n",
              "            RFM_Score  \n",
              "CustomerID             \n",
              "13160.0             5  \n",
              "15539.0             8  \n",
              "15228.0            10  \n",
              "13366.0             5  \n",
              "17320.0             8  "
            ],
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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",
              "          '<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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              "\n",
              "  [theme=dark] .colab-df-quickchart {\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",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\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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              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\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-c4649c6b-cded-4829-ad9c-97ef83a5a257 button');\n",
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              "\n",
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            ],
            "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\": 1713.9506410629215,\n        \"min\": 13160.0,\n        \"max\": 17320.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          15539.0,\n          17320.0,\n          15228.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36,\n        \"min\": 7,\n        \"max\": 100,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7,\n          100,\n          66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 1,\n        \"max\": 125,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          41,\n          73,\n          125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1382.9610942828435,\n        \"min\": 56.160000000000004,\n        \"max\": 3386.71,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          538.46,\n          1200.6200000000001,\n          3386.71\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          4,\n          2\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          2,\n          3,\n          1\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          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RFM_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 5,\n        \"max\": 10,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          8,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "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": "fd324300-b862-46dd-fcde-cb193d10c004"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary R_Segment F_Segment M_Segment  \\\n",
              "CustomerID                                                               \n",
              "18198.0           4        159   5425.56         4         4         4   \n",
              "18210.0           2        134   2621.38         4         4         4   \n",
              "18225.0           3        271   5509.12         4         4         4   \n",
              "18283.0           4        756   2094.88         4         4         4   \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\": 228,\n        \"min\": 1,\n        \"max\": 7847,\n        \"num_unique_values\": 460,\n        \"samples\": [\n          476,\n          310,\n          488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8989.230441338681,\n        \"min\": 3.75,\n        \"max\": 280206.02,\n        \"num_unique_values\": 4253,\n        \"samples\": [\n          2666.36,\n          1628.12,\n          199.48000000000002\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": 61
        }
      ]
    },
    {
      "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": "20d55602-b1a2-41c5-8d43-e3983e9c84b4"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  InvoiceNo StockCode                         Description  Quantity  \\\n",
              "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                 WHITE METAL LANTERN         6   \n",
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              "          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",
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              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "      <td>12</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    }\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",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df4"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "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": "edeeccf0-b476-431d-e1f0-d970351d3841"
      },
      "execution_count": 63,
      "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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              "      <th>InvoiceDate</th>\n",
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              "      <th>CustomerID</th>\n",
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              "      <th>12346.0</th>\n",
              "      <td>2011-01-18 10:01:00</td>\n",
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              "      <td>2011-12-07 15:52:00</td>\n",
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              "      <th>12348.0</th>\n",
              "      <td>2011-09-25 13:13:00</td>\n",
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              "      <th>12349.0</th>\n",
              "      <td>2011-11-21 09:51:00</td>\n",
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              "      <th>12350.0</th>\n",
              "      <td>2011-02-02 16:01:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> datetime64[ns]</label>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(customer_last_purchase)"
      ],
      "metadata": {
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          "height": 203
        },
        "id": "YuzJWXHd4V-o",
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      },
      "execution_count": 64,
      "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": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "current_date"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSfeTxS54q0d",
        "outputId": "f84e0d19-80c9-4122-c5ac-f3483c80bde5"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2011-12-10 12:50:00')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "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": "40117e6e-83b9-4575-9c5c-6cbf8d06b018"
      },
      "execution_count": 66,
      "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",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>InvoiceDate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <th></th>\n",
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              "      <td>326</td>\n",
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              "      <td>2</td>\n",
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              "      <td>75</td>\n",
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              "      <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": 66
        }
      ]
    },
    {
      "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": "d88305a6-6683-4f30-de23-4f2318aa9606"
      },
      "execution_count": 68,
      "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": 68
        }
      ]
    },
    {
      "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": "7b0bdb85-7fb1-4112-a028-d5f8d9ada3bd"
      },
      "execution_count": 70,
      "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": "code",
      "source": [
        "print(f\"[{pd.__name__}] total time taken: {time.time() - stime} sec\")"
      ],
      "metadata": {
        "id": "-a3PPo6Z6Cs8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}