Data_Cleaning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMmDR2aGVZ3gGdtTTbmqWuQ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/udhaya2823/DataSpark-Illuminating-Insights-for-Global-Electronics/blob/Projects/Data_Cleaning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importing Libraries\n"
      ],
      "metadata": {
        "id": "PWIQEd1vgGF2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import missingno as msno\n",
        "import datetime as dt\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import mysql.connector\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "nKbQgAEMXWTf"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#cutomers"
      ],
      "metadata": {
        "id": "dbz9MC6HXzz0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V7GK1ZRroaoW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e9ff989e-6a0b-461a-9b34-a12ba0651f7e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerKey  Gender               Name            City State Code  \\\n",
              "0          301  Female      Lilly Harding  WANDEARAH EAST         SA   \n",
              "1          325  Female       Madison Hull      MOUNT BUDD         WA   \n",
              "2          554  Female      Claire Ferres       WINJALLOK        VIC   \n",
              "3          786    Male  Jai Poltpalingada    MIDDLE RIVER         SA   \n",
              "4         1042    Male    Aidan Pankhurst   TAWONGA SOUTH        VIC   \n",
              "\n",
              "               State Zip Code    Country  Continent    Birthday  \n",
              "0    South Australia     5523  Australia  Australia    7/3/1939  \n",
              "1  Western Australia     6522  Australia  Australia   9/27/1979  \n",
              "2           Victoria     3380  Australia  Australia   5/26/1947  \n",
              "3    South Australia     5223  Australia  Australia   9/17/1957  \n",
              "4           Victoria     3698  Australia  Australia  11/19/1965  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5dbab973-5584-4992-81f4-7e9ec11e437c\" 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>CustomerKey</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Name</th>\n",
              "      <th>City</th>\n",
              "      <th>State Code</th>\n",
              "      <th>State</th>\n",
              "      <th>Zip Code</th>\n",
              "      <th>Country</th>\n",
              "      <th>Continent</th>\n",
              "      <th>Birthday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>301</td>\n",
              "      <td>Female</td>\n",
              "      <td>Lilly Harding</td>\n",
              "      <td>WANDEARAH EAST</td>\n",
              "      <td>SA</td>\n",
              "      <td>South Australia</td>\n",
              "      <td>5523</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>7/3/1939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>325</td>\n",
              "      <td>Female</td>\n",
              "      <td>Madison Hull</td>\n",
              "      <td>MOUNT BUDD</td>\n",
              "      <td>WA</td>\n",
              "      <td>Western Australia</td>\n",
              "      <td>6522</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9/27/1979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>554</td>\n",
              "      <td>Female</td>\n",
              "      <td>Claire Ferres</td>\n",
              "      <td>WINJALLOK</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>3380</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>5/26/1947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>786</td>\n",
              "      <td>Male</td>\n",
              "      <td>Jai Poltpalingada</td>\n",
              "      <td>MIDDLE RIVER</td>\n",
              "      <td>SA</td>\n",
              "      <td>South Australia</td>\n",
              "      <td>5223</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9/17/1957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1042</td>\n",
              "      <td>Male</td>\n",
              "      <td>Aidan Pankhurst</td>\n",
              "      <td>TAWONGA SOUTH</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>3698</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>11/19/1965</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-5dbab973-5584-4992-81f4-7e9ec11e437c')\"\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-5dbab973-5584-4992-81f4-7e9ec11e437c 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-5dbab973-5584-4992-81f4-7e9ec11e437c');\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-03f1e61f-6334-4c36-ab2e-182bfc606349\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-03f1e61f-6334-4c36-ab2e-182bfc606349')\"\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-03f1e61f-6334-4c36-ab2e-182bfc606349 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": "customer_dt",
              "summary": "{\n  \"name\": \"customer_dt\",\n  \"rows\": 15266,\n  \"fields\": [\n    {\n      \"column\": \"CustomerKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 612709,\n        \"min\": 301,\n        \"max\": 2099937,\n        \"num_unique_values\": 15266,\n        \"samples\": [\n          1163087,\n          943859,\n          402983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15118,\n        \"samples\": [\n          \"Aagje Maring\",\n          \"Spencer Clayton\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8258,\n        \"samples\": [\n          \"Oberpframmern\",\n          \"Schleching\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 467,\n        \"samples\": [\n          \"CE\",\n          \"VI\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 512,\n        \"samples\": [\n          \"Mid Suffolk\",\n          \"Idaho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zip Code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9505,\n        \"samples\": [\n          \"90461\",\n          \"66865\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Canada\",\n          \"Netherlands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Australia\",\n          \"North America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Birthday\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 11270,\n        \"samples\": [\n          \"1/20/1996\",\n          \"4/27/1938\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "path = \"/content/Customers.csv\"\n",
        "customer_dt=pd.read_csv(path,encoding='latin-1') #character encoding standard (like é, ö, etc.)\n",
        "customer_dt.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_dt.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQnhmR5MQJtj",
        "outputId": "1c018fb9-3886-4b13-cb2f-4c2fed63d180"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15266 entries, 0 to 15265\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   CustomerKey  15266 non-null  int64 \n",
            " 1   Gender       15266 non-null  object\n",
            " 2   Name         15266 non-null  object\n",
            " 3   City         15266 non-null  object\n",
            " 4   State Code   15256 non-null  object\n",
            " 5   State        15266 non-null  object\n",
            " 6   Zip Code     15266 non-null  object\n",
            " 7   Country      15266 non-null  object\n",
            " 8   Continent    15266 non-null  object\n",
            " 9   Birthday     15266 non-null  object\n",
            "dtypes: int64(1), object(9)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(customer_dt.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFDZKbBGRm5A",
        "outputId": "91c33214-54b2-47c8-cec9-fb927f0062a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CustomerKey     0\n",
            "Gender          0\n",
            "Name            0\n",
            "City            0\n",
            "State Code     10\n",
            "State           0\n",
            "Zip Code        0\n",
            "Country         0\n",
            "Continent       0\n",
            "Birthday        0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Droping useless columns\n",
