Retail Case Study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mp\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Customers = pd.read_csv('Customer.csv') \n",
    "Product = pd.read_csv('prod_cat_info.csv')\n",
    "Transactions = pd.read_csv('Transactions.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now merging to get all customers who have done transactions with us "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Customer_Final1 = pd.merge(left = Transactions,\n",
    "        right = Customers[['customer_Id', 'DOB', 'Gender', 'city_code']],\n",
    "        left_on = ['cust_id'],\n",
    "        right_on = ['customer_Id'],\n",
    "        how = 'left',\n",
    "        indicator = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>_merge</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>405.3</td>\n",
       "      <td>-4265.3</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>both</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>4</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>405.3</td>\n",
       "      <td>-4265.3</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>both</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</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  28-02-2014                 1              1   -5   \n",
       "1     80712190438   270351  28-02-2014                 1              1   -5   \n",
       "\n",
       "   Rate    Tax  total_amt Store_type  customer_Id         DOB Gender  \\\n",
       "0  -772  405.3    -4265.3     e-Shop       270351  26-09-1981      M   \n",
       "1  -772  405.3    -4265.3     e-Shop       270351  26-09-1981      M   \n",
       "\n",
       "   city_code _merge  prod_cat  prod_sub_cat_code prod_subcat  \n",
       "0        5.0   both  Clothing                  4        Mens  \n",
       "1        5.0   both  Clothing                  1       Women  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final = pd.merge(left = Customer_Final1,\n",
    "        right = Product[['prod_cat_code', 'prod_cat', 'prod_sub_cat_code', 'prod_subcat']],\n",
    "        left_on = ['prod_cat_code'],\n",
    "        right_on = ['prod_cat_code'],\n",
    "        how = 'left',\n",
    "        indicator = False)\n",
    "Customer_Final.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Prepare a summary report for the merged data set.\n",
    " - a. Get the column names and their corresponding data types \n",
    " - b. Top/Bottom 10 observations\n",
    " - c. “Five-number summary” for continuous variables (min, Q1, median, Q3 and max) \n",
    " - d. Frequency tables for all the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id          int64\n",
       "cust_id                 int64\n",
       "tran_date              object\n",
       "prod_subcat_code        int64\n",
       "prod_cat_code           int64\n",
       "Qty                     int64\n",
       "Rate                    int64\n",
       "Tax                   float64\n",
       "total_amt             float64\n",
       "Store_type             object\n",
       "customer_Id             int64\n",
       "DOB                    object\n",
       "Gender                 object\n",
       "city_code             float64\n",
       "_merge               category\n",
       "prod_cat               object\n",
       "prod_sub_cat_code       int64\n",
       "prod_subcat            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a. column names along with their data types\n",
    "Customer_Final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing the dates into date format plus droping the unnecessary variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Customer_Final['tran_date'] = pd.to_datetime(Customer_Final['tran_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Customer_Final['DOB'] = pd.to_datetime(Customer_Final['DOB'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Customer_Final.drop(['prod_subcat_code', 'prod_cat_code', '_merge', 'prod_sub_cat_code', 'cust_id'], axis = 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id             int64\n",
       "tran_date         datetime64[ns]\n",
       "Qty                        int64\n",
       "Rate                       int64\n",
       "Tax                      float64\n",
       "total_amt                float64\n",
       "Store_type                object\n",
       "customer_Id                int64\n",
       "DOB               datetime64[ns]\n",
       "Gender                    object\n",
       "city_code                float64\n",
       "prod_cat                  object\n",
       "prod_subcat               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#b.top 10 and bottom 10 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-772</td>\n",
       "      <td>405.300</td>\n",
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       "      <td>Women</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>2014-02-28</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>1981-09-26</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Kids</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29258453508</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1497</td>\n",
       "      <td>785.925</td>\n",
       "      <td>-8270.925</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>270384</td>\n",
       "      <td>1973-11-05</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29258453508</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1497</td>\n",
       "      <td>785.925</td>\n",
       "      <td>-8270.925</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>270384</td>\n",
       "      <td>1973-11-05</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Computers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29258453508</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1497</td>\n",
       "      <td>785.925</td>\n",
       "      <td>-8270.925</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>270384</td>\n",
       "      <td>1973-11-05</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Personal Appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29258453508</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1497</td>\n",
       "      <td>785.925</td>\n",
       "      <td>-8270.925</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>270384</td>\n",
       "      <td>1973-11-05</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Cameras</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29258453508</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1497</td>\n",
       "      <td>785.925</td>\n",
       "      <td>-8270.925</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>270384</td>\n",
       "      <td>1973-11-05</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Audio and video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>51750724947</td>\n",
       "      <td>2014-02-24</td>\n",
       "      <td>-2</td>\n",
       "      <td>-791</td>\n",
       "      <td>166.110</td>\n",
       "      <td>-1748.110</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>273420</td>\n",
       "      <td>1992-07-27</td>\n",
       "      <td>M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>51750724947</td>\n",
       "      <td>2014-02-24</td>\n",
       "      <td>-2</td>\n",
       "      <td>-791</td>\n",
       "      <td>166.110</td>\n",
       "      <td>-1748.110</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>273420</td>\n",
       "      <td>1992-07-27</td>\n",
       "      <td>M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Academic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   transaction_id  tran_date  Qty  Rate      Tax  total_amt Store_type  \\\n",
       "0     80712190438 2014-02-28   -5  -772  405.300  -4265.300     e-Shop   \n",
       "1     80712190438 2014-02-28   -5  -772  405.300  -4265.300     e-Shop   \n",
       "2     80712190438 2014-02-28   -5  -772  405.300  -4265.300     e-Shop   \n",
       "3     29258453508 2014-02-27   -5 -1497  785.925  -8270.925     e-Shop   \n",
       "4     29258453508 2014-02-27   -5 -1497  785.925  -8270.925     e-Shop   \n",
       "5     29258453508 2014-02-27   -5 -1497  785.925  -8270.925     e-Shop   \n",
       "6     29258453508 2014-02-27   -5 -1497  785.925  -8270.925     e-Shop   \n",
       "7     29258453508 2014-02-27   -5 -1497  785.925  -8270.925     e-Shop   \n",
       "8     51750724947 2014-02-24   -2  -791  166.110  -1748.110   TeleShop   \n",
       "9     51750724947 2014-02-24   -2  -791  166.110  -1748.110   TeleShop   \n",
       "\n",
       "   customer_Id        DOB Gender  city_code     prod_cat          prod_subcat  \n",
       "0       270351 1981-09-26      M        5.0     Clothing                 Mens  \n",
       "1       270351 1981-09-26      M        5.0     Clothing                Women  \n",
       "2       270351 1981-09-26      M        5.0     Clothing                 Kids  \n",
       "3       270384 1973-11-05      F        8.0  Electronics              Mobiles  \n",
       "4       270384 1973-11-05      F        8.0  Electronics            Computers  \n",
       "5       270384 1973-11-05      F        8.0  Electronics  Personal Appliances  \n",
       "6       270384 1973-11-05      F        8.0  Electronics              Cameras  \n",
       "7       270384 1973-11-05      F        8.0  Electronics      Audio and video  \n",
       "8       273420 1992-07-27      M        8.0        Books              Fiction  \n",
       "9       273420 1992-07-27      M        8.0        Books             Academic  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>tran_date</th>\n",
       "      <th>Qty</th>\n",
