Sales_Analytics.ipynb

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        "# import python libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt # visualizing data\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ]
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      "source": [
        "\n",
        "# import csv file\n",
        "df = pd.read_csv('Diwali Sales Data.csv', encoding= 'unicode_escape')"
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              "(11251, 15)"
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              "   User_ID  Cust_name Product_ID Gender Age Group  Age  Marital_Status  \\\n",
              "0  1002903  Sanskriti  P00125942      F     26-35   28               0   \n",
              "1  1000732     Kartik  P00110942      F     26-35   35               1   \n",
              "2  1001990      Bindu  P00118542      F     26-35   35               1   \n",
              "3  1001425     Sudevi  P00237842      M      0-17   16               0   \n",
              "4  1000588       Joni  P00057942      M     26-35   28               1   \n",
              "\n",
              "            State      Zone       Occupation Product_Category  Orders  \\\n",
              "0     Maharashtra   Western       Healthcare             Auto       1   \n",
              "1  Andhra Pradesh  Southern             Govt             Auto       3   \n",
              "2   Uttar Pradesh   Central       Automobile             Auto       3   \n",
              "3       Karnataka  Southern     Construction             Auto       2   \n",
              "4         Gujarat   Western  Food Processing             Auto       2   \n",
              "\n",
              "    Amount  Status  unnamed1  \n",
              "0  23952.0     NaN       NaN  \n",
              "1  23934.0     NaN       NaN  \n",
              "2  23924.0     NaN       NaN  \n",
              "3  23912.0     NaN       NaN  \n",
              "4  23877.0     NaN       NaN  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11251,\n  \"fields\": [\n    {\n      \"column\": \"User_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1716,\n        \"min\": 1000001,\n        \"max\": 1006040,\n        \"num_unique_values\": 3755,\n        \"samples\": [\n          1005905,\n          1003730,\n          1005326\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cust_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1250,\n        \"samples\": [\n          \"Nida\",\n          \"Lacy\",\n          \"Caudle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2351,\n        \"samples\": [\n          \"P00224442\",\n          \"P00205242\",\n          \"P00347442\"\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          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"26-35\",\n          \"0-17\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 12,\n        \"max\": 92,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          18,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marital_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Maharashtra\",\n          \"Andhra\\u00a0Pradesh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Southern\",\n          \"Eastern\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Retail\",\n          \"Aviation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Auto\",\n          \"Hand & Power Tools\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5222.355869186455,\n        \"min\": 188.0,\n        \"max\": 23952.0,\n        \"num_unique_values\": 6584,\n        \"samples\": [\n          19249.0,\n          13184.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unnamed1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDSRLsAf6rVr",
        "outputId": "b174533b-3ac1-4155-9f52-fdc746c29a20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11251 entries, 0 to 11250\n",
            "Data columns (total 15 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   User_ID           11251 non-null  int64  \n",
            " 1   Cust_name         11251 non-null  object \n",
            " 2   Product_ID        11251 non-null  object \n",
            " 3   Gender            11251 non-null  object \n",
            " 4   Age Group         11251 non-null  object \n",
            " 5   Age               11251 non-null  int64  \n",
            " 6   Marital_Status    11251 non-null  int64  \n",
            " 7   State             11251 non-null  object \n",
            " 8   Zone              11251 non-null  object \n",
            " 9   Occupation        11251 non-null  object \n",
            " 10  Product_Category  11251 non-null  object \n",
            " 11  Orders            11251 non-null  int64  \n",
            " 12  Amount            11239 non-null  float64\n",
            " 13  Status            0 non-null      float64\n",
            " 14  unnamed1          0 non-null      float64\n",
            "dtypes: float64(3), int64(4), object(8)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#drop unrelated/blank columns\n",
        "df.drop(['Status', 'unnamed1'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "JhkAwfnO6uz9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check for null values\n",
        "pd.isnull(df).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WhdVUU2j6wiq",
        "outputId": "fabcca8c-e504-42f9-d5cb-f3e9ba78a24c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User_ID              0\n",
              "Cust_name            0\n",
              "Product_ID           0\n",
              "Gender               0\n",
              "Age Group            0\n",
              "Age                  0\n",
              "Marital_Status       0\n",
              "State                0\n",
              "Zone                 0\n",
              "Occupation           0\n",
              "Product_Category     0\n",
              "Orders               0\n",
              "Amount              12\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop null values\n",
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "M1hChySo6yZR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# change data type\n",
        "df['Amount'] = df['Amount'].astype('int')"
      ],
      "metadata": {
        "id": "fBaT_f1s60GN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Amount'].dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oTtLmOT66lU",
        "outputId": "42522055-fe40-4ee0-cac2-c1422eb4674d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDBGkO6Z68QT",
        "outputId": "117435ab-9e6c-4f58-8cb2-5c9018ddd077"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
              "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
              "       'Orders', 'Amount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#rename column\n",
        "df.rename(columns= {'Marital_Status':'Shaadi'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "u_6dQUhe69wc",
        "outputId": "700619d7-b8ba-441f-8234-f64560091c7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       User_ID    Cust_name Product_ID Gender Age Group  Age  Shaadi  \\\n",
              "0      1002903    Sanskriti  P00125942      F     26-35   28       0   \n",
              "1      1000732       Kartik  P00110942      F     26-35   35       1   \n",
              "2      1001990        Bindu  P00118542      F     26-35   35       1   \n",
              "3      1001425       Sudevi  P00237842      M      0-17   16       0   \n",
              "4      1000588         Joni  P00057942      M     26-35   28       1   \n",
              "...        ...          ...        ...    ...       ...  ...     ...   \n",
              "11246  1000695      Manning  P00296942      M     18-25   19       1   \n",
              "11247  1004089  Reichenbach  P00171342      M     26-35   33       0   \n",
              "11248  1001209        Oshin  P00201342      F     36-45   40       0   \n",
              "11249  1004023       Noonan  P00059442      M     36-45   37       0   \n",
              "11250  1002744      Brumley  P00281742      F     18-25   19       0   \n",
              "\n",
              "                State      Zone       Occupation Product_Category  Orders  \\\n",
              "0         Maharashtra   Western       Healthcare             Auto       1   \n",
              "1      Andhra Pradesh  Southern             Govt             Auto       3   \n",
              "2       Uttar Pradesh   Central       Automobile             Auto       3   \n",
              "3           Karnataka  Southern     Construction             Auto       2   \n",
              "4             Gujarat   Western  Food Processing             Auto       2   \n",
              "...               ...       ...              ...              ...     ...   \n",
              "11246     Maharashtra   Western         Chemical           Office       4   \n",
              "11247         Haryana  Northern       Healthcare       Veterinary       3   \n",
              "11248  Madhya Pradesh   Central          Textile           Office       4   \n",
              "11249       Karnataka  Southern      Agriculture           Office       3   \n",
              "11250     Maharashtra   Western       Healthcare           Office       3   \n",
              "\n",
              "       Amount  \n",
              "0       23952  \n",
              "1       23934  \n",
              "2       23924  \n",
              "3       23912  \n",
              "4       23877  \n",
              "...       ...  \n",
              "11246     370  \n",
              "11247     367  \n",
              "11248     213  \n",
              "11249     206  \n",
              "11250     188  \n",
              "\n",
              "[11239 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ec57117-bddd-49ea-b1bd-d9f9017cd55c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Cust_name</th>\n",
              "      <th>Product_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age Group</th>\n",
              "      <th>Age</th>\n",
              "      <th>Shaadi</th>\n",
              "      <th>State</th>\n",
              "      <th>Zone</th>\n",
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              "      <th>Product_Category</th>\n",
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              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1002903</td>\n",
              "      <td>Sanskriti</td>\n",
              "      <td>P00125942</td>\n",
              "      <td>F</td>\n",
              "      <td>26-35</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>Western</td>\n",
