Credit Card Case Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "803edfd7",
   "metadata": {},
   "source": [
    "# CASE STUDY: DATA EXPLORATORY ANALYSIS FOR CREDIT CARD DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "83fd6f56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary libraries (pandas, numpy)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import seaborn as sns\n",
    "\n",
    "# set the graphs to show in the jupyter notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# set seaborn graphs to a better style\n",
    "sns.set(style= 'ticks')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92daf2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the files \n",
    "\n",
    "Customer = pd.read_csv(r'C:\\Users\\91896\\OneDrive\\Desktop\\AnalytixLabs\\PYTHON ASSIGNMENT AND CASE STUDIES\\PYTHON CASE STUDIES\\Case Study 2 - Credit Card Case Study\\Customer Acqusition.csv',usecols= ['Customer','Age','City','Product','Limit','Company','Segment'])\n",
    "Spend =  pd.read_csv(r'C:\\Users\\91896\\OneDrive\\Desktop\\AnalytixLabs\\PYTHON ASSIGNMENT AND CASE STUDIES\\PYTHON CASE STUDIES\\Case Study 2 - Credit Card Case Study\\spend.csv', usecols=['Customer','Month','Type','Amount'])\n",
    "Repayment =  pd.read_csv(r'C:\\Users\\91896\\OneDrive\\Desktop\\AnalytixLabs\\PYTHON ASSIGNMENT AND CASE STUDIES\\PYTHON CASE STUDIES\\Case Study 2 - Credit Card Case Study\\Repayment.csv', usecols= ['Customer','Month','Amount']) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32218a2d",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>71</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>34</td>\n",
       "      <td>COCHIN</td>\n",
       "      <td>Platimum</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>C3</td>\n",
       "      <td>Salaried_Pvt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer  Age       City   Product     Limit Company        Segment\n",
       "0       A1   76  BANGALORE      Gold  500000.0      C1  Self Employed\n",
       "1       A2   71   CALCUTTA    Silver  100000.0      C2   Salaried_MNC\n",
       "2       A3   34     COCHIN  Platimum   10000.0      C3   Salaried_Pvt"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# details of customer at the time of card issuing by company\n",
    "Customer.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab53fba7",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Month</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>12-Jan-04</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>3-Jan-04</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>15-Jan-04</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer      Month       Type     Amount\n",
       "0       A1  12-Jan-04  JEWELLERY  485470.80\n",
       "1       A1   3-Jan-04      PETRO  410556.13\n",
       "2       A1  15-Jan-04    CLOTHES   23740.46"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# credit card spend by customer\n",
    "Spend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b4b21cd",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Month</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>12-Jan-04</td>\n",
       "      <td>495414.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>3-Jan-04</td>\n",
       "      <td>245899.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>15-Jan-04</td>\n",
       "      <td>259490.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>25-Jan-04</td>\n",
       "      <td>437555.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>17-Jan-05</td>\n",
       "      <td>165972.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer      Month     Amount\n",
       "0       A1  12-Jan-04  495414.75\n",
       "1       A1   3-Jan-04  245899.02\n",
       "2       A1  15-Jan-04  259490.06\n",
       "3       A1  25-Jan-04  437555.12\n",
       "4       A1  17-Jan-05  165972.88"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# credit card payment done by the customers\n",
    "Repayment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9af8fe",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c527c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 7)\n",
      "(1500, 4)\n",
      "(1523, 3)\n"
     ]
    }
   ],
   "source": [
    "print(Customer.shape)\n",
    "print(Spend.shape)\n",
    "print(Repayment.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a5ba63",
   "metadata": {},
   "source": [
    "### Renaming the Month column of Spend and Repayment to Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53b603c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Spend.rename(columns= {'Month': 'Date'}, inplace= True)\n",
    "Repayment.rename(columns= {'Month': 'Date'}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa08590f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gold', 'Silver', 'Platimum'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer.Product.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a23cf55",
   "metadata": {},
   "outputs": [],
   "source": [
    "corrections = {'Gold':'Gold', 'Silver':'Silver','Platimum': 'Platinum'} # Correct the misspelling\n",
    "    \n",
    "\n",
    "# Apply the corrections to the \"Product\" column using the map function\n",
    "Customer['Product'] = Customer['Product'].map(corrections)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b4bd8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Gold\n",
       "1       Silver\n",
       "2     Platinum\n",
       "3     Platinum\n",
       "4     Platinum\n",
       "        ...   \n",
       "95      Silver\n",
       "96    Platinum\n",
       "97    Platinum\n",
       "98    Platinum\n",
       "99      Silver\n",
       "Name: Product, Length: 100, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer.Product"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94dd4a1a",
   "metadata": {},
   "source": [
    "### checking for dataypes in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f82d99ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer     object\n",
       "Age           int64\n",
       "City         object\n",
       "Product      object\n",
       "Limit       float64\n",
       "Company      object\n",
       "Segment      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc9bb276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer     object\n",
       "Date         object\n",
       "Type         object\n",
       "Amount      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we have to change the datatype of Date column datetime\n",
    "Spend.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f25e6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "Spend['Date'] = pd.to_datetime(Spend.Date,format ='%d-%b-%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a9b41963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2004-01-12\n",
       "1   2004-01-03\n",
       "2   2004-01-15\n",
       "3   2004-01-25\n",
       "4   2005-01-17\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spend['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "42e13fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer     object\n",
       "Date         object\n",
       "Amount      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18e5ea10",
   "metadata": {},
   "outputs": [],
   "source": [
    "Repayment['Date'] = pd.to_datetime(Repayment.Date,format ='%d-%b-%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "586342e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2004-01-12\n",
       "1   2004-01-03\n",
       "2   2004-01-15\n",
       "3   2004-01-25\n",
       "4   2005-01-17\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment['Date'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a1898d",
   "metadata": {},
   "source": [
    "### checking for null values in the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4695d253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer    0\n",
       "Age         0\n",
       "City        0\n",
       "Product     0\n",
       "Limit       0\n",
       "Company     0\n",
       "Segment     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be221b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer    0\n",
       "Date        0\n",
       "Type        0\n",
       "Amount      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spend.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb906f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer    23\n",
       "Date        23\n",
       "Amount      23\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b292d11f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the null values in Rapayment dataset\n",
    "\n",
    "Repayment.dropna(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bcf39a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer    0\n",
       "Date        0\n",
       "Amount      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "30989f5f",
   "metadata": {},
   "outputs": [
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       "      <td>495414.75</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>259490.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>437555.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>165972.88</td>\n",
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       "      <th>1495</th>\n",
       "      <td>A67</td>\n",
       "      <td>2005-05-09</td>\n",
       "      <td>55638.77</td>\n",
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       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>A68</td>\n",
       "      <td>2006-05-10</td>\n",
       "      <td>319836.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>A69</td>\n",
       "      <td>2006-07-11</td>\n",
       "      <td>247628.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>A70</td>\n",
       "      <td>2005-08-12</td>\n",
       "      <td>454016.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>A71</td>\n",
       "      <td>2004-09-13</td>\n",
       "      <td>56286.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer       Date     Amount\n",
       "0          A1 2004-01-12  495414.75\n",
       "1          A1 2004-01-03  245899.02\n",
       "2          A1 2004-01-15  259490.06\n",
       "3          A1 2004-01-25  437555.12\n",
       "4          A1 2005-01-17  165972.88\n",
       "...       ...        ...        ...\n",
       "1495      A67 2005-05-09   55638.77\n",
       "1496      A68 2006-05-10  319836.49\n",
       "1497      A69 2006-07-11  247628.45\n",
       "1498      A70 2005-08-12  454016.51\n",
       "1499      A71 2004-09-13   56286.33\n",
       "\n",
       "[1500 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a01f3a0",
   "metadata": {},
   "source": [
    "### checking for duplicates in the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b318aa19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No duplicates present in the dataset\n",
    "\n",
    "Customer.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "78698292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spend.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "daedde6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6769b729",
   "metadata": {},
   "source": [
    "### Following are some of Mr. Watson’s questions to a Consultant (like you) to understand the customers spend & repayment behavior.\n",
    "### 1. In the above dataset,\n",
    "### a. In case age is less than 18, replace it with mean of age values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bef75787",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of age column is 46.49\n"
     ]
    }
   ],
   "source": [
    "# mean of age column\n",
    "\n",
    "print('Mean of age column is',Customer.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ea21e5b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing age less than 18 with mean of age values\n",
    "\n",
    "Customer.loc[Customer.Age < 18,'Age'] = Customer.Age.mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "52ae3c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Age, dtype: float64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can see we got an empty series and the age has been replaced by mean age\n",
    "Customer.loc[Customer.Age<18,'Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "855787f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All the customers who have age less than 18 have been replaced by mean of the age column\n"
     ]
    }
   ],
   "source": [
    "print('All the customers who have age less than 18 have been replaced by mean of the age column')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef98fec",
   "metadata": {},
   "source": [
    "### b. In case spend amount is more than the limit, replace it with 50% of that customer’s limit. \n",
    "### (customer’s limit provided in acquisition table is the per transaction limit on his card)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3e443c58",
   "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",
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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></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed\n",
       "1       A2  71.0   CALCUTTA  Silver  100000.0      C2   Salaried_MNC"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "64bb741f",
   "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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       "        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>Customer</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       Date       Type     Amount\n",
       "0       A1 2004-01-12  JEWELLERY  485470.80\n",
       "1       A1 2004-01-03      PETRO  410556.13"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spend.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a763582d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In order to get both limit and spend in one table, we need to merge both the table on the basis of Customer col \n",
    "# present in both table\n",
    "\n",
    "customer_spend = pd.merge(left=Customer, right=Spend, on='Customer', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "86d2de5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>484342.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>CAMERA</td>\n",
       "      <td>369694.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "3       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "4       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  \n",
       "0 2004-01-12  JEWELLERY  485470.80  \n",
       "1 2004-01-03      PETRO  410556.13  \n",
       "2 2004-01-15    CLOTHES   23740.46  \n",
       "3 2004-01-25       FOOD  484342.47  \n",
       "4 2005-01-17     CAMERA  369694.07  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "134ce223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 10)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "34a8b4ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>RENTAL</td>\n",
       "      <td>199554.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2005-02-28</td>\n",
       "      <td>TRAIN TICKET</td>\n",
       "      <td>376887.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>BUS TICKET</td>\n",
       "      <td>439648.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2004-11-23</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>384078.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2005-09-03</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>117089.16</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486</th>\n",
       "      <td>A87</td>\n",
       "      <td>27.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100003.0</td>\n",
       "      <td>C10</td>\n",
       "      <td>Govt</td>\n",
       "      <td>2006-03-30</td>\n",
       "      <td>RENTAL</td>\n",
       "      <td>483886.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>A97</td>\n",
       "      <td>58.0</td>\n",
       "      <td>TRIVANDRUM</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>C20</td>\n",
       "      <td>Govt</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>139018.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>A98</td>\n",
       "      <td>51.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>10001.0</td>\n",
       "      <td>C21</td>\n",
       "      <td>Normal Salary</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>SHOPPING</td>\n",
       "      <td>284521.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>A99</td>\n",
       "      <td>35.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>10002.0</td>\n",
