Diwali Sales Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "43e831f8",
   "metadata": {},
   "source": [
    "# Python_Diwali_Sales_Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2821b7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79818cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <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>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11248</th>\n",
       "      <td>1001209</td>\n",
       "      <td>Oshin</td>\n",
       "      <td>P00201342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Textile</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>213.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11249</th>\n",
       "      <td>1004023</td>\n",
       "      <td>Noonan</td>\n",
       "      <td>P00059442</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>206.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>1002744</td>\n",
       "      <td>Brumley</td>\n",
       "      <td>P00281742</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>188.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11251 rows × 15 columns</p>\n",
       "</div>"
      ],
      "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     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]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Aayush\\Downloads\\Diwali Sales Data.csv\",encoding = 'unicode_escape')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "592bbc69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d425da29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5868f4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Status','unnamed1'],axis =1, inplace =  True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5e9c1ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b8a7254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID               int64\n",
       "Cust_name            object\n",
       "Product_ID           object\n",
       "Gender               object\n",
       "Age Group            object\n",
       "Age                   int64\n",
       "Marital_Status        int64\n",
       "State                object\n",
       "Zone                 object\n",
       "Occupation           object\n",
       "Product_Category     object\n",
       "Orders                int64\n",
       "Amount              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a3f825a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID              0\n",
       "Cust_name            0\n",
       "Product_ID           0\n",
       "Gender               0\n",
       "Age Group            0\n",
       "Age                  0\n",
       "Marital_Status       0\n",
       "State                0\n",
       "Zone                 0\n",
       "Occupation           0\n",
       "Product_Category     0\n",
       "Orders               0\n",
       "Amount              12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08760567",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values\n",
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d93abf02",
   "metadata": {},
   "outputs": [
    {
     "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",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2200a6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change data types of Amount\n",
    "df['Amount'] = df['Amount'].astype(int)\n",
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c195aaee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00dfc148",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID             3752\n",
       "Cust_name           1250\n",
       "Product_ID          2350\n",
       "Gender                 2\n",
       "Age Group              7\n",
       "Age                   81\n",
       "Marital_Status         2\n",
       "State                 16\n",
       "Zone                   5\n",
       "Occupation            15\n",
       "Product_Category      18\n",
       "Orders                 4\n",
       "Amount              6583\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bc4744c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1e3119d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.410357</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.753866</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age        Orders        Amount\n",
       "count  11239.000000  11239.000000  11239.000000\n",
       "mean      35.410357      2.489634   9453.610553\n",
       "std       12.753866      1.114967   5222.355168\n",
       "min       12.000000      1.000000    188.000000\n",