        "customer_dt.drop([\"State Code\",\"Zip Code\"],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "nXKVTGv9SJuM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicate rows\n",
        "customer_dt.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "NEAMvQ3cT7wg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert Birthday column to datetime\n",
        "customer_dt['Birthday'] = pd.to_datetime(customer_dt['Birthday'], format = '%m/%d/%Y')\n",
        "customer_dt.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pYDiy3Q4X7x4",
        "outputId": "be31451f-fb34-4f86-9cd1-a811e45bdb0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerKey  Gender               Name            City State Code  \\\n",
              "0          301  Female      Lilly Harding  WANDEARAH EAST         SA   \n",
              "1          325  Female       Madison Hull      MOUNT BUDD         WA   \n",
              "2          554  Female      Claire Ferres       WINJALLOK        VIC   \n",
              "3          786    Male  Jai Poltpalingada    MIDDLE RIVER         SA   \n",
              "4         1042    Male    Aidan Pankhurst   TAWONGA SOUTH        VIC   \n",
              "\n",
              "               State Zip Code    Country  Continent   Birthday  \n",
              "0    South Australia     5523  Australia  Australia 1939-07-03  \n",
              "1  Western Australia     6522  Australia  Australia 1979-09-27  \n",
              "2           Victoria     3380  Australia  Australia 1947-05-26  \n",
              "3    South Australia     5223  Australia  Australia 1957-09-17  \n",
              "4           Victoria     3698  Australia  Australia 1965-11-19  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff025fa1-3531-4335-9e05-38ea1ffd1e6d\" 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>CustomerKey</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Name</th>\n",
              "      <th>City</th>\n",
              "      <th>State Code</th>\n",
              "      <th>State</th>\n",
              "      <th>Zip Code</th>\n",
              "      <th>Country</th>\n",
              "      <th>Continent</th>\n",
              "      <th>Birthday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>301</td>\n",
              "      <td>Female</td>\n",
              "      <td>Lilly Harding</td>\n",
              "      <td>WANDEARAH EAST</td>\n",
              "      <td>SA</td>\n",
              "      <td>South Australia</td>\n",
              "      <td>5523</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1939-07-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>325</td>\n",
              "      <td>Female</td>\n",
              "      <td>Madison Hull</td>\n",
              "      <td>MOUNT BUDD</td>\n",
              "      <td>WA</td>\n",
              "      <td>Western Australia</td>\n",
              "      <td>6522</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1979-09-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>554</td>\n",
              "      <td>Female</td>\n",
              "      <td>Claire Ferres</td>\n",
              "      <td>WINJALLOK</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>3380</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1947-05-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>786</td>\n",
              "      <td>Male</td>\n",
              "      <td>Jai Poltpalingada</td>\n",
              "      <td>MIDDLE RIVER</td>\n",
              "      <td>SA</td>\n",
              "      <td>South Australia</td>\n",
              "      <td>5223</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1957-09-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1042</td>\n",
              "      <td>Male</td>\n",
              "      <td>Aidan Pankhurst</td>\n",
              "      <td>TAWONGA SOUTH</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>3698</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1965-11-19</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-ff025fa1-3531-4335-9e05-38ea1ffd1e6d')\"\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-ff025fa1-3531-4335-9e05-38ea1ffd1e6d 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-ff025fa1-3531-4335-9e05-38ea1ffd1e6d');\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-c58d5744-4894-41bc-a1cf-7e4f6b73eeb2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c58d5744-4894-41bc-a1cf-7e4f6b73eeb2')\"\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-c58d5744-4894-41bc-a1cf-7e4f6b73eeb2 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": "customer_dt",
              "summary": "{\n  \"name\": \"customer_dt\",\n  \"rows\": 15266,\n  \"fields\": [\n    {\n      \"column\": \"CustomerKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 612709,\n        \"min\": 301,\n        \"max\": 2099937,\n        \"num_unique_values\": 15266,\n        \"samples\": [\n          1163087,\n          943859,\n          402983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15118,\n        \"samples\": [\n          \"Aagje Maring\",\n          \"Spencer Clayton\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8258,\n        \"samples\": [\n          \"Oberpframmern\",\n          \"Schleching\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 468,\n        \"samples\": [\n          \"CE\",\n          \"VI\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 512,\n        \"samples\": [\n          \"Mid Suffolk\",\n          \"Idaho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zip Code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9505,\n        \"samples\": [\n          \"90461\",\n          \"66865\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Canada\",\n          \"Netherlands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Australia\",\n          \"North America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Birthday\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1935-02-03 00:00:00\",\n        \"max\": \"2002-02-18 00:00:00\",\n        \"num_unique_values\": 11270,\n        \"samples\": [\n          \"1996-01-20 00:00:00\",\n          \"1938-04-27 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking null set and data types\n",
        "customer_dt.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PuPRWsA_a1t_",
        "outputId": "304c7863-028c-4d83-dcb6-b2b44099a1c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15266 entries, 0 to 15265\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype         \n",
            "---  ------       --------------  -----         \n",
            " 0   CustomerKey  15266 non-null  int64         \n",
            " 1   Gender       15266 non-null  object        \n",
            " 2   Name         15266 non-null  object        \n",
            " 3   City         15266 non-null  object        \n",
            " 4   State        15266 non-null  object        \n",
            " 5   Country      15266 non-null  object        \n",
            " 6   Continent    15266 non-null  object        \n",
            " 7   Birthday     15266 non-null  datetime64[ns]\n",
            "dtypes: datetime64[ns](1), int64(1), object(6)\n",
            "memory usage: 954.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize gender details\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.subplot(1,2,1)\n",
        "plt.pie(customer_dt[\"Gender\"].value_counts(), autopct='%1.0f%%', labels=['Male', 'Female'])\n",
        "plt.subplot(1,2,2)\n",
        "sns.countplot(x= customer_dt[\"Gender\"],data=customer_dt,hue=\"Gender\")\n",
        "plt.xlabel(\"Gender\")\n",
        "plt.ylabel(\"counts\")\n",
        "plt.title(\"counts as per Gender\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "gyacaHjXa1qm",
        "outputId": "cda15b56-fc92-489b-9357-860d086bc311"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize country counts\n",
        "plt.figure(figsize=(12,5))\n",
        "sns.countplot(x= customer_dt[\"Country\"],data=customer_dt,hue=\"Country\")\n",
        "plt.xlabel(\"Country\")\n",
        "plt.ylabel(\"Counts\")\n",
        "plt.title(\"Counts as per Country\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "b5ftBVPFa1nv",