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       "      <th>99283</th>\n",
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       "      <td>2011-01-25</td>\n",
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       "      <td>359.730</td>\n",
       "      <td>3785.730</td>\n",
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       "      <td>3</td>\n",
       "      <td>1142</td>\n",
       "      <td>359.730</td>\n",
       "      <td>3785.730</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>270911</td>\n",
       "      <td>1970-05-22</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99285</th>\n",
       "      <td>72870271171</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>3</td>\n",
       "      <td>1142</td>\n",
       "      <td>359.730</td>\n",
       "      <td>3785.730</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>270911</td>\n",
       "      <td>1970-05-22</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Comics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99286</th>\n",
       "      <td>72870271171</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>3</td>\n",
       "      <td>1142</td>\n",
       "      <td>359.730</td>\n",
       "      <td>3785.730</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>270911</td>\n",
       "      <td>1970-05-22</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>DIY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99287</th>\n",
       "      <td>77960931771</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>447</td>\n",
       "      <td>46.935</td>\n",
       "      <td>493.935</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>271961</td>\n",
       "      <td>1982-01-15</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99288</th>\n",
       "      <td>77960931771</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>447</td>\n",
       "      <td>46.935</td>\n",
       "      <td>493.935</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>271961</td>\n",
       "      <td>1982-01-15</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Academic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99289</th>\n",
       "      <td>77960931771</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>447</td>\n",
       "      <td>46.935</td>\n",
       "      <td>493.935</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>271961</td>\n",
       "      <td>1982-01-15</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Non-Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99290</th>\n",
       "      <td>77960931771</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>447</td>\n",
       "      <td>46.935</td>\n",
       "      <td>493.935</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>271961</td>\n",
       "      <td>1982-01-15</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99291</th>\n",
       "      <td>77960931771</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>447</td>\n",
       "      <td>46.935</td>\n",
       "      <td>493.935</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>271961</td>\n",
       "      <td>1982-01-15</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Comics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99292</th>\n",
       "      <td>77960931771</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>447</td>\n",
       "      <td>46.935</td>\n",
       "      <td>493.935</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>271961</td>\n",
       "      <td>1982-01-15</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>DIY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  tran_date  Qty  Rate      Tax  total_amt Store_type  \\\n",
       "99283     72870271171 2011-01-25    3  1142  359.730   3785.730   TeleShop   \n",
       "99284     72870271171 2011-01-25    3  1142  359.730   3785.730   TeleShop   \n",
       "99285     72870271171 2011-01-25    3  1142  359.730   3785.730   TeleShop   \n",
       "99286     72870271171 2011-01-25    3  1142  359.730   3785.730   TeleShop   \n",
       "99287     77960931771 2011-01-25    1   447   46.935    493.935   TeleShop   \n",
       "99288     77960931771 2011-01-25    1   447   46.935    493.935   TeleShop   \n",
       "99289     77960931771 2011-01-25    1   447   46.935    493.935   TeleShop   \n",
       "99290     77960931771 2011-01-25    1   447   46.935    493.935   TeleShop   \n",
       "99291     77960931771 2011-01-25    1   447   46.935    493.935   TeleShop   \n",
       "99292     77960931771 2011-01-25    1   447   46.935    493.935   TeleShop   \n",
       "\n",
       "       customer_Id        DOB Gender  city_code prod_cat  prod_subcat  \n",
       "99283       270911 1970-05-22      M        2.0    Books  Non-Fiction  \n",
       "99284       270911 1970-05-22      M        2.0    Books     Children  \n",
       "99285       270911 1970-05-22      M        2.0    Books       Comics  \n",
       "99286       270911 1970-05-22      M        2.0    Books          DIY  \n",
       "99287       271961 1982-01-15      M        1.0    Books      Fiction  \n",
       "99288       271961 1982-01-15      M        1.0    Books     Academic  \n",
       "99289       271961 1982-01-15      M        1.0    Books  Non-Fiction  \n",
       "99290       271961 1982-01-15      M        1.0    Books     Children  \n",
       "99291       271961 1982-01-15      M        1.0    Books       Comics  \n",
       "99292       271961 1982-01-15      M        1.0    Books          DIY  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# c.“Five-number summary” for continuous variables (min, Q1, median, Q3 and max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "describe = Customer_Final.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>transaction_id</th>\n",
       "      <td>3268991.000</td>\n",
       "      <td>2.492150e+10</td>\n",
       "      <td>5.011083e+10</td>\n",
       "      <td>7.528121e+10</td>\n",
       "      <td>9.998755e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qty</th>\n",
       "      <td>-5.000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rate</th>\n",
       "      <td>-1499.000</td>\n",
       "      <td>3.130000e+02</td>\n",
       "      <td>7.130000e+02</td>\n",
       "      <td>1.109000e+03</td>\n",
       "      <td>1.500000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tax</th>\n",
       "      <td>7.350</td>\n",
       "      <td>9.828000e+01</td>\n",
       "      <td>1.999200e+02</td>\n",
       "      <td>3.669750e+02</td>\n",
       "      <td>7.875000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_amt</th>\n",
       "      <td>-8270.925</td>\n",
       "      <td>7.624500e+02</td>\n",
       "      <td>1.761370e+03</td>\n",
       "      <td>3.585725e+03</td>\n",
       "      <td>8.287500e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_Id</th>\n",
       "      <td>266783.000</td>\n",
       "      <td>2.689560e+05</td>\n",
       "      <td>2.709820e+05</td>\n",
       "      <td>2.731200e+05</td>\n",
       "      <td>2.752650e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_code</th>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        min           25%           50%           75%  \\\n",
       "transaction_id  3268991.000  2.492150e+10  5.011083e+10  7.528121e+10   \n",
       "Qty                  -5.000  1.000000e+00  3.000000e+00  4.000000e+00   \n",
       "Rate              -1499.000  3.130000e+02  7.130000e+02  1.109000e+03   \n",
       "Tax                   7.350  9.828000e+01  1.999200e+02  3.669750e+02   \n",
       "total_amt         -8270.925  7.624500e+02  1.761370e+03  3.585725e+03   \n",
       "customer_Id      266783.000  2.689560e+05  2.709820e+05  2.731200e+05   \n",
       "city_code             1.000  3.000000e+00  5.000000e+00  8.000000e+00   \n",
       "\n",
       "                         max  \n",
       "transaction_id  9.998755e+10  \n",
       "Qty             5.000000e+00  \n",
       "Rate            1.500000e+03  \n",
       "Tax             7.875000e+02  \n",
       "total_amt       8.287500e+03  \n",
       "customer_Id     2.752650e+05  \n",
       "city_code       1.000000e+01  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "five_number_summary = describe[['min', '25%', '50%', '75%', 'max']].copy()\n",
    "five_number_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d. Frequency tables for all the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_variables = ('Store_type', 'Gender', 'prod_cat', 'prod_subcat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = Customer_Final.groupby(by='Store_type')[['Store_type']].count().add_prefix(\"CountOf_\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot('Store_type', y='CountOf_Store_type', data=df,\n",
