CASE STUDY 1 - CUSTOMER ANALYSIS FOR RETAIL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "79597355",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "3507f2e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import datasets\n",
    "\n",
    "cust_data = pd.read_csv(\"C://Users//SANAM KANDAR//OneDrive//Desktop//Case Study//PYTHON//Python Foundation Case Study 1 - Retail Case Study//Case Study 1 - Retail Case Study//Customer.csv\") \n",
    "prod_data = pd.read_csv(\"C://Users//SANAM KANDAR//OneDrive//Desktop//Case Study//PYTHON//Python Foundation Case Study 1 - Retail Case Study//Case Study 1 - Retail Case Study//prod_cat_info.csv\")\n",
    "trans_data = pd.read_csv(\"C://Users//SANAM KANDAR//OneDrive//Desktop//Case Study//PYTHON//Python Foundation Case Study 1 - Retail Case Study//Case Study 1 - Retail Case Study//Transactions.csv\")                         \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914a1230",
   "metadata": {},
   "source": [
    "###### 1. Merge the datasets Customers, Product Hierarchy and Transactions as Customer_Final. Ensure to keep all customers who have done transactions with us and select the join type accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "81fbf5d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_cust_data = pd.merge(left=trans_data,\n",
    "                           right=cust_data,\n",
    "                           how=\"inner\",\n",
    "                           left_on=\"cust_id\",\n",
    "                           right_on=\"customer_Id\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "7a745e1b",
   "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>transaction_id</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>tran_date</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Tax</th>\n",
       "      <th>total_amt</th>\n",
       "      <th>Store_type</th>\n",
       "      <th>customer_Id</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Gender</th>\n",
       "      <th>city_code</th>\n",
       "      <th>prod_cat</th>\n",
       "      <th>prod_sub_cat_code</th>\n",
       "      <th>prod_subcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>270351</td>\n",
       "      <td>28-02-2014</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>-772</td>\n",
       "      <td>405.300</td>\n",
       "      <td>-4265.300</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>270351</td>\n",
       "      <td>26-09-1981</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>270351</td>\n",
       "      <td>20-02-2014</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>772</td>\n",
       "      <td>405.300</td>\n",
       "      <td>4265.300</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>270351</td>\n",
       "      <td>26-09-1981</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18505840838</td>\n",
       "      <td>271509</td>\n",
       "      <td>16-12-2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1229</td>\n",
       "      <td>387.135</td>\n",
       "      <td>4074.135</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>271509</td>\n",
       "      <td>08-06-1981</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92814475704</td>\n",
       "      <td>267750</td>\n",
       "      <td>16-08-2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>-284</td>\n",
       "      <td>119.280</td>\n",
       "      <td>-1255.280</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>267750</td>\n",
       "      <td>13-10-1986</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92814475704</td>\n",
       "      <td>267750</td>\n",
       "      <td>07-08-2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>284</td>\n",
       "      <td>119.280</td>\n",
       "      <td>1255.280</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>267750</td>\n",
       "      <td>13-10-1986</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</td>\n",
       "      <td>Women</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23048</th>\n",
       "      <td>7173864364</td>\n",
       "      <td>271157</td>\n",
       "      <td>09-04-2011</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>788</td>\n",
       "      <td>413.700</td>\n",
       "      <td>4353.700</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>271157</td>\n",
       "      <td>15-10-1973</td>\n",
       "      <td>F</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>4</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23049</th>\n",
       "      <td>5618131425</td>\n",
       "      <td>272010</td>\n",
       "      <td>03-03-2011</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1150</td>\n",
       "      <td>241.500</td>\n",
       "      <td>2541.500</td>\n",
       "      <td>MBR</td>\n",
       "      <td>272010</td>\n",
       "      <td>22-12-1972</td>\n",
       "      <td>F</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>4</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23050</th>\n",
       "      <td>18727956164</td>\n",
       "      <td>267161</td>\n",
       "      <td>23-02-2011</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>668</td>\n",
       "      <td>350.700</td>\n",
       "      <td>3690.700</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>267161</td>\n",
       "      <td>08-05-1981</td>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>4</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23051</th>\n",
       "      <td>60416814232</td>\n",
       "      <td>273281</td>\n",
       "      <td>18-02-2011</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>202</td>\n",
       "      <td>84.840</td>\n",
       "      <td>892.840</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>273281</td>\n",
       "      <td>14-12-1988</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>4</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23052</th>\n",
       "      <td>83245680995</td>\n",
       "      <td>273723</td>\n",
       "      <td>26-01-2011</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1477</td>\n",
       "      <td>620.340</td>\n",
       "      <td>6528.340</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>273723</td>\n",
       "      <td>21-01-1984</td>\n",
       "      <td>F</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>4</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23053 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  cust_id   tran_date  prod_subcat_code  prod_cat_code  \\\n",
       "0         80712190438   270351  28-02-2014                 1              1   \n",
       "1         80712190438   270351  20-02-2014                 1              1   \n",
       "2         18505840838   271509  16-12-2013                 1              1   \n",
