SalesAnalysis.ipynb

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          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.23.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "pip install pandas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "dD8Y8tpnx7YU"
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      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data=pd.read_csv(\"/content/Sales Data.csv\")\n",
        "data=pd.read_csv(\"/content/Sales Data.csv\",encoding='unicode_escape')"
      ],
      "metadata": {
        "id": "XeMUORJUyAnb"
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      "execution_count": 120,
      "outputs": []
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      "source": [
        "data.shape"
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        "id": "znoxiKaeyWTj",
        "outputId": "1da55939-aca2-4244-d97a-eb931fd39308"
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      "execution_count": 121,
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              "(11251, 15)"
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      "cell_type": "code",
      "source": [
        "#data.head()\n",
        "data[:10]"
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      "execution_count": 122,
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          "output_type": "execute_result",
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            "text/plain": [
              "   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",
              "5  1000588       Joni  P00057942      M     26-35   28               1   \n",
              "6  1001132       Balk  P00018042      F     18-25   25               1   \n",
              "7  1002092   Shivangi  P00273442      F       55+   61               0   \n",
              "8  1003224     Kushal  P00205642      M     26-35   35               0   \n",
              "9  1003650      Ginny  P00031142      F     26-35   26               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",
              "5  Himachal Pradesh  Northern  Food Processing             Auto       1   \n",
              "6     Uttar Pradesh   Central           Lawyer             Auto       4   \n",
              "7       Maharashtra   Western        IT Sector             Auto       1   \n",
              "8     Uttar Pradesh   Central             Govt             Auto       2   \n",
              "9    Andhra Pradesh  Southern            Media             Auto       4   \n",
              "\n",
              "     Amount  Status  unnamed1  \n",
              "0  23952.00     NaN       NaN  \n",
              "1  23934.00     NaN       NaN  \n",
              "2  23924.00     NaN       NaN  \n",
              "3  23912.00     NaN       NaN  \n",
              "4  23877.00     NaN       NaN  \n",
              "5  23877.00     NaN       NaN  \n",
              "6  23841.00     NaN       NaN  \n",
              "7       NaN     NaN       NaN  \n",
              "8  23809.00     NaN       NaN  \n",
              "9  23799.99     NaN       NaN  "
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              "      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-2a553c2d-2699-46ba-95b0-659add4b7442 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XW_Vlsudy2UD",
        "outputId": "0d445377-10c7-41fa-8d77-46c9992e2d6d"
      },
      "execution_count": 123,
      "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": [
        "#as from above 2 columns has zero values either we fill or delete\n",
        "data.fillna\n",
        "#df.drop(['unnamed','Status'],axis=1,inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIAPSYHnzaL3",
        "outputId": "be117457-9800-4618-da7c-1e36746e0374"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.fillna of        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",
              "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  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  \n",
              "...        ...     ...       ...  \n",
              "11246    370.0     NaN       NaN  \n",
              "11247    367.0     NaN       NaN  \n",
              "11248    213.0     NaN       NaN  \n",
              "11249    206.0     NaN       NaN  \n",
              "11250    188.0     NaN       NaN  \n",
              "\n",
              "[11251 rows x 15 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.fillna(method='ffill', axis=1)\n",
        "#data = data.fillna(method='bfill', axis=0)\n",
        "'''data.fillna(value={'Unit_Price': data['Unit_Price'].mean(),\n",
        "                  'Quantity': data['Quantity'].mean(),\n",
        "                  'Total_Value': data['Total_Value'].mean(),\n",
        "                  'Profit': data['Profit'].mean()})'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "40w1yJLczuM-",
        "outputId": "a3fec039-3955-461e-ca90-e70c4b467d45"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"data.fillna(value={'Unit_Price': data['Unit_Price'].mean(),\\n                  'Quantity': data['Quantity'].mean(),\\n                  'Total_Value': data['Total_Value'].mean(),\\n                  'Profit': data['Profit'].mean()})\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xSzDegD1Pu4",
        "outputId": "b285c313-3d81-4801-c970-26a55220efa0"
      },
      "execution_count": 126,
      "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  object\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  object\n",
            " 6   Marital_Status    11251 non-null  object\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  object\n",
            " 12  Amount            11251 non-null  object\n",
            " 13  Status            11251 non-null  object\n",
            " 14  unnamed1          11251 non-null  object\n",
            "dtypes: object(15)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#delete blank null\n",
        "data.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "xjKTwxxy1gTw"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check null and count\n",
        "pd.isnull(data).sum()\n",
        "#pd.isnull(data).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73K1Z9L216le",
        "outputId": "ece19b00-7821-4d48-b8a6-a05409a50563"
      },
      "execution_count": 128,
      "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              0\n",
              "Status              0\n",
              "unnamed1            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        },
        "id": "Xcjj2ZiOzJZN",
        "outputId": "e3630786-0402-4652-a3f4-b8f024b6b3fe"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        User_ID Cust_name Product_ID Gender Age Group    Age  Marital_Status  \\\n",
              "count     11251     11251      11251  11251     11251  11251           11251   \n",
              "unique     3755      1250       2351      2         7     81               2   \n",
              "top     1001680  Vishakha  P00265242      F     26-35     30               0   \n",
              "freq         24        42         53   7842      4543    522            6522   \n",
              "\n",
              "                State     Zone Occupation    Product_Category  Orders  \\\n",
              "count           11251    11251      11251               11251   11251   \n",
              "unique             16        5         15                  18       4   \n",
              "top     Uttar Pradesh  Central  IT Sector  Clothing & Apparel       2   \n",
              "freq             1946     4296       1588                2655    2895   \n",
              "\n",
              "         Amount   Status  unnamed1  \n",
              "count   11251.0  11251.0   11251.0  \n",
              "unique   6588.0   6588.0    6588.0  \n",
              "top      7907.0   7907.0    7907.0  \n",
              "freq       11.0     11.0      11.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5026920b-716e-4085-aa37-e813ff7fff7d\" 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",
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              "      <th>Occupation</th>\n",
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              "      <th>unnamed1</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
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              "      <td>11251.0</td>\n",
              "      <td>11251.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>3755</td>\n",
              "      <td>1250</td>\n",
              "      <td>2351</td>\n",
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              "      <td>7</td>\n",
              "      <td>81</td>\n",
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              "      <td>5</td>\n",