    "                 palette=\"Blues_d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF = Customer_Final.groupby(by='Gender')[['Gender']].count().add_prefix('CountOf_').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF.plot(kind='bar')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Df = Customer_Final.groupby(by = 'prod_cat')[['prod_cat']].count().add_prefix('CountOf_').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vmtl0M2sD9AXedvct7l5FVFhGmdkXgXx33xBv8+E4ngcMAJ5Ijgfsi4iIHIRgh8vc/Y3a92Z2BtFhs/7AJcAk4EPgSeA/gT1ERanWdqATcHID8Q7AR3FBSo7vw8wKgIKU8H7tREQkjKBzMgBmdhawCvhvd3fgyqRlc4BriUYkNUmrJYBqopFWOnHieKpbgKmH2AURETlIQc8uM7OLgLXAbe6+wMzOMbORSU0SQCVQCpyUFO8IbGsk/j5wnJnlxPGT4niqWcBpKa/+h9ovERFJT7CRjJl9AVgOjHb3dXE4Acwys3VEh8gmAguAjdEqdjqwBRgDzHf3rWZWbmYXufuzwDhgjbtXmlkJMBpYRDQaWpOag7uXAWUpeQXorYiI1Cfk4bJbgaOBmUlf7HOBu4BngTxgibs/CmBm44El8Tqr+cek/ljgITNrB/wemB3HJwELzKwQ+DPw9YB9ETnsVVTupW1ezoEbttD9ScsUcuL/ZuDmBhYX1dN+LdCjnvgrRGefpca3Ep1EICJA27wchk1ekbH9rbxveMb2JS2XrvgXEZFgVGRERCQYFRkREQlGRUZERIJRkRERkWBUZEREJBgVGRERCUZFRkREglGRERGRYFRkREQkGBUZEREJRkVGRESCSbvImFmX+L9DzGyKmR0XLi0REWkN0ioyZvYT4Dtm1h14COgCzA+ZmIiItHzpjmTOA/4v0aOTF7j7vwNfDJaViIi0CukWmTbuXg38K1D7lMt/CpOSiIi0FukWmXfMbDXRYbKnzGwh8Gq4tEREpDVIt8j8O7AIuNjdK4GSOCYiItKgdIvMbHcvdvd3Adx9LvBIsKxERKRVyG1soZn9GDgF6G9mJyQtyiM6dCYiItKgRosM8DPgbKAHsCQpXgVsONDGzWwqcHX8cZW7f9vMLgNmAvnAYncvjNv2BOYB7YCngevdvcrMOgPFwImAA2PdfY+ZFQALiYrdTuBqd9+RRp9FRCRDGj1c5u4vuvvDwEXuviDptdDd/9jYunEx+TLQC+gJnGdmXye6vmY40B3oY2aD41WKgRvcvSuQACbE8SKgyN27AS8CU+L4DKDE3Wuv3XmgKR0XEZHwDjSSqfUFM3sE+GeiAgCAu5/byDrbgcnuXgFgZm8CXYG33X1LHCsGRpnZZiDf3WtHRw8D081sHjAAGJEUXw98BxgSLwN4FPiRmeXFJyYQb78AKEjJq1OafRYRkUOUbpH5CdEX/O+BmnRWcPc3at+b2RlEh83mEBWfWtuJvvRPbiDeAfjI3atS4iSvEx9W+wg4AdiWtJ1bgKnp5CsiIs0v3SJT5e4zD2YHZnYWsAr4b6K5nK5JixNANdFhu5o04sTx2jbJEknLas0iKo7JOhGdgi0iIoGlW2ReN7Nz3P21pmzczC4iOmHgFnd/zMwuBk5KatKRaORR2kD8feA4M8tx971xm9qRyl/jdqVmlgscC+xK3r+7lwFlKTk1pQsiInII0r1Opgvwkpm9bWav1r4aW8HMvgAsB8a4+2NxeGO0yE43sxxgDLDG3bcC5XFRAhgXx2sv/Bwdx68F1sTvV8efiZeXJM/HiIhI9qU7kvnuQWz7VuBoYGbS6GEuMJ5odHM0UaF4Il42FnjIzNoRzf3MjuOTgAVmVgj8Gfh6HJ8CPGxmbxCNVsYeRI4iIhJQukWmSYfJANz9ZuDmBhb3qKf9K0DfeuJbgUvqie8GvtLUvEREJHPSLTJ/I5qAT/CPifjkM71ERET2k1aRcfe6uRsza0s0l6IZdBERaVTaj1+u5e4V8V0A/rX50xERkdYkrZGMmf1z0scE0BtoHyQjERFpNQ5mTgai61duCpKRiIi0Gk2ekxEREUlXuofL2hBd9zKY6Fkyvwa+n3RPMRERkf2kO0K5CxhIdDv9mcC/APeGSkpERFqHdOdk/g3oXXvbFjNbBbwC/FeoxEREpOVLdyTTJvm+YO7+GaD7hImISKPSHclsMrP7gQeJzjK7EWj0BpkiIiLpjmS+RXRdzO+I7qTcgajQiIiINKjRkUx8C5mHgOXuPj6OrQL2Ah8Fz05ERFq0A41k7gDaAc8mxSYABcC0QDmJiEgrcaAiM5TooWPv1wbcfRvRw8KuDJmYiMiRoqJyb6vd34Em/ivc/dPUoLt/ZGafBcpJROSI0jYvh2GTV2RsfyvvG56xfR1oJLPXzI5NDcaxvDApiYhIa3GgIvMoMM/MPlcbiN/PI3qEsoiISIMOdLhsFjAX2GFmbxAVpe7AQqKTAkRERBrUaJFx92pgopndCZwHVAMb3X17ujsws3ZE19cMdfd3zeznQD/g47jJdHdfZmaXEd0XLR9Y7O6F8fo9iUZO7YCngevdvcrMOgPFwImAA2PdfU+6eYmISHhpXYzp7lvdfam7L29igTkfeAbomhTuDQxw957xa5mZ5QPzgeFEI6U+ZjY4bl8M3ODuXYmeZzMhjhcBRe7eDXgRmJJuXiIikhmhnxMzgehuAdsAzOyfgM7AfDN71cymx48R6Au87e5b4scHFAOjzOyLQL67b4i393AczwMGAE8kxwP3RUREmijde5cdFHe/DsDMakMdgXXAJOBD4EngP4E9QPIIaTvQCTi5gXgH4KOk59nUxvdhZgVEF44m26+diIiEEbTIpHL3P5F0EaeZzSG6sPMJohtv1koQzf+0STNOHE91CzD1kBMXEZGDktHHKpvZOWY2MimUIHpkQClwUlK8I9Ehtobi7wPHmVlOHD8pjqeaBZyW8up/6D0REZF0ZHQkQ1RUZpnZOqJDZBOBBUR3djYzOx3YAowB5rv7VjMrN7OL3P1ZYBywxt0rzawEGA0sIhoNrUndmbuXAWXJsaRDdyIiElhGRzLu/irRo5yfBTYDm9z9UXcvB8YTXeC5GXiLf0zqjwXuN7O3gGOA2XF8EtHp1ZuJRieFmeqHiIikJyMjGXc/Nel9EdHpx6lt1gI96om/QnT2WWp8K3BJc+YpIiLNK6MjGRERObKoyIiISDAqMiIiEoyKjIiIBKMiIyIiwajIiIhIMCoyIiISjIqMiIgEoyIjIiLBqMiIiEgwKjIiIhKMioyIiASjIiMiIsGoyIiISDAqMiIiEoyKjIiIBKMiIyIiwajIiIhIMCoyIiISjIqMiIgEkxty42bWDvgdMNTd3zWzy4CZQD6w2N0L43Y9gXlAO+Bp4Hp3rzKzzkAxcCLgwFh332NmBcBCoAuwE7ja3XeE7IuIiDRdsJGMmZ0PPAN0jT/nA/OB4UB3oI+ZDY6bFwM3uHtXIAFMiONFQJG7dwNeBKbE8RlAibt3Bx4CHgjVDxEROXghRzITgG8Bj8Sf+wJvu/sWADMrBkaZ2WYg3903xO0eBqab2TxgADAiKb4e+A4wJF4G8CjwIzPLc/fK5ATiEU9BSl6dmqV3IiJyQMFGMu5+nbuXJIVOBrYnfd5O9IXfULwD8JG7V6XE99lWvPwj4IR60rgF2JLyKqmnnYiIBBB0TiZFG6Am6XMCqG5CnDhe2yZZImlZsllEI6BknVChERHJiEwWmVLgpKTPHYFtjcTfB44zsxx33xu32Ra3+WvcrtTMcoFjgV2pO3T3MqAsOWZmzdIZERE5sEyewrwRMDM73cxygDHAGnffCpSb2UVxu3FxvJJoxDE6jl8LrInfr44/Ey8vSZ2PERGR7MtYkXH3cmA8sATYDLwFPBEvHgvcb2ZvAccAs+P4JGBifHJAf6Awjk8BLjCzN+I238pEH0REpGmCHy5z91OT3q8FetTT5hWis89S41uBS+qJ7wa+0px5iohI89MV/yJy2Kuo3Nuq99eaZXLiXySrKir30jYvp9XurzVrm5fDsMkrMra/lfcNz9i+WjsVGTli6ItKJPN0uExERIJRkRERkWBUZEREJBgVGRERCUZFRkREglGRERGRYFRkREQkGBUZEREJRkVGRESCUZEREZFgVGRERCQYFRkREQlGRUZERIJRkRERkWBUZEREJBgVGRERCSYrDy0zs98CJwKVceibwP8BCoE8YJa7/yhuexkwE8gHFrt7YRzvCcwD2gFPA9e7e1Um+yEiIo3L+EjGzBJAV6CHu/d0955AKXAn0A/oCUw0szPNLB+YDwwHugN9zGxwvKli4AZ37wokgAkZ7oqIiBxANkYyFv/312Z2PPAQ8HdgnbvvBjCzJ4CrgPXA2+6+JY4XA6PMbDOQ7+4b4m09DEwHfpyxXoiIyAFlo8i0B9YCNxIdGnsKWAxsT2qzHegLnFxPvFMj8X2YWQFQkBLer52IiISR8SLj7s8Bz9V+NrOfEc25zEhqlgCqiQ7n1TQhnuoWYGqzJC4iIk2W8SJjZv2Ao9x9bRxKAO8CJyU16whsI5qraUo81SyiQ2nJOgElB5e9iIg0RTYOlxUAd5jZvxAdLvsGcA1QbGYnAB8DI4GJwKuAmdnpwBZgDDDf3beaWbmZXeTuzwLjgDWpO3L3MqAsOWZmqc1ERCSQjJ9d5u5PAquAl4GXiIrGs8B3gd8Cm4BF7v68u5cD44ElwGbgLeCJeFNjgfvN7C3gGGB2JvshIiIHlpXrZNx9CjAlJbYIWFRP27VAj3rirxCdHCAiIocpXfHfBBWVe1v1/kREmltWRjItVdu8HIZNXpGx/a28b3jG9iUiEoJGMiIiEoyKjIiIBKMiIyIiwajIiIhIMCoyIiISjIqMiIgEoyIjIiLBqMiIiEgwKjIiIhKMioyIiASjIiMiIsGoyIiISDAqMiIiEoyKjIiIBKMiIyIiwajIiIhIMCoyIiISjIqMiIgE06Ifv2xmY4BCIA+Y5e4/ynJKIiKSpMWOZMzsFOBOoB/QE5hoZmdmNysREUnWkkcylwHr3H03gJk9AVwF3FHbwMwKgIKU9b4IsGPHjoPaaeUnuw9qvYNRWlqasX3VUv+aj/rXvFpz3+Dw71/Sd2ZOU9ZL1NTUNHlnhwMzux34nLsXxp+vA/q6+8SkNtOAqdnJUESkVerv7s+k27glj2TaAMkVMgFUp7SZBTycEmsLdAHeBvaGSi5JJ6AE6A9k/s+j8NS/lq0196819w0y378c4CTghaas1JKLTCnRP26tjsC25AbuXgaU1bPuHwLmtQ8zq31b6u7vZmq/maL+tWytuX+tuW+Qtf79sakrtOQi87/ANDM7AfgYGAlMbHwVERHJpBZ7dpm7/xX4LvBbYBOwyN2fz25WIiKSrCWPZHD3RcCibOchIiL1a7EjmRakDJhO/XNDrYH617K15v615r5BC+lfiz2FWUREDn8ayYiISDAqMiIiEoyKjIiIBNOizy47XJlZN6L7qHUiugvBNuCX7v5iVhOTAzKz4UBnYLW7/zEpPtHdf5q9zJqHmZ0BfOzu2+JbMZ0LPOPuv8hyakGY2X3uPjnbeTQHM+vj7i/E7wcBVwCVwDJ335jV5Bqhif9mZmaTiC4KfQLYTnS7m45EF4sWu/t9WUxPGmFmdwO9gTeBUcCt7l4cL/u9u38pm/kdKjP7L+BGotuDrCUqpkuB4USF5ntZTO+Qmdn8esJfAf4fgLv/R2Yzal61v4Nm9i3geuBnRN8v3wDmufuDWU2wARrJNL+bgV7u/kly0MxmAr8HWnSRMbPOjS139z9nKpcAhhD97KrMbDbwazP7zN0fJ/qfuaX7D+BM4PPAG0AHdy83s3lE96Nq0UUG2A1cS/QIkNrTegcB67OWURgTgEvcfRdA0s/vsCwympNpflVED1FLlU80tG3pVhHd++0pov95k19PZS2r5pEgvumqu78NDAUeMLNL2PdmrC1VG+Azd98K/NDdy5OWtfg/ON39VuDrwNeAre6+ANjt7gvi9y1dnpm1AXYBnyXFK9j/5sCHjRb/i3UYuhN42czWEh0uqwFOBgYS3QanpbuI6M6vk9z92Wwn08weB54ys8nu/ry7v2Fmo4BlwFFZzq05LAHWm9ml7j4NwMx6AA8Bi7OZWHNx97Vm9jIw18yG0sRnnxzm/gbUHil4EBhvZgOBHxD97h6WNCcTgJmdTPRQtZOJ/nosBf7X3bc1umILYWZ9geuSn93TWsQTqtvc/c2k2BeAye5+S/Yyax5mNsDdn076bEAXd1+TxbSCiE9suNrdv5ztXJpT/DNr7+4bzOwioMDdV2U7r4aoyIjK1an2AAAAJklEQVSISDCakxERkWBUZEREJBgVGRERCUZFRkREglGRERGRYP4/R4Ve6CxE2XkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Df.plot(kind ='bar')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dF= Customer_Final.groupby(by='prod_subcat')[['prod_subcat']].count().add_prefix('CountOf_').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot('prod_subcat', y='CountOf_prod_subcat', data=dF,\n",
    "                 palette=\"Greens_d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Generate histograms for all continuous variables and frequency bars for categorical variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_variables = ('customer_Id', 'Qty', 'Rate', 'Tax', 'total_amt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Customer_Final.hist(bins=5, edgecolor = \"black\", color=\"red\", figsize = (15,10))\n",
    "plt.xlabel('frequency')\n",
    "plt.title('Histogram')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_variables = ('Store_type', 'Gender', 'prod_cat', 'prod_subcat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d071ea50b8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Customer_Final['Store_type'].value_counts().plot('bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d071a8f278>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Customer_Final['Gender'].value_counts().plot('bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d071a7b9e8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Customer_Final['prod_cat'].value_counts().plot('bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d07105f550>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Customer_Final['prod_subcat'].value_counts().plot('bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Frequency bars for categorical variables\n",
    "df1 = pd.crosstab(index=Customer_Final['Gender'], \n",
    "                           columns=Customer_Final['Store_type'])\n",
    "\n",
    "df1.index= ['F','M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(kind='bar')\n",
    "plt.ylabel('count')\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Calculate the following information using the merged dataset :   \n",
    " - a. Time period of the available transaction data  \n",
    " - b. Count of transactions where the total amount of transaction was negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#for a\n",
    "Customer_Final.set_index(Customer_Final.tran_date, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1130"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_days = (Customer_Final.tran_date[0] - Customer_Final.tran_date[-1]).days\n",
    "number_of_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9294"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#b. count of transaction where transaction is in negative amounts\n",
    "#method 1\n",
    "Customer_Final.Qty[Customer_Final.Qty + Customer_Final.total_amt < 0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9294"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method2\n",
    "Customer_Final.Qty.where(Customer_Final.total_amt < 0).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 5. Analyze which product categories are more popular among females vs male customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prod_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bags</th>\n",
       "      <td>1988</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Books</th>\n",
       "      <td>17694</td>\n",
       "      <td>18696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clothing</th>\n",
       "      <td>4317</td>\n",
       "      <td>4554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronics</th>\n",
       "      <td>11640</td>\n",
       "      <td>12850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Footwear</th>\n",
       "      <td>4587</td>\n",
       "      <td>4407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Home and kitchen</th>\n",
       "      <td>7976</td>\n",
       "      <td>8536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender                F      M\n",
       "prod_cat                      \n",
       "Bags               1988   2008\n",
       "Books             17694  18696\n",