       "25%       27.000000      2.000000   5443.000000\n",
       "50%       33.000000      2.000000   8109.000000\n",
       "75%       43.000000      3.000000  12675.000000\n",
       "max       92.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age','Orders','Amount']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7933dd2b",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2cd49372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAGwCAYAAABIC3rIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3qElEQVR4nO3df3QU9b3/8deaX4aQjJCQXVaCBkkpSLAaekKoFeQ3Jaa99oAaukJBwCJglF9F+63UYxPBCrTNFdGLoIBN770l1mtpINiSK0IgRKOAgL+i/MqSVJNNoiHBZL5/eJjrkogYSDZhno9z5hz2M++deX9yTtyXn52ZOEzTNAUAAGBjVwS6AQAAgEAjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsLDnQDnUVTU5NOnjypyMhIORyOQLcDAAAugGmaqqmpkdvt1hVXfP06EIHoAp08eVJxcXGBbgMAALTCsWPH1KtXr6/dTyC6QJGRkZK+/IFGRUUFuBsAAHAhqqurFRcXZ32Ofx0C0QU6+zVZVFQUgQgAgE7mmy534aJqAABgewQiAABgewQiAABgewQiXJauvfZaORyOZtt9990nSaqtrdWcOXPUq1cvhYeHq3///lq9erXfMWbNmqXrrrtO4eHh6tGjh3784x/r8OHD1v6PPvpI06dPV3x8vMLDw3XdddfpkUceUUNDQ7vOFQBw8QIaiL744gv96le/sj5Q+vTpo0cffVRNTU1WjWmaWrp0qdxut8LDwzV8+HAdPHjQ7zj19fWaO3euYmJiFBERobS0NB0/ftyvprKyUh6PR4ZhyDAMeTweVVVVtcc0EQBFRUUqKyuztvz8fEnSxIkTJUkPPPCA8vLytHHjRh06dEgPPPCA5s6dq7/+9a/WMZKSkrRu3TodOnRIW7dulWmaGjNmjBobGyVJhw8fVlNTk9asWaODBw9q5cqVevrpp/XQQw+1/4QBABfHDKDHHnvMjI6ONl955RWztLTU/K//+i+za9eu5qpVq6yaxx9/3IyMjDT/8pe/mPv37zfvuOMOs2fPnmZ1dbVVc++995pXX321mZ+fb77xxhvmrbfeat5www3mF198YdWMGzfOHDhwoLlr1y5z165d5sCBA83U1NQL7tXn85mSTJ/Pd2kmj3Z1//33m9ddd53Z1NRkmqZpXn/99eajjz7qV3PTTTeZv/rVr772GG+99ZYpyXz//fe/tmb58uVmfHz8pWkaAHDRLvTzO6CBaMKECea0adP8xm6//XbzZz/7mWmaptnU1GS6XC7z8ccft/afPn3aNAzDfPrpp03TNM2qqiozJCTEzMnJsWpOnDhhXnHFFWZeXp5pmqb5zjvvmJLMwsJCq2b37t2mJPPw4cMt9nb69GnT5/NZ27FjxwhEnVR9fb0ZHR1t/va3v7XGZs2aZQ4ePNg8fvy42dTUZP7jH/8wu3btar722mstHqO2ttbMyMgw4+Pjzfr6+q8918MPP2wmJSVd8jkAAFrnQgNRQL8yu/nmm/Xqq6/q3XfflSS99dZb2rlzp370ox9JkkpLS+X1ejVmzBjrPWFhYRo2bJh27dolSSouLtaZM2f8atxutwYOHGjV7N69W4ZhKDk52aoZMmSIDMOwas6VlZVlfb1mGAZPqe7EXnrpJVVVVWnq1KnW2B/+8AcNGDBAvXr1UmhoqMaNG6ennnpKN998s997n3rqKXXt2lVdu3ZVXl6e8vPzFRoa2uJ5PvjgA/3xj3/Uvffe25bTAQC0gYAGosWLF+uuu+7Sd7/7XYWEhOjGG29URkaG7rrrLkmS1+uVJDmdTr/3OZ1Oa5/X61VoaKi6det23prY2Nhm54+NjbVqzrVkyRL5fD5rO3bs2MVNFgGzdu1ajR8/Xm632xr7wx/+oMLCQr388ssqLi7Wk08+qdmzZ2v79u1+7508ebLefPNNFRQUKCEhQZMmTdLp06ebnePkyZMaN26cJk6cqHvuuafN5wQAuLQC+qTqP//5z9q4caNefPFFXX/99SopKVFGRobcbremTJli1Z37dEnTNL/xiZPn1rRUf77jhIWFKSws7NtMBx3Qxx9/rO3bt2vz5s3WWF1dnR566CHl5uZqwoQJkqRBgwappKREv/vd7zRq1Cir9uwKYUJCgoYMGaJu3bopNzfXCu3Sl2Ho1ltvVUpKip555pn2mxwA4JIJaCBauHChfvnLX+rOO++UJCUmJurjjz9WVlaWpkyZIpfLJenLFZ6ePXta7ysvL7dWjVwulxoaGlRZWem3SlReXq6hQ4daNadOnWp2/oqKimarT7i8rFu3TrGxsVbwkaQzZ87ozJkzzf7qcVBQkN8dji0xTVP19fXW6xMnTujWW2+17kg7319SBgB0XAH9r/fnn39+3g+l+Ph4uVwu65ZpSWpoaFBBQYEVdpKSkhQSEuJXU1ZWpgMHDlg1KSkp8vl82rt3r1WzZ88e+Xw+qwaXn6amJq1bt05TpkxRcPD/Zf+oqCgNGzZMCxcu1I4dO1RaWqr169frhRde0L/9279Jkj788ENlZWWpuLhYR48e1e7duzVp0iSFh4db17idPHlSw4cPV1xcnH73u9+poqJCXq/3a7+GBQB0YO1wgffXmjJlinn11Vdbt91v3rzZjImJMRctWmTVPP7446ZhGObmzZvN/fv3m3fddVeLt9336tXL3L59u/nGG2+YI0aMaPG2+0GDBpm7d+82d+/ebSYmJnLb/WVu69atpiTzyJEjzfaVlZWZU6dONd1ut3nllVea/fr1M5988knrtvwTJ06Y48ePN2NjY82QkBCzV69eZnp6ut9dievWrTMltbgBADqGTnHbfXV1tXn//febvXv3Nq+88kqzT58+5sMPP+x3W3NTU5P5yCOPmC6XywwLCzNvueUWc//+/X7HqaurM+fMmWN2797dDA8PN1NTU82jR4/61XzyySfm5MmTzcjISDMyMtKcPHmyWVlZecG9EogAAOh8LvTz22GaphmgxalOpbq6WoZhyOfzKSoqKtDtAACAC3Chn98BvagazSUtfCHQLQAdTvETdwe6BQCXOW6JAQAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAgAAthfQQHTttdfK4XA02+677z5JkmmaWrp0qdxut8LDwzV8+HAdPHjQ7xj19fWaO3euYmJiFBERobS0NB0/ftyvprKyUh6PR4ZhyDAMeTweVVVVtdc0AQBABxfQQFRUVKSysjJry8/PlyRNnDhRkrR8+XKtWLFC2dnZKioqksvl0ujRo1VTU2MdIyMjQ7m5ucrJydHOnTtVW1ur1NRUNTY2WjXp6ekqKSlRXl6e8vLyVFJSIo/H076TBQAAHZbDNE0z0E2clZGRoVdeeUXvvfeeJMntdisjI0OLFy+W9OVqkNPp1LJlyzRr1iz5fD716NFDGzZs0B133CFJOnnypOLi4rRlyxaNHTtWhw4d0oABA1RYWKjk5GRJUmFhoVJSUnT48GH169fvgnqrrq6WYRjy+XyKiopqg9l/KWnhC212bKCzKn7i7kC3AKCTutDP7w5zDVFDQ4M2btyoadOmyeFwqLS0VF6vV2PGjLFqwsLCNGzYMO3atUuSVFxcrDNnzvjVuN1uDRw40KrZvXu3DMOwwpAkDRkyRIZhWDUtqa+vV3V1td8GAAAuTx0mEL300kuqqqrS1KlTJUler1eS5HQ6/eqcTqe1z+v1KjQ0VN26dTtvTWxsbLPzxcbGWjUtycrKsq45MgxDcXFxrZ4bAADo2DpMIFq7dq3Gjx8vt9vtN+5wOPxem6bZbOxc59a0VP9Nx1myZIl8Pp+1HTt27EKmAQAAOqEOEYg+/vhjbd++Xffcc4815nK5JKnZKk55ebm1auRyudTQ0KDKysrz1pw6darZOSsqKpqtPn1VWFiYoqKi/DYAAHB56hCBaN26dYqNjdWECROssfj4eLlcLuvOM+nL64wKCgo0dOhQSVJSUpJCQkL8asrKynTgwAGrJiUlRT6fT3v37rVq9uzZI5/PZ9UAAAB7Cw50A01NTVq3bp2mTJmi4OD/a8fhcCgjI0OZmZlKSEhQQkKCMjMz1aVLF6Wnp0uSDMPQ9OnTNX/+fEVHR6t79+5asGCBEhMTNWrUKElS//79NW7cOM2YMUNr1qyRJM2cOVOpqakXfIcZAAC4vAU8EG3