Diwali_Sales_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0979edbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import python libraries\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt # visualizing data\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7fbfb436",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv file\n",
    "df = pd.read_csv(r'C:\\Users\\teena\\Downloads\\Python_Diwali_Sales_Analysis-main\\Diwali Sales Data.csv', encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b8db4a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "662c471c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
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       "      <th>Occupation</th>\n",
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       "      <th>Orders</th>\n",
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       "      <th>unnamed1</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
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       "      <td>P00125942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
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       "      <td>F</td>\n",
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       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
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       "      <td>Uttar Pradesh</td>\n",
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       "      <td>Automobile</td>\n",
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       "      <td>0-17</td>\n",
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       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
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       "      <td>23912.00</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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>23877.00</td>\n",
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       "      <td>Himachal Pradesh</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Food Processing</td>\n",
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       "      <td>1</td>\n",
       "      <td>23877.00</td>\n",
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       "    </tr>\n",
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       "      <th>6</th>\n",
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       "      <td>Balk</td>\n",
       "      <td>P00018042</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>Auto</td>\n",
       "      <td>4</td>\n",
       "      <td>23841.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>1002092</td>\n",
       "      <td>Shivangi</td>\n",
       "      <td>P00273442</td>\n",
       "      <td>F</td>\n",
       "      <td>55+</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>IT Sector</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>8</th>\n",
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       "      <td>Kushal</td>\n",
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       "      <td>M</td>\n",
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       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23809.00</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1003650</td>\n",
       "      <td>Ginny</td>\n",
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       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
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       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Media</td>\n",
       "      <td>Auto</td>\n",
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       "      <td>23799.99</td>\n",
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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  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0814c45b",
   "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": 25,
   "id": "29afa95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unrelated/blank columns\n",
    "df.drop(['Status', 'unnamed1'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5cee58eb",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "65de74e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e01aef9c-8e33-4d3c-8caa-0caff67cd5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change data type\n",
    "df['Amount'] = df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5a856bc8-96eb-4491-b97f-f56ef123cf51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f723a99",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a85bb3c3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
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       "      <td>F</td>\n",
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       "      <td>Southern</td>\n",
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       "      <td>23934</td>\n",
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       "      <th>2</th>\n",
       "      <td>1001990</td>\n",
       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>1000695</td>\n",
       "      <td>Manning</td>\n",
       "      <td>P00296942</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
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       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11247</th>\n",
       "      <td>1004089</td>\n",
       "      <td>Reichenbach</td>\n",
       "      <td>P00171342</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Veterinary</td>\n",
       "      <td>3</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11248</th>\n",
       "      <td>1001209</td>\n",
       "      <td>Oshin</td>\n",