        "outputId": "6ab26074-f9b2-4d2c-ca76-80b180e64df9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Counts as per Country')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Exchange_Rates"
      ],
      "metadata": {
        "id": "XHiVE7jLgVum"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_exchange=pd.read_csv(\"/content/Exchange_Rates.csv\")\n",
        "df_exchange"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "fM87drJpa1k7",
        "outputId": "65c8226a-6fd6-4736-d4d3-a9cd995c2c39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Date Currency  Exchange\n",
              "0       1/1/2015      USD    1.0000\n",
              "1       1/1/2015      CAD    1.1583\n",
              "2       1/1/2015      AUD    1.2214\n",
              "3       1/1/2015      EUR    0.8237\n",
              "4       1/1/2015      GBP    0.6415\n",
              "...          ...      ...       ...\n",
              "11210  2/20/2021      USD    1.0000\n",
              "11211  2/20/2021      CAD    1.2610\n",
              "11212  2/20/2021      AUD    1.2723\n",
              "11213  2/20/2021      EUR    0.8238\n",
              "11214  2/20/2021      GBP    0.7126\n",
              "\n",
              "[11215 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6c9293d-9423-4be2-ada2-757890bc7f1b\" 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>Date</th>\n",
              "      <th>Currency</th>\n",
              "      <th>Exchange</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1/1/2015</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/1/2015</td>\n",
              "      <td>CAD</td>\n",
              "      <td>1.1583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1/1/2015</td>\n",
              "      <td>AUD</td>\n",
              "      <td>1.2214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/1/2015</td>\n",
              "      <td>EUR</td>\n",
              "      <td>0.8237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1/1/2015</td>\n",
              "      <td>GBP</td>\n",
              "      <td>0.6415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11210</th>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11211</th>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>CAD</td>\n",
              "      <td>1.2610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11212</th>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>AUD</td>\n",
              "      <td>1.2723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11213</th>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>EUR</td>\n",
              "      <td>0.8238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11214</th>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>GBP</td>\n",
              "      <td>0.7126</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11215 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6c9293d-9423-4be2-ada2-757890bc7f1b')\"\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-f6c9293d-9423-4be2-ada2-757890bc7f1b 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-f6c9293d-9423-4be2-ada2-757890bc7f1b');\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-6dcd2ac6-ce92-44a7-9beb-5637ad9fdae3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6dcd2ac6-ce92-44a7-9beb-5637ad9fdae3')\"\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-6dcd2ac6-ce92-44a7-9beb-5637ad9fdae3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_234eb4d3-6997-414a-b9c9-22e2b9eac64a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_exchange')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_234eb4d3-6997-414a-b9c9-22e2b9eac64a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_exchange');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_exchange",
              "summary": "{\n  \"name\": \"df_exchange\",\n  \"rows\": 11215,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2243,\n        \"samples\": [\n          \"12/28/2016\",\n          \"6/26/2017\",\n          \"3/28/2019\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Currency\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"CAD\",\n          \"GBP\",\n          \"AUD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Exchange\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24551875667014184,\n        \"min\": 0.6285,\n        \"max\": 1.7253,\n        \"num_unique_values\": 3473,\n        \"samples\": [\n          0.9063,\n          0.9478,\n          1.354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_exchange.isnull().sum())\n",
        "# If there are missing values, decide on a strategy for handling them (e.g., drop rows, impute with mean/median).\n",
        "\n",
        "# Summary statistics\n",
        "print(df_exchange.describe())\n",
        "\n",
        "# Check data types\n",
        "print(df_exchange.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMFfvoU1iSPv",
        "outputId": "8c72fc5d-8ef9-41d4-9387-04d817458fdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date        0\n",
            "Currency    0\n",
            "Exchange    0\n",
            "dtype: int64\n",
            "           Exchange\n",
            "count  11215.000000\n",
            "mean       1.061682\n",
            "std        0.245519\n",
            "min        0.628500\n",
            "25%        0.857800\n",
            "50%        1.000000\n",
            "75%        1.311900\n",
            "max        1.725300\n",
            "Date         object\n",
            "Currency     object\n",
            "Exchange    float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Date' column to datetime objects\n",
        "df_exchange['Date'] = pd.to_datetime(df_exchange['Date'])"
      ],
      "metadata": {
        "id": "RzoZmv41iSEY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the distribution of 'Exchange' rates\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(df_exchange['Exchange'], bins=20, kde=True)\n",
        "plt.title('Distribution of Exchange Rates')\n",
        "plt.xlabel('Exchange Rate')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "hBeR0ycyicvE",
        "outputId": "319ef058-9924-447b-8536-a0ca83653673"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "# Analyze the 'Exchange' rate over time\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(x='Date', y='Exchange', data=df_exchange)\n",
        "plt.title('Exchange Rate Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Exchange Rate')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-85L8I5UkHDq",
        "outputId": "a960dcde-25be-4bb5-ccc3-77b0aea9a1ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Products"
      ],
      "metadata": {
        "id": "AnZGllySolDI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_products=pd.read_csv(\"/content/Products.csv\")\n",
        "df_products"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "gGf16WRnhjVJ",
        "outputId": "21b3f5b0-fcd4-45de-d7ee-40b1d8fcc559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ProductKey                                   Product Name    Brand  \\\n",
              "0              1            Contoso 512MB MP3 Player E51 Silver  Contoso   \n",
              "1              2              Contoso 512MB MP3 Player E51 Blue  Contoso   \n",
              "2              3               Contoso 1G MP3 Player E100 White  Contoso   \n",
              "3              4              Contoso 2G MP3 Player E200 Silver  Contoso   \n",
              "4              5                 Contoso 2G MP3 Player E200 Red  Contoso   \n",
              "...          ...                                            ...      ...   \n",
              "2512        2513    Contoso Bluetooth Active Headphones L15 Red  Contoso   \n",
              "2513        2514  Contoso Bluetooth Active Headphones L15 White  Contoso   \n",
              "2514        2515             Contoso In-Line Coupler E180 White  Contoso   \n",
              "2515        2516             Contoso In-Line Coupler E180 Black  Contoso   \n",
              "2516        2517            Contoso In-Line Coupler E180 Silver  Contoso   \n",
              "\n",