       "      <td>C22</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>AIR TICKET</td>\n",
       "      <td>90184.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>A100</td>\n",
       "      <td>36.0</td>\n",
       "      <td>COCHIN</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C5</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>BUS TICKET</td>\n",
       "      <td>224786.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>607 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer   Age        City   Product     Limit Company        Segment  \\\n",
       "18         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "19         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "20         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "21         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "22         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "...       ...   ...         ...       ...       ...     ...            ...   \n",
       "1486      A87  27.0    CALCUTTA    Silver  100003.0     C10           Govt   \n",
       "1496      A97  58.0  TRIVANDRUM  Platinum   10000.0     C20           Govt   \n",
       "1497      A98  51.0    CALCUTTA  Platinum   10001.0     C21  Normal Salary   \n",
       "1498      A99  35.0    CALCUTTA  Platinum   10002.0     C22  Self Employed   \n",
       "1499     A100  36.0      COCHIN    Silver  100000.0      C5   Salaried_MNC   \n",
       "\n",
       "           Date          Type     Amount  \n",
       "18   2004-01-03        RENTAL  199554.72  \n",
       "19   2005-02-28  TRAIN TICKET  376887.49  \n",
       "20   2004-04-06    BUS TICKET  439648.53  \n",
       "21   2004-11-23         PETRO  384078.73  \n",
       "22   2005-09-03     JEWELLERY  117089.16  \n",
       "...         ...           ...        ...  \n",
       "1486 2006-03-30        RENTAL  483886.62  \n",
       "1496 2004-01-12          AUTO  139018.26  \n",
       "1497 2004-01-03      SHOPPING  284521.06  \n",
       "1498 2004-01-15    AIR TICKET   90184.22  \n",
       "1499 2004-01-25    BUS TICKET  224786.88  \n",
       "\n",
       "[607 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All the customers whose spend amount is more than the limit\n",
    "\n",
    "customer_spend[customer_spend['Amount']>customer_spend['Limit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f7c68173",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All the customers whose spend amount is more than the limit, replacing it with 50% of customer limit\n",
    "\n",
    "customer_spend.loc[customer_spend['Amount']>customer_spend['Limit'],'Amount'] = (50*customer_spend['Limit'])/(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8d2e601c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
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       "      <th>Amount</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Customer, Age, City, Product, Limit, Company, Segment, Date, Type, Amount]\n",
       "Index: []"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can see there is no customers left, where spend amount is more than the limit\n",
    "customer_spend[customer_spend['Amount']>customer_spend['Limit']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "325f85f5",
   "metadata": {},
   "source": [
    "### c. Incase the repayment amount is more than the limit, replace the repayment with the limit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "93539703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed\n",
       "1       A2  71.0   CALCUTTA  Silver  100000.0      C2   Salaried_MNC"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "307e42cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>495414.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       Date     Amount\n",
       "0       A1 2004-01-12  495414.75\n",
       "1       A1 2004-01-03  245899.02"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "961df2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging table Customer and Repayment on Customer column\n",
    "\n",
    "customer_repay = pd.merge(left=Customer, right=Repayment, on='Customer', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b6e9069c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
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       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
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       "      <td>2004-01-12</td>\n",
       "      <td>495414.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>259490.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>437555.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>165972.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "3       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "4       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date     Amount  \n",
       "0 2004-01-12  495414.75  \n",
       "1 2004-01-03  245899.02  \n",
       "2 2004-01-15  259490.06  \n",
       "3 2004-01-25  437555.12  \n",
       "4 2005-01-17  165972.88  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_repay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b41cac8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>18</th>\n",
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       "      <td>185955.07</td>\n",
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       "      <th>19</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
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       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2005-02-28</td>\n",
       "      <td>412783.34</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>148038.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2004-11-23</td>\n",
       "      <td>179144.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "      <td>2005-08-03</td>\n",
       "      <td>381635.66</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1486</th>\n",
       "      <td>A87</td>\n",
       "      <td>27.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100003.0</td>\n",
       "      <td>C10</td>\n",
       "      <td>Govt</td>\n",
       "      <td>2006-03-30</td>\n",
       "      <td>462283.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>A96</td>\n",
       "      <td>54.0</td>\n",
       "      <td>CHENNAI</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C19</td>\n",
       "      <td>Salaried_Pvt</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>310992.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>A97</td>\n",
       "      <td>58.0</td>\n",
       "      <td>TRIVANDRUM</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>C20</td>\n",
       "      <td>Govt</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>121874.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>A98</td>\n",
       "      <td>51.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>10001.0</td>\n",
       "      <td>C21</td>\n",
       "      <td>Normal Salary</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>337815.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>A99</td>\n",
       "      <td>35.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>10002.0</td>\n",
       "      <td>C22</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>25682.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer   Age        City   Product     Limit Company        Segment  \\\n",
       "18         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "19         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "20         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "21         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "23         A2  71.0    CALCUTTA    Silver  100000.0      C2   Salaried_MNC   \n",
       "...       ...   ...         ...       ...       ...     ...            ...   \n",
       "1486      A87  27.0    CALCUTTA    Silver  100003.0     C10           Govt   \n",
       "1495      A96  54.0     CHENNAI    Silver  100000.0     C19   Salaried_Pvt   \n",
       "1496      A97  58.0  TRIVANDRUM  Platinum   10000.0     C20           Govt   \n",
       "1497      A98  51.0    CALCUTTA  Platinum   10001.0     C21  Normal Salary   \n",
       "1498      A99  35.0    CALCUTTA  Platinum   10002.0     C22  Self Employed   \n",
       "\n",
       "           Date     Amount  \n",
       "18   2004-01-03  185955.07  \n",
       "19   2005-02-28  412783.34  \n",
       "20   2004-04-06  148038.67  \n",
       "21   2004-11-23  179144.68  \n",
       "23   2005-08-03  381635.66  \n",
       "...         ...        ...  \n",
       "1486 2006-03-30  462283.02  \n",
       "1495 2004-01-25  310992.30  \n",
       "1496 2004-01-12  121874.90  \n",
       "1497 2004-01-03  337815.57  \n",
       "1498 2004-01-15   25682.73  \n",
       "\n",
       "[600 rows x 9 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# customers whose repay amount is more than the limit\n",
    "\n",
    "customer_repay[customer_repay['Amount'] > customer_repay['Limit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a0f5efd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# customers whose repay amount is more than the limit, replacing it with limit\n",
    "\n",
    "customer_repay.loc[customer_repay['Amount'] > customer_repay['Limit'],'Amount'] = customer_repay['Limit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "72d7d01b",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Customer, Age, City, Product, Limit, Company, Segment, Date, Amount]\n",
       "Index: []"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so there is no customers left where repayment amount is more than the limit\n",
    "\n",
    "customer_repay[customer_repay['Amount']>customer_repay['Limit']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523c073e",
   "metadata": {},
   "source": [
    "### 2. From the above dataset create the following summaries:\n",
    "### a. How many distinct customers exist?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "adfcd287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer['Customer'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "299001c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of distinct customers are  100\n"
     ]
    }
   ],
   "source": [
    "print('The number of distinct customers are ', Customer['Customer'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "974a51e9",
   "metadata": {},
   "source": [
    "### b. How many distinct categories exist?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2a042db6",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>Silver</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>C2</td>\n",
       "      <td>Salaried_MNC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed\n",
       "1       A2  71.0   CALCUTTA  Silver  100000.0      C2   Salaried_MNC"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Customer.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "92aac924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Govt             29\n",
       "Self Employed    23\n",
       "Normal Salary    22\n",
       "Salaried_MNC     13\n",
       "Salaried_Pvt     13\n",
       "Name: Segment, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# customers with different segments/categories\n",
    "\n",
    "Customer[\"Segment\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "08a42078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph of different segments\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(data=Customer, x='Segment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a3de3c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#len(Customer[\"Segment\"].value_counts())   -- 1st method\n",
    "Customer['Segment'].nunique()   # 2nd method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d4fe349",
   "metadata": {},
   "source": [
    "*So there are 5 distinct segments/categories of customers are present as we can see from the countplot also.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac310560",
   "metadata": {},
   "source": [
    "### c. What is the average monthly spend by customers?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3ef5cadd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Customer</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>484342.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>CAMERA</td>\n",
       "      <td>369694.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       Date       Type     Amount\n",
       "0       A1 2004-01-12  JEWELLERY  485470.80\n",
       "1       A1 2004-01-03      PETRO  410556.13\n",
       "2       A1 2004-01-15    CLOTHES   23740.46\n",
       "3       A1 2004-01-25       FOOD  484342.47\n",
       "4       A1 2005-01-17     CAMERA  369694.07"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "abb49557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer            object\n",
       "Date        datetime64[ns]\n",
       "Type                object\n",
       "Amount             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Spend.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "41d788d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making new column monthly from Month column\n",
    "Spend['Monthly']= Spend['Date'].apply(lambda x: pd.Timestamp.strftime(x,format= \"%B\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6d44c31e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making new column yearly from Month column\n",
    "Spend['Yearly'] = Spend['Date'].apply(lambda x: pd.Timestamp.strftime(x, format='%Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "23fcec98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer            object\n",
       "Date        datetime64[ns]\n",
       "Type                object\n",
       "Amount             float64\n",
       "Monthly             object\n",
       "Yearly              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spend.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b9df7f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Yearly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>484342.47</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>CAMERA</td>\n",
       "      <td>369694.07</td>\n",
       "      <td>January</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       Date       Type     Amount  Monthly Yearly\n",
       "0       A1 2004-01-12  JEWELLERY  485470.80  January   2004\n",
       "1       A1 2004-01-03      PETRO  410556.13  January   2004\n",
       "2       A1 2004-01-15    CLOTHES   23740.46  January   2004\n",
       "3       A1 2004-01-25       FOOD  484342.47  January   2004\n",
       "4       A1 2005-01-17     CAMERA  369694.07  January   2005"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b2054b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# average monthly spend by customers, by grouping yearly and monthly\n",
    "cust_avg_monthly_spend = round(Spend.groupby(by=['Yearly','Monthly'])[['Amount']].mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b98cf188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly</th>\n",
       "      <th>Monthly</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2004</th>\n",
       "      <th>April</th>\n",
       "      <td>235272.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>221215.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>251712.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>232146.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>217539.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>270486.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>310923.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2005</th>\n",
       "      <th>April</th>\n",
       "      <td>252181.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>233734.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>147503.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>229802.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>292741.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>229116.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>262688.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>245696.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>286143.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>194568.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>188666.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2006</th>\n",
       "      <th>April</th>\n",