fvl1Hjx7VtGnTmu1btGiR6urqNHv2bFVWVio5OVnbtm1TZGSkVbNy5UoFBwdr0qRJqqur08iRI7V+/XoFBQVZNZs2bdK8efOsu9HS0tKUnZ3d9pMDAACdQod6DlFHxnOIgMDhOUQAWqvTPYcIAAAgUAhEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9gIeiE6cOKGf/exnio6OVpcuXfS9731PxcXF1n7TNLV06VK53W6Fh4dr+PDhOnjwoN8x6uvrNXfuXMXExCgiIkJpaWk6fvy4X01lZaU8Ho8Mw5BhGPJ4PKqqqmqPKQIAgA4uoIGosrJSP/jBDxQSEqK///3veuedd/Tkk0/qqquusmqWL1+uFStWKDs7W0VFRXK5XBo9erRqamqsmoyMDOXm5ionJ0c7d+5UbW2tUlNT1djYaNWkp6erpKREeXl5ysvLU0lJiTweT3tOFwAAdFAO0zTNQJ38l7/8pV5//XW99tprLe43TVNut1sZGRlavHixpC9Xg5xOp5YtW6ZZs2bJ5/OpR48e2rBhg+644w5J0smTJxUXF6ctW7Zo7NixOnTokAYMGKDCwkIlJydLkgoLC5WSkqLDhw+rX79+39hrdXW1DMOQz+dTVFTUJfoJNJe08IU2OzbQWRU/cXegWwDQSV3o53dAV4hefvllDR48WBMnTlRsbKxuvPFGPfvss9b+0tJSeb1ejRkzxhoLCwvTsGHDtGvXLklScXGxzpw541fjdrs1cOBAq2b37t0yDMMKQ5I0ZMgQGYZh1Zyrvr5e1dXVfhsAALg8BTQQffjhh1q9erUSEhK0detW3XvvvZo3b55eeOHLVRKv1ytJcjqdfu9zOp3WPq/Xq9DQUHXr1u28NbGxsc3OHxsba9WcKysry7reyDAMxcXFXdxkAQBAhxXQQNTU1KSbbrpJmZmZuvHGGzVr1izNmDFDq1ev9qtzOBx+r03TbDZ2rnNrWqo/33GWLFkin89nbceOHbvQaQEAgE4moIGoZ8+eGjBggN9Y//79dfToUUmSy+WSpGarOOXl5daqkcvlUkNDgyorK89bc+rUqWbnr6ioaLb6dFZYWJiioqL8NgAAcHkKaCD6wQ9+oCNHjviNvfvuu7rmmmskSfHx8XK5XMrPz7f2NzQ0qKCgQEOHDpUkJSUlKSQkxK+mrKxMBw4csGpSUlLk8/m0d+9eq2bPnj3y+XxWDQAAsK/gQJ78gQce0NChQ5WZmalJkyZp7969euaZZ/TMM89I+vJrroyMDGVmZiohIUEJCQnKzMxUly5dlJ6eLkkyDEPTp0/X/PnzFR0dre7du2vBggVKTEzUqFGjJH256jRu3DjNmDFDa9askSTNnDlTqampF3SHGQAAuLwFNBB9//vfV25urpYsWaJHH31U8fHxWrVqlSZPnmzVLFq0SHV1dZo9e7YqKyuVnJysbdu2KTIy0qpZuXKlgoODNWnSJNXV1WnkyJFav369goKCrJpNmzZp3rx51t1oaWlpys7Obr/JAgCADiugzyHqTHgOERA4PIcIQGt1iucQAQAAdAQEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsBDURLly6Vw+Hw21wul7XfNE0tXbpUbrdb4eHhGj58uA4ePOh3jPr6es2dO1cxMTGKiIhQWlqajh8/7ldTWVkpj8cjwzBkGIY8Ho+qqqraY4oAAKATCPgK0fXXX6+ysjJr279/v7Vv+fLlWrFihbKzs1VUVCSXy6XRo0erpqbGqsnIyFBubq5ycnK0c+dO1dbWKjU1VY2NjVZNenq6SkpKlJeXp7y8PJWUlMjj8bTrPAEAQMcVHPAGgoP9VoXOMk1Tq1at0sMPP6zbb79dkvT888/L6XTqxRdf1KxZs+Tz+bR27Vpt2LBBo0aNkiRt3LhRcXFx2r59u8aOHatDhw4pLy9PhYWFSk5OliQ9++yzSklJ0ZEjR9SvX7/2mywAAOiQAr5C9N5778ntdis+Pl533nmnPvzwQ0lSaWmpvF6vxowZY9WGhYVp2LBh2rVrlySpuLhYZ86c8atxu90aOHCgVbN7924ZhmGFIUkaMmSIDMOwalpSX1+v6upqvw0AAFyeAhqIkpOT9cILL2jr1q169tln5fV6NXToUH3yySfyer2SJKfT6fcep9Np7fN6vQoNDVW3bt3OWxMbG9vs3LGxsVZNS7KysqxrjgzDUFxc3EXNFQAAdFwBDUTjx4/XT3/6UyUmJmrUqFH629/+JunLr8bOcjgcfu8xTbPZ2LnOrWmp/puOs2TJEvl8Pms7duzYBc0JAAB0PgH/yuyrIiIilJiYqPfee8+6rujcVZzy8nJr1cjlcqmhoUGVlZXnrTl16lSzc1VUVDRbffqqsLAwRUVF+W0AAODy1KECUX19vQ4dOqSePXsqPj5eLpdL+fn51v6GhgYVFBRo6NChkqSkpCSFhIT41ZSVlenAgQNWTUpKinw+n/bu3WvV7NmzRz6fz6oBAAD2FtC7zBYsWKDbbrtNvXv3Vnl5uR577DFVV1drypQpcjgcysjIUGZmphISEpSQkKDMzEx16dJF6enpkiTDMDR9+nTNnz9f0dHR6t69uxYsWGB9BSdJ/fv317hx4zRjxgytWbNGkjRz5kylpqZyhxkAAJAU4EB0/Phx3XXXXfrXv/6lHj16aMiQISosLNQ111wjSVq0aJHq6uo0e/ZsVVZWKjk5Wdu2bVNkZKR1jJUrVyo4OFiTJk1SXV2dRo4cqfXr1ysoKMiq2bRpk+bNm2fdjZaWlqbs7Oz2nSwAAOiwHKZpmoFuojOorq6WYRjy+Xxtej1R0sIX2uzYQGdV/MTdgW4BQCd1oZ/fHeoaIgAAgEAgEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANtrVSAaMWKEqqqqmo1XV1drxIgRF9sTAABAu2pVINqxY4caGhqajZ8+fVqvvfbaRTcFAADQnoK/TfHbb79t/fudd96R1+u1Xjc2NiovL09XX331pesOAACgHXyrQPS9731PDodDDoejxa/GwsPD9cc//vGSNQcAANAevlUgKi0tlWma6tOnj/bu3asePXpY+0JDQxUbG6ugoKBL3iQAAEBb+laB6JprrpEkNTU1tUkzAAAAgfCtAtFXvfvuu9qxY4fKy8ubBaRf//rXF90YAABAe2lVIHr22Wf1i1/8QjExMXK5XHI4HNY+h8NBIAIAAJ1KqwLRY489pt/+9rdavHjxpe4HAACg3bXqOUSVlZWaOHHipe4FAAAgIFoViCZOnKht27Zd6l4AAAAColVfmfXt21f/7//9PxUWFioxMVEhISF+++fNm3dJmgMAAGgPrQpEzzzzjLp27aqCggIVFBT47XM4HAQiAADQqbQqEJWWll7qPgAAAAKmVdcQAQAAXE5atUI0bdq08+5/7rnnWtUMAABAILQqEFVWVvq9PnPmjA4cOKCqqqoW/+grAABAR9aqQJSbm9tsrKmpSbNnz1afPn0uuikAAID2dMmuIbriiiv0wAMPaOXKla16f1ZWlhwOhzIyMqwx0zS1dOlSud1uhYeHa/jw4Tp48KDf++rr6zV37lzFxMQoIiJCaWlpOn78uF9NZWWlPB6PDMOQYRjyeDyqqqpqVZ8AAODyc0kvqv7ggw/0xRdffOv3FRUV6ZlnntGgQYP8xpcvX64VK1YoOztbRUVFcrlcGj16tGpqaqyajIwM5ebmKicnRzt37lRtba1SU1PV2Nho1aSnp6ukpER5eXnKy8tTSUmJPB5P6ycKAAAuK636yuzBBx/0e22apsrKyvS3v/1NU6ZM+VbHqq2t1eTJk/Xss8/qscce8zvmqlWr9PDDD+v222+XJD3//PNyOp168cUXNWvWLPl8Pq1du1YbNmzQqFGjJEkbN25UXFyctm/frrFjx+rQoUPKy8tTYWGhkpOTJX35x2lTUlJ05MgR9evXrzU/AgAAcBlp1QrRm2++6be9/fbbkqQnn3xSq1at+lbHuu+++zRhwgQr0JxVWloqr9erMWPGWGNhYWEaNmyYdu3aJUkqLi7WmTNn/GrcbrcGDhxo1ezevVuGYVhhSJKGDBkiwzCsmpbU19erurrabwMAAJenVq0Q/fOf/7wkJ8/JydEbb7yhoqKiZvu8Xq8kyel0+o07nU59/PHHVk1oaKi6devWrObs+71er2JjY5sdPzY21qppSVZWln7zm998uwkBAIBO6aKuIaqoqNDOnTv1+uuvq6Ki4lu999ixY7r//vu1ceNGXXnllV9b53A4/F6bptls7Fzn1rRU/03HWbJkiXw+n7UdO3bsvOcEAACdV6sC0WeffaZp06apZ8+euuWWW/TDH/5Qbrdb06dP1+eff35BxyguLlZ5ebmSkpIUHBys4OBgFRQU6A9/+IOCg4OtlaFzV3HKy8utfS6XSw0NDc2ei3RuzalTp5qdv6Kiotnq01eFhYUpKirKbwMAAJenVgWiBx98UAUFBfqf//kfVVVVqaqqSn/9619VUFCg+fPnX9AxRo4cqf3796ukpMTaBg