       "Clothing           4317   4554\n",
       "Electronics       11640  12850\n",
       "Footwear           4587   4407\n",
       "Home and kitchen   7976   8536"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.pivot_table(index='prod_cat', columns='Gender', aggfunc='size', fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city_code\n",
       "4.0     10.648595\n",
       "3.0     10.550591\n",
       "7.0     10.322462\n",
       "5.0     10.186208\n",
       "10.0    10.051956\n",
       "8.0     10.039751\n",
       "2.0      9.913821\n",
       "1.0      9.777585\n",
       "9.0      9.283352\n",
       "6.0      9.189759\n",
       "Name: customer_Id, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_cust_city = Customer_Final.groupby('city_code').customer_Id.sum()\n",
    "sums = Customer_Final.customer_Id.sum()\n",
    "pcent_of_cust = max_cust_city.sort_values(ascending = False)/sums * 100\n",
    "pcent_of_cust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Which store type sells the maximum products by value and by quantity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store_type</th>\n",
       "      <th>total_amt</th>\n",
       "      <th>Qty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tran_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-03-23</th>\n",
       "      <td>e-Shop</td>\n",
       "      <td>8287.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-23</th>\n",
       "      <td>e-Shop</td>\n",
       "      <td>8287.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-23</th>\n",
       "      <td>e-Shop</td>\n",
       "      <td>8287.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-23</th>\n",
       "      <td>MBR</td>\n",
       "      <td>8287.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-23</th>\n",
       "      <td>MBR</td>\n",
       "      <td>8287.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Store_type  total_amt  Qty\n",
       "tran_date                            \n",
       "2013-03-23     e-Shop     8287.5    5\n",
       "2013-03-23     e-Shop     8287.5    5\n",
       "2013-03-23     e-Shop     8287.5    5\n",
       "2012-08-23        MBR     8287.5    5\n",
       "2012-08-23        MBR     8287.5    5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.sort_values(by=['total_amt','Qty'] ,ascending=[False, False]).loc[:,['Store_type','total_amt', 'Qty']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 8. What was the total amount earned from the \"Electronics\" and \"Clothing\" categories from Flagship Stores?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41910611.03999948"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final.total_amt.where(Customer_Final.Store_type == 'Flagship store', Customer_Final['prod_cat'].isin(['Clothing', 'Electronics'])).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 9. What was the total amount earned from \"Male\" customers under the \"Electronics\" category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28515547.125"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer_Final['total_amt'][(Customer_Final['Gender'] == 'M') & (Customer_Final['prod_cat'] == 'Electronics')].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>tran_date</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_subcat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tran_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
       "      <td>28396777609</td>\n",
       "      <td>2014-02-19</td>\n",
       "      <td>4</td>\n",
       "      <td>1386</td>\n",
       "      <td>582.12</td>\n",
       "      <td>6126.12</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>269101</td>\n",
       "      <td>1980-06-27</td>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
       "      <td>28396777609</td>\n",
       "      <td>2014-02-19</td>\n",
       "      <td>4</td>\n",
       "      <td>1386</td>\n",
       "      <td>582.12</td>\n",
       "      <td>6126.12</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>269101</td>\n",
       "      <td>1980-06-27</td>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            transaction_id  tran_date  Qty  Rate     Tax  total_amt  \\\n",
       "tran_date                                                             \n",
       "2014-02-19     28396777609 2014-02-19    4  1386  582.12    6126.12   \n",
       "2014-02-19     28396777609 2014-02-19    4  1386  582.12    6126.12   \n",
       "\n",
       "           Store_type  customer_Id        DOB Gender  city_code  prod_cat  \\\n",
       "tran_date                                                                   \n",
       "2014-02-19   TeleShop       269101 1980-06-27      M        7.0  Clothing   \n",
       "2014-02-19   TeleShop       269101 1980-06-27      M        7.0  Clothing   \n",
       "\n",
       "           prod_subcat  \n",
       "tran_date               \n",
       "2014-02-19        Mens  \n",
       "2014-02-19       Women  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = Customer_Final.drop(Customer_Final[(Customer_Final['Qty']< 0)].index)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
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       "      <td>2014-02-19</td>\n",
       "      <td>4</td>\n",
       "      <td>1386</td>\n",
       "      <td>582.120</td>\n",
       "      <td>6126.120</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>269101</td>\n",
       "      <td>1980-06-27</td>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Mens</td>\n",
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       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
       "      <td>22391667321</td>\n",
       "      <td>2014-02-19</td>\n",
       "      <td>5</td>\n",
       "      <td>723</td>\n",
       "      <td>379.575</td>\n",
       "      <td>3994.575</td>\n",
       "      <td>MBR</td>\n",
       "      <td>270842</td>\n",
       "      <td>1984-07-12</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            transaction_id  tran_date  Qty  Rate      Tax  total_amt  \\\n",
       "tran_date                                                              \n",
       "2014-02-19     28396777609 2014-02-19    4  1386  582.120   6126.120   \n",
       "2014-02-19     22391667321 2014-02-19    5   723  379.575   3994.575   \n",
       "\n",
       "           Store_type  customer_Id        DOB Gender  city_code  prod_cat  \\\n",
       "tran_date                                                                   \n",
       "2014-02-19   TeleShop       269101 1980-06-27      M        7.0  Clothing   \n",
       "2014-02-19        MBR       270842 1984-07-12      M        2.0  Footwear   \n",
       "\n",
       "           prod_subcat  \n",
       "tran_date               \n",
       "2014-02-19        Mens  \n",
       "2014-02-19        Mens  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.loc[-df.transaction_id.duplicated()]\n",
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-12-06</th>\n",
       "      <td>4236411732</td>\n",
       "      <td>2011-12-06</td>\n",
       "      <td>1</td>\n",
       "      <td>1238</td>\n",
       "      <td>129.990</td>\n",
       "      <td>1367.990</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>266788</td>\n",
       "      <td>1972-03-20</td>\n",
       "      <td>F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-31</th>\n",
       "      <td>5914139604</td>\n",
       "      <td>2012-10-31</td>\n",
       "      <td>2</td>\n",
       "      <td>672</td>\n",
       "      <td>141.120</td>\n",
       "      <td>1485.120</td>\n",
       "      <td>MBR</td>\n",
       "      <td>266788</td>\n",
       "      <td>1972-03-20</td>\n",
       "      <td>F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-09</th>\n",
       "      <td>42434369409</td>\n",
       "      <td>2012-11-09</td>\n",
       "      <td>2</td>\n",
       "      <td>325</td>\n",
       "      <td>68.250</td>\n",
       "      <td>718.250</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>266794</td>\n",
       "      <td>1971-02-28</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-29</th>\n",
       "      <td>42435707527</td>\n",
       "      <td>2013-06-29</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>12.915</td>\n",
       "      <td>135.915</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266794</td>\n",
       "      <td>1971-02-28</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-03</th>\n",
       "      <td>38727202803</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>5</td>\n",
       "      <td>1294</td>\n",
       "      <td>679.350</td>\n",
       "      <td>7149.350</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>266799</td>\n",
       "      <td>1970-03-16</td>\n",
       "      <td>F</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-18</th>\n",
       "      <td>22473497751</td>\n",
       "      <td>2012-10-18</td>\n",
       "      <td>1</td>\n",
       "      <td>468</td>\n",
       "      <td>49.140</td>\n",
       "      <td>517.140</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266806</td>\n",
       "      <td>1991-08-12</td>\n",
       "      <td>F</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-05</th>\n",
       "      <td>75039992579</td>\n",
       "      <td>2012-09-05</td>\n",
       "      <td>3</td>\n",
       "      <td>348</td>\n",
       "      <td>109.620</td>\n",
       "      <td>1153.620</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266814</td>\n",
       "      <td>1982-10-18</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-11</th>\n",
       "      <td>34749917660</td>\n",
       "      <td>2012-01-11</td>\n",
       "      <td>4</td>\n",
       "      <td>632</td>\n",