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              "      <td>Auto</td>\n",
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              "      <td>23952</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000732</td>\n",
              "      <td>Kartik</td>\n",
              "      <td>P00110942</td>\n",
              "      <td>F</td>\n",
              "      <td>26-35</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>Southern</td>\n",
              "      <td>Govt</td>\n",
              "      <td>Auto</td>\n",
              "      <td>3</td>\n",
              "      <td>23934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1001990</td>\n",
              "      <td>Bindu</td>\n",
              "      <td>P00118542</td>\n",
              "      <td>F</td>\n",
              "      <td>26-35</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>Central</td>\n",
              "      <td>Automobile</td>\n",
              "      <td>Auto</td>\n",
              "      <td>3</td>\n",
              "      <td>23924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1001425</td>\n",
              "      <td>Sudevi</td>\n",
              "      <td>P00237842</td>\n",
              "      <td>M</td>\n",
              "      <td>0-17</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Southern</td>\n",
              "      <td>Construction</td>\n",
              "      <td>Auto</td>\n",
              "      <td>2</td>\n",
              "      <td>23912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000588</td>\n",
              "      <td>Joni</td>\n",
              "      <td>P00057942</td>\n",
              "      <td>M</td>\n",
              "      <td>26-35</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>Western</td>\n",
              "      <td>Food Processing</td>\n",
              "      <td>Auto</td>\n",
              "      <td>2</td>\n",
              "      <td>23877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>11246</th>\n",
              "      <td>1000695</td>\n",
              "      <td>Manning</td>\n",
              "      <td>P00296942</td>\n",
              "      <td>M</td>\n",
              "      <td>18-25</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>Western</td>\n",
              "      <td>Chemical</td>\n",
              "      <td>Office</td>\n",
              "      <td>4</td>\n",
              "      <td>370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11247</th>\n",
              "      <td>1004089</td>\n",
              "      <td>Reichenbach</td>\n",
              "      <td>P00171342</td>\n",
              "      <td>M</td>\n",
              "      <td>26-35</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>Northern</td>\n",
              "      <td>Healthcare</td>\n",
              "      <td>Veterinary</td>\n",
              "      <td>3</td>\n",
              "      <td>367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11248</th>\n",
              "      <td>1001209</td>\n",
              "      <td>Oshin</td>\n",
              "      <td>P00201342</td>\n",
              "      <td>F</td>\n",
              "      <td>36-45</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>Central</td>\n",
              "      <td>Textile</td>\n",
              "      <td>Office</td>\n",
              "      <td>4</td>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11249</th>\n",
              "      <td>1004023</td>\n",
              "      <td>Noonan</td>\n",
              "      <td>P00059442</td>\n",
              "      <td>M</td>\n",
              "      <td>36-45</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Southern</td>\n",
              "      <td>Agriculture</td>\n",
              "      <td>Office</td>\n",
              "      <td>3</td>\n",
              "      <td>206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11250</th>\n",
              "      <td>1002744</td>\n",
              "      <td>Brumley</td>\n",
              "      <td>P00281742</td>\n",
              "      <td>F</td>\n",
              "      <td>18-25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>Western</td>\n",
              "      <td>Healthcare</td>\n",
              "      <td>Office</td>\n",
              "      <td>3</td>\n",
              "      <td>188</td>\n",
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              "<p>11239 rows × 13 columns</p>\n",
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              "    [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-0ec57117-bddd-49ea-b1bd-d9f9017cd55c 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-0ec57117-bddd-49ea-b1bd-d9f9017cd55c');\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-0abd9845-5c98-46d7-8e6e-190c5cf5c2be\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0abd9845-5c98-46d7-8e6e-190c5cf5c2be')\"\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-0abd9845-5c98-46d7-8e6e-190c5cf5c2be 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\",\n  \"rows\": 11239,\n  \"fields\": [\n    {\n      \"column\": \"User_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1716,\n        \"min\": 1000001,\n        \"max\": 1006040,\n        \"num_unique_values\": 3752,\n        \"samples\": [\n          1002014,\n          1003491,\n          1001842\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cust_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1250,\n        \"samples\": [\n          \"Hallsten\",\n          \"Shubham\",\n          \"Riya\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2350,\n        \"samples\": [\n          \"P00133342\",\n          \"P00302142\",\n          \"P00227542\"\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          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"26-35\",\n          \"0-17\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 12,\n        \"max\": 92,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          38,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shaadi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Maharashtra\",\n          \"Andhra\\u00a0Pradesh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Southern\",\n          \"Eastern\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"IT Sector\",\n          \"Hospitality\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Auto\",\n          \"Hand & Power Tools\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5222,\n        \"min\": 188,\n        \"max\": 23952,\n        \"num_unique_values\": 6583,\n        \"samples\": [\n          19247,\n          5293\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# describe() method returns description of the data in the DataFrame (i.e. count, mean, std, etc)\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "zAUK0Hkp6_ec",
        "outputId": "2d1a82c7-496d-46fa-d3b0-0d0697122800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            User_ID           Age  Marital_Status        Orders        Amount\n",
              "count  1.123900e+04  11239.000000    11239.000000  11239.000000  11239.000000\n",
              "mean   1.003004e+06     35.410357        0.420055      2.489634   9453.610553\n",
              "std    1.716039e+03     12.753866        0.493589      1.114967   5222.355168\n",
              "min    1.000001e+06     12.000000        0.000000      1.000000    188.000000\n",
              "25%    1.001492e+06     27.000000        0.000000      2.000000   5443.000000\n",
              "50%    1.003064e+06     33.000000        0.000000      2.000000   8109.000000\n",
              "75%    1.004426e+06     43.000000        1.000000      3.000000  12675.000000\n",
              "max    1.006040e+06     92.000000        1.000000      4.000000  23952.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a85d68cf-d2c7-4f1a-9b8a-93c9106364dc\" 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>User_ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Orders</th>\n",
              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.123900e+04</td>\n",
              "      <td>11239.000000</td>\n",
              "      <td>11239.000000</td>\n",
              "      <td>11239.000000</td>\n",
              "      <td>11239.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.003004e+06</td>\n",
              "      <td>35.410357</td>\n",
              "      <td>0.420055</td>\n",
              "      <td>2.489634</td>\n",
              "      <td>9453.610553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.716039e+03</td>\n",
              "      <td>12.753866</td>\n",
              "      <td>0.493589</td>\n",
              "      <td>1.114967</td>\n",
              "      <td>5222.355168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000001e+06</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>188.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.001492e+06</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5443.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.003064e+06</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8109.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.004426e+06</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>12675.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.006040e+06</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>23952.000000</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-a85d68cf-d2c7-4f1a-9b8a-93c9106364dc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a85d68cf-d2c7-4f1a-9b8a-93c9106364dc 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-a85d68cf-d2c7-4f1a-9b8a-93c9106364dc');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fbc3037-1437-4b0c-8cfa-187f4018fbd5')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --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",
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              "    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-8fbc3037-1437-4b0c-8cfa-187f4018fbd5 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\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"User_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 461312.82997958694,\n        \"min\": 1716.0388257054724,\n        \"max\": 1006040.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1003003.5217546045,\n          1003064.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3960.7779927819724,\n        \"min\": 12.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.41035679330901,\n          33.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marital_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3973.439417307323,\n        \"min\": 0.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.42005516505027135,\n          1.0,\n          0.4935894048750261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3972.7985251347,\n        \"min\": 1.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11239.0,\n          2.4896343091022333,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7024.070687950829,\n        \"min\": 188.0,\n        \"max\": 23952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9453.610552540262,\n          8109.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# use describe() for specific columns\n",