8erMmTJ6ukpER9+vSRy+VSfn6+9Z6GhgYVFBRo6NChkqSkpCSFhIT41ZSVlenAgQNWTUpKinw+n/bu3WvV7NmzRz6fz6oBAAD21qpriP7yl7/ov//7vzV8+HBr7Ec/+pHCw8M1adIkrV69+huPERkZqYEDB/qNRUREKDo62hrPyMhQZmamEhISlJCQoMzMTHXp0kXp6emSJMMwNH36dM2fP1/R0dHq3r27FixYoMTEROsi7f79+2vcuHGaMWOG1qxZI0maOXOmUlNTucMMAABIamUg+vzzz1v8uik2NvaCvzK7EIsWLVJdXZ1mz56tyspKJScna9u2bYqMjLRqVq5cqeDgYE2aNEl1dXUaOXKk1q9fr6CgIKtm06ZNmjdvnnU3WlpamrKzsy9ZnwAAoHNzmKZpfts3jRw5UtHR0XrhhResC6Lr6uo0ZcoUffrpp9q+ffslbzTQqqurZRiGfD5fm15PlLTwhTY7NtBZFT9xd6BbANBJXejnd6tWiFatWqXx48erV69euuGGG+RwOFRSUqKwsDBt27at1U0DAAAEQqsCUWJiot577z1t3LhRhw8flmmauvPOOzV58mSFh4df6h4BAADaVKsCUVZWlpxOp2bMmOE3/txzz6miokKLFy++JM0BAAC0h1bddr9mzRp997vfbTZ+/fXX6+mnn77opgAAANpTqwKR1+tVz549m4336NFDZWVlF90UAABAe2pVIIqLi9Prr7/ebPz111+X2+2+6KYAAADaU6uuIbrnnnuUkZGhM2fOaMSIEZKkV199VYsWLbrgJ1UDAAB0FK1aIVq0aJGmT5+u2bNnq0+fPurTp4/mzp2refPmacmSJZe6RwAALKtXr9agQYOsvzOZkpKiv//97y3Wzpo1Sw6HQ6tWrfIbr6+v19y5cxUTE6OIiAilpaXp+PHj1v4dO3bI4XC0uBUVFbXl9BAgrQpEDodDy5YtU0VFhQoLC/XWW2/p008/1a9//etL3R8AAH569eqlxx9/XPv27dO+ffs0YsQI/fjHP9bBgwf96l566SXt2bOnxUs5MjIylJubq5ycHO3cuVO1tbVKTU1VY2OjJGno0KEqKyvz2+655x5de+21Gjx4cLvME+2rVV+ZndW1a1d9//vfv1S9AADwjW677Ta/17/97W+1evVqFRYW6vrrr5cknThxQnPmzNHWrVs1YcIEv3qfz6e1a9dqw4YN1t+93Lhxo+Li4rR9+3aNHTtWoaGhcrlc1nvOnDmjl19+WXPmzJHD4WjjGSIQWrVCBABAR9DY2KicnBx99tlnSklJkSQ1NTXJ4/Fo4cKFVkD6quLiYp05c8b6+5aS5Ha7NXDgQO3atavF87z88sv617/+palTp7bJPBB4F7VCBABAIOzfv18pKSk6ffq0unbtqtzcXA0YMECStGzZMgUHB2vevHktvtfr9So0NFTdunXzG3c6nfJ6vS2+Z+3atRo7dqzi4uIu7UTQYRCIAACdTr9+/VRSUqKqqir95S9/0ZQpU1RQUKC6ujr9/ve/1xtvvPGtv9oyTbPF9xw/flxbt27Vf/7nf16q9tEBEYgAAJ1OaGio+vbtK0kaPHiwioqK9Pvf/179+/dXeXm5evfubdU2NjZq/vz5WrVqlT766CO5XC41NDSosrLSb5WovLxcQ4cObXaudevWKTo6WmlpaW0/MQQM1xABADo90zRVX18vj8ejt99+WyUlJdbmdru1cOFCbd26VZKUlJSkkJAQ5efnW+8vKyvTgQMHmgUi0zS1bt063X333QoJCWnXOaF9sUIEAOhUHnroIY0fP15xcXGqqalRTk6OduzYoby8PEVHRys6OtqvPiQkRC6XS/369ZMkGYah6dOna/78+YqOjlb37t21YMECJSYmWnednfWPf/xDpaWlmj59ervND4FBIAIAdCqnTp2Sx+NRWVmZDMPQoEGDlJeXp9GjR1/wMVauXKng4GBNmjRJdXV1GjlypNavX6+goCC/urVr12ro0KHq37//pZ4GOhiHaZpmoJvoDKqrq2UYhnw+n6KiotrsPEkLX2izYwOdVfETdwe6BQCd1IV+fnMNEQAAsD0CEQAAsD2uIQKAdnL00cRAtwB0OL1/vT/QLUhihQgAAIBABAAAQCACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2F9BAtHr1ag0aNEhRUVGKiopSSkqK/v73v1v7TdPU0qVL5Xa7FR4eruHDh+vgwYN+x6ivr9fcuXMVExOjiIgIpaWl6fjx4341lZWV8ng8MgxDhmHI4/GoqqqqPaYIAAA6gYAGol69eunxxx/Xvn37tG/fPo0YMUI//vGPrdCzfPlyrVixQtnZ2SoqKpLL5dLo0aNVU1NjHSMjI0O5ubnKycnRzp07VVtbq9TUVDU2Nlo16enpKikpUV5envLy8lRSUiKPx9Pu8wUAAB2TwzRNM9BNfFX37t31xBNPaNq0aXK73crIyNDixYslfbka5HQ6tWzZMs2aNUs+n089evTQhg0bdMcdd0iSTp48qbi4OG3ZskVjx47VoUOHNGDAABUWFio5OVmSVFhYqJSUFB0+fFj9+vW7oL6qq6tlGIZ8Pp+ioqLaZvKSkha+0GbHBjqr4ifuDnQLl8TRRxMD3QLQ4fT+9f42Pf6Ffn53mGuIGhsblZOTo88++0wpKSkqLS2V1+vVmDFjrJqwsDANGzZMu3btkiQVFxfrzJkzfjVut1sDBw60anbv3i3DMKwwJElDhgyRYRhWTUvq6+tVXV3ttwEAgMtTwAPR/v371bVrV4WFhenee+9Vbm6uBgwYIK/XK0lyOp1+9U6n09rn9XoVGhqqbt26nbcmNja22XljY2OtmpZkZWVZ1xwZhqG4uLiLmicAAOi4Ah6I+vXrp5KSEhUWFuoXv/iFpkyZonfeecfa73A4/OpN02w2dq5za1qq/6bjLFmyRD6fz9qOHTt2oVMCAACdTMADUWhoqPr27avBgwcrKytLN9xwg37/+9/L5XJJUrNVnPLycmvVyOVyqaGhQZWVleetOXXqVLPzVlRUNFt9+qqwsDDr7rezGwAAuDwFPBCdyzRN1dfXKz4+Xi6XS/n5+da+hoYGFRQUaOjQoZKkpKQkhYSE+NWUlZXpwIEDVk1KSop8Pp/27t1r1ezZs0c+n8+qAQAA9hYcyJM/9NBDGj9+vOLi4lRTU6OcnBzt2LFDeXl5cjgcysjIUGZmphISEpSQkKDMzEx16dJF6enpkiTDMDR9+nTNnz9f0dHR6t69uxYsWKDExESNGjVKktS/f3+NGzdOM2bM0Jo1ayRJM2fOVGpq6gXfYQYAAC5vAQ1Ep06dksfjUVlZmQzD0KBBg5SXl6fRo0dLkhYtWqS6ujrNnj1blZWVSk5O1rZt2xQZGWkdY+XKlQoODtakSZNUV1enkSNHav369QoKCrJqNm3apHnz5ll3o6WlpSk7O7t9JwsAADqsDvccoo6K5xABgcNziIDLF88hAgAA6CAIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYCGoiysrL0/e9/X5GRkYqNjdVPfvITHTlyxK/GNE0tXbpUbrdb4eHhGj58uA4ePOhXU19fr7lz5yomJkYRERFKS0vT8ePH/WoqKyvl8XhkGIYMw5DH41FVVVVbTxEAAHQCAQ1EBQUFuu+++1RYWKj8/Hx98cUXGjNmjD777DOrZvny5VqxYoWys7NVVFQkl8ul0aNHq6amxqrJyMhQbm6ucnJytHPnTtXW1io1NVWNjY1WTXp6ukpKSpSXl6e8vDyVlJTI4/G063wBAEDH5DBN0wx0E2dVVFQoNjZWBQUFuuWWW2SaptxutzIyMrR48WJJX64GOZ1OLVu2TLNmzZLP51OPHj20YcMG3XHHHZKkkydPKi4uTlu2bNHYsWN16NAhDRgwQIWFhUpOTpYkFRYWKiUlRYcPH1a/fv2+sbfq6moZhiGfz6eoqKg2+xkkLXyhzY4NdFbFT9wd6BYuiaOPJga6BaDD6f3r/W16/Av9/O5Q1xD5fD5JUvfu3SVJpaWl8nq9GjNmjFUTFhamYcOGadeuXZKk4uJinTlzxq/G7XZr4MCBVs3u3btlGIYVhiRpyJAhMgzDqjlXfX29qqur/TYAAHB56jCByDRNPfjgg7r55ps1cOBASZLX65UkOZ1Ov1qn02nt83q9Cg0NVbdu3c5bExsb2+ycsbGxVs25srKyrOuNDMNQXFzcxU0QAAB0WB0mEM2ZM0dvv/22/vSnPzXb53A4/F6bptls7Fzn1rRUf77jLFmyRD6fz9qOHTt2IdMAAACdUIcIRHPnztXLL7+sf/7zn+rVq5c17nK5JKnZKk55ebm1auRyudTQ0KDKysrz1pw6darZeSsqKpqtPp0VFhamqKgovw0AAFyeAhqITNPUnDlztHnzZv3jH/9QfHy83/74+Hi5XC7l5+dbYw0NDSooKNDQoUMlSUlJSQoJCfGrKSsr04EDB6yalJQU+Xw+7d2716rZs2ePfD6fVQMAAOwrOJAnv++++/Tiiy/qr3/9qyIjI62VIMMwFB4eLofDoYyMDGVmZiohIUEJCQnKzMxUly5dlJ6ebtVOnz5d8+fPV3R0tLp3764FCxYoMTFRo0aNkiT1799f48aN04wZM7RmzRpJ0syZM5WamnpBd5gBAIDLW0AD0erVqyVJw4cP9xtft26dpk6dKklatGiR6urqNHv2bFVWVio5OVnbtm1TZGSkVb9y5UoFBwdr0qRJqqur08iRI7V+/XoFBQVZNZs2bdK8efOsu9HS0tKUnZ3dthMEAACdQod6DlFHxnOIgMDhOUTA5YvnEAEAAHQQBCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7AQ1E//u//6vbbrtNbrdbDodDL730kt9+0zS1dOlSud1uhYeHa/jw4Tp48KBfTX19vebOnauYmBhFREQoLS1Nx48f96uprKyUx+ORYRgyDEMej0dVVVVtPDsAANBZBDQQffbZZ7rhhhuUnZ3d4v7ly5drxYoVys7OVlFRkVwul0aPHq2amhqrJiMjQ7m5ucrJydHOnTtVW1ur1NRUNTY2WjXp6ekqKSlRXl6e8vLyVFJSIo/H0+bzAwAAnUNwIE8+fvx4jR8/vsV9pmlq1apVevjhh3X77bdLkp5//nk5nU69+OKLmjVrlnw+n9auXasNGzZo1KhRkqSNGzcqLi5O27dv19ixY3Xo0CHl5eWpsLBQycnJkqRnn31WKSkpOnLkiPr169c+kwUAAB1Wh72GqLS0VF6vV2PGjLHGwsLCNGzYMO3atUuSVFxcrDNnzvjVuN1uDRw40KrZvXu3DMOwwpAkDRkyRIZhWDUtqa+vV3V1td8GAAAuTx02EHm9XkmS0+n0G3c6ndY+r9er0NBQdevW7bw1sbGxzY4fGxtr1bQkKyvLuubIMAzFxcVd1HwAAEDH1WED0VkOh8PvtWmazcbOdW5NS/XfdJwlS5bI5/NZ27Fjx75l5wAAoLPosIHI5XJJUrNVnPLycmvVyOVyqaGhQZWVleetOXXqVLPjV1RUNFt9+qqwsDBFRUX5bQAA4PLUYQNRfHy8XC6X8vPzrbGGhgYVFBRo6NChkqSkpCSFhIT41ZSVlenAgQNWTUpKinw+n/bu3WvV7NmzRz6fz6oBAAD2FtC7zGpra/X+++9br0tLS1VSUqLu3burd+/eysjIUGZmphISEpSQkKDMzEx16dJF6enpkiTDMDR9+nTNnz9f0dHR6t69uxYsWKDExETrrrP+/ftr3LhxmjFjhtasWSNJmjlzplJTU7nDDAAASApwINq3b59uvfVW6/WDDz4oSZoyZYrWr1+vRYsWqa6uTrNnz1ZlZaWSk5O1bds2RUZGWu9ZuXKlgoODNWnSJNXV1WnkyJFav369goKCrJpNmzZp3rx51t1oaWlpX/vsIwAAYD8O0zTNQDfRGVRXV8swDPl8vja9nihp4Qttdmygsyp+4u5At3BJHH00MdAtAB1O71/vb9PjX+jnd4e9hggAAKC9EIgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDt2SoQPfXUU4qPj9eVV16ppKQkvfbaa4FuCQAAdAC2CUR//vOflZGRoYcfflhvvvmmfvjDH2r8+PE6evRooFsDAAABZptAtGLFCk2fPl333HOP+vfvr1WrVikuLk6rV68OdGsAACDAggPdQHtoaGhQcXGxfvnLX/qNjxkzRrt27WrxPfX19aqvr7de+3w+SVJ1dXXbNSqpsb6uTY8PdEZt/XvXXmpONwa6BaDDaevf77PHN03zvHW2CET/+te/1NjYKKfT6TfudDrl9XpbfE9WVpZ+85vfNBuPi4trkx4BfD3jj/cGugUAbSXLaJfT1NTUyDC+/ly2CERnORwOv9emaTYbO2vJkiV68MEHrddNTU369NNPFR0d/bXvweWjurpacXFxOnbsmKKiogLdDoBLiN9vezFNUzU1NXK73eets0UgiomJUVBQULPVoPLy8marRmeFhYUpLCzMb+yqq65qqxbRQUVFRfEfTOAyxe+3fZxvZegsW1xUHRoaqqSkJOXn5/uN5+fna+jQoQHqCgAAdBS2WCGSpAcffFAej0eDBw9WSkqKnnnmGR09elT33su1CQAA2J1tAtEdd9yhTz75RI8++qjKyso0cOBAbdmyRddcc02gW0MHFBYWpkceeaTZ16YAOj9+v9ESh/lN96EBAABc5mxxDREAAMD5EIgAAIDtEYgAAIDtEYgAAIDtEYiAr5g6daocDkez7f333w90awBa6ezvdUuPWZk9e7YcDoemTp3a/o2hQyEQAecYN26cysrK/Lb4+PhAtwXgIsTFxSknJ0d1df/3B7RPnz6tP/3pT+rdu3cAO0NHQSACzhEWFiaXy+W3BQUFBbotABfhpptuUu/evbV582ZrbPPmzYqLi9ONN94YwM7QURCIAAC28POf/1zr1q2zXj/33HOaNm1aADtCR0IgAs7xyiuvqGvXrtY2ceLEQLcE4BLweDzauXOnPvroI3388cd6/fXX9bOf/SzQbaGDsM2f7gAu1K233qrVq1dbryMiIgLYDYBLJSYmRhMmTNDzzz8v0zQ1YcIExcTEBLotdBAEIuAcERER6tu3b6DbANAGpk2bpjlz5kiS/v3f/z3A3aAjIRABAGxj3LhxamhokCSNHTs2wN2gIyEQAQBsIygoSIcOHbL+DZxFIAIA2EpUVFSgW0AH5DBN0wx0EwAAAIHEbfcAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAcAGGDx+ujIyMQLcBoI0QiAB0Gl6vV/fff7/69u2rK6+8Uk6nUzfffLOefvppff7554FuD0Anxt8yA9ApfPjhh/rBD36gq666SpmZmUpMTNQXX3yhd999V88995zcbrfS0tIC3ebXamxslMPh0BVX8P+hQEfEbyaATmH27NkKDg7Wvn37NGnSJPXv31+JiYn66U9/qr/97W+67bbbJEk+n08zZ85UbGysoqKiNGLECL311lvWcZYuXarvfe972rBhg6699loZhqE777xTNTU1Vs1nn32mu+++W127dlXPnj315JNPNuunoaFBixYt0tVXX62IiAglJydrx44d1v7169frqquu0iuvvKIBAwYoLCxMH3/8cdv9gABcFAIRgA7vk08+0bZt23TfffcpIiKixRqHwyHTNDVhwgR5vV5t2bJFxcXFuummmzRy5Eh9+umnVu0HH3ygl156Sa+88opeeeUVFRQU6PHHH7f2L1y4UP/85z+Vm5urbdu2aceOHSouLvY7389//nO9/vrrysnJ0dtvv62JEydq3Lhxeu+996yazz//XFlZWfqP//gPHTx4ULGxsZf4JwPgkjEBoIMrLCw0JZmbN2/2G4+OjjYjIiLMiIgIc9GiRearr75qRkVFmadPn/aru+6668w1a9aYpmmajzzyiNmlSxezurra2r9w4UIzOTnZNE3TrKmpMUNDQ82cnBxr/yeffGKGh4eb999/v2mapvn++++bDofDPHHihN95Ro4caS5ZssQ0TdNct26dKcksKSm5ND8EAG2Ka4gAdBoOh8Pv9d69e9XU1KTJkyervr5excXFqq2tVXR0tF9dXV2dPvjgA+v1tddeq8jISOt1z549VV5eLunL1aOGhgalpKRY+7t3765+/fpZr9944w2ZpqnvfOc7fuepr6/3O3doaKgGDRp0ETMG0F4IRAA6vL59+8rhcOjw4cN+43369JEkhYeHS5KamprUs2dPv2t5zrrqqqusf4eEhPjtczgcampqkiSZpvmN/TQ1NSkoKEjFxcUKCgry29e1a1fr3+Hh4c1CHICOiUAEoMOLjo7W6NGjlZ2drblz537tdUQ33XSTvF6vgoODde2117bqXH379lVISIgKCwvVu3dvSVJlZaXeffddDRs2TJJ04403qrGxUeXl5frhD3/YqvMA6Fi4qBpAp/DUU0/piy++0ODBg/XnP/9Zhw4d0pEjR7Rx40YdPnxYQUFBGjVqlFJSUvSTn/xEW7du1UcffaRdu3bpV7/6lfbt23dB5+nataumT5+uhQsX6tVXX9WBAwc0depUv9vlv/Od72jy5Mm6++67tXnzZpWWlqqoqEjLli3Tli1b2upHAKANsUIEoFO47rrr9OabbyozM1NLlizR8ePHFRYWpgEDBmjBggWaPXu2HA6HtmzZoocffljTpk1TRUWFXC6XbrnlFjmdzgs+1xNPPKHa2lqlpaUpMjJS8+fPl8/n86tZt26dHnvsMc2fP18nTpxQdHS0UlJS9KMf/ehSTx1AO3CYF/KFOQAAwGWMr8wAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDt/X8gBm1BYaoDQQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a bar chart for Gender and it's count\n",