       "      <td>P00201342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Textile</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11249</th>\n",
       "      <td>1004023</td>\n",
       "      <td>Noonan</td>\n",
       "      <td>P00059442</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>1002744</td>\n",
       "      <td>Brumley</td>\n",
       "      <td>P00281742</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11239 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       User_ID    Cust_name Product_ID Gender Age Group  Age  Shaadi  \\\n",
       "0      1002903    Sanskriti  P00125942      F     26-35   28       0   \n",
       "1      1000732       Kartik  P00110942      F     26-35   35       1   \n",
       "2      1001990        Bindu  P00118542      F     26-35   35       1   \n",
       "3      1001425       Sudevi  P00237842      M      0-17   16       0   \n",
       "4      1000588         Joni  P00057942      M     26-35   28       1   \n",
       "...        ...          ...        ...    ...       ...  ...     ...   \n",
       "11246  1000695      Manning  P00296942      M     18-25   19       1   \n",
       "11247  1004089  Reichenbach  P00171342      M     26-35   33       0   \n",
       "11248  1001209        Oshin  P00201342      F     36-45   40       0   \n",
       "11249  1004023       Noonan  P00059442      M     36-45   37       0   \n",
       "11250  1002744      Brumley  P00281742      F     18-25   19       0   \n",
       "\n",
       "                State      Zone       Occupation Product_Category  Orders  \\\n",
       "0         Maharashtra   Western       Healthcare             Auto       1   \n",
       "1      Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2       Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3           Karnataka  Southern     Construction             Auto       2   \n",
       "4             Gujarat   Western  Food Processing             Auto       2   \n",
       "...               ...       ...              ...              ...     ...   \n",
       "11246     Maharashtra   Western         Chemical           Office       4   \n",
       "11247         Haryana  Northern       Healthcare       Veterinary       3   \n",
       "11248  Madhya Pradesh   Central          Textile           Office       4   \n",
       "11249       Karnataka  Southern      Agriculture           Office       3   \n",
       "11250     Maharashtra   Western       Healthcare           Office       3   \n",
       "\n",
       "       Amount  \n",
       "0       23952  \n",
       "1       23934  \n",
       "2       23924  \n",
       "3       23912  \n",
       "4       23877  \n",
       "...       ...  \n",
       "11246     370  \n",
       "11247     367  \n",
       "11248     213  \n",
       "11249     206  \n",
       "11250     188  \n",
       "\n",
       "[11239 rows x 13 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename column\n",
    "df.rename(columns= {'Marital_Status':'Shaadi'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a2484863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.123900e+04</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003004e+06</td>\n",
       "      <td>35.410357</td>\n",
       "      <td>0.420055</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.716039e+03</td>\n",
       "      <td>12.753866</td>\n",
       "      <td>0.493589</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001492e+06</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003064e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004426e+06</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID           Age  Marital_Status        Orders        Amount\n",
       "count  1.123900e+04  11239.000000    11239.000000  11239.000000  11239.000000\n",
       "mean   1.003004e+06     35.410357        0.420055      2.489634   9453.610553\n",
       "std    1.716039e+03     12.753866        0.493589      1.114967   5222.355168\n",
       "min    1.000001e+06     12.000000        0.000000      1.000000    188.000000\n",
       "25%    1.001492e+06     27.000000        0.000000      2.000000   5443.000000\n",
       "50%    1.003064e+06     33.000000        0.000000      2.000000   8109.000000\n",
       "75%    1.004426e+06     43.000000        1.000000      3.000000  12675.000000\n",
       "max    1.006040e+06     92.000000        1.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() method returns description of the data in the DataFrame (i.e. count, mean, std, etc)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2c380030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use describe() for specific columns\n",
    "df[['Age', 'Orders', 'Amount']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc649558",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504b419c",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a3fea6fd-87b3-4c38-836a-295df5780f72",
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f536def2-e3bc-476e-815d-6af20570e694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 50,
     "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": [
    "sns.countplot(x = 'Gender',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "84c7918d",
   "metadata": {},
   "outputs": [
    {
     "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 and it's count\n",
    "\n",
    "ax = sns.countplot(x = 'Gender',data = df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "035b9da2-140a-4481-8ce9-bc2242af56f5",
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c5fd4566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 53,