              "       Color Unit Cost USD Unit Price USD  SubcategoryKey  \\\n",
              "0     Silver        $6.62         $12.99              101   \n",
              "1       Blue        $6.62         $12.99              101   \n",
              "2      White        $7.40         $14.52              101   \n",
              "3     Silver       $11.00         $21.57              101   \n",
              "4        Red       $11.00         $21.57              101   \n",
              "...      ...           ...            ...             ...   \n",
              "2512     Red       $43.07        $129.99              505   \n",
              "2513   White       $43.07        $129.99              505   \n",
              "2514   White        $1.71          $3.35              505   \n",
              "2515   Black        $1.71          $3.35              505   \n",
              "2516  Silver        $1.71          $3.35              505   \n",
              "\n",
              "                  Subcategory  CategoryKey     Category  \n",
              "0                     MP4&MP3            1        Audio  \n",
              "1                     MP4&MP3            1        Audio  \n",
              "2                     MP4&MP3            1        Audio  \n",
              "3                     MP4&MP3            1        Audio  \n",
              "4                     MP4&MP3            1        Audio  \n",
              "...                       ...          ...          ...  \n",
              "2512  Cell phones Accessories            5  Cell phones  \n",
              "2513  Cell phones Accessories            5  Cell phones  \n",
              "2514  Cell phones Accessories            5  Cell phones  \n",
              "2515  Cell phones Accessories            5  Cell phones  \n",
              "2516  Cell phones Accessories            5  Cell phones  \n",
              "\n",
              "[2517 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1d405787-1bdf-4545-8ff8-12732bbb15e2\" 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>ProductKey</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Color</th>\n",
              "      <th>Unit Cost USD</th>\n",
              "      <th>Unit Price USD</th>\n",
              "      <th>SubcategoryKey</th>\n",
              "      <th>Subcategory</th>\n",
              "      <th>CategoryKey</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Contoso 512MB MP3 Player E51 Silver</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>Silver</td>\n",
              "      <td>$6.62</td>\n",
              "      <td>$12.99</td>\n",
              "      <td>101</td>\n",
              "      <td>MP4&amp;MP3</td>\n",
              "      <td>1</td>\n",
              "      <td>Audio</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Contoso 512MB MP3 Player E51 Blue</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>Blue</td>\n",
              "      <td>$6.62</td>\n",
              "      <td>$12.99</td>\n",
              "      <td>101</td>\n",
              "      <td>MP4&amp;MP3</td>\n",
              "      <td>1</td>\n",
              "      <td>Audio</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Contoso 1G MP3 Player E100 White</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>White</td>\n",
              "      <td>$7.40</td>\n",
              "      <td>$14.52</td>\n",
              "      <td>101</td>\n",
              "      <td>MP4&amp;MP3</td>\n",
              "      <td>1</td>\n",
              "      <td>Audio</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Contoso 2G MP3 Player E200 Silver</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>Silver</td>\n",
              "      <td>$11.00</td>\n",
              "      <td>$21.57</td>\n",
              "      <td>101</td>\n",
              "      <td>MP4&amp;MP3</td>\n",
              "      <td>1</td>\n",
              "      <td>Audio</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Contoso 2G MP3 Player E200 Red</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>Red</td>\n",
              "      <td>$11.00</td>\n",
              "      <td>$21.57</td>\n",
              "      <td>101</td>\n",
              "      <td>MP4&amp;MP3</td>\n",
              "      <td>1</td>\n",
              "      <td>Audio</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>2512</th>\n",
              "      <td>2513</td>\n",
              "      <td>Contoso Bluetooth Active Headphones L15 Red</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>Red</td>\n",
              "      <td>$43.07</td>\n",
              "      <td>$129.99</td>\n",
              "      <td>505</td>\n",
              "      <td>Cell phones Accessories</td>\n",
              "      <td>5</td>\n",
              "      <td>Cell phones</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2513</th>\n",
              "      <td>2514</td>\n",
              "      <td>Contoso Bluetooth Active Headphones L15 White</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>White</td>\n",
              "      <td>$43.07</td>\n",
              "      <td>$129.99</td>\n",
              "      <td>505</td>\n",
              "      <td>Cell phones Accessories</td>\n",
              "      <td>5</td>\n",
              "      <td>Cell phones</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2514</th>\n",
              "      <td>2515</td>\n",
              "      <td>Contoso In-Line Coupler E180 White</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>White</td>\n",
              "      <td>$1.71</td>\n",
              "      <td>$3.35</td>\n",
              "      <td>505</td>\n",
              "      <td>Cell phones Accessories</td>\n",
              "      <td>5</td>\n",
              "      <td>Cell phones</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2515</th>\n",
              "      <td>2516</td>\n",
              "      <td>Contoso In-Line Coupler E180 Black</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>Black</td>\n",
              "      <td>$1.71</td>\n",
              "      <td>$3.35</td>\n",
              "      <td>505</td>\n",
              "      <td>Cell phones Accessories</td>\n",
              "      <td>5</td>\n",
              "      <td>Cell phones</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2516</th>\n",
              "      <td>2517</td>\n",
              "      <td>Contoso In-Line Coupler E180 Silver</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>Silver</td>\n",
              "      <td>$1.71</td>\n",
              "      <td>$3.35</td>\n",
              "      <td>505</td>\n",
              "      <td>Cell phones Accessories</td>\n",
              "      <td>5</td>\n",
              "      <td>Cell phones</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2517 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d405787-1bdf-4545-8ff8-12732bbb15e2')\"\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-1d405787-1bdf-4545-8ff8-12732bbb15e2 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-1d405787-1bdf-4545-8ff8-12732bbb15e2');\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-a3045623-f8f0-4569-a2fc-f2239896f5cc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a3045623-f8f0-4569-a2fc-f2239896f5cc')\"\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-a3045623-f8f0-4569-a2fc-f2239896f5cc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6a12df5f-6656-4aa9-95b0-6c3d3e71ea19\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_products')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6a12df5f-6656-4aa9-95b0-6c3d3e71ea19 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_products');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_products",