    "ax = sns.countplot(x='Gender',data = df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "127073bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>74335853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>31913276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender    Amount\n",
       "0      F  74335853\n",
       "1      M  31913276"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'],as_index = False)['Amount'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ec466f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a bar chart for gender vs total amount\n",
    "sales_gender = df.groupby(['Gender'],as_index = False)['Amount'].sum().sort_values(by='Amount',ascending = False)\n",
    "sns.barplot(x = 'Gender',y='Amount',data = sales_gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "222fc814",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "29d0afa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x='Age Group', hue = 'Gender',data = df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "72c54b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age Group\n",
       "0-17      2699653\n",
       "18-25    17240732\n",
       "26-35    42613442\n",
       "36-45    22144994\n",
       "46-50     9207844\n",
       "51-55     8261477\n",
       "55+       4080987\n",
       "Name: Amount, dtype: int32"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Amount vs Age Group\n",
    "df.groupby(['Age Group'])['Amount'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "07eb2af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='Amount'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_age=df.groupby(['Age Group'],as_index= False)['Amount'].sum().sort_values(by='Amount',ascending = False)\n",
    "\n",
    "sns.barplot(x='Age Group',y='Amount', data = sales_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8389b467",
   "metadata": {},
   "source": [
    "# States"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e42b5c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>4807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>3810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>3240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>2740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>2252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>1568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>1109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               State  Orders\n",
       "14     Uttar Pradesh    4807\n",
       "10       Maharashtra    3810\n",
       "7          Karnataka    3240\n",
       "2              Delhi    2740\n",
       "9     Madhya Pradesh    2252\n",
       "0     Andhra Pradesh    2051\n",
       "5   Himachal Pradesh    1568\n",
       "8             Kerala    1137\n",
       "4            Haryana    1109\n",
       "3            Gujarat    1066"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of orders from top 10 states\n",
    "df.groupby(['State'],as_index=False)['Orders'].sum().sort_values(by='Orders',ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "86d31b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['State'],as_index=False)['Orders'].sum().sort_values(by='Orders',ascending = False).head(10)\n",
    "\n",
    "sns.barplot(x='State',y='Orders', data = sales_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ca1980c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total amount/sales fromt top 10 states\n",
    "sales_state=df.groupby(['State'],as_index = False)['Amount'].sum().sort_values(by='Amount',ascending = False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(data = sales_state, x= 'State',y='Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf3d2a5c",
   "metadata": {},
   "source": [
    "## From above graph we can see that most of the orders and sales are from Uttar Pradesh, Maharashtra and Karnataka resp."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0943193c",
   "metadata": {},
   "source": [
    "# Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2df2e01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= sns.countplot(data = df, x='Marital_Status')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(8,6)})\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fee8b5fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>43786646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>18338738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>30549207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>13574538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marital_Status Gender    Amount\n",
       "0               0      F  43786646\n",