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              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>6588.0</td>\n",
              "      <td>6588.0</td>\n",
              "      <td>6588.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>1001680</td>\n",
              "      <td>Vishakha</td>\n",
              "      <td>P00265242</td>\n",
              "      <td>F</td>\n",
              "      <td>26-35</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>Central</td>\n",
              "      <td>IT Sector</td>\n",
              "      <td>Clothing &amp; Apparel</td>\n",
              "      <td>2</td>\n",
              "      <td>7907.0</td>\n",
              "      <td>7907.0</td>\n",
              "      <td>7907.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>24</td>\n",
              "      <td>42</td>\n",
              "      <td>53</td>\n",
              "      <td>7842</td>\n",
              "      <td>4543</td>\n",
              "      <td>522</td>\n",
              "      <td>6522</td>\n",
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              "      <td>1588</td>\n",
              "      <td>2655</td>\n",
              "      <td>2895</td>\n",
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              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5026920b-716e-4085-aa37-e813ff7fff7d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-5026920b-716e-4085-aa37-e813ff7fff7d 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-5026920b-716e-4085-aa37-e813ff7fff7d');\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",
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              "\n",
              "\n",
              "<div id=\"df-79a1b766-f465-49e6-a5d3-60f500e43df7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79a1b766-f465-49e6-a5d3-60f500e43df7')\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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-79a1b766-f465-49e6-a5d3-60f500e43df7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to check data type of each column\n",
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_URXnOUpzRhz",
        "outputId": "e7111505-4f09-4470-b81d-0679c0a9ea72"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User_ID             object\n",
              "Cust_name           object\n",
              "Product_ID          object\n",
              "Gender              object\n",
              "Age Group           object\n",
              "Age                 object\n",
              "Marital_Status      object\n",
              "State               object\n",
              "Zone                object\n",
              "Occupation          object\n",
              "Product_Category    object\n",
              "Orders              object\n",
              "Amount              object\n",
              "Status              object\n",
              "unnamed1            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# : to check data type of each row of Amount as amount must be in Integer\n",
        "data.dtypes.loc[\"Amount\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-em710B2dbe",
        "outputId": "f4c32a4b-aaed-43ee-b2c2-9a98372e9547"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#changing amount column datatype object to integer\n",
        "data[\"Amount\"].astype('int')\n",
        "data['Amount'].dtypes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDwS6Ib53KKZ",
        "outputId": "8fb8f047-2441-4bc1-cb4e-3d8fd4bd646d"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#to check columns\n",
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXCYS2VT5mIX",
        "outputId": "e4240915-5788-4964-dcfb-8f7db50cf2a8"
      },
      "execution_count": 133,
      "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', 'Status', 'unnamed1'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to check names of all rows\n",
        "data.index.to_list()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TIPtCQh3xe9",
        "outputId": "62a1be70-39c8-4222-9f84-cb6fade6a910"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 996,\n",
              " 997,\n",
              " 998,\n",
              " 999,\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#rename columns\n",
        "data.rename(columns={'Martial_Status':'Shaadishuda'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "GCLPqiMP57Ak",
        "outputId": "19b11e86-29c3-4358-a96e-1c09024c627e"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       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",
              "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   Status unnamed1  \n",
              "0      23952.0  23952.0  23952.0  \n",
              "1      23934.0  23934.0  23934.0  \n",
              "2      23924.0  23924.0  23924.0  \n",
              "3      23912.0  23912.0  23912.0  \n",
              "4      23877.0  23877.0  23877.0  \n",
              "...        ...      ...      ...  \n",
              "11246    370.0    370.0    370.0  \n",
              "11247    367.0    367.0    367.0  \n",
              "11248    213.0    213.0    213.0  \n",
              "11249    206.0    206.0    206.0  \n",
              "11250    188.0    188.0    188.0  \n",
              "\n",
              "[11251 rows x 15 columns]"
            ],
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              "      <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>Marital_Status</th>\n",
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              "      <th>Occupation</th>\n",
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              "      <th>Orders</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Status</th>\n",
              "      <th>unnamed1</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",
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              "      <td>Maharashtra</td>\n",
              "      <td>Western</td>\n",
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              "      <td>23952.0</td>\n",
              "      <td>23952.0</td>\n",
              "      <td>23952.0</td>\n",
              "    </tr>\n",
              "    <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>23934.0</td>\n",
              "      <td>23934.0</td>\n",
              "      <td>23934.0</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>23924.0</td>\n",
              "      <td>23924.0</td>\n",
              "      <td>23924.0</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>23912.0</td>\n",
              "      <td>23912.0</td>\n",
              "      <td>23912.0</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>23877.0</td>\n",
              "      <td>23877.0</td>\n",
              "      <td>23877.0</td>\n",
              "    </tr>\n",
              "    <tr>\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>370.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>370.0</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>367.0</td>\n",
              "      <td>367.0</td>\n",
              "      <td>367.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11248</th>\n",
              "      <td>1001209</td>\n",
              "      <td>Oshin</td>\n",
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              "      <td>Madhya Pradesh</td>\n",
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              "      <td>Textile</td>\n",
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              "      <td>213.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>213.0</td>\n",
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              "      <td>1004023</td>\n",
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              "      <td>Karnataka</td>\n",
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              "      <td>Agriculture</td>\n",
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              "      <td>3</td>\n",
              "      <td>206.0</td>\n",
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              "    <tr>\n",
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              "      <td>1002744</td>\n",
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              "      <td>3</td>\n",
              "      <td>188.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>188.0</td>\n",
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              "    .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-e61a0d80-d6c4-4a51-ba0b-779bb17c8958 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-e61a0d80-d6c4-4a51-ba0b-779bb17c8958');\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-c0ff04b2-a1c9-488f-8251-956c1121f641\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c0ff04b2-a1c9-488f-8251-956c1121f641')\"\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-c0ff04b2-a1c9-488f-8251-956c1121f641 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  to add columns name and row names\n",