              "summary": "{\n  \"name\": \"df_products\",\n  \"rows\": 2517,\n  \"fields\": [\n    {\n      \"column\": \"ProductKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 726,\n        \"min\": 1,\n        \"max\": 2517,\n        \"num_unique_values\": 2517,\n        \"samples\": [\n          2196,\n          411,\n          1390\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2517,\n        \"samples\": [\n          \"Adventure Works Desk Lamp E1300 Black\",\n          \"Proseware Laptop15 M510 White\",\n          \"Contoso Private Automatic Branch Exchange M65 Grey\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Litware\",\n          \"Contoso\",\n          \"The Phone Company\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Silver\",\n          \"Blue\",\n          \"Green\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit Cost USD\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 480,\n        \"samples\": [\n          \"$101.46 \",\n          \"$33.75 \",\n          \"$4.13 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit Price USD\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 426,\n        \"samples\": [\n          \"$210.00 \",\n          \"$480.00 \",\n          \"$25.90 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SubcategoryKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 229,\n        \"min\": 101,\n        \"max\": 808,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          807,\n          405,\n          802\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subcategory\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"Air Conditioners\",\n          \"Camcorders\",\n          \"Refrigerators\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CategoryKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2,\n          6,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"TV and Video\",\n          \"Music, Movies and Audio Books\",\n          \"Audio\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the data\n",
        "print(df_products.info())  # Get information about the dataframe\n",
        "print(df_products.describe())  # Summary statistics for numerical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bz536KipMcq",
        "outputId": "1a008c00-3269-4d7e-eea4-87e7fd56aae6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2517 entries, 0 to 2516\n",
            "Data columns (total 10 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   ProductKey      2517 non-null   int64 \n",
            " 1   Product Name    2517 non-null   object\n",
            " 2   Brand           2517 non-null   object\n",
            " 3   Color           2517 non-null   object\n",
            " 4   Unit Cost USD   2517 non-null   object\n",
            " 5   Unit Price USD  2517 non-null   object\n",
            " 6   SubcategoryKey  2517 non-null   int64 \n",
            " 7   Subcategory     2517 non-null   object\n",
            " 8   CategoryKey     2517 non-null   int64 \n",
            " 9   Category        2517 non-null   object\n",
            "dtypes: int64(3), object(7)\n",
            "memory usage: 196.8+ KB\n",
            "None\n",
            "        ProductKey  SubcategoryKey  CategoryKey\n",
            "count  2517.000000     2517.000000  2517.000000\n",
            "mean   1259.000000      491.810091     4.878824\n",
            "std     726.739637      229.887134     2.299170\n",
            "min       1.000000      101.000000     1.000000\n",
            "25%     630.000000      305.000000     3.000000\n",
            "50%    1259.000000      406.000000     4.000000\n",
            "75%    1888.000000      801.000000     8.000000\n",
            "max    2517.000000      808.000000     8.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values in each column\n",
        "print(df_products.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "937IFaqApMZG",
        "outputId": "bcc515e3-f39c-45da-e0ad-5d487a14fe6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ProductKey        0\n",
            "Product Name      0\n",
            "Brand             0\n",
            "Color             0\n",
            "Unit Cost USD     0\n",
            "Unit Price USD    0\n",
            "SubcategoryKey    0\n",
            "Subcategory       0\n",
            "CategoryKey       0\n",
            "Category          0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Type Conversion\n",
        "# Convert columns to the appropriate data types if needed\n",
        "# Example: Convert 'Unit Cost USD' and 'Unit Price USD' to numeric type\n",
        "df_products['Unit Cost USD'] = df_products['Unit Cost USD'].str.replace('$', '').str.replace(\",\", \"\").str.strip().astype(float)\n",
        "df_products['Unit Price USD'] = df_products['Unit Price USD'].str.replace('$', '').str.replace(\",\", \"\").str.strip().astype(float)"
      ],
      "metadata": {
        "id": "WXBrR5mSpMW8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Duplicates\n",
        "# Check for duplicate rows and remove them if necessary\n",
        "df_products.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "aKl6uSKZptW_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_products.drop([\"SubcategoryKey\"],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "VZuvhayWptRV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize the brand and category\n",
        "plt.figure(figsize=(50,8))\n",
        "plt.subplot(1,2,1)\n",
        "sns.countplot(x=df_products[\"Brand\"],data=df_products,hue=\"Brand\")\n",
        "plt.xlabel(\"Brand\")\n",
        "plt.ylabel(\"counts\")\n",
        "plt.title(\"counts as per Brand\")\n",
        "plt.show()\n",
        "plt.figure(figsize=(40,8))\n",
        "plt.subplot(1,2,2)\n",
        "sns.countplot(x=df_products[\"Category\"],data=df_products,hue=\"Category\")\n",
        "plt.xlabel(\"Category\")\n",
        "plt.ylabel(\"counts\")\n",
        "plt.title(\"counts as per Category\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        },
        "id": "Y8CXjT_Qqd2F",
        "outputId": "c63f0aa6-2b4c-4826-98e6-8d50047354e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 5000x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 4000x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Sales"
      ],
      "metadata": {
        "id": "dyV2YiiTsy4T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales=pd.read_csv(\"/content/Sales.csv\")\n",
        "df_sales"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "X6HLNBbxox4h",
        "outputId": "2ed6554f-402b-4889-d932-a6501c4053f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Order Number  Line Item Order Date Delivery Date  CustomerKey  \\\n",
              "0            366000          1   1/1/2016           NaN       265598   \n",
              "1            366001          1   1/1/2016     1/13/2016      1269051   \n",
              "2            366001          2   1/1/2016     1/13/2016      1269051   \n",
              "3            366002          1   1/1/2016     1/12/2016       266019   \n",
              "4            366002          2   1/1/2016     1/12/2016       266019   \n",
              "...             ...        ...        ...           ...          ...   \n",
              "62879       2243030          1  2/20/2021           NaN      1216913   \n",
              "62880       2243031          1  2/20/2021     2/24/2021       511229   \n",
              "62881       2243032          1  2/20/2021     2/23/2021       331277   \n",
              "62882       2243032          2  2/20/2021     2/23/2021       331277   \n",
              "62883       2243032          3  2/20/2021     2/23/2021       331277   \n",
              "\n",
              "       StoreKey  ProductKey  Quantity Currency Code  \n",
              "0            10        1304         1           CAD  \n",
              "1             0        1048         2           USD  \n",
              "2             0        2007         1           USD  \n",
              "3             0        1106         7           CAD  \n",
              "4             0         373         1           CAD  \n",
              "...         ...         ...       ...           ...  \n",
              "62879        43         632         3           USD  \n",
              "62880         0          98         4           EUR  \n",
              "62881         0        1613         2           CAD  \n",
              "62882         0        1717         2           CAD  \n",
              "62883         0         464         7           CAD  \n",
              "\n",