       "1               0      M  18338738\n",
       "2               1      F  30549207\n",
       "3               1      M  13574538"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Marital_Status','Gender'],as_index = False)['Amount'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7291ad9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_marital_status = df.groupby(['Marital_Status','Gender'],as_index = False)['Amount'].sum()\n",
    "\n",
    "sns.set(rc={'figure.figsize':(6,5)})\n",
    "sns.barplot(x='Marital_Status',y='Amount', hue='Gender',data = sales_marital_status)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ce2d45e",
   "metadata": {},
   "source": [
    "## Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2ae8475d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "ax=sns.countplot(x='Occupation',data=df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "022cf976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IT Sector</td>\n",
       "      <td>14755079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Healthcare</td>\n",
       "      <td>13034586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aviation</td>\n",
       "      <td>12602298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Banking</td>\n",
       "      <td>10770610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Govt</td>\n",
       "      <td>8517212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hospitality</td>\n",
       "      <td>6376405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Media</td>\n",
       "      <td>6295832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Automobile</td>\n",
       "      <td>5368596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chemical</td>\n",
       "      <td>5297436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lawyer</td>\n",
       "      <td>4981665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Retail</td>\n",
       "      <td>4783170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food Processing</td>\n",
       "      <td>4070670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Construction</td>\n",
       "      <td>3597511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Textile</td>\n",
       "      <td>3204972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>2593087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Occupation    Amount\n",
       "10        IT Sector  14755079\n",
       "8        Healthcare  13034586\n",
       "2          Aviation  12602298\n",
       "3           Banking  10770610\n",
       "7              Govt   8517212\n",
       "9       Hospitality   6376405\n",
       "12            Media   6295832\n",
       "1        Automobile   5368596\n",
       "4          Chemical   5297436\n",
       "11           Lawyer   4981665\n",
       "13           Retail   4783170\n",
       "6   Food Processing   4070670\n",
       "5      Construction   3597511\n",
       "14          Textile   3204972\n",
       "0       Agriculture   2593087"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Occupation'],as_index = False)['Amount'].sum().sort_values(by='Amount',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f32ae1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_occupation = df.groupby(['Occupation'],as_index = False)['Amount'].sum().sort_values(by='Amount',ascending = False)\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(20,5)})\n",
    "sns.barplot(x='Occupation',y='Amount',data = sales_occupation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f225bc6",
   "metadata": {},
   "source": [
    "## Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "52440a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x='Product_Category',data=df)\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c486b05d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food</td>\n",
       "      <td>33933883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clothing &amp; Apparel</td>\n",
       "      <td>16495019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Electronics &amp; Gadgets</td>\n",
       "      <td>15643846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Footwear &amp; Shoes</td>\n",
       "      <td>15575209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>5440051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Games &amp; Toys</td>\n",
       "      <td>4331694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sports Products</td>\n",
       "      <td>3635933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beauty</td>\n",