        "#data.columns = ['column1', 'column2', 'column3', ..., 'columnN']\n",
        "#data.index = ['row1', 'row2', 'row3', ..., 'rowN']\n",
        "#number of rows\n",
        "data.shape[0]\n",
        "len(data.index)\n",
        "#number of columns\n",
        "data.shape[1]\n",
        "len(data.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2B1KX_aA5YPg",
        "outputId": "d10ecb08-62cb-422b-8af3-151e3f82d26a"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        },
        "id": "iFMyi-c460CD",
        "outputId": "dcfa31a7-88d7-47f0-c05b-596aa4b8b3bd"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        User_ID Cust_name Product_ID Gender Age Group    Age  Marital_Status  \\\n",
              "count     11251     11251      11251  11251     11251  11251           11251   \n",
              "unique     3755      1250       2351      2         7     81               2   \n",
              "top     1001680  Vishakha  P00265242      F     26-35     30               0   \n",
              "freq         24        42         53   7842      4543    522            6522   \n",
              "\n",
              "                State     Zone Occupation    Product_Category  Orders  \\\n",
              "count           11251    11251      11251               11251   11251   \n",
              "unique             16        5         15                  18       4   \n",
              "top     Uttar Pradesh  Central  IT Sector  Clothing & Apparel       2   \n",
              "freq             1946     4296       1588                2655    2895   \n",
              "\n",
              "         Amount   Status  unnamed1  \n",
              "count   11251.0  11251.0   11251.0  \n",
              "unique   6588.0   6588.0    6588.0  \n",
              "top      7907.0   7907.0    7907.0  \n",
              "freq       11.0     11.0      11.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ac5b856-4471-467e-9cac-933c3831a252\" 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>Cust_name</th>\n",
              "      <th>Product_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age Group</th>\n",
              "      <th>Age</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>State</th>\n",
              "      <th>Zone</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Product_Category</th>\n",
              "      <th>Orders</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Status</th>\n",
              "      <th>unnamed1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251.0</td>\n",
              "      <td>11251.0</td>\n",
              "      <td>11251.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>3755</td>\n",
              "      <td>1250</td>\n",
              "      <td>2351</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>81</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>6588.0</td>\n",
              "      <td>6588.0</td>\n",
              "      <td>6588.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>1001680</td>\n",
              "      <td>Vishakha</td>\n",
              "      <td>P00265242</td>\n",
              "      <td>F</td>\n",
              "      <td>26-35</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>Central</td>\n",
              "      <td>IT Sector</td>\n",
              "      <td>Clothing &amp; Apparel</td>\n",
              "      <td>2</td>\n",
              "      <td>7907.0</td>\n",
              "      <td>7907.0</td>\n",
              "      <td>7907.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>24</td>\n",
              "      <td>42</td>\n",
              "      <td>53</td>\n",
              "      <td>7842</td>\n",
              "      <td>4543</td>\n",
              "      <td>522</td>\n",
              "      <td>6522</td>\n",
              "      <td>1946</td>\n",
              "      <td>4296</td>\n",
              "      <td>1588</td>\n",
              "      <td>2655</td>\n",
              "      <td>2895</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ac5b856-4471-467e-9cac-933c3831a252')\"\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-0ac5b856-4471-467e-9cac-933c3831a252 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-0ac5b856-4471-467e-9cac-933c3831a252');\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-fe6adb3e-6088-4f37-8621-902c9aca7897\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fe6adb3e-6088-4f37-8621-902c9aca7897')\"\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-fe6adb3e-6088-4f37-8621-902c9aca7897 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[['Age','Orders','Amount']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "F3iC0Hga7ttO",
        "outputId": "e8352752-be03-4f1f-f86c-f4280cc8eeaa"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Age  Orders   Amount\n",
              "count   11251   11251  11251.0\n",
              "unique     81       4   6588.0\n",
              "top        30       2   7907.0\n",
              "freq      522    2895     11.0"
            ],
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              "      <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>11251</td>\n",
              "      <td>11251</td>\n",
              "      <td>11251.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>81</td>\n",
              "      <td>4</td>\n",
              "      <td>6588.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>7907.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>522</td>\n",
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              "      <td>11.0</td>\n",
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              "\n",
              "    .colab-df-convert {\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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "        document.querySelector('#df-c3d161dd-adc5-43fa-8712-766ef84b5476 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-c3d161dd-adc5-43fa-8712-766ef84b5476');\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",
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              "    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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    box-shadow: none;\n",
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              "\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-f32a25b6-7614-44b5-8f76-5ade04bfbfa0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "EDA : Exploratory Data Analysis\n"
      ],
      "metadata": {
        "id": "wcDJaSqP8D6R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install seaborn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mdf7wh_e7_Uj",
        "outputId": "885691c8-e2dd-4e11-e6af-76abdb8432cb"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.1)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.23.5)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.5.3)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.10/dist-packages (from seaborn) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.47.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "bBtBIDJS8hr9"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot a bar chart for gender and its Count\n",
        "chart=sns.countplot(x='Gender',data=data)\n",
        "#chart=sns.countplot(x='Gender',data=data,color=\"blue\")\n",
        "chart.set_xlabel(\"Gender\",color=\"black\",size=15)\n",
        "chart.set_ylabel(\"Count\",color=\"black\",size=15)\n",
        "#chart.legend(labels=['Male', 'Female'], loc='upper right')\n",
        "for bars in chart.containers:\n",
        "  chart.bar_label(bars)#for number at top of bars"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "hlcUnBUJ8KW-",
        "outputId": "4cd0aed4-ebfb-48a8-cbfb-d467d405c2fb"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot a bar chart for Gender vs Total Amount\n",
        "sale=data.groupby(['Gender'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
        "#sale.plot.bar(x='Gender',y='Amount')\n",
        "sns.barplot(x = 'Gender',y= 'Amount' ,data = sale)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "u4dzeA5E-7Fw",