       "      <td>232468.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>240699.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>270470.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>253858.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>282058.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>287505.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>177252.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>247166.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>236162.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>182841.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>236136.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>158520.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Amount\n",
       "Yearly Monthly             \n",
       "2004   April      235272.04\n",
       "       February   221215.27\n",
       "       January    251712.07\n",
       "       March      232146.12\n",
       "       May        217539.24\n",
       "       November   270486.40\n",
       "       September  310923.38\n",
       "2005   April      252181.33\n",
       "       August     233734.63\n",
       "       December   147503.06\n",
       "       February   229802.28\n",
       "       January    292741.37\n",
       "       July       229116.66\n",
       "       June       262688.11\n",
       "       May        245696.52\n",
       "       November   286143.32\n",
       "       October    194568.64\n",
       "       September  188666.20\n",
       "2006   April      232468.97\n",
       "       August     240699.72\n",
       "       December   270470.98\n",
       "       February   253858.26\n",
       "       January    282058.01\n",
       "       July       287505.10\n",
       "       June       177252.39\n",
       "       March      247166.08\n",
       "       May        236162.74\n",
       "       November   182841.29\n",
       "       October    236136.34\n",
       "       September  158520.09"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_avg_monthly_spend"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00219939",
   "metadata": {},
   "source": [
    "### d. What is the average monthly repayment by customers?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9bf00feb",
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>495414.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>259490.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>437555.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>165972.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       Date     Amount\n",
       "0       A1 2004-01-12  495414.75\n",
       "1       A1 2004-01-03  245899.02\n",
       "2       A1 2004-01-15  259490.06\n",
       "3       A1 2004-01-25  437555.12\n",
       "4       A1 2005-01-17  165972.88"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "acfb4479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer            object\n",
       "Date        datetime64[ns]\n",
       "Amount             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e93ccc62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making new column monthly and Yearly from date column\n",
    "Repayment['Monthly'] = Repayment['Date'].apply(lambda x: pd.Timestamp.strftime(x,format=\"%B\"))\n",
    "Repayment['Yearly'] = Repayment['Date'].apply(lambda x: pd.Timestamp.strftime(x,format=\"%Y\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "07526358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer            object\n",
       "Date        datetime64[ns]\n",
       "Amount             float64\n",
       "Monthly             object\n",
       "Yearly              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a94c466a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Yearly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>495414.75</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       Date     Amount  Monthly Yearly\n",
       "0       A1 2004-01-12  495414.75  January   2004\n",
       "1       A1 2004-01-03  245899.02  January   2004"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6b74134f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_avg_monthly_repay = round(Repayment.groupby(by= ['Yearly','Monthly'])[['Amount']].mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4e25c65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly</th>\n",
       "      <th>Monthly</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2004</th>\n",
       "      <th>April</th>\n",
       "      <td>259141.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>256702.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>243489.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>252585.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>226162.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>209592.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>184487.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2005</th>\n",
       "      <th>April</th>\n",
       "      <td>212688.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>264430.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>320379.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>238829.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>240548.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>239871.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>210199.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>263207.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>256695.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>219849.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>247700.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2006</th>\n",
       "      <th>April</th>\n",
       "      <td>275472.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>224143.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>252736.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>284489.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>293475.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>257936.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>251990.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>240851.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>256920.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>199399.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>260765.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>262533.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Amount\n",
       "Yearly Monthly             \n",
       "2004   April      259141.68\n",
       "       February   256702.63\n",
       "       January    243489.65\n",
       "       March      252585.28\n",
       "       May        226162.72\n",
       "       November   209592.24\n",
       "       September  184487.85\n",
       "2005   April      212688.63\n",
       "       August     264430.92\n",
       "       December   320379.34\n",
       "       February   238829.27\n",
       "       January    240548.10\n",
       "       July       239871.48\n",
       "       June       210199.82\n",
       "       May        263207.37\n",
       "       November   256695.12\n",
       "       October    219849.96\n",
       "       September  247700.17\n",
       "2006   April      275472.25\n",
       "       August     224143.99\n",
       "       December   252736.57\n",
       "       February   284489.57\n",
       "       January    293475.39\n",
       "       July       257936.38\n",
       "       June       251990.45\n",
       "       March      240851.27\n",
       "       May        256920.88\n",
       "       November   199399.20\n",
       "       October    260765.48\n",
       "       September  262533.88"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_avg_monthly_repay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846fe474",
   "metadata": {},
   "source": [
    "### e. If the monthly rate of interest is 2.9%, what is the profit for the bank for each month?\n",
    "### (Profit is defined as interest earned on Monthly Profit. Monthly Profit = Monthly repayment – Monthly spend. Interest is earned only on positive profits and not on negative amounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "23c18568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
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       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>484342.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>CAMERA</td>\n",
       "      <td>369694.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "3       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "4       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  \n",
       "0 2004-01-12  JEWELLERY  485470.80  \n",
       "1 2004-01-03      PETRO  410556.13  \n",
       "2 2004-01-15    CLOTHES   23740.46  \n",
       "3 2004-01-25       FOOD  484342.47  \n",
       "4 2005-01-17     CAMERA  369694.07  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dfd3c86f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>259490.06</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>437555.12</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>165972.88</td>\n",
       "      <td>January</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       Date     Amount  Monthly Yearly\n",
       "0       A1 2004-01-12  495414.75  January   2004\n",
       "1       A1 2004-01-03  245899.02  January   2004\n",
       "2       A1 2004-01-15  259490.06  January   2004\n",
       "3       A1 2004-01-25  437555.12  January   2004\n",
       "4       A1 2005-01-17  165972.88  January   2005"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Repayment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ed01ad42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To get the repayment and spend of customer merge customer_spend and repay table\n",
    "\n",
    "customer_spend_repay = pd.merge(left=customer_spend, right=Repayment, on=['Customer','Date'], how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "13556c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
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       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
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       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.8</td>\n",
       "      <td>495414.75</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.8</td>\n",
       "      <td>350446.82</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type  Amount_x   Amount_y  Monthly Yearly  \n",
       "0 2004-01-12  JEWELLERY  485470.8  495414.75  January   2004  \n",
       "1 2004-01-12  JEWELLERY  485470.8  350446.82  January   2004  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend_repay.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7fa1fa1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming columns\n",
    "\n",
    "customer_spend_repay.rename(columns={'Amount_x':'Spend_Amount','Amount_y':'Repay_Amount'}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "33a6f893",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Spend_Amount</th>\n",
       "      <th>Repay_Amount</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Yearly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.8</td>\n",
       "      <td>495414.75</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.8</td>\n",
       "      <td>350446.82</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type  Spend_Amount  Repay_Amount  Monthly Yearly  \n",
       "0 2004-01-12  JEWELLERY      485470.8     495414.75  January   2004  \n",
       "1 2004-01-12  JEWELLERY      485470.8     350446.82  January   2004  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend_repay.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "19a8f663",
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_card_profit = customer_spend_repay.groupby(by=['Yearly','Monthly'])[['Spend_Amount','Repay_Amount']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "02a58f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Spend_Amount</th>\n",
       "      <th>Repay_Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly</th>\n",
       "      <th>Monthly</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2004</th>\n",
       "      <th>April</th>\n",
       "      <td>676643.02</td>\n",
       "      <td>1801933.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1613276.92</td>\n",
       "      <td>2440340.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>54964451.63</td>\n",
       "      <td>90130133.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1071905.01</td>\n",
       "      <td>1385447.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>613440.97</td>\n",
       "      <td>2179617.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Spend_Amount  Repay_Amount\n",
       "Yearly Monthly                             \n",
       "2004   April        676643.02    1801933.80\n",
       "       February    1613276.92    2440340.81\n",
       "       January    54964451.63   90130133.46\n",
       "       March       1071905.01    1385447.54\n",
       "       May          613440.97    2179617.48"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_profit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d97cc09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating Monthly Profit = Monthly repayment – Monthly spend.\n",
    "\n",
    "credit_card_profit['Monthly_Profit'] = credit_card_profit['Repay_Amount'] - credit_card_profit['Spend_Amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "61584e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Spend_Amount</th>\n",
       "      <th>Repay_Amount</th>\n",
       "      <th>Monthly_Profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly</th>\n",
       "      <th>Monthly</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2004</th>\n",
       "      <th>April</th>\n",
       "      <td>676643.02</td>\n",
       "      <td>1801933.80</td>\n",
       "      <td>1125290.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1613276.92</td>\n",
       "      <td>2440340.81</td>\n",
       "      <td>827063.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>54964451.63</td>\n",
       "      <td>90130133.46</td>\n",
       "      <td>35165681.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1071905.01</td>\n",
       "      <td>1385447.54</td>\n",
       "      <td>313542.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>613440.97</td>\n",
       "      <td>2179617.48</td>\n",
       "      <td>1566176.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Spend_Amount  Repay_Amount  Monthly_Profit\n",
       "Yearly Monthly                                             \n",
       "2004   April        676643.02    1801933.80      1125290.78\n",
       "       February    1613276.92    2440340.81       827063.89\n",
       "       January    54964451.63   90130133.46     35165681.83\n",
       "       March       1071905.01    1385447.54       313542.53\n",
       "       May          613440.97    2179617.48      1566176.51"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_profit.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "956dc649",
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_card_profit_new = credit_card_profit[credit_card_profit['Monthly_Profit']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3a8ac23a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91896\\AppData\\Local\\Temp\\ipykernel_27448\\316425692.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  credit_card_profit_new['profit_as_interest'] = (credit_card_profit_new['Monthly_Profit']*2.9)/100\n"
     ]
    }
   ],
   "source": [
    "# profit at monthly rate of interest 2.9%\n",
    "credit_card_profit_new['profit_as_interest'] = (credit_card_profit_new['Monthly_Profit']*2.9)/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "12fbfafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Spend_Amount</th>\n",
       "      <th>Repay_Amount</th>\n",
       "      <th>Monthly_Profit</th>\n",
       "      <th>profit_as_interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly</th>\n",
       "      <th>Monthly</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2004</th>\n",
       "      <th>April</th>\n",
       "      <td>676643.02</td>\n",
       "      <td>1801933.80</td>\n",
       "      <td>1125290.78</td>\n",
       "      <td>3.263343e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1613276.92</td>\n",
       "      <td>2440340.81</td>\n",
       "      <td>827063.89</td>\n",
       "      <td>2.398485e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>54964451.63</td>\n",