       "      <td>1959484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Auto</td>\n",
       "      <td>1958609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Stationery</td>\n",
       "      <td>1676051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Household items</td>\n",
       "      <td>1569337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Tupperware</td>\n",
       "      <td>1155642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Books</td>\n",
       "      <td>1061478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decor</td>\n",
       "      <td>730360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pet Care</td>\n",
       "      <td>482277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hand &amp; Power Tools</td>\n",
       "      <td>405618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Veterinary</td>\n",
       "      <td>112702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Office</td>\n",
       "      <td>81936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Product_Category    Amount\n",
       "6                    Food  33933883\n",
       "3      Clothing & Apparel  16495019\n",
       "5   Electronics & Gadgets  15643846\n",
       "7        Footwear & Shoes  15575209\n",
       "8               Furniture   5440051\n",
       "9            Games & Toys   4331694\n",
       "14        Sports Products   3635933\n",
       "1                  Beauty   1959484\n",
       "0                    Auto   1958609\n",
       "15             Stationery   1676051\n",
       "11        Household items   1569337\n",
       "16             Tupperware   1155642\n",
       "2                   Books   1061478\n",
       "4                   Decor    730360\n",
       "13               Pet Care    482277\n",
       "10     Hand & Power Tools    405618\n",
       "17             Veterinary    112702\n",
       "12                 Office     81936"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Product_Category'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1b42e804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_product_category=df.groupby(['Product_Category'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending = False).head(10)\n",
    "\n",
    "sns.barplot(x='Product_Category',y='Amount',data = sales_product_category)\n",
    "sns.set(rc={'figure.figsize':(20,5)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9609e905",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ddcbc039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>P00265242</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>P00110942</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>P00237542</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>P00184942</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>P00114942</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>P00066142</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>P00213442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>P00064342</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>P00064142</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>P00142742</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2350 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Product_ID  Orders\n",
       "1679  P00265242     127\n",
       "644   P00110942     116\n",
       "1504  P00237542      91\n",
       "1146  P00184942      82\n",
       "679   P00114942      79\n",
       "...         ...     ...\n",
       "427   P00066142       1\n",
       "1333  P00213442       1\n",
       "418   P00064342       1\n",
       "416   P00064142       1\n",
       "875   P00142742       1\n",
       "\n",
       "[2350 rows x 2 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Product_ID'],as_index= False)['Orders'].sum().sort_values(by='Orders',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "55af34a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_product_id = df.groupby(['Product_ID'],as_index= False)['Orders'].sum().sort_values(by='Orders',ascending = False).head(10)\n",
    "\n",
    "sns.barplot(x='Product_ID',y='Orders',data = sales_product_id)\n",
    "sns.set(rc={'figure.figsize':(20,5)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c7e5f13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## top 10 most sold products(same thing as above)\n",
    "fig1, ax1 = plt.subplots(figsize=(12,7))\n",
    "df.groupby('Product_ID')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df220589",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}