              "[62884 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e0bde36b-eb46-4541-9a77-65a7ba67d168\" 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>Order Number</th>\n",
              "      <th>Line Item</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Delivery Date</th>\n",
              "      <th>CustomerKey</th>\n",
              "      <th>StoreKey</th>\n",
              "      <th>ProductKey</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Currency Code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>366000</td>\n",
              "      <td>1</td>\n",
              "      <td>1/1/2016</td>\n",
              "      <td>NaN</td>\n",
              "      <td>265598</td>\n",
              "      <td>10</td>\n",
              "      <td>1304</td>\n",
              "      <td>1</td>\n",
              "      <td>CAD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>366001</td>\n",
              "      <td>1</td>\n",
              "      <td>1/1/2016</td>\n",
              "      <td>1/13/2016</td>\n",
              "      <td>1269051</td>\n",
              "      <td>0</td>\n",
              "      <td>1048</td>\n",
              "      <td>2</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>366001</td>\n",
              "      <td>2</td>\n",
              "      <td>1/1/2016</td>\n",
              "      <td>1/13/2016</td>\n",
              "      <td>1269051</td>\n",
              "      <td>0</td>\n",
              "      <td>2007</td>\n",
              "      <td>1</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>366002</td>\n",
              "      <td>1</td>\n",
              "      <td>1/1/2016</td>\n",
              "      <td>1/12/2016</td>\n",
              "      <td>266019</td>\n",
              "      <td>0</td>\n",
              "      <td>1106</td>\n",
              "      <td>7</td>\n",
              "      <td>CAD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>366002</td>\n",
              "      <td>2</td>\n",
              "      <td>1/1/2016</td>\n",
              "      <td>1/12/2016</td>\n",
              "      <td>266019</td>\n",
              "      <td>0</td>\n",
              "      <td>373</td>\n",
              "      <td>1</td>\n",
              "      <td>CAD</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62879</th>\n",
              "      <td>2243030</td>\n",
              "      <td>1</td>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1216913</td>\n",
              "      <td>43</td>\n",
              "      <td>632</td>\n",
              "      <td>3</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62880</th>\n",
              "      <td>2243031</td>\n",
              "      <td>1</td>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>2/24/2021</td>\n",
              "      <td>511229</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>4</td>\n",
              "      <td>EUR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62881</th>\n",
              "      <td>2243032</td>\n",
              "      <td>1</td>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>2/23/2021</td>\n",
              "      <td>331277</td>\n",
              "      <td>0</td>\n",
              "      <td>1613</td>\n",
              "      <td>2</td>\n",
              "      <td>CAD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62882</th>\n",
              "      <td>2243032</td>\n",
              "      <td>2</td>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>2/23/2021</td>\n",
              "      <td>331277</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>2</td>\n",
              "      <td>CAD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62883</th>\n",
              "      <td>2243032</td>\n",
              "      <td>3</td>\n",
              "      <td>2/20/2021</td>\n",
              "      <td>2/23/2021</td>\n",
              "      <td>331277</td>\n",
              "      <td>0</td>\n",
              "      <td>464</td>\n",
              "      <td>7</td>\n",
              "      <td>CAD</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>62884 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e0bde36b-eb46-4541-9a77-65a7ba67d168')\"\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-e0bde36b-eb46-4541-9a77-65a7ba67d168 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-e0bde36b-eb46-4541-9a77-65a7ba67d168');\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-00d0c8bf-2e58-47eb-a133-dd4ffc5e8b13\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-00d0c8bf-2e58-47eb-a133-dd4ffc5e8b13')\"\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-00d0c8bf-2e58-47eb-a133-dd4ffc5e8b13 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_55c04958-9ed5-4f75-8181-6cef630047a1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_sales')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_55c04958-9ed5-4f75-8181-6cef630047a1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_sales');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_sales",
              "summary": "{\n  \"name\": \"df_sales\",\n  \"rows\": 62884,\n  \"fields\": [\n    {\n      \"column\": \"Order Number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 453296,\n        \"min\": 366000,\n        \"max\": 2243032,\n        \"num_unique_values\": 26326,\n        \"samples\": [\n          1774035,\n          1424034,\n          1125004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Line Item\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1641,\n        \"samples\": [\n          \"2/18/2018\",\n          \"2/24/2020\",\n          \"1/29/2019\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delivery Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2016-01-06 00:00:00\",\n        \"max\": \"2021-02-27 00:00:00\",\n        \"num_unique_values\": 1492,\n        \"samples\": [\n          \"6/20/2019\",\n          \"3/25/2019\",\n          \"8/25/2020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 585963,\n        \"min\": 301,\n        \"max\": 2099937,\n        \"num_unique_values\": 11887,\n        \"samples\": [\n          1474251,\n          959269,\n          513426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StoreKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 0,\n        \"max\": 66,\n        \"num_unique_values\": 58,\n        \"samples\": [\n          10,\n          29,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ProductKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 709,\n        \"min\": 1,\n        \"max\": 2517,\n        \"num_unique_values\": 2492,\n        \"samples\": [\n          2371,\n          809,\n          726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Currency Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"USD\",\n          \"AUD\",\n          \"GBP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get information about the dataframe, including data types and missing values\n",
        "print(df_sales.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWhpMrwdtNJP",
        "outputId": "ce9b0a2d-0c2f-4449-b282-ce87aa0c989f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 62884 entries, 0 to 62883\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Order Number   62884 non-null  int64 \n",
            " 1   Line Item      62884 non-null  int64 \n",
            " 2   Order Date     62884 non-null  object\n",
            " 3   Delivery Date  13165 non-null  object\n",
            " 4   CustomerKey    62884 non-null  int64 \n",
            " 5   StoreKey       62884 non-null  int64 \n",
            " 6   ProductKey     62884 non-null  int64 \n",
            " 7   Quantity       62884 non-null  int64 \n",
            " 8   Currency Code  62884 non-null  object\n",
            "dtypes: int64(6), object(3)\n",
            "memory usage: 4.3+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate descriptive statistics for numerical columns\n",
        "print(df_sales.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jof3rDLktNDb",
        "outputId": "801422ab-a437-41bf-f2f7-a82167ca4f86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Order Number     Line Item   CustomerKey      StoreKey    ProductKey  \\\n",