       "      <td>90130133.46</td>\n",
       "      <td>35165681.83</td>\n",
       "      <td>1.019805e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1071905.01</td>\n",
       "      <td>1385447.54</td>\n",
       "      <td>313542.53</td>\n",
       "      <td>9.092733e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>613440.97</td>\n",
       "      <td>2179617.48</td>\n",
       "      <td>1566176.51</td>\n",
       "      <td>4.541912e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>572731.66</td>\n",
       "      <td>1107455.00</td>\n",
       "      <td>534723.34</td>\n",
       "      <td>1.550698e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2005</th>\n",
       "      <th>April</th>\n",
       "      <td>937391.46</td>\n",
       "      <td>2677783.73</td>\n",
       "      <td>1740392.27</td>\n",
       "      <td>5.047138e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1128247.40</td>\n",
       "      <td>2356578.55</td>\n",
       "      <td>1228331.15</td>\n",
       "      <td>3.562160e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>892073.15</td>\n",
       "      <td>1993057.32</td>\n",
       "      <td>1100984.17</td>\n",
       "      <td>3.192854e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>19857706.36</td>\n",
       "      <td>29032454.09</td>\n",
       "      <td>9174747.73</td>\n",
       "      <td>2.660677e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>3153534.09</td>\n",
       "      <td>3490382.91</td>\n",
       "      <td>336848.82</td>\n",
       "      <td>9.768616e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>347293.80</td>\n",
       "      <td>1516706.01</td>\n",
       "      <td>1169412.21</td>\n",
       "      <td>3.391295e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>2779376.13</td>\n",
       "      <td>4523946.92</td>\n",
       "      <td>1744570.79</td>\n",
       "      <td>5.059255e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>8352824.26</td>\n",
       "      <td>14092297.62</td>\n",
       "      <td>5739473.36</td>\n",
       "      <td>1.664447e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>2337536.73</td>\n",
       "      <td>3757882.79</td>\n",
       "      <td>1420346.06</td>\n",
       "      <td>4.119004e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>570860.46</td>\n",
       "      <td>1383682.65</td>\n",
       "      <td>812822.19</td>\n",
       "      <td>2.357184e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>663419.22</td>\n",
       "      <td>1128231.04</td>\n",
       "      <td>464811.82</td>\n",
       "      <td>1.347954e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2006</th>\n",
       "      <th>April</th>\n",
       "      <td>3506096.05</td>\n",
       "      <td>8561217.81</td>\n",
       "      <td>5055121.76</td>\n",
       "      <td>1.465985e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>918882.48</td>\n",
       "      <td>922682.18</td>\n",
       "      <td>3799.70</td>\n",
       "      <td>1.101913e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>2744167.15</td>\n",
       "      <td>4224494.12</td>\n",
       "      <td>1480326.97</td>\n",
       "      <td>4.292948e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1270011.03</td>\n",
       "      <td>1832436.44</td>\n",
       "      <td>562425.41</td>\n",
       "      <td>1.631034e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>1494112.19</td>\n",
       "      <td>2138597.55</td>\n",
       "      <td>644485.36</td>\n",
       "      <td>1.869008e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>423460.22</td>\n",
       "      <td>1206400.69</td>\n",
       "      <td>782940.47</td>\n",
       "      <td>2.270527e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>15387180.98</td>\n",
       "      <td>24727574.88</td>\n",
       "      <td>9340393.90</td>\n",
       "      <td>2.708714e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>2246785.99</td>\n",
       "      <td>6233206.28</td>\n",
       "      <td>3986420.29</td>\n",
       "      <td>1.156062e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>1649658.67</td>\n",
       "      <td>2429426.80</td>\n",
       "      <td>779768.13</td>\n",
       "      <td>2.261328e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>989641.47</td>\n",
       "      <td>1040526.27</td>\n",
       "      <td>50884.80</td>\n",
       "      <td>1.475659e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>733211.92</td>\n",
       "      <td>1276369.16</td>\n",
       "      <td>543157.24</td>\n",
       "      <td>1.575156e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Spend_Amount  Repay_Amount  Monthly_Profit  \\\n",
       "Yearly Monthly                                                 \n",
       "2004   April         676643.02    1801933.80      1125290.78   \n",
       "       February     1613276.92    2440340.81       827063.89   \n",
       "       January     54964451.63   90130133.46     35165681.83   \n",
       "       March        1071905.01    1385447.54       313542.53   \n",
       "       May           613440.97    2179617.48      1566176.51   \n",
       "       November      572731.66    1107455.00       534723.34   \n",
       "2005   April         937391.46    2677783.73      1740392.27   \n",
       "       August       1128247.40    2356578.55      1228331.15   \n",
       "       December      892073.15    1993057.32      1100984.17   \n",
       "       February    19857706.36   29032454.09      9174747.73   \n",
       "       January      3153534.09    3490382.91       336848.82   \n",
       "       July          347293.80    1516706.01      1169412.21   \n",
       "       June         2779376.13    4523946.92      1744570.79   \n",
       "       May          8352824.26   14092297.62      5739473.36   \n",
       "       November     2337536.73    3757882.79      1420346.06   \n",
       "       October       570860.46    1383682.65       812822.19   \n",
       "       September     663419.22    1128231.04       464811.82   \n",
       "2006   April        3506096.05    8561217.81      5055121.76   \n",
       "       August        918882.48     922682.18         3799.70   \n",
       "       February     2744167.15    4224494.12      1480326.97   \n",
       "       January      1270011.03    1832436.44       562425.41   \n",
       "       July         1494112.19    2138597.55       644485.36   \n",
       "       June          423460.22    1206400.69       782940.47   \n",
       "       March       15387180.98   24727574.88      9340393.90   \n",
       "       May          2246785.99    6233206.28      3986420.29   \n",
       "       November     1649658.67    2429426.80       779768.13   \n",
       "       October       989641.47    1040526.27        50884.80   \n",
       "       September     733211.92    1276369.16       543157.24   \n",
       "\n",
       "                  profit_as_interest  \n",
       "Yearly Monthly                        \n",
       "2004   April            3.263343e+04  \n",
       "       February         2.398485e+04  \n",
       "       January          1.019805e+06  \n",
       "       March            9.092733e+03  \n",
       "       May              4.541912e+04  \n",
       "       November         1.550698e+04  \n",
       "2005   April            5.047138e+04  \n",
       "       August           3.562160e+04  \n",
       "       December         3.192854e+04  \n",
       "       February         2.660677e+05  \n",
       "       January          9.768616e+03  \n",
       "       July             3.391295e+04  \n",
       "       June             5.059255e+04  \n",
       "       May              1.664447e+05  \n",
       "       November         4.119004e+04  \n",
       "       October          2.357184e+04  \n",
       "       September        1.347954e+04  \n",
       "2006   April            1.465985e+05  \n",
       "       August           1.101913e+02  \n",
       "       February         4.292948e+04  \n",
       "       January          1.631034e+04  \n",
       "       July             1.869008e+04  \n",
       "       June             2.270527e+04  \n",
       "       March            2.708714e+05  \n",
       "       May              1.156062e+05  \n",
       "       November         2.261328e+04  \n",
       "       October          1.475659e+03  \n",
       "       September        1.575156e+04  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_profit_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff41d84",
   "metadata": {},
   "source": [
    "### f. What are the top 5 product types?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3a6e2a61",
   "metadata": {
    "scrolled": true
   },
   "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>Customer</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Yearly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       Date       Type     Amount  Monthly Yearly\n",
       "0       A1 2004-01-12  JEWELLERY  485470.80  January   2004\n",
       "1       A1 2004-01-03      PETRO  410556.13  January   2004\n",
       "2       A1 2004-01-15    CLOTHES   23740.46  January   2004"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f445968f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PETRO           200\n",
       "FOOD            160\n",
       "CAMERA          160\n",
       "AIR TICKET      147\n",
       "TRAIN TICKET    132\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 products in which customers are spending\n",
    "\n",
    "Spend.Type.value_counts().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d316e140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Spend.Type.value_counts().head(5).plot(kind='bar')\n",
    "plt.xticks(rotation=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5767f682",
   "metadata": {},
   "source": [
    "*The top 5 products where customers are spending is Petro, Food, Camera, Air Ticket, Train Ticket.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2921abde",
   "metadata": {},
   "source": [
    "### g. Which city is having maximum spend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "19838cf5",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>484342.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>CAMERA</td>\n",
       "      <td>369694.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "3       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "4       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  \n",
       "0 2004-01-12  JEWELLERY  485470.80  \n",
       "1 2004-01-03      PETRO  410556.13  \n",
       "2 2004-01-15    CLOTHES   23740.46  \n",
       "3 2004-01-25       FOOD  484342.47  \n",
       "4 2005-01-17     CAMERA  369694.07  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "510e91b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping the data by city, and finding the amount spend by each city and sorting the values in decreasing order\n",
    "\n",
    "city_spend = customer_spend.groupby(by= ['City'])[['Amount']].sum().sort_values(by= 'Amount',ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b8988b16",
   "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>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COCHIN</th>\n",
       "      <td>45963513.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALCUTTA</th>\n",
       "      <td>41108132.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANGALORE</th>\n",
       "      <td>40461647.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOMBAY</th>\n",
       "      <td>28603127.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHENNAI</th>\n",
       "      <td>22128635.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRIVANDRUM</th>\n",
       "      <td>15337155.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATNA</th>\n",
       "      <td>15260586.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELHI</th>\n",
       "      <td>10918286.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Amount\n",
       "City                   \n",
       "COCHIN      45963513.50\n",
       "CALCUTTA    41108132.30\n",
       "BANGALORE   40461647.02\n",
       "BOMBAY      28603127.38\n",
       "CHENNAI     22128635.65\n",
       "TRIVANDRUM  15337155.87\n",
       "PATNA       15260586.97\n",
       "DELHI       10918286.54"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_spend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1cf2a401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph of above reporting table\n",
    "\n",
    "city_spend.plot(kind='pie',y='Amount',autopct='%1.2f%%', shadow = True,explode=[0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0], legend= False)\n",
    "plt.title('Amount spend by customers through credit card in different cities')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f231a5a",
   "metadata": {},
   "source": [
    "*From the above pie chart we can see that Cochin is having maximum spend through credit cards, whereas Bangalore and Calcutta is also having  almost similar spend in terms of using credit card.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a45c448",
   "metadata": {},
   "source": [
    "### h. Which age group is spending more money?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "da668d3b",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  \n",
       "0 2004-01-12  JEWELLERY  485470.80  \n",
       "1 2004-01-03      PETRO  410556.13  \n",
       "2 2004-01-15    CLOTHES   23740.46  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "98782b17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a new column Age_cat with different age buckets\n",
    "customer_spend['Age_cat'] = pd.cut(customer_spend['Age'], bins=[18,28,38,48,58,68,78,88] ,labels =['18-28','28-38','38-48','48-58','58-68','68-78','78-88'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3f25b0f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-28    329\n",
       "38-48    285\n",
       "48-58    275\n",
       "28-38    254\n",
       "68-78    170\n",
       "58-68    169\n",
       "78-88     18\n",
       "Name: Age_cat, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.Age_cat.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a8ba1450",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Age_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "      <td>68-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "      <td>68-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "      <td>68-78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount Age_cat  \n",
       "0 2004-01-12  JEWELLERY  485470.80   68-78  \n",
       "1 2004-01-03      PETRO  410556.13   68-78  \n",
       "2 2004-01-15    CLOTHES   23740.46   68-78  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "41a34c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping data by age group, and finding the amount spend by each group and arranging it in descending order\n",
    "\n",
    "age_spend = customer_spend.groupby(by='Age_cat')[['Amount']].sum().sort_values(by='Amount', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "367bc60f",
   "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>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_cat</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38-48</th>\n",
       "      <td>52350765.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-28</th>\n",
       "      <td>44458290.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28-38</th>\n",
       "      <td>40669446.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48-58</th>\n",
       "      <td>32043137.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68-78</th>\n",
       "      <td>28731480.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58-68</th>\n",
       "      <td>20623443.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78-88</th>\n",
       "      <td>904520.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Amount\n",
       "Age_cat             \n",
       "38-48    52350765.92\n",
       "18-28    44458290.95\n",
       "28-38    40669446.09\n",
       "48-58    32043137.38\n",
       "68-78    28731480.99\n",
       "58-68    20623443.21\n",
       "78-88      904520.69"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_spend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "321fe8b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph of above reporting table\n",
    "\n",
    "age_spend.plot(kind='pie', y='Amount', autopct='%1.2f%%', shadow= True, explode=[0.1,0.0,0.0,0.0,0.0,0.0,0.0] ,legend= False)\n",