        "outputId": "3b8a24f6-d05f-4271-8369-8abe8bd2585d"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Gender', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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oBgDAdqNcLkW/fr2iocENFQDwVrW2VmLVqnXC2V+IZgAAbDfK5VI0NJTj8u8vjmXPry56HAB42xi4S9+YNungKJdLotlfiGYAAGx3lj2/Op5ctrLoMQCAtzHXrQMAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQNJY9ABvdNddd8V3vvOdeOyxx2Lt2rUxYMCAOOKII+Kss86KPn36FD0eAAAAAF1EXUWzVatWxbBhw2LKlCnRr1+/WLJkSVx22WWxZMmSuPrqq4seDwAAAIAuoq6i2bHHHrvZ5yNGjIju3bvHeeedF8uXL48BAwYUNBkAAAAAXUndP9OsX79+ERGxYcOGYgcBAAAAoMuoqyvNXtPa2hobN26Mxx57LC6//PIYM2ZM7Lbbbm1as7Gx7vtgmzQ0bN/fHwB0NH9Ltw9+jwDQNv6Wvq4uo9lhhx0Wy5cvj4iIQw89NP7jP/6jTeuVy6Xo3793e4wGAGynmpp6Fj0CAEDhnBO9ri6j2VVXXRXr16+Pxx57LK688so444wz4pprromGhoaa1qtUqtHSsq6dp6wvDQ1l/2EDQBu0tKyP1tZK0WPQRs6JAKBttvdzoqamntt8NV1dRrN99tknIiIOOOCAGDp0aBx77LFx5513xtFHH13zmhs3br+/cACg7VpbK84XAIAuzznR6+r+RtXBgwdHt27d4umnny56FAAAAAC6iLqPZvfff39s2LChzW8EAAAAAADbqq5uzzzrrLNiv/32i8GDB0ePHj3if//3f2PmzJkxePDgOOKII4oeDwAAAIAuoq6i2bBhw2L+/Plx1VVXRbVajYEDB8ZHPvKROOWUU6J79+5FjwcAAABAF1FX0ez000+P008/vegxAAAAAOji6v6ZZgAAAADQ2UQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEgaix7gjW677baYPXt2PPTQQ9HS0hJ77rlnTJkyJY477rgolUpFjwcAAABAF1FX0ezaa6+NgQMHxowZM6J///5x9913x3nnnRfPPfdcnHXWWUWPBwAAAEAXUVfR7Morr4zm5uZNn48cOTJWrVoV11xzTZx55plRLrubFAAAAICOV1cV6o3B7DXvfe97Y+3atbFu3boCJgIAAACgK6qrK8225re//W0MGDAgdtxxxzat09hYV32w3TU0bN/fHwB0NH9Ltw9+jwDQNv6Wvq6uo9lvfvObmD9/fnzuc59r0zrlcin69+/dTlMBANujpqaeRY8AAFA450Svq9to9txzz8X06dNjxIgRcdJJJ7VprUqlGi0t2/ftnQ0NZf9hA0AbtLSsj9bWStFj0EbOiQCgbbb3c6Kmpp7bfDVdXUazlpaWOO2006Jfv35x2WWXtcsbAGzcuP3+wgGAtmttrThfAAC6POdEr6u7aPbyyy/H1KlTY82aNfHDH/4w+vTpU/RIAAAAAHQxdRXNNm7cGJ/+9KfjiSeeiBtvvDEGDBhQ9EgAAAAAdEF1Fc0uuOCCWLRoUcyYMSPWrl0b991336Z9++67b3Tv3r244QAAAADoMuoqmi1evDgiIi666KIt9i1YsCB22223zh4JAAAAgC6orqLZwoULix4BAAAAAKLtb0sJAAAAANsZ0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAEtEMAAAAABLRDAAAAAAS0QwAAAAAksaiB3ijp556KmbOnBn3339/LFmyJPbee++YO3du0WMBAAAA0MXUVTRbsmRJ3HXXXTF8+PCoVCpRrVaLHgkAAACALqiubs8cM2ZM3HXXXfGf//mfMWTIkKLHAQAAAKCLqqtoVi7X1TgAAAAAdFF1dXtmR2ps3L6DXEPD9v39AUBH87d0++D3CABt42/p67pENCuXS9G/f++ixwAA6lhTU8+iRwAAKJxzotd1iWhWqVSjpWVd0WN0qIaGsv+wAaANWlrWR2trpegxaCPnRADQNtv7OVFTU89tvpquS0SziIiNG7ffXzgA0HatrRXnCwBAl+ec6HVuVAUAAACARDQDAAAAgKSubs9cv3593HXXXRERsWzZsli7dm3cfvvtERFx0EEHRXNzc5HjAQAAANBF1FU0+9Of/hSf+tSnNtv22ufXXXddjBgxooixAAAAAOhi6iqa7bbbbvHII48UPQYAAAAAXZxnmgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAIpoBAAAAQCKaAQAAAEAimgEAAABAUnfR7PHHH49PfOITsf/++8fBBx8c//Zv/xavvPJK0WMBAAAA0IU0Fj3AG61evTo+9rGPxV577RWXXXZZLF++PC666KJ4+eWX4/zzzy96PAAAAAC6iLqKZj/4wQ/ipZdeim9+85vRr1+/iIhobW2NCy64IKZOnRoDBgwodkAAAAAAuoS6uj3zpz/9aYwcOXJTMIuIGDt2bFQqlVi8eHFxgwEAAADQpdTVlWZPPPFEHHfccZtta2pqip133jmeeOKJmtctl0vR3Ny7rePVtVLp1X9+7pQx0dpaKXYYAHgbaWh49f9D7Nu3Z1SrBQ9DmzknAoDadJVzonK5tM3H1lU0a2lpiaampi229+3bN1avXl3zuqVSKRoatv2H8nbWd8ceRY8AAG9L5XJdXYBPGzknAoDaOCd6nZ8EAAAAACR1Fc2amppizZo1W2xfvXp19O3bt4CJAAAAAOiK6iqa7b333ls8u2zNmjXxwgsvxN57713QVAAAAAB0NXUVzT7wgQ/E3XffHS0tLZu23X777VEul+Pggw8ucDIAAAAAupJStVo/74mwevXqOOaYY2LQoEExderUWL58eVx00UUxYcKEOP/884seDwAAAIAuoq6iWUTE448/Hv/6r/8a9957b/Tu3TuOPfbYmD59enTv3r3o0QAAAADoIuoumgEAAABA0erqmWYAAAAAUA9EMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzgA5y2WWXxeDBg7f4GD9+fNGjAQB0uNfOhQ499NCoVCpb7D/hhBNi8ODBMWPGjAKmA/jbGoseAGB71qNHj5g1a9YW2wAAuoJu3brFypUr49e//nWMGDFi0/Zly5bFfffdF7169SpwOoC/TjQD6EDlcjn233//oscAAChEt27dYuTIkTFv3rzNotm8efPiPe95T5TLbn4C6pf/hQIAAKDDjB8/Pn7yk5/Ehg0bNm2bO3euR1YAdU80A+hgGzdu3OyjWq0WPRIAQKc57LDD4pVXXonFixdHRMRjjz0WjzzySIwbN67gyQD+OtEMoAOtW7cuhgwZstnH7Nmzix4LAKDT9OzZM8aMGRPz5s2LiFevMjvggANi9913L3gygL/OM80AOlCPHj3ihhtu2GybE0QAoKsZP358nHPOOfHyyy/H/PnzY8qUKUWPBPA3iWYAHahcLsfQoUOLHgMAoFCHHHJIdOvWLS699NJYunRpjB07tuiRAP4mt2cCAADQobp16xZHHnlkXHvttfH+978/3vGOdxQ9EsDf5EozAAAAOtxHPvKR+NOf/hQTJ04sehSAbSKaAQAA0OGGDRsWV1xxRdFjAGwzt2cCAAAAQFKqVqvVoocAAAAAgHriSjMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAANjNmzJiYMWNG0WMAABSqsegBAAC6smeeeSauueaaWLx4cTz33HMRETFw4MAYMWJEHH/88bHPPvsUPCEAQNckmgEAFGTRokUxffr0aGhoiAkTJsQ+++wT5XI5nnjiibjjjjvi+9//fixYsCAGDhxY9KgAAF2OaAYAUICnn346PvOZz8Suu+4a1157beyyyy6b7f+nf/qn+N73vhfl8tv7aRobN26MSqUS3bt3L3oUAIC35O19FgYA8Db13e9+N9atWxdf/epXtwhmERGNjY1x0kknxbve9a5N2x5//PH45Cc/GQcddFAMHTo0/vEf/zEWLFiw2etuueWWGDx4cPz2t7+Nr371q/H+978/9t9//5g2bVqsWLFis2Or1WpcccUV8YEPfCCGDx8eU6ZMiSVLlmx13paWlrjwwgtj9OjRsd9++8WHPvShuOqqq6JSqWw6ZunSpTF48OCYOXNmXHvttXHEEUfE0KFD4/HHH2/LjwoAoBCuNAMAKMCiRYtizz33jOHDh2/T8UuWLIlJkybFgAED4rTTTotevXrFbbfdFtOmTYvLLrssPvShD212/Je//OVoamqKs846K5YtWxazZs2KL33pS/GNb3xj0zGXXnppXHnllTF69OgYPXp0PPTQQ3HyySfHhg0bNltr/fr18dGPfjSWL18eJ5xwQrzrXe+Ke++9N77+9a/HCy+8EP/yL/+y2fG33HJL/PnPf46JEydG9+7do2/fvrX9kAAACiSaAQB0srVr18bzzz8fRxxxxBb7WlpaYuPGjZs+79WrV/To0SMuvPDCeNe73hU333zzplsdTzzxxJg0aVJcfPHFW0Szfv36xdVXXx2lUikiIiqVSlx//fWxZs2a6NOnT6xYsSK++93vxgc/+MH41re+tem4Sy65JL71rW9tttY111wTzzzzTNx6662x1157RUTECSecELvsskvMnDkzTj755M2uiHvuuefizjvvjObm5rb/sAAACuL2TACATrZ27dqIeDWIZVOmTImRI0du+rjxxhtj1apV8ctf/jLGjh0ba9eujRUrVsSKFSti5cqVccghh8STTz4Zy5cv32ydiRMnbgphEREHHnhgtLa2xrJlyyIi4u67744NGzbERz/60c2O+9jHPrbFTLfffnu8733vi6ampk1fe8WKFTFq1KhobW2NX//615sdf+SRRwpmAMDbnivNAAA6We/evSMiYt26dVvs+9KXvhQvvfRSvPjii/HZz342Il5904BqtRqXXnppXHrppVtd809/+lMMGDBg0+e77rrrZvubmpoi4tUr2SIinn322YiITVeOvaa5uXmL2ymfeuqpeOSRR2LkyJFb/dr5WWm77bbbVo8DAHg7Ec0AADpZnz59Yuedd97qQ/dfe8bZ0qVLN2177WH7J598chx66KFbXXOPPfbY7PM3e9fNarX6luetVCpx8MEHx6mnnrrV/Tm89ejR4y1/DQCAeiOaAQAU4IMf/GDcdNNN8cADD8SwYcP+6rG77757RER069YtRo0a1S5f/7Ur0Z588slN60e8etXY6tWrNzt2jz32iHXr1rXb1wYAeDvwTDMAgAKceuqp0bNnzzj33HPjxRdf3GL/G68I22mnneKggw6KH/7wh/H8889vcWy+PXJbjBo1Krp16xY33HDDZl9r1qxZWxw7duzYuPfee+NnP/vZFvvyGxcAAGwvXGkGAFCAvfbaKy6++OI455xz4uijj44JEybEPvvsE9VqNZYuXRpz586Ncrkc73znOyMi4otf/GKceOKJMWHChJg4cWLsvvvu8eKLL8Z9990Xzz33XMyePfstff3m5uY4+eST49vf/nZMnTo1Ro8eHX/4wx/ipz/9afTv33+zY0855ZRYuHBhnHHGGfEP//APMWTIkFi/fn08+uij8ZOf/CQWLFjgwf8AwHZHNAMAKMgRRxwRc+bMiauvvjoWL14cN998c5RKpdh1111j9OjRMWnSpNhnn30iIuLd73533HzzzfHNb34zbr311li1alU0NzfHvvvuG9OmTavp63/605+O7t27xw9+8IO45557YtiwYXH11VfH1KlTNzuuZ8+ecf3118e3v/3tuP322+O//uu/Yscdd4y99torzj777OjTp0+bfxYAAPWmVK3labAAAAAAsB3zTDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABIRDMAAAAASEQzAAAAAEhEMwAAAABI/h9lXOIodWnX5QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The provided line of code is used to group the DataFrame by the `Gender` column, calculate the sum of the `Amount` column for each group, and then sort the results by the `Amount` column in descending order.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. **`data.groupby(['Gender'],as_index=False)`**:\n",
        "\n",
        "   - This line groups the DataFrame by the `Gender` column.\n",
        "   - `as_index=False` ensures that the resulting DataFrame has a separate index column instead of using the original `Gender` column as the index.\n",
        "\n",
        "2. **`['Amount'].sum()`**:\n",
        "\n",
        "   - This line calculates the sum of the `Amount` column for each group.\n",
        "\n",
        "3. **`.sort_values(by='Amount',ascending=False)`**:\n",
        "\n",
        "   - This line sorts the resulting DataFrame by the `Amount` column in descending order.\n",
        "\n",
        "So, the final result is a new DataFrame that shows the total amount of sales for each gender, sorted from highest to lowest. This information can be used to analyze the sales performance based on gender and identify any potential trends or differences."
      ],
      "metadata": {
        "id": "VTALDV30A8MM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**From Chart we conclude FEmales are more Engaged in Shopping.**"
      ],
      "metadata": {
        "id": "bXZ4uU9lBHuF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chart=sns.countplot(data=data,x=\"Age Group\",hue='Gender')\n",
        "for bars in chart.containers:\n",
        "  chart.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "3sUWO18MBZCP",
        "outputId": "02034dca-3989-4e4b-dbd8-3e388e949916"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total Amount vs Age Group\n",
        "sale_age=data.groupby(['Age Group'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
        "sns.barplot(x='Age Group',y='Amount',data=sale_age)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "id": "g_0UevD9CS6g",
        "outputId": "8410d279-1ac6-46be-fd08-63308a7b4a81"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age Group', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**From this graph we conclude that 26 - 35 age people more spend.**"
      ],
      "metadata": {
        "id": "1loaMzXoD9Gw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "GleqajMHELtX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "SdyxxcwQIgAm"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#total number of orders from top 10 states\n",
        "#max(data['State'])only state\n",
        "order_state=data.groupby(['State'])['Orders'].sum()\n",
        "order_state\n",
        "order_state=data.groupby(['State'],as_index=False)['Orders'].sum().sort_values(by='Orders',ascending=False).head(10)\n",
        "order_state\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "LGnPayX9DtVn",
        "outputId": "a201bd43-97c6-42e8-c71f-c04bf45d3d17"
      },
      "execution_count": 161,