        "df[['Age', 'Orders', 'Amount']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "w-bYIJhF7BjW",
        "outputId": "060c9402-2768-41da-81b5-1af2ec7b9cfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age        Orders        Amount\n",
              "count  11239.000000  11239.000000  11239.000000\n",
              "mean      35.410357      2.489634   9453.610553\n",
              "std       12.753866      1.114967   5222.355168\n",
              "min       12.000000      1.000000    188.000000\n",
              "25%       27.000000      2.000000   5443.000000\n",
              "50%       33.000000      2.000000   8109.000000\n",
              "75%       43.000000      3.000000  12675.000000\n",
              "max       92.000000      4.000000  23952.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a97b386d-a648-40ef-8aa3-720d8d980761\" 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>Age</th>\n",
              "      <th>Orders</th>\n",
              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11239.000000</td>\n",
              "      <td>11239.000000</td>\n",
              "      <td>11239.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.410357</td>\n",
              "      <td>2.489634</td>\n",
              "      <td>9453.610553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.753866</td>\n",
              "      <td>1.114967</td>\n",
              "      <td>5222.355168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>188.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5443.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8109.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>12675.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>92.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>23952.000000</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-a97b386d-a648-40ef-8aa3-720d8d980761')\"\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-a97b386d-a648-40ef-8aa3-720d8d980761 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-a97b386d-a648-40ef-8aa3-720d8d980761');\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-8e6d8c00-1682-4661-9ae7-8dec41f4e062\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8e6d8c00-1682-4661-9ae7-8dec41f4e062')\"\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-8e6d8c00-1682-4661-9ae7-8dec41f4e062 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[['Age', 'Orders', 'Amount']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3960.7779927819724,\n        \"min\": 12.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.41035679330901,\n          33.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3972.7985251347,\n        \"min\": 1.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11239.0,\n          2.4896343091022333,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7024.070687950829,\n        \"min\": 188.0,\n        \"max\": 23952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9453.610552540262,\n          8109.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gender"
      ],
      "metadata": {
        "id": "wZIXBKLT7JWi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting a bar chart for Gender and it's count\n",
        "\n",
        "ax = sns.countplot(x = 'Gender',data = df)\n",
        "\n",
        "for bars in ax.containers:\n",
        "    ax.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "xn0QbmhJ7DYD",
        "outputId": "d6c0663f-629f-45de-bdcd-ff505073b23c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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1ycnJ2r17t3JycpSdna3169frkUceMcf9fr/69++vTp06KT8/X9OnT9ezzz6r1157rUHXCwAAGiebYRhGsA7+m9/8Rp988ok+/vjjc44bhqGYmBg98cQTevLJJyVJPp9PLpdLWVlZGjZsmD777DPFx8dr69atuvHGGyVJK1as0F133aWvv/5aMTExmj17tp566il5vV6Fhoaax166dKn27Nnzb/v0+/1yOp3y+XxyOBx1tPqaemW8VW9zA01Z/vSUYLcAoAm6kPfvoJ4h+uCDD3TjjTdq6NChioqK0g033KDXX3/dHN+/f7+8Xq8SEhLMfU6nU71791ZeXp4kKS8vTxEREWYYkqSEhAQ1a9ZMmzdvNmv69u1rhiFJSkxMVFFRkY4ePVqjr/Lycvn9/oANAABcuoIaiL788kvNnj1bV111lVauXKkxY8boscce0/z58yVJXq9XkuRyuQKe53K5zDGv16uoqKiA8ebNm6tt27YBNeea4+xjnG3KlClyOp3mFhsbWwerBQAAjVVQA1FVVZV69uyp559/XjfccIMeeeQRPfzww5ozZ04w21JmZqZ8Pp+5HThwIKj9AACA+hXUQBQdHa34+PiAfd26dVNxcbEkye12S5JKSkoCakpKSswxt9ut0tLSgPEzZ87oyJEjATXnmuPsY5zNbrfL4XAEbAAA4NIV1EB00003qaioKGDf559/rk6dOkmS4uLi5Ha7tWbNGnPc7/dr8+bN8ng8kiSPx6OysjLl5+ebNWvXrlVVVZV69+5t1qxfv16nT582a3JycnTNNdcE3NEGAACsKaiBaPz48dq0aZOef/55ffHFF1qwYIFee+01paWlSZJsNpvGjRun5557Th988IEKCwuVkpKimJgY3XPPPZL+dUbpzjvv1MMPP6wtW7bok08+0dixYzVs2DDFxMRIkn75y18qNDRUo0aN0u7du/XOO+9o5syZSk9PD9bSAQBAI9I8mAf/6U9/qiVLligzM1OTJ09WXFycXnzxRSUnJ5s1EyZM0IkTJ/TII4+orKxMN998s1asWKGWLVuaNW+//bbGjh2r22+/Xc2aNdOQIUP00ksvmeNOp1OrVq1SWlqaevXqpXbt2mnSpEkB31UEAACsK6jfQ9RU8D1EQHDxPUQAaqPJfA8RAABAY0AgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlhfUQPTss8/KZrMFbF27djXHT506pbS0NEVGRqp169YaMmSISkpKAuYoLi5WUlKSWrVqpaioKGVkZOjMmTMBNevWrVPPnj1lt9vVpUsXZWVlNcTyAABAExH0M0TXXnutDh06ZG4bNmwwx8aPH68PP/xQ7777rnJzc3Xw4EENHjzYHK+srFRSUpIqKiq0ceNGzZ8/X1lZWZo0aZJZs3//fiUlJalfv34qKCjQuHHj9NBDD2nlypUNuk4AANB4NQ96A82by+1219jv8/k0d+5cLViwQLfddpskad68eerWrZs2bdqkPn36aNWqVfr000+1evVquVwuXX/99fr973+viRMn6tlnn1VoaKjmzJmjuLg4vfDCC5Kkbt26acOGDZoxY4YSExMbdK0AAKBxCvoZor179yomJkZXXHGFkpOTVVxcLEnKz8/X6dOnlZCQYNZ27dpVHTt2VF5eniQpLy9P3bt3l8vlMmsSExPl9/u1e/dus+bsOaprquc4l/Lycvn9/oANAABcuoIaiHr37q2srCytWLFCs2fP1v79+3XLLbfo2LFj8nq9Cg0NVURERMBzXC6XvF6vJMnr9QaEoerx6rEfq/H7/Tp58uQ5+5oyZYqcTqe5xcbG1sVyAQBAIxXUj8wGDBhg/t2jRw/17t1bnTp10qJFixQWFha0vjIzM5Wenm4+9vv9hCIAAC5hQf/I7GwRERG6+uqr9cUXX8jtdquiokJlZWUBNSUlJeY1R263u8ZdZ9WP/12Nw+H4wdBlt9vlcDgCNgAAcOlqVIHo+PHj2rdvn6Kjo9WrVy+1aNFCa9asMceLiopUXFwsj8cjSfJ4PCosLFRpaalZk5OTI4fDofj4eLPm7Dmqa6rnAAAACGogevLJJ5Wbm6t//vOf2rhxo/7rv/5LISEhuv/+++V0OjVq1Cilp6fro48+Un5+vkaMGCGPx6M+ffpIkvr376/4+HgNHz5cO3bs0MqVK/X0008rLS1NdrtdkjR69Gh9+eWXmjBhgvbs2aNZs2Zp0aJFGj9+fDCXDgAAGpGgXkP09ddf6/7779e3336r9u3b6+abb9amTZvUvn17SdKMGTPUrFkzDRkyROXl5UpMTNSsWbPM54eEhCg7O1tjxoyRx+NReHi4UlNTNXnyZLMmLi5Oy5Yt0/jx4zVz5kx16NBBb7zxBrfcAwAAk80wDCPYTTR2fr9fTqdTPp+vXq8n6pXxVr3NDTRl+dNTgt0CgCboQt6/G9U1RAAAAMFAIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZXq0B02223qaysrMZ+v9+v22677WJ7AgAAaFC1CkTr1q1TRUVFjf2nTp3Sxx9/fNFNAQAANKTmF1K8c+dO8+9PP/1UXq/XfFxZWakVK1bo8ssvr7vuAAAAGsAFBaLrr79eNptNNpvtnB+NhYWF6c9//nOdNQcAANAQLigQ7d+/X4Zh6IorrtCWLVvUvn17cyw0NFRRUVEKCQmp8yYBAADq0wUFok6dOkmSqqqq6qUZAACAYLigQHS2vXv36qOPPlJpaWmNgDRp0qSLbgwAAKCh1CoQvf766xozZozatWsnt9stm81mjtlsNgIRAABoUmoViJ577jn94Q9/0MSJE+u6HwAAgAZXq+8hOnr0qIYOHVrXvQAAAARFrQLR0KFDtWrVqrruBQAAIChq9ZFZly5d9Mwzz2jTpk3q3r27WrRoETD+2GOP1UlzAAAADaFWgei1115T69atlZubq9zc3IAxm81GIAIAAE1KrQLR/v3767oPAACAoKnVNUQAAACXklqdIRo5cuSPjr/55pu1agYAACAYahWIjh49GvD49OnT2rVrl8rKys75o68AAACNWa0C0ZIlS2rsq6qq0pgxY3TllVdedFMAAAANqc6uIWrWrJnS09M1Y8aMWj1/6tSpstlsGjdunLnv1KlTSktLU2RkpFq3bq0hQ4aopKQk4HnFxcVKSkpSq1atFBUVpYyMDJ05cyagZt26derZs6fsdru6dOmirKysWvUIAAAuTXV6UfW+fftqhJHzsXXrVr366qvq0aNHwP7x48frww8/1Lvvvqvc3FwdPHhQgwcPNscrKyuVlJSkiooKbdy4UfPnz1dWVlbAb6nt379fSUlJ6tevnwoKCjRu3Dg99NBDWrlyZe0XCgAALim1+sgsPT094LFhGDp06JCWLVum1NTUC5rr+PHjSk5O1uuvv67nnnvO3O/z+TR37lwtWLDAvC5p3rx56tatmzZt2qQ+ffpo1apV+vTTT7V69Wq5XC5df/31+v3vf6+JEyfq2WefVWhoqObMmaO4uDi98MILkqRu3bppw4YNmjFjhhITE2uzfAAAcImp1Rmi7du3B2w7d+6UJL3wwgt68cUXL2iutLQ0JSUlKSEhIWB/fn6+Tp8+HbC/a9eu6tixo/Ly8iRJeXl56t69u1wul1mTmJgov9+v3bt3mzXfnzsxMdGc41zKy8vl9/sDNgAAcOmq1Rmijz76qE4OvnDhQv3jH//Q1q1ba4x5vV6FhoYqIiIiYL/L5ZLX6zVrzg5D1ePVYz9W4/f7dfLkSYWFhdU49pQpU/S73/2u1usCAABNy0VdQ3T48GFt2LBBGzZs0OHDhy/ouQcOHNDjjz+ut99+Wy1btryYNupcZmamfD6fuR04cCDYLQEAgHpUq0B04sQJjRw5UtHR0erbt6/69u2rmJgYjRo1St999915zZGfn6/S0lL17NlTzZs3V/PmzZWbm6uXXnpJzZs3l8vlUkVFhcrKygKeV1JSIrfbLUlyu9017jqrfvzvahwOxznPDkmS3W6Xw+EI2AAAwKWrVoEoPT1dubm5+vDDD1VWVqaysjK9//77ys3N1RNPPHFec9x+++0qLCxUQUGBud14441KTk42/27RooXWrFljPqeoqEjFxcXyeDySJI/Ho8LCQpWWlpo1OTk5cjgcio+PN2vOnqO6pnoOAACAWl1D9N577+lvf/ubbr31VnPfXXfdpbCwMN13332aPXv2v52jTZs2+slPfhKwLzw8XJGRkeb+UaNGKT09XW3btpXD4dCjjz4qj8ejPn36SJL69++v+Ph4DR8+XNOmTZPX69XTTz+ttLQ02e12SdLo0aP18ssva8KECRo5cqTWrl2rRYsWadmyZbVZOgAAuATVKhB99913NS5UlqSoqKjz/sjsfMyYMUPNmjXTkCFDVF5ersTERM2aNcscDwkJUXZ2tsaMGSOPx6Pw8HClpqZq8uTJZk1cXJyWLVum8ePHa+bMmerQoYPeeOMNbrkHAAAmm2EYxoU+6fbbb1dkZKTeeust84LokydPKjU1VUeOHNHq1avrvNFg8vv9cjqd8vl89Xo9Ua+Mt+ptbqApy5+eEuwWADRBF/L+XaszRC+++KLuvPNOdejQQdddd50kaceOHbLb7Vq1alVtpgQAAAiaWgWi7t27a+/evXr77be1Z88eSdL999+v5OTkH7xzCwAAoLGqVSCaMmWKXC6XHn744YD9b775pg4fPqyJEyfWSXMAAAANoVa33b/66qvq2rVrjf3XXnut5syZc9FNAQAANKRaBSKv16vo6Oga+9u3b69Dhw5ddFMAAAANqVaBKDY2Vp988kmN/Z988oliYmIuuikAAICGVKtriB5++GGNGzdOp0+f1m233SZJWrNmjSZMmHDe31QNAADQWNTqDFFGRoZGjRqlX//617riiit0xRVX6NFHH9Vjjz2mzMzMuu4RAADT7Nmz1aNHD/O3Jj0ej5YvX16jzjAMDRgwQDabTUuXLg0YKy4uVlJSklq1aqWoqChlZGTozJkz5viDDz4om81WY7v22mvre3kIklqdIbLZbPrjH/+oZ555Rp999pnCwsJ01VVXmT+XAQBAfenQoYOmTp2qq666SoZhaP78+Ro0aJC2b98eEFhefPFF2Wy2Gs+vrKxUUlKS3G63Nm7cqEOHDiklJUUtWrTQ888/L0maOXOmpk6daj7nzJkzuu666zR06ND6XyCColbfVG01fFM1EFx8UzX+nbZt22r69OkaNWqUJKmgoEADBw7Utm3bFB0drSVLluiee+6RJC1fvlwDBw7UwYMHzZ+hmjNnjiZOnKjDhw8rNDS0xvxLly7V4MGDtX//fnXq1KnB1oWLcyHv37X6yAwAgMagsrJSCxcu1IkTJ+TxeCT96/c2f/nLX+qVV16R2+2u8Zy8vDx179494Dc5ExMT5ff7tXv37nMeZ+7cuUpISCAMXcJq9ZEZAADBVFhYKI/Ho1OnTql169ZasmSJ4uPjJUnjx4/Xf/7nf2rQoEHnfK7X663xA+XVj71eb436gwcPavny5VqwYEEdrwKNCYEIANDkXHPNNSooKJDP59Pf/vY3paamKjc3V1988YXWrl2r7du319mx5s+fr4iICPMjN1yaCEQAgCYnNDRUXbp0kST16tVLW7du1cyZMxUWFqZ9+/YpIiIioH7IkCG65ZZbtG7dOrndbm3ZsiVgvKSkRJJqfMRmGIbefPNNDR8+/JzXFuHSwTVEAIAmr6qqSuXl5frNb36jnTt3qqCgwNwkacaMGZo3b54kyePxqLCwUKWlpebzc3Jy5HA4zI/dqlWfdaq+WBuXLs4QAQCalMzMTA0YMEAdO3bUsWPHtGDBAq1bt04rV66U2+0+54XUHTt2VFxcnCSpf//+io+P1/DhwzVt2jR5vV49/fTTSktLq/H1MXPnzlXv3r31k5/8pEHWhuAhEAEAmpTS0lKlpKTo0KFDcjqd6tGjh1auXKk77rjjvJ4fEhKi7OxsjRkzRh6PR+Hh4UpNTdXkyZMD6nw+n9577z3NnDmzPpaBRoZABABoUubOnXtB9ef6ur1OnTrp73//+48+z+l06rvvvrugY6Hp4hoiAABgeQQiAABgeXxkBgANgJ/mAc6tsfw0D2eIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5QU1EM2ePVs9evSQw+GQw+GQx+PR8uXLzfFTp04pLS1NkZGRat26tYYMGaKSkpKAOYqLi5WUlKRWrVopKipKGRkZOnPmTEDNunXr1LNnT9ntdnXp0kVZWVkNsTwAANBEBDUQdejQQVOnTlV+fr62bdum2267TYMGDdLu3bslSePHj9eHH36od999V7m5uTp48KAGDx5sPr+yslJJSUmqqKjQxo0bNX/+fGVlZWnSpElmzf79+5WUlKR+/fqpoKBA48aN00MPPaSVK1c2+HoBAEDjZDMMwwh2E2dr27atpk+frnvvvVft27fXggULdO+990qS9uzZo27duikvL099+vTR8uXLNXDgQB08eFAul0uSNGfOHE2cOFGHDx9WaGioJk6cqGXLlmnXrl3mMYYNG6aysjKtWLHivHry+/1yOp3y+XxyOBx1v+