       "      <td>265.440</td>\n",
       "      <td>2793.440</td>\n",
       "      <td>MBR</td>\n",
       "      <td>266814</td>\n",
       "      <td>1982-10-18</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-06</th>\n",
       "      <td>11256528141</td>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>3</td>\n",
       "      <td>774</td>\n",
       "      <td>243.810</td>\n",
       "      <td>2565.810</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>266818</td>\n",
       "      <td>1981-08-10</td>\n",
       "      <td>F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-06</th>\n",
       "      <td>71308225311</td>\n",
       "      <td>2011-03-06</td>\n",
       "      <td>4</td>\n",
       "      <td>1023</td>\n",
       "      <td>429.660</td>\n",
       "      <td>4521.660</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266822</td>\n",
       "      <td>1970-09-20</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-30</th>\n",
       "      <td>85773906672</td>\n",
       "      <td>2011-03-30</td>\n",
       "      <td>2</td>\n",
       "      <td>154</td>\n",
       "      <td>32.340</td>\n",
       "      <td>340.340</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>266822</td>\n",
       "      <td>1970-09-20</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-29</th>\n",
       "      <td>15868818159</td>\n",
       "      <td>2013-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>522</td>\n",
       "      <td>54.810</td>\n",
       "      <td>576.810</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>266824</td>\n",
       "      <td>1973-04-06</td>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-11</th>\n",
       "      <td>70203705196</td>\n",
       "      <td>2011-11-11</td>\n",
       "      <td>2</td>\n",
       "      <td>1381</td>\n",
       "      <td>290.010</td>\n",
       "      <td>3052.010</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266824</td>\n",
       "      <td>1973-04-06</td>\n",
       "      <td>M</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-31</th>\n",
       "      <td>44566096268</td>\n",
       "      <td>2011-07-31</td>\n",
       "      <td>5</td>\n",
       "      <td>667</td>\n",
       "      <td>350.175</td>\n",
       "      <td>3685.175</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266830</td>\n",
       "      <td>1985-05-13</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>283737262</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>4</td>\n",
       "      <td>286</td>\n",
       "      <td>120.120</td>\n",
       "      <td>1264.120</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>266831</td>\n",
       "      <td>1978-08-10</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-03</th>\n",
       "      <td>41995387726</td>\n",
       "      <td>2011-09-03</td>\n",
       "      <td>1</td>\n",
       "      <td>360</td>\n",
       "      <td>37.800</td>\n",
       "      <td>397.800</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266831</td>\n",
       "      <td>1978-08-10</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-12</th>\n",
       "      <td>15639583679</td>\n",
       "      <td>2011-06-12</td>\n",
       "      <td>4</td>\n",
       "      <td>122</td>\n",
       "      <td>51.240</td>\n",
       "      <td>539.240</td>\n",
       "      <td>MBR</td>\n",
       "      <td>266836</td>\n",
       "      <td>1973-11-21</td>\n",
       "      <td>F</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-07</th>\n",
       "      <td>11284485790</td>\n",
       "      <td>2012-08-07</td>\n",
       "      <td>3</td>\n",
       "      <td>858</td>\n",
       "      <td>270.270</td>\n",
       "      <td>2844.270</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>266838</td>\n",
       "      <td>1974-11-14</td>\n",
       "      <td>M</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-04</th>\n",
       "      <td>18301188842</td>\n",
       "      <td>2011-09-04</td>\n",
       "      <td>5</td>\n",
       "      <td>1322</td>\n",
       "      <td>694.050</td>\n",
       "      <td>7304.050</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266841</td>\n",
       "      <td>1991-02-03</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-11</th>\n",
       "      <td>16775867233</td>\n",
       "      <td>2013-09-11</td>\n",
       "      <td>3</td>\n",
       "      <td>1052</td>\n",
       "      <td>331.380</td>\n",
       "      <td>3487.380</td>\n",
       "      <td>MBR</td>\n",
       "      <td>266842</td>\n",
       "      <td>1984-05-15</td>\n",
       "      <td>F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-20</th>\n",
       "      <td>58064640073</td>\n",
       "      <td>2011-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1223</td>\n",
       "      <td>128.415</td>\n",
       "      <td>1351.415</td>\n",
       "      <td>MBR</td>\n",
       "      <td>266843</td>\n",
       "      <td>1987-02-01</td>\n",
       "      <td>F</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-13</th>\n",
       "      <td>44027985600</td>\n",
       "      <td>2013-04-13</td>\n",
       "      <td>2</td>\n",
       "      <td>973</td>\n",
       "      <td>204.330</td>\n",
       "      <td>2150.330</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266844</td>\n",
       "      <td>1986-03-10</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-18</th>\n",
       "      <td>40615508857</td>\n",
       "      <td>2012-03-18</td>\n",
       "      <td>4</td>\n",
       "      <td>1194</td>\n",
       "      <td>501.480</td>\n",
       "      <td>5277.480</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266846</td>\n",
       "      <td>1979-04-24</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-24</th>\n",
       "      <td>30944211836</td>\n",
       "      <td>2012-03-24</td>\n",
       "      <td>4</td>\n",
       "      <td>1166</td>\n",
       "      <td>489.720</td>\n",
       "      <td>5153.720</td>\n",
       "      <td>MBR</td>\n",
       "      <td>266846</td>\n",
       "      <td>1979-04-24</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-09</th>\n",
       "      <td>17468457642</td>\n",
       "      <td>2013-10-09</td>\n",
       "      <td>3</td>\n",
       "      <td>855</td>\n",
       "      <td>269.325</td>\n",
       "      <td>2834.325</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266847</td>\n",
       "      <td>1990-01-24</td>\n",
       "      <td>F</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-20</th>\n",
       "      <td>35946237363</td>\n",
       "      <td>2012-12-20</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>38.640</td>\n",
       "      <td>406.640</td>\n",
       "      <td>MBR</td>\n",
       "      <td>266847</td>\n",
       "      <td>1990-01-24</td>\n",
       "      <td>F</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-11</th>\n",
       "      <td>3433540543</td>\n",
       "      <td>2011-12-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1136</td>\n",
       "      <td>119.280</td>\n",
       "      <td>1255.280</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266848</td>\n",
       "      <td>1987-07-10</td>\n",
       "      <td>F</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-16</th>\n",
       "      <td>90667600281</td>\n",
       "      <td>2011-02-16</td>\n",
       "      <td>5</td>\n",
       "      <td>1377</td>\n",
       "      <td>722.925</td>\n",
       "      <td>7607.925</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>266849</td>\n",
       "      <td>1979-12-10</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-20</th>\n",
       "      <td>34958490122</td>\n",
       "      <td>2011-02-20</td>\n",
       "      <td>4</td>\n",
       "      <td>1155</td>\n",
       "      <td>485.100</td>\n",
       "      <td>5105.100</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>266849</td>\n",
       "      <td>1979-12-10</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-06</th>\n",
       "      <td>65066509062</td>\n",
       "      <td>2011-12-06</td>\n",
       "      <td>2</td>\n",
       "      <td>717</td>\n",
       "      <td>150.570</td>\n",
       "      <td>1584.570</td>\n",
       "      <td>MBR</td>\n",
       "      <td>266853</td>\n",
       "      <td>1990-09-18</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-30</th>\n",
       "      <td>58687778228</td>\n",
       "      <td>2013-05-30</td>\n",
       "      <td>4</td>\n",
       "      <td>1168</td>\n",
       "      <td>490.560</td>\n",
       "      <td>5162.560</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275184</td>\n",
       "      <td>1976-01-11</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-09</th>\n",
       "      <td>29866196355</td>\n",
       "      <td>2013-09-09</td>\n",
       "      <td>4</td>\n",
       "      <td>866</td>\n",
       "      <td>363.720</td>\n",
       "      <td>3827.720</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275186</td>\n",
       "      <td>1974-06-05</td>\n",
       "      <td>F</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-30</th>\n",
       "      <td>63742635156</td>\n",
       "      <td>2011-03-30</td>\n",
       "      <td>1</td>\n",
       "      <td>785</td>\n",
       "      <td>82.425</td>\n",
       "      <td>867.425</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>275187</td>\n",
       "      <td>1984-04-07</td>\n",
       "      <td>F</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-22</th>\n",
       "      <td>30395695659</td>\n",
       "      <td>2012-06-22</td>\n",
       "      <td>3</td>\n",
       "      <td>484</td>\n",
       "      <td>152.460</td>\n",