       "3         92814475704   267750  16-08-2013                 1              1   \n",
       "4         92814475704   267750  07-08-2013                 1              1   \n",
       "...               ...      ...         ...               ...            ...   \n",
       "23048      7173864364   271157  09-04-2011                 4              4   \n",
       "23049      5618131425   272010  03-03-2011                 4              4   \n",
       "23050     18727956164   267161  23-02-2011                 4              4   \n",
       "23051     60416814232   273281  18-02-2011                 4              4   \n",
       "23052     83245680995   273723  26-01-2011                 4              4   \n",
       "\n",
       "       Qty  Rate      Tax  total_amt      Store_type  customer_Id         DOB  \\\n",
       "0       -5  -772  405.300  -4265.300          e-Shop       270351  26-09-1981   \n",
       "1        5   772  405.300   4265.300          e-Shop       270351  26-09-1981   \n",
       "2        3  1229  387.135   4074.135  Flagship store       271509  08-06-1981   \n",
       "3       -4  -284  119.280  -1255.280  Flagship store       267750  13-10-1986   \n",
       "4        4   284  119.280   1255.280  Flagship store       267750  13-10-1986   \n",
       "...    ...   ...      ...        ...             ...          ...         ...   \n",
       "23048    5   788  413.700   4353.700  Flagship store       271157  15-10-1973   \n",
       "23049    2  1150  241.500   2541.500             MBR       272010  22-12-1972   \n",
       "23050    5   668  350.700   3690.700          e-Shop       267161  08-05-1981   \n",
       "23051    4   202   84.840    892.840  Flagship store       273281  14-12-1988   \n",
       "23052    4  1477  620.340   6528.340          e-Shop       273723  21-01-1984   \n",
       "\n",
       "      Gender  city_code  prod_cat  prod_sub_cat_code prod_subcat  \n",
       "0          M        5.0  Clothing                  1       Women  \n",
       "1          M        5.0  Clothing                  1       Women  \n",
       "2          M        3.0  Clothing                  1       Women  \n",
       "3          M        1.0  Clothing                  1       Women  \n",
       "4          M        1.0  Clothing                  1       Women  \n",
       "...      ...        ...       ...                ...         ...  \n",
       "23048      F        6.0      Bags                  4       Women  \n",
       "23049      F        5.0      Bags                  4       Women  \n",
       "23050      M        9.0      Bags                  4       Women  \n",
       "23051      F        9.0      Bags                  4       Women  \n",
       "23052      F        4.0      Bags                  4       Women  \n",
       "\n",
       "[23053 rows x 17 columns]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final = pd.merge(left=trans_cust_data, \n",
    "                         right=prod_data,\n",
    "                         how=\"inner\", \n",
    "                         left_on=[\"prod_cat_code\",  \"prod_subcat_code\"],\n",
    "                         right_on=[\"prod_cat_code\", \"prod_sub_cat_code\"],)\n",
    "\n",
    "Customer_Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "ec3f0ead",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping duplicated columns\n",
    "\n",
    "Customer_Final.drop(labels=[\"customer_Id\", \"prod_sub_cat_code\"],  axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "91c4b457",
   "metadata": {},
   "outputs": [],
   "source": [
    "# coverting dtype of date variables\n",
    "\n",
    "Customer_Final.tran_date = pd.to_datetime(Customer_Final.tran_date, format=\"%d-%m-%Y\")\n",
    "Customer_Final.DOB = pd.to_datetime(Customer_Final.DOB, format=\"%d-%m-%Y\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "b3263de8",
   "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>transaction_id</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>tran_date</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Tax</th>\n",
       "      <th>total_amt</th>\n",
       "      <th>Store_type</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Gender</th>\n",
       "      <th>city_code</th>\n",
       "      <th>prod_cat</th>\n",
       "      <th>prod_subcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>270351</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>-772</td>\n",
       "      <td>405.300</td>\n",
       "      <td>-4265.300</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1981-09-26</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>270351</td>\n",
       "      <td>2014-02-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>772</td>\n",
       "      <td>405.300</td>\n",
       "      <td>4265.300</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1981-09-26</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18505840838</td>\n",
       "      <td>271509</td>\n",
       "      <td>2013-12-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1229</td>\n",
       "      <td>387.135</td>\n",
       "      <td>4074.135</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1981-06-08</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92814475704</td>\n",
       "      <td>267750</td>\n",
       "      <td>2013-08-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>-284</td>\n",
       "      <td>119.280</td>\n",
       "      <td>-1255.280</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1986-10-13</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92814475704</td>\n",
       "      <td>267750</td>\n",
       "      <td>2013-08-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>284</td>\n",
       "      <td>119.280</td>\n",
       "      <td>1255.280</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1986-10-13</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</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>23048</th>\n",
       "      <td>7173864364</td>\n",
       "      <td>271157</td>\n",
       "      <td>2011-04-09</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>788</td>\n",
       "      <td>413.700</td>\n",
       "      <td>4353.700</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1973-10-15</td>\n",
       "      <td>F</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23049</th>\n",
       "      <td>5618131425</td>\n",
       "      <td>272010</td>\n",
       "      <td>2011-03-03</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1150</td>\n",
       "      <td>241.500</td>\n",
       "      <td>2541.500</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1972-12-22</td>\n",
       "      <td>F</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23050</th>\n",