j/r1fGW/U2N9CU5U9PCXYLF43XN3Bu9fn6vpD370ZzDVFlZaUWLlyoEydOyOPxKD8/X6dPn1ZCQoJZ07VrV3Xs2FF5eXmSpLy8PHXv3t0MQ5KUmJgov99vnmXKy8sLmKO6pnqOcykvL5ff7w/YAADApSvogaiwsFCtW7eW3W7X6NGjtWTJEsXHx8vr9So0NFQREREB9S6XS16vV5Lk9XoDwlD1ePXYj9X4/X6dPHnynD1NmTJFTqfT3GJjY+tiqQAAoJEKeiC65pprVFBQoM2bN2vMmDFKTU3Vp59+GtSeMjMz5fP5zO3AgQNB7QcAANSv5sFuIDQ0VF26dJEk9erVS1u3btXMmTP1i1/8QhUVFSorKws4S1RSUiK32y1Jcrvd2rJlS8B81XehnV3z/TvTSkpK5HA4FBYWds6e7Ha77HZ7nawPAAA0fkE/Q/R9VVVVKi8vV69evdSiRQutWbPGHCsqKlJxcbE8Ho8kyePxqLCwUKWlpWZNTk6OHA6H4uPjzZqz56iuqZ4DAAAgqGeIMjMzNWDAAHXs2FHHjh3TggULtG7dOq1cuVJOp1OjRo1Senq62rZtK4fDoUcffVQej0d9+vSRJPXv31/x8fEaPny4pk2bJq/Xq6efflppaWnmGZ7Ro0fr5Zdf1oQJEzRy5EitXbtWixYt0rJly4K5dAAA0IgENRCVlpYqJSVFhw4dktPpVI8ePbRy5UrdcccdkqQZM2aoWbNmGjJkiMrLy5WYmKhZs2aZzw8JCVF2drbGjBkjj8ej8PBwpaamavLkyWZNXFycli1bpvHjx2vmzJnq0KGD3njjDSUmJjb4egEAQOPU6L6HqDHie4iA4OJ7iIBLF99DBAAA0EgQiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOUFNRBNmTJFP/3pT9WmTRtFRUXpnnvuUVFRUUDNqVOnlJaWpsjISLVu3VpDhgxRSUlJQE1xcbGSkpLUqlUrRUVFKSMjQ2fOnAmoWbdunXr27Cm73a4uXbooKyurvpcHAACaiKAGotzcXKWlpWnTpk3KycnR6dOn1b9/f504ccKsGT9+vD788EO9++67ys3N1cGDBzV48GBzvLKyUklJSaqoqNDGjRs1f/58ZWVladKkSWbN/v37lZSUpH79+qmgoEDjxo3TQw89pJUrVzboegEAQONkMwzDCHYT1Q4fPqyoqCjl5uaqb9++8vl8at++vRYsWKB7771XkrRnzx5169ZNeXl56tOnj5YvX66BAwfq4MGDcrlckqQ5c+Zo4sSJOnz4sEJDQzVx4kQtW7ZMu3btMo81bNgwlZWVacWKFf+2L7/fL6fTKZ/PJ4fDUT+Ll9Qr4616mxtoyvKnpwS7hYvG6xs4t/p8fV/I+3ejuobI5/NJktq2bStJys/P1+nTp5WQkGDWdO3aVR07dlReXp4kKS8vT927dzfDkCQlJibK7/dr9+7dZs3Zc1TXVM/xfeXl5fL7/QEbAAC4dDWaQFRVVaVx48bppptu0k9+8hNJktfrVWhoqCIiIgJqXS6XvF6vWXN2GKoerx77sRq/36+TJ0/W6GXKlClyOp3mFhsbWydrBAAAjVOjCURpaWnatWuXFi5cGOxWlJmZKZ/PZ24HDhwIdksAAKAeNQ92A5I0duxYZWdna/369erQoYO53+12q6KiQmVlZQFniUpKSuR2u82aLVu2BMxXfRfa2TXfvzOtpKREDodDYWFhNfqx2+2y2+11sjYAAND4BfUMkWEYGjt2rJYsWaK1a9cqLi4uYLxXr15q0aKF1qxZY+4rKipScXGxPB6PJMnj8aiwsFClpaVmTU5OjhwOh+Lj482as+eorqmeAwAAWFtQzxClpaVpwYIFev/999WmTRvzmh+n06mwsDA5nU6NGjVK6enpatu2rRwOhx599FF5PB716dNHktS/f3/Fx8dr+PDhmjZtmrxer55++mmlpaWZZ3lGjx6tl19+WRMmTNDIkSO1du1aLVq0SMuWLQva2gEAQOMR1DNEs2fPls/n06233qro6Ghze+edd8yaGTNmaODAgRoyZIj69u0rt9utxYsXm+MhISHKzs5WSEiIPB6PHnjgAaWkpGjy5MlmTVxcnJYtW6acnBxdd911euGFF/TGG28oMTGxQdcLAAAap6CeITqfr0Bq2bKlXnnlFb3yyis/WNOpUyf9/e9//9F5br31Vm3fvv2CewQAAJe+RnOXGQAAQLAQiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOUFNRCtX79ed999t2JiYmSz2bR06dKAccMwNGnSJEVHRyssLEwJCQnau3dvQM2RI0eUnJwsh8OhiIgIjRo1SsePHw+o2blzp2655Ra1bNlSsbGxmjZtWn0vDQAANCFBDUQnTpzQddddp1deeeWc49OmTdNLL72kOXPmaPPmzQoPD1diYqJOnTpl1iQnJ2v37t3KyclRdna21q9fr0ceecQc9/v96t+/vzp16qT8/HxNnz5dzz77rF577bV6Xx8AAGgamgfz4AMGDNCAAQPOOWYYhl588UU9/fTTGjRokCTprbfeksvl0tKlSzVs2DB99tlnWrFihbZu3aobb7xRkvTnP/9Zd911l/77v/9bMTExevvtt1VRUaE333xToaGhuvbaa1VQUKA//elPAcEJAABYV6O9hmj//v3yer1KSEgw9zmdTvXu3Vt5eXmSpLy8PEVERJhhSJISEhLUrFkzbd682azp27evQkNDzZrExEQVFRXp6NGj5zx2eXm5/H5/wAYAAC5djTYQeb1eSZLL5QrY73K5zDGv16uoqKiA8ebNm6tt27YBNeea4+xjfN+UKVPkdDrNLTY29uIXBAAAGq1GG4iCKTMzUz6fz9wOHDgQ7JYAAEA9arSByO12S5JKSkoC9peUlJhjbrdbpaWlAeNnzpzRkSNHAmrONcfZx/g+u90uh8MRsAEAgEtXow1EcXFxcrvdWrNmjbnP7/dr8+bN8ng8kiSPx6OysjLl5+ebNWvXrlVVVZV69+5t1qxfv16nT582a3JycnTNNdfosssua6DVAACAxiyogej48eMqKChQQUGBpH9dSF1QUKDi4mLZbDaNGzdOzz33nD744AMVFhYqJSVFMTExuueeeyRJ3bp105133qmHH35YW7Zs0SeffKKxY8dq2LBhiomJkST98pe/VGhoqEaNGqXdu3frnXfe0cyZM5Wenh6kVQMAgMYmqLfdb9u2Tf369TMfV4eU1NRUZWVlacKECTpx4oQeeeQRlZWV6eabb9aKFSvUsmVL8zlvv/22xo4dq9tvv13NmjXTkCFD9NJLL5njTqdTq1atUlpamnr16qV27dpp0qRJ3HIPAABMNsMwjGA30dj5/X45nU75fL56vZ6oV8Zb9TY30JTlT08JdgsXjdc3cG71+fq+kPfvRnsNEQAAQEMhEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMuzVCB65ZVX1LlzZ7Vs2VK9e/fWli1bgt0SAABoBCwTiN555x2lp6frt7/9rf7xj3/ouuuuU2JiokpLS4PdGgAACDLLBKI//elPevjhhzVixAjFx8drzpw5atWqld58881gtwYAAIKsebAbaAgVFRXKz89XZmamua9Zs2ZKSEhQXl5ejfry8nKVl5ebj30+nyTJ7/fXa5+V5SfrdX6gqarv115D4PUNnFt9vr6r5zYM49/WWiIQ/e///q8qKyvlcrkC9rtcLu3Zs6dG/ZQpU/S73/2uxv7Y2Nh66xHAD3P+eXSwWwBQTxri9X3s2DE5nc4frbFEILpQmZmZSk9PNx9XVVXpyJEjioyMlM1mC2JnaAh+v1+xsbE6cOCAHA5HsNsBUId4fVuLYRg6duyYYmJi/m2tJQJRu3btFBISopKSkoD9JSUlcrvdNertdrvsdnvAvoiIiPpsEY2Qw+Hg/zCBSxSvb+v4d2eGqlniourQ0FD16tVLa9asMfdVVVVpzZo18ng8QewMAAA0BpY4QyRJ6enpSk1N1Y033qj/+I//0IsvvqgTJ05oxIgRwW4NAAAEmWUC0S9+8QsdPnxYkyZNktfr1fXXX68VK1bUuNAasNvt+u1vf1vjY1MATR+vb/wQm3E+96IBAABcwixxDREAAMCPIRABAADLIxABAADLIxABAADLIxABZ3nwwQdls9lqbF988UWwWwNQS9Wv69Gja/5ERFpammw2mx588MGGbwyNCoEI+J4777xThw4dCtji4uKC3RaAixAbG6uFCxfq5Mn/+5HdU6dOacGCBerYsWMQO0NjQSACvsdut8vtdgdsISEhwW4LwEXo2bOnYmNjtXjxYnPf4sWL1bFjR91www1B7AyNBYEIAGAJI0eO1Lx588zHb775Jr9WABOBCPie7OxstW7d2tyGDh0a7JYA1IEHHnhAGzZs0FdffaWvvvpKn3zyiR544IFgt4VGwjI/3QGcr379+mn27Nnm4/Dw8CB2A6CutG/fXklJScrKypJhGEpKSlK7du2C3RYaCQIR8D3h4eHq0qVLsNsAUA9GjhypsWPHSpJeeeWVIHeDxoRABACwjDvvvFMVFRWy2WxKTEwMdjtoRAhEAADLCAkJ0WeffWb+DVQjEAEALMXhcAS7BTRCNsMwjGA3AQAAEEzcdg8AACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQA5+HWW2/VuHHjgt0GgHpCIALQZHi9Xj3++OPq0qWLWrZsKZfLpZtuukmzZ8/Wd999F+z2ADRh/JYZgCbhyy+/1E033aSIiAg9//zz6t69u+x2uwoLC/Xaa6/p8ssv189//vNgt/mDKisrZbPZ1KwZ/x0KNEa8MgE0Cb/+9a/VvHlzbdu2Tffdd5+6deumK664QoMGDdKyZct09913S5LKysr00EMPqX379nI4HLrtttu0Y8cOc55nn31W119/vf7nf/5HnTt3ltPp1LBhw3Ts2DGz5sSJE0pJSVHr1q0VHR2tF154oUY/5eXlevLJJ3X55ZcrPDxcvXv31rp168zxrKwsRURE6IMPPlB8fLzsdruKi4vr7x8IwEUhEAFo9L799lutWrVKaWlpCg8PP2eNzWaTJA0dOlSlpaVavny58vPz1bNnT91+++06cuSIWbtv3z4tXbpU2dnZys7OVm5urqZOnWqOZ2RkKDc3V++//75WrVqldevW6R//+EfA8caOHau8vDwtXLhQO3fu1NChQ3XnnXdq7969Zs13332nP/7xj3rjjTe0e/duRUVF1eU/C4C6ZABAI7dp0yZDkrF48eKA/ZGRkUZ4eLgRHh5uTJgwwfj4448Nh8NhnDp1KqDuyiuvNF599VXDMAzjt7/9rdGqVSvD7/eb4xkZGUbv3r0NwzCMY8eOGaGhocaiRYvM8W+//dYICwszHn/8ccMwDOOrr74yQkJCjG+++SbgOLfffruRmZlpGIZhzJs3z5BkFBQU1M0/AoB6xTVEAJqsLVu2qKqqSsnJySovL9eOHTt0/PhxRUZGBtSdPHlS+/btMx937txZbdq0MR9HR0ertLRU0r/OHlVUVKh3797meNu2bXXNNdeYjwsLC1VZWamrr7464Djl5eUBxw4NDVWPHj3qZrEA6hWBCECj16VLF9lsNhUVFQXsv+KKKyRJYWFhkqTjx48rOjo64FqeahEREebfLVq0CBiz2Wyqqqo6736OHz+ukJAQ5efnKyQkJGCsdevW5t9hYWHmR3kAGjcCEYBGLzIyUnfccYdefvllPfrooz94HVHPnj3l9XrVvHlzde7cuVbHuvLKK9WiRQtt3rxZHTt2lCQdPXpUn3/+uX72s59Jkm644QZVVlaqtLRUt9xyS62OA6Bx4aJqAE3CrFmzdObMGd14441655139Nlnn6moqEh/+ctftGfPHoWEhCghIUEej0f33HOPVq1apX/+85/auHGjnnrqKW3btu28jtO6dWuNGjVKGRkZWrt2rXbt2qUHH3ww4Hb5q6++WsnJyUpJSdHixYu1f/9+bdmyRVOmTNGyZcvq658AQD3iDBGAJuHKK6/U9u3b9fzzzyszM1Nff/217Ha74uPj9eSTT+rXv/61bDab/v73v+upp57SiBEjdPjwYbndbvXt21cul+u8jzV9+nQdP35cd999t9q0aaMnnnhCPp8voGbevHl67rnn9MQTT+ibb75Ru3bt1KdPHw0cOLCulw6gAdgMwzCC3QQAAEAw8ZEZAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwvP8HiUT2OO+RW/YAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# plotting a bar chart for gender vs total amount\n",
        "\n",
        "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
        "\n",
        "sns.barplot(x = 'Gender',y= 'Amount' ,data = sales_gen)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "8qQu3it47PZH",
        "outputId": "43a142ca-a1cb-415b-ff45-e5775f8a0d84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Gender', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ages"
      ],
      "metadata": {
        "id": "2ruIF9cQ7TLk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.countplot(data = df, x = 'Age Group', hue = 'Gender')\n",
        "\n",
        "for bars in ax.containers:\n",
        "    ax.bar_label(bars)\n",
        "\n",
        "    #From above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "RISFZjTA7RIf",
        "outputId": "266d7bf5-922e-45bf-c2d7-a667019a806a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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PoVKp0Lp1awCAj48PPDw88OGHH2LWrFlITU3FN998g8DAQGmEZ+TIkfj5558xfvx4DBs2DDExMVi7di22bdumy64TERFRJaLThCg9PR2DBg3CrVu3YGFhAU9PT+zYsQNvvfUWAGDevHkwMDBAnz59kJubC19fXyxatEh6vaGhIbZu3YpRo0ZBpVLBzMwMgwcPxpQpU6QYV1dXbNu2DWPHjsWPP/4IJycnLFu2DL6+vlrvLxEREVVOle45RJURn0NERESVHZ9D9Io8h4iIiIhIV5gQERERkd7T+W33REREVLFSpjTW6v5cJp5+ofghQ4YgMjISH3/8MZYsWaJWFhgYiEWLFmHw4MEVOjk7R4iIiIhI55ydnfH777+rzSLx6NEjrF69Gi4uLhW+fyZEREREpHPNmzeHs7MzNmzYIG3bsGEDXFxc0KxZswrfPxMiIiIiqhSGDRuG8PBwaX358uUYOnSoVvbNhIiIiIgqhYEDB2L//v34559/8M8//+DAgQMYOHCgVvbNi6qJiIioUrC1tYW/vz8iIiIghIC/vz9sbGy0sm8mRERERFRpDBs2DEFBQQCAhQsXam2/TIiIiIio0vDz80NeXh5kMplWp9liQkRERESVhqGhIc6fPy/9rC1MiIiIiKhSKWvesYrAhIiIiOgV96JPjta2sp5AvWnTpgpvA2+7JyIiIr3HhIiIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiJ6hQghdN0ErdJUf5kQERERvQKqVasGAHjw4IGOW6JdeXl5AF7+mUW87Z6IiOgVYGhoCEtLS6SnpwMATE1NIZPJdNyqilVYWIjbt2/D1NQURkYvl9IwISIiInpF2NvbA4CUFOkDAwMDuLi4vHTyx4SIiIjoFSGTyeDg4AA7Ozvk5+frujlaIZfLYWDw8lcAMSEiIiJ6xRgaGmp1HrBXAS+qJiIiIr3HhIiIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO8xISIiIiK9x4SIiIiI9B4TIiIiItJ7TIiIiIhI7zEhIiIiIr3HhIiIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO/pNCEKDQ1Fy5YtUb16ddjZ2aFXr15ISkpSi+nQoQNkMpnaMnLkSLWYlJQU+Pv7w9TUFHZ2dhg3bhweP36sFhMbG4vmzZtDoVDAzc0NERERFd09IiIiqiJ0mhDFxcUhMDAQhw4dQnR0NPLz8+Hj44OcnBy1uOHDh+PWrVvSMmvWLKmsoKAA/v7+yMvLw8GDBxEZGYmIiAhMnDhRiklOToa/vz86duyIxMREjBkzBh999BF27Nihtb4SERFR5WWky51HRUWprUdERMDOzg4JCQlo166dtN3U1BT29vYl1rFz506cO3cOu3btQs2aNdG0aVNMnToVEyZMwOTJkyGXy7FkyRK4urpizpw5AAB3d3fs378f8+bNg6+vb8V1kIiIiKqESnUNUWZmJgDAyspKbfuqVatgY2OD119/HSEhIXjw4IFUFh8fj8aNG6NmzZrSNl9fX2RlZeHs2bNSTJcuXdTq9PX1RXx8fIntyM3NRVZWltpCREREry6djhA9rbCwEGPGjEHbtm3x+uuvS9s/+OAD1KpVC46Ojjh16hQmTJiApKQkbNiwAQCQmpqqlgwBkNZTU1OfGZOVlYWHDx/CxMRErSw0NBTfffedxvtIRERElVOlSYgCAwNx5swZ7N+/X237iBEjpJ8bN24MBwcHdO7cGVeuXEHdunUrpC0hISEIDg6W1rOysuDs7Fwh+yIiIiLdqxSnzIKCgrB161bs2bMHTk5Oz4z19vYGAFy+fBkAYG9vj7S0NLWYovWi645Ki1EqlcVGhwBAoVBAqVSqLURERPTq0mlCJIRAUFAQNm7ciJiYGLi6upb5msTERACAg4MDAEClUuH06dNIT0+XYqKjo6FUKuHh4SHF7N69W62e6OhoqFQqDfWEiIiIqjKdJkSBgYFYuXIlVq9ejerVqyM1NRWpqal4+PAhAODKlSuYOnUqEhIScPXqVWzZsgWDBg1Cu3bt4OnpCQDw8fGBh4cHPvzwQ5w8eRI7duzAN998g8DAQCgUCgDAyJEj8ffff2P8+PG4cOECFi1ahLVr12Ls2LE66zsRERFVHjpNiBYvXozMzEx06NABDg4O0rJmzRoAgFwux65du+Dj44OGDRvi888/R58+ffDnn39KdRgaGmLr1q0wNDSESqXCwIEDMWjQIEyZMkWKcXV1xbZt2xAdHY0mTZpgzpw5WLZsGW+5JyIiIgCATAghdN2Iyi4rKwsWFhbIzMws8Xoir3ErtN6mhNmDtL5PIiKiqqSs/99PqxQXVRMRERHpEhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO8xISIiIiK9x4SIiIiI9B4TIiIiItJ7TIiIiIhI7zEhIiIiIr3HhIiIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO8xISIiIiK9x4SIiIiI9B4TIiIiItJ7TIiIiIhI7zEhIiIiIr3HhIiIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO8xISIiIiK9x4SIiIiI9B4TIiIiItJ7TIiIiIhI7zEhIiIiIr3HhIiIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO/pNCEKDQ1Fy5YtUb16ddjZ2aFXr15ISkpSi3n06BECAwNhbW0Nc3Nz9OnTB2lpaWoxKSkp8Pf3h6mpKezs7DBu3Dg8fvxYLSY2NhbNmzeHQqGAm5sbIiIiKrp7REREVEXoNCGKi4tDYGAgDh06hOjoaOTn58PHxwc5OTlSzNixY/Hnn39i3bp1iIuLw82bN9G7d2+pvKCgAP7+/sjLy8PBgwcRGRmJiIgITJw4UYpJTk6Gv78/OnbsiMTERIwZMwYfffQRduzYodX+EhERUeUkE0IIXTeiyO3bt2FnZ4e4uDi0a9cOmZmZsLW1xerVq/Huu+8CAC5cuAB3d3fEx8ejdevW2L59O7p164abN2+iZs2aAIAlS5ZgwoQJuH37NuRyOSZMmIBt27bhzJkz0r769++PjIwMREVFFWtHbm4ucnNzpfWsrCw4OzsjMzMTSqWyWLzXuBWaPhRlSpg9SOv7JCIiqkqysrJgYWFR6v/vp1Wqa4gyMzMBAFZWVgCAhIQE5Ofno0uXLlJMw4YN4eLigvj4eABAfHw8GjduLCVDAODr64usrCycPXtWinm6jqKYojr+KzQ0FBYWFtLi7OysuU4SERFRpVNpEqLCwkKMGTMGbdu2xeuvvw4ASE1NhVwuh6WlpVpszZo1kZqaKsU8nQwVlReVPSsmKysLDx8+LNaWkJAQZGZmSsu1a9c00kciIiKqnIx03YAigYGBOHPmDPbv36/rpkChUEChUOi6GURERKQllWKEKCgoCFu3bsWePXvg5OQkbbe3t0deXh4yMjLU4tPS0mBvby/F/Peus6L1smKUSiVMTEw03R0iIiKqYnSaEAkhEBQUhI0bNyImJgaurq5q5V5eXqhWrRp2794tbUtKSkJKSgpUKhUAQKVS4fTp00hPT5dioqOjoVQq4eHhIcU8XUdRTFEdREREpN90esosMDAQq1evxubNm1G9enXpmh8LCwuYmJjAwsICAQEBCA4OhpWVFZRKJT799FOoVCq0bt0aAODj4wMPDw98+OGHmDVrFlJTU/HNN98gMDBQOu01cuRI/Pzzzxg/fjyGDRuGmJgYrF27Ftu2bdNZ34mIiKjy0OkI0eLFi5GZmYkOHTrAwcFBWtasWSPFzJs3D926dUOfPn3Qrl072NvbY8OGDVK5oaEhtm7dCkNDQ6hUKgwcOBCDBg3ClClTpBhXV1ds27YN0dHRaNKkCebMmYNly5bB19dXq/0lIiKiyqlSPYeosirrOQZ8DhEREVHlU2WfQ0RERESkC0yIiIiISO8xISIiIiK9x4SIiIiI9B4TIiIiItJ7TIiIiIhI75UrIerUqVOx6TSAJ7e3derU6WXbRERERKRV5UqIYmNjkZeXV2z7o0ePsG/fvpduFBEREZE2vdDUHadOnZJ+PnfunDTVBgAUFBQgKioKr732muZaR0RERKQFL5QQNW3aFDKZDDKZrMRTYyYmJliwYIHGGkdERESkDS+UECUnJ0MIgTp16uDIkSOwtbWVyuRyOezs7GBoaKjxRhIRERFVpBdKiGrVqgUAKCwsrJDGEBEREenCCyVET7t06RL27NmD9PT0YgnSxIkTX7phRERERNpSroTo119/xahRo2BjYwN7e3vIZDKpTCaTMSEiIiKiKqVcCdG0adMwffp0TJgwQdPtISIiItK6cj2H6N69e3jvvfc03RYiIiIinShXQvTee+9h586dmm4LERERkU6U65SZm5sbvv32Wxw6dAiNGzdGtWrV1MpHjx6tkcYRERERaUO5EqKlS5fC3NwccXFxiIuLUyuTyWRMiIiIiKhKKVdClJycrOl2EBEREelMua4hIiIiInqVlGuEaNiwYc8sX758ebkaQ0RERKQL5UqI7t27p7aen5+PM2fOICMjo8RJX4mIiIgqs3IlRBs3biy2rbCwEKNGjULdunVfulFERERE2qSxa4gMDAwQHByMefPmaapKIiIiIq3Q6EXVV65cwePHjzVZJREREVGFK1dCFBwcrLaMHTsW/fv3R79+/dCvXz9Nt5GIiIjKYe/evejevTscHR0hk8mwadMmtfIhQ4ZAJpOpLX5+flJ5bGxssfKi5ejRo1Lcjh070Lp1a1SvXh22trbo06cPrl69qqVeaka5riE6ceKE2rqBgQFsbW0xZ86cMu9AIyIiIu3IyclBkyZNMGzYMPTu3bvEGD8/P4SHh0vrCoVC+rlNmza4deuWWvy3336L3bt3o0WLFgCePJuwZ8+eCA4OxqpVq5CZmYmxY8eid+/eOH78eAX0qmKUKyHas2ePpttBREREGta1a1d07dr1mTEKhQL29vYllsnlcrWy/Px8bN68GZ9++ilkMhkAICEhAQUFBZg2bRoMDJ6cePriiy/Qs2dP5OfnF5veq7J6qWuIbt++jf3792P//v24ffu2ptpEREREWhIbGws7Ozs0aNAAo0aNwp07d0qN3bJlC+7cuYOhQ4dK27y8vGBgYIDw8HAUFBQgMzMT//vf/9ClS5cqkwwB5UyIcnJyMGzYMDg4OKBdu3Zo164dHB0dERAQgAcPHmi6jURERFQB/Pz8sGLFCuzevRvff/894uLi0LVrVxQUFJQYHxYWBl9fXzg5OUnbXF1dsXPnTnz11VdQKBSwtLTE9evXsXbtWm11QyPKfVF1XFwc/vzzT2RkZCAjIwObN29GXFwcPv/8c023kYiIiCpA//790aNHDzRu3Bi9evXC1q1bcfToUcTGxhaLvX79Onbs2IGAgAC17ampqRg+fDgGDx6Mo0ePIi4uDnK5HO+++y6EEFrqycsr1zVEf/zxB9avX48OHTpI295++22YmJigb9++WLx4sabaR0RERFpSp04d2NjY4PLly+jcubNaWXh4OKytrdGjRw+17QsXLoSFhQVmzZolbVu5ciWcnZ1x+PBhtG7dWittf1nlGiF68OABatasWWy7nZ0dT5kRERFVUdevX8edO3fg4OCgtl0IgfDwcAwaNKjYdUEPHjyQLqYuYmhoCODJLBZVRbkSIpVKhUmTJuHRo0fStocPH+K7776DSqXSWOOIiIio/LKzs5GYmIjExEQAT26RT0xMREpKCrKzszFu3DgcOnQIV69exe7du9GzZ0+4ubnB19dXrZ6YmBgkJyfjo48+KrYPf39/HD16FFOmTMGlS5dw/PhxDB06FLVq1UKzZs200U2NKNcps/nz58PPzw9OTk5o0qQJAODkyZNQKBTYuXOnRhtIRERE5XPs2DF07NhRWg8ODgYADB48GIsXL8apU6cQGRmJjIwMODo6wsfHB1OnTlV7FhHw5GLqNm3aoGHDhsX20alTJ6xevRqzZs3CrFmzYGpqCpVKhaioKJiYmFRsBzVIJsp5xdODBw+watUqXLhwAQDg7u6OAQMGVKnOP6+srCxYWFggMzMTSqWyWLnXuBVab1PC7EFa3ycREVFVUtb/76eVa4QoNDQUNWvWxPDhw9W2L1++HLdv38aECRPKUy0RERGRTpQrIfrll1+wevXqYtsbNWqE/v37MyEiIiKqRLR9JqMqnsUo10XVqampxa5ABwBbW9tic54QERERVXblSoicnZ1x4MCBYtsPHDgAR0fHl24UERERkTaV65TZ8OHDMWbMGOTn56NTp04AgN27d2P8+PF8UjURERFVOeVKiMaNG4c7d+7gk08+QV5eHgDA2NgYEyZMQEhIiEYbSERERFTRynXKTCaT4fvvv8ft27dx6NAhnDx5Enfv3sXEiRNfqJ69e/eie/fucHR0hEwmw6ZNm9TKhwwZAplMprb4+fmpxdy9excDBgyAUqmEpaUlAgICkJ2drRZz6tQpvPnmmzA2Noazs7Pa48WJiIiIypUQFTE3N0fLli3x+uuvF3uI0/PIyclBkyZNsHDhwlJj/Pz8cOvWLWn57bff1MoHDBiAs2fPIjo6Glu3bsXevXsxYsQIqTwrKws+Pj6oVasWEhISMHv2bEyePBlLly594fYSERHRq6lcp8w0pWvXrujateszYxQKBezt7UssO3/+PKKionD06FG0aNECALBgwQK8/fbb+OGHH+Do6IhVq1YhLy8Py5cvh1wuR6NGjZCYmIi5c+eqJU5Py83NRW5urrSelZVVzh4SERFRVfBSI0TaEBsbCzs7OzRo0ACjRo3CnTt3pLL4+HhYWlpKyRAAdOnSBQYGBjh8+LAU065dO8jlcinG19cXSUlJuHfvXon7DA0NhYWFhbQ4OztXUO+IiIioMqjUCZGfnx9WrFiB3bt34/vvv0dcXBy6du2KgoICAE+eh2RnZ6f2GiMjI1hZWSE1NVWKqVmzplpM0XpRzH+FhIQgMzNTWq5du6bprhEREVElotNTZmXp37+/9HPjxo3h6emJunXrIjY2Fp07d66w/SoUinJdE0VERERVU6UeIfqvOnXqwMbGBpcvXwYA2NvbIz09XS3m8ePHuHv3rnTdkb29PdLS0tRiitZLuzaJiIiI9EuVSoiuX7+OO3fuSNOGqFQqZGRkICEhQYqJiYlBYWEhvL29pZi9e/ciPz9fiomOjkaDBg1Qo0YN7XaAiIiIKiWdJkTZ2dlITExEYmIiACA5ORmJiYlISUlBdnY2xo0bh0OHDuHq1avYvXs3evbsCTc3N/j6+gIA3N3d4efnh+HDh+PIkSM4cOAAgoKC0L9/f2kKkQ8++AByuRwBAQE4e/Ys1qxZgx9//BHBwcG66jYRERFVMjpNiI4dO4ZmzZqhWbNmAIDg4GA0a9YMEydOhKGhIU6dOoUePXqgfv36CAgIgJeXF/bt26d2fc+qVavQsGFDdO7cGW+//TbeeOMNtWcMWVhYYOfOnUhOToaXlxc+//xzTJw4sdRb7omIiEj/6PSi6g4dOkAIUWr5jh07yqzDysoKq1evfmaMp6cn9u3b98LtIyIiIv1Qpa4hIiIiIqoITIiItKysOfwmT56Mhg0bwszMDDVq1ECXLl2kB40WKWsOv6tXrxabB1Amk+HQoUPa6CIRUZXDhIhIy8qaw69+/fr4+eefcfr0aezfvx+1a9eGj48Pbt++LcWUNYdfkV27dqnNBejl5VVh/SIiqsoq9YMZiV5FZc3h98EHH6itz507F2FhYTh16hQ6d+78XHP4FbG2tubztoiIngNHiIgqsby8PCxduhQWFhZo0qQJgOebw69Ijx49YGdnhzfeeANbtmzRatuJiKoSJkRVzP1rF3B5w7wSrz/Jz8/HhAkT0LhxY5iZmcHR0RGDBg3CzZs3pZjY2NgSry2RyWQ4evSoFNOzZ084ODjAzMwMTZs2xapVq7TdVb22detWmJubw9jYGPPmzUN0dDRsbGwAPN8cfubm5pgzZw7WrVuHbdu24Y033kCvXr2YFBERlYIJURVTmJ8LUzvnEq8/efDgAY4fP45vv/0Wx48fx4YNG5CUlIQePXpIMW3atFG7puTWrVv46KOP4OrqKo04HDx4EJ6envjjjz9w6tQpDB06FIMGDcLWrVu11k9917FjRyQmJuLgwYPw8/ND3759i01T8yw2NjYIDg6Gt7c3WrZsiZkzZ2LgwIGYPXt2BbaaiKjq4jVEVYxFnSawqNME77zzTvEyCwtER0erbfv555/RqlUrpKSkwMXFBXK5XO2akvz8fGzevBmffvopZDIZAOCrr75Sq+Ozzz7Dzp07sWHDBnTr1q0CekX/ZWZmBjc3N7i5uaF169aoV68ewsLCEBIS8lxz+JXE29u72PuDiIie4AjRKy4zMxMymQyWlpYllm/ZsgV37tzB0KFDy6zHysqqAlpIz6OwsBC5ubkAnm8Ov5IkJiZK8wASEZE6jhC9wh49eoQJEybg/fffh1KpLDEmLCwMvr6+cHJyKrWetWvX4ujRo/jll18qqql6JTs7G5cvX5bWi+bws7KygrW1NaZPn44ePXrAwcEB//77LxYuXIgbN27gvffeA6A+h9+SJUuQn59fbA6/yMhIyOVyaVqcDRs2YPny5Vi2bJn2O0xEVAUwIXpF5efno2/fvhBCYPHixSXGXL9+HTt27MDatWtLrWfPnj0YOnQofv31VzRq1KiimqtXjh07ho4dO0rrRRMNDx48GEuWLMGFCxcQGRmJf//9F9bW1mjZsiX27dundvxXrVqFoKAgdO7cGQYGBujTpw9++ukntf1MnToV//zzD4yMjNCwYUOsWbMG7777rnY6SURUxTAhegUVJUP//PMPYmJiSh0dCg8Ph7W1tdpF10+Li4tD9+7dMW/ePAwaNKgim6xXyprDb8OGDWXWUdYcfoMHD8bgwYPL1T4iIn3EhOgVU5QMXbp0CXv27IG1tXWJcUIIhIeHY9CgQahWrVqx8tjYWHTr1g3ff/99iU9AJiIiepUwIapiCvIeITcjDYmJiQDUrz9xcHDAu+++i+PHj2Pr1q0oKCiQnktjZWUFuVwu1RMTE4Pk5GR89NFHxfaxZ88edOvWDZ999hn69Okj1SGXy3lhtQZ5jVuh9X0mzOZIHxFRSXiXWRXzIDUZF1ZMlC6WDQ4ORrNmzTBx4kTcuHEDW7ZswfXr19G0aVM4ODhIy8GDB9XqCQsLQ5s2bdCwYcNi+4iMjMSDBw8QGhqqVkfv3r210kciIiJt4whRFVPdxR3Nv4gs9Zv+s65Nedqzrj+JiIhAREREeZpHRERUJXGEiIiIiPQeR4iqqJQpjbW6P5eJp7W6PyIiIm3iCBERERHpPSZEREREpPeYEBEREZHeY0JEREREeo8JEREREek9JkRERESk95gQERERkd5jQkRERER6jwkRERER6T0mRERERKT3mBARERGR3mNCRERERHqPCRERERHpPSZEREREpPeYEBEREZHeY0JEREREeo8JEREREek9JkRERESk95gQERERkd5jQkRERER6jwkRERER6T0mRERERKT3mBARERGR3mNCRERERHpPpwnR3r170b17dzg6OkImk2HTpk1q5UIITJw4EQ4ODjAxMUGXLl1w6dIltZi7d+9iwIABUCqVsLS0REBAALKzs9ViTp06hTfffBPGxsZwdnbGrFmzKrprREREVIXoNCHKyclBkyZNsHDhwhLLZ82ahZ9++glLlizB4cOHYWZmBl9fXzx69EiKGTBgAM6ePYvo6Ghs3boVe/fuxYgRI6TyrKws+Pj4oFatWkhISMDs2bMxefJkLF26tML7R0RERFWDkS533rVrV3Tt2rXEMiEE5s+fj2+++QY9e/YEAKxYsQI1a9bEpk2b0L9/f5w/fx5RUVE4evQoWrRoAQBYsGAB3n77bfzwww9wdHTEqlWrkJeXh+XLl0Mul6NRo0ZITEzE3Llz1RInIiIi0l+V9hqi5ORkpKamokuXLtI2CwsLeHt7Iz4+HgAQHx8PS0tLKRkCgC5dusDAwACHDx+WYtq1awe5XC7F+Pr6IikpCffu3Stx37m5ucjKylJbiIiI6NVVaROi1NRUAEDNmjXVttesWVMqS01NhZ2dnVq5kZERrKys1GJKquPpffxXaGgoLCwspMXZ2fnlO0RERESVVqVNiHQpJCQEmZmZ0nLt2jVdN4mIiIgqUKVNiOzt7QEAaWlpatvT0tKkMnt7e6Snp6uVP378GHfv3lWLKamOp/fxXwqFAkqlUm0hIiKiV1elTYhcXV1hb2+P3bt3S9uysrJw+PBhqFQqAIBKpUJGRgYSEhKkmJiYGBQWFsLb21uK2bt3L/Lz86WY6OhoNGjQADVq1NBSb4iIiKgy02lClJ2djcTERCQmJgJ4ciF1YmIiUlJSIJPJMGbMGEybNg1btmzB6dOnMWjQIDg6OqJXr14AAHd3d/j5+WH48OE4cuQIDhw4gKCgIPTv3x+Ojo4AgA8++AByuRwBAQE4e/Ys1qxZgx9//BHBwcE66jURERFVNjq97f7YsWPo2LGjtF6UpAwePBgREREYP348cnJyMGLECGRkZOCNN95AVFQUjI2NpdesWrUKQUFB6Ny5MwwMDNCnTx/89NNPUrmFhQV27tyJwMBAeHl5wcbGBhMnTuQt90RERCTRaULUoUMHCCFKLZfJZJgyZQqmTJlSaoyVlRVWr179zP14enpi37595W4nERERvdoq7TVERERERNrChIiIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO8xISIiIiK9x4SIiIiI9B4TIiIiItJ7TIiIiIhI7zEhIiIiIr3HhIiIiIgqRO3atSGTyYotgYGBAIBHjx4hMDAQ1tbWMDc3R58+fZCWlqZWR0mv//333zXeViZERERV1I0bNzBw4EBYW1vDxMQEjRs3xrFjx6TytLQ0DBkyBI6OjjA1NYWfnx8uXbqkVsfHH3+MunXrwsTEBLa2tujZsycuXLig7a7QK+ro0aO4deuWtERHRwMA3nvvPQDA2LFj8eeff2LdunWIi4vDzZs30bt372L1hIeHq9XTq1cvjbfVSOM1EhFRhbt37x7atm2Ljh07Yvv27bC1tcWlS5dQo0YNAIAQAr169UK1atWwefNmKJVKzJ07F126dMG5c+dgZmYGAPDy8sKAAQPg4uKCu3fvYvLkyfDx8UFycjIMDQ112UV6Bdja2qqtz5w5E3Xr1kX79u2RmZmJsLAwrF69Gp06dQLwJPFxd3fHoUOH0Lp1a+l1lpaWsLe3r9C2coSIiKgK+v777+Hs7Izw8HC0atUKrq6u8PHxQd26dQEAly5dwqFDh7B48WK0bNkSDRo0wOLFi/Hw4UP89ttvUj0jRoxAu3btULt2bTRv3hzTpk3DtWvXcPXqVR31jF5VeXl5WLlyJYYNGwaZTIaEhATk5+ejS5cuUkzDhg3h4uKC+Ph4tdcGBgbCxsYGrVq1wvLlyyGE0Hj7mBAREVVBW7ZsQYsWLfDee+/Bzs4OzZo1w6+//iqV5+bmAgCMjY2lbQYGBlAoFNi/f3+Jdebk5CA8PByurq5wdnau2A6Q3tm0aRMyMjIwZMgQAEBqairkcjksLS3V4mrWrInU1FRpfcqUKVi7di2io6PRp08ffPLJJ1iwYIHG28eEiIioCvr777+xePFi1KtXDzt27MCoUaMwevRoREZGAvj/37RDQkJw79495OXl4fvvv8f169dx69YttboWLVoEc3NzmJubY/v27YiOjoZcLtdFt+gVFhYWhq5du8LR0fGFXvftt9+ibdu2aNasGSZMmIDx48dj9uzZGm8fEyIioiqosLAQzZs3x4wZM9CsWTOMGDECw4cPx5IlSwAA1apVw4YNG3Dx4kVYWVnB1NQUe/bsQdeuXWFgoP6nf8CAAThx4gTi4uJQv3599O3bF48ePdJFt+gV9c8//2DXrl346KOPpG329vbIy8tDRkaGWmxaWtozrxfy9vbG9evXpVFQTWFCRERUBTk4OMDDw0Ntm7u7O1JSUqR1Ly8vJCYmIiMjA7du3UJUVBTu3LmDOnXqqL3OwsIC9erVQ7t27bB+/XpcuHABGzdu1Eo/SD+Eh4fDzs4O/v7+0jYvLy9Uq1YNu3fvlrYlJSUhJSUFKpWq1LoSExNRo0YNKBQKjbaRd5kREVVBbdu2RVJSktq2ixcvolatWsViLSwsADy50PrYsWOYOnVqqfUKISCE0Pi3b9JfhYWFCA8Px+DBg2Fk9P/TDgsLCwQEBCA4OBhWVlZQKpX49NNPoVKppDvM/vzzT6SlpaF169YwNjZGdHQ0ZsyYgS+++ELj7WRCRERUBY0dOxZt2rTBjBkz0LdvXxw5cgRLly7F0qVLpZh169bB1tYWLi4uOH36ND777DP06tULPj4+AJ5ch7RmzRr4+PjA1tYW169fx8yZM2FiYoK3335bV12jV8yuXbuQkpKCYcOGFSubN28eDAwM0KdPH+Tm5sLX1xeLFi2SyqtVq4aFCxdi7NixEELAzc0Nc+fOxfDhwzXeTiZERERVUMuWLbFx40aEhIRgypQpcHV1xfz58zFgwAAp5tatWwgODkZaWhocHBwwaNAgfPvtt1K5sbEx9u3bh/nz5+PevXuoWbMm2rVrh4MHD8LOzk4X3aJXkI+PT6m3yRsbG2PhwoVYuHBhieV+fn7w8/OryOZJmBAREVVR3bp1Q7du3UotHz16NEaPHl1quaOjI/7666+KaBpRlcOEiIiIiDQqZUpjre7PZeLpl66DCRERURXiNW6FVveXMHuQVvdHpCu87Z5ID02ePLnY7NENGzYEAFy9erXE2aVlMhnWrVtXrK47d+7AyckJMpms2PNEiIiqCo4QEempRo0aYdeuXdJ60e2wzs7OxZ5kvHTpUsyePRtdu3YtVk9AQAA8PT1x48aNim0wEVEF4ggRkZ4yMjKCvb29tNjY2AAADA0N1bbb29tj48aN6Nu3L8zNzdXqWLx4MTIyMirkmSDAs0eyAKBDhw7FykeOHCmV37lzB35+fnB0dIRCoYCzszOCgoKQlZVVIe0loqqLI0REeurSpUtwdHSEsbExVCoVQkND4eLiUiwuISEBiYmJxW6LPXfuHKZMmYLDhw/j77//rrB2ljaSVWT48OGYMmWKtG5qair9bGBggJ49e2LatGmwtbXF5cuXERgYiLt372L16tUV1mYiqnqYEBHpIW9vb0RERKBBgwa4desWvvvuO7z55ps4c+YMqlevrhYbFhYGd3d3tGnTRtqWm5uL999/H7Nnz4aLi0uFJkRFI1mlMTU1LbW8Ro0aGDVqlLReq1YtfPLJJxUyMSQRVW08ZUakh7p27Yr33nsPnp6e8PX1xV9//YWMjAysXbtWLe7hw4dYvXo1AgIC1LaHhITA3d0dAwcOrPC2Fo1k1alTBwMGDFCbqwsAVq1aBRsbG7z++usICQnBgwcPSq3r5s2b2LBhA9q3b1/RzSaiKoYJERHB0tIS9evXx+XLl9W2r1+/Hg8ePMCgQeq3XsfExGDdunUwMjKCkZEROnfuDACwsbHBpEmTNNauopGsqKgoLF68GMnJyXjzzTdx//59AMAHH3yAlStXYs+ePQgJCcH//ve/EpO0999/H6ampnjttdegVCqxbNkyjbWRiF4NPGVGRMjOzsaVK1fw4Ycfqm0PCwtDjx49YGtrq7b9jz/+wMOHD6X1o0ePYtiwYdi3bx/q1q2rsXY9fVebp6cnvL29UatWLaxduxYBAQEYMWKEVN64cWM4ODigc+fOuHLlilo75s2bh0mTJuHixYsICQlBcHCw2nxJRERMiIj00BdffIHu3bujVq1auHnzJiZNmgRDQ0O8//77Uszly5exd+/eEqd2+G/S8++//wIA3N3dYWlpWWHtLm0kq4i3tzeAJ21/uo1Fd8s1bNgQVlZWePPNN/Htt9/CwcGhwtpKRFULT5kR6aHr