       "      <td>1604.460</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275190</td>\n",
       "      <td>1971-02-11</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-17</th>\n",
       "      <td>77508007471</td>\n",
       "      <td>2012-11-17</td>\n",
       "      <td>5</td>\n",
       "      <td>412</td>\n",
       "      <td>216.300</td>\n",
       "      <td>2276.300</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>275192</td>\n",
       "      <td>1987-08-06</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-29</th>\n",
       "      <td>22224473979</td>\n",
       "      <td>2011-08-29</td>\n",
       "      <td>4</td>\n",
       "      <td>1238</td>\n",
       "      <td>519.960</td>\n",
       "      <td>5471.960</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>275197</td>\n",
       "      <td>1979-10-07</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-12</th>\n",
       "      <td>67494771515</td>\n",
       "      <td>2011-04-12</td>\n",
       "      <td>1</td>\n",
       "      <td>628</td>\n",
       "      <td>65.940</td>\n",
       "      <td>693.940</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275200</td>\n",
       "      <td>1970-04-12</td>\n",
       "      <td>M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-08</th>\n",
       "      <td>24722252527</td>\n",
       "      <td>2013-11-08</td>\n",
       "      <td>3</td>\n",
       "      <td>305</td>\n",
       "      <td>96.075</td>\n",
       "      <td>1011.075</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275202</td>\n",
       "      <td>1981-03-14</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-14</th>\n",
       "      <td>39578452941</td>\n",
       "      <td>2011-07-14</td>\n",
       "      <td>2</td>\n",
       "      <td>210</td>\n",
       "      <td>44.100</td>\n",
       "      <td>464.100</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275202</td>\n",
       "      <td>1981-03-14</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-29</th>\n",
       "      <td>31728298398</td>\n",
       "      <td>2011-04-29</td>\n",
       "      <td>2</td>\n",
       "      <td>250</td>\n",
       "      <td>52.500</td>\n",
       "      <td>552.500</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275204</td>\n",
       "      <td>1973-01-24</td>\n",
       "      <td>F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-12</th>\n",
       "      <td>20630217348</td>\n",
       "      <td>2011-04-12</td>\n",
       "      <td>4</td>\n",
       "      <td>779</td>\n",
       "      <td>327.180</td>\n",
       "      <td>3443.180</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>275205</td>\n",
       "      <td>1981-07-28</td>\n",
       "      <td>F</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-06</th>\n",
       "      <td>71557636302</td>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>2</td>\n",
       "      <td>829</td>\n",
       "      <td>174.090</td>\n",
       "      <td>1832.090</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>275210</td>\n",
       "      <td>1973-09-13</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-11</th>\n",
       "      <td>50250308685</td>\n",
       "      <td>2013-09-11</td>\n",
       "      <td>2</td>\n",
       "      <td>1106</td>\n",
       "      <td>232.260</td>\n",
       "      <td>2444.260</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275214</td>\n",
       "      <td>1984-04-06</td>\n",
       "      <td>M</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-20</th>\n",
       "      <td>95566895752</td>\n",
       "      <td>2013-06-20</td>\n",
       "      <td>4</td>\n",
       "      <td>528</td>\n",
       "      <td>221.760</td>\n",
       "      <td>2333.760</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275218</td>\n",
       "      <td>1985-04-10</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-27</th>\n",
       "      <td>37545092475</td>\n",
       "      <td>2013-07-27</td>\n",
       "      <td>2</td>\n",
       "      <td>390</td>\n",
       "      <td>81.900</td>\n",
       "      <td>861.900</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>275226</td>\n",
       "      <td>1973-10-26</td>\n",
       "      <td>M</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-30</th>\n",
       "      <td>71604893404</td>\n",
       "      <td>2011-03-30</td>\n",
       "      <td>4</td>\n",
       "      <td>1425</td>\n",
       "      <td>598.500</td>\n",
       "      <td>6298.500</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275226</td>\n",
       "      <td>1973-10-26</td>\n",
       "      <td>M</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-25</th>\n",
       "      <td>28972634039</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>334</td>\n",
       "      <td>35.070</td>\n",
       "      <td>369.070</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275227</td>\n",
       "      <td>1989-09-23</td>\n",
       "      <td>M</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-09</th>\n",
       "      <td>9827954893</td>\n",
       "      <td>2012-11-09</td>\n",
       "      <td>2</td>\n",
       "      <td>977</td>\n",
       "      <td>205.170</td>\n",
       "      <td>2159.170</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275229</td>\n",
       "      <td>1988-11-16</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Furnishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-16</th>\n",
       "      <td>51997466118</td>\n",
       "      <td>2013-10-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1085</td>\n",
       "      <td>113.925</td>\n",
       "      <td>1198.925</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275230</td>\n",
       "      <td>1990-06-28</td>\n",
       "      <td>F</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-29</th>\n",
       "      <td>26701622498</td>\n",
       "      <td>2011-04-29</td>\n",
       "      <td>5</td>\n",
       "      <td>1212</td>\n",
       "      <td>636.300</td>\n",
       "      <td>6696.300</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275236</td>\n",
       "      <td>1972-08-06</td>\n",
       "      <td>F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-14</th>\n",
       "      <td>35471264649</td>\n",
       "      <td>2013-04-14</td>\n",
       "      <td>5</td>\n",
       "      <td>1215</td>\n",
       "      <td>637.875</td>\n",
       "      <td>6712.875</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>275241</td>\n",
       "      <td>1989-03-16</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-14</th>\n",
       "      <td>52151291726</td>\n",
       "      <td>2011-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1190</td>\n",
       "      <td>124.950</td>\n",
       "      <td>1314.950</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275243</td>\n",
       "      <td>1988-03-09</td>\n",
       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-20</th>\n",
       "      <td>84072629443</td>\n",
       "      <td>2011-02-20</td>\n",
       "      <td>2</td>\n",
       "      <td>1194</td>\n",
       "      <td>250.740</td>\n",
       "      <td>2638.740</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275244</td>\n",
       "      <td>1979-01-02</td>\n",
       "      <td>M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-27</th>\n",
       "      <td>87384005745</td>\n",
       "      <td>2013-10-27</td>\n",
       "      <td>5</td>\n",
       "      <td>266</td>\n",
       "      <td>139.650</td>\n",
       "      <td>1469.650</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>275244</td>\n",
       "      <td>1979-01-02</td>\n",
       "      <td>M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-19</th>\n",
       "      <td>90022277289</td>\n",
       "      <td>2011-05-19</td>\n",
       "      <td>4</td>\n",
       "      <td>223</td>\n",
       "      <td>93.660</td>\n",
       "      <td>985.660</td>\n",
       "      <td>Flagship store</td>\n",
       "      <td>275245</td>\n",
       "      <td>1981-07-01</td>\n",
       "      <td>M</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-18</th>\n",
       "      <td>60089273284</td>\n",
       "      <td>2013-07-18</td>\n",
       "      <td>4</td>\n",
       "      <td>991</td>\n",
       "      <td>416.220</td>\n",
       "      <td>4380.220</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275245</td>\n",
       "      <td>1981-07-01</td>\n",
       "      <td>M</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-29</th>\n",
       "      <td>2906545289</td>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>9.030</td>\n",
       "      <td>95.030</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275250</td>\n",
       "      <td>1970-02-05</td>\n",
       "      <td>F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-14</th>\n",
       "      <td>7046193868</td>\n",
       "      <td>2013-11-14</td>\n",
       "      <td>1</td>\n",
       "      <td>652</td>\n",
       "      <td>68.460</td>\n",
       "      <td>720.460</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275252</td>\n",
       "      <td>1977-09-03</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-13</th>\n",
       "      <td>37292690651</td>\n",
       "      <td>2011-08-13</td>\n",
       "      <td>2</td>\n",
       "      <td>792</td>\n",
       "      <td>166.320</td>\n",
       "      <td>1750.320</td>\n",
       "      <td>MBR</td>\n",
       "      <td>275262</td>\n",
       "      <td>1973-06-06</td>\n",
       "      <td>M</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Mobiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-26</th>\n",
       "      <td>38961184788</td>\n",