    "plt.title('Amount spend by different age groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0da28c62",
   "metadata": {},
   "source": [
    "*From the above pie chart we can say that age group 38-48 is spending more money using cards which is around 24% of their contribution, also the age group 18-20 have spend around 20.23% and age group 28-38 have their contribution of around 18.5% as the 3rd highest spend amoung different age groups.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3fb619",
   "metadata": {},
   "source": [
    "### i. Who are the top 10 customers in terms of repayment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5b2ceed3",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>495414.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>259490.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>437555.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>165972.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "3       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "4       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date     Amount  \n",
       "0 2004-01-12  495414.75  \n",
       "1 2004-01-03  245899.02  \n",
       "2 2004-01-15  259490.06  \n",
       "3 2004-01-25  437555.12  \n",
       "4 2005-01-17  165972.88  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_repay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4a28040a",
   "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>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A61</th>\n",
       "      <td>10539142.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A60</th>\n",
       "      <td>9876290.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A13</th>\n",
       "      <td>9572000.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A43</th>\n",
       "      <td>8489871.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A45</th>\n",
       "      <td>8448334.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A12</th>\n",
       "      <td>8334760.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A14</th>\n",
       "      <td>7943268.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A44</th>\n",
       "      <td>7744730.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A39</th>\n",
       "      <td>7622483.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A42</th>\n",
       "      <td>7615460.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Amount\n",
       "Customer             \n",
       "A61       10539142.91\n",
       "A60        9876290.74\n",
       "A13        9572000.66\n",
       "A43        8489871.46\n",
       "A45        8448334.87\n",
       "A12        8334760.16\n",
       "A14        7943268.63\n",
       "A44        7744730.12\n",
       "A39        7622483.30\n",
       "A42        7615460.86"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping by customer and finding top 10 customers\n",
    "\n",
    "customer_repay.groupby(by='Customer')[['Amount']].sum().sort_values(by='Amount',ascending= False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6345f6a9",
   "metadata": {},
   "source": [
    "*The above customers are those customers who have made payments of their credit card bills.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3316a77",
   "metadata": {},
   "source": [
    "### 3. Calculate the city wise spend on each product on yearly basis. Also include a graphical representation for the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bf5cbbf9",
   "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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       "        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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  \n",
       "0 2004-01-12  JEWELLERY  485470.80  \n",
       "1 2004-01-03      PETRO  410556.13  \n",
       "2 2004-01-15    CLOTHES   23740.46  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "40c55ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new column year in customer_spend df\n",
    "\n",
    "customer_spend['Year'] = customer_spend['Date'].apply(lambda x: pd.Timestamp.strftime(x, format='%Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9b6bd42d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  Year  \n",
       "0 2004-01-12  JEWELLERY  485470.80  2004  \n",
       "1 2004-01-03      PETRO  410556.13  2004  \n",
       "2 2004-01-15    CLOTHES   23740.46  2004  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1845c48d",
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_spend_pivot = customer_spend.pivot_table(index=['City','Year'], columns='Product', aggfunc='sum', values='Amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ae3104a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### just tried this after submitting by changing the values in index nd columns\n",
    "\n",
    "customer_spend.pivot_table(index=['City','Product'], columns='Year', aggfunc='sum', values='Amount').plot(kind='bar',figsize = (18,10), width=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25e108c",
   "metadata": {},
   "source": [
    "*From all the 3 products - Gold, Silver and Platinum - the highest purchases are for Gold.*\n",
    "\n",
    "*Also from all the years Gold purchasing is more in the year 2005.*\n",
    "\n",
    "*From the above chart we can infer that among all the cities, Bangalore and Cochin is having same trends for Gold purchase for every consecutive years that means, from 2004 to 2005 it gradually increased and then decreased in 2006.*\n",
    "\n",
    "*For Bombay also it is follwing the same tend in purchasing of Gold as in Cohin and Bangalore.*\n",
    "\n",
    "*The decreasing trend of gold purchases between the year 2005-06, is the point of focus and one should find out the possible reason for this, and make some strategies to improve the customer aquisition.* \n",
    "\n",
    "*But for Calcutta we can see that the Gold purchasing trends are gradually increasing in almost all the years. So the marketing team should make strategies on giving some offers and cashback to the people who are purchasing Gold.*\n",
    "\n",
    "*In Chennai, there is a trend of purchasing Platinum among the 3 products, also the purchasing has increased in every consecutive years. So there one can focus on selling Platinum products with different varieties and options by keeping in view peoples demand.*\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "cd66ed77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Platinum</th>\n",
       "      <th>Silver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">BANGALORE</th>\n",
       "      <th>2004</th>\n",
       "      <td>9289878.54</td>\n",
       "      <td>1112732.30</td>\n",
       "      <td>1291852.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>12892362.99</td>\n",
       "      <td>912284.97</td>\n",
       "      <td>830612.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>12030611.09</td>\n",
       "      <td>685831.27</td>\n",
       "      <td>1415480.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BOMBAY</th>\n",
       "      <th>2004</th>\n",
       "      <td>6987853.53</td>\n",
       "      <td>897264.94</td>\n",
       "      <td>532089.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>8983018.28</td>\n",
       "      <td>711804.94</td>\n",
       "      <td>508988.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Product                Gold    Platinum      Silver\n",
       "City      Year                                     \n",
       "BANGALORE 2004   9289878.54  1112732.30  1291852.58\n",
       "          2005  12892362.99   912284.97   830612.34\n",
       "          2006  12030611.09   685831.27  1415480.94\n",
       "BOMBAY    2004   6987853.53   897264.94   532089.03\n",
       "          2005   8983018.28   711804.94   508988.54"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "adae5309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_spend_pivot.plot(kind='bar', figsize = (18,10), width=0.8)\n",
    "plt.title('City wise Amount spend on each product on yearly basis')\n",
    "plt.ylabel('Amount spend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a063f5fe",
   "metadata": {},
   "source": [
    "### 4. Create graphs for\n",
    "### a. Monthly comparison of total spends, city wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9e4a5b40",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  Year  \n",
       "0 2004-01-12  JEWELLERY  485470.80  2004  \n",
       "1 2004-01-03      PETRO  410556.13  2004  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3eb55807",
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_spend['Monthly'] = customer_spend['Date'].apply(lambda x: pd.Timestamp.strftime(x,format=\"%B\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "fa4d75ea",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Year</th>\n",
       "      <th>Monthly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  Year  Monthly  \n",
       "0 2004-01-12  JEWELLERY  485470.80  2004  January  \n",
       "1 2004-01-03      PETRO  410556.13  2004  January  \n",
       "2 2004-01-15    CLOTHES   23740.46  2004  January  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1ea852d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Monthly</th>\n",
       "      <th>City</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>April</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>4207980.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>April</td>\n",
       "      <td>BOMBAY</td>\n",
       "      <td>1873785.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>April</td>\n",
       "      <td>CALCUTTA</td>\n",
       "      <td>3457507.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>April</td>\n",
       "      <td>CHENNAI</td>\n",
       "      <td>2518656.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>April</td>\n",
       "      <td>COCHIN</td>\n",
       "      <td>3882744.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>September</td>\n",
       "      <td>CHENNAI</td>\n",
       "      <td>745759.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>September</td>\n",
       "      <td>COCHIN</td>\n",
       "      <td>1382242.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>September</td>\n",
       "      <td>DELHI</td>\n",
       "      <td>50001.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>September</td>\n",
       "      <td>PATNA</td>\n",
       "      <td>70329.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>September</td>\n",
       "      <td>TRIVANDRUM</td>\n",
       "      <td>186063.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Monthly        City      Amount\n",
       "0       April   BANGALORE  4207980.48\n",
       "1       April      BOMBAY  1873785.01\n",
       "2       April    CALCUTTA  3457507.95\n",
       "3       April     CHENNAI  2518656.99\n",
       "4       April      COCHIN  3882744.94\n",
       "..        ...         ...         ...\n",
       "91  September     CHENNAI   745759.40\n",
       "92  September      COCHIN  1382242.90\n",
       "93  September       DELHI    50001.00\n",
       "94  September       PATNA    70329.95\n",
       "95  September  TRIVANDRUM   186063.70\n",
       "\n",
       "[96 rows x 3 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data based on monthly and city columns\n",
    "\n",
    "customer_spend.groupby(by= ['Monthly','City'])[['Amount']].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "id": "2321b4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_wise_monthly_spend = customer_spend.pivot_table(index='City', columns='Monthly', aggfunc='sum', values='Amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "id": "7a2e0b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Monthly</th>\n",
       "      <th>April</th>\n",
       "      <th>August</th>\n",
       "      <th>December</th>\n",
       "      <th>February</th>\n",
       "      <th>January</th>\n",
       "      <th>July</th>\n",
       "      <th>June</th>\n",
       "      <th>March</th>\n",
       "      <th>May</th>\n",
       "      <th>November</th>\n",
       "      <th>October</th>\n",
       "      <th>September</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</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>BANGALORE</th>\n",
       "      <td>4207980.48</td>\n",
       "      <td>695061.95</td>\n",
       "      <td>422167.24</td>\n",
       "      <td>6734494.76</td>\n",
       "      <td>9041796.84</td>\n",
       "      <td>2228217.69</td>\n",
       "      <td>1330685.90</td>\n",
       "      <td>5362903.07</td>\n",
       "      <td>6383115.13</td>\n",
       "      <td>2260603.07</td>\n",
       "      <td>1091602.61</td>\n",
       "      <td>703018.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOMBAY</th>\n",
       "      <td>1873785.01</td>\n",
       "      <td>1066744.75</td>\n",
       "      <td>140040.71</td>\n",
       "      <td>4276988.30</td>\n",
       "      <td>5948993.33</td>\n",
       "      <td>177726.32</td>\n",
       "      <td>1172594.97</td>\n",
       "      <td>5227850.50</td>\n",
       "      <td>3891945.60</td>\n",
       "      <td>2872049.40</td>\n",
       "      <td>676005.70</td>\n",
       "      <td>1278402.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALCUTTA</th>\n",
       "      <td>3457507.95</td>\n",
       "      <td>1548354.02</td>\n",
       "      <td>55001.50</td>\n",
       "      <td>8150545.61</td>\n",
       "      <td>9064864.90</td>\n",
       "      <td>2234573.27</td>\n",
       "      <td>2293045.81</td>\n",
       "      <td>4939040.04</td>\n",
       "      <td>5578246.87</td>\n",
       "      <td>2078880.93</td>\n",
       "      <td>196357.83</td>\n",
       "      <td>1511713.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHENNAI</th>\n",
       "      <td>2518656.99</td>\n",
       "      <td>305875.97</td>\n",
       "      <td>223628.27</td>\n",
       "      <td>4385815.95</td>\n",
       "      <td>4466961.36</td>\n",
       "      <td>693825.77</td>\n",
       "      <td>1244242.16</td>\n",
       "      <td>3183643.71</td>\n",
       "      <td>2856199.62</td>\n",
       "      <td>1347779.28</td>\n",
       "      <td>156247.17</td>\n",
       "      <td>745759.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COCHIN</th>\n",
       "      <td>3882744.94</td>\n",
       "      <td>2317306.61</td>\n",
       "      <td>909533.05</td>\n",
       "      <td>5747886.38</td>\n",
       "      <td>11714373.54</td>\n",
       "      <td>1290897.30</td>\n",
       "      <td>2495745.07</td>\n",
       "      <td>5645146.53</td>\n",
       "      <td>6581085.69</td>\n",
       "      <td>3127999.60</td>\n",
       "      <td>868551.89</td>\n",
       "      <td>1382242.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Monthly         April      August   December    February      January  \\\n",
       "City                                                                    \n",
       "BANGALORE  4207980.48   695061.95  422167.24  6734494.76   9041796.84   \n",
       "BOMBAY     1873785.01  1066744.75  140040.71  4276988.30   5948993.33   \n",
       "CALCUTTA   3457507.95  1548354.02   55001.50  8150545.61   9064864.90   \n",
       "CHENNAI    2518656.99   305875.97  223628.27  4385815.95   4466961.36   \n",
       "COCHIN     3882744.94  2317306.61  909533.05  5747886.38  11714373.54   \n",
       "\n",
       "Monthly          July        June       March         May    November  \\\n",
       "City                                                                    \n",
       "BANGALORE  2228217.69  1330685.90  5362903.07  6383115.13  2260603.07   \n",
       "BOMBAY      177726.32  1172594.97  5227850.50  3891945.60  2872049.40   \n",
       "CALCUTTA   2234573.27  2293045.81  4939040.04  5578246.87  2078880.93   \n",
       "CHENNAI     693825.77  1244242.16  3183643.71  2856199.62  1347779.28   \n",
       "COCHIN     1290897.30  2495745.07  5645146.53  6581085.69  3127999.60   \n",
       "\n",
       "Monthly       October   September  \n",
       "City                               \n",
       "BANGALORE  1091602.61   703018.28  \n",
       "BOMBAY      676005.70  1278402.79  \n",
       "CALCUTTA    196357.83  1511713.57  \n",
       "CHENNAI     156247.17   745759.40  \n",
       "COCHIN      868551.89  1382242.90  "
      ]
     },
     "execution_count": 807,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_wise_monthly_spend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 808,