16/j/fffR4MGDdC3b19YW1vj0KFDaiNBy5cvh5OTkzQzemVQNJJVWiKTmJgIAM9MdAoLCwE8uTCciKgIR4iI9NDvv/9eZsyMGTMwY8aM56qvQ4cOpc5m/TKeNZJ15coVrF69Gm+//Tasra1x6tQpjB07Fu3atYOnpycA4K+//kJaWhpatmwJc3NznD17FuPGjUPbtm1Ru3ZtjbeXiKouJkREVGkVjWTduXMHtra2eOONN6SRrEePHmHXrl2YP38+cnJy4OzsjD59+uCbb76RXm9iYoJff/0VY8eORW5uLpydndG7d298+eWXOuwVEVVGTIiI9EhVm4H6WSNZzs7OiIuLe+brO3bsiIMHD75UG4hIP/AaIiIiItJ7HCEiokrHa9wKre4vYfagsoOI6JXGESIiIiLSe5U6ISprputHjx4hMDAQ1tbWMDc3R58+fZCWlqZWR0pKCvz9/WFqago7OzuMGzcOjx8/1nZXiIiIqBKr9KfMnjXT9dixY7Ft2zasW7cOFhYWCAoKQu/evXHgwAEAQEFBAfz9/WFvb4+DBw/i1q1bGDRoEKpVq/bctxMTERHRq6/SJ0SlzXSdmZmJsLAwrF69Gp06dQIAhIeHw93dHYcOHULr1q2xc+dOnDt3Drt27ULNmjXRtGlTTJ06FRMmTMDkyZMhl8u13R0iIiKqhCr1KTOg9JmuExISkJ+fjy5dukixDRs2hIuLC+Lj4wEA8fHxaNy4MWrWrCnF+Pr6IisrC2fPni11n7m5ucjKylJbiIiI6NVVqROiZ810nZqaCrlcXmzepJo1ayI1NRUAkJqaqpYMFZUXlZUmNDQUFhYW0uLs7KzZjhEREVGlUqlPmT1rpmsTE5MK22/RbNhFsrKymBQRERG9wir1CNF/PT3Ttb29PfLy8pCRkaEWk5aWJl1zZG9vX+yus6L1kq5LKqJQKKBUKtUWIiIienVVqYTo6Zmuvby8UK1aNezevVsqT0pKQkpKClQqFQBApVLh9OnTSE9Pl2Kio6OhVCrh4eGh9fYTERFR5VSpT5k9a6ZrCwsLBAQEIDg4GFZWVlAqlfj000+hUqnQunVrAICPjw88PDzw4YcfYtasWUhNTcU333yDwMBAKBQKHfeOiIiIKotKnRA9a6ZrAJg3bx4MDAzQp08f5ObmwtfXF4sWLZJeb2hoiK1bt2LUqFFQqVQwMzPD4MGDMWXKFF11iYiIiCqhSp0QPWumawAwNjbGwoULsXDhwlJjatWqhb/++kvTTSMiIqJXSJW6hoiIiIioIjAhIiIiIr3HhIiIiIj0HhMiIiIqU2hoKFq2bInq1avDzs4OvXr1QlJSklrMlStX8M4778DW1hZKpRJ9+/Yt9iy4u3fvYsCAAVAqlbC0tERAQACys7O12RWiEjEhIiKiMsXFxSEwMBCHDh1CdHQ08vPz4ePjg5ycHABATk4OfHx8IJPJEBMTgwMHDiAvLw/du3dHYWGhVM+AAQNw9uxZREdHY+vWrdi7dy9GjBihq24RSZgQERH9n7JGQa5evQqZTFbism7dOgBAREREqTFPPyS2qomKisKQIUPQqFEjNGnSBBEREUhJSUFCQgIA4MCBA7h69SoiIiLQuHFjNG7cGJGRkTh27BhiYmIAAOfPn0dUVBSWLVsGb29vvPHGG1iwYAF+//133Lx5U2Nt1cTv8eTJk3j//ffh7OwMExMTuLu748cff9RYG6nyYUJERPR/yhoFcXZ2xq1bt9SW7777Dubm5tLci/369SsW4+vri/bt28POzk6X3dOozMxMAICVlRUAIDc3FzKZTO2ht8bGxjAwMMD+/fsBAPHx8bC0tESLFi2kmC5dusDAwACHDx/WWNs08XtMSEiAnZ0dVq5cibNnz+Lrr79GSEgIfv75Z421kyqXSv0cIiIibYqKilJbj4iIgJ2dHRISEtCuXTsYGhoWmwdx48aN6Nu3L8zNzQEAJiYmapNP3759GzExMQgLC6v4DmhJYWEhxowZg7Zt2+L1118HALRu3RpmZmaYMGECZsyYASEEvvzySxQUFODWrVsAgNTU1GJJoZGREaysrJCamqqx9mni9zhs2DC18jp16iA+Ph4bNmxAUFCQxtpKlQdHiIiISvHfUZD/SkhIQGJiIgICAkqtY8WKFTA1NcW7775bIW3UhcDAQJw5c0bt4bm2trZYt24d/vzzT5ibm8PCwgIZGRlo3rw5DAx0+69GE7/HonpKq4OqPo4QERGVoKRRkP8KCwuDu7s72rRpU2o9YWFh+OCDD9RGjaqyoKAg6WJoJycntTIfHx9cuXIF//77L4yMjGBpaQl7e3vUqVMHAGBvb1/sOqrHjx/j7t27xUZsNEVTv8eDBw9izZo12LZtW4W0k3SPI0RERCUoaRTkaQ8fPsTq1aufOaoQHx+P8+fPlznyUBUIIRAUFISNGzciJiYGrq6upcba2NjA0tISMTExSE9PR48ePQAAKpUKGRkZ0oXYABATE4PCwkJ4e3tXSLs18Xs8c+YMevbsiUmTJsHHx6dC2km6xxEiIqL/eNYoSJH169fjwYMHGDRoUKn1LFu2DE2bNoWXl1dFNVVrAgMDsXr1amzevBnVq1eXrvmxsLCQRr/Cw8Ph7u4OW1tbxMfH47PPPsPYsWPRoEEDAIC7uzv8/PwwfPhwLFmyBPn5+QgKCkL//v3h6Oio8TZr4vd47tw5dO7cGSNGjMA333yj8TZS5cGEiIjo/wgh8Omnn2Ljxo2IjY195ihIWFgYevToAVtb2xLLs7OzsXbtWoSGhlZUc7Vq8eLFAIAOHTqobQ8PD8eQIUMAAElJSQgJCcHdu3dRu3ZtfP311xg7dqxa/KpVqxAUFITOnTvDwMAAffr0wU8//aTRtmrq93j27Fl06tQJgwcPxvTp0zXaRqp8mBAREf2f5xkFAYDLly9j7969+Ouvv0qta82aNXj8+DEGDhxY4e3WBiFEmTEzZ87EzJkznxljZWWF1atXa6pZJdLE7/HMmTPo1KkTfH19ERwcLNVhaGhYahJMVRuvISIi+j+LFy9GZmYmOnToAAcHB2lZs2aNWtzy5cvh5OT0zOtJwsLC0Lt3b1haWlZwq+m/NPF7XL9+PW7fvo2VK1eq1dGyZUttdYO0jCNERET/53lGQQBgxowZmDFjxjNjDh48qIkm6VzKlMZa36fLxNMv9XpN/B4nT56MyZMnv1Q7qGrhCBERERHpPY4Qkdbs3bsXs2fPRkJCAm7duoWNGzeiV69eajHnz5/HhAkTEBcXh8ePH8PDwwN//PEHXFxccPfuXUyaNAk7d+5ESkoKbG1t0atXL0ydOhUWFha66RS9EqriKAgV5zVuhVb3lzC79DsMqerhCBFpTU5ODpo0aYKFCxeWWH7lyhW88cYbaNiwIWJjY3Hq1Cl8++23MDY2BgDcvHkTN2/exA8//IAzZ84gIiICUVFRr8QzXoioatu7dy+6d+8OR0dHyGQybNq0Sa18yJAhxSaS9fPzU4upXbt2sZiyLlInzeEIEWlN165dpYkTS/L111/j7bffxqxZs6RtdevWlX5+/fXX8ccff6iVTZ8+HQMHDsTjx49hZMS3MxHpRtEXvmHDhqF3794lxvj5+SE8PFxaf3oi3CJTpkzB8OHDpfXq1atrvrFUIo4QUaVQWFiIbdu2oX79+vD19YWdnR28vb2Lfcv6r8zMTCiVyhKTobK+sU2ePBkNGzaEmZkZatSogS5dupQ643Zubi6aNm0KmUyGxMTEcvaSiF5VXbt2xbRp0/DOO++UGqNQKGBvby8tNWrUKBZTvXp1tRgzM7OKbDY9hQkRVQrp6enIzs7GzJkz4efnh507d+Kdd95B7969ERcXV+Jr/v33X0ydOhUjRowosbysU3T169fHzz//jNOnT2P//v2oXbs2fHx8cPv27WKx48ePr5An6RKR/oiNjYWdnR0aNGiAUaNG4c6dO8ViZs6cCWtrazRr1gyzZ8/G48ePddBS/cRzDFQpFBYWAgB69uwpPdm2adOmOHjwIJYsWYL27durxWdlZcHf3x8eHh6l3hpb1im6Dz74QG197ty5CAsLw6lTp9C5c2dp+/bt27Fz50788ccf2L59e3m6R0R6zs/PD71794arqyuuXLmCr776Cl27dkV8fDwMDQ0BAKNHj0bz5s1hZWWFgwcPIiQkBLdu3cLcuXN13Hr9wISIKgUbGxsYGRnBw8NDbbu7uzv279+vtu3+/fvw8/ND9erVsXHjRlSrVu2l95+Xl4elS5fCwsICTZo0kbanpaVh+PDh2LRpE0xNTV96P0Skn/r37y/93LhxY3h6eqJu3bqIjY2VvoAFBwdLMZ6enpDL5fj4448RGhpa4vVGpFk8ZUaVglwuR8uWLZGUlKS2/eLFi6hVq5a0npWVBR8fH8jlcmzZskW6A628tm7dCnNzcxgbG2PevHmIjo6GjY0NgCcPdxsyZAhGjhyJFi1avNR+iIieVqdOHdjY2ODy5culxnh7e+Px48e4evWq9hqmxzhCRFqTnZ2t9uFPTk5GYmIirKys4OLignHjxqFfv35o164dOnbsiKioKPz555+IjY0F8P+ToQcPHmDlypXIyspCVlYWAMDW1lYadn4RHTt2RGJiIv7991/8+uuv6Nu3Lw4fPgw7OzssWLAA9+/fR0hIiEb6T0RU5Pr167hz5w4cHBxKjUlMTISBgQHs7Oy02DL9xYSItObYsWPo2LGjtF40PDx48GBERETgnXfewZIlSxAaGorRo0ejQYMG+OOPP/DGG28AAI4fPy7dBebm5qZWd3JyMmrXrv3CbTIzM4Obmxvc3NzQunVr1KtXD2FhYQgJCUFMTAzi4+OLDVW3aNECAwYMQGRk5Avvj4heTc/6wmdlZYXvvvsOffr0gb29Pa5cuYLx48fDzc0Nvr6+AID4+HgcPnwYHTt2RPXq1REfH4+xY8di4MCBJd6NRprHhIi0pkOHDmXOMTRs2DAMGzas3K9/WYWFhcjNzQUA/PTTT5g2bZpUdvPmTfj6+mLNmjXw9vau0HYQUdXyrC98ixcvxqlTpxAZGYmMjAw4OjrCx8cHU6dOlb5wKRQK/P7775g8eTJyc3Ph6uqKsWPHql1XRBWLCRG9sp71jc3a2hrTp09Hjx494ODggH///RcLFy7EjRs38N577wEAXFxc1OozNzcH8OSBkE5OTtrrCBFVemV9YduxY8czX9+8eXMcOnRI082iF8CEiCqUtucWAv7//ELP+sa2ZMkSXLhwAZGRkfj3339hbW2Nli1bYt++fWjUqJHW20xERLrFhIheWWV9Y9uwYcML1Ve7du0KP2VHRFWHticF5oTAFYu33RMREZHe4wgRvXL4rY2IiF4UR4iIiIhI7zEhIiIiIr3HhIiIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO8xISIiIiK9p1cJ0cKFC1G7dm0YGxvD29sbR44c0XWTiIiIqBLQm4RozZo1CA4OxqRJk3D8+HE0adIEvr6+SE9P13XTiIiISMf0JiGaO3cuhg8fjqFDh8LDwwNLliyBqakpli9fruumERERkY7pxWz3eXl5SEhIQEhIiLTNwMAAXbp0QXx8fLH43Nxc5ObmSuuZmZkAgKysrBLrL8h9qOEWl+1+tQKt7q+0vpeFx6Z0PDal0/ax0fZxAXhsnoXHpmTlPS6A/h6bou1CiLIrEXrgxo0bAoA4ePCg2vZx48aJVq1aFYufNGmSAMCFCxcuXLhweQWWa9eulZkr6MUI0YsKCQlBcHCwtF5YWIi7d+/C2toaMplMhy17IisrC87Ozrh27RqUSqWum1Op8NiUjsemZDwupeOxKR2PTekq07ERQuD+/ftwdHQsM1YvEiIbGxsYGhoiLS1NbXtaWhrs7e2LxSsUCigUCrVtlpaWFdnEclEqlTp/s1VWPDal47EpGY9L6XhsSsdjU7rKcmwsLCyeK04vLqqWy+Xw8vLC7t27pW2FhYXYvXs3VCqVDltGRERElYFejBABQHBwMAYPHowWLVqgVatWmD9/PnJycjB06FBdN42IiIh0TG8Son79+uH27duYOHEiUlNT0bRpU0RFRaFmzZq6btoLUygUmDRpUrHTesRj8yw8NiXjcSkdj03peGxKV1WPjUyI57kXjYiIiOjVpRfXEBERERE9CxMiIiIi0ntMiIiIiEjvMSEiIiIivceESItCQ0PRsmVLVK9eHXZ2dujVqxeSkpKKxcXHx6NTp04wMzODUqlEu3bt8PBh6fPQ3LlzB35+fnB0dIRCoYCzszOCgoLU5naJjY2FTCYrtqSmplZIXzVl4cKFqF27NoyNjeHt7Y0jR448M3769Olo06YNTE1NS3yYZkRERInHQSaTIT09vYJ68XL27t2L7t27w9HRETKZDJs2bVIrz87ORlBQEJycnGBiYiJNXvwsV69eRUBAAFxdXWFiYoK6deti0qRJyMvLU4sp6TgdOnSoIrr5wiZPnlysbQ0bNpTKly5dig4dOkCpVEImkyEjI+O56i2pz7///rtUXhU/SzNnzoRMJsOYMWPUtr/o3xqg7OMDPDlGzZs3h0KhgJubGyIiIjTcI80r6/3UoUOHYuUjR47UYYs1b/HixfD09JQeqKhSqbB9+3a1mPK8Z4rcuXMHTk5OxT6PleUzpTe33VcGcXFxCAwMRMuWLfH48WN89dVX8PHxwblz52BmZgbgyZvNz88PISEhWLBgAYyMjHDy5EkYGJSeuxoYGKBnz56YNm0abG1tcfnyZQQGBuLu3btYvXq1WmxSUpLak0Pt7OwqprMasGbNGgQHB2PJkiXw9vbG/Pnz4evri6SkpFLbnZeXh/feew8qlQphYWHFyvv16wc/Pz+1bUOGDMGjR48q7bHIyclBkyZNMGzYMPTu3btYeXBwMGJiYrBy5UrUrl0bO3fuxCeffAJHR0f06NGjxDovXLiAwsJC/PLLL3Bzc8OZM2cwfPhw5OTk4IcfflCL3bVrFxo1aiStW1tba7aDL6FRo0bYtWuXtG5k9P//pD148AB+fn7S5+lFhIeHq71PSkquq8pn6ejRo/jll1/g6emptr08f2uKPOv4JCcnw9/fHyNHjsSqVauwe/dufPTRR3BwcICvr6/G+lURnvV+AoDhw4djypQp0rqpqWmpdcXGxmLIkCG4evWqxttZUZycnDBz5kzUq1cPQghERkaiZ8+eOHHiBBo1avRS7xkACAgIgKenJ27cuFFiuc4/UxqZPZXKJT09XQAQcXFx0jZvb2/xzTffvHTdP/74o3BycpLW9+zZIwCIe/fuvXTd2tKqVSsRGBgorRcUFAhHR0cRGhpa5mvDw8OFhYVFmXHp6emiWrVqYsWKFS/TVK0BIDZu3Ki2rVGjRmLKlClq25o3by6+/vrrF6p71qxZwtXVVVpPTk4WAMSJEyfK29wKNWnSJNGkSZMy4170vV/SMX6Z+nTp/v37ol69eiI6Olq0b99efPbZZ1JZef/WlHV8xo8fLxo1aqS2rV+/fsLX1/eF96VNZb2f/nv8yrJnzx5Rq1atl26XrtWoUUMsW7ZMCPFy/58WLVok2rdvL3bv3l3s81NZPlM8ZaZDmZmZAAArKysAQHp6Og4fPgw7Ozu0adMGNWvWRPv27bF///4XqvfmzZvYsGED2rdvX6ysadOmcHBwwFtvvYUDBw68fCcqSF5eHhISEtClSxdpm4GBAbp06YL4+HiN7WfFihUwNTXFu+++q7E6ta1NmzbYsmULbty4ASEE9uzZg4sXL8LHx+eF6snMzJTei0/r0aMH7Ozs8MYbb2DLli2aarZGXLp0CY6OjqhTpw4GDBiAlJQUjdQbGBgIGxsbtGrVCsuXL4co4XFtVeGzFBgYCH9/f7XPEfDyf2uedXzi4+OL7c/X11ejn9uKUtb7adWqVbCxscHrr7+OkJAQPHjwQEctrXgFBQX4/fffkZOTA5VK9VLvmXPnzmHKlClYsWLFM0eTdP6Z0mk6pscKCgqEv7+/aNu2rbQtPj5eABBWVlZi+fLl4vjx42LMmDFCLpeLixcvllln//79hYmJiQAgunfvLh4+fCiVXbhwQSxZskQcO3ZMHDhwQAwdOlQYGRmJhISECunfy7px44YAIA4ePKi2fdy4caJVq1Zlvv55R4jc3d3FqFGjyttMrUMJ384fPXokBg0aJAAIIyMjIZfLRWRk5AvVe+nSJaFUKsXSpUulbbdv3xZz5swRhw4dEkeOHBETJkwQMplMbN68WRNdeWl//fWXWLt2rTh58qSIiooSKpVKuLi4iKysLLW4F/32OWXKFLF//35x/PhxMXPmTKFQKMSPP/4olVeVz9Jvv/0mXn/9denvwNMjHC/zt6as41OvXj0xY8YMtdds27ZNABAPHjzQbCc1qKz30y+//CKioqLEqVOnxMqVK8Vrr70m3nnnnVLrq6ojRKdOnRJmZmbC0NBQWFhYiG3btgkhyv+eefTokfD09BT/+9//hBAlfx4ry2eKCZGOjBw5UtSqVUtcu3ZN2nbgwAEBQISEhKjFNm7cWHz55ZdCCCH8/PyEmZmZMDMzEx4eHmpxt27dEufPnxebN28WHh4eZf6jb9eunRg4cKCGeqRZZSVEH3/8sXQczMzMir3+eRKigwcPCgDi2LFjmmx6hSopIZo9e7aoX7++2LJlizh58qRYsGCBMDc3F9HR0UIIUeaxun79uqhbt64ICAgoc/8ffviheOONNzTSF027d++eUCqV0vB+kdISomd9lp727bffqp1+Lkll+yylpKQIOzs7cfLkSWnb0wnRy/6tedp/j09VTYj+q7T3U5GiUz+XL1+Wtj39OTM2NhYymUxt28cff6yt5pdbbm6uuHTpkjh27Jj48ssvhY2NjTh79my53zNjx44V/fr1k+Kf9wuKLj5TvKhaB4KCgrB161bs3bsXTk5O0nYHBwcAgIeHh1q8u7u7NHS7bNky6Yr+atWqqcXZ29vD3t4eDRs2hJWVFd588018++23Ur3/1apVqxc+HactNjY2MDQ0RFpamtr2tLQ02NvbY8qUKfjiiy9eah/Lli1D06ZN4eXl9VL16NLDhw/x1VdfYePGjfD39wcAeHp6IjExET/88AO6dOnyzGN18+ZNdOzYEW3atMHSpUvL3J+3tzeio6M12gdNsbS0RP369XH58uXnin/WZ+lp3t7emDp1KnJzc0udm6myfZYSEhKQnp6O5s2bS9sKCgqwd+9e/Pzzz9LdreX9W/O0/x4fe3v7Ej+3SqUSJiYmGumfNpT1fvL29gYAXL58GXXr1gUAJCYmSuWHDx/GhAkTEBsbK217+oLhykoul8PNzQ0A4OXlhaNHj+LHH3/El19+CeDF3zMxMTE4ffo01q9fDwDS6VUbGxt8/fXX+O6770pshy4+U0yItEgIgU8//RQbN25EbGwsXF1d1cpr164NR0fHYrfiX7x4EV27dgUAvPbaa8+1r8LCQgBAbm5uqTGJiYmlJku6JpfL4eXlhd27d6NXr14AnvRp9+7dCAoKgp2d3UvdgZCdnY21a9ciNDRUQy3Wjfz8fOTn5xc7L29oaCi9B0o7Vjdu3EDHjh3h5eWF8PDw57pTpDK/Z7Kzs3HlyhV8+OGHzxX/vJ+lxMRE1KhR45kTVVa249K5c2ecPn1abdvQoUPRsGFDTJgwAXXq1NHY35r/Hh+VSoW//vpLLSY6Ohoqlaq83dGJst5PRcnP07/3okQCAK5fvw4jIyO1bVVRYWEhcnNzy/3/6Y8//lC7Lf/o0aMYNmwY9u3bJyWSJdHJZ0qr41F6btSoUcLCwkLExsaKW7duScvTw8jz5s0TSqVSrFu3Tly6dEl88803wtjYWG1Y9r+2bdsmli9fLk6fPi2Sk5PF1q1bhbu7u9r1SfPmzRObNm0Sly5dEqdPnxafffaZMDAwELt27arQPr+M33//XSgUChERESHOnTsnRowYISwtLUVqamqpr/nnn3/EiRMnxHfffSfMzc3FiRMnxIkTJ8T9+/fV4pYtWyaMjY11flfD87h//77UDwBi7ty54sSJE+Kff/4RQjw5FdKoUSOxZ88e8ffff4vw8HBhbGwsFi1aVGqd169fF25ubqJz587i+vXrau/HIhEREWL16tXi/Pnz4vz582L69OnCwMBALF++vML7/Dw+//xzERsbK5KTk8WBAwdEly5dhI2NjUhPTxdCPDmFfOLECfHrr78KAGLv3r3ixIkT4s6dO6XWuWXLFvHrr7+K06dPi0uXLolFixYJU1NTMXHiRCmmKn6WhCh+l1R5/tY8z/H5+++/hampqRg3bpw4f/68WLhwoTA0NBRRUVEV2b2X9qz30+XLl8WUKVPEsWPHRHJysti8ebOoU6eOaNeuXan1VcVriL788ksRFxcnkpOTxalTp8SXX34pZDKZ2LlzpxCifO+Z/yrplFll+UwxIdIiACUu4eHhanGhoaHCyclJmJqaCpVKJfbt2/fMemNiYoRKpRIWFhbC2NhY1KtXT0yYMEHtDff999+LunXrCmNjY2FlZSU6dOggYmJiKqCXmrVgwQLh4uIi5HK5aNWqlTh06NAz4wcPHlziMd6zZ49anEqlEh988EEFtlxziv6A/HcZPHiwEOLJP/4hQ4YIR0dHYWxsLBo0aCDmzJkjCgsLS60zPDy81PdjkYiICOHu7i5MTU2FUqkUrVq1EuvWravo7j63fv36CQcHByGXy8Vrr70m+vXrp/aHedKkSc/1eXva9u3bRdOmTYW5ubkwMzMTTZo0EUuWLBEFBQVSTFX9LJV02/iL/q15nuMjxJP3bNOmTYVcLhd16tR55jGvLJ71fkpJSRHt2rUTVlZWQqFQCDc3NzFu3DiRmZlZan1VMSEaNmyYqFWrlpDL5cLW1lZ07txZSoaKvOh75r9KSogqy2dKJkQJ95MSERER6RE+h4iIiIj0HhMiIiIi0ntMiIiIiEjvMSEiIiIivceEiIiIiPQeEyIiIiLSe0yIiIiISO8xISIiIiK9x4SIiIiI9B4TIiLSqfj4eBgaGsLf31+r+83Ly8Ps2bPRvHlzmJmZwcLCAk2aNME333yDmzdvarUtRKR7nLqDiHTqo48+grm5OcLCwpCUlARHR8cK32dubi58fHxw6tQpfPfdd2jbti1sbW2RnJyM3377DTVq1EBoaGiJr83Ly4NcLq/wNhKRdnGEiIh0Jjs7G2vWrMGoUaPg7++PiIiIYjFbtmxBvXr1YGxsjI4dOyIyMhIymQwZGRlSzP79+/Hmm2/CxMQEzs7OGD16NHJyckrd77x587B//37ExMRg9OjR8PLygouLC9q3b48lS5ZgxowZUmyHDh0QFBSEMWPGwMbGBr6+vgCAuLg4tGrVCgqFAg4ODvjyyy/x+PFj6XW1a9fG/Pnz1fbbtGlTTJ48WVqXyWRYvHgxunbtChMTE9SpUwfr169/sYNIRBrBhIiIdGbt2rVo2LAhGjRogIEDB2L58uV4etA6OTkZ7777Lnr16oWTJ0/i448/xtdff61Wx5UrV+Dn54c+ffrg1KlTWLNmDfbv34+goKBS9/vbb7/hrbfeQrNmzUosl8lkauuRkZGQy+U4cOAAlixZghs3buDtt99Gy5YtcfLkSSxevBhhYWGYNm3aCx+Db7/9Fn369MHJkycxYMAA9O/fH+fPn3/heojoJQkiIh1p06aNmD9/vhBCiPz8fGFjYyP27NkjlU+YMEG8/vrraq/5+uuvBQBx7949IYQQAQEBYsSIEWox+/btEwYGBuLhw4cl7tfY2FiMHj1abVuvXr2EmZmZMDMzEyqVStrevn170axZM7XYr776SjRo0EAUFhZK2xYuXCjMzc1FQUGBEEKIWrVqiXnz5qm9rkmTJmLSpEnSOgAxcuRItRhvb28xatSoEttNRBWHI0REpBNJSUk4cuQI3n//fQCAkZER+vXrh7CwMLWYli1bqr2uVatWausnT55EREQEzM3NpcXX1xeFhYVITk5+7vYsWrQIiYmJGDZsGB48eKBW5uXlpbZ+/vx5qFQqtZGktm3bIjs7G9evX3/ufQKASqUqts4RIiLtM9J1A4hIP4WFheHx48dqF1ELIaBQKPDzzz/DwsLiuerJzs7Gxx9/jNGjRxcrc3FxKfE19erVQ1JSkto2BwcHAICVlVWxeDMzs+dqy9MMDAzUTv8BQH5+/gvXQ0TawREiItK6x48fY8WKFZgzZw4SExOl5eTJk3B0dMRvv/0GAGjQoAGOHTum9tqjR4+qrTdv3hznzp2Dm5tbsaW0u8Hef/99REdH48SJE+Vqv7u7O+Lj49USngMHDqB69epwcnICANja2uLWrVtSeVZWVokjVocOHSq27u7uXq52EdFL0PEpOyLSQxs3bhRyuVxkZGQUKxs/frxo0aKFEEKIv//+W1SrVk2MHz9eJCUliTVr1ggnJycBQHrtyZMnhYmJiQgMDBQnTpwQFy9eFJs2bRKBgYGl7v/hw4eibdu2okaNGmL+/PkiISFB/P333yIqKkq0atVKNG/eXIpt3769+Oyzz9Ref/36dWFqaioCAwPF+fPnxaZNm4SNjY3a9UFffvmlsLe3F3v37hWnTp0SvXr1Eubm5sWuIbKxsRFhYWEiKSlJTJw4URgYGIizZ8+W46gS0ctgQkREWtetWzfx9ttvl1h2+PBhAUCcPHlSCCHE5s2bhZubm1AoFKJDhw5i8eLFAoDaBdNHjhwRb731ljA3NxdmZmbC09NTTJ8+/ZltePTokZg5c6Zo0qSJMDExEQqFQjRs2FCMHTtWpKSkSHElJURCCBEbGytatmwp5HK5sLe3FxMmTBD5+flSeWZmpujXr59QKpXC2dlZRERElHhR9cKFC8Vbb70lFAqFqF27tlizZs3zHEIi0jA+mJGIqpTp06djyZIluHbtmq6b8tJkMhk2btyIXr166bopRHqPF1UTUaW2aNEitGzZEtbW1jhw4ABmz579zGcMERGVBxMiIqrULl26hGnTpuHu3btwcXHB559/jpCQEF03i4heMTxlRkRERHqPt90TERGR3mNCRERERHqPCRERERHpPSZEREREpPeYEBEREZHeY0JEREREeo8JEREREek9JkRERESk9/4fSSPeqdOfciQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Total Amount vs Age Group\n",
        "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
        "\n",
        "sns.barplot(x = 'Age Group',y= 'Amount' ,data = sales_age)\n",
        "\n",
        "#From above graphs we can see that most of the buyers are of age group between 26-35 yrs female"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "y69eQFyj7XmJ",
        "outputId": "d648266c-823f-4c6d-b3f8-397a843d759e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age Group', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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pe/fueumll9SpU6cS+xYUFKigoMCxnpubW64aAQBA9VbqcJOWlibpp5mbjIwMeXt7O7Z5e3srIiJCTz75ZJkLKSoq0uTJk9WvXz917tzZZb927dppxYoVCg8PV05Ojl5++WX17dtXhw8fVrNmzYr1j4+P16xZs8pcFwAAqFlspqS7g29izJgxWrhwoQICAiq0kAkTJmjr1q3as2dPiSHFlWvXrqlDhw4aNWqUnn/++WLbS5q5CQsLU05OTonXEDl1VdkuoAZJnRvj6RIAAHBLbm6uAgMDXf77fSO377lZuXJlmQtzJTY2Vps3b9bu3bvdCjaSVKdOHXXr1k0nTpwocbuPj498fHwqokwAAFADuB1u8vPzNWfOHCUmJur8+fMqKipy2v7111+X+ljGGP3lL3/R+vXrlZSU5PbTVtJP9/tkZGToN7/5jdv7AgAA63E73IwbN067du3Sn/70J4WEhJT45FRpTZw4UatXr9bGjRtVv359ZWVlSZICAwNVt25dSVJMTIyaNm2q+Ph4SdLs2bPVp08ftWnTRhcvXtTcuXN16tQpjRs3rsx1AAAA63A73GzdulUffPCB+vXrV+6TL168WJLUv39/p/aVK1fqoYcekvTTlwZ6ef3763i+//57jR8/XllZWWrQoIEiIyO1b98+dezYsdz1AACAms/tcNOgQYNbPqpdWqW5lzkpKclpff78+Zo/f36FnB8AAFiP299Q/Pzzz+u5557TDz/8UBn1AAAAlIvbMzfz5s3TV199paCgILVs2VJ16tRx2v7ZZ59VWHEAAADucjvcREdHV0IZAAAAFcPtcDNz5szKqAMAAKBCuH3PDQAAQHXm9syNl5fXTb/bprCwsFwFAQAAlIfb4Wb9+vVO69euXVNaWpreeustfqASAAB4nNvhZvjw4cXaHnjgAXXq1Elr167V2LFjK6QwAACAsqiwe2769OmjxMTEijocAABAmVRIuLl8+bL+53/+R02bNq2IwwEAAJRZmX5+4cYbio0xunTpkvz8/PT2229XaHEAAADucjvcLFiwwGndy8tLjRs3Vu/evdWgQYOKqgsAAKBM3A43o0eProw6AAAAKoTb4UaSLl68qOXLl+vo0aOSpE6dOunhhx9WYGBghRYHAADgLrdvKE5JSVHr1q01f/58XbhwQRcuXNArr7yi1q1b86OZAADA49yeuXn88cd1//33680331Tt2j/t/uOPP2rcuHGaPHmydu/eXeFFAgAAlJbb4SYlJcUp2EhS7dq1NW3aNPXo0aNCiwMAAHCX2+EmICBAmZmZat++vVP76dOnVb9+/QorDNVP5NRVni6h0qXOjfF0CQCAcnL7npuRI0dq7NixWrt2rU6fPq3Tp09rzZo1GjdunEaNGlUZNQIAAJSa2zM3L7/8smw2m2JiYvTjjz9KkurUqaMJEyZozpw5FV4gAACAO9wON97e3lq4cKHi4+P11VdfSZJat24tPz+/Ci8OAADAXWX6nhtJ8vPzU5cuXSqyFgAAgHJzO9xcuXJFr776qnbu3Knz58+rqKjIaTvfdQMAADzJ7XAzduxYffTRR3rggQfUq1cvpx/RBAAA8DS3w83mzZu1ZcsW9evXrzLqAQAAKBe3HwVv2rQp32cDAACqLbfDzbx58zR9+nSdOnWqMuoBAAAoF7c/lurRo4euXLmiO+64Q35+fqpTp47T9gsXLlRYcQAAAO5yO9yMGjVKZ8+e1UsvvaSgoCBuKAYAANWK2+Fm3759Sk5OVkRERGXUAwAAUC5u33PTvn17Xb58uUJOHh8fr549e6p+/fpq0qSJoqOjdezYsVvut27dOrVv316+vr7q0qWLtmzZUiH1AACAms/tcDNnzhw98cQTSkpK0nfffafc3FynxR27du3SxIkTtX//fm3fvl3Xrl3Tfffdp/z8fJf77Nu3T6NGjdLYsWOVlpam6OhoRUdH6/PPP3f3UgAAgAXZjDHGnR28vH7KQz+/18YYI5vNpsLCwjIX8+2336pJkybatWuX7r777hL7jBw5Uvn5+dq8ebOjrU+fPuratauWLFlyy3Pk5uYqMDBQOTk5CggIKLY9cuqqMtdfU6TOjSnTfowNAMBTbvXv943cvudm586dLrdlZGS4ezgnOTk5kqSGDRu67JOcnKwpU6Y4tQ0aNEgbNmwo17kBAIA1uB1uoqKinNYvXbqkd999V8uWLVNqaqpiY2PLVEhRUZEmT56sfv36qXPnzi77ZWVlKSgoyKktKChIWVlZJfYvKChQQUGBY93dj84AAEDN4vY9N9ft3r1bo0ePVkhIiF5++WX9+te/1v79+8tcyMSJE/X5559rzZo1ZT5GSeLj4xUYGOhYwsLCKvT4AACgenFr5iYrK0sJCQlavny5cnNz9Z//+Z8qKCjQhg0b1LFjxzIXERsbq82bN2v37t1q1qzZTfsGBwcrOzvbqS07O1vBwcEl9o+Li3P6GCs3N5eAAwCAhZV65mbYsGFq166dDh06pAULFuibb77Rq6++Wq6TG2MUGxur9evXa8eOHWrVqtUt97Hb7UpMTHRq2759u+x2e4n9fXx8FBAQ4LQAAADrKvXMzdatW/XYY49pwoQJatu2bYWcfOLEiVq9erU2btyo+vXrO+6bCQwMVN26dSVJMTExatq0qeLj4yVJkyZNUlRUlObNm6ehQ4dqzZo1SklJ0dKlSyukJgAAULOVeuZmz549unTpkiIjI9W7d2+99tpr+te//lWuky9evFg5OTnq37+/QkJCHMvatWsdfTIzM3Xu3DnHet++fbV69WotXbpUERERev/997Vhw4ab3oQMAAB+OUo9c9OnTx/16dNHCxYs0Nq1a7VixQpNmTJFRUVF2r59u8LCwlS/fn23Tl6ar9hJSkoq1jZixAiNGDHCrXMBAIBfBreflvL399fDDz+sPXv2KCMjQ0888YTmzJmjJk2a6P7776+MGgEAAEqtzI+CS1K7du30t7/9TWfOnNG7775bUTUBAACUWbnCzXW1atVSdHS0Nm3aVBGHAwAAKLMKCTcAAADVBeEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYSm1PFwBYQeTUVZ4uodKlzo3xdAkAUCrM3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEvxaLjZvXu3hg0bptDQUNlsNm3YsOGm/ZOSkmSz2YotWVlZVVMwAACo9jwabvLz8xUREaFFixa5td+xY8d07tw5x9KkSZNKqhAAANQ0tT158iFDhmjIkCFu79ekSRPddtttFV8QAACo8WrkPTddu3ZVSEiIBg4cqL1793q6HAAAUI14dObGXSEhIVqyZIl69OihgoICLVu2TP3799eBAwfUvXv3EvcpKChQQUGBYz03N7eqygUAAB5Qo8JNu3bt1K5dO8d637599dVXX2n+/Pn6+9//XuI+8fHxmjVrVlWVCAAAPKxGfix1o169eunEiRMut8fFxSknJ8exnD59ugqrAwAAVa1GzdyUJD09XSEhIS63+/j4yMfHpworAgAAnuTRcJOXl+c063Ly5Emlp6erYcOGat68ueLi4nT27FmtWrVKkrRgwQK1atVKnTp10pUrV7Rs2TLt2LFDH330kacuAQAAVDMeDTcpKSm65557HOtTpkyRJI0ePVoJCQk6d+6cMjMzHduvXr2qJ554QmfPnpWfn5/Cw8P18ccfOx0DAAD8snk03PTv31/GGJfbExISnNanTZumadOmVXJVAACgJqvxNxQDAADciHADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAsxaPhZvfu3Ro2bJhCQ0Nls9m0YcOGW+6TlJSk7t27y8fHR23atFFCQkKl1wkAAGoOj4ab/Px8RUREaNGiRaXqf/LkSQ0dOlT33HOP0tPTNXnyZI0bN04ffvhhJVcKAABqitqePPmQIUM0ZMiQUvdfsmSJWrVqpXnz5kmSOnTooD179mj+/PkaNGhQZZUJAABqkBp1z01ycrIGDBjg1DZo0CAlJye73KegoEC5ublOCwAAsK4aFW6ysrIUFBTk1BYUFKTc3Fxdvny5xH3i4+MVGBjoWMLCwqqiVAAA4CE1KtyURVxcnHJychzL6dOnPV0SAACoRB6958ZdwcHBys7OdmrLzs5WQECA6tatW+I+Pj4+8vHxqYryAABANVCjZm7sdrsSExOd2rZv3y673e6higAAQHXj0XCTl5en9PR0paenS/rpUe/09HRlZmZK+ukjpZiYGEf/Rx99VF9//bWmTZumL774Qq+//rree+89Pf74454oHwAAVEMeDTcpKSnq1q2bunXrJkmaMmWKunXrpueee06SdO7cOUfQkaRWrVrpgw8+0Pbt2xUREaF58+Zp2bJlPAYOAAAcPHrPTf/+/WWMcbm9pG8f7t+/v9LS0iqxKgAAUJPVqBuKAdQ8kVNXebqESpc6N+bWnQBUmRp1QzEAAMCtEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAICl8Cg4AHiI1R+T5xF5eAozNwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFKqRbhZtGiRWrZsKV9fX/Xu3VuffPKJy74JCQmy2WxOi6+vbxVWCwAAqjOPh5u1a9dqypQpmjlzpj777DNFRERo0KBBOn/+vMt9AgICdO7cOcdy6tSpKqwYAABUZx4PN6+88orGjx+vMWPGqGPHjlqyZIn8/Py0YsUKl/vYbDYFBwc7lqCgoCqsGAAAVGceDTdXr15VamqqBgwY4Gjz8vLSgAEDlJyc7HK/vLw8tWjRQmFhYRo+fLgOHz7ssm9BQYFyc3OdFgAAYF0eDTf/+te/VFhYWGzmJSgoSFlZWSXu065dO61YsUIbN27U22+/raKiIvXt21dnzpwpsX98fLwCAwMdS1hYWIVfBwAAqD48/rGUu+x2u2JiYtS1a1dFRUXpH//4hxo3bqw33nijxP5xcXHKyclxLKdPn67iigEAQFWq7cmTN2rUSLVq1VJ2drZTe3Z2toKDg0t1jDp16qhbt246ceJEidt9fHzk4+NT7loBAEDN4NGZG29vb0VGRioxMdHRVlRUpMTERNnt9lIdo7CwUBkZGQoJCamsMgEAQA3i0ZkbSZoyZYpGjx6tHj16qFevXlqwYIHy8/M1ZswYSVJMTIyaNm2q+Ph4SdLs2bPVp08ftWnTRhcvXtTcuXN16tQpjRs3zpOXAQAAqgmPh5uRI0fq22+/1XPPPaesrCx17dpV27Ztc9xknJmZKS+vf08wff/99xo/fryysrLUoEEDRUZGat++ferYsaOnLgEAAFQjHg83khQbG6vY2NgStyUlJTmtz58/X/Pnz6+CqgAAQE1U456WAgAAuJlqMXMDAMCNIqeu8nQJlSp1boynS7A0Zm4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAICl8PMLAADUIPw0xa0xcwMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACylWoSbRYsWqWXLlvL19VXv3r31ySef3LT/unXr1L59e/n6+qpLly7asmVLFVUKAACqO4+Hm7Vr12rKlCmaOXOmPvvsM0VERGjQoEE6f/58if337dunUaNGaezYsUpLS1N0dLSio6P1+eefV3HlAACgOvJ4uHnllVc0fvx4jRkzRh07dtSSJUvk5+enFStWlNh/4cKFGjx4sKZOnaoOHTro+eefV/fu3fXaa69VceUAAKA68mi4uXr1qlJTUzVgwABHm5eXlwYMGKDk5OQS90lOTnbqL0mDBg1y2R8AAPyy1Pbkyf/1r3+psLBQQUFBTu1BQUH64osvStwnKyurxP5ZWVkl9i8oKFBBQYFjPScnR5KUm5tbYv/Cgsulrr+mcnXtt8LYuMbYuMbYuGb1sSnruEiMzc38Usfmersx5pbH8Gi4qQrx8fGaNWtWsfawsDAPVFM9BL76qKdLqLYYG9cYG9cYm5IxLq4xNq7damwuXbqkwMDAm/bxaLhp1KiRatWqpezsbKf27OxsBQcHl7hPcHCwW/3j4uI0ZcoUx3pRUZEuXLig22+/XTabrZxXUH65ubkKCwvT6dOnFRAQ4OlyqhXGpmSMi2uMjWuMjWuMjWvVaWyMMbp06ZJCQ0Nv2dej4cbb21uRkZFKTExUdHS0pJ/CR2JiomJjY0vcx263KzExUZMnT3a0bd++XXa7vcT+Pj4+8vHxcWq77bbbKqL8ChUQEODxF051xdiUjHFxjbFxjbFxjbFxrbqMza1mbK7z+MdSU6ZM0ejRo9WjRw/16tVLCxYsUH5+vsaMGSNJiomJUdOmTRUfHy9JmjRpkqKiojRv3jwNHTpUa9asUUpKipYuXerJywAAANWEx8PNyJEj9e233+q5555TVlaWunbtqm3btjluGs7MzJSX178f6urbt69Wr16tZ555Rk899ZTatm2rDRs2qHPnzp66BAAAUI14PNxIUmxsrMuPoZKSkoq1jRgxQiNGjKjkqqqGj4+PZs6cWeyjMzA2rjAurjE2rjE2rjE2rtXUsbGZ0jxTBQAAUEN4/BuKAQAAKhLhBgAAWArhBgAAWArhpozi4+PVs2dP1a9fX02aNFF0dLSOHTtWrF9ycrJ+/etfy9/fXwEBAbr77rt1+bLrr87+7rvvNHjwYIWGhsrHx0dhYWGKjY11+jrqpKQk2Wy2Yourn6CoaosXL1Z4eLjjexHsdru2bt3q1MfdcbnRd999p2bNmslms+nixYuO9uo+LpK0e/duDRs2TKGhobLZbNqwYYPT9ry8PMXGxqpZs2aqW7eu48dkb+b//u//NHbsWLVq1Up169ZV69atNXPmTF29etWpT0ljs3///sq4zHKbM2eObDab0/dZSWV73ZR03WvWrHHqk5SUpO7du8vHx0dt2rRRQkJCBV9R2f31r38tVn/79u0d25cuXar+/fsrICCg2HviZm41LjXh/VSSW41X//79i21/9NFfzrcFL1q0SC1btpSvr6969+6tTz755Kb9X3zxRfXt21d+fn4lfkdcQkJCia8Tm82m8+fPV9JV3Fq1eFqqJtq1a5cmTpyonj176scff9RTTz2l++67T0eOHJG/v7+kn/4QDx48WHFxcXr11VdVu3ZtHTx40OnR9p/z8vLS8OHD9cILL6hx48Y6ceKEJk6cqAsXLmj16tVOfY8dO+b0pUpNmjSpnIt1U7NmzTRnzhy1bdtWxhi99dZbGj58uNLS0tSpU6cyjcuNxo4dq/DwcJ09e7bE7dV1XCQpPz9fERERevjhh/Uf//EfxbZPmTJFO3bs0Ntvv62WLVvqo48+0v/7f/9PoaGhuv/++0s85hdffKGioiK98cYbatOmjT7//HONHz9e+fn5evnll536fvzxx+rUqZNj/fbbb6/YC6wAn376qd544w2Fh4c7tZfndbNy5UoNHjzYsX7jH+mTJ09q6NChevTRR/XOO+8oMTFR48aNU0hIiAYNGlRh11UenTp10scff+xYr13733+6f/jhBw0ePNgxNu642bhcV53fT67cbLwkafz48Zo9e7Zj3c/Pz+WxkpKS9NBDD+n//u//KrzOqrZ27VpNmTJFS5YsUe/evbVgwQINGjRIx