       "      <td>18727956164</td>\n",
       "      <td>267161</td>\n",
       "      <td>2011-02-23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>668</td>\n",
       "      <td>350.700</td>\n",
       "      <td>3690.700</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1981-05-08</td>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23051</th>\n",
       "      <td>60416814232</td>\n",
       "      <td>273281</td>\n",
       "      <td>2011-02-18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>202</td>\n",
       "      <td>84.840</td>\n",
       "      <td>892.840</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1988-12-14</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23052</th>\n",
       "      <td>83245680995</td>\n",
       "      <td>273723</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1477</td>\n",
       "      <td>620.340</td>\n",
       "      <td>6528.340</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1984-01-21</td>\n",
       "      <td>F</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23053 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  cust_id  tran_date  prod_subcat_code  prod_cat_code  \\\n",
       "0         80712190438   270351 2014-02-28                 1              1   \n",
       "1         80712190438   270351 2014-02-20                 1              1   \n",
       "2         18505840838   271509 2013-12-16                 1              1   \n",
       "3         92814475704   267750 2013-08-16                 1              1   \n",
       "4         92814475704   267750 2013-08-07                 1              1   \n",
       "...               ...      ...        ...               ...            ...   \n",
       "23048      7173864364   271157 2011-04-09                 4              4   \n",
       "23049      5618131425   272010 2011-03-03                 4              4   \n",
       "23050     18727956164   267161 2011-02-23                 4              4   \n",
       "23051     60416814232   273281 2011-02-18                 4              4   \n",
       "23052     83245680995   273723 2011-01-26                 4              4   \n",
       "\n",
       "       Qty  Rate      Tax  total_amt      Store_type        DOB Gender  \\\n",
       "0       -5  -772  405.300  -4265.300          e-Shop 1981-09-26      M   \n",
       "1        5   772  405.300   4265.300          e-Shop 1981-09-26      M   \n",
       "2        3  1229  387.135   4074.135  Flagship store 1981-06-08      M   \n",
       "3       -4  -284  119.280  -1255.280  Flagship store 1986-10-13      M   \n",
       "4        4   284  119.280   1255.280  Flagship store 1986-10-13      M   \n",
       "...    ...   ...      ...        ...             ...        ...    ...   \n",
       "23048    5   788  413.700   4353.700  Flagship store 1973-10-15      F   \n",
       "23049    2  1150  241.500   2541.500             MBR 1972-12-22      F   \n",
       "23050    5   668  350.700   3690.700          e-Shop 1981-05-08      M   \n",
       "23051    4   202   84.840    892.840  Flagship store 1988-12-14      F   \n",
       "23052    4  1477  620.340   6528.340          e-Shop 1984-01-21      F   \n",
       "\n",
       "       city_code  prod_cat prod_subcat  \n",
       "0            5.0  Clothing       Women  \n",
       "1            5.0  Clothing       Women  \n",
       "2            3.0  Clothing       Women  \n",
       "3            1.0  Clothing       Women  \n",
       "4            1.0  Clothing       Women  \n",
       "...          ...       ...         ...  \n",
       "23048        6.0      Bags       Women  \n",
       "23049        5.0      Bags       Women  \n",
       "23050        9.0      Bags       Women  \n",
       "23051        9.0      Bags       Women  \n",
       "23052        4.0      Bags       Women  \n",
       "\n",
       "[23053 rows x 15 columns]"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a01d6da",
   "metadata": {},
   "source": [
    "##### 2. Prepare a summary report for the merged data set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb6be86",
   "metadata": {},
   "source": [
    "##### a. Get the column names and their corresponding data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "7e5634cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 23053 entries, 0 to 23052\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   transaction_id    23053 non-null  int64         \n",
      " 1   cust_id           23053 non-null  int64         \n",
      " 2   tran_date         23053 non-null  datetime64[ns]\n",
      " 3   prod_subcat_code  23053 non-null  int64         \n",
      " 4   prod_cat_code     23053 non-null  int64         \n",
      " 5   Qty               23053 non-null  int64         \n",
      " 6   Rate              23053 non-null  int64         \n",
      " 7   Tax               23053 non-null  float64       \n",
      " 8   total_amt         23053 non-null  float64       \n",
      " 9   Store_type        23053 non-null  object        \n",
      " 10  DOB               23053 non-null  datetime64[ns]\n",
      " 11  Gender            23044 non-null  object        \n",
      " 12  city_code         23045 non-null  float64       \n",
      " 13  prod_cat          23053 non-null  object        \n",
      " 14  prod_subcat       23053 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(3), int64(6), object(4)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "Customer_Final.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b51d22",
   "metadata": {},
   "source": [
    "##### b. Top/Bottom 10 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "99dd0c52",
   "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>transaction_id</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>tran_date</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Tax</th>\n",
       "      <th>total_amt</th>\n",
       "      <th>Store_type</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Gender</th>\n",
       "      <th>city_code</th>\n",
       "      <th>prod_cat</th>\n",
       "      <th>prod_subcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>270351</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>-772</td>\n",
       "      <td>405.300</td>\n",
       "      <td>-4265.300</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1981-09-26</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>270351</td>\n",
       "      <td>2014-02-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>772</td>\n",
       "      <td>405.300</td>\n",
       "      <td>4265.300</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1981-09-26</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18505840838</td>\n",