            "count  6.288400e+04  62884.000000  6.288400e+04  62884.000000  62884.000000   \n",
            "mean   1.430905e+06      2.164207  1.180797e+06     31.802144   1125.859344   \n",
            "std    4.532963e+05      1.365170  5.859634e+05     22.978188    709.244010   \n",
            "min    3.660000e+05      1.000000  3.010000e+02      0.000000      1.000000   \n",
            "25%    1.121017e+06      1.000000  6.808580e+05      8.000000    437.000000   \n",
            "50%    1.498016e+06      2.000000  1.261200e+06     37.000000   1358.000000   \n",
            "75%    1.788010e+06      3.000000  1.686496e+06     53.000000   1650.000000   \n",
            "max    2.243032e+06      7.000000  2.099937e+06     66.000000   2517.000000   \n",
            "\n",
            "           Quantity  \n",
            "count  62884.000000  \n",
            "mean       3.144790  \n",
            "std        2.256371  \n",
            "min        1.000000  \n",
            "25%        1.000000  \n",
            "50%        2.000000  \n",
            "75%        4.000000  \n",
            "max       10.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values in each column\n",
        "print(df_sales.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4sBQP-hCtMdB",
        "outputId": "1a02b6ab-f7a3-4c81-df6e-0c55186e0907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Order Number         0\n",
            "Line Item            0\n",
            "Order Date           0\n",
            "Delivery Date    49719\n",
            "CustomerKey          0\n",
            "StoreKey             0\n",
            "ProductKey           0\n",
            "Quantity             0\n",
            "Currency Code        0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Order Date' column to datetime objects\n",
        "df_sales['Order Date'] = pd.to_datetime(df_sales['Order Date'])"
      ],
      "metadata": {
        "id": "yXyYqHfVtMXq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales['Delivery Date'] = pd.to_datetime(df_sales['Delivery Date'], format=\"%m/%d/%Y\", errors='coerce').dt.date"
      ],
      "metadata": {
        "id": "ZxR0STDvt0Zm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fill the missing values\n",
        "df_sales[\"Delivery Date\"]=df_sales[\"Delivery Date\"].fillna(0)"
      ],
      "metadata": {
        "id": "nMcUHmtVuj9T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates\n",
        "print(df_sales.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWBPhtXAu1mz",
        "outputId": "2a692e9a-e880-407a-c4a9-3218d05b398a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore distributions of numerical features\n",
        "numerical_features = ['Quantity', 'StoreKey', 'ProductKey', 'CustomerKey']\n",
        "for feature in numerical_features:\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.histplot(df_sales[feature], bins=30, kde=True)\n",
        "    plt.title(f\"Distribution of {feature}\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "U9k6kOdYu1aq",
        "outputId": "8c24ea78-19c1-4d31-84cf-66deed92a5af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Stores"
      ],
      "metadata": {
        "id": "KZjl5He3veYq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_stores=pd.read_csv(\"/content/Stores.csv\")\n",
        "df_stores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "1p3kkdjGu1T3",
        "outputId": "5a72b55d-bf1a-4a4c-88ba-58dd37d6d94f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    StoreKey        Country                         State  Square Meters  \\\n",
              "0          1      Australia  Australian Capital Territory          595.0   \n",
              "1          2      Australia            Northern Territory          665.0   \n",
              "2          3      Australia               South Australia         2000.0   \n",
              "3          4      Australia                      Tasmania         2000.0   \n",
              "4          5      Australia                      Victoria         2000.0   \n",
              "..       ...            ...                           ...            ...   \n",
              "62        63  United States                          Utah         2000.0   \n",
              "63        64  United States                 Washington DC         1330.0   \n",
              "64        65  United States                 West Virginia         1785.0   \n",
              "65        66  United States                       Wyoming          840.0   \n",
              "66         0         Online                        Online            NaN   \n",
              "\n",
              "    Open Date  \n",
              "0    1/1/2008  \n",
              "1   1/12/2008  \n",
              "2    1/7/2012  \n",
              "3    1/1/2010  \n",
              "4   12/9/2015  \n",
              "..        ...  \n",
              "62   3/6/2008  \n",
              "63   1/1/2010  \n",
              "64   1/1/2012  \n",
              "65   1/1/2014  \n",
              "66   1/1/2010  \n",
              "\n",
              "[67 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-990071e7-b066-4b5f-b5d6-b869d9fbe607\" 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>StoreKey</th>\n",
              "      <th>Country</th>\n",
              "      <th>State</th>\n",
              "      <th>Square Meters</th>\n",
              "      <th>Open Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australian Capital Territory</td>\n",
              "      <td>595.0</td>\n",
              "      <td>1/1/2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Northern Territory</td>\n",
              "      <td>665.0</td>\n",
              "      <td>1/12/2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Australia</td>\n",
              "      <td>South Australia</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>1/7/2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Tasmania</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>1/1/2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>12/9/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>63</td>\n",
              "      <td>United States</td>\n",
              "      <td>Utah</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>3/6/2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>64</td>\n",
              "      <td>United States</td>\n",
              "      <td>Washington DC</td>\n",
              "      <td>1330.0</td>\n",
              "      <td>1/1/2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>65</td>\n",
              "      <td>United States</td>\n",
              "      <td>West Virginia</td>\n",
              "      <td>1785.0</td>\n",
              "      <td>1/1/2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>66</td>\n",
              "      <td>United States</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>840.0</td>\n",
              "      <td>1/1/2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0</td>\n",
              "      <td>Online</td>\n",
              "      <td>Online</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1/1/2010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-990071e7-b066-4b5f-b5d6-b869d9fbe607')\"\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-990071e7-b066-4b5f-b5d6-b869d9fbe607 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-990071e7-b066-4b5f-b5d6-b869d9fbe607');\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-2736aace-9671-4f89-b7e7-73d55de021fd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2736aace-9671-4f89-b7e7-73d55de021fd')\"\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-2736aace-9671-4f89-b7e7-73d55de021fd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_42472ab3-ca37-4e2e-9b67-bdc819150ff5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_stores')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_42472ab3-ca37-4e2e-9b67-bdc819150ff5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_stores');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_stores",