      "outputs": [
        {
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            "text/plain": [
              "               State  Orders\n",
              "14     Uttar Pradesh    4813\n",
              "10       Maharashtra    3811\n",
              "7          Karnataka    3241\n",
              "2              Delhi    2744\n",
              "9     Madhya Pradesh    2259\n",
              "0     Andhra Pradesh    2054\n",
              "5   Himachal Pradesh    1568\n",
              "8             Kerala    1137\n",
              "4            Haryana    1109\n",
              "3            Gujarat    1070"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#order_state = pd.DataFrame(order_state)\n",
        "# Set the figure size\n",
        "sns.set(rc={'figure.figsize':(15,10)})\n",
        "# Create a bar plot using the order_state DataFrame\n",
        "sns.barplot(data=order_state,x='State',y='Orders')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "id": "JZR5R8jHEb7L",
        "outputId": "26e0442d-c98d-4891-9f09-2028e658da84"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='State', ylabel='Orders'>"
            ]
          },
          "metadata": {},
          "execution_count": 162
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#total Amt/sales from 10 states\n",
        "sale_state=data.groupby(['State'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
        "sns.set(rc={'figure.figsize':(15,5)})\n",
        "sns.barplot(data=sale_state,x='State',y='Amount')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "m9vpqFYxHXvA",
        "outputId": "adc827c2-42ef-4e69-eeb6-0b34b98527a5"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='State', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 168
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From Above Graph most of orders & total sales/amount are from U.P"
      ],
      "metadata": {
        "id": "UGlfByOfMJoC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "l2W2TODvLNr3",
        "outputId": "1206c068-9bb8-4e36-e01d-64d8bc1f305c"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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   Amount  \\\n",
              "0     Maharashtra   Western       Healthcare             Auto      1  23952.0   \n",
              "1  Andhra Pradesh  Southern             Govt             Auto      3  23934.0   \n",
              "2   Uttar Pradesh   Central       Automobile             Auto      3  23924.0   \n",
              "3       Karnataka  Southern     Construction             Auto      2  23912.0   \n",
              "4         Gujarat   Western  Food Processing             Auto      2  23877.0   \n",
              "\n",
              "    Status unnamed1  \n",
              "0  23952.0  23952.0  \n",
              "1  23934.0  23934.0  \n",
              "2  23924.0  23924.0  \n",
              "3  23912.0  23912.0  \n",
              "4  23877.0  23877.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-244d6740-2176-4b86-9054-4d8cc61300e7\" 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>Cust_name</th>\n",
              "      <th>Product_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age Group</th>\n",
              "      <th>Age</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>State</th>\n",
              "      <th>Zone</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Product_Category</th>\n",
              "      <th>Orders</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Status</th>\n",
              "      <th>unnamed1</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",
              "      <td>Healthcare</td>\n",
              "      <td>Auto</td>\n",
              "      <td>1</td>\n",
              "      <td>23952.0</td>\n",
              "      <td>23952.0</td>\n",
              "      <td>23952.0</td>\n",
              "    </tr>\n",
              "    <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>23934.0</td>\n",
              "      <td>23934.0</td>\n",
              "      <td>23934.0</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>23924.0</td>\n",
              "      <td>23924.0</td>\n",
              "      <td>23924.0</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>23912.0</td>\n",
              "      <td>23912.0</td>\n",
              "      <td>23912.0</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>23877.0</td>\n",
              "      <td>23877.0</td>\n",
              "      <td>23877.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-244d6740-2176-4b86-9054-4d8cc61300e7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-244d6740-2176-4b86-9054-4d8cc61300e7 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-244d6740-2176-4b86-9054-4d8cc61300e7');\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-0a2a1637-4ad4-4155-a6a3-72d91b1c65d2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a2a1637-4ad4-4155-a6a3-72d91b1c65d2')\"\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-0a2a1637-4ad4-4155-a6a3-72d91b1c65d2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "martial=sns.countplot(data=data,x='Marital_Status')\n",
        "sns.set(rc={'figure.figsize':(7,5)})\n",
        "for bars in martial.containers:\n",
        "  martial.bar_label(bars)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "Kczrz_l_OUcL",
        "outputId": "1473bda1-0cc0-4726-a82b-457e9a023fbf"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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gYEI6AAAAACiYkA4AAAAACiakAwAAAICCCekAAAAAoGBCOgAAAAAomJAOAAAAAAompAMAAACAggnpAAAAAKBgQjoAAAAAKJiQDgAAAAAKJqQDAAAAgIIJ6QAAAACgYK0qpPvlL3+Zfv36NfvfhRde2KTupptuysiRI9O/f/8ceOCBueeee5qNVVtbm4kTJ2a33XbLoEGDcvzxx+eVV15pVvfII4/k61//egYMGJC99torV111VRobG9fbHAEAAADgn7UtuoF38pOf/CSbbbZZ6XOPHj1Kf3377bfnjDPOyDHHHJPdd989s2bNyrHHHpuf//znGThwYKnuxBNPzLPPPpuzzjorHTp0yJQpU3LkkUfmlltuSdu2q6f9wgsvZPz48Rk6dGhOPPHE/O1vf8uFF16YNm3aZPz48R/ZfAEAAADYuLXKkG7HHXdMt27d3vHcpZdemv322y8nnnhikmT33XfP008/ncsuuyzTp09PksybNy/33XdfZsyYkWHDhiVJ+vTpk9GjR+fOO+/M6NGjkyQzZsxI165dc/HFF6d9+/YZMmRIlixZkiuuuCLjxo1L+/bt1/9kAQAAANjotarHXd/PggUL8vzzz2fUqFFNjo8ePTpz585NXV1dkmTOnDmpqKjI0KFDSzWVlZXZfvvtM2fOnNKxOXPmZO+9924Sxo0ePTo1NTWZN2/eep4NAAAAAKzWKlfS7b///nn99dfz6U9/Ol/72tfyne98J23atElVVVWS1avi3q5v375ZuXJlFixYkL59+6aqqip9+vRJWVlZk7rKysrSGMuXL88//vGPVFZWNqspKytLVVVVBg8evB5nCQAAwAfxs5/NyPTpl6dPn8pce+2NSZJ//OPvOfjgA9/1mgMO+FJOOeX0JMlTT/0lv/3tbXnkkT/n5Zf/noqKztlxx/458sjvZuutt2l27S23/CK//OVN+fvfF6Zz5y7Ze+8v5Dvf+W46duy4fiYIkFYW0nXv3j3HHXdcPve5z6WsrCy///3vM2XKlCxatCiTJk1KdXV1kqSioqLJdWs+rzlfU1PTZE+7NTp37pwnnngiyeoXS7zTWO3bt0/Hjh1LY7VU27Yfq0WKLdKmzYY/RwBYn/wsBXh/r7yyKNdee006duyYsrKy0p+1PvnJzXPmmec0q3/ggT/ljjt+m913H1Kqvf76mXn88ccyYsQ+2Xbbz+S1117LzTf/IuPHfzM/+cnP0rfvtqXrp037Ua677mcZMWKffP3rh2b+/KrcfPONef75qvzoRz/+aCYNbJRaVUi3xx57ZI899ih9HjZsWDp06JCf/exnOeaYYwrs7MMpLy9L166dim4DAGjlKiqsyAB4Pz/4wdQMHDgwDQ0Nef3110t/1uratVMOPfRrzervvHNWNt100xxwwKh06NAhSXLUUd/JTjvt1GSro69+dUwOOOCA3HDDtbnwwguTJK+88kpuuOHnGTNmTC644IJS7Wc/+5mcc845mTfvwYwYMWJ9ThfYiLWqkO6djBo1KldffXWeeuqpdO7cOcnqVXDdu3cv1dTU1CRJ6XxFRUVefvnlZmNVV1eXatastFuzom6Nurq6rFixolTXEg0NjampWd7i6z8u2rQp94cLAFgLNTUrUl/fUHQbAK3WvHl/zh133JGf/ez6XHTRBamvb8jrry971/pXX12cBx98MKNG7Zfly1dl+fJVSZI+ffpl2bKVWbZsZam2c+fu6dOnMk8//UxpzPvueyCrVq3Knnvu3eR7hg79fJJz8t///ZsMGmRbJODDqajo+IGeoGj1Id3brdk/rqqqqsleclVVVWnXrl169epVqps7d24aGxub7Es3f/78bLfddkmSTTbZJJ/61KdKe9S9vaaxsbHZXnUf1qpVbrgBgPdWX9/gngHgXdTX1+fCCy/I/vuPSe/efdPY2JjGxsb3/H3zjjtmp6GhIfvss+/7/v7a2NiYJUuWpE+fylLtm2++lSRp27Z9k+vbtl29Iu+vf33S79vAetPqN0KZNWtW2rRpkx122CG9evVK7969M3v27GY1Q4YMKS1dHj58eKqrqzN37txSzfz58/Pkk09m+PDhpWPDhw/P3XffnZUrVzYZq6KiIoMGDVrPMwMAAODd/OpXt2TRon/kO9/57ge+5s47Z2fzzT+Zf/mXXT9A7W+zePErGTHiC6VjW2/dO0ny+OOPNql97LF5SZLFixd/4F4APqxWtZJu/PjxGTx4cPr165ckufvuu3PjjTfmsMMOKz3eetxxx+Xkk0/O1ltvncGDB2fWrFl5/PHHc91115XGGTRoUIYNG5aJEyfmlFNOSYcOHXLJJZekX79++eIXv9jk+2699dacdNJJOeSQQ/L0009nxowZmTBhQpO9CgAAAPjoVFe/kRkzrszhh38nXbt2/UDXvPjiC/nb357K179+aMrL33s9ygsvPJ+LLz4/O+00IKNG7V863q/fZ7PDDjvl5z+fme7dt8jOO++S55+fn4sumpy2bdumru6ttZoXwHtpVSFdnz59csstt+Tll19OQ0NDevfunYkTJ2bcuHGlmv333z8rVqzI9OnTc9VVV6VPnz6ZNm1as5VvU6ZMyXnnnZdJkyZl1apVGTZsWE4//fS0bft/U95mm20yY8aMTJ48OUcddVS6deuW448/Pt/+9rc/sjkDAADQ1PTpl6eioiIHHfT1D3zNXXetfuLqC18Y9Z51r732av79309Ip06b5txzz0+bNm2anP/P/7wgkyadlvPO+0GSpE2bNvn61w/NvHmPZMGCFz7kTAA+uLLGxsbGopvY0NTXN2TJknffzHRD0bZtebp27ZSJP5qV5xe+XnQ7APCx0XurrvnhCaPz+uvL7G0E8E8WLHgx3/jGQTn++H/L0KH/t13RmWdOTG1tTS6+eFo6deqUioqmL/sbO/bLKS8vz/XX3/KuYy9dujTHHXdUFi1alMsum54+fd59L/IFC17MkiWvpWfPXtl8809mzJh9s8UWW2T69JlrP0lgo9KtW6cN78URAAAAbNgWL34lDQ0NmTLlwkyZcmGz8wcffGAOPviQnHDCSaVjf/nLE3nppQX5zneOeddx33rrrZxyyoQsWPBipkz58XsGdEnSq9fW6dVr6yTJ/PlVee21VzN69AEtnBXA+xPSAQAA0GpUVm6bH/6weTg3ffqPs3z58pxwwsnZaqueTc7936Ou+77jmPX19TnzzNPyxBOPZ/Lki7LTTgM+cD8NDQ25/PJL84lPfCJjxnz1Q8wE4MMR0gEAANBqdOnSJcOHf77Z8RtvvD5Jmp2rr6/P739/V3bcsX+z8G6NadOm5L775mTo0D1SU1OTO+6Y1eT8yJGjS389ZcqFqat7K5/5TL+sWrUqd901O0899Zd8//tnZcstt1y7yQG8ByEdAAAAH1sPP/xQlix5LYcddsS71jzzzN+SJPff/8fcf/8fm51/e0i33Xb9cuON/5W77pqdsrLybL/9jvnRjy7Pzjvvsu6bB3gbL45YD7w4AgB4L14cAQCw8figL454/woAAAAAYL0S0gEAAABAwexJBwAAa6m8vCzl5WVFtwEAHzsNDY1paLATWyKkAwCAtVJeXpYuXTb5QHvNAABN1dc35I03lgvqIqQDAIC1Ul5eljZtynPZf92fha9UF90OAHxsbLVF53zvkKEpLy8T0kVIBwAA68TCV6q98R4AaDFr8gEAAACgYEI6AAAAACiYkA4AAAAACiakAwAAAICCCekAAAAAoGBCOgAAAAAomJAOAAAAAAompAMAAACAggnpAAAAAKBgQjoAAAAAKJiQDgAAAAAKJqQDAAAAgIIJ6QAAAACgYEI6AAAAACiYkA4AAAAACiakAwAAAICCCekAAAAAoGBCOgAAAAAomJAOAAAAAAompAMAAACAggnpAAAAAKBgQjoAAAAAKJiQDgAAAAAKJqQDAAAAgIIJ6QAAAACgYEI6AAAAACiYkA4AAAAACiakAwAAAICCCekAAAAAoGBCOgAAAAAomJAOAAAAAAompAMAAACAgrXakG7ZsmUZPnx4+vXrl//93/9tcu6mm27KyJEj079//xx44IG55557ml1fW1ubiRMnZrfddsugQYNy/PHH55VXXmlW98gjj+TrX/96BgwYkL322itXXXVVGhsb19u8AAAAAOCftdqQ7sc//nHq6+ubHb/99ttzxhlnZNSoUZk+fXoGDhyYY489No8++miTuhNPPDH3339/zjrrrFx44YWZP39+jjzyyKxatapU88ILL2T8+PHp3r17rrzyyhx++OG59NJLc/XVV6/v6QEAAABASduiG3gnzz33XK6//vqccsopOfPMM5ucu/TSS7PffvvlxBNPTJLsvvvuefrpp3PZZZdl+vTpSZJ58+blvvvuy4wZMzJs2LAkSZ8+fTJ69OjceeedGT16dJJkxowZ6dq1ay6++OK0b98+Q4YMyZIlS3LFFVdk3Lhxad++/Uc3aQAAAAA2Wq1yJd25556bsWPHpk+fPk2OL1iwIM8//3xGjRrV5Pjo0aMzd+7c1NXVJUnmzJmTioqKDB06tFRTWVmZ7bffPnPmzCkdmzNnTvbee+8mYdzo0aNTU1OTefPmrY+pAQAAAEAzrS6kmz17dp5++ul873vfa3auqqoqSZqFd3379s3KlSuzYMGCUl2fPn1SVlbWpK6ysrI0xvLly/OPf/wjlZWVzWrKyspKdQAAAACwvrWqx11XrFiRyZMnZ8KECdl0002bna+urk6SVFRUNDm+5vOa8zU1Ndlss82aXd+5c+c88cQTSVa/WOKdxmrfvn06duxYGqul2rZtdfnnOtemzYY/RwBYn/ws3TD4+wgAa8fP0tVaVUh3+eWXZ/PNN89Xv/rVoltZK+XlZenatVPRbQAArVxFRceiWwAAKJx7otVaTUi3cOHCXH311bnssstKq9yWL19e+v9ly5alc+fOSVavguvevXvp2pqamiQpna+oqMjLL7/c7Duqq6tLNWtW2q35rjXq6uqyYsWKUl1LNDQ0pqZmeYuv/7ho06bcv0gAsBZqalakvr6h6DZYS+6JAGDtbOj3RBUVHT/QasFWE9K99NJLWblyZY466qhm5w477LB87nOfy0UXXZRk9Z5zb99LrqqqKu3atUuvXr2SrN5Xbu7cuWlsbGyyL938+fOz3XbbJUk22WSTfOpTn2q299z8+fPT2NjYbK+6D2vVqg33Hy4AYN2or29wzwAAbPTcE63Wah763X777TNz5swm/zvttNOSJGeffXbOPPPM9OrVK717987s2bObXDtr1qwMGTKk9JbW4cOHp7q6OnPnzi3VzJ8/P08++WSGDx9eOjZ8+PDcfffdWblyZZOxKioqMmjQoPU5XQAAAAAoaTUr6SoqKjJ48OB3PLfjjjtmxx13TJIcd9xxOfnkk7P11ltn8ODBmTVrVh5//PFcd911pfpBgwZl2LBhmThxYk455ZR06NAhl1xySfr165cvfvGLpbrx48fn1ltvzUknnZRDDjkkTz/9dGbMmJEJEyaUAj8AAAAAWN9aTUj3Qe2///5ZsWJFpk+fnquuuip9+vTJtGnTmq18mzJlSs4777xMmjQpq1atyrBhw3L66aenbdv/m/I222yTGTNmZPLkyTnqqKPSrVu3HH/88fn2t7/9UU8LAAAAgI1YWWNjY2PRTWxo6usbsmTJsqLbWO/ati1P166dMvFHs/L8wteLbgcAPjZ6b9U1PzxhdF5/fZn9VzYA7okAoGU2lnuibt06faAXR7SaPekAAAAAYGMlpAMAAACAggnpAAAAAKBgQjoAAAAAKFiLQ7pf/epXeemll971/EsvvZRf/epXLR0eAAAAADYaLQ7pTjvttMybN+9dzz/++OM57bTTWjo8AAAAAGw0WhzSNTY2vuf55cuXp02bNi0dHgAAAAA2Gm0/TPFf//rX/PWvfy19fvjhh1NfX9+srqamJjfccEP69Omz9h0CAAAAwAbuQ4V0v/vd7zJt2rQkSVlZWX7xi1/kF7/4xTvWVlRU5Pzzz1/7DgEAAABgA/ehQrqvfe1r+fznP5/GxsYcfPDBOf744zN8+PAmNWVlZenYsWO23nrrtG37oYYHAAAAgI3Sh0rRtthii2yxxRZJkpkzZ6Zv377ZfPPN10tjAAAAALCxaPFSt912221d9gEAAAAAG621eh71j3/8Y26++eYsWLAgNTU1zd74WlZWlt/97ndr1SAAAAAAbOhaHNL95Cc/yUUXXZTNN988AwYMSL9+/dZlXwAAAACw0WhxSDdz5szsvvvuueqqq9KuXbt12RMAAAAAbFTKW3phTU1NRo4cKaADAAAAgLXU4pCuf//+mT9//rrsBQAAAAA2Si0O6c4666zcddddufXWW9dlPwAAAACw0WnxnnQnnnhiVq1alf/4j//IWWedlS233DLl5U0zv7KysvzmN79Z6yYBAAAAYEPW4pCuS5cu6dKlS7bZZpt12Q8AAAAAbHRaHNJde+2167IPAAAAANhotXhPOgAAAABg3WjxSrr/+Z//+UB1u+66a0u/AgAAAAA2Ci0O6caNG5eysrL3rXvqqada+hUAAAAAsFFocUg3c+bMZsfq6+uzcOHC3HjjjWloaMhJJ520Vs0BAAAAwMagxSHdbrvt9q7nvvKVr+TQQw/NQw89lCFDhrT0KwAAAABgo7BeXhxRXl6e/fbbLzfddNP6GB4AAAAANijr7e2u1dXVqa2tXV/DAwAAAMAGo8WPu/79739/x+M1NTV5+OGHM2PGjOyyyy4tbgwAAAAANhYtDulGjBjxrm93bWxszMCBA3P22We3uDEAAAAA2Fi0OKT74Q9/2CykKysrS0VFRbbeeutsu+22a90cAAAAAGwMWhzSfeUrX1mXfQAAAADARqvFId3bPfvss1m4cGGSZKuttrKKDgAAAAA+hLUK6X73u99l8uTJpYBujZ49e+bUU0/N3nvvvVbNAQAAAMDGoMUh3b333pvjjz8+n/70pzNhwoT07ds3SfLcc8/lxhtvzHHHHZcrrrgiw4cPX2fNAgAAAMCGqMUh3Y9//OP069cvP//5z7PJJpuUju+999755je/mUMPPTSXXXaZkA4AAAAA3kd5Sy/829/+li996UtNAro1Ntlkk3z5y1/O3/72t7VqDgAAAAA2Bi0O6Tp06JDq6up3PV9dXZ0OHTq0dHgAAAAA2Gi0OKQbPHhwZs6cmXnz5jU799hjj+Xaa6/NkCFD1qo5AAAAANgYtHhPun//93/P2LFjc+ihh2bAgAHp06dPkmT+/Pl5/PHHs/nmm+fkk09eZ40CAAAAwIaqxSvpevXqld/85jcZN25cqqurM2vWrMyaNSvV1dU57LDD8utf/zo9e/Zcl70CAAAAwAapxSvpVq1alQ4dOmTixImZOHFis/NLly7NqlWr0rZti78CAAAAADYKLV5Jd+6552bs2LHvev6QQw7J5MmTWzo8AAAAAGw0WhzS/fGPf8zIkSPf9fzIkSMzZ86cDzXmvffem29+85vZfffds9NOO2XvvffOeeedl9ra2iZ1v//973PggQemf//+GTlyZG655ZZmY9XV1eX888/P0KFDM3DgwBxxxBGpqqpqVvfcc8/liCOOyMCBAzN06NBccMEFqaur+1B9AwAAAMDaaPGzqK+88kp69Ojxrue32GKLLFq06EON+cYbb2TAgAEZN25cunTpkmeeeSZTp07NM888k6uvvjpJ8vDDD+fYY4/NQQcdlIkTJ+aBBx7I97///XTq1Cn77rtvaaxzzz03s2bNyqmnnpoePXrkiiuuyLe+9a3cfvvt2WyzzZIk1dXVOfzww9O7d+9MnTo1ixYtyuTJk/Pmm29m0qRJLfhVAQAAAIAPr8UhXZcuXTJ//vx3Pf/cc89l0003/VBjjhkzpsnnwYMHp3379jnjjDOyaNGi9OjRI5dffnkGDBiQH/zgB0mS3XffPQsWLMill15aCulefvnl3HzzzTnzzDNz0EEHJUn69++fvfbaKzfccEOOPPLIJMkNN9yQZcuWZdq0aenSpUuSpL6+PmeffXaOPvro9wwhAQAAAGBdafHjrnvssUduuOGGPPnkk83O/eUvf8mNN96Y4cOHr1VzSUrh2cqVK1NXV5cHH3ywyYq5JBk9enSee+65vPTSS0mS++67Lw0NDU3qunTpkqFDhzZ5BHfOnDkZMmRI6TuSZNSoUWloaMj999+/1r0DAAAAwAfR4pV0J5xwQv74xz/m4IMPzogRI7LtttsmSZ555pncc8896datW0444YQWjV1fX59Vq1bl2WefzWWXXZYRI0akZ8+eefbZZ7Ny5cpUVlY2qe/bt2+SpKqqKj179kxVVVU233zzdO7cuVndzTffXPpcVVWVr371q01qKioq0r1793fcvw4AAAAA1ocWh3Q9evTILbfckosuuih333137rrrriTJpptumgMOOCATJkxo8eOie+21V2k/uz322CMXXXRRktV7yCWrg7S3W/N5zfmamprSvnP/XLemZk3dP4+VJJ07d25S1xJt27Z4keLHRps2G/4cAWB98rN0w+DvIwCsHT9LV2txSJesfjnE+eefn8bGxixZsiRJ0q1bt5SVla1VU1dddVVWrFiRZ599NpdffnmOOeaYXHPNNWs15kepvLwsXbt2KroNAKCVq6joWHQLAACFc0+02lqFdGuUlZVl8803XxdDJUk++9nPJkkGDRqU/v37Z8yYMbnrrrtKj9TW1tY2qa+pqUmS0uOtFRUVWbp0abNxa2pqmjwCW1FR0WysZPWKvH9+VPbDaGhoTE3N8hZf/3HRpk25f5EAYC3U1KxIfX1D0W2wltwTAcDa2dDviSoqOn6g1YLrJKRbn/r165d27drlxRdfzIgRI9KuXbtUVVVljz32KNWs2T9uzV51lZWVefXVV5uFbVVVVU32s6usrGy291xtbW0WL17cbN+7D2vVqg33Hy4AYN2or29wzwAAbPTcE63W6h/6feyxx7Jy5cr07Nkz7du3z+DBg3PHHXc0qZk1a1b69u2bnj17JkmGDRuW8vLy3HnnnaWa6urq3HfffU3eODt8+PD86U9/Kq3ES5LZs2envLw8Q4cOXc8zAwAAAIDVWtVKumOPPTY77bRT+vXrl0984hP561//mhkzZqRfv37ZZ599kiTf/e53c9hhh+Wss87KqFGj8uCDD+a2227LJZdcUhpnyy23zEEHHZQLLrgg5eXl6dGjR6688spsttlmGTt2bKlu7Nixufbaa/O9730vRx99dBYtWpQLLrggY8eObfFLLwAAAADgw2pVId2AAQMya9asXHXVVWlsbMxWW22Vgw8+OOPHj0/79u2TJLvsskumTp2aKVOm5Oabb86nP/3pnHvuuRk1alSTsU4//fR06tQpF110UZYtW5add94511xzTZO3vnbu3Dk/+9nPcs455+R73/teOnXqlIMOOigTJkz4SOcNAAAAwMatrLGxsbHoJjY09fUNWbJkWdFtrHdt25ana9dOmfijWXl+4etFtwMAHxu9t+qaH54wOq+/vsz+KxsA90QA0DIbyz1Rt26dPtCLI1r9nnQAAAAAsKET0gEAAABAwYR0AAAAAFAwIR0AAAAAFExIBwAAAAAFE9IBAAAAQMGEdAAAAABQMCEdAAAAABRMSAcAAAAABRPSAQAAAEDBhHQAAAAAUDAhHQAAAAAUTEgHAAAAAAUT0gEAAABAwYR0AAAAAFAwIR0AAAAAFExIBwAAAAAFE9IBAAAAQMGEdAAAAABQMCEdAAAAABRMSAcAAAAABRPSAQAAAEDBhHQAAAAAUDAhHQAAAAAUTEgHAAAAAAUT0gEAAABAwYR0AAAAAFAwIR0AAAAAFExIBwAAAAAFE9IBAAAAQMGEdAAAAABQMCEdAAAAABRMSAcAAAAABRPSAQAAAEDBhHQAAAAAUDAhHQAAAAAUTEgHAAAAAAUT0gEAAABAwYR0AAAAAFAwIR0AAAAAFExIBwAAAAAFE9IBAAAAQMGEdAAAAABQMCEdAAAAABSsVYV0v/3tb/Pd7343w4cPz8CBAzNmzJjcfPPNaWxsbFJ30003ZeTIkenfv38OPPDA3HPPPc3Gqq2tzcSJE7Pbbrtl0KBBOf744/PKK680q3vkkUfy9a9/PQMGDMhee+2Vq666qtn3AQAAAMD61KpCup/+9Kfp2LFjTj311Fx++eUZPnx4zjjjjFx22WWlmttvvz1nnHFGRo0alenTp2fgwIE59thj8+ijjzYZ68QTT8z999+fs846KxdeeGHmz5+fI488MqtWrSrVvPDCCxk/fny6d++eK6+8MocffnguvfTSXH311R/VlAEAAAAgbYtu4O0uv/zydOvWrfR5yJAheeONN3LNNdfkX//1X1NeXp5LL700++23X0488cQkye67756nn346l112WaZPn54kmTdvXu67777MmDEjw4YNS5L06dMno0ePzp133pnRo0cnSWbMmJGuXbvm4osvTvv27TNkyJAsWbIkV1xxRcaNG5f27dt/tL8AAAAAAGyUWtVKurcHdGtsv/32Wbp0aZYvX54FCxbk+eefz6hRo5rUjB49OnPnzk1dXV2SZM6cOamoqMjQoUNLNZWVldl+++0zZ86c0rE5c+Zk7733bhLGjR49OjU1NZk3b966nh4AAAAAvKNWFdK9kz//+c/p0aNHNt1001RVVSVZvSru7fr27ZuVK1dmwYIFSZKqqqr06dMnZWVlTeoqKytLYyxfvjz/+Mc/UllZ2aymrKysVAcAAAAA61uretz1nz388MOZNWtWTjnllCRJdXV1kqSioqJJ3ZrPa87X1NRks802azZe586d88QTTyRZ/WKJdxqrffv26dixY2mslmrbttXnn2utTZsNf44AsD75Wbph8PcRANaOn6WrtdqQ7uWXX86ECRMyePDgHHbYYUW386GUl5ela9dORbcBALRyFRUdi24BAKBw7olWa5UhXU1NTY488sh06dIlU6dOTXn56kS1c+fOSVavguvevXuT+refr6ioyMsvv9xs3Orq6lLNmpV2a1bUrVFXV5cVK1aU6lqioaExNTXLW3z9x0WbNuX+RQKAtVBTsyL19Q1Ft8Fack8EAGtnQ78nqqjo+IFWC7a6kO7NN9/M0Ucfndra2vziF79o8tjqmv3jqqqqmuwlV1VVlXbt2qVXr16lurlz56axsbHJvnTz58/PdtttlyTZZJNN8qlPfarZ3nPz589PY2Njs73qPqxVqzbcf7gAgHWjvr7BPQMAsNFzT7Raq3rod9WqVTnxxBNTVVWVn/zkJ+nRo0eT87169Urv3r0ze/bsJsdnzZqVIUOGlN7SOnz48FRXV2fu3Lmlmvnz5+fJJ5/M8OHDS8eGDx+eu+++OytXrmwyVkVFRQYNGrQ+pggAAAAAzbSqlXRnn3127rnnnpx66qlZunRpHn300dK5HXbYIe3bt89xxx2Xk08+OVtvvXUGDx6cWbNm5fHHH891111Xqh00aFCGDRuWiRMn5pRTTkmHDh1yySWXpF+/fvniF79Yqhs/fnxuvfXWnHTSSTnkkEPy9NNPZ8aMGZkwYUIp8AMAAACA9a1VhXT3339/kmTy5MnNzt19993p2bNn9t9//6xYsSLTp0/PVVddlT59+mTatGnNVr5NmTIl5513XiZNmpRVq1Zl2LBhOf3009O27f9NeZtttsmMGTMyefLkHHXUUenWrVuOP/74fPvb316/EwUAAACAtylrbGxsLLqJDU19fUOWLFlWdBvrXdu25enatVMm/mhWnl/4etHtAMDHRu+tuuaHJ4zO668vs//KBsA9EQC0zMZyT9StW6cP9OKIVrUnHQAAAABsjIR0AAAAAFAwIR0AAAAAFExIBwAAAAAFE9IBAAAAQMGEdAAAAABQMCEdAAAAABRMSAcAAAAABRPSAQAAAEDBhHQAAAAAUDAhHQAAAAAUTEgHAAAAAAUT0gEAAABAwYR0AAAAAFAwIR0AAAAAFExIBwAAAAAFE9IBAAAAQMGEdAAAAABQMCEdAAAAABRMSAcAAAAABRPSAQAAAEDBhHQAAAAAUDAhHQAAAAAUTEgHAAAAAAUT0gEAAABAwYR0AAAAAFAwIR0AAAAAFExIBwAAAAAFE9IBAAAAQMGEdAAAAABQMCEdAAAAABRMSAcAAAAABRPSAQAAAEDBhHQAAAAAUDAhHQAAAAAUTEgHAAAAAAUT0gEAAABAwYR0AAAAAFAwIR0AAAAAFExIBwAAAAAFE9IBAAAAQMGEdAAAAABQMCEdAAAAABSsVYV0L7zwQiZNmpQxY8Zkhx12yP777/+OdTfddFNGjhyZ/v3758ADD8w999zTrKa2tjYTJ07MbrvtlkGDBuX444/PK6+80qzukUceyde//vUMGDAge+21V6666qo0Njau87kBAAAAwLtpVSHdM888k3vvvTfbbLNN+vbt+441t99+e84444yMGjUq06dPz8CBA3Psscfm0UcfbVJ34okn5v77789ZZ52VCy+8MPPnz8+RRx6ZVatWlWpeeOGFjB8/Pt27d8+VV16Zww8/PJdeemmuvvrq9TlNAAAAAGiibdENvN2IESOyzz77JElOPfXUPPHEE81qLr300uy333458cQTkyS77757nn766Vx22WWZPn16kmTevHm57777MmPGjAwbNixJ0qdPn4wePTp33nlnRo8enSSZMWNGunbtmosvvjjt27fPkCFDsmTJklxxxRUZN25c2rdv/xHMGgAAAICNXataSVde/t7tLFiwIM8//3xGjRrV5Pjo0aMzd+7c1NXVJUnmzJmTioqKDB06tFRTWVmZ7bffPnPmzCkdmzNnTvbee+8mYdzo0aNTU1OTefPmrYspAQAAAMD7alUh3fupqqpKsnpV3Nv17ds3K1euzIIFC0p1ffr0SVlZWZO6ysrK0hjLly/PP/7xj1RWVjarKSsrK9UBAAAAwPrWqh53fT/V1dVJkoqKiibH13xec76mpiabbbZZs+s7d+5ceoS2trb2Hcdq3759OnbsWBqrpdq2/Vjlny3Sps2GP0cAWJ/8LN0w+PsIAGvHz9LVPlYh3cdFeXlZunbtVHQbAEArV1HRsegWAAAK555otY9VSNe5c+ckq1fBde/evXS8pqamyfmKioq8/PLLza6vrq4u1axZabdmRd0adXV1WbFiRamuJRoaGlNTs7zF139ctGlT7l8kAFgLNTUrUl/fUHQbrCX3RACwdjb0e6KKio4faLXgxyqkW7N/XFVVVZO95KqqqtKuXbv06tWrVDd37tw0NjY22Zdu/vz52W677ZIkm2yyST71qU8123tu/vz5aWxsbLZX3Ye1atWG+w8XALBu1Nc3uGcAADZ67olW+1g99NurV6/07t07s2fPbnJ81qxZGTJkSOktrcOHD091dXXmzp1bqpk/f36efPLJDB8+vHRs+PDhufvuu7Ny5comY1VUVGTQoEHreTYAAAAAsFqrWkm3YsWK3HvvvUmShQsXZunSpaVAbrfddku3bt1y3HHH5eSTT87WW2+dwYMHZ9asWXn88cdz3XXXlcYZNGhQhg0blokTJ+aUU05Jhw4dcskll6Rfv3754he/WKobP358br311px00kk55JBD8vTTT2fGjBmZMGFCKfADAAAAgPWtVYV0r732Wk444YQmx9Z8njlzZgYPHpz9998/K1asyPTp03PVVVelT58+mTZtWrOVb1OmTMl5552XSZMmZdWqVRk2bFhOP/30tG37f1PeZpttMmPGjEyePDlHHXVUunXrluOPPz7f/va31/9kAQAAAOD/V9bY2NhYdBMbmvr6hixZsqzoNta7tm3L07Vrp0z80aw8v/D1otsBgI+N3lt1zQ9PGJ3XX19m/5UNgHsiAGiZjeWeqFu3Th/oxREfqz3pAAAAAGBDJKQDAAAAgIIJ6QAAAACgYEI6AAAAACiYkA4AAAAACiakAwAAAICCCekAAAAAoGBCOgAAAAAomJAOAAAAAAompAMAAACAggnpAAAAAKBgQjoAAAAAKJiQDgAAAAAKJqQDAAAAgIIJ6QAAAACgYEI6AAAAACiYkA4AAAAACiakAwAAAICCCekAAAAAoGBCOgAAAAAomJAOAAAAAAompAMAAACAggnpAAAAAKBgQjoAAAAAKJiQDgAAAAAKJqQDAAAAgIIJ6QAAAACgYEI6AAAAACiYkA4AAAAACiakAwAAAICCCekAAAAAoGBCOgAAAAAomJAOAAAAAAompAMAAACAggnpAAAAAKBgQjoAAAAAKJiQDgAAAAAKJqQDAAAAgIIJ6QAAAACgYEI6AAAAACiYkA4AAAAACiakAwAAAICCCekAAAAAoGBCuiTPPfdcjjjiiAwcODBDhw7NBRdckLq6uqLbAgAAAGAj0bboBopWXV2dww8/PL17987UqVOzaNGiTJ48OW+++WYmTZpUdHsAAAAAbAQ2+pDuhhtuyLJlyzJt2rR06dIlSVJfX5+zzz47Rx99dHr06FFsgwAAAABs8Db6x13nzJmTIUOGlAK6JBk1alQaGhpy//33F9cYAAAAABuNjT6kq6qqSmVlZZNjFRUV6d69e6qqqgrqCgAAAICNyUb/uGtNTU0qKiqaHe/cuXOqq6tbNGZ5eVm6deu0tq21emVlq///lPEjUl/fUGwzAPAx0qbN6v9O2rlzxzQ2FtwMa809EQC0zMZyT1ReXvaB6jb6kG59KCsrS5s2H+xvwIag86afKLoFAPhYKi/f6B9q2KC4JwKAlnFPtNpG/6tQUVGR2traZserq6vTuXPnAjoCAAAAYGOz0Yd0lZWVzfaeq62tzeLFi5vtVQcAAAAA68NGH9INHz48f/rTn1JTU1M6Nnv27JSXl2fo0KEFdgYAAADAxqKssXFD3prv/VVXV2e//fZLnz59cvTRR2fRokWZPHlyDjjggEyaNKno9gAAAADYCGz0IV2SPPfccznnnHMyb968dOrUKWPGjMmECRPSvn37olsDAAAAYCMgpAMAAACAgm30e9IBAAAAQNGEdAAAAABQMCEdAAAAABRMSAcAAAAABRPSAQAAAEDBhHQAAAAAUDAhHcAG5LnnnssRRxyRgQMHZujQobngggtSV1dXdFsAAB+pF154IZMmTcqYMWOyww47ZP/99y+6JYD31bboBgBYN6qrq3P44Yend+/emTp1ahYtWpTJkyfnzTffzKRJk4puDwDgI/PMM8/k3nvvzec+97k0NDSksbGx6JYA3peQDmADccMNN2TZsmWZNm1aunTpkiSpr6/P2WefnaOPPjo9evQotkEAgI/IiBEjss8++yRJTj311DzxxBMFdwTw/jzuCrCBmDNnToYMGVIK6JJk1KhRaWhoyP33319cYwAAH7Hycn/UBT5+/M4FsIGoqqpKZWVlk2MVFRXp3r17qqqqCuoKAACAD0JIB7CBqKmpSUVFRbPjnTt3TnV1dQEdAQAA8EEJ6QAAAACgYEI6gA1ERUVFamtrmx2vrq5O586dC+gIAACAD0pIB7CBqKysbLb3XG1tbRYvXtxsrzoAAABaFyEdwAZi+PDh+dOf/pSamprSsdmzZ6e8vDxDhw4tsDMAAADeT9uiGwBg3Rg7dmyuvfbafO9738vRRx+dRYsW5YILLsjYsWPTo0ePotsDAPjIrFixIvfee2+SZOHChVm6dGlmz56dJNltt93SrVu3ItsDeEdljY2NjUU3AcC68dxzz+Wcc87JvHnz0qlTp4wZMyYTJkxI+/bti24NAOAj89JLL2Xvvfd+x3MzZ87M4MGDP+KOAN6fkA4AAAAACmZPOgAAAAAomJAOAAAAAAompAMAAACAggnpAAAAAKBgQjoAAAAAKJiQDgAAAAAKJqQDAAAAgIIJ6QAAAACgYEI6AICNTL9+/TJ16tR1Oua4ceMybty4dTomAMDGREgHAFCAX/7yl+nXr1/69euXhx9+uNn5xsbG7LnnnunXr1+OPvro9drLI488kqlTp6ampma9fs8/a2hoyK9+9ascfPDB2W233TJo0KCMHDky//Ef/5FHH320VPfss89m6tSpeemll1r8Xbfeemt++tOfrn3TAADriZAOAKBAHTp0yG233dbs+EMPPZSXX3457du3X+ff+fjjj+e73/1u6fO8efMybdq0jzykO/fcc3PKKaeke/fuOfbYY3PyySdnjz32yGOPPZY//vGPpbpnn30206ZNy8KFC1v8Xbfddltmzpy5LtoGAFgv2hbdAADAxmzPPffM7Nmzc/rpp6dt2/+7Nbvtttuy44475o033lgn39PQ0JCVK1emQ4cO6dChwzoZc228+uqruf766/O1r30t55xzTpNzjY2NWbJkSUGdAQAUw0o6AIAC7bfffnnjjTdy//33l47V1dXljjvuyAEHHNCsfsaMGRk7dmwGDx6cAQMG5Ctf+Upmz57drK5fv375wQ9+kN/85jfZb7/90r9//9LqtLfvSTd16tRccMEFSZK999679AjumkdLb7nllhx22GEZMmRIdtppp4wePTrXX3/9Ws/7pZdeSmNjY3beeedm58rKyrL55psnWf1Y8AknnJAkOeyww0r9Pfjgg0mS3/3udznqqKMybNiw7LTTTtlnn31y2WWXpb6+vjTeuHHj8oc//CELFy4sXT9ixIjS+G+f7xoPPvhgk+9Jkueffz7HHXdchg4dmv79+2f48OGZMGFCamtr1/rXAwDASjoAgAJttdVWGThwYG6//fbsueeeSZI5c+aktrY2o0ePzrXXXtukfubMmRkxYkQOOOCArFy5MrfffntOOOGEXHnllfn85z/fpPaBBx7Ib3/723zjG99I165ds9VWWzX7/i984Qt5/vnnc9ttt+W0005L165dkyTdunVLkvzXf/1XPvOZz2TEiBFp27Zt7rnnnpx99tlpbGzMN77xjRbP+9Of/nSSZPbs2dl3333TsWPHd6zbddddM27cuFx77bU55phjUllZmSTp27dvkuS///u/s8kmm+SII47IJptskgceeCCXXnppli5dmlNOOSVJcswxx6S2tjYvv/xyTjvttCRJp06dPlS/dXV1GT9+fOrq6vLNb34zn/zkJ7No0aL84Q9/SE1NTTbbbLMW/ToAAKwhpAMAKNgBBxyQiy66KG+++WY+8YlP5NZbb82uu+6aHj16NKu944478olPfKL0+Rvf+Ea+8pWv5JprrmkW0s2fPz+33nprtt1223f97s9+9rPZYYcdctttt2WfffZJz549m5y/7rrrmnzfN7/5zYwfPz7XXHPNWoV0W2yxRb70pS/lV7/6Vfbcc8/stttu2XnnnbPnnnuWArgk6dWrV3bZZZdce+21+X//7/9l8ODBTca56KKLmvR3yCGHZNKkSfmv//qvTJgwIe3bt8/QoUMzc+bM1NTUZMyYMS3q97nnnstLL72UH/3oR9l3331Lx4899tgWjQcA8M887goAULBRo0blrbfeyj333JOlS5fmD3/4wzs+6pqkSSBVXV2d2tra/Mu//EuefPLJZrW77rrrewZ0H8Tbv6+2tjZLlizJbrvtlgULFqz1Y57nnXdeJk2alJ49e+auu+7K+eefn9GjR+fwww/PokWLPnR/S5cuzZIlS7LLLrtkxYoVqaqqWqv+3m7TTTdNktx3331ZsWLFOhsXAGANK+kAAArWrVu3DBkyJLfddlvefPPN1NfXZ+TIke9Ye8899+Tyyy/PU089lbq6utLxsrKyZrX/vCquJf785z9n6tSpefTRR5uFU7W1tWv1mGd5eXm+8Y1v5Bvf+EZef/31PPLII7nhhhsyZ86cTJgw4QPtfffMM89kypQpeeCBB7J06dJm/a0rvXr1yhFHHJFrrrkmt956a3bZZZeMGDEiBx54oEddAYB1QkgHANAK7L///jnjjDPy6quvZvjw4amoqGhW8/DDD+e73/1udt1115x55pnp3r172rVrl1tuuSW33XZbs/q3rzJriRdffDHf+ta3UllZmVNPPTWf+tSn0q5du9x777356U9/moaGhrUa/+26du2avffeO3vvvXfGjRuXhx56KAsXLnzHffTWqKmpyTe/+c1suummOf7447P11lunQ4cO+ctf/pILL7zwA/X3TuFmkne89tRTT82Xv/zl3H333bn//vtz7rnn5sorr8yNN96YLbfc8oNPFgDgHQjpAABagS984Qs588wz8+ijj+aSSy55x5o77rgjHTp0yIwZM9K+ffvS8VtuuWWtvvvdgqrf//73qaury+WXX1560UOSJm88XR922mmnPPTQQ1m8eHG22mqrd+3voYceyhtvvJFp06Zl1113LR3/5ze1Ju8+xzVh6D+vulu4cOE71q95O+y//uu/5pFHHskhhxxS2v8OAGBtCOkAAFqBTp065ayzzsrChQszYsSId6xp06ZNysrKUl9fXzr20ksv5e67716r717zZtV/DqratGmTJGlsbCwdq62tXetQMEkWL16c6urqZnvm1dXVZe7cuSkvL8/WW2/9nv2Vl5c366+uru4dH5Pt2LHjOz7+uuY7/ud//ifbb799kqS+vj433nhjk7qlS5fmE5/4RNq2/b/b5+222y7l5eVNHjsGAGgpIR0AQCvx5S9/+T3P77nnnrnmmmvyne98J/vvv39ee+21XH/99dl6663zt7/9rcXfu+OOOyZJLrnkkowePTrt2rXLXnvtlaFDh6Zdu3Y55phjMnbs2Cxbtiw33XRTNt988yxevLjF35ckL7/8cg4++ODsvvvuGTJkSD75yU/mtddey+23356//vWvOfzww9OtW7ckyfbbb582bdpk+vTpqa2tTfv27bP77rtn0KBB6dy5c0499dSMGzcuZWVl+fWvf90ktHv7HGfNmpXzzjsv/fv3zyabbJIRI0bkM5/5TAYOHJiLL7441dXV6dy5c2bNmpVVq1Y1uf6BBx7ID37wg+y7777p3bt36uvr8+tf/zpt2rR51/0DAQA+DCEdAMDHxJAhQ/Kf//mfmT59en74wx+mZ8+eOfnkk7Nw4cK1CukGDBiQE044ITfccEP++Mc/pqGhIXfffXcqKytz6aWXZsqUKTn//PPzyU9+Mocccki6deuWiRMnrtVc+vTpk4kTJ+bee+/N9ddfn9deey3t27fPdtttl3PPPTcHHXRQqbZ79+45++yzc+WVV+b73/9+6uvrM3PmzAwePDhXXHFFzj///EyZMiUVFRU58MADM2TIkIwfP77J9x166KF56qmn8stf/jI//elPs9VWW5VWLF544YWZNGlSrrrqqlRUVOSggw7K4MGDc8QRR5Su79evX4YNG5Z77rknixYtSseOHdOvX79Mnz49AwcOXKtfCwCAJClrfKf/1AgAAAAAfGTKi24AAAAAADZ2HncFAGCdWrJkSZOXW/yzdu3apUuXLh9dQwAAHwMedwUAYJ0aMWJEFi5c+K7nd9ttt1x77bUfYUcAAK2fkA4AgHXqz3/+c9566613PV9RUZGddtrpI+wIAKD1E9IBAAAAQMG8OAIAAAAACiakAwAAAICCCekAAAAAoGBCOgAAAAAomJAOAAAAAAompAMAAACAggnpAAAAAKBgQjoAAAAAKNj/ByL2ylIUfRjJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "martial=data.groupby(['Marital_Status','Gender'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
        "#sns.boxplot(data=data,x='Marital_Status',y='Orders')\n",
        "sns.set(rc={'figure.figsize':(6,5)})\n",
        "sns.barplot(data=data,x='Marital_Status',y='Amount',hue='Gender')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "3wzKrC7HPF-D",
        "outputId": "f6e1d32c-1d76-40e7-acf6-335c30a8b940"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 181
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(20,5)})\n",
        "ax = sns.countplot(data = data, x = 'Occupation')\n",
        "\n",
        "for bars in ax.containers:\n",
        "    ax.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "hUPM5a0JRv5w",
        "outputId": "dd92ccbd-a788-48fb-c594-9487b2287042"
      },
      "execution_count": 183,
      "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 = data.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": 343
        },
        "id": "-woG5S5lTFI6",
        "outputId": "508a9cf6-6d8e-4bc2-aeaf-a9ba0ec8b310"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Occupation', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 184
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(20,5)})\n",
        "ax = sns.countplot(data = data, x = 'Product_Category')\n",