       "      <td>271509</td>\n",
       "      <td>2013-12-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1229</td>\n",
       "      <td>387.135</td>\n",
       "      <td>4074.135</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1981-06-08</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92814475704</td>\n",
       "      <td>267750</td>\n",
       "      <td>2013-08-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>-284</td>\n",
       "      <td>119.280</td>\n",
       "      <td>-1255.280</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1986-10-13</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92814475704</td>\n",
       "      <td>267750</td>\n",
       "      <td>2013-08-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>284</td>\n",
       "      <td>119.280</td>\n",
       "      <td>1255.280</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1986-10-13</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4737317330</td>\n",
       "      <td>269345</td>\n",
       "      <td>2011-07-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1141</td>\n",
       "      <td>599.025</td>\n",
       "      <td>6304.025</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1970-06-26</td>\n",
       "      <td>F</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>44425889101</td>\n",
       "      <td>274987</td>\n",
       "      <td>2012-03-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>897</td>\n",
       "      <td>376.740</td>\n",
       "      <td>3964.740</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1983-10-08</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>90501340928</td>\n",
       "      <td>271817</td>\n",
       "      <td>2012-02-19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1122</td>\n",
       "      <td>117.810</td>\n",
       "      <td>1239.810</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>1989-12-24</td>\n",
       "      <td>M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>99335419136</td>\n",
       "      <td>268755</td>\n",
       "      <td>2012-12-13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1181</td>\n",
       "      <td>372.015</td>\n",
       "      <td>3915.015</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1984-07-15</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35030444164</td>\n",
       "      <td>268129</td>\n",
       "      <td>2011-11-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1047</td>\n",
       "      <td>549.675</td>\n",
       "      <td>5784.675</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1982-08-07</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  cust_id  tran_date  prod_subcat_code  prod_cat_code  Qty  \\\n",
       "0     80712190438   270351 2014-02-28                 1              1   -5   \n",
       "1     80712190438   270351 2014-02-20                 1              1    5   \n",
       "2     18505840838   271509 2013-12-16                 1              1    3   \n",
       "3     92814475704   267750 2013-08-16                 1              1   -4   \n",
       "4     92814475704   267750 2013-08-07                 1              1    4   \n",
       "5      4737317330   269345 2011-07-29                 1              1    5   \n",
       "6     44425889101   274987 2012-03-18                 1              1    4   \n",
       "7     90501340928   271817 2012-02-19                 1              1    1   \n",
       "8     99335419136   268755 2012-12-13                 1              1    3   \n",
       "9     35030444164   268129 2011-11-18                 1              1    5   \n",
       "\n",
       "   Rate      Tax  total_amt      Store_type        DOB Gender  city_code  \\\n",
       "0  -772  405.300  -4265.300          e-Shop 1981-09-26      M        5.0   \n",
       "1   772  405.300   4265.300          e-Shop 1981-09-26      M        5.0   \n",
       "2  1229  387.135   4074.135  Flagship store 1981-06-08      M        3.0   \n",
       "3  -284  119.280  -1255.280  Flagship store 1986-10-13      M        1.0   \n",
       "4   284  119.280   1255.280  Flagship store 1986-10-13      M        1.0   \n",
       "5  1141  599.025   6304.025             MBR 1970-06-26      F       10.0   \n",
       "6   897  376.740   3964.740  Flagship store 1983-10-08      M        2.0   \n",
       "7  1122  117.810   1239.810        TeleShop 1989-12-24      M        8.0   \n",
       "8  1181  372.015   3915.015          e-Shop 1984-07-15      F        8.0   \n",
       "9  1047  549.675   5784.675             MBR 1982-08-07      F        9.0   \n",
       "\n",
       "   prod_cat prod_subcat  \n",
       "0  Clothing       Women  \n",
       "1  Clothing       Women  \n",
       "2  Clothing       Women  \n",
       "3  Clothing       Women  \n",
       "4  Clothing       Women  \n",
       "5  Clothing       Women  \n",
       "6  Clothing       Women  \n",
       "7  Clothing       Women  \n",
       "8  Clothing       Women  \n",
       "9  Clothing       Women  "
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "7f1c3ff1",
   "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>transaction_id</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>tran_date</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Tax</th>\n",
       "      <th>total_amt</th>\n",
       "      <th>Store_type</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Gender</th>\n",
       "      <th>city_code</th>\n",
       "      <th>prod_cat</th>\n",
       "      <th>prod_subcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23043</th>\n",
       "      <td>3387244829</td>\n",
       "      <td>269114</td>\n",
       "      <td>2011-07-15</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>388</td>\n",
       "      <td>40.740</td>\n",
       "      <td>428.740</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1989-01-22</td>\n",
       "      <td>F</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23044</th>\n",
       "      <td>76906459516</td>\n",
       "      <td>267940</td>\n",
       "      <td>2011-06-15</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1263</td>\n",
       "      <td>265.230</td>\n",
       "      <td>2791.230</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1979-06-09</td>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23045</th>\n",
       "      <td>73549617163</td>\n",
       "      <td>271334</td>\n",
       "      <td>2011-07-05</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>263</td>\n",
       "      <td>138.075</td>\n",
       "      <td>1453.075</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1983-12-08</td>\n",