   "id": "489b5829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_wise_monthly_spend.plot(kind='bar',figsize=(18,7), width=0.8)\n",
    "plt.title('City Wise Monthly Spend by Customer')\n",
    "plt.xticks(rotation = 25)\n",
    "plt.ylabel('Amount spend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dbe782a",
   "metadata": {},
   "source": [
    "*Among all the months customers are spending more in January.*\n",
    "\n",
    "*Among all the cities in Bangalore, Bombay, Calcutta, Chochin people are spending the most in the month of January, this is may be due the end of an year, and most of the e-platform run clearance sale on their platform, and also give lots of discount and offers for their year end sale, so that they can acquire lots and lots of potential customer.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db11c669",
   "metadata": {},
   "source": [
    "### b. Comparison of yearly spend on air tickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 809,
   "id": "7619cc47",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Age_cat</th>\n",
       "      <th>Year</th>\n",
       "      <th>Monthly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "      <td>68-78</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "      <td>68-78</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "      <td>68-78</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount Age_cat  Year  Monthly  \n",
       "0 2004-01-12  JEWELLERY  485470.80   68-78  2004  January  \n",
       "1 2004-01-03      PETRO  410556.13   68-78  2004  January  \n",
       "2 2004-01-15    CLOTHES   23740.46   68-78  2004  January  "
      ]
     },
     "execution_count": 809,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "id": "19d1e0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "air_ticket = customer_spend[customer_spend['Type']== 'AIR TICKET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "id": "08964ae6",
   "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>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>4357225.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>10550152.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>5248469.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Amount\n",
       "Year             \n",
       "2004   4357225.35\n",
       "2005  10550152.21\n",
       "2006   5248469.56"
      ]
     },
     "execution_count": 811,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_air = air_ticket.groupby(by='Year')[['Amount']].sum()\n",
    "yearly_air"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "id": "db3a35f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2004', '2005', '2006'], dtype='object', name='Year')"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_air.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "id": "ce3bc8e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7V6+++qqGDBmiy5cvKywsTA888MAd9xs3bpxln4EDB+rWrVtasWKFsrKyLH+s71WLFi20a9cuffLJJ2rYsKESExO1bNky2dnZ5Xtg5L1wdXXVt99+qyNHjuS7zWhvb6+RI0dq5syZCgkJUWBgoM6cOaOFCxeqf//+BT63Z/Lkyfriiy80ffp0ffTRRwoICJCvr6+GDRumYcOGqWHDhjp69KgWL14sf39/yzHy6jh06JC8vLzUpk0bzZgxQ/PmzVP79u2Vnp6uiIgI1a9fX56envc9buB+EHTuUkJCgo4ePWr1jJG8e+6nTp2yCjoxMTF66qmn7rhWAPg9Nzc3rVu3TqGhoQoJCVFmZqbq16+v2bNnq2/fvpKkXr16KSkpSZs2bVJ0dLTc3NwUEBCgF198UdOmTdPJkycLfFvynXTo0EGhoaF3XIScZ+bMmVq4cKFWrlypCxcuqHr16urbt6/efPNNq35/+9vftGXLFi1fvlwPP/ywwsPDrcLQnDlztHz5cq1bt04pKSmqXr26unbtqtGjR99xIfEf1a9fX2vXrtXcuXM1ZswYVa9eXZMmTdLcuXPvuF/btm310UcfKTw8XGPHjpXJZJKPj4/mzZunRo0a3fX5C/LWW2/p1q1bWrhwobKysuTh4aE33nhDJ0+e1P79+61mke7F66+/rqVLlyo4ONjq2TZ5BgwYoEqVKikqKkobN26Um5ubgoKCLO9w+6OaNWtqzJgxmjlzpjZt2qQ+ffpoxYoVWrRokZYvX66LFy/Kzc1NgwcPtrz7LO883333nYKDgzVnzhz169dPt27d0rp16xQdHS1HR0e1bdtWEyZM+NPgCZQ0OzOfzFagt956S7/88ovl7eUtWrRQUFCQevXqla9vzZo1LdPUly5d0pNPPqnIyMh8C06BsuaDDz7Qhx9+qIMHD97305nzHrGwevVqtW7dupgqBID7w4zOXWrUqJF++uknq8V4hw8f1scff6yQkBBL0Pnmm29kZ2cnPz8/W5UK/KktW7boxx9/VHR0tIYOHcpHUAAwLBYj36Xg4GDt3r1bixcv1unTp3Xo0CFNnjxZ6enpVm8rT0xMVN26dfO9NRYoSxITExUdHa0uXbpYns4MAEbEratC/PHWlSTt2rVLy5cv18mTJ1W5cmV17NhREyZMsFoIGhISoh9++MEmnygMAACsEXQAAIBhcesKAAAYFkEHAAAY1v/8u658fHyUlZVltaAYAACUbRcuXJDJZNLXX399x37/80EnMzPzvh/iBQAASld2dvaffliuRNCxfBbVHz8fBgAAlF2dO3e+q36s0QEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0AEMypyba+sSUIZwPeB/VUVbFwCgZNhVqKDTOz5QxsVkW5cCG3OqXlsNugfbugzAJgg6gIFlXExWRurPti4DAGyGW1cAAMCwCDoAAMCwCDoAAMCwCDoAAMCwCDoAAMCwCDoAAMCwCDoAAMCwCDoAAMCwCDoAAMCwCDoAAMCwCDoAAMCwylTQWbp0qV566aU79rl8+bLGjRsnX19f+fr6atq0abpx40YpVQgAAMqTMhN0Vq1apfDw8D/tN2rUKJ07d87S/6uvvtLbb79dChUCAIDyxuafXp6amqopU6YoPj5eDRo0uGPfb7/9VocPH1ZsbKwaNmwoSZo5c6aGDBmisWPHys3NrTRKBgAA5YTNZ3S+//57Va5cWdu2bZO3t/cd+3799deqWbOmJeRIkp+fn+zs7BQfH1/SpQIAgHLG5jM6nTp1UqdOne6qb2pqqmrXrm3VZjKZVKVKFSUnJxe6X+fOnQvdlpycnO+YAADAGGw+o1MUGRkZMplM+dodHByUmZlpg4oAAEBZZvMZnaJwdHRUVlZWvvbMzExVqlSp0P327dtX6LY7zfYAAIDyrVzN6Li7uystLc2qLSsrS1euXGEhMgAAyKdcBR1fX1+lpKTo7Nmzlra4uDhJUqtWrWxVFgAAKKPKdNDJycnRhQsXdPPmTUmSt7e3WrVqpTFjxujo0aP697//rRkzZqhnz57M6AAAgHzKdNBJTk6Wv7+/YmNjJUl2dnaKiIiQh4eHBg0apNGjR6t9+/YKCQmxbaEAAKBMKlOLkefOnWv1s4eHh44fP27VVr169bt6gjIAAECZntEBAAC4HwQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWDYPOrm5uQoPD1e7du3k7e2toKAgnT17ttD+Fy5c0NixY9W6dWu1bt1ab775plJSUkqxYgAAUF7YPOgsXbpU69at06xZs7R+/XrZ2dkpODhYWVlZBfYfM2aMkpOT9dFHH+mjjz5SSkqKhg0bVspVAwCA8sCmQScrK0srV67UyJEjFRAQIE9PT4WFhSk1NVV79uzJ1z89PV1HjhxRcHCwvLy85OXlpaFDh+r777/X5cuXbTACAABQllW05ckTExN1/fp1tWnTxtLm6uoqLy8vHTlyRN26dbPq7+DgoEqVKikmJkZ+fn6SpK1bt6p+/fqqXLlyoefp3LlzoduSk5NVu3bt+xwJAAAoi2wadPLW1vwxaNSqVUvJycn5+js4OGj27NmaOXOmfHx8ZGdnp5o1a2rt2rWqUMHmd+EAAEAZY9Ogk5GRIUkymUxW7Q4ODrp69Wq+/mazWcePH1fLli01ZMgQ5eTkKCwsTMOHD9cnn3wiZ2fnAs+zb9++Qmu402wPAAAo32wadBwdHSXdXquT970kZWZmysnJKV//nTt3Kjo6WgcOHLCEmsjISHXs2FGbNm3SoEGDSqdwAABQLtj0fk/eLau0tDSr9rS0NLm7u+frHx8frwYNGljN3FSuXFkNGjTQmTNnSrRWAABQ/tg06Hh6esrZ2VlxcXGWtvT0dCUkJMjHxydf/9q1a+vs2bPKzMy0tGVkZCgpKUn16tUrlZoBAED5YdOgYzKZNHDgQC1YsED79u1TYmKixowZI3d3dwUGBionJ0cXLlzQzZs3JUk9e/aUJI0ePVqJiYmW/iaTSb1797bhSAAAQFlk87cqjRo1Sn379tXUqVPVv39/2dvbKyoqSiaTScnJyfL391dsbKyk2+/Gio6Oltls1qBBg/TKK6/ogQce0CeffCJXV1cbjwQAAJQ1Nl2MLEn29vaaMGGCJkyYkG+bh4eHjh8/btXWsGFDRUZGllZ5AACgHLP5jA4AAEBJIegAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDIugAAADDKnLQOXLkiK5fv17gtvT0dO3cufO+iwIAACgORQ46L7/8sk6dOlXgtoSEBE2ePPm+iwIAACgOFe+m06RJk5ScnCxJMpvNCgkJkbOzc75+Z86cUY0aNYq3QgAAgHt0VzM6Tz/9tMxms8xms6Ut7+e8rwoVKuixxx7TnDlzSqxYAACAorirGZ1OnTqpU6dOkqSXXnpJISEhatiwYYkWBgAAcL/uKuj83po1a0qiDgAAgGJX5KCTkZGhyMhIHThwQBkZGcrNzbXabmdnp7179xZbgQAAAPeqyEFn9uzZ2rRpk/z8/NSkSRNVqHB/j+LJzc1VRESENmzYoPT0dD3++OOaMWOG6tWrV2D/W7duKTw8XDExMbp27ZqaNWumKVOmqEmTJvdVBwAAMJ4iB53du3drzJgxGjp0aLEUsHTpUq1bt05z5syRm5ub5s+fr+DgYO3YsUMmkylf/5CQEO3fv19z5sxR3bp1FRYWpuDgYO3atUsuLi7FUhMAADCGIk/HZGdnq0WLFsVy8qysLK1cuVIjR45UQECAPD09FRYWptTUVO3Zsydf/3Pnzmnjxo2aM2eOOnTooIYNG+rdd9+VyWTSd999Vyw1AQAA4yhy0PH399cXX3xRLCdPTEzU9evX1aZNG0ubq6urvLy8dOTIkXz9v/zyS7m6uqp9+/ZW/ffv36+2bdsWS00AAMA4inzrqmvXrpoxY4YuXbokb29vOTk55evTs2fPuzpWSkqKJKl27dpW7bVq1bI8oPD3zpw5o7p162r37t1asWKFUlNT5eXlpbfeeuuOb3fv3LlzoduSk5PznR8AABhDkYPO6NGjJUkxMTGKiYnJt93Ozu6ug05GRoYk5VuL4+DgoKtXr+br/9tvv+nnn3/W0qVLNXHiRLm6umrZsmV68cUXFRsbq+rVqxdpLAAAwNiKHHT27dtXbCd3dHSUdHutTt73kpSZmVngTNEDDzyga9euKSwszDKDExYWpoCAAG3ZskVDhgwpcs13mu0BAADlW5GDTp06dYrt5Hm3jNLS0vTQQw9Z2tPS0uTp6Zmvv7u7uypWrGh1m8rR0VF169ZVUlJSsdUFAACMochBJyIi4k/7jBgx4q6O5enpKWdnZ8XFxVmCTnp6uhISEjRw4MB8/X18fJSdna1jx46pefPmkqSbN2/q3Llz6tatWxFGAQAA/hcUa9BxdnZWrVq17jromEwmDRw4UAsWLFC1atVUp04dzZ8/X+7u7goMDFROTo4uXbokFxcXOTo6ysfHR0888YQmTZqkmTNnqkqVKgoPD5e9vb2ee+65og4FAAAYXJGDTmJiYr62GzduKD4+XiEhIZo2bVqRjjdq1ChlZ2dr6tSpunnzpnx9fRUVFSWTyaSkpCR17txZc+bMUe/evSVJixcv1oIFCzRixAjdvHlTrVq10urVq1WtWrWiDgUAABicndlsNhfXwTZv3qw1a9Zoy5YtxXXIEpe3GLk4F1kDZUXCxzOVkfqzrcuAjTm5PSSvQdNtXQZQrO727/f9fVDVH9SuXVunTp0qzkMCAADcsyLfuiqI2WxWcnKyPvjgg2J9VxYAAMD9KHLQ8fT0lJ2dXYHbzGaz3nvvvfsuCgAAoDgUOegMHz68wKDj7OysDh06qH79+sVRFwAAwH0rctAZOXJkSdQBAABQ7O5pjU5WVpY2b96suLg4paenq2rVqvLx8VGvXr3k4OBQ3DUCAADckyIHnfT0dL388stKTEzUgw8+qJo1a+r06dPasWOH/v73vys6OlouLi4lUSsAAECRFPnt5aGhoUpJSdHatWu1f/9+rV+/Xvv379fatWt18eJFLVq0qCTqBAAAKLIiB519+/Zp9OjR8vHxsWr38fHRqFGjtHv37mIrDgAA4H4UOehcv35ddevWLXBb3bp1deXKlfutCQAAoFgUOeg8/PDDOnDgQIHb9u3bp3r16t13UQAAAMWhyIuRX331VY0dO1ZZWVnq0aOHatSooV9//VXbt2/Xhg0bFBISUgJlAgAAFF2Rg07Xrl115swZRUZGasOGDZJuPxHZZDJp+PDheuGFF4q9SAAAgHtxT8/RGTZsmAYOHKhvv/1W6enpqly5sry9vVW5cuXirg8AAOCe3fOHerq6uiogIKA4awEAAChWRQ4658+f18yZM/XNN9/o2rVr+bbb2dkpISGhWIoDAAC4H0UOOlOmTNF//vMf9enTR1WqVCmBkgAAAIpHkYPOf/7zH02bNk29e/cuiXoAAACKTZGfo1OzZk0WHQMAiiw3N9fWJaAMKa3rocgzOq+99pqWLFkiT09P1alTpyRqAgAYUIUKFbT8n6t1/mqqrUuBjT1Y2U2vBbxcKucqctDp0KGDPvzwQ3Xp0kXVqlWTo6Oj1XY7Ozvt3bu32AoEABjH+aupOnsxydZl4H9IkYPO5MmTde7cOT355JOqWbNmSdQEAABQLIocdA4fPqzp06fr//7v/0qiHgAAgGJT5MXIrq6uevDBB0uiFgAAgGJV5KDz4osvasWKFfrtt99Koh4AAIBic09PRk5ISJC/v78efvhhOTs75+uzevXqYikOAADgfhQ56Jw+fVpNmjSx/Gw2m622Z2dn339VAAAAxaDIQWfNmjUFticlJenTTz/V5s2b77soAACA4nDPn14u3Z7NOXDggNatW6evvvpKOTk5atiwYXHVBgAAcF/uKeikpaVpw4YN2rhxo1JSUuTq6qoXXnhBPXv2VIsWLYq7RgAAgHtSpKDz1Vdfad26dTpw4IDMZrNat26tlJQURUREyNfXt6RqBAAAuCd3FXQ+/PBDffrpp/r555/VoEEDjRo1Sr169ZKDg4P8/PxKukYAAIB7cldBZ8GCBWrcuLHWrFljNXNz7dq1EisMAADgft3VAwOfffZZ/fzzzxoyZIhee+017dq1S1lZWSVdGwAAwH25qxmd9957T9evX9eOHTu0efNmjRkzRpUrV1bnzp1lZ2cnOzu7kq4TAACgyO76IyD+8pe/6IUXXtD69eu1c+dO9e7dW1988YXMZrMmTZqksLAw/fjjjyVZKwAAQJEU+bOuJKlhw4aaNGmS/vnPfyoiIkKNGjVSVFSUnnvuOT377LPFXSMAAMA9ua8HBtrb26tLly7q0qWLLl68qM2bNysmJqaYSgMAALg/9zSjU5Dq1asrODhYO3fuLK5DAgAA3JdiCzoAAABlDUEHAAAYFkEHAAAYFkEHAAAYFkEHAAAYFkEHAAAYFkEHAAAYFkEHAAAYFkEHAAAYFkGnmOTmmm1dAsoQrgcAKBvu67Ou8P9UqGCnJZ98pV/Srtq6FNhYnVqVNbz/k7YuAwAggk6x+iXtqs78ctnWZQAAgP8ft64AAIBhEXQAAIBhEXQAAIBhEXQAAIBh2Tzo5ObmKjw8XO3atZO3t7eCgoJ09uzZu9p3+/btaty4sZKSkkq4SgAAUB7ZPOgsXbpU69at06xZs7R+/XrZ2dkpODhYWVlZd9zvl19+0dtvv11KVQIAgPLIpkEnKytLK1eu1MiRIxUQECBPT0+FhYUpNTVVe/bsKXS/3NxcTZgwQU2bNi3FagEAQHlj06CTmJio69evq02bNpY2V1dXeXl56ciRI4XuFxkZqVu3bum1114rjTIBAEA5ZdMHBqakpEiSateubdVeq1YtJScnF7jP0aNHtXLlSm3cuFGpqal3dZ7OnTsXui05OTnf+QEAgDHYdEYnIyNDkmQymazaHRwclJmZma//jRs3NH78eI0fP17169cvjRIBAEA5ZtMZHUdHR0m31+rkfS9JmZmZcnJyytd/1qxZql+/vvr161ek8+zbt6/QbXea7QEAAOWbTYNO3i2jtLQ0PfTQQ5b2tLQ0eXp65uu/adMmmUwmtWzZUpKUk5MjSerevbueffZZzZw5sxSqBgAA5YVNg46np6ecnZ0VFxdnCTrp6elKSEjQwIED8/XfvXu31c///e9/NWHCBK1YsUINGzYslZoBAED5YdOgYzKZNHDgQC1YsEDVqlVTnTp1NH/+fLm7uyswMFA5OTm6dOmSXFxc5OjoqHr16lntn7eY+cEHH1T16tVtMQQAAFCG2fyBgaNGjVLfvn01depU9e/fX/b29oqKipLJZFJycrL8/f0VGxtr6zIBAEA5ZNMZHUmyt7fXhAkTNGHChHzbPDw8dPz48UL3bd269R23AwCA/202n9EBAAAoKQQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWAQdAABgWDYPOrm5uQoPD1e7du3k7e2toKAgnT17ttD+J06c0NChQ9W6dWu1bdtWo0aN0vnz50uxYgAAUF7YPOgsXbpU69at06xZs7R+/XrZ2dkpODhYWVlZ+fpevnxZr7zyiv7yl79o7dq1+uCDD3T58mUNGTJEmZmZNqgeAACUZTYNOllZWVq5cqVGjhypgIAAeXp6KiwsTKmpqdqzZ0++/nv37lVGRobmzp2rRo0aqVmzZpo/f75OnTqlb775xgYjAAAAZZlNg05iYqKuX7+uNm3aWNpcXV3l5eWlI0eO5Ovftm1bLVmyRA4ODvm2Xb16tURrBQAA5U9FW548JSVFklS7dm2r9lq1aik5OTlffw8PD3l4eFi1LV++XA4ODvL19S30PJ07dy50W3Jycr7zAwAAY7DpjE5GRoYkyWQyWbU7ODjc1Zqb1atXKzo6WmPHjlX16tVLpEYAAFB+2XRGx9HRUdLttTp530tSZmamnJycCt3PbDZr0aJFWrZsmV577TUNHjz4jufZt29fodvuNNsDAADKN5vO6OTdMkpLS7NqT0tLk7u7e4H73Lp1SxMmTFBkZKQmTpyosWPHlnidAACgfLJp0PH09JSzs7Pi4uIsbenp6UpISJCPj0+B+0ycOFH/+Mc/FBoaqldffbW0SgUAAOWQTW9dmUwmDRw4UAsWLFC1atVUp04dzZ8/X+7u7goMDFROTo4uXbokFxcXOTo6avPmzYqNjdXEiRPl5+enCxcuWI6V1wcAACCPzR8YOGrUKPXt21dTp05V//79ZW9vr6ioKJlMJiUnJ8vf31+xsbGSpB07dkiS3nvvPfn7+1t95fUBAADIY9MZHUmyt7fXhAkTNGHChHzbPDw8dPz4ccvPK1euLM3SAABAOWfzGR0AAICSQtABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGRdABAACGZfOgk5ubq/DwcLVr107e3t4KCgrS2bNnC+1/+fJljRs3Tr6+vvL19dW0adN048aNUqwYAACUFzYPOkuXLtW6des0a9YsrV+/XnZ2dgoODlZWVlaB/UeNGqVz585p1apVCg8P11dffaW33367lKsGAADlgU2DTlZWllauXKmRI0cqICBAnp6eCgsLU2pqqvbs2ZOv/7fffqvDhw9rzpw5atq0qdq2bauZM2dq69atSk1NtcEIAABAWVbRlidPTEzU9evX1aZNG0ubq6urvLy8dOTIEXXr1s2q/9dff62aNWuqYcOGljY/Pz/Z2dkpPj5eXbt2LfA8nTt3LrSGpKQk2dvb37HP3Ur/7aayc3Pv+zgo305UqKC4baG2LkOSlH3jmsy5ObYuAzZmV+GoKq7+p63LkCRdu/mbsrkm/+clVLDXl4v+cV/HSE5Olr29/Z/2s2nQSUlJkSTVrl3bqr1WrVpKTk7O1z81NTVfX5PJpCpVqhTY/27Y2dmpYsXieRlcnR2L5TjlVd7v4I+/I9hOxUouti7Bprgmyx4XR2dbl2BTXJPFp2LFijKZTH/erxRqKVRGRoYk5SvUwcFBV69eLbB/QYNycHBQZmZmoefZt2/ffVaKu5E3K8brjbKCaxJlDddk6bPpGh1Hx9szIH9ceJyZmSknJ6cC+xe0SDkzM1OVKlUqmSIBAEC5ZdOgkzd1l5aWZtWelpYmd3f3fP3d3d3z9c3KytKVK1fk5uZWcoUCAIByyaZBx9PTU87OzoqLi7O0paenKyEhQT4+Pvn6+/r6KiUlxeo5O3n7tmrVquQLBgAA5YpN1+iYTCYNHDhQCxYsULVq1VSnTh3Nnz9f7u7uCgwMVE5Oji5duiQXFxc5OjrK29tbrVq10pgxYxQSEqIbN25oxowZ6tmzJzM6AAAgH5s/MHDUqFHq27evpk6dqv79+8ve3l5RUVEymUxKTk6Wv7+/YmNjJd1+h1RERIQ8PDw0aNAgjR49Wu3bt1dISIhtBwEAAMokO7PZbLZ1ETAG3k2AsoZrEmUN12TpI+gAAADDsvmtKwAAgJJC0AEAAIZF0AEAAIZF0AEAAIZF0AEAAIZF0IGVK1euaPr06Wrfvr1atWql/v376+uvv7Zs/+GHHzRw4EA99thj6tChg6Kioqz2z83NVXh4uNq1aydvb28FBQVZPcn6j6ZOnapOnTqV2HhQ/pXGNTl58mQ1btzY6qt9+/alMj6UT6VxXaalpWns2LHy8fFR69atNW7cOF26dKlUxmcoZuB3XnnlFfOzzz5rPnLkiPnUqVPmd955x9yiRQvzyZMnzZcuXTK3bt3aPGXKFPPJkyfNGzduNDdv3ty8ceNGy/6LFy82t23b1vz555+bf/jhB3NQUJA5MDDQnJmZme9ce/bsMT/66KPmjh07luYQUc6UxjXZq1cv8/vvv29OS0uzfF28eNEWw0U5UdLXZWZmprlbt27mvn37mo8ePWr+9ttvzc8884x5yJAhthpyuUXQgcWZM2fMjz76qDk+Pt7Slpubaw4MDDQvXLjQHBkZaW7Xrp351q1blu2hoaHmp59+2mw23/4Ps2XLlubo6GjL9qtXr5pbtGhh3rFjh9W5UlNTzW3atDEPHDiQoINClcY1mZ2dbW7evLl5z549pTQqlHelcV1u2rTJ/Nhjj5kvXLhg6fPFF1+YO3fubL527VpJD9FQuHUFi6pVq2rFihVq1qyZpc3Ozk5ms1lXr17V119/LV9fX1Ws+P8+Iq1NmzY6ffq0Ll68qMTERF2/fl1t2rSxbHd1dZWXl5eOHDliaTObzXrrrbf03HPPyc/Pr3QGh3KpNK7JM2fOKDMzUw0bNiy9gaFcK43r8uDBg2rTpo1q1Khh6dOuXTvt3btXzs7OpTBK4yDowMLV1VUBAQEymUyWtl27dunnn3+Wv7+/UlJS5O7ubrVPrVq1JEnnz59XSkqKJKl27dr5+iQnJ1t+XrVqlS5cuKCxY8eW1FBgEKVxTf7444+ys7PTxx9/rE6dOqlLly565513dO3atZIcGsqx0rguz5w5Iw8PDy1ZskSBgYHq2LGjpk2bpvT09JIcmiERdFCo+Ph4/e1vf1Pnzp3VqVMn3bx50+o/bElycHCQJGVmZiojI0OSCuyTmZkpSUpMTFRERITmz5+frx/wZ0rimjxx4oQqVKigOnXqKDIyUpMmTdI///lPDRs2TLm5uaUwKpR3JXFd/vbbb4qJidHx48cVGhqqmTNnKj4+XsOGDZOZT24qkop/3gX/i/bu3avx48fL29tb77//viTJ0dFRWVlZVv3y/qOsVKmSHB0dJUlZWVmW7/P6ODk5KTMzU+PHj9cbb7whT0/PUhoJjKIkrklJGjlypAYPHixXV1dJ0qOPPqqaNWvqhRde0LFjx+Tt7V3iY0P5VVLX5QMPPKBKlSopNDRUDzzwgCSpcuXKev7553Xs2DG1aNGixMdmFMzoIJ+1a9dq5MiRat++vT744APLf4ju7u5KS0uz6pv3s5ubm2UatqA+7u7u+u9//6sTJ04oIiJCLVu2VMuWLbV8+XKdP39eLVu21LZt20phdCiPSuqalG6vrcgLOXkeffRRSbLcYgAKUpLXpbu7uxo0aGAJOZLUqFEjSVJSUlLJDMigCDqwEh0drXfeeUcDBgzQwoULraZWfX19FR8fr5ycHEvboUOH1KBBA1WvXl2enp5ydnZWXFycZXt6eroSEhLk4+OjFi1aaPfu3dq6datiYmIUExOjfv36qVatWoqJieF5OihQSV6TkjRu3Di9+uqrVuc8duyYJOmRRx4pyaGhHCvp69LHx0eJiYm6efOmpc+PP/4oSapXr15JD89YbPqeL5QpP/30k7lp06bm4cOHWz1PJC0tzZyenm7+9ddfzb6+vuZJkyaZT5w4Yd60aZO5efPm5s2bN1uO8f7775v9/PzMe/futTwb4qmnnirwOTpms9kcHh7O28tRqNK4Jvfv329u3LixeenSpeazZ8+aP//8c3OnTp3MY8eOtdWwUcaVxnV58eJF8xNPPGEeNmyY+fjx4+avv/7a3L17d/NLL71kq2GXW3ZmM6uacFtkZKTCwsIK3NarVy/NnTtXR48e1ezZs5WQkKCaNWsqKChIAwcOtPTLycnR+++/r82bN+vmzZvy9fXV9OnT5eHhUeBxFy9erC1btmj//v0lMiaUb6V1TX722WeKjIzUTz/9JBcXF/Xo0UOjR4+2LCAFfq+0rsszZ85ozpw5iouLk8lkUpcuXTR58mS5uLiU+BiNhKADAAAMizU6AADAsAg6AADAsAg6AADAsAg6AADAsAg6AADAsAg6AADAsAg6AADAsAg6AADAsAg6AMqs8ePHq3Hjxvryyy8L3H7w4EE1btxY8+bNK+XKAJQXPBkZQJl15coVde/eXQ4ODtqxY4ecnJws265fv64ePXqoUqVK2rx5s9WHKgJAHmZ0AJRZVapUUUhIiJKSkrRw4UKrbaGhoUpLS9P8+fMJOQAKRdABUKZ16dJF3bt315o1a3Ts2DFJ0jfffKPo6GiNGDFCTZo00fnz5zV27Fj5+fnJ29tbgwYNUkJCgtVxkpKSNHHiRPn7+6tp06Zq27atJk6cqMuXL1v6dOrUSe+++64GDRqkVq1aafr06aU6VgDFj1tXAMq8y5cvq3v37nrwwQcVHR2tPn36yMnJSdHR0bp69ap69uwpJycnjRgxQk5OTvr444/13XffaePGjWrYsKEyMjLUrVs3Va1aVa+//rpcXFwUHx+vJUuWqE+fPnrnnXck3Q46qampGjBggAICAuTo6KjHH3/cxqMHcD8q2roAAPgzVatWVUhIiEaMGKGgoCD9/PPPiomJkb29vT7++GNduXJFn3zyierUqSNJat++vbp27apFixYpPDxcZ86ckbu7u+bOnauHHnpIktSmTRsdO3ZMhw8ftjpXrVq19NZbb6lCBSa8ASMg6AAoFwIDA9W1a1fFxsZq+vTpql+/viTp0KFDatKkidzc3JSdnS1JqlChgtq3b69t27ZJkpo0aaLo6Gjl5ubq3LlzOnPmjE6cOKGffvrJsk+ehg0bEnIAAyHoACg32rVrp9jYWAUEBFjarly5orNnz6pp06YF7pORkSEnJyd99NFHWr58uS5fvqwaNWqoadOmcnJy0rVr16z616hRo0THAKB0EXQAlGsuLi7y8/PTxIkTC9xuMpm0fft2zZ07V+PGjVPfvn1VrVo1SdKbb75pWeAMwJgIOgDKNT8/P23fvl0NGjSQs7OzpX327NnKzMzUzJkzFR8fLxcXFw0dOtSy/fr164qPj1fFivxvEDAybkQDKNcGDx6s3NxcDR48WLGxsTp06JCmTZum1atX6+GHH5YktWjRQteuXdPcuXMVFxen7du3a8CAAfr111+VkZFh4xEAKEn8UwZAuebm5qZ169YpNDRUISEhyszMVP369TV79mz17dtXktSrVy8lJSVp06ZNio6OlpubmwICAvTiiy9q2rRpOnnypB555BEbjwRASeA5OgAAwLC4dQUAAAyLoAMAAAyLoAMAAAyLoAMAAAyLoAMAAAyLoAMAAAyLoAMAAAyLoAMAAAyLoAMAAAyLoAMAAAyLoAMAAAzr/wNYDOOSCFDC+QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data= yearly_air, x= yearly_air.index , y='Amount')\n",
    "plt.title('Yearly spend on air tickets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e940424",
   "metadata": {},
   "source": [
    "*In 2005 the spend on air tickets is high.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d996a6a",
   "metadata": {},
   "source": [
    "### c. Comparison of monthly spend for each product (look for any seasonality that exists in terms of spend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d7d05e60",
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Year</th>\n",