     "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",
    "\n",
    "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.barplot(x = 'Gender',y= 'Amount' ,data = sales_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e108cec",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc927f9",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f712ad54-87fe-4178-9e4e-9c2717eb1caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='count'>"
      ]
     },
     "execution_count": 54,
     "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": [
    "sns.countplot(data = df, x = 'Age Group', hue = 'Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "88563dec",
   "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(data = df, x = 'Age Group', hue = 'Gender')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bfbe3937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='Amount'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VqXv37nrppZfUqVOnEvsWFBSooKDAsZ6bm1uuGgEAQPVW6nCTlpYm6aeZm4yMDHl7ezu2eXt7KyIiQk8++WSZCykqKtLkyZPVr18/de7c2WW/du3aacWKFQoPD1dOTo5efvll9e3bV4cPH1azZs2K9Y+Pj9esWbPKXBcAAKhZbKaku4NvYsyYMVq4cKECAgIqtJAJEyZo69at2rNnT4khxZVr166pQ4cOGjVqlJ5//vli20uauQkLC1NOTk6J1xA5dVXZLqAGSZ0b4+kSAABwS25urgIDA13++30jt++5WblyZZkLcyU2NlabN2/W7t273Qo2klSnTh1169ZNJ06cKHG7j4+PfHx8KqJMAABQA7gdbvLz8zVnzhwlJibq/PnzKioqctr+9ddfl/pYxhj95S9/0fr165WUlOT201bST/f7ZGRk6De/+Y3b+wIAAOtxO9yMGzdOu3bt0p/+9CeFhISU+ORUaU2cOFGrV6/Wxo0bVb9+fWVlZUmSAgMDVbduXUlSTEyMmjZtqvj4eEnS7Nmz1adPH7Vp00YXL17U3LlzderUKY0bN67MdQAAAOtwO9xs3bpVH3zwgfr161fuky9evFiS1L9/f6f2lStX6qGHHpL005cGenn9++t4vv/+e40fP15ZWVlq0KCBIiMjtW/fPnXs2LHc9QAAgJrP7XDToEGDWz6qXVqluZc5KSnJaX3+/PmaP39+hZwfAABYj9vfUPz888/rueee0w8//FAZ9QAAAJSL2zM38+bN01dffaWgoCC1bNlSderUcdr+2WefVVhxAAAA7nI73ERHR1dCGQAAABXD7XAzc+bMyqgDAACgQrh9zw0AAEB15vbMjZeX102/26awsLBcBQEAAJSH2+Fm/fr1TuvXrl1TWlqa3nrrLX6gEgAAeJzb4Wb48OHF2h544AF16tRJa9eu1dixYyukMAAAgLKosHtu+vTpo8TExIo6HAAAQJlUSLi5fPmy/ud//kdNmzatiMMBAACUWZl+fuHGG4qNMbp06ZL8/Pz09ttvV2hxAAAA7nI73CxYsMBp3cvLS40bN1bv3r3VoEGDiqoLAACgTNwON6NHj66MOgAAACqE2+FGki5evKjly5fr6NGjkqROnTrp4YcfVmBgYIUWBwAA4C63byhOSUlR69atNX/+fF24cEEXLlzQK6+8otatW/OjmQAAwOPcnrl5/PHHdf/99+vNN99U7do/7f7jjz9q3Lhxmjx5snbv3l3hRQIAAJSW2+EmJSXFKdhIUu3atTVt2jT16NGjQosDAABwl9vhJiAgQJmZmWrfvr1T++nTp1W/fv0KKwzVT+TUVZ4uodKlzo3xdAkAgHJy+56bkSNHauzYsVq7dq1Onz6t06dPa82aNRo3bpxGjRpVGTUCAACUmtszNy+//LJsNptiYmL0448/SpLq1KmjCRMmaM6cORVeIAAAgDvcDjfe3t5auHCh4uPj9dVXX0mSWrduLT8/vwovDgAAwF1l+p4bSfLz81OXLl0qshYAAIByczvcXLlyRa+++qp27typ8+fPq6ioyGk733UDAAA8ye1wM3bsWH300Ud64IEH1KtXL6cf0QQAAPA0t8PN5s2btWXLFvXr168y6gEAACgXtx8Fb9q0Kd9nAwAAqi23w828efM0ffp0nTp1qjLqAQAAKBe3P5bq0aOHrly5ojvuuEN+fn6qU6eO0/YLFy5UWHEAAADucjvcjBo1SmfPntVLL72koKAgbigGAADVitvhZt++fUpOTlZERERl1AMAAFAubt9z0759e12+fLlCTh4fH6+ePXuqfv36atKkiaKjo3Xs2LFb7rdu3Tq1b99evr6+6tKli7Zs2VIh9QAAgJrP7XAzZ84cPfHEE0pKStJ3332n3Nxcp8Udu3bt0sSJE7V//35t375d165d03333af8/HyX++zbt0+jRo3S2LFjlZaWpujoaEVHR+vzzz9391IAAIAF2Ywxxp0dvLx+ykM/v9fGGCObzabCwsIyF/Ptt9+qSZMm2rVrl+6+++4S+4wcOVL5+fnavHmzo61Pnz7q2rWrlixZcstz5ObmKjAwUDk5OQoICCi2PXLqqjLXX1Okzo0p036MDQDAU2717/eN3L7nZufOnS63ZWRkuHs4Jzk5OZKkhg0buuyTnJysKVOmOLUNGjRIGzZsKNe5AQCANbgdbqKiopzWL126pHfffVfLli1TamqqYmNjy1RIUVGRJk+erH79+qlz584u+2VlZSkoKMipLSgoSFlZWSX2LygoUEFBgWPd3Y/OAABAzeL2PTfX7d69W6NHj1ZISIhefvll/frXv9b+/fvLXMjEiRP1+eefa82aNWU+Rkni4+MVGBjoWMLCwir0+AAAoHpxa+YmKytLCQkJWr58uXJzc/Wf//mfKigo0IYNG9SxY8cyFxEbG6vNmzdr9+7datas2U37BgcHKzs726ktOztbwcHBJfaPi4tz+hgrNzeXgAMAgIWVeuZm2LBhateunQ4dOqQFCxbom2++0auvvlqukxtjFBsbq/Xr12vHjh1q1arVLfex2+1KTEx0atu+fbvsdnuJ/X18fBQQEOC0AAAA6yr1zM3WrVv12GOPacKECWrbtm2FnHzixIlavXq1Nm7cqPr16zvumwkMDFTdunUlSTExMWratKni4+MlSZMmTVJUVJTmzZunoUOHas2aNUpJSdHSpUsrpCYAAFCzlXrmZs+ePbp06ZIiIyPVu3dvvfbaa/rXv/5VrpMvXrxYOTk56t+/v0JCQhzL2rVrHX0yMzN17tw5x3rfvn21evVqLV26VBEREXr//fe1YcOGm96EDAAAfjlKPXPTp08f9enTRwsWLNDatWu1YsUKTZkyRUVFRdq+fbvCwsJUv359t05emq/YSUpKKtY2YsQIjRgxwq1zAQCAXwa3n5by9/fXww8/rD179igjI0NPPPGE5syZoyZNmuj++++vjBoBAABKrcyPgktSu3bt9Le//U1nzpzRu+++W1E1AQAAlFm5ws11tWrVUnR0tDZt2lQRhwMAACizCgk3AAAA1QXhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWEptTxcAWEHk1FWeLqHSpc6N8XQJAFAqzNwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABL8Wi42b17t4YNG6bQ0FDZbDZt2LDhpv2TkpJks9mKLVlZWVVTMAAAqPY8Gm7y8/MVERGhRYsWubXfsWPHdO7cOcfSpEmTSqoQAADUNLU9efIhQ4ZoyJAhbu/XpEkT3XbbbRVfEAAAqPFq5D03Xbt2VUhIiAYOHKi9e/d6uhwAAFCNeHTmxl0hISFasmSJevTooYKCAi1btkz9+/fXgQMH1L179xL3KSgoUEFBgWM9Nze3qsoFAAAeUKPCTbt27dSuXTvHet++ffXVV19p/vz5+vvf/17iPvHx8Zo1a1ZVlQgAADysRn4sdaNevXrpxIkTLrfHxcUpJyfHsZw+fboKqwMAAFWtRs3clCQ9PV0hISEut/v4+MjHx6cKKwIAAJ7k0XCTl5fnNOty8uRJpaenq2HDhmrevLni4uJ09uxZrVq1SpK0YMECtWrVSp06ddKVK1e0bNky7dixQx999JGnLgEAAFQzHg03KSkpuueeexzrU6ZMkSSNHj1aCQkJOnfunDIzMx3br169qieeeEJnz56Vn5+fwsPD9fHHHzsdAwAA/LJ5NNz0799fxhiX2xMSEpzWp02bpmnTplVyVQAAoCar8TcUAwAA3IhwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALMWj4Wb37t0aNmyYQkNDZbPZtGHDhlvuk5SUpO7du8vHx0dt2rRRQkJCpdcJAABqDo+Gm/z8fEVERGjRokWl6n/y5EkNHTpU99xzj9LT0zV58mSNGzdOH374YSVXCgAAaoranjz5kCFDNGTIkFL3X7JkiVq1aqV58+ZJkjp06KA9e/Zo/vz5GjRoUGWVCQAAapAadc9NcnKyBgwY4NQ2aNAgJScnu9ynoKBAubm5TgsAALCuGhVusrKyFBQU5NQWFBSk3NxcXb58ucR94uPjFRgY6FjCwsKqolQAAOAhNSrclEVcXJxycnIcy+nTpz1dEgAAqEQevefGXcHBwcrOznZqy87OVkBAgOrWrVviPj4+PvLx8amK8gAAQDVQo2Zu7Ha7EhMTndq2b98uu93uoYoAAEB149Fwk5eXp/T0dKWnp0v66VHv9PR0ZWZmSvrpI6WYmBhH/0cffVRff/21pk2bpi+++EKvv/663nvvPT3++OOeKB8AAFRDHg03KSkp6tatm7p16yZJmjJlirp166bnnntOknTu3DlH0JGkVq1a6YMPPtD27dsVERGhefPmadmyZTwGDgAAHDx6z03//v1ljHG5vaRvH+7fv7/S0tIqsSoAAFCT1agbigHUPJFTV3m6hEqXOjfm1p0AVJkadUMxAADArRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApfAoOAB4iNUfk+cReXgKMzcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSqkW4WbRokVq2bClfX1/17t1bn3zyicu+CQkJstlsTouvr28VVgsAAKozj4ebtWvXasqUKZo5c6Y+++wzRUREaNCgQTp//rzLfQICAnTu3DnHcurUqSqsGAAAVGceDzevvPKKxo8frzFjxqhjx45asmSJ/Pz8tGLFCpf72Gw2BQcHO5agoKAqrBgAAFRnHg03V69eVWpqqgYMGOBo8/Ly0oABA5ScnOxyv7y8PLVo0UJhYWEaPny4Dh8+7LJvQUGBcnNznRYAAGBdHg03//rXv1RYWFhs5iUoKEhZWVkl7tOuXTutWLFCGzdu1Ntvv62ioiL17dtXZ86cKbF/fHy8AgMDHUtYWFiFXwcAAKg+PP6xlLvsdrtiYmLUtWtXRUVF6R//+IcaN26sN954o8T+cXFxysnJcSynT5+u4ooBAEBVqu3Jkzdq1Ei1atVSdna2U3t2draCg4NLdYw6deqoW7duOnHiRInbfXx85OPjU+5aAQBAzeDRmRtvb29FRkYqMTHR0VZUVKTExETZ7fZSHaOwsFAZGRkKCQmprDIBAEAN4tGZG0maMmWKRo8erR49eqhXr15asGCB8vPzNWbMGElSTEyMmjZtqvj4eEnS7Nmz1adPH7Vp00YXL17U3LlzderUKY0bN86TlwEAAKoJj4ebkSNH6ttvv9Vzzz2nrKwsde3aVdu2bXPcZJyZmSkvr39PMH3//fcaP368srKy1KBBA0VGRmrfvn3q2LGjpy4BAABUIx4PN5IUGxur2NjYErclJSU5rc+fP1/z58+vgqoAAEBNVOOelgIAALiZajFzAwDAjSKnrvJ0CZUqdW6Mp0uwNGZuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApfDzCwAA1CD8NMWtMXMDAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAspVqEm0WLFqlly5by9fVV79699cknn9y0/7p169S+fXv5+vqqS5cu2rJlSxVVCgAAqjuPh5u1a9dqypQpmjlzpj777DNFRERo0KBBOn/+fIn99+3bp1GjRmns2LFKS0tTdHS0oqOj9fnnn1dx5QAAoDryeLh55ZVXNH78eI0ZM0YdO3bUkiVL5OfnpxUrVpTYf+HChRo8eLCmTp2qDh066Pnnn1f37t312muvVXHlAACgOvJouLl69apSU1M1YMAAR5uXl5cGDBig5OTkEvdJTk526i9JgwYNctkfAAD8stT25Mn/9a9/qbCwUEFBQU7tQUFB+uKLL0rcJysrq8T+WVlZJfYvKChQQUGBYz0nJ0eSlJubW2L/woLLpa6/pnJ17bfC2LjG2LjG2Lhm9bEp67hIjM3N/FLH5nq7MeaWx/BouKkK8fHxmjVrVrH2sLAwD1RTPQS++qinS6i2GBvXGBvXGJuSMS6uMTau3WpsLl26pMDAwJv28Wi4adSokWrVqqXs7Gyn9uzsbAUHB5e4T3BwsFv94+LiNGXKFMd6UVGRLly4oNtvv102m62cV1B+ubm5CgsL0+nTpxUQEODpcqoVxqZkjItrjI1rjI1rjI1r1WlsjDG6dOmSQkNDb9nXo+HG29tbkZGRSkxMVHR0tKSfwkdiYqJiY2NL3MdutysxMVGTJ092tG3fvl12u73E/j4+PvLx8XFqu+222yqi/AoVEBDg8RdOdcXYlIxxcY2xcY2xcY2xca26jM2tZmyu8/jHUlOmTNHo0aPVo0cP9erVSwsWLFB+fr7GjBkjSYqJiVHTpk0VHx8vSZo0aZKioqI0b948DR06VGvWrFFKSoqWLl3qycsAAADVhMfDzciRI/Xtt9/queeeU1ZWlrp27apt27Y5bhrOzMyUl9e/H+rq27evVq9erWeeeUZPPfWU2rZtqw0bNqhz586eugQAAFCNeDzcSFJsbKzLj6GSkpKKtY0YMUIjRoyo5Kqqho+Pj2bOnFnsozMwNq4wLq4xNq4xNq4xNq7V1LGxmdI8UwUAAFBDePwbigEAACoS4QYAAFgK4QYAAFgK4aaM4uPj1bNnT9WvX19NmjRRdHS0jh07VqxfcnKyfv3rX8vf318BAQG6++67dfmy66/O/u677zR48GCFhobKx8dHYWFhio2Ndfo66qSkJNlstmKLq5+gqGqLFy9WeHi443sR7Ha7tm7d6tTH3XG50XfffadmzZrJZrPp4sWLjvbqPi6StHv3bg0bNkyhoaGy2WzasGGD0/a8vDzFxsaqWbNmqlu3ruPHZG/m//7v/zR27Fi1atVKdevWVevWrTVz5kxdvXrVqU9JY7N///7KuMxymzNnjmw2m9P3WUlle92UdN1r1qxx6pOUlKTu3bvLx8dHbdq0UUJCQgVfUdn99a9/LVZ/+/btHduXLl2q/v37KyAgoNh74mZuNS414f1UkluNV//+/Yttf/TRX863BS9atEgtW7aUr6+vevfurU8++eSm/V988UX17dtXfn5+JX5HXEJCQomvE5vNpvPnz1fSVdxatXhaqibatWuXJk6cqJ49e+rHH3/UU089pfvuu09HjhyRv7+/pJ/+EA8ePFhxcXF69dVXVbt2bR08eNDp0faf8/Ly0vDhw/XCCy+ocePGOnHihCZOnKgLFy5o9erVTn2PHTvm9KVKTZo0qZyLdVOzZs00Z84ctW3bVsYYvfXWWxo+fLjS0tLUqVOnMo3LjcaOHavw8HCdPXu2xO3VdVwkKT8/XxEREXr44Yf1H//xH8W2T5kyRTt27NDbb7+tli1b6qOPPtL/+3//T6Ghobr//vtLPOYXX3yhoqIivfHGG2rTpo0+//xzjR8/Xvn5+Xr55Zed+n788cfq1KmTY/3222+v2AusAJ9++qneeOMNhYeHO7WX53WzcuVKDR482LF+4x/pkydPaujQoXr00Uf1zjvvKDExUePGjVNISIgGDRpUYddVHp06ddLHH3/sWK9d+99/un/44QcNHjzYMTbuuNm4XFed30+u3Gy8JGn8+PGaPXu2Y93Pz8/lsZKSkvTQQw/p//7v/yq8zqq2du1aTZkyRUuWLFHv3r21YMECDRo0SMeOHXP53/Xq1asaMWKE7Ha7li9fXmz7yJEjnV5DkvTQQw/pypUrnn2tGFSI8+fPG0lm165djrbevXubZ555ptzHXrhwoWnWrJljfefOnUaS+f7778t97KrSoEEDs2zZMmNM+cbl9ddfN1FRUSYxMbHYGNS0cZFk1q9f79TWqVMnM3v2bKe27t27m6efftqtY//tb38zrVq1cqyfPHnSSDJpaWllLbdKXLp0ybRt29Zs377dREVFmUmTJjm2lfV1U9I432jatGmmU6dOTm0jR440gwYNcvtclWHmzJkmIiLilv3cff3falxq2vvpuluN189fV7eyc+dO06JFi3LXVR306tXLTJw40bFeWFhoQkNDTXx8/C33XblypQkMDLxlv/Pnz5s6deqYVatWlafUcuNjqQpy/dfGGzZsKEk6f/68Dhw4oCZNmqhv374KCgpSVFSU9uzZ49Zxv/nmG/3jH/9QVFRUsW1du3ZVSEiIBg4cqL1795b/IipBYWGh1qxZo/z8fNnt9nKNy5EjRzR79mytWrXqpv+3XhPGxZW+fftq06ZNOnv2rIwx2rlzp7788kvdd999bh0nJyfH8Vq80f33368mTZroV7/6lTZt2lRRZVeYiRMnaujQoRowYIBTe3nfTxMnTlSjRo3Uq1cvrVixwulXhZOTk4udb9CgQUpOTi7/BVWQ48ePKzQ0VHfccYcefPBBZWZmVshxbzYu19XE99Otxuudd95Ro0aN1LlzZ8XFxemHH37wUKVV5+rVq0pNTXV6rXt5eWnAgAEV+lpftWqV/Pz89MADD1TYMcvEo9HKIgoLC83QoUNNv379HG3JyclGkmnYsKFZsWKF+eyzz8zkyZONt7e3+fLLL295zD/84Q+mbt26RpIZNmyYuXz5smPbF198YZYsWWJSUlLM3r17zZgxY0zt2rVNampqpVxfWRw6dMj4+/ubWrVqmcDAQPPBBx8YY8o+LleuXDHh4eHm73//uzGm5P+rrAnjciOV8H/OV65cMTExMUaSqV27tvH29jZvvfWWW8c9fvy4CQgIMEuXLnW0ffvtt2bevHlm//795pNPPjHTp083NpvNbNy4sSIupUK8++67pnPnzo7X+o3/h12e99Ps2bPNnj17zGeffWbmzJljfHx8zMKFCx3b27Zta1566SWnfT744AMjyfzwww8Ve5FlsGXLFvPee++ZgwcPmm3bthm73W6aN29ucnNznfq5O9Nyq3Gpae+n6241Xm+88YbZtm2bOXTokHn77bdN06ZNze9+9zuXx7PKzM3Zs2eNJLNv3z6n9qlTp5pevXrdcv/Sztx06NDBTJgwoaxlVhjCTQV49NFHTYsWLczp06cdbXv37jWSTFxcnFPfLl26mBkzZhhjjBk8eLDx9/c3/v7+pmPHjk79zp07Z44ePWo2btxoOnbseMsXy913323++Mc/VtAVlV9BQYE5fvy4SUlJMTNmzDCNGjUyhw8fLvO4PP7442bkyJGO/qX9Q17dxuVGJYWbuXPnmjvvvNNs2rTJHDx40Lz66qumXr16Zvv27cYYYx555BHH2Pj7+xc75pkzZ0zr1q3N2LFjb3n+P/3pT+ZXv/pVhVxLeWVmZpomTZqYgwcPOtpuDDflfT/d6Nlnn3X6mLe6h5uf+/77701AQIDjY97rXL0nyjouJanO7ydXXI3Xddc/4j5x4oSj7cb3mK+vr7HZbE5tjzzySFWVX2FuFW5u9belNOFm3759RpJJSUmpyNLLhBuKyyk2NlabN2/W7t271axZM0d7SEiIJKljx45O/Tt06OCYIl22bJnjSY86deo49QsODlZwcLDat2+vhg0b6q677tKzzz7rOO7P9erVy+2PvCqTt7e32rRpI0mKjIzUp59+qoULF2rGjBmS3B+XHTt2KCMjQ++//74kOabPGzVqpKefflqzZs0qsY7qNi43c/nyZT311FNav369hg4dKkkKDw9Xenq6Xn75ZQ0YMECzZ8/Wk08+WeL+33zzje655x717du3VD8k27t3b23fvr1Cr6GsUlNTdf78eXXv3t3RVlhYqN27d+u1115zPIlY1vfTjXr37q3nn39eBQUF8vHxUXBwsLKzs536ZGdnKyAgQHXr1q2Q66tIt912m+68806dOHGiVP3LOi4lqUnvp+tuNV69e/eWJJ04cUKtW7eWJKWnpzu2HzhwQNOnT3f6KaDq8OvY7mrUqJFq1apV4ms9ODj4pn9bSmvZsmXq2rWrIiMjy3WcikC4KSNjjP7yl79o/fr1SkpKUqtWrZy2t2zZUqGhocUeD//yyy81ZMgQSVLTpk1Lda6ioiJJUkFBgcs+6enpLoNPdVBUVKSCgoIyj8v//u//Oj3y++mnn+rhhx/WP//5T8cfpJJU93G50bVr13Tt2rVi9xPVqlXL8Rpo0qRJiU8gnD17Vvfcc48iIyO1cuXKUj1BVJ3G5t5771VGRoZT25gxY9S+fXtNnz5dd9xxR4W9n9LT09WgQQPHP+B2u11btmxx6rN9+3bZ7fayXk6lysvL01dffaU//elPpepf1nFx1ae6vGZK61bjdT