       "      <td>F</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23046</th>\n",
       "      <td>75339646315</td>\n",
       "      <td>274827</td>\n",
       "      <td>2011-05-02</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1381</td>\n",
       "      <td>580.020</td>\n",
       "      <td>6104.020</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1988-12-27</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23047</th>\n",
       "      <td>6650926717</td>\n",
       "      <td>268110</td>\n",
       "      <td>2011-04-05</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1036</td>\n",
       "      <td>435.120</td>\n",
       "      <td>4579.120</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1976-03-06</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23048</th>\n",
       "      <td>7173864364</td>\n",
       "      <td>271157</td>\n",
       "      <td>2011-04-09</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>788</td>\n",
       "      <td>413.700</td>\n",
       "      <td>4353.700</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1973-10-15</td>\n",
       "      <td>F</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23049</th>\n",
       "      <td>5618131425</td>\n",
       "      <td>272010</td>\n",
       "      <td>2011-03-03</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1150</td>\n",
       "      <td>241.500</td>\n",
       "      <td>2541.500</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1972-12-22</td>\n",
       "      <td>F</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23050</th>\n",
       "      <td>18727956164</td>\n",
       "      <td>267161</td>\n",
       "      <td>2011-02-23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>668</td>\n",
       "      <td>350.700</td>\n",
       "      <td>3690.700</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1981-05-08</td>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23051</th>\n",
       "      <td>60416814232</td>\n",
       "      <td>273281</td>\n",
       "      <td>2011-02-18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>202</td>\n",
       "      <td>84.840</td>\n",
       "      <td>892.840</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1988-12-14</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23052</th>\n",
       "      <td>83245680995</td>\n",
       "      <td>273723</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1477</td>\n",
       "      <td>620.340</td>\n",
       "      <td>6528.340</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1984-01-21</td>\n",
       "      <td>F</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  cust_id  tran_date  prod_subcat_code  prod_cat_code  \\\n",
       "23043      3387244829   269114 2011-07-15                 4              4   \n",
       "23044     76906459516   267940 2011-06-15                 4              4   \n",
       "23045     73549617163   271334 2011-07-05                 4              4   \n",
       "23046     75339646315   274827 2011-05-02                 4              4   \n",
       "23047      6650926717   268110 2011-04-05                 4              4   \n",
       "23048      7173864364   271157 2011-04-09                 4              4   \n",
       "23049      5618131425   272010 2011-03-03                 4              4   \n",
       "23050     18727956164   267161 2011-02-23                 4              4   \n",
       "23051     60416814232   273281 2011-02-18                 4              4   \n",
       "23052     83245680995   273723 2011-01-26                 4              4   \n",
       "\n",
       "       Qty  Rate      Tax  total_amt      Store_type        DOB Gender  \\\n",
       "23043    1   388   40.740    428.740          e-Shop 1989-01-22      F   \n",
       "23044    2  1263  265.230   2791.230  Flagship store 1979-06-09      M   \n",
       "23045    5   263  138.075   1453.075          e-Shop 1983-12-08      F   \n",
       "23046    4  1381  580.020   6104.020          e-Shop 1988-12-27      F   \n",
       "23047    4  1036  435.120   4579.120             MBR 1976-03-06      M   \n",
       "23048    5   788  413.700   4353.700  Flagship store 1973-10-15      F   \n",
       "23049    2  1150  241.500   2541.500             MBR 1972-12-22      F   \n",
       "23050    5   668  350.700   3690.700          e-Shop 1981-05-08      M   \n",
       "23051    4   202   84.840    892.840  Flagship store 1988-12-14      F   \n",
       "23052    4  1477  620.340   6528.340          e-Shop 1984-01-21      F   \n",
       "\n",
       "       city_code prod_cat prod_subcat  \n",
       "23043        5.0     Bags       Women  \n",
       "23044        9.0     Bags       Women  \n",
       "23045       10.0     Bags       Women  \n",
       "23046        8.0     Bags       Women  \n",
       "23047        2.0     Bags       Women  \n",
       "23048        6.0     Bags       Women  \n",
       "23049        5.0     Bags       Women  \n",
       "23050        9.0     Bags       Women  \n",
       "23051        9.0     Bags       Women  \n",
       "23052        4.0     Bags       Women  "
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2eec22",
   "metadata": {},
   "source": [
    "##### c. “Five-number summary” for continuous variables (min, Q1, median, Q3 and max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "f0f327bf",
   "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>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Qty</th>\n",
       "      <td>-5.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rate</th>\n",
       "      <td>-1499.000</td>\n",
       "      <td>312.00</td>\n",
       "      <td>710.00</td>\n",
       "      <td>1109.000</td>\n",
       "      <td>1500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tax</th>\n",
       "      <td>7.350</td>\n",
       "      <td>98.28</td>\n",
       "      <td>199.08</td>\n",
       "      <td>365.715</td>\n",
       "      <td>787.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_amt</th>\n",
       "      <td>-8270.925</td>\n",
       "      <td>762.45</td>\n",
       "      <td>1754.74</td>\n",
       "      <td>3569.150</td>\n",
       "      <td>8287.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                min     25%      50%       75%     max\n",
       "Qty          -5.000    1.00     3.00     4.000     5.0\n",
       "Rate      -1499.000  312.00   710.00  1109.000  1500.0\n",
       "Tax           7.350   98.28   199.08   365.715   787.5\n",
       "total_amt -8270.925  762.45  1754.74  3569.150  8287.5"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_var = Customer_Final.describe(include=[\"int64\", \"float64\"]).loc[::, [\"Qty\",\"Rate\",\"Tax\",\"total_amt\"]]\n",
    "\n",