       "      <td>2011-12-26</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "      <td>59.220</td>\n",
       "      <td>623.220</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>275265</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>M</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3254 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            transaction_id  tran_date  Qty  Rate      Tax  total_amt  \\\n",
       "tran_date                                                              \n",
       "2011-12-06      4236411732 2011-12-06    1  1238  129.990   1367.990   \n",
       "2012-10-31      5914139604 2012-10-31    2   672  141.120   1485.120   \n",
       "2012-11-09     42434369409 2012-11-09    2   325   68.250    718.250   \n",
       "2013-06-29     42435707527 2013-06-29    1   123   12.915    135.915   \n",
       "2012-11-03     38727202803 2012-11-03    5  1294  679.350   7149.350   \n",
       "2012-10-18     22473497751 2012-10-18    1   468   49.140    517.140   \n",
       "2012-09-05     75039992579 2012-09-05    3   348  109.620   1153.620   \n",
       "2012-01-11     34749917660 2012-01-11    4   632  265.440   2793.440   \n",
       "2012-10-06     11256528141 2012-10-06    3   774  243.810   2565.810   \n",
       "2011-03-06     71308225311 2011-03-06    4  1023  429.660   4521.660   \n",
       "2011-03-30     85773906672 2011-03-30    2   154   32.340    340.340   \n",
       "2013-01-29     15868818159 2013-01-29    1   522   54.810    576.810   \n",
       "2011-11-11     70203705196 2011-11-11    2  1381  290.010   3052.010   \n",
       "2011-07-31     44566096268 2011-07-31    5   667  350.175   3685.175   \n",
       "2014-02-01       283737262 2014-02-01    4   286  120.120   1264.120   \n",
       "2011-09-03     41995387726 2011-09-03    1   360   37.800    397.800   \n",
       "2011-06-12     15639583679 2011-06-12    4   122   51.240    539.240   \n",
       "2012-08-07     11284485790 2012-08-07    3   858  270.270   2844.270   \n",
       "2011-09-04     18301188842 2011-09-04    5  1322  694.050   7304.050   \n",
       "2013-09-11     16775867233 2013-09-11    3  1052  331.380   3487.380   \n",
       "2011-05-20     58064640073 2011-05-20    1  1223  128.415   1351.415   \n",
       "2013-04-13     44027985600 2013-04-13    2   973  204.330   2150.330   \n",
       "2012-03-18     40615508857 2012-03-18    4  1194  501.480   5277.480   \n",
       "2012-03-24     30944211836 2012-03-24    4  1166  489.720   5153.720   \n",
       "2013-10-09     17468457642 2013-10-09    3   855  269.325   2834.325   \n",
       "2012-12-20     35946237363 2012-12-20    4    92   38.640    406.640   \n",
       "2011-12-11      3433540543 2011-12-11    1  1136  119.280   1255.280   \n",
       "2011-02-16     90667600281 2011-02-16    5  1377  722.925   7607.925   \n",
       "2011-02-20     34958490122 2011-02-20    4  1155  485.100   5105.100   \n",
       "2011-12-06     65066509062 2011-12-06    2   717  150.570   1584.570   \n",
       "...                    ...        ...  ...   ...      ...        ...   \n",
       "2013-05-30     58687778228 2013-05-30    4  1168  490.560   5162.560   \n",
       "2013-09-09     29866196355 2013-09-09    4   866  363.720   3827.720   \n",
       "2011-03-30     63742635156 2011-03-30    1   785   82.425    867.425   \n",
       "2012-06-22     30395695659 2012-06-22    3   484  152.460   1604.460   \n",
       "2012-11-17     77508007471 2012-11-17    5   412  216.300   2276.300   \n",
       "2011-08-29     22224473979 2011-08-29    4  1238  519.960   5471.960   \n",
       "2011-04-12     67494771515 2011-04-12    1   628   65.940    693.940   \n",
       "2013-11-08     24722252527 2013-11-08    3   305   96.075   1011.075   \n",
       "2011-07-14     39578452941 2011-07-14    2   210   44.100    464.100   \n",
       "2011-04-29     31728298398 2011-04-29    2   250   52.500    552.500   \n",
       "2011-04-12     20630217348 2011-04-12    4   779  327.180   3443.180   \n",
       "2012-10-06     71557636302 2012-10-06    2   829  174.090   1832.090   \n",
       "2013-09-11     50250308685 2013-09-11    2  1106  232.260   2444.260   \n",
       "2013-06-20     95566895752 2013-06-20    4   528  221.760   2333.760   \n",
       "2013-07-27     37545092475 2013-07-27    2   390   81.900    861.900   \n",
       "2011-03-30     71604893404 2011-03-30    4  1425  598.500   6298.500   \n",
       "2011-01-25     28972634039 2011-01-25    1   334   35.070    369.070   \n",
       "2012-11-09      9827954893 2012-11-09    2   977  205.170   2159.170   \n",
       "2013-10-16     51997466118 2013-10-16    1  1085  113.925   1198.925   \n",
       "2011-04-29     26701622498 2011-04-29    5  1212  636.300   6696.300   \n",
       "2013-04-14     35471264649 2013-04-14    5  1215  637.875   6712.875   \n",
       "2011-05-14     52151291726 2011-05-14    1  1190  124.950   1314.950   \n",
       "2011-02-20     84072629443 2011-02-20    2  1194  250.740   2638.740   \n",
       "2013-10-27     87384005745 2013-10-27    5   266  139.650   1469.650   \n",
       "2011-05-19     90022277289 2011-05-19    4   223   93.660    985.660   \n",
       "2013-07-18     60089273284 2013-07-18    4   991  416.220   4380.220   \n",
       "2012-12-29      2906545289 2012-12-29    1    86    9.030     95.030   \n",
       "2013-11-14      7046193868 2013-11-14    1   652   68.460    720.460   \n",
       "2011-08-13     37292690651 2011-08-13    2   792  166.320   1750.320   \n",
       "2011-12-26     38961184788 2011-12-26    3   188   59.220    623.220   \n",
       "\n",
       "                Store_type  customer_Id        DOB Gender  city_code  \\\n",
       "tran_date                                                              \n",
       "2011-12-06  Flagship store       266788 1972-03-20      F        2.0   \n",
       "2012-10-31             MBR       266788 1972-03-20      F        2.0   \n",
       "2012-11-09  Flagship store       266794 1971-02-28      F        9.0   \n",
       "2013-06-29          e-Shop       266794 1971-02-28      F        9.0   \n",
       "2012-11-03  Flagship store       266799 1970-03-16      F        5.0   \n",
       "2012-10-18          e-Shop       266806 1991-08-12      F       10.0   \n",
       "2012-09-05          e-Shop       266814 1982-10-18      F        9.0   \n",
       "2012-01-11             MBR       266814 1982-10-18      F        9.0   \n",
       "2012-10-06        TeleShop       266818 1981-08-10      F        2.0   \n",
       "2011-03-06          e-Shop       266822 1970-09-20      F        9.0   \n",
       "2011-03-30        TeleShop       266822 1970-09-20      F        9.0   \n",
       "2013-01-29        TeleShop       266824 1973-04-06      M        7.0   \n",
       "2011-11-11          e-Shop       266824 1973-04-06      M        7.0   \n",
       "2011-07-31          e-Shop       266830 1985-05-13      M        1.0   \n",
       "2014-02-01        TeleShop       266831 1978-08-10      M        6.0   \n",
       "2011-09-03          e-Shop       266831 1978-08-10      M        6.0   \n",
       "2011-06-12             MBR       266836 1973-11-21      F        7.0   \n",
       "2012-08-07  Flagship store       266838 1974-11-14      M        4.0   \n",
       "2011-09-04          e-Shop       266841 1991-02-03      M        1.0   \n",
       "2013-09-11             MBR       266842 1984-05-15      F        1.0   \n",
       "2011-05-20             MBR       266843 1987-02-01      F        5.0   \n",
       "2013-04-13          e-Shop       266844 1986-03-10      M        5.0   \n",
       "2012-03-18          e-Shop       266846 1979-04-24      M        2.0   \n",
       "2012-03-24             MBR       266846 1979-04-24      M        2.0   \n",
       "2013-10-09          e-Shop       266847 1990-01-24      F        7.0   \n",
       "2012-12-20             MBR       266847 1990-01-24      F        7.0   \n",
       "2011-12-11          e-Shop       266848 1987-07-10      F       10.0   \n",
       "2011-02-16  Flagship store       266849 1979-12-10      M        1.0   \n",
       "2011-02-20          e-Shop       266849 1979-12-10      M        1.0   \n",
       "2011-12-06             MBR       266853 1990-09-18      M        6.0   \n",
       "...                    ...          ...        ...    ...        ...   \n",
       "2013-05-30             MBR       275184 1976-01-11      M        5.0   \n",
       "2013-09-09             MBR       275186 1974-06-05      F        3.0   \n",
       "2011-03-30  Flagship store       275187 1984-04-07      F        7.0   \n",
       "2012-06-22             MBR       275190 1971-02-11      M        6.0   \n",
       "2012-11-17  Flagship store       275192 1987-08-06      F        8.0   \n",
       "2011-08-29        TeleShop       275197 1979-10-07      F        8.0   \n",
       "2011-04-12             MBR       275200 1970-04-12      M        8.0   \n",
       "2013-11-08             MBR       275202 1981-03-14      F        9.0   \n",
       "2011-07-14          e-Shop       275202 1981-03-14      F        9.0   \n",
       "2011-04-29             MBR       275204 1973-01-24      F        1.0   \n",
       "2011-04-12  Flagship store       275205 1981-07-28      F       10.0   \n",
       "2012-10-06        TeleShop       275210 1973-09-13      M        2.0   \n",
       "2013-09-11             MBR       275214 1984-04-06      M        9.0   \n",
       "2013-06-20             MBR       275218 1985-04-10      M        5.0   \n",
       "2013-07-27  Flagship store       275226 1973-10-26      M        4.0   \n",
       "2011-03-30          e-Shop       275226 1973-10-26      M        4.0   \n",