3I3Hhd1//HTJLOnDmj2rVrO7XVRN7e3oqMjFRiYqKio6Ml/fR3OTExUbGxsS7/tpRWXl6e3nvvPcXHx1dQxeXk6amjmmrChAkmMDDQJCUlmXPnzjmWG6ex58+fbwICAsy6devM8ePHzTPPPGN8fX2dpj9/7oMPPjArVqwwGRkZ5uTJk2bz5s2mQ4cOTvfzzJ8/32zYsMEcP37cZGRkmEmTJhkvLy/z8ccfV+o1l9aMGTPMrl27zMmTJ82hQ4fMjBkzjM1mMx999JExpmzj8nMlTcFX93Ex5qengdLS0kxaWpqRZF555RWTlpZmTp06ZYz56aOYTp06mZ07d5qvv/7arFy50vj6+prXX3/d5THPnDlj2rRpY+69915z5swZp9fjdQkJCWb16tXm6NGj5ujRo+bFF180Xl5eZsWKFZV+zWX186dayvK62bRpk3nzzTdNRkaGOX78uHn99deNn5+fee655xx9vv76a+Pn52emTp1qjh49ahYtWmRq1apltm3bVpmXV2pPPPGESUpKMidPnjR79+41AwYMMI0aNTLnz583xvz0EXZaWpp58803jSSze/duk5aWZr777juXxyzNuNSE91NJbjZeJ06cMLNnzzYpKSnm5MmTZuPGjeaOO+4wd999t8vjWeWeG2OMWbNmjfHx8TEJCQnmyJEj5s9//rO57bbbTFZWlst9Tp06ZdLS0sysWbNMvXr1HH+/Ll265NRv2bJlxtfXt9o8XUe4KSNJJS4rV6506hcfH2+aNWtm/Pz8jN1uN//85z9vetwdO3YYu91uAgMDja+vr2nbtq2ZPn260wvmv//7v03r1q2Nr6+vadiwoenfv7/ZsWNHJVxl2Tz88MOmRYsWxtvb2zRu3Njce++9jmBznbvj8nMlhZvqPi7G/Lvuny+jR482xvz0D9VDDz1kQkNDja+vr2nXrp2ZN2+eKSoqcnnMlStXunw9XpeQkGA6dOhg/Pz8TEBAgOnVq5dZt25dZV9uuZT0yK67r5utW7earl27mnr16hl/f38TERFhlixZYgoLC5367dy503Tt2tV4e3ubO+64o9j72JNGjhxpQkJCjLe3t2natKkZOXKkU6CbOXNmqf4W3ag041IT3k8ludl4ZWZmmrvvvts0bNjQ+Pj4mDZt2pipU6eanJwcl8ezUrgxxphXX33VNG/e3Hh7e5tevXqZ/fv337T/6NGjS3x97dy506mf3W43//Vf/1WJlbuHXwUHAACWwvfcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAKgQycnJqlWrluMXzavK1atXNXfuXHXv3l3+/v4KDAxURESEnnnmGX3zzTdVWguA6oGfXwBQIcaNG6d69epp+fLlOnbsmEJDQyv9nAUFBbrvvvt06NAhzZo1S/369VPjxo118uRJvfvuu2rQoIHLXym+evWqvL29K71GAFWPmRsA5ZaXl6e1a9dqwoQJGjp0qBISEor12bRpk9q2bStfX1/dc889euutt2Sz2XTx4kVHnz179uiuu+5S3bp1FRYWpscee0z5+fkuzzt//nzt2bNHO3bs0GOPPabIyEg1b95cUVFRWrJkiV566SVH3/79+ys2NlaTJ09Wo0aNNGjQIEnSrl271KtXL/n4+CgkJEQzZszQjz/+6NivZcuWWrBggdN5u3btqr/+9a+OdZvNpsWLF2vIkCGqW7eu7rjjDr3//vvuDSKACkO4AVBu7733ntq3b6927drpj3/8o1asWKEbJ4VPnjypBx54QNHR0Tp48KAeeeQRPf30007H+OqrrzR48GD9/ve/16FDh7R27Vrt2bNHsbGxLs/77rvvauDAgerWrVuJ2202m9P6W2+9JW9vb+3du1dLlizR2bNn9Zvf/EY9e/bUwYMHtXjxYi1fvlwvvPCC22Pw7LPP6ve//70OHjyoBx98UH/4wx909OhRt48DoAJ49DfJAVhC3759zYIFC4wxxly7ds00atTI7Ny507F9+vTppnPnzk77PP3000aS+f77740xxowdO9b8+c9/durzz3/+03h5eZnLly+XeF5fX1/z2GOPObVFR0cbf39/4+/vb+x2u6M9KirKdOvWzanvU089Zdq1a2eKioocbYsWLTL16tUzhYWFxhhjWrRoYebPn++0X0REhJk5c6ZjXZJ59NFHnfr07t3bTJgwocS6AVQuZm4AlMuxY8f0ySefaNSoUZKk2rVra+TIkVq+fLlTn549ezrt16tXL6f1gwcPKiEhQfXq1XMsgwYNUlFRkU6ePFnqel5//XWlp6fr4Ycf1g8//OC0LTIy0mn96NGjstvtTjM8/fr1U15ens6cOVPqc0qS3W4vts7MDeAZtT1dAICabfny5frxxx+dbiA2xsjHx0evvfaaAgMDS3WcvLw8PfLII3rssceKbWvevHmJ+7Rt21bHjh1zagsJCZEkNWzYsFh/f3//UtVyIy8vL6eP2CTp2rVrbh8HQNVh5gZAmf34449atWqV5s2bp/T0dMdy8OBBhYaG6t1335UktWvXTikpKU77fvrpp07r3bt315EjR9SmTZtii6unmkaNGqXt27crLS2tTPV36NBBycnJTuFl7969ql+/vpo1ayZJaty4sc6dO+fYnpubW+JM0v79+4utd+jQoUx1ASgnD38sBqAGW79+vfH29jYXL14stm3atGmmR48exhhjvv76a1OnTh0zbdo0c+zYMbN27VrTrFkzI8mx78GDB03dunXNxIkTTVpamvnyyy/Nhg0bzMSJE12e//Lly6Zfv36mQYMGZsGCBSY1NdV8/fXXZtu2baZXr16me/fujr5RUVFm0qRJTvufOXPG+Pn5mYkTJ5qjR4+aDRs2mEaNGjndTzNjxgwTHBxsdu/ebQ4dOmSio6NNvXr1it1z06hRI7N8+XJz7Ngx89xzzxkvLy9z+PDhMowqgPIi3AAos9/+9rfmN7/5TYnbDhw4YCSZgwcPGmOM2bhxo2nTpo3x8fEx/fv3N4sXLzaSnG4W/uSTT8zAgQNNvXr1jL+/vwkPDzcvvvjiTWu4cuWKmTNnjomIiDB169Y1Pj4+pn379ubxxx83mZmZjn4lhRtjjElKSjI9e/Y03t7eJjg42EyfPt1cu3bNsT0nJ8eMHDnSBAQEmLCwMJOQkFDiDcWLFi0yAwcOND4+PqZly5Zm7dq1pRlCAJWAL/ED4BEvvviilixZotOnT3u6lHKz2Wxav369oqOjPV0KAHFDMYAq8vrrr6tnz566/fbbtXfvXs2dO/em32EDAGVFuAFQJY4fP64XXnhBFy5cUPPmzfXEE08oLi7O02UBsCA+lgIAAJbCo+AAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBS/j989fwHahz/CAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Amount vs Age Group\n",
    "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.barplot(x = 'Age Group',y= 'Amount' ,data = sales_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abaa6c07",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are of age group between 26-35 yrs female*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a49098",
   "metadata": {},
   "source": [
    "### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f8146ce6-c834-4b5e-9d7b-b87831777f6c",
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dfac4f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total number of orders from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a66617bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 68,
     "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 from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d46e9e",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f865a5",
   "metadata": {},
   "source": [
    "### Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "77e6635e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data = df, x = 'Marital_Status')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(5,3)})\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2d8c7b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 73,
     "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_state = df.groupby(['Marital_Status', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(6,5)})\n",
    "sns.barplot(data = sales_state, x = 'Marital_Status',y= 'Amount', hue='Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37afbb83",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are married (women) and they have high purchasing power*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230cfe66",
   "metadata": {},
   "source": [
    "### Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4deb41f4",
   "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(data = df, x = 'Occupation')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4eb620f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 24,