    "continuous_var.loc[[\"min\",\"25%\",\"50%\",\"75%\", \"max\"],::].T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7612f2c9",
   "metadata": {},
   "source": [
    "##### d. Frequency tables for all the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "4e9ac3e9",
   "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store_type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>prod_cat</th>\n",
       "      <th>prod_subcat</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Flagship store</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Bags</th>\n",
       "      <th>Mens</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Women</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Books</th>\n",
       "      <th>Academic</th>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comics</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">e-Shop</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>Footwear</th>\n",
       "      <th>Women</th>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Home and kitchen</th>\n",
       "      <th>Bath</th>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furnishing</th>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kitchen</th>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tools</th>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0\n",
       "Store_type     Gender prod_cat         prod_subcat     \n",
       "Flagship store F      Bags             Mens          90\n",
       "                                       Women        110\n",
       "                      Books            Academic     111\n",
       "                                       Children     113\n",
       "                                       Comics        91\n",
       "...                                                 ...\n",
       "e-Shop         M      Footwear         Women        204\n",
       "                      Home and kitchen Bath         206\n",
       "                                       Furnishing   213\n",
       "                                       Kitchen      202\n",
       "                                       Tools        225\n",
       "\n",
       "[184 rows x 1 columns]"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Customer_Final.groupby(by=[\"Store_type\",\"Gender\",\"prod_cat\",\"prod_subcat\"]).size())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ad17e3",
   "metadata": {},
   "source": [
    "##### 3. Generate histograms for all continuous variables and frequency bars for categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "5f0c3d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Qty', ylabel='Count'>"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuous\n",
    "sns.histplot(x=Customer_Final.Qty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "6a8afe96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rate', ylabel='Count'>"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuous\n",
    "sns.histplot(x=Customer_Final.Rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "3d55e199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Tax', ylabel='Count'>"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuous\n",
    "sns.histplot(x=Customer_Final.Tax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "aed1ec42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_amt', ylabel='Count'>"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuous\n",
    "\n",
    "plt.figure(figsize=(13,7))\n",
    "sns.histplot(x=Customer_Final.total_amt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "c4a3e2e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='Store_type'>"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categotical\n",
    "\n",
    "plt.figure(figsize=(13,3))\n",
    "sns.histplot(y=Customer_Final.Store_type)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "6d5e5b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='Gender'>"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1300x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categotical\n",
    "\n",
    "plt.figure(figsize=(13,2))\n",
    "sns.histplot(y=Customer_Final.Gender)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "246dca88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='city_code'>"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categorical\n",
    "\n",
    "plt.figure(figsize=(13,5))\n",
    "sns.histplot(y=Customer_Final.city_code )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "f58f87b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='prod_cat'>"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#categorical\n",
    "\n",
    "plt.figure(figsize=(13,5))\n",
    "sns.histplot(y=Customer_Final.prod_cat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "3c90db6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='prod_subcat'>"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#categorical\n",
    "\n",
    "plt.figure(figsize=(13,7))\n",
    "sns.histplot(y=Customer_Final.prod_subcat)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32d91df",
   "metadata": {},
   "source": [
    "##### 4. Calculate the following information using the merged dataset :\n",
    "###### a. Time period of the available transaction data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "a17d0085",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transaction data available 1130 days which is approx. 3 years 9 days time period.\n"
     ]
    }
   ],
   "source": [
    "start_date = Customer_Final.tran_date.min()\n",
    "\n",
    "end_date = Customer_Final.tran_date.max()\n",
    "\n",
    "time_period = end_date-start_date\n",
    "\n",
    "print(\"Transaction data available \"+ str(time_period.days) +\" days which is approx. 3 years 9 days time period.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e162ba26",
   "metadata": {},
   "source": [
    "##### b. Count of transactions where the total amount of transaction was negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "73514d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2177"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final[Customer_Final.total_amt<0].shape[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8255730",
   "metadata": {},
   "source": [
    "##### 5. Analyze which product categories are more popular among females vs male customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "d3fc162b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product categories are more popular among male customers.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prod_cat\n",
       "Books               3116\n",
       "Electronics         2570\n",
       "Home and kitchen    2134\n",
       "Clothing            1518\n",