        "\n",
        "for bars in ax.containers:\n",
        "    ax.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "2Wcl0CncTK08",
        "outputId": "fbf47044-ff31-4a2d-b1fa-9f45a0b56013"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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CQnYoNPS04uLiVaZMGXXq9KI6dXohT+8/AAByq8f5bJDq9u0ExccnKD4+TgcP7ldw8HrVrFmLL8ggw/3110E1bNjYHNhIkru7u7y96+m330IUFxcnJycnpaSk6JtvVsnPr4WqV6+ppKQkJSUlmU+MAUBOdCEySuEXbmR3GXhMly9HqH//3ipUqJBefLGbnJ2ddfToYQUFLdCJE8c1deoMSdLPP2/Wtm0/y8vLS+7uxbK56ryhXr1n9OmncxUbG6M///xDp0+fUnx8fHaX9UCENnnItWtXJSnNb90VLequ6Ogo3blzRw4ODrp27aqKFHGzmlQ0ddurV69mfsFAOiUkJOi998bI0dFRb7wxLLvLyVadO7+oZ55pYLHMZDJp+vQPVbLkUypWrLhCQ09r5syP9fTT9dW9+ytyciqovXv/p08+maqjRw9r/Pj3s6l6AACQWR7ns0GqNWtWacGCOebHTz/dQOPGBWZ+schzEhPvyNHR0Wq5o2N+JSYmKjT0jGrWrKXw8FBdvXpFFStW0rRpH2jz5g1KTExUxYqVNGLEW6pX75lsqB4AkBf99FOwYmNjNG/eYvNt/Dt3flEpKSnavHmjoqOj5ezsrIEDh2jMmPGyt7fXf/4zUqGhZ7K58tzPza2oeQqGFi1aa+nSLzRq1BCtWvUdc9og+92+fVuSlC+fg9U6BwdHcxsHBwfz/63bOVj0Bdia5ORkTZjwjsLDwzR9+qw8/62FmjVrq2bN2hbL/vrroBISEtS27bOSJDc3d3311SqLuYGef76Lpkx5X8HB6/Xaa/1UpkzZLK0bAABkrsf5bJCqTZt2qlq1mm7evKHffgvR9evX+VyATFGuXHkdPXpYycnJ5qu+ExMTdezYEUnS1auRkqRz585Jktas+VqFC7vo7bfHSbo7X9Po0cO0aNFSVapUORtGAADIa27duiXJen7mokXdZTQalS9fPknK8+epbEHz5q20cOE87dq1Q88/3yW7y0kTM1vlIanfVEpMvGO17s6d2xZtHB0ddedOWu3uWLQDbM20aZP1228hGjdugp5+un52l2OTfv55swwGg9q0uRvauLq6WgQ2qfz8WkiSwsPDsrQ+AACQ+R7ns0GqkiVLqX79hmrT5llNmDBZTz1VWiNHDtbt2wmZXzDylBdeeEnnzv2jqVMnKSwsVKGhpzV5cqD5CrHUsDA+Pk6SFBcXp88+myd//47y9++omTPnyWQy6euvl2bbGAAAeYu399OSpKlT/6tTp07o8uUIbdu2RT/88K1eeqm7ChQokM0VIlXq+4hbt2KzuZL740qbPCT1cq/UN7r3unbtqpydXczfpCta1F379++TyWSyuEVa6rbu7rZ56RjytrlzP1Nw8HoNHz7aHEjAUlJSkrZv/1k1a9ZWqVJPPbDt9evXJN0NdQAAQO7yOJ8N7qdFi1Zav/57HTx4QA0bNs6UOpE3Pf/8S7p8+bJWrlxmnp+xatXq6tmzl5Yu/UIFCjhJ+r9gsVatOipRoqR5+5IlS6p27bo6fPhQ1hcPAMiTGjVqon79BmnZsiUKCdlpXt6r1+saMGBwNlaWd928eVMuLi5W039s2PCDpLvvLWwVoU0eUqxYcbm6FtHffx+3Wnfs2FFVrlzF/LhSpSpav/4HhYeHycPD07z86NG7l6NXruyV+QUDj+Hrr5dq5cplCgjoo27demR3OTbr99/3KCoqSm3btn9gu8TERK1Zs1KlSpW26T9iAAAgfR7ns8H9pF5hExtru99SRM41cOAQ9egRoLCwUBUqVEgVK1bSggVzJUlly5aT9H+3mClSxM1q+yJFiujkyRNZVzAAIM8rVeop1alTT82bt5SLi4t++y1Ey5YtUdGiRdWlS/fsLi9HWLt2tWJiYszzqe/evVORkZclSS+99LIKFSqkiIhL2rx5oySZ38t++eViSXevDH/22eckSVu2BOuHH9bK17e5nnqqtOLi4rR37x798cfvatrU16bv0ENok8c0b95SmzZt0OXLEeZvIu3bt1fnzv2j7t17mtv5+jbT7Nkz9P333+jNN8dIujt5+bp1a1WsWHGrOTLykuPHj2rTpg3av/9PRURclLOzi2rUqKX+/d9QuXLlLdqmpKRo3brvtG7dd/rnn7PKnz+/KlWqrGHD3nykD8K5WUYehHfs2K5582apTJlyqlDBQz/9FGzxXPXrN7S6p2he9fPPm2Vvb6+WLVs/sN2MGR8pPDxUH388U/b2/KkAACA3etTPBjdu3FCRIkWstt+wYZ0MBoO8vKpmWc3IW5ydnVWnTl3z4337flfx4iVUvnwFSVLFipVkb2+vq1evWG179epVubpav24BAMgMW7f+pI8++kArV36n4sVLSJKaNWspk8mkzz+frdat28nFxTV7i8wBVq5croiIS+bHO3Zs144d2yVJ7dr5q1ChQrp48YIWL55vsV3q47p165nPF6Zedbt160+6ceO67OzsVLZseQ0bNsrmQzTOxOUij3ISPCCgj7Zv36rhwwepa9eXFR8fr6+/XqaKFSvJ37+Tua/ixUuoW7ce+vrrZUpKSlK1ajW0a9ev+uuvAwoMnGyeDDIvWrHiKx0+/JdatGitihUr69q1q/ruu2/Ut++rWrBgiTw9K5nbfvjhf7VlyyY9++xz6tKlm+Lj43Xq1AndvHk9G0dgGzLyIHz69ElJ0vnz/2jSpECr55o1az6hje7e6zskZIcaNGj8wDcKX3+9VOvXf69+/QapcWOfrCsQAABkmIz8bLB06Rc6fPgvNWzYWCVKlFR0dJR27PhFx48f00svdVeZMmWzZYzIW7Zt26Ljx49pyJCRMhrvTs/r5FRQjRs31W+/hejs2XBzmBMeHqYjRw6pU6cXs7FiAEBe8v3336pKFS9zYJOqaVM/BQev18mTJ1S/fsNsqi7n+Pbb9Q9tU6/eMwoJ2ffQdlWrVtekSVMzoqwsR2iTizzKSfASJUpq9uyFmjPnU82fP0f29vnUpImPhg4daXXP6kGDhqlwYWetW/edNm3aoDJlyiowcJLats3bc4V07/6KJkz4QPny5TMva9WqrXr3flnLl3+lwMBJkqRt237Wpk0b9MEHH6tZsxbZVa7NysiDcN++A9W378CMKCtX27XrVyUkJDzwdzg4eL0+/3y2nn++i157rV+W1QYAADJWRn42aNKkqS5cOK+NG3/UzZs35ODgqIoVK2ncuAlq375Dlo8Nud/Bg/u1ZMliNWjQUC4uLjp69IiCg9erYcMm6tr1ZYu2AwYM0b59f2j48EF66aW76779dpUKF3ZWr159sqN8AEAedP36NRUu7Gy1PCkpSZKUnJyc1SUhByO0yUUe5SS4JHl6VtSMGXMe2s5oNCogoI8CAnije69atepYLStbtpwqVPBUeHiYednq1StUrVoNNWvWQikpKbp9+7YKFCiQlaUCFrZs2awCBZzk49MszfW7dv2qadMmq1mzFubbIgIAgJwpIz8b1K/fSPXrN8qIsoBHUqxYcdnZGbVy5TLFxcWpVKmn1K/fG3r55Vesbt3r4eGpOXMW6vPPZ2np0iAZDEY9/fQzGjx4hIoVK55NIwAA5DVly5bXH3/8T//8c9Zi+oStW3+S0WhUpUqVs7E65DSENkAGMJlMunHjujw8PCVJt27F6vjxo3rhhZe0YMFcffvtasXHx6lUqdIaNGioWrVqk80VI6+5ceOG9u37Xa1bt1P+/Pmt1h88uF8TJryrOnW8FRg42XzLCQAAACCrlS5d5pG+aJjKy6uqZs6cl4kVAQDwYD17Buj333/TkCH91aVLNzk7u+i333bpf//7TR07Pi9392KSpNOnTykkZIck6fz5c4qNjTXP31ypUhX5+Phl2xgel9FokNFoyO4ybEpKikkpKaYn7ofQBsgAW7Zs0pUrkeZbdF24cF4mk0nbtm2RnZ29Bg8eroIFC+mbb1Zq4sRxKliwoBo1apLNVT86DsLWMuognFV++WWLkpOT1bZte6t1ERGXNHbsmzIYpObNW2n79q0W6ytWrMw3QgAAAAAAAO6jbt16+vzzIH3xxUJ9//03ioqKUqlST2nAgMHq2bOXud3Jk3/fd/7m9u075JjQxmg0yNXVSXZ2fOn3XsnJKbp5M+6JzxkS2mQTToJby2knwVOdPRuuGTOmqWbN2uZ7esfHx0uSoqKitGDBl6pRo6YkycfHT926ddJXXwXlmNCGg3DaMuognFW2bNmsIkXc9MwzDazWXbx4QbGxsZKkGTOmWa3v06c/oQ0AAJmIzwbWcupnAwAAkHdVr15T06fPemAbf/+O8vfvmEUVZR6j0SA7O6PmrtytC5FR2V2OTShd3EVDejSV0WggtMmJOAmetpx2ElySrl27qrffHqGCBQtp8uRpsrOzkyQ5OjpKkkqVKm0ObCTJyclJTZr4asuWTUpKSrK6H7Mt4iBsLSMPwlllwYIl911Xr94zCgnZl4XVAACAVHw2SFtO/GyQVxAyWiNkBADkVRcioxR+4UZ2l5Hr2P4Z41yIk+DWcuJJ8NjYWL311nDFxsZq7txF5ntTSjL/283NzWq7IkXclJSUpISEBBUqVCjL6n1SHIQBAAAyHp8NrOXEzwZ5BSFj2ggZAQBARiK0yUacBM+5bt++rTFjRuncuX80c+Y8eXh4Wqx3dy+mokWL6sqVSKttr169IgcHRzk5OWVVuQAAALBxfDZATkDIaI2QEQBsH1eJWuMqUdtGaAM8puTkZE2Y8I6OHDmkqVM/Uc2atdNs17JlW33zzUr98cf/VL9+I0nSzZs3FRKyQ08//YyMRr6dltfxpsEabxoAAABsHyEjACCn4CrRtHGVqG0jtAEe05w5MxUSslNNm/oqOjpaP/0UbLG+XTt/SVJAwGv65Zef9e67Y9S9e08VKlRIP/ywVklJSRowYEh2lA4bwpuGtPGmAQAAAAAAZBSuErXGVaK2j9AGeEynTp2QJO3evUu7d++yWp8a2ri5FdW8eYs1d+5nWrPmayUlJal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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_state = data.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": 333
        },
        "id": "6lyXnYQlVZbZ",
        "outputId": "5f7712a1-a098-460b-d442-7549bef90599"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Product_ID', ylabel='Orders'>"
            ]
          },
          "metadata": {},
          "execution_count": 195
        },
        {
          "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": [
        "pip install matplotlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EcU8kD3gV7_a",
        "outputId": "bc602c5b-3606-44e3-c6f0-f8604f9ce542"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.47.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install numpy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwhhhK9RWcAD",
        "outputId": "d64c9398-6cc1-4bea-d7d0-5edac6b5370f"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.23.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "A-2A0Ag2WMKZ"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Orders'].dtype\n",
        "data['Orders'] = pd.to_numeric(data['Orders'])"
      ],
      "metadata": {
        "id": "8uuTj9SvW8oC"
      },
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig1, ax1 = plt.subplots(figsize=(12,7))\n",
        "data.groupby('Product_ID')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "SnOicC6XV1wG",
        "outputId": "07dc1cc9-ac2d-440b-b73e-db7a600eca64"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Product_ID'>"
            ]
          },
          "metadata": {},
          "execution_count": 210
        },
        {
          "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": [
        "\n",
        "\n",
        "The provided line of code performs the following operations on the `data` DataFrame:\n",
        "\n",
        "1. **`data.groupby('Product_ID')['Orders'].sum()`**:\n",
        "   - This line groups the DataFrame by the `Product_ID` column and calculates the sum of the `Orders` column for each group.\n",
        "2. **`.nlargest(10)`**:\n",
        "   - This line selects the top 10 groups with the highest sum of orders.\n",
        "3. **`.sort_values(ascending=False)`**:\n",
        "   - This line sorts the resulting Series in descending order based on the sum of orders.\n",
        "4. **`.plot(kind='bar')`**:\n",
        "   - This line creates a bar plot using the sorted Series.\n",
        "\n",
        "The resulting plot visually depicts the top 10 product IDs with the highest number of orders, making it easier to identify the most popular products in the dataset. This information can be valuable for understanding customer preferences, optimizing inventory management, and making informed business decisions.\n",
        "\n",
        "Here's a breakdown of the output:\n",
        "\n",
        "- The x-axis represents the product IDs.\n",
        "- The y-axis represents the total number of orders for each product ID.\n",
        "- The bars are arranged in descending order, with the product ID with the highest number of orders on the leftmost side.\n",
        "\n",
        "By analyzing this plot, you can quickly identify the top-selling products and gain insights into customer preferences and market trends."
      ],
      "metadata": {
        "id": "4SAPnrQ4XiU2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**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": "bUu4empzXQI_"
      }
    }
  ]
}