44dc/nf9erVqxoxYoTsdruWL19ebPvIkSOdXkOS9NBDD+nKlSuefa0YVIjz588bSWbXrl2Ott69e5tnnnmm3MdeuHChadasmWN9586dRpL5/vvvy33sqtKgQQOzbNkyY0z5xuX11183UVFRJjExsdgY1LRxkWTWr1/v1NapUycze/Zsp7bu3bubp59+2q1j/+1vfzOtWrVyrJ88edJIMmlpaWUtt0pcunTJtG3b1mzfvt1ERUWZSZMmObaV9XVT0jjfaNq0aaZTp05ObSNHjjSDBg1y+1yVYebMmSYiIuKW/dx9/d9qXGra++m6W43Xz19Xt7Jz507TokWLctdVHfTq1ctMnDjRsV5YWGhCQ0NNfHz8LfdduXKlCQwMvGW/8+fPmzp16phVq1aVp9Ry42OpCnL918YbNmwoSTp//rwOHDigJk2aqG/fvgoKClJUVJT27Nnj1nG/+eYb/eMf/1BUVFSxbV27dlVISIgGDhyovXv3lv8iKkFhYaHWrFmj/Px82e32co3LkSNHNHv2bK1ateqm/7deE8bFlb59+2rTpk06e/asjDHauXOnvvzyS913331uHScnJ8fxWrzR/fffryZNmuhXv/qVNm3aVFFlV5iJEydq6NChGjBggFN7ed9PEydOVKNGjdSrVy+tWLHC6VeFk5OTi51v0KBBSk5OLv8FVZDjx48rNDRUd9xxhx588EFlZmZWyHFvNi7X1cT3063G65133lGjRo3UuXNnxcXF6YcffvBQpVXn6tWrSk1NdXqte3l5acCAARX6Wl+1apX8/Pz0wAMPVNgxy8Sj0coiCgsLzdChQ02/fv0cbcnJyUaSadiwoVmxYoX57LPPzOTJk423t7f58ssvb3nMP/zhD6Zu3bpGkhk2bJi5fPmyY9sXX3xhlixZYlJSUszevXvNmDFjTO3atU1qamqlXF9ZHDp0yPj7+5tatWqZwMBA88EHHxhjyj4uV65cMeHh4ebvf/+7Mabk/6usCeNyI5Xwf85XrlwxMTExRpKpXbu28fb2Nm+99ZZbxz1+/LgJCAgwS5cudbR9++23Zt68eWb//v3mk08+MdOnTzc2m81s3LixIi6lQrz77rumc+fOjtf6jf+HXZ730+zZs82ePXvMZ599ZubMmWN8fHzMwoULHdvbtm1rXnrpJad9PvjgAyPJ/PDDDxV7kWWwZcsW895775mDBw+abdu2Gbvdbpo3b25yc3Od+rk703Krcalp76frbjVeb7zxhtm2bZs5dOiQefvtt03Tpk3N7373O5fHs8rMzdmzZ40ks2/fPqf2qVOnml69et1y/9LO3HTo0MFMmDChrGVWGMJNBXj00UdNixYtzOnTpx1te/fuNZJMXFycU98uXbqYGTNmGGOMGTx4sPH39zf+/v6mY8eOTv3OnTtnjh49ajZu3Gg6dux4yxfL3Xffbf74xz9W0BWVX0FBgTl+/LhJSUkxM2bMMI0aNTKHDx8u87g8/vjjZuTIkY7+pf1DXt3G5UYlhZu5c+eaO++802zatMkcPHjQvPrqq6ZevXpm+/btxhhjHnnkEcfY+Pv7FzvmmTNnTOvWrc3YsWNvef4//elP5le/+lWFXEt5ZWZmmiZNmpiDBw862m4MN+V9P93o2WefdfqYt7qHm5/7/vvvTUBAgONj3utcvSfKOi4lqc7vJ1dcjdd11z/iPnHihKPtxveYr6+vsdlsTm2PPPJIVZVfYW4Vbm71t6U04Wbfvn1GkklJSanI0suEG4rLKTY2Vps3b9bu3bvVrFkzR3tISIgkqWPHjk79O3To4JgiXbZsmeNJjzp16jj1Cw4OVnBwsNq3b6+GDRvqrrvu0rPPPus47s/16tXL7Y+8KpO3t7fatGkjSYqMjNSnn36qhQsXasaMGZLcH5cdO3YoIyND77//viQ5ps8bNWqkp59+WrNmzSqxjuo2Ljdz+fJlPfXUU1q/fr2GDh0qSQoPD1d6erpefvllDRgwQLNnz9aTTz5Z4v7ffPON7rnnHvXt27dUPyTbu3dvbd++vUKvoaxSU1N1/vx5de/e3dFWWFio3bt367XXXnM8iVjW99ONevfureeff14FBQXy8fFRcHCwsrOznfpkZ2crICBAdevWrZDrq0i33Xab7rzzTp04caJU/cs6LiWpSe+n6241Xr1795YknThxQq1bt5YkpaenO7YfOHBA06dPd/opoOrw69juatSokWrVqlXiaz04OPimf1tKa9myZeratasiIyPLdZyKQLgpI2OM/vKXv2j9+vVKSkpSq1atnLa3bNlSoaGhxR4P//LLLzVkyBBJUtOmTUt1rqKiIklSQUGByz7p6ekug091UFRUpIKCgjKPy//+7/86PfL76aef6uGHH9Y///lPxx+kklT3cbnRtWvXdO3atWL3E9WqVcvxGmjSpEmJTyCcPXtW99xzjyIjI7Vy5cpSPUFUncbm3nvvVUZGhlPbmDFj1L59e02fPl133HFHhb2f0tPT1aBBA8c/4Ha7XVu2bHHqs337dtnt9rJeTqXKy8vTV199pT/96U+l6l/WcXHVp7q8ZkrrVuN1PcjceF3X/8dMks6cOaPatWs7tdVE3t7eioyMVGJioqKjoyX99Hc5MTFRsbGxLv+2lFZeXp7ee+89xcfHV1DF5eTpqaOaasKECSYwMNAkJSWZc+fOOZYbp7Hnz59vAgICzLp168zx48fNM888Y3x9fZ2mP3/ugw8+MCtWrDAZGRnm5MmTZvPmzaZDhw5O9/PMnz/fbNiwwRw/ftxkZGSYSZMmGS8vL/Pxxx9X6jWX1owZM8yuXbvMyZMnzaFDh8yMGTOMzWYzH330kTGmbOPycyVNwVf3cTHmp6eB0tLSTFpampFkXnnlFZOWlmZOnTpljPnpo5hOnTqZnTt3mq+//tqsXLnS+Pr6mtdff93lMc+cOWPatGlj7r33XnPmzBmn1+N1CQkJZvXq1ebo0aPm6NGj5sUXXzReXl5mxYoVlX7NZfXzp1rK8rrZtGmTefPNN01GRoY5fvy4ef31142fn5957rnnHH2+/vpr4+fnZ6ZOnWqOHj1qFi1aZGrVqmW2bdtWmZdXak888YRJSkoyJ0+eNHv37jUDBgwwjRo1MufPnzfG/PQRdlpamnnzzTeNJLN7926TlpZmvvvuO5fHLM241IT3U0luNl4nTpwws2fPNikpKebkyZNm48aN5o477jB33323y+NZ5Z4bY4xZs2aN8fHxMQkJCebIkSPmz3/+s7nttttMVlaWy31OnTpl0tLSzKxZs0y9evUcf78uXbrk1G/ZsmXG19e32jxdR7gpI0klLitXrnTqFx8fb5o1a2b8/PyM3W43//znP2963B07dhi73W4CAwONr6+vadu2rZk+fbrTC+a///u/TevWrY2vr69p2LCh6d+/v9mxY0clXGXZPPzww6ZFixbG29vbNG7c2Nx7772OYHOdu+PycyWFm+o+Lsb8u+6fL6NHjzbG/PQP1UMPPWRCQ0ONr6+vadeunZk3b54pKipyecyVK1e6fD1el5CQYDp06GD8/PxMQECA6dWrl1m3bl1lX265lPTIrruvm61bt5quXbuaevXqGX9/fxMREWGWLFliCgsLnfrt3LnTdO3a1Xh7e5s77rij2PvYk0aOHGlCQkKMt7e3adq0qRk5cqRToJs5c2ap/hbdqDTjUhPeTyW52XhlZmaau+++2zRs2ND4+PiYNm3amKlTp5qcnByXx7NSuDHGmFdffdU0b97ceHt7m169epn9+/fftP/o0aNLfH3t3LnTqZ/dbjf/9V//VYmVu4dfBQcAAJbC99wAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAqBDJycmqVauW4xfNq8rVq1c1d+5cde/eXf7+/goMDFRERISeeeYZffPNN1VaC4DqgZ9fAFAhxo0bp3r16mn58uU6duyYQkNDK/2cBQUFuu+++3To0CHNmjVL/fr1U+PGjXXy5Em9++67atCggctfKb569aq8vb0rvUYAVY+ZGwDllpeXp7Vr12rChAkaOnSoEhISivXZtGmT2rZtK19fX91zzz166623ZLPZdPHiRUefPXv26K677lLdunUVFhamxx57TPn5+S7PO3/+fO3Zs0c7duzQY489psjISDVv3lxRUVFasmSJXnrpJUff/v37KzY2VpMnT1ajRo00aNAgSdKuXbvUq1cv+fj4KCQkRDNmzNCPP/7o2K9ly5ZasGCB03m7du2qv/71r451m82mxYsXa8iQIapbt67uuOMOvf/+++4NIoAKQ7gBUG7vvfee2rdvr3bt2umPf/yjVqxYoRsnhU+ePKkHHnhA0dHROnjwoB555BE9/fTTTsf46quvNHjwYP3+97/XoUOHtHbtWu3Zs0exsbEuz/vuu+9q4MCB6tatW4nbbTab0/pbb70lb29v7d27V0uWLNHZs2f1m9/8Rj179tTBgwe1ePFiLV++XC+88ILbY/Dss8/q97//vQ4ePKgHH3xQf/jDH3T06FG3jwOgAnj0N8kBWELfvn3NggULjDHGXLt2zTRq1Mjs3LnTsX369Ommc+fOTvs8/fTTRpL5/vvvjTHGjB071vz5z3926vPPf/7TeHl5mcuXL5d4Xl9fX/PYY485tUVHRxt/f3/j7+9v7Ha7oz0qKsp069bNqe9TTz1l2rVrZ4qKihxtixYtMvXq1TOFhYXGGGNatGhh5s+f77RfRESEmTlzpmNdknn00Ued+vTu3dtMmDChxLoBVC5mbgCUy7Fjx/TJJ59o1KhRkqTatWtr5MiRWr58uVOfnj17Ou3Xq1cvp/WDBw8qISFB9erVcyyDBg1SUVGRTp48Wep6Xn/9daWnp+vhhx/WDz/84LQtMjLSaf3o0aOy2+1OMzz9+vVTXl6ezpw5U+pzSpLdbi+2zswN4Bm1PV0AgJpt+fLl+vHHH51uIDbGyMfHR6+99poCAwNLdZy8vDw98sgjeuyxx4pta968eYn7tG3bVseOHXNqCwkJkSQ1bNiwWH9/f/9S1XIjLy8vp4/YJOnatWtuHwdA1WHmBkCZ/fjjj1q1apXmzZun9PR0x3Lw4EGFhobq3XfflSS1a9dOKSkpTvt++umnTuvdu3fXkSNH1KZNm2KLq6eaRo0ape3btystLa1M9Xfo0EHJyclO4WXv3r2qX7++mjVrJklq3Lixzp0759iem5tb4kzS/v37i6136NChTHUBKCcPfywGoAZbv3698fb2NhcvXiy2bdq0aaZHjx7GGGO+/vprU6dOHTNt2jRz7Ngxs3btWtOsWTMjybHvwYMHTd26dc3EiRNNWlqa+fLLL82GDRvMxIkTXZ7/8uXLpl+/fqZBgwZmwYIFJjU11Xz99ddm27ZtplevXqZ79+6OvlFRUWbSpElO+585c8b4+fmZiRMnmqNHj5oNGzaYRo0aOd1PM2PGDBMcHGx2795tDh06ZKKjo029evWK3XPTqFEjs3z5cnPs2DHz3HPPGS8vL3P48OEyjCqA8iLcACiz3/72t+Y3v/lNidsOHDhgJJmDBw8aY4zZuHGjadOmjfHx8TH9+/c3ixcvNpKcbhb+5JNPzMCBA029evWMv7+/CQ8PNy+++OJNa7hy5YqZM2eOiYiIMHXr1jU+Pj6mffv25vHHHzeZmZmOfiWFG2OMSUpKMj179jTe3t4mODjYTJ8+3Vy7ds2xPScnx4wcOdIEBASYsLAwk5CQUOINxYsWLTIDBw40Pj4+pmXLlmbt2rWlGUIAlYAv8QPgES+++KKWLFmi06dPe7qUcrPZbFq/fr2io6M9XQoAcUMxgCry+uuvq2fPnrr99tu1d+9ezZ0796bfYQMAZUW4AVAljh8/rhdeeEEXLlxQ8+bN9cQTTyguLs7TZQGwID6WAgAAlsKj4AAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFL+P3z1/AdqHP8IAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **State**"
      ],
      "metadata": {
        "id": "VJ2MUvay7kVL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# total number of orders from top 10 states\n",
        "\n",
        "sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
        "\n",
        "sns.set(rc={'figure.figsize':(15,5)})\n",
        "sns.barplot(data = sales_state, x = 'State',y= 'Orders')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "ZOIxJOww7gJ4",
        "outputId": "a6b35ddb-89cf-4ff2-f5f3-ec152c8e6353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='State', ylabel='Orders'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# total amount/sales from top 10 states\n",
        "\n",
        "sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
        "\n",
        "sns.set(rc={'figure.figsize':(15,5)})\n",
        "sns.barplot(data = sales_state, x = 'State',y= 'Amount')\n",
        "\n",
        "#From above graphs we can see that most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "vrC2ffrs7oeI",
        "outputId": "095b6604-ca8c-4f5b-8659-6b14743d3893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='State', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Marital Status**"
      ],
      "metadata": {
        "id": "hfrbQMdj7t9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "ax = sns.countplot(data = df, x = 'Marital_Status')\n",
        "\n",
        "sns.set(rc={'figure.figsize':(7,5)})\n",
        "for bars in ax.containers:\n",
        "    ax.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "JW0ybN3G7qmd",
        "outputId": "5a98d116-ec56-4d17-c45f-a9b5a550ddf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_state = df.groupby(['Marital_Status', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
        "\n",
        "sns.set(rc={'figure.figsize':(6,5)})\n",
        "sns.barplot(data = sales_state, x = 'Marital_Status',y= 'Amount', hue='Gender')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "cW_r38k37x-D",
        "outputId": "50bb47a5-0dd9-4bec-87a4-71ba44323575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Occupation**"
      ],
      "metadata": {
        "id": "KThFdvmq71sz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(20,5)})\n",
        "ax = sns.countplot(data = df, x = 'Occupation')\n",
        "\n",
        "for bars in ax.containers:\n",
        "    ax.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "x6Moqfwy7zoc",
        "outputId": "be4b76b7-7cae-4357-f697-f7e6a84b80b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
        "\n",
        "sns.set(rc={'figure.figsize':(20,5)})\n",
        "sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "vche9Q3h76hY",
        "outputId": "bd8b9ae0-879f-48ee-c4bd-305c157e1725"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Occupation', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Product Category**"
      ],
      "metadata": {
        "id": "ld2xb4so8ESj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(20,5)})\n",
        "ax = sns.countplot(data = df, x = 'Product_Category')\n",
        "\n",
        "for bars in ax.containers:\n",
        "    ax.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "iEo1Uy8478UN",
        "outputId": "f348a796-088a-410e-d2eb-46474736adec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
        "\n",
        "sns.set(rc={'figure.figsize':(20,5)})\n",
        "sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "QbzOEGZM8IeM",
        "outputId": "fd3a26fa-0449-4526-f8f0-ee6ddfc41a97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Product_Category', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_state = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
        "\n",
        "sns.set(rc={'figure.figsize':(20,5)})\n",
        "sns.barplot(data = sales_state, x = 'Product_ID',y= 'Orders')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "1Lkfw7rj8Loe",
        "outputId": "6c98779e-95b5-47e0-e39d-09630bb82d30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Product_ID', ylabel='Orders'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# top 10 most sold products (same thing as above)\n",
        "\n",
        "fig1, ax1 = plt.subplots(figsize=(12,7))\n",
        "df.groupby('Product_ID')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "jZNL4IOT8Tf6",
        "outputId": "99a1fcd2-7a67-4703-921c-1f053cfe7b78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Product_ID'>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusion:**\n",
        "Married women age group 26-35 yrs from UP, Maharastra and Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category"
      ],
      "metadata": {
        "id": "56ikkaSN8aQL"
      }
    }
  ]
}