       "Footwear            1469\n",
       "Bags                1004\n",
       "Name: prod_cat, dtype: int64"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M_data = Customer_Final[Customer_Final.Gender==\"M\"]\n",
    "\n",
    "\n",
    "print(\"product categories are more popular among male customers.\")\n",
    "\n",
    "M_data.groupby(by=\"prod_cat\").prod_cat.count().sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "1e9b9d3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product categories are more popular among females customers.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "prod_cat\n",
       "Books               2949\n",
       "Electronics         2328\n",
       "Home and kitchen    1994\n",
       "Footwear            1529\n",
       "Clothing            1439\n",
       "Bags                 994\n",
       "Name: prod_cat, dtype: int64"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_data = Customer_Final[Customer_Final.Gender==\"F\"]\n",
    "\n",
    "\n",
    "print(\"product categories are more popular among females customers.\")\n",
    "\n",
    "F_data.groupby(by=\"prod_cat\").prod_cat.count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a99d4a",
   "metadata": {},
   "source": [
    "##### 6. Which City code has the maximum customers and what was the percentage of customers from that city?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "bc4b6d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Customer_in_city = pd.DataFrame( Customer_Final.groupby(by=\"city_code\").cust_id.count().sort_values(ascending=False) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "3fa14284",
   "metadata": {},
   "outputs": [],
   "source": [
    "Customer_in_city[\"percentage of cust.\"]= round( ( Customer_in_city.cust_id / Customer_in_city.cust_id.sum() )*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "e15ab105",
   "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>cust_id</th>\n",
       "      <th>percentage of cust.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2422</td>\n",
       "      <td>10.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cust_id  percentage of cust.\n",
       "city_code                              \n",
       "4.0           2422                10.51"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_in_city.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f049e83",
   "metadata": {},
   "source": [
    "##### 7. Which store type sells the maximum products by value and by quantity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "5164e2df",
   "metadata": {
    "scrolled": false
   },
   "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>Qty</th>\n",
       "      <th>total_amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e-Shop</th>\n",
       "      <td>22763</td>\n",
       "      <td>1.982482e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MBR</th>\n",
       "      <td>11194</td>\n",
       "      <td>9.674486e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flagship store</th>\n",
       "      <td>11133</td>\n",
       "      <td>9.715688e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TeleShop</th>\n",
       "      <td>10984</td>\n",
       "      <td>9.364781e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Qty     total_amt\n",
       "Store_type                         \n",
       "e-Shop          22763  1.982482e+07\n",
       "MBR             11194  9.674486e+06\n",
       "Flagship store  11133  9.715688e+06\n",
       "TeleShop        10984  9.364781e+06"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.groupby(by=\"Store_type\").agg({ \"Qty\": np.sum,\n",
    "                                              \"total_amt\": np.sum }).sort_values(by=[\"Qty\",\"total_amt\"], \n",
    "                                                                                 ascending=[False, False])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4f2a0a",
   "metadata": {},
   "source": [
    "##### 8. What was the total amount earned from the \"Electronics\" and \"Clothing\" categories from Flagship Stores?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "50fcb1c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3409559.27"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.loc[\n",
    "    (Customer_Final[\"Store_type\"] == \"Flagship store\") & ( Customer_Final[\"prod_cat\"].isin([\"Clothing\",\"Electronics\"]) )\n",
    "                    ].total_amt.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3f36c4",
   "metadata": {},
   "source": [
    "##### 9. What was the total amount earned from \"Male\" customers under the \"Electronics\" category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "05ae0d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5703109.425000001"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.loc[ (Customer_Final[\"Gender\"] == \"M\") & (Customer_Final[\"prod_cat\"]==\"Electronics\") ].total_amt.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d422cc75",
   "metadata": {},
   "source": [
    "##### 10. How many customers have more than 10 unique transactions, after removing all transactions which have any negative amounts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "fda35270",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#droped -ve rows\n",
    "Customer_Final.drop(Customer_Final.loc[Customer_Final.total_amt<0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "4151df7d",
   "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>transaction_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>266794</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270535</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270803</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272741</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273014</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274227</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         transaction_id\n",
       "cust_id                \n",
       "266794               11\n",
       "270535               11\n",
       "270803               11\n",
       "272741               11\n",
       "273014               11\n",
       "274227               11"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary\n",
    "customers = Customer_Final.groupby(by=\"cust_id\").agg({\"transaction_id\": \"nunique\"})\n",
    "\n",
    "customers[customers.transaction_id>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "ce34ae64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of customers\n",
    "\n",
    "customers[customers.transaction_id>10].index.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdd04ad",
   "metadata": {},
   "source": [
    "###### 11. For all customers aged between 25 - 35, find out:\n",