       "      <th>Monthly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>JEWELLERY</td>\n",
       "      <td>485470.80</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>PETRO</td>\n",
       "      <td>410556.13</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>CLOTHES</td>\n",
       "      <td>23740.46</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>484342.47</td>\n",
       "      <td>2004</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2005-01-17</td>\n",
       "      <td>CAMERA</td>\n",
       "      <td>369694.07</td>\n",
       "      <td>2005</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "3       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "4       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date       Type     Amount  Year  Monthly  \n",
       "0 2004-01-12  JEWELLERY  485470.80  2004  January  \n",
       "1 2004-01-03      PETRO  410556.13  2004  January  \n",
       "2 2004-01-15    CLOTHES   23740.46  2004  January  \n",
       "3 2004-01-25       FOOD  484342.47  2004  January  \n",
       "4 2005-01-17     CAMERA  369694.07  2005  January  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e712cf6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating pivot table based on \"Monthly\" and \"Product\" columns\n",
    "product_wise_spend = customer_spend.pivot_table(index='Product', columns='Monthly', aggfunc='sum', values='Amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "68d3cda3",
   "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>Monthly</th>\n",
       "      <th>April</th>\n",
       "      <th>August</th>\n",
       "      <th>December</th>\n",
       "      <th>February</th>\n",
       "      <th>January</th>\n",
       "      <th>July</th>\n",
       "      <th>June</th>\n",
       "      <th>March</th>\n",
       "      <th>May</th>\n",
       "      <th>November</th>\n",
       "      <th>October</th>\n",
       "      <th>September</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</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>Gold</th>\n",
       "      <td>13170572.59</td>\n",
       "      <td>4954030.42</td>\n",
       "      <td>2332940.96</td>\n",
       "      <td>24779179.55</td>\n",
       "      <td>32413831.83</td>\n",
       "      <td>5167416.37</td>\n",
       "      <td>6538862.64</td>\n",
       "      <td>19256972.48</td>\n",
       "      <td>21548169.76</td>\n",
       "      <td>9203267.46</td>\n",
       "      <td>3018223.38</td>\n",
       "      <td>3816887.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platinum</th>\n",
       "      <td>5182493.86</td>\n",
       "      <td>1714762.80</td>\n",
       "      <td>750156.19</td>\n",
       "      <td>9134054.58</td>\n",
       "      <td>14951578.51</td>\n",
       "      <td>1288156.26</td>\n",
       "      <td>2965712.71</td>\n",
       "      <td>7288837.68</td>\n",
       "      <td>8151926.49</td>\n",
       "      <td>2741618.42</td>\n",
       "      <td>550275.04</td>\n",
       "      <td>1667840.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silver</th>\n",
       "      <td>1902322.65</td>\n",
       "      <td>293109.82</td>\n",
       "      <td>200537.90</td>\n",
       "      <td>2087018.37</td>\n",
       "      <td>4206383.62</td>\n",
       "      <td>530153.72</td>\n",
       "      <td>689537.61</td>\n",
       "      <td>3909405.83</td>\n",
       "      <td>2140940.01</td>\n",
       "      <td>691102.63</td>\n",
       "      <td>100001.50</td>\n",
       "      <td>442803.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Monthly         April      August    December     February      January  \\\n",
       "Product                                                                   \n",
       "Gold      13170572.59  4954030.42  2332940.96  24779179.55  32413831.83   \n",
       "Platinum   5182493.86  1714762.80   750156.19   9134054.58  14951578.51   \n",
       "Silver     1902322.65   293109.82   200537.90   2087018.37   4206383.62   \n",
       "\n",
       "Monthly         July        June        March          May    November  \\\n",
       "Product                                                                  \n",
       "Gold      5167416.37  6538862.64  19256972.48  21548169.76  9203267.46   \n",
       "Platinum  1288156.26  2965712.71   7288837.68   8151926.49  2741618.42   \n",
       "Silver     530153.72   689537.61   3909405.83   2140940.01   691102.63   \n",
       "\n",
       "Monthly      October   September  \n",
       "Product                           \n",
       "Gold      3018223.38  3816887.62  \n",
       "Platinum   550275.04  1667840.84  \n",
       "Silver     100001.50   442803.13  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_wise_spend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "42afb564",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "product_wise_spend.plot(kind='bar', figsize=(15,6), width=0.8)\n",
    "plt.title('Monthly spend for products')\n",
    "plt.xticks(rotation=1)\n",
    "plt.ylabel('Amount Spend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd92685",
   "metadata": {},
   "source": [
    "*So we can see that sales are high for all the product in the months :-*\n",
    "\n",
    "*January, February, March, April, May*\n",
    "\n",
    "*Among all these months sales are very high in January*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d54d5d",
   "metadata": {},
   "source": [
    "### 5. Write user defined PYTHON function to perform the following analysis:\n",
    "### You need to find top 10 customers for each city in terms of their repayment amount by different products and by different time periods i.e. year or month. The user should be able to specify the product (Gold/Silver/Platinum) and time period (yearly or monthly) and the function should automatically take these inputs while identifying the top 10 customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "id": "5eb6b9df",
   "metadata": {
    "scrolled": true
   },
   "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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>495414.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>259490.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date     Amount  \n",
       "0 2004-01-12  495414.75  \n",
       "1 2004-01-03  245899.02  \n",
       "2 2004-01-15  259490.06  "
      ]
     },
     "execution_count": 818,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_repay.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "33e5e079",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding month and year column in df\n",
    "\n",
    "customer_repay['Monthly'] = customer_repay['Date'].apply(lambda x: pd.Timestamp.strftime(x, format='%B'))\n",
    "customer_repay['Yearly'] = customer_repay['Date'].apply(lambda x: pd.Timestamp.strftime(x, format='%Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e855265d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Customer</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Company</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Yearly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>495414.75</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>245899.02</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>Gold</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>C1</td>\n",
       "      <td>Self Employed</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>259490.06</td>\n",
       "      <td>January</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer   Age       City Product     Limit Company        Segment  \\\n",
       "0       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "1       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "2       A1  76.0  BANGALORE    Gold  500000.0      C1  Self Employed   \n",
       "\n",
       "        Date     Amount  Monthly Yearly  \n",
       "0 2004-01-12  495414.75  January   2004  \n",
       "1 2004-01-03  245899.02  January   2004  \n",
       "2 2004-01-15  259490.06  January   2004  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_repay.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "44042ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def summary(product,timeperiod):\n",
    "    print('Please provide input for product and timeperiod')\n",
    "    \n",
    "    if (product.lower()=='gold' and timeperiod.lower()=='monthly'):\n",
    "        pivot = customer_repay.pivot_table(index=['Product','City','Customer'],columns='Monthly',aggfunc='sum', values='Amount')\n",
    "        result= pivot.loc[('Gold',['BANGALORE', 'CALCUTTA', 'COCHIN', 'BOMBAY', 'DELHI', 'PATNA',\n",
    "       'CHENNAI', 'TRIVANDRUM']),:]\n",
    "        \n",
    "    elif(product.lower()=='gold' and timeperiod.lower()=='yearly'):\n",
    "        pivot = customer_repay.pivot_table(index=['Product','City','Customer'],columns='Yearly', aggfunc='sum', values='Amount')\n",
    "        result= pivot.loc[('Gold',['BANGALORE', 'CALCUTTA', 'COCHIN', 'BOMBAY', 'DELHI', 'PATNA',\n",
    "       'CHENNAI', 'TRIVANDRUM']),:]\n",
    "        \n",
    "    elif(product.lower()=='silver' and timeperiod.lower()=='monthly'):\n",
    "        pivot = customer_repay.pivot_table(index=['Product','City','Customer'],columns='Monthly', aggfunc='sum', values='Amount')\n",
    "        result= pivot.loc[('Silver',['BANGALORE', 'CALCUTTA', 'COCHIN', 'BOMBAY', 'DELHI', 'PATNA',\n",
    "       'CHENNAI', 'TRIVANDRUM']),:]\n",
    "        \n",
    "    elif(product.lower()=='silver' and timeperiod.lower()=='yearly'):\n",
    "        pivot = customer_repay.pivot_table(index=['Product','City','Customer'],columns='Yearly', aggfunc='sum', values='Amount')\n",
    "        result= pivot.loc[('Silver',['BANGALORE', 'CALCUTTA', 'COCHIN', 'BOMBAY', 'DELHI', 'PATNA',\n",
    "       'CHENNAI', 'TRIVANDRUM']),:]\n",
    "        \n",
    "    elif(product.lower()=='platinum' and timeperiod.lower()=='monthly'):\n",
    "        pivot = customer_repay.pivot_table(index=['Product','City','Customer'],columns='Monthly', aggfunc='sum', values='Amount')\n",
    "        result= pivot.loc[('Platinum',['BANGALORE', 'CALCUTTA', 'COCHIN', 'BOMBAY', 'DELHI', 'PATNA',\n",
    "       'CHENNAI', 'TRIVANDRUM']),:]\n",
    "        \n",
    "    elif(product.lower()=='platinum' and timeperiod.lower()=='yearly'):\n",
    "        pivot = customer_repay.pivot_table(index=['Product','City','Customer'],columns='Yearly', aggfunc='sum', values='Amount')\n",
    "        result= pivot.loc[('Platinum',['BANGALORE', 'CALCUTTA', 'COCHIN', 'BOMBAY', 'DELHI', 'PATNA',\n",
    "       'CHENNAI', 'TRIVANDRUM']),:]\n",
    "        \n",
    "    return result\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9737c7e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide input for product and timeperiod\n"
     ]
    },
    {
     "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",
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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></th>\n",
       "      <th>Monthly</th>\n",
       "      <th>April</th>\n",
       "      <th>August</th>\n",
       "      <th>December</th>\n",
       "      <th>February</th>\n",
       "      <th>January</th>\n",
       "      <th>July</th>\n",
       "      <th>June</th>\n",
       "      <th>March</th>\n",
       "      <th>May</th>\n",
       "      <th>November</th>\n",
       "      <th>October</th>\n",
       "      <th>September</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer</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 rowspan=\"38\" valign=\"top\">Gold</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">BANGALORE</th>\n",
       "      <th>A1</th>\n",
       "      <td>508949.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>781873.80</td>\n",
       "      <td>2407806.29</td>\n",
       "      <td>297176.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>571458.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186427.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A13</th>\n",
       "      <td>494392.40</td>\n",
       "      <td>873304.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2049808.15</td>\n",
       "      <td>2014186.98</td>\n",
       "      <td>424603.55</td>\n",
       "      <td>837101.91</td>\n",
       "      <td>1183569.05</td>\n",
       "      <td>632600.75</td>\n",
       "      <td>622592.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>439840.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A14</th>\n",
       "      <td>812582.20</td>\n",
       "      <td>198623.13</td>\n",
       "      <td>388821.96</td>\n",
       "      <td>1482923.34</td>\n",
       "      <td>993114.87</td>\n",
       "      <td>41962.19</td>\n",
       "      <td>41128.98</td>\n",
       "      <td>1682886.51</td>\n",
       "      <td>1418286.63</td>\n",
       "      <td>420638.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>462300.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A30</th>\n",
       "      <td>425694.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1321469.80</td>\n",
       "      <td>608066.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72609.24</td>\n",
       "      <td>599331.38</td>\n",
       "      <td>3086549.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19761.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A43</th>\n",
       "      <td>612541.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>763846.93</td>\n",
       "      <td>1978038.15</td>\n",
       "      <td>803935.53</td>\n",
       "      <td>23525.91</td>\n",
       "      <td>1378774.72</td>\n",
       "      <td>2058983.99</td>\n",
       "      <td>454364.16</td>\n",
       "      <td>415860.27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A63</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>372179.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>416676.34</td>\n",
       "      <td>211117.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A81</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>207780.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A88</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146821.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A92</th>\n",
       "      <td>459105.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">CALCUTTA</th>\n",
       "      <th>A10</th>\n",
       "      <td>478747.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>652033.51</td>\n",
       "      <td>1091589.54</td>\n",
       "      <td>358194.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720391.63</td>\n",
       "      <td>394690.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17981.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>516544.51</td>\n",
       "      <td>1051502.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49334.38</td>\n",
       "      <td>1738696.08</td>\n",
       "      <td>1953768.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227585.03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>534103.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>627147.67</td>\n",
       "      <td>1474900.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1269579.93</td>\n",
       "      <td>2612139.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A31</th>\n",
       "      <td>13054.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>962656.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130109.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>845269.25</td>\n",
       "      <td>1432949.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A60</th>\n",
       "      <td>2397565.91</td>\n",
       "      <td>735660.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1403731.08</td>\n",
       "      <td>2465584.81</td>\n",
       "      <td>273877.36</td>\n",
       "      <td>657931.74</td>\n",
       "      <td>109367.55</td>\n",
       "      <td>1189900.27</td>\n",
       "      <td>294357.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>348314.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A89</th>\n",
       "      <td>497237.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">COCHIN</th>\n",
       "      <th>A11</th>\n",
       "      <td>958466.08</td>\n",
       "      <td>332677.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1069789.26</td>\n",
       "      <td>958556.39</td>\n",
       "      <td>128484.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>508638.02</td>\n",
       "      <td>1354322.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A16</th>\n",
       "      <td>338710.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331143.85</td>\n",