       "2011-01-25             MBR       275227 1989-09-23      M        4.0   \n",
       "2012-11-09          e-Shop       275229 1988-11-16      M        3.0   \n",
       "2013-10-16          e-Shop       275230 1990-06-28      F        3.0   \n",
       "2011-04-29          e-Shop       275236 1972-08-06      F        2.0   \n",
       "2013-04-14  Flagship store       275241 1989-03-16      M        3.0   \n",
       "2011-05-14          e-Shop       275243 1988-03-09      M        5.0   \n",
       "2011-02-20             MBR       275244 1979-01-02      M        8.0   \n",
       "2013-10-27        TeleShop       275244 1979-01-02      M        8.0   \n",
       "2011-05-19  Flagship store       275245 1981-07-01      M       10.0   \n",
       "2013-07-18             MBR       275245 1981-07-01      M       10.0   \n",
       "2012-12-29          e-Shop       275250 1970-02-05      F        1.0   \n",
       "2013-11-14          e-Shop       275252 1977-09-03      M        6.0   \n",
       "2011-08-13             MBR       275262 1973-06-06      M       10.0   \n",
       "2011-12-26          e-Shop       275265 1990-01-01      M        3.0   \n",
       "\n",
       "                    prod_cat prod_subcat  \n",
       "tran_date                                 \n",
       "2011-12-06             Books     Fiction  \n",
       "2012-10-31              Bags        Mens  \n",
       "2012-11-09       Electronics     Mobiles  \n",
       "2013-06-29       Electronics     Mobiles  \n",
       "2012-11-03       Electronics     Mobiles  \n",
       "2012-10-18  Home and kitchen  Furnishing  \n",
       "2012-09-05  Home and kitchen  Furnishing  \n",
       "2012-01-11  Home and kitchen  Furnishing  \n",
       "2012-10-06             Books     Fiction  \n",
       "2011-03-06       Electronics     Mobiles  \n",
       "2011-03-30       Electronics     Mobiles  \n",
       "2013-01-29       Electronics     Mobiles  \n",
       "2011-11-11             Books     Fiction  \n",
       "2011-07-31          Footwear        Mens  \n",
       "2014-02-01             Books     Fiction  \n",
       "2011-09-03          Footwear        Mens  \n",
       "2011-06-12          Clothing        Mens  \n",
       "2012-08-07          Clothing        Mens  \n",
       "2011-09-04  Home and kitchen  Furnishing  \n",
       "2013-09-11             Books     Fiction  \n",
       "2011-05-20  Home and kitchen  Furnishing  \n",
       "2013-04-13             Books     Fiction  \n",
       "2012-03-18  Home and kitchen  Furnishing  \n",
       "2012-03-24          Clothing        Mens  \n",
       "2013-10-09       Electronics     Mobiles  \n",
       "2012-12-20       Electronics     Mobiles  \n",
       "2011-12-11       Electronics     Mobiles  \n",
       "2011-02-16          Footwear        Mens  \n",
       "2011-02-20  Home and kitchen  Furnishing  \n",
       "2011-12-06  Home and kitchen  Furnishing  \n",
       "...                      ...         ...  \n",
       "2013-05-30             Books     Fiction  \n",
       "2013-09-09             Books     Fiction  \n",
       "2011-03-30       Electronics     Mobiles  \n",
       "2012-06-22       Electronics     Mobiles  \n",
       "2012-11-17       Electronics     Mobiles  \n",
       "2011-08-29       Electronics     Mobiles  \n",
       "2011-04-12             Books     Fiction  \n",
       "2013-11-08             Books     Fiction  \n",
       "2011-07-14  Home and kitchen  Furnishing  \n",
       "2011-04-29  Home and kitchen  Furnishing  \n",
       "2011-04-12  Home and kitchen  Furnishing  \n",
       "2012-10-06  Home and kitchen  Furnishing  \n",
       "2013-09-11       Electronics     Mobiles  \n",
       "2013-06-20             Books     Fiction  \n",
       "2013-07-27              Bags        Mens  \n",
       "2011-03-30             Books     Fiction  \n",
       "2011-01-25       Electronics     Mobiles  \n",
       "2012-11-09  Home and kitchen  Furnishing  \n",
       "2013-10-16       Electronics     Mobiles  \n",
       "2011-04-29       Electronics     Mobiles  \n",
       "2013-04-14       Electronics     Mobiles  \n",
       "2011-05-14          Footwear        Mens  \n",
       "2011-02-20              Bags        Mens  \n",
       "2013-10-27          Clothing        Mens  \n",
       "2011-05-19             Books     Fiction  \n",
       "2013-07-18          Footwear        Mens  \n",
       "2012-12-29       Electronics     Mobiles  \n",
       "2013-11-14             Books     Fiction  \n",
       "2011-08-13       Electronics     Mobiles  \n",
       "2011-12-26             Books     Fiction  \n",
       "\n",
       "[3254 rows x 13 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sort_values('customer_Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3254"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.customer_Id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3254"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.transaction_id.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "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",
    " - b. What was the total amount spent by these customers between 1st Jan, 2014 to 1st Mar, 2014?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>tran_date</th>\n",
       "      <th>Qty</th>\n",
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       "    <tr>\n",
       "      <th>2014-02-28</th>\n",
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       "      <td>-772</td>\n",
       "      <td>405.3</td>\n",
       "      <td>-4265.3</td>\n",
       "      <td>e-Shop</td>\n",
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       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-28</th>\n",
       "      <td>80712190438</td>\n",
       "      <td>2014-02-28</td>\n",
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       "      <td>-772</td>\n",
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       "      <td>e-Shop</td>\n",
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       "      <td>M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            transaction_id  tran_date  Qty  Rate    Tax  total_amt Store_type  \\\n",
       "tran_date                                                                       \n",
       "2014-02-28     80712190438 2014-02-28   -5  -772  405.3    -4265.3     e-Shop   \n",
       "2014-02-28     80712190438 2014-02-28   -5  -772  405.3    -4265.3     e-Shop   \n",
       "\n",
       "            customer_Id        DOB Gender  city_code  prod_cat prod_subcat  \\\n",
       "tran_date                                                                    \n",
       "2014-02-28       270351 1981-09-26      M        5.0  Clothing        Mens   \n",
       "2014-02-28       270351 1981-09-26      M        5.0  Clothing       Women   \n",
       "\n",
       "             age  \n",
       "tran_date         \n",
       "2014-02-28  37.0  \n",
       "2014-02-28  37.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = pd.Timestamp('now')\n",
    "Customer_Final['age'] = (now - Customer_Final['DOB']).astype('<m8[Y]') \n",
    "Customer_Final.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2014-02-24</th>\n",
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       "      <td>2014-02-24</td>\n",
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       "      <td>-791</td>\n",
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       "      <td>-1748.11</td>\n",
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       "      <td>M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>-1748.11</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>273420</td>\n",
       "      <td>1992-07-27</td>\n",
       "      <td>M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Books</td>\n",
       "      <td>Academic</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            transaction_id  tran_date  Qty  Rate     Tax  total_amt  \\\n",
       "tran_date                                                             \n",
       "2014-02-24     51750724947 2014-02-24   -2  -791  166.11   -1748.11   \n",
       "2014-02-24     51750724947 2014-02-24   -2  -791  166.11   -1748.11   \n",
       "\n",
       "           Store_type  customer_Id        DOB Gender  city_code prod_cat  \\\n",
       "tran_date                                                                  \n",
       "2014-02-24   TeleShop       273420 1992-07-27      M        8.0    Books   \n",
       "2014-02-24   TeleShop       273420 1992-07-27      M        8.0    Books   \n",
       "\n",
       "           prod_subcat   age  \n",
       "tran_date                     \n",
       "2014-02-24     Fiction  27.0  \n",
       "2014-02-24    Academic  27.0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_series = Customer_Final['age'].between(25.0, 35.0, inclusive = True)\n",
    "cust_final4 = Customer_Final[bool_series]\n",
    "cust_final4.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53256444.08499999"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_final4.total_amt.where(cust_final4['prod_cat'].isin(['Electronics', 'Books'])).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all customers aged between 25 - 35, find out:\n",
    "a. What was the total amount spent for “Electronics” and “Books” product categories?\n",
    "b. What was the total amount spent by these customers between 1st Jan, 2014 to 1st Mar, 2014?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2649188.88"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_final4[(cust_final4['tran_date'] >= '2014-01-01') & (cust_final4['tran_date'] <= '2014-03-01')]['total_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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