    "###### a. What was the total amount spent for “Electronics” and “Books” product categories?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "d9e78950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       32 days 09:39:32.895277207\n",
       "2       32 days 16:53:13.429158111\n",
       "4       27 days 08:33:30.677618070\n",
       "5       43 days 15:43:14.661190965\n",
       "6       30 days 08:54:12.566735113\n",
       "                   ...            \n",
       "23048   40 days 08:24:38.439425051\n",
       "23049   41 days 03:55:33.880903491\n",
       "23050   32 days 18:55:26.488706365\n",
       "23051   25 days 04:27:06.283367556\n",
       "23052   30 days 02:00:14.784394250\n",
       "Name: DOB, Length: 20876, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Date = Customer_Final.tran_date.max()    # from which age will be calculated\n",
    "\n",
    "Cal_Age = (Date - Customer_Final.DOB)/365.25        # in years\n",
    "\n",
    "Cal_Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "c1b282e2",
   "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>transaction_id</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>tran_date</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Tax</th>\n",
       "      <th>total_amt</th>\n",
       "      <th>Store_type</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Gender</th>\n",
       "      <th>city_code</th>\n",
       "      <th>prod_cat</th>\n",
       "      <th>prod_subcat</th>\n",
       "      <th>cust_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>270351</td>\n",
       "      <td>2014-02-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>772</td>\n",
       "      <td>405.300</td>\n",
       "      <td>4265.300</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1981-09-26</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18505840838</td>\n",
       "      <td>271509</td>\n",
       "      <td>2013-12-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1229</td>\n",
       "      <td>387.135</td>\n",
       "      <td>4074.135</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1981-06-08</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92814475704</td>\n",
       "      <td>267750</td>\n",
       "      <td>2013-08-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>284</td>\n",
       "      <td>119.280</td>\n",
       "      <td>1255.280</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1986-10-13</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4737317330</td>\n",
       "      <td>269345</td>\n",
       "      <td>2011-07-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1141</td>\n",
       "      <td>599.025</td>\n",
       "      <td>6304.025</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1970-06-26</td>\n",
       "      <td>F</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>44425889101</td>\n",
       "      <td>274987</td>\n",
       "      <td>2012-03-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>897</td>\n",
       "      <td>376.740</td>\n",
       "      <td>3964.740</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>1983-10-08</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  cust_id  tran_date  prod_subcat_code  prod_cat_code  Qty  \\\n",
       "1     80712190438   270351 2014-02-20                 1              1    5   \n",
       "2     18505840838   271509 2013-12-16                 1              1    3   \n",
       "4     92814475704   267750 2013-08-07                 1              1    4   \n",
       "5      4737317330   269345 2011-07-29                 1              1    5   \n",
       "6     44425889101   274987 2012-03-18                 1              1    4   \n",
       "\n",
       "   Rate      Tax  total_amt      Store_type        DOB Gender  city_code  \\\n",
       "1   772  405.300   4265.300          e-Shop 1981-09-26      M        5.0   \n",
       "2  1229  387.135   4074.135  Flagship store 1981-06-08      M        3.0   \n",
       "4   284  119.280   1255.280  Flagship store 1986-10-13      M        1.0   \n",
       "5  1141  599.025   6304.025             MBR 1970-06-26      F       10.0   \n",
       "6   897  376.740   3964.740  Flagship store 1983-10-08      M        2.0   \n",
       "\n",
       "   prod_cat prod_subcat  cust_age  \n",
       "1  Clothing       Women        32  \n",
       "2  Clothing       Women        32  \n",
       "4  Clothing       Women        27  \n",
       "5  Clothing       Women        43  \n",
       "6  Clothing       Women        30  "
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final[\"cust_age\"] = Cal_Age.dt.days      # created age column\n",
    "\n",
    "Customer_Final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "a4cb8eef",
   "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>total_amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prod_cat</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Books</th>\n",
       "      <td>6714704.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronics</th>\n",
       "      <td>5663232.185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               total_amt\n",
       "prod_cat                \n",
       "Books        6714704.880\n",
       "Electronics  5663232.185"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.loc[(Customer_Final.cust_age>=25) & \n",
    "                   (Customer_Final.cust_age<=35) & \n",
    "                   ( Customer_Final.prod_cat.isin([\"Electronics\",\"Books\"]) )\n",
    "                   \n",
    "                  ].groupby(by=\"prod_cat\").agg({\"total_amt\":np.sum})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26808245",
   "metadata": {},
   "source": [
    "###### b. What was the total amount spent by these customers between 1st Jan, 2014 to 1st Mar, 2014?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "3487b531",
   "metadata": {},
   "outputs": [],
   "source": [
    "strt_d = pd.to_datetime(\"01-jan-2014\", format=\"%d-%b-%Y\")\n",
    "end_d  = pd.to_datetime(\"01-mar-2014\", format=\"%d-%b-%Y\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "1c8834bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1205210.2400000002"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.loc[(Customer_Final.tran_date>=strt_d) & \n",
    "                                  (Customer_Final.tran_date<=end_d) &\n",
    "                                  (Customer_Final.cust_age>=25) & \n",
    "                                   (Customer_Final.cust_age<=35)].total_amt.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c4795d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee6f9188",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}