              "summary": "{\n  \"name\": \"df_stores\",\n  \"rows\": 67,\n  \"fields\": [\n    {\n      \"column\": \"StoreKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 66,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          37,\n          17,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"United States\",\n          \"Canada\",\n          \"Netherlands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 67,\n        \"samples\": [\n          \"Ayrshire\",\n          \"Martinique\",\n          \"Victoria\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Square Meters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 576.4040575231386,\n        \"min\": 245.0,\n        \"max\": 2105.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          840.0,\n          1715.0,\n          1800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"4/4/2015\",\n          \"8/8/2012\",\n          \"1/1/2008\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display basic information about the dataframe\n",
        "print(df_stores.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ihwk-Y_r6smW",
        "outputId": "2d359408-d4d0-42ba-968a-ff484e1b5aae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 67 entries, 0 to 66\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   StoreKey       67 non-null     int64  \n",
            " 1   Country        67 non-null     object \n",
            " 2   State          67 non-null     object \n",
            " 3   Square Meters  66 non-null     float64\n",
            " 4   Open Date      67 non-null     object \n",
            "dtypes: float64(1), int64(1), object(3)\n",
            "memory usage: 2.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_stores.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PKDMcPB6si2",
        "outputId": "f6215858-853f-466e-8257-faba7c4d22e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "StoreKey         0\n",
            "Country          0\n",
            "State            0\n",
            "Square Meters    1\n",
            "Open Date        0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Cleaning\n",
        "# Convert 'Open Date' to datetime objects\n",
        "df_stores['Open Date'] = pd.to_datetime(df_stores['Open Date'])"
      ],
      "metadata": {
        "id": "uhawg52V659Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_stores.fillna(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "28Fu32sh7033",
        "outputId": "72b14b8b-99ea-4026-d736-3f4563890075"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    StoreKey        Country                         State  Square Meters  \\\n",
              "0          1      Australia  Australian Capital Territory          595.0   \n",
              "1          2      Australia            Northern Territory          665.0   \n",
              "2          3      Australia               South Australia         2000.0   \n",
              "3          4      Australia                      Tasmania         2000.0   \n",
              "4          5      Australia                      Victoria         2000.0   \n",
              "..       ...            ...                           ...            ...   \n",
              "62        63  United States                          Utah         2000.0   \n",
              "63        64  United States                 Washington DC         1330.0   \n",
              "64        65  United States                 West Virginia         1785.0   \n",
              "65        66  United States                       Wyoming          840.0   \n",
              "66         0         Online                        Online            0.0   \n",
              "\n",
              "    Open Date  \n",
              "0  2008-01-01  \n",
              "1  2008-01-12  \n",
              "2  2012-01-07  \n",
              "3  2010-01-01  \n",
              "4  2015-12-09  \n",
              "..        ...  \n",
              "62 2008-03-06  \n",
              "63 2010-01-01  \n",
              "64 2012-01-01  \n",
              "65 2014-01-01  \n",
              "66 2010-01-01  \n",
              "\n",
              "[67 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c236a3e-b776-4bc7-bdcd-7501ade64577\" 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>StoreKey</th>\n",
              "      <th>Country</th>\n",
              "      <th>State</th>\n",
              "      <th>Square Meters</th>\n",
              "      <th>Open Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Australian Capital Territory</td>\n",
              "      <td>595.0</td>\n",
              "      <td>2008-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Northern Territory</td>\n",
              "      <td>665.0</td>\n",
              "      <td>2008-01-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Australia</td>\n",
              "      <td>South Australia</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2012-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Tasmania</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2010-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2015-12-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>63</td>\n",
              "      <td>United States</td>\n",
              "      <td>Utah</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2008-03-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>64</td>\n",
              "      <td>United States</td>\n",
              "      <td>Washington DC</td>\n",
              "      <td>1330.0</td>\n",
              "      <td>2010-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>65</td>\n",
              "      <td>United States</td>\n",
              "      <td>West Virginia</td>\n",
              "      <td>1785.0</td>\n",
              "      <td>2012-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>66</td>\n",
              "      <td>United States</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>840.0</td>\n",
              "      <td>2014-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0</td>\n",
              "      <td>Online</td>\n",
              "      <td>Online</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2010-01-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c236a3e-b776-4bc7-bdcd-7501ade64577')\"\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-8c236a3e-b776-4bc7-bdcd-7501ade64577 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-8c236a3e-b776-4bc7-bdcd-7501ade64577');\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-81cbfa61-c4c2-426d-ba62-1e8c73876d62\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-81cbfa61-c4c2-426d-ba62-1e8c73876d62')\"\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-81cbfa61-c4c2-426d-ba62-1e8c73876d62 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\": \"df_stores\",\n  \"rows\": 67,\n  \"fields\": [\n    {\n      \"column\": \"StoreKey\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 66,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          37,\n          17,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"United States\",\n          \"Canada\",\n          \"Netherlands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 67,\n        \"samples\": [\n          \"Ayrshire\",\n          \"Martinique\",\n          \"Victoria\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Square Meters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 597.1208339744566,\n        \"min\": 0.0,\n        \"max\": 2105.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          1610.0,\n          1715.0,\n          2105.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2005-03-04 00:00:00\",\n        \"max\": \"2019-03-05 00:00:00\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"2015-04-04 00:00:00\",\n          \"2012-08-08 00:00:00\",\n          \"2008-01-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicate rows (if any)\n",
        "df_stores.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "UeNc3pQ96551"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "egbsUdy366NX"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-k1CIrN4YEp9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}