     "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_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c22532",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are working in IT, Healthcare and Aviation sector*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40518579",
   "metadata": {},
   "source": [
    "### Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e7e4d39",
   "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(data = df, x = 'Product_Category')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99f5c6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e5vDhw/ZhNpvN1KxZ036hLurCyfXr1+1tpk6danx9fe0/P7ic+vXrm6+//tqhpjlz5pg///zTHDhwwHh7e5u//vrLPu7IkSPm4MGDj/zsjh8/bqpXr24qV65s/Pz8TIcOHYwx90/wo07QY1KlSpVodfz555/RPv+ovjJjxgxz6NAhh/b16tUzPXr0MMYYM2TIEFOhQgWHZU6ZMsXeb+7cuWO/4BnFZrOZWrVq2S+uDh8+3NSoUcNhGVHTRX1+D1/MqFGjhuncubP9d+7mzZtm06ZN5u7du49c97h63P4oaplPu0+Kad2qV69uxowZY4wxZvz48aZly5YOAV+DBg3M6NGjjTHGlCtXzn4QGmXevHkmf/785u7du05vD2KyatUqU7x4cfPBBx8Yb29vM2vWLPvn8CRHjx4133zzjSldurTx9vY23t7epkyZMvYLj8bc/7zr1q3rMF3r1q1NixYtjDHmiZ/l7du3Tf78+R3maYwxI0eOtH++U6ZMMaVLlzaBgYH28du3bzdnz5594jq8yJ7U12MbssT0RwMP9s+QkBDj7e1tlixZYoxxDFmMiXnb/KSQxZl9GWIv6jhv48aNDsNbtWrl0Ffef/99Y4yJ9TandOnSJjIy0hhjzO3bt+0Ba5RZs2bZ96f//POP8fHxMcHBwQ41fPLJJ/Y++MUXX5gmTZqYGzduGGPu/zHM33//bUJCQh65bqNHjzb58+c31apVM35+fua///4zxsRum2ZM9GPOqPV8cF9ujDFt2rSxn7RHHQvu2rXLGGPMiRMnHI57/f39zU8//WTCwsKMMfe3cVu2bLF/Vi+a3bt3m44dO9r/OMPb29tUqVLF7Nixw96mQoUKplWrVg7TdejQwdSvX98YY0z79u1N7dq1HcYfPHjQeHt7m3Xr1tnn8eWXXzq0edrjiqjjwAd/D6ICu4EDB9qPi6JClgf74pNqjvojtn/++cc+/vLlyyZfvnyxDlniejy8atUqky9fPrN37157271795oTJ07EuP6InaiQMKqP+Pr6Gm9vb1OvXj1z8+bNWB27HDhwwH7MFWX9+vXG29vbfoHwSftpYxy/7/DwcFO6dGn7OXFkZKQpV66c/dgUL6bw8HBTqlQph3PBVq1amWLFitm3dY8LWYyJfiwWm3MdPD+etF9++Nj9SdcKjHn08X1szqWjzjEe3EZG7S937doVq21oTOcpSXEbyDtZkrCKFSuqc+fODsM8PDx04sQJhYeHy9/f32FcsWLFNG3aNF29elVHjhxRvnz57LdoS/eflfz666/ryJEjCgwMVHh4uPLnz28fnyJFCuXNmzd+V+oFFfXuh+vXr8d52iNHjkR7oX2xYsXs4x71IrWcOXM6/JwuXTrdu3fvkcvJlSuXw89p0qRReHi4JGn//v2qUqWKw/iiRYtq6tSpj5xf165ddezYMS1ZskShoaGqX7++OnfurLp166p9+/ZaunSp3njjjWjTzZ8/X8mTJ7cvL+r/582bp7Nnzypbtmz2tsWLF7f/f/LkyZUvXz4dOXLEPszPz8/hZcSFChVSeHi4AgMD7beQT58+XUeOHNGpU6fsj2968NFkr732mkN9Bw4c0N69ex1e4Gn+/yN0jh8/ruzZsz/yM3kaMf2+S3J4hNDDdQYFBalQoULRxh0/flwpU6ZUeHi4ChQo4DDu66+/fmwdD34eR44cUZo0aeTt7W0fZrFYVKRIEYcX3GbMmNHhpW0P9q2HxdTf69evb///6tWrq3Xr1sqUKZNKly6t8uXLq3LlyjHO686dO2ratKny58+vYcOGadWqVerUqZPy5s2ry5cva+XKlVq7dq0sFku0ab28vKLdAvz2229r0aJFkqSLFy+qcePG9r7SsGFD/fXXX1q4cKFOnjypY8eO6cyZM/Y+fuTIEeXNm9fh/S2FCxe2///x48d19+5dFSxY0OGz9Pf3t/fLAwcO6OjRo9G+03v37jk8fu1BX331lbp06aIVK1bI399fZcqUUfXq1ZUiRYoY2zvrUfsjSc9kn/SoZf7zzz/68ssvtWnTJlWtWlXBwcHasmWLXnvtNe3Zs0c9e/bUtWvXdOHCBQ0bNkwjR460T2+z2XTv3j2dOXNGuXLlcmp78LD//vtP7dq1U4cOHdSyZUv17NlT/fv3l7e3t/r166fcuXNryJAhj5w+d+7c+vHHHyXd7xMbNmzQzJkz1b59e4fHm8S0fY963MCTPstjx47p3r176tSpk6zW/3vqbEREhMLCwnT37l3VqFFD8+fP17vvvqvcuXOrVKlSevfdd/XKK688dv2Tgsf19dh6+PuTHPfBUe+weNR20hmJZV/2ovLy8pKkaPuNPn362B/vOmPGDP3111+SpFdffTVW25zs2bPbf0+j9vc5cuSwj/fw8HA4VjPGqEKFCg41hIWF2Y//Pv/8c7Vu3VolS5aUn5+fSpcurQ8++OCR7/2ZNm2aJk2apAkTJqhAgQL65JNP9NVXX2nSpEn68MMP1atXrzi91FWS/bFeD+8TihYtan9/lbe3t/Lnz69FixapUKFCWrRokfz8/OzHvB07dlT//v0VEBCgYsWKqWzZsqpevbrDNu1FUrBgQRUsWFA2m02HDh3S+vXrNXPmTH3++edatWqVXnrpJUmOx8XS/ePedevWSbq/byhdurTD+Dx58ihNmjQ6fPiw/X2JT9rPOXNckTlzZs2YMUPS/WObqHdbPXz89dJLLzn0xSfVHPWYlQfPVzNmzOjwO/IkUee8sT0eLlu2rAoVKqS6desqe/bs9kfo5suXL9bLRMw+/vhj+6MxIyIiFBQUpOHDh+uTTz5R3759n3js8tZbbyldunSaMGGCTpw4oaCgoBi3q3GRLFky1ahRQ3/88YdatWqlLVu26Nq1a6pevfrTrzASrfXr1+vKlSsO56TVqlXT2rVrtXz5ctWsWTPO83TmXAeJ15P2yw970rWCx4nNuXSUB+eXOnVqSffPJ2Jz/hflwfOUpLgNJGRJwlKlShXjgfCFCxdibB/17PlkyZI98rnqNptNyZMntx/0PtwuWTK6nDNy5MihjBkzateuXTG+MO348eP68ccf1aNHD7355psO42L6rh78Lh8lppdxP+p7l+Sww3+4fbJkyeL0Pp5bt25p9erVGj58uP0dNMOGDVPr1q21e/du5cqVK8YdSnh4uBYvXqzw8HCVKlXKoQ6bzaa5c+eqY8eO9uEPr39kZKTDTiN58uQO46PWwc3NTceOHVPDhg3l6+urUqVKqUqVKvLy8op2seDhi2c2m00tWrRQrVq1otUfny8ne9Tv+6PYbDZ98MEHat26dbRxGTJkcPr5rw8/GzkmxhiH7yamvvUoT9rGDB06VF9++aX+/vtv/fPPP+rSpYv8/f0d3tsSZfPmzbp48aIWLlyoFClSqHr16jp69KiGDx8uT09PffjhhzEGLNL9iz9z5sxRWFiYvf5UqVIpVapUkhx/v2w2m1q1aqWjR4+qevXqev/99+Xr66vvv//e3sZisUT7HXpwXaP+/3G/ozabTSVKlFCvXr2ijXvUBbLKlStrw4YN2rBhg/755x9NmTJFY8aM0dy5c6Nta57G4/rn4/Y3Uuz2STGpWLGifv31V125ckW7d+9W3759deHCBW3dulWvvPKKsmTJojx58ujKlSuS7j/j9sHtSpSsWbM6vT142JIlS5QzZ061bNlSkvT9998rMDBQbdq00Y0bN2L8fZTuB4LDhg1TnTp19NZbb0m6f9E9V65cqlGjhipUqKCNGzfaQ5aYtu9RnvRZRo0fMWJEjNthd3d3eXh46I8//tDu3bu1adMmbdy4UdOnT1fbtm3j/SXLiV1ct8URERHRhsV0MfJx++C4iuliUmLZl72ocuTIoUyZMmnr1q0Ox3kvv/yy/f8f/GOD2G5zYtr+PSpIsNlsSp06tcM7zqJE9a9ChQpp/fr12rRpkzZv3qxFixZp3LhxmjRpkkqWLBltukWLFqlmzZoqUaKEJGns2LGqW7euGjRoIEkqV67cIz+TuHr42KFOnToaPny4vv32Wy1ZskQtWrSwj/vkk0/03nvvaf369dq8ebNGjRqlcePGadGiRcqYMeMzq8nVLly4oF9++UWtWrVSlixZZLValTdvXuXNm1fvvPOOqlevru3bt+u9996TFP0Yymaz2fvL447ZHuxnT9rPOXNckSxZslhtNx9e9pNqjjqGe9zxVUwe3EY+6hjjUVKkSKHp06frwIED2rhxozZu3KjWrVurZs2aGjBgQJzmBUfp0qVz6Ce5cuVSunTp1LBhQ/t7VR537LJt2zY1b95c5cuXl7+/vz744AOFhobqyy+/fOxyY9pPP6hOnTr69ddf9d9//2nx4sWqVKmSw/YcL56o/WhMx7yzZ89+ZMjyuL7kzLkOEp/Y7pcfFJtrBY8Tm3PpKI86n4jN+V+Uh89Tkto28MX8cx08lVy5cil58uQOLwGX7r9gM1OmTEqXLp18fHy0b98+h5f0XblyRUFBQcqVK5def/11pUiRQrt27bKPj4iIiPaiZsSO1WpV3bp1tWDBAvtLmB80adIk7du3z+EujSg+Pj4O34N0/7uUot99El/y5Mnj8GJV6f6LKB8lefLkSpYsmf3ipnT/RLx+/fq6ceOG/a/lHrZu3Tpdu3ZNvXr10qJFi+z//vjjD3l7e2v+/PkOBy8Pvsw1LCxM+/fvd3jh7P79+x1OpHbv3i1PT0+9/vrrmj17tl566SVNmTJFn3/+ud5++217vY+7sPXmm28qMDBQr732mv3fhQsXNHjwYN2+ffuR0yW0N998U8eOHXOoMyIiQgMGDND58+f12muvKXny5A6foXT/rpHH3aH0IB8fH928edPhr2+MMdq5c+cj77B6kly5ckWracCAAWrXrp3+/fdf9e/fX2+88YaaNm2qCRMmqH///tqyZYvDX3BEifqr36gXw0tShw4d9Prrr+vWrVsqX778I+v4+OOPFRERoZ9//jnG8Q+G2QcPHtTff/+tkSNHqnPnzqpRo4ZeffVVnTp1yt6X8uTJo//++89hm/vff//Z//+1116Th4eH9uzZ47CcB3/v3nzzTR0/flxZs2a1f6fp0qVT//797d/Bg6FRWFiYBgwYoNOnT+v9999Xv379tHr1almtVvtftiaEZ7FPkhQtEPPz81O6dOk0fvx4vfTSS8qZM6dKliypnTt3auXKlapYsaKk+38ZmyFDBp0+fdrh92H//v0aMWKEJDm9PXiYp6enQkJC7OuRPHlyDRkyRLdv35aHh0eMFzGl+xeWlixZotmzZ0cblypVKrm5udn/UvlJnvRZvvHGG0qWLJnOnTvn8HmsX79ev/76q6xWqxYvXqzffvvN/lLOuXPnql69elq2bFmsP4ukKHny5Lp165b9Z5vNptOnT8f7MiU5LPfkyZNPnO552Zc9L9zc3NSkSRMtWrTokcfKDx7/PattzoO8vb1169YthYeHO3yvEydO1Jo1ayRJo0aN0s6dO1WpUiV99913WrFihXLkyGF/6fzDPD09HY7lsmTJom+++UY3btxQvnz5HEKk2IoKi2PaJzx47FC9enXdu3dPU6ZM0ZUrV+x/NXn16lX17dtX4eHhql27tn766SctXrxYly9fdvoF7ImVu7u7fv/992gv95aktGnTSpJDqPTw8dOuXbvsTyHw8fGJ9pkfOnRIt27deuz5hCuPK55Uc9QfJTx4nhQSEqJTp07Zf37SNjKux8Pr16/XmDFjlDdvXrVs2VLTp09Xu3bt2D/GkwePo5907DJ58mQVL15co0ePVtOmTVW6dGn7djdqPs7sp3PlyqVChQpp+fLlWrNmjWrXrh1Pa4vE4OrVq1q/fr1q167tcD1i0aJFqlOnjnbv3q0jR45E60u3bt1yOB99+LwlNuc6SPxiu19+8PuPzbWCx4nNufSTxOb873HLT0rbQEIWRJM6dWp99NFHGjVqlJYuXaqgoCDNmjVLAQEBatasmSwWixo0aKDbt2+rS5cuOnTokPbu3av27dvLy8tL1apVU6pUqdSoUSONGjVKK1eu1PHjx9WrVy9dvHjR1av33GrdurVy5syphg0batGiRTp16pT27t2rHj16aNGiRfrhhx9ifPRTixYttHLlSo0dO1aBgYFau3atfvjhB1WoUMG+Q06VKpXOnj37yLuYntbnn3+u//3vf5oyZYpOnjyp+fPna+bMmY9snyJFCtWvX19jxozRypUrdfr0aU2dOlULFy5UwYIFNWXKFPtjlx40f/58Zc2aVR999JG8vb0d/n322We6fPmyVq9ebW8/YsQIrVu3TseOHdM333xjfyxZlLNnz6pPnz46fvy4Vq5cqVGjRqlRo0by9PRUlixZdOHCBa1fv15nz57VypUr1bt3b0lyOPiJ6bNYsWKFxowZo8DAQG3evFk9evTQzZs34/Wvf+/evavLly/H+C+meps1a6YDBw7Y13/37t3q1KmTTp48qZw5c8rT01ONGjXSyJEjtWbNGp06dUrDhg3TkSNH7H+ZmjJlSh0/flzBwcEx1lSmTBm99dZb6tSpk7Zt26bjx4+rb9++OnLkiD799FOn1rNly5ZatmyZZsyYoVOnTmnJkiX67bffVLFiRaVOnVoBAQH66aefFBQUpCNHjmjZsmXKmTOn/VEtDypatKjy5Mmjbt26affu3Tp27Jj69OmjM2fOKE+ePPrmm2+0f//+GOvIkSOHBgwYoKlTp6p9+/bavHmzzp49q3379mnw4MFq3ry5cuTIoWzZsiljxoxKliyZli9frtOnT2vfvn3q0KGDw3fToEEDhYaG6ptvvtHx48e1du1ajR492r48T09PNW7cWKNGjdLq1asVGBioQYMGOYQsDRs21M2bN9W5c2cdOnRIhw4dUseOHbVv3z77I9tSpkypGzduKDAwUBaLRfv27dP333+vPXv26MyZM1qwYIHu3LkT42Pk4suz2Cc9vG7h4eGyWCwqX7685syZYw8vChcuLGOMVq1apUqVKkm6f5Lz+eefa8aMGZo5c6ZOnTqlVatWqXfv3vLw8JC7u7vT24OH1a1bV7dv31aPHj107Ngx7dq1S507d1aWLFmUKlUqtWrVSteuXYs2ndVqVefOnTV79mz16tVLe/fu1ZkzZ+yPQ8uaNav9L5Wf5EmfZZo0afTxxx9r5MiR+uOPP3T69GnNmzdPP/30kzJnzizp/iPoBg0apEWLFunMmTPasWOHtm/fnqD95nlUsGBBbdq0SX///beCgoL0ww8/KCQk5Jkv58Ftc+bMmZUtWzZNmzZNx48f186dOzVy5MhH3qUXxVX7shdZixYtVKFCBTVs2FDjx4/XoUOHdObMGf31119q1qyZ5s+fb78j5Fltcx5UtmxZvfXWW+rYsaO2bNmioKAgDRgwQAsWLLAfL54+fVq9evWy79NWrFihc+fOPfJ3u3Hjxlq9erUmTpxo33YOGTJE+fPn1969e9W7d+8n/hX4w3LlyqUKFSqoT58+WrdunQIDAzVmzBitWbNGzZo1s7dLkyaNKleurLFjx6pSpUr2ixfp0qXTunXr9N133+ngwYM6ffq0Zs+ebX9s7IskQ4YMatGihUaOHKnhw4fb13ft2rX66quvVLx4cRUpUsTe/s8//9SsWbN08uRJTZo0SatWrbLfAfTZZ5/p8OHD+uGHH3T8+HFt3bpVnTt3Vt68eR/5BwCSa48rnlTzq6++qvfee099+/bVP//8oyNHjqhr164Ov0MFCxaUxWLR6NGjdebMGS1fvlwLFy60j4/r8XDy5Mn1888/a+rUqTp9+rT+++8/rVu3jv3jM3Dnzh37uc2lS5e0Y8cO9e/fX5kzZ1bp0qWfeOySNWtWHT58WDt27NCZM2c0f/58+2Nio/pEbPbTMZ3/1KlTRzNnzpSHh0e0R9jhxbJ48WJFRETo888/j3Y9onXr1rJarZo9e7YKFiyoZcuWadeuXfZrEQ/eaR51XefIkSO6efNmrM51kPjFdr+cMmVKXbp0SadPn47VtQLp0ddeYnMu/SSxOf97nKS0DeTZTYhRjx495OXlpSFDhujKlSvKmTOnevbsab8AnT17ds2cOVM//fSTPvroI7m7u6t06dL66aef7CcxnTp1UooUKdS3b1/dvn1bVatWtf9lMOLO09NTM2fO1OTJkzVx4kSdO3dOHh4eyps3r2bMmOFwkvSgd999V8OGDdO4ceM0duxYZciQQdWrV1e7du3sbT7++GN169ZNNWrU0ObNm5957eXKlVPfvn31yy+/aOjQocqXL58aNGjw2KDlm2++UcqUKdW3b19dv35duXPn1oABA1S1alUNGjRIGzZscLjV9sqVK9qwYYPatm0b46NwqlevrmHDhmn27Nnq16+fpPsXEwcNGqRz586pQIECmjFjhsNOomDBgva7iNKkSaMmTZroiy++kCQ1adJEJ06csJ+M5cyZU19//bVGjRqlffv2PfIRGO+9956GDx+uX375RePHj1f69Okf+b6UZ2n58uVavnx5jONGjhwZ7QJswYIFNWnSJI0cOVK1atVSypQpVbJkSXXr1s1+O+jXX38tNzc39erVSzdv3lSePHk0YcIE+y2kzZo106RJk3T8+HF999130Zbr5uamyZMna9CgQfrqq68UFhamfPnyaerUqQ7vFomLihUrqm/fvpo4caIGDRqkbNmyqUePHva+Mnr0aI0ZM0YBAQGyWq0qUaKEJk6cGONfXyRLlkwTJ05U//791aJFC9lsNhUqVEgBAQF69dVX9eWXX2rfvn0Odz89qGrVqvL29tb06dPVs2dPXbhwQR4eHvbgpnbt2vZHWwwcOFCjR4/WrFmzlClTJpUvX15Nmza1P3//5Zdf1rRp09S/f3/VqlVLWbNm1RdffKE+ffrYl9e+fXuFh4fru+++U2hoqCpUqKBKlSrZn6WfI0cOzZw5U0OHDlWDBg3k5uamwoULa/r06fb3PlWpUkVz585VjRo1NHPmTA0fPlwDBgzQF198oZs3b+qNN97QkCFDHrm9iS/PYp/08LoVKFBAFSpU0Lx58+zPoXd3d5e/v7/+++8/FS1a1L78Zs2aKUWKFJoxY4YGDhyojBkzqn79+vbtqLPbg4dlz55dU6ZM0eDBg+2/dxUrVtTw4cN17do1ff311zp37pz9+3pQvXr1lClTJk2bNk2ff/65bt++rYwZM6pSpUoaPHhwrN/7EZvPMur7GDlypC5duqSsWbOqXbt29otx9erV0/Xr1zV27FidP39e6dKl07vvvhvv27nnXbNmzXTq1Cm1b99e7u7uqlu3rqpVq+b0nQmPW07Utnn8+PEaPHiw+vfvrw8//FCvvfaaevToYX9k3aO4al/2IrNarRoxYoSWL1+u+fPna/r06QoJCVHGjBlVpEgRzZw5075delbbnAdF7ZN/+ukndejQQaGhocqVK5fGjBljv4jeq1cvDRo0SF26dNH169eVLVs2de7cWR9++GGM86xatapCQ0M1YcIEjRw5UpkzZ1bt2rXVqlUrrVq1SpMmTdLt27fj/NiIYcOGadiwYfr2228VEhIib29vjR49Oto71mrXrq0lS5Y4/NVk1L590KBBatq0qUJDQ/XWW29pwoQJevXVV+P4qSV+HTp0UM6cOTV37lzNmjVLd+/e1SuvvKKqVauqVatWDm1r1aqlVatWaeDAgcqZM6dGjBhhv3u8QIECmjRpkkaMGKGaNWsqderUeuedd9SpU6fHPqrGlccVsal50KBBGjRokDp27CibzaaPPvrI4Y8ZcuTIoT59+uiXX35RQECA/P391bVrV3Xr1s3eJi7Hw+PHj9ePP/6oyZMna/jw4fLw8NDbb7+t7t27P/P1T2omT56syZMnS7q/PU2fPr2KFCmiIUOGyNPT84nHLu3atdOVK1fsj2bNnTu3+vfvry5dumjfvn3KlStXrPbTD3/f0v1t4Q8//KCaNWs+9pGteP4tWLBApUqVivGRSq+++qreeecdLV68WH/++aeuX7+uzz77TGnSpFGzZs0cAjsvLy/VqVNHgwcPVlBQkL777rsnHp/j+RCb/XLNmjW1atUqVa9eXStXrnzitQLp8ddennQuHRtP2oY+TlLaBlrMsz5zA4CHbNu2TRkzZnQ42Bg/frzmzZvncGdJXBhjYpW6x+TMmTOqVKmSpk+fHu0ln1G6d++us2fP2l+0CTzsafpgfFi1apX8/f0dLsA3a9ZMWbJkUf/+/V1YGZ6VxNbnAOBpxPc2bcGCBRo9erTWrFnzwr7U/lmpWLGiatWqpbZt27q6FOCFc/r0aVWpUkXLly93eCk0ACQFSWkbyJ0sAOLdxo0btWTJEg0YMECvvvqqDh48qGnTpqlhw4ZOz5MLjXC1xNYHf/31VwUEBKhr165KnTq11qxZoy1bttj/qg/Pv8TW5wDgacTXNm3//v06ceKE/TGvBCwAXOH8+fPau3evAgICVLZs2Rf+4iIAPCgpbgMJWQDEu6+++kp37txR165dde3aNWXNmlVNmzaN1a2FAGJnyJAhGjhwoJo2baq7d+8qd+7cGjlypP0Z/gAAJAV79uzR4MGDVb58eaff8QYATys4OFjdu3dXzpw5NWbMGFeXAwAJKiluA3lcGAAAAAAAAAAAgBO4dxoAAAAAAAAAAMAJhCwAAAAAAAAAAABOIGQBAAAAAAAAAABwAiELAAAAAAAAAACAEwhZAAAAALyQjDGuLsEuMdUCAAAA4NkhZAEAAACgxo0by8fHx+Ffvnz5VL58efXp00c3btyIl+UuWLBAPj4+OnPmzDOd7++//65BgwY5Pf2KFSvUvHlzlSpVSgULFlT16tU1duxY3bp1K87zWrNmjbp16+Z0LQAAAAASr2SuLgAAAABA4pA3b1716tXL/nN4eLj279+vYcOG6eDBg/rtt99ksVhcWGHsjRs3TsWKFYvzdDabTV26dNH//vc/1alTRw0aNFCqVKm0Z88e/frrr1q9erWmTp2qtGnTxnqeU6dOjXMdAAAAAJ4PhCwAAAAAJEmpU6dWwYIFHYYVLVpUt2/f1qhRo/Tvv/9GG/+imTRpkpYuXaoxY8aocuXK9uElS5ZUsWLF9Mknn+jnn39Wjx49XFglAAAAgMSCx4UBAAAAeKx8+fJJks6dO6fGjRurc+fOateunQoWLKjPPvtMknTz5k0NGDBA77zzjvLnz6/q1atr3rx5DvOx2WwaO3asypcvrwIFCqhNmzbRHkPWvXt3VaxY0WHYmTNn5OPjowULFtiHXbp0Sd26dVPJkiVVqFAhNWrUSLt375YkVaxYUWfPntXChQvj9Ciy8PBwTZ48WeXKlXMIWKL4+/urXbt2yp07t0NtXbt2VZkyZeTr66uSJUuqa9euCg4OlnT/MWzbtm3Ttm3b5OPjo61bt0qSrl+/rp49e6pUqVLKnz+/6tevr82bNzss79atW+rZs6d9HTt27KipU6fKx8fHod2yZctUu3ZtFSpUSKVLl1bPnj0dPtfRo0ercuXKGjNmjIoVK6YyZcrou+++k5+fn27evOkwr7Fjx8rf31+hoaGx+swAAACApI47WQAAAAA8VmBgoCQpR44ckqTly5erRo0aGjdunGw2m+7evauGDRvq6tWrateunbJly6bVq1fr22+/1ZUrV9S6dWtJ0k8//aTp06friy++UIECBbR8+XINHTo0zvXcvn1bDRo0UGRkpLp06aKXX35ZkydPVrNmzbRw4UKNGTNGLVu2VN68edWmTRtlzpw5VvPdv3+/goODVaFChUe2adOmjf3/Q0ND1aRJE3l5ealXr15KkyaNdu/erTFjxsjDw0N9+/ZVr1691KVLF0lSr169lDt3bt27d0+ffvqprly5oo4dOypz5syaP3++WrRooUmTJqlkyZL2ZR08eFAdO3bUK6+8ooCAgGif19ixYzVq1Cg1bNhQHTt21OnTpzVy5Ejt2bNHc+fOlYeHh6T7Adn69es1fPhwXb9+XXny5NHvv/+u//3vf6pXr559fn/88Yfef/99eXp6xu7LAAAAAJI4QhYAAAAAkiRjjCIiIuw/37hxQ9u2bdO4ceNUqFAh+x0tyZMnV58+feTu7i5JCggI0JEjRzR79mwVKlRIklS2bFlFRERo7Nix+vjjj2W1WjVjxgx99tln+uqrr+xtLl26pA0bNsSpzoULF9rvVHnrrbckSYULF1bNmjW1fft21atXT+7u7sqQIUOcHm92/vx5SVL27Nlj1f7kyZPKkiWLBg0aZA+gSpQooX///Vfbtm2TJOXOnVupU6eWJHstc+fO1aFDhzR37lwVKFBAklSuXDk1btxYQ4YM0fz587V582Zt3bpVo0ePVpUqVextqlevruPHj0u6//2MGzdO9evXV8+ePe11eXt765NPPtH8+fP1ySefSJIiIiLUrVs3FSlSxN6uUKFC+uOPP+why65du3Ty5EkNHDgw1p8ZAAAAkNTxuDAAAAAAkqTt27fL19fX/q9UqVL6+uuvlS9fPg0dOtT+0vs33njDHrBI0rZt25QtWzZ7wBKlRo0aunfvnv7991/t2bNH4eHh0e4SqVq1apzr3Llzp7Jnz24PWCTJ09NTK1ascLgrI66SJbv/N2g2my1W7d966y0FBAQoW7ZsOnnypNavX69ff/1VJ06cUFhY2COn27x5szJlyiRfX19FREQoIiJCkZGRqlChgv777z/duHFDW7ZsUfLkyfXOO+/Yp7NarXr//fftP+/Zs0dhYWGqXr26w/yLFCmibNmy2YOeB+t9UJ06dbRjxw6dPXtW0v3w6vXXX4/2PQIAAAB4NO5kAQAAACBJ8vX1VZ8+fSRJFotFKVKkUNasWe13YkRJlSqVw883btxQpkyZos0vY8aMkqSQkBD7MC8vL4c2MU33JNevX9dLL70U5+me5JVXXpEke+gQk2vXril16tT2kGnKlCkaP368rl+/rowZMypfvnzy9PSM9q6TB12/fl2XL1+Wr69vjOMvX76s4OBgpU+fXlar49/FPbjeUe9difqcH5QxY8ZoNTz8vb3//vvq37+//vjjDzVv3lzLly9Xy5YtH1k3AAAAgOgIWQAAAABIun8RPn/+/HGeLl26dAoKCoo2/PLly5Icg5WrV6/qjTfesP98/fp1h2ksFosiIyMdht25c8fh5zRp0sT4Mvtdu3YpXbp0ypUrV5zXQbp/p0fGjBn1999/2x+z9bDvvvtOe/bs0bp167RixQoNHDhQXbp0Ue3atZUhQwZJUvv27bVv375HLidNmjTKmTOnhgwZEuP47Nmz6+WXX1ZwcLBsNptD0HL16lX7/6dLl06SdOXKFYfPVLr/2Uc9wuxRUqVKpffee0/Lly+Xt7e37ty5ow8//PCx0wAAAABwxOPCAAAAADyVokWL6uzZs9q9e7fD8MWLFyt58uTy8/NToUKF5OHhof/9738ObdauXevwc6pUqRQcHKx79+7Zh+3cudOhTZEiRXT69GkdPXrUPuzevXtq27at5s2bJ0nR7gCJDavVqqZNm2rdunX666+/oo3fsmWL1q9fr/fee0/u7u7auXOn0qZNqxYtWtgDltu3b2vnzp0Ojxx7uJZixYrp/Pnzeumll5Q/f377v02bNmnSpElyc3NTsWLFFBER4VCHMUarV6+2/1ygQAG5u7tr6dKlDvPfsWOHzp07p8KFCz9xnevWrasjR45o2rRpKlWqlF5++eXYfVgAAAAAJHEnCwAAAICnVLt2bQUEBOjLL79Uu3btlD17dv3111+aP3++vvrqK6VNm1aS1KZNG40YMUKenp4qUaKE1q9fHy1kqVChgmbMmKFvv/3WHgBMmTJFbm5uDsubMWOGvvjiC7Vr105eXl6aPn26wsPD1bBhQ0lS2rRpdeDAAW3btk1+fn7y8PCI1bo0bdpU27dvV9u2bVW/fn29/fbbslqt2r59u2bMmKG33npLnTp1kiT5+fnpt99+08CBA1WhQgVdunRJv/76q65cuWK/yySqlt27d2vz5s3KmzevateurZkzZ+qzzz5T69atlTVrVv3zzz+aOHGiGjVqpOTJk6to0aIqXbq0vv32W125ckWvvPKK5s2bp8OHD9vfjZM+fXq1bNlSP//8s5InT64KFSrozJkzGjlypHLnzq1atWo9cX39/f31+uuva9u2bRo+fHisPiMAAAAA/8dijDGuLgIAAACAazVu3FiSNGPGDKfaXbt2TUOHDtVff/2lW7du6Y033lDjxo1Vt25dh3YzZszQtGnTdPHiRRUqVEhVq1ZV7969tWbNGmXPnl2SNHnyZM2YMUNXrlyRr6+vvv/+e3388cfq06ePateuLUm6ePGiBg8erL///ls2m00FCxZUly5dlCdPHknS0qVL1b9/f928eVNTpkxRkSJFYv1ZREREaM6cOfrjjz8UFBSksLAw5ciRQ9WrV1ejRo2UMmVKSffvLBk9erTmz5+v4OBgvfzyy3r77bfl7e2t77//XsuWLVOuXLm0ZcsW9ejRQ5cvX9aAAQP0wQcf6OrVqxo6dKjWrVunmzdvKlu2bKpbt66aNWtmv/Plxo0bGjhwoFavXq2IiAhVqlRJadOm1aJFi7Rr1y57vb/99ptmzpypoKAgpU+fXpUrV1aHDh3sQc/o0aM1ZswYHT58OMb1HThwoBYsWKCNGzfa3zUDAAAAIHYIWQAAAPD/2rtjHIOiKAzAB4lNKBU2oZC8Ep1WYQNqbMAK9Hag12pEJVHoNC92QPcw3SSSick8Mmb4vvrm3NP/95wL/DH7/T7W63UkSXI1hdPv9yNN05jNZg+553K5RLPZjHq9HqPR6CE1AQDgnVgXBgAAvLTT6RTfvS0rFApXK8merVgsxmAwiCRJotPpRKlUisViEfP5PMbj8d31D4dDTKfT2Gw2kabp54QSAADwMyZZAACAl9btdmO1Wt08U6lUvvzs/pmWy2VMJpPYbreRZVlUq9Xo9XrRarXurp1lWTQajTifzzEcDqPdbj+gYwAAeD9CFgAA4KXtdrs4Ho83z5TL5ajVar/UEQAA8CqELAAAAAAAADkUn90AAAAAAADAfyRkAQAAAAAAyEHIAgAAAAAAkIOQBQAAAAAAIAchCwAAAAAAQA5CFgAAAAAAgByELAAAAAAAADkIWQAAAAAAAHL4AAONgAq/lsx/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be54973",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the sold products are from Food, Clothing and Electronics category*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f33d23c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='Orders'>"
      ]
     },
     "execution_count": 27,
     "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_state = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_ID',y= 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17d2bb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID'>"
      ]
     },
     "execution_count": 28,
     "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",
    "\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": "markdown",
   "id": "31f88f63",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "\n",
    "### "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e52bca",
   "metadata": {},
   "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*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bfbc58",
   "metadata": {},
   "source": [
    "Thank you!"
   ]
  }
 ],
 "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": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}