       "      <td>1071852.72</td>\n",
       "      <td>367100.15</td>\n",
       "      <td>92055.06</td>\n",
       "      <td>886861.02</td>\n",
       "      <td>572216.09</td>\n",
       "      <td>260514.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>339373.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A32</th>\n",
       "      <td>333042.82</td>\n",
       "      <td>99528.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>851127.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137150.37</td>\n",
       "      <td>284410.26</td>\n",
       "      <td>540858.16</td>\n",
       "      <td>312176.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A45</th>\n",
       "      <td>1435443.21</td>\n",
       "      <td>431769.44</td>\n",
       "      <td>410935.52</td>\n",
       "      <td>1241580.90</td>\n",
       "      <td>2033076.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>303151.38</td>\n",
       "      <td>869686.35</td>\n",
       "      <td>1657916.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64774.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A61</th>\n",
       "      <td>3412860.60</td>\n",
       "      <td>197804.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1345911.54</td>\n",
       "      <td>1755251.93</td>\n",
       "      <td>441593.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>382632.66</td>\n",
       "      <td>1819382.29</td>\n",
       "      <td>423828.84</td>\n",
       "      <td>289863.84</td>\n",
       "      <td>470014.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A65</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>468922.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230667.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A90</th>\n",
       "      <td>109931.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A94</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153465.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">BOMBAY</th>\n",
       "      <th>A12</th>\n",
       "      <td>601326.07</td>\n",
       "      <td>120471.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1978117.59</td>\n",
       "      <td>1220387.18</td>\n",
       "      <td>1201928.94</td>\n",
       "      <td>526225.82</td>\n",
       "      <td>1001134.17</td>\n",
       "      <td>1048233.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347528.59</td>\n",
       "      <td>289407.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A17</th>\n",
       "      <td>391463.29</td>\n",
       "      <td>98341.04</td>\n",
       "      <td>479227.30</td>\n",
       "      <td>374865.01</td>\n",
       "      <td>761645.74</td>\n",
       "      <td>118112.45</td>\n",
       "      <td>257937.78</td>\n",
       "      <td>2007441.50</td>\n",
       "      <td>382881.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>412459.19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A42</th>\n",
       "      <td>173199.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>476213.23</td>\n",
       "      <td>2516249.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>579721.80</td>\n",
       "      <td>3305341.39</td>\n",
       "      <td>362899.70</td>\n",
       "      <td>197897.82</td>\n",
       "      <td>3937.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A62</th>\n",
       "      <td>1115760.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>478764.07</td>\n",
       "      <td>996722.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>546251.18</td>\n",
       "      <td>758067.14</td>\n",
       "      <td>798121.60</td>\n",
       "      <td>276130.73</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A91</th>\n",
       "      <td>247781.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">DELHI</th>\n",
       "      <th>A44</th>\n",
       "      <td>346650.76</td>\n",
       "      <td>539494.72</td>\n",
       "      <td>79696.21</td>\n",
       "      <td>745752.19</td>\n",
       "      <td>2415712.91</td>\n",
       "      <td>501962.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>696222.43</td>\n",
       "      <td>1988158.24</td>\n",
       "      <td>431079.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A64</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212614.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110614.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A82</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342200.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A93</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342326.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PATNA</th>\n",
       "      <th>A26</th>\n",
       "      <td>558432.60</td>\n",
       "      <td>398164.37</td>\n",
       "      <td>417177.67</td>\n",
       "      <td>1298000.32</td>\n",
       "      <td>1129827.37</td>\n",
       "      <td>213185.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>416810.29</td>\n",
       "      <td>2215839.66</td>\n",
       "      <td>255915.07</td>\n",
       "      <td>351333.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A66</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>477039.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113094.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A95</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>369405.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CHENNAI</th>\n",
       "      <th>A27</th>\n",
       "      <td>379529.81</td>\n",
       "      <td>472123.57</td>\n",
       "      <td>136860.63</td>\n",
       "      <td>841114.02</td>\n",
       "      <td>1215350.93</td>\n",
       "      <td>241912.50</td>\n",
       "      <td>484419.68</td>\n",
       "      <td>976107.81</td>\n",
       "      <td>1855518.11</td>\n",
       "      <td>409963.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A67</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67845.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55638.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRIVANDRUM</th>\n",
       "      <th>A28</th>\n",
       "      <td>115326.71</td>\n",
       "      <td>437823.07</td>\n",
       "      <td>451630.26</td>\n",
       "      <td>968709.79</td>\n",
       "      <td>1034461.46</td>\n",
       "      <td>380460.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1547067.86</td>\n",
       "      <td>2233253.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Monthly                           April     August   December    February  \\\n",
       "Product City       Customer                                                 \n",
       "Gold    BANGALORE  A1         508949.16        NaN        NaN   781873.80   \n",
       "                   A13        494392.40  873304.51        NaN  2049808.15   \n",
       "                   A14        812582.20  198623.13  388821.96  1482923.34   \n",
       "                   A30        425694.16        NaN        NaN  1321469.80   \n",
       "                   A43        612541.80        NaN        NaN   763846.93   \n",
       "                   A63              NaN        NaN        NaN         NaN   \n",
       "                   A81              NaN        NaN        NaN         NaN   \n",
       "                   A88              NaN        NaN        NaN         NaN   \n",
       "                   A92        459105.69        NaN        NaN         NaN   \n",
       "        CALCUTTA   A10        478747.41        NaN        NaN   652033.51   \n",
       "                   A15              NaN   17981.25        NaN   516544.51   \n",
       "                   A29              NaN  534103.41        NaN   627147.67   \n",
       "                   A31         13054.67        NaN        NaN   962656.24   \n",
       "                   A60       2397565.91  735660.40        NaN  1403731.08   \n",
       "                   A89        497237.97        NaN        NaN         NaN   \n",
       "        COCHIN     A11        958466.08  332677.61        NaN  1069789.26   \n",
       "                   A16        338710.86        NaN        NaN   331143.85   \n",
       "                   A32        333042.82   99528.43        NaN   851127.91   \n",
       "                   A45       1435443.21  431769.44  410935.52  1241580.90   \n",
       "                   A61       3412860.60  197804.13        NaN  1345911.54   \n",
       "                   A65              NaN        NaN        NaN         NaN   \n",
       "                   A90        109931.32        NaN        NaN         NaN   \n",
       "                   A94              NaN        NaN        NaN         NaN   \n",
       "        BOMBAY     A12        601326.07  120471.17        NaN  1978117.59   \n",
       "                   A17        391463.29   98341.04  479227.30   374865.01   \n",
       "                   A42        173199.87        NaN        NaN   476213.23   \n",
       "                   A62       1115760.01        NaN        NaN   478764.07   \n",
       "                   A91        247781.00        NaN        NaN         NaN   \n",
       "        DELHI      A44        346650.76  539494.72   79696.21   745752.19   \n",
       "                   A64              NaN        NaN  212614.48         NaN   \n",
       "                   A82              NaN        NaN        NaN         NaN   \n",
       "                   A93              NaN        NaN        NaN         NaN   \n",
       "        PATNA      A26        558432.60  398164.37  417177.67  1298000.32   \n",
       "                   A66              NaN        NaN        NaN         NaN   \n",
       "                   A95              NaN        NaN        NaN         NaN   \n",
       "        CHENNAI    A27        379529.81  472123.57  136860.63   841114.02   \n",
       "                   A67              NaN        NaN        NaN         NaN   \n",
       "        TRIVANDRUM A28        115326.71  437823.07  451630.26   968709.79   \n",
       "\n",
       "Monthly                         January        July       June       March  \\\n",
       "Product City       Customer                                                  \n",
       "Gold    BANGALORE  A1        2407806.29   297176.74        NaN         NaN   \n",
       "                   A13       2014186.98   424603.55  837101.91  1183569.05   \n",
       "                   A14        993114.87    41962.19   41128.98  1682886.51   \n",
       "                   A30        608066.49         NaN   72609.24   599331.38   \n",
       "                   A43       1978038.15   803935.53   23525.91  1378774.72   \n",
       "                   A63        372179.26         NaN        NaN         NaN   \n",
       "                   A81              NaN         NaN        NaN   207780.32   \n",
       "                   A88              NaN         NaN        NaN   146821.30   \n",
       "                   A92              NaN         NaN        NaN         NaN   \n",
       "        CALCUTTA   A10       1091589.54   358194.65        NaN   720391.63   \n",
       "                   A15       1051502.61         NaN   49334.38  1738696.08   \n",
       "                   A29       1474900.52         NaN        NaN  1269579.93   \n",
       "                   A31              NaN   130109.14        NaN   845269.25   \n",
       "                   A60       2465584.81   273877.36  657931.74   109367.55   \n",
       "                   A89              NaN         NaN        NaN         NaN   \n",
       "        COCHIN     A11        958556.39   128484.37        NaN   508638.02   \n",
       "                   A16       1071852.72   367100.15   92055.06   886861.02   \n",
       "                   A32              NaN   137150.37  284410.26   540858.16   \n",
       "                   A45       2033076.43         NaN  303151.38   869686.35   \n",
       "                   A61       1755251.93   441593.08        NaN   382632.66   \n",
       "                   A65        468922.60         NaN        NaN         NaN   \n",
       "                   A90              NaN         NaN        NaN         NaN   \n",
       "                   A94        153465.89         NaN        NaN         NaN   \n",
       "        BOMBAY     A12       1220387.18  1201928.94  526225.82  1001134.17   \n",
       "                   A17        761645.74   118112.45  257937.78  2007441.50   \n",
       "                   A42       2516249.24         NaN        NaN   579721.80   \n",
       "                   A62        996722.42         NaN        NaN   546251.18   \n",
       "                   A91              NaN         NaN        NaN         NaN   \n",
       "        DELHI      A44       2415712.91   501962.99        NaN   696222.43   \n",
       "                   A64              NaN         NaN        NaN         NaN   \n",
       "                   A82              NaN         NaN        NaN   342200.74   \n",
       "                   A93        342326.14         NaN        NaN         NaN   \n",
       "        PATNA      A26       1129827.37   213185.89        NaN   416810.29   \n",
       "                   A66        477039.78         NaN        NaN         NaN   \n",
       "                   A95        369405.96         NaN        NaN         NaN   \n",
       "        CHENNAI    A27       1215350.93   241912.50  484419.68   976107.81   \n",
       "                   A67         67845.60         NaN        NaN         NaN   \n",
       "        TRIVANDRUM A28       1034461.46   380460.40        NaN  1547067.86   \n",
       "\n",
       "Monthly                             May   November    October  September  \n",
       "Product City       Customer                                               \n",
       "Gold    BANGALORE  A1               NaN  571458.18        NaN  186427.50  \n",
       "                   A13        632600.75  622592.45        NaN  439840.91  \n",
       "                   A14       1418286.63  420638.02        NaN  462300.80  \n",
       "                   A30       3086549.14        NaN        NaN   19761.75  \n",
       "                   A43       2058983.99  454364.16  415860.27        NaN  \n",
       "                   A63        416676.34  211117.12        NaN        NaN  \n",
       "                   A81              NaN        NaN        NaN        NaN  \n",
       "                   A88              NaN        NaN        NaN        NaN  \n",
       "                   A92              NaN        NaN        NaN        NaN  \n",
       "        CALCUTTA   A10        394690.78        NaN        NaN        NaN  \n",
       "                   A15       1953768.97        NaN  227585.03        NaN  \n",
       "                   A29       2612139.63        NaN        NaN        NaN  \n",
       "                   A31       1432949.95        NaN        NaN        NaN  \n",
       "                   A60       1189900.27  294357.01        NaN  348314.61  \n",
       "                   A89              NaN        NaN        NaN        NaN  \n",
       "        COCHIN     A11       1354322.30        NaN        NaN        NaN  \n",
       "                   A16        572216.09  260514.95        NaN  339373.25  \n",
       "                   A32        312176.83        NaN        NaN        NaN  \n",
       "                   A45       1657916.98        NaN        NaN   64774.66  \n",
       "                   A61       1819382.29  423828.84  289863.84  470014.00  \n",
       "                   A65        230667.34        NaN        NaN        NaN  \n",
       "                   A90              NaN        NaN        NaN        NaN  \n",
       "                   A94              NaN        NaN        NaN        NaN  \n",
       "        BOMBAY     A12       1048233.62        NaN  347528.59  289407.01  \n",
       "                   A17        382881.69        NaN  412459.19        NaN  \n",
       "                   A42       3305341.39  362899.70  197897.82    3937.81  \n",
       "                   A62        758067.14  798121.60  276130.73        NaN  \n",
       "                   A91              NaN        NaN        NaN        NaN  \n",
       "        DELHI      A44       1988158.24  431079.67        NaN        NaN  \n",
       "                   A64        110614.61        NaN        NaN        NaN  \n",
       "                   A82              NaN        NaN        NaN        NaN  \n",
       "                   A93              NaN        NaN        NaN        NaN  \n",
       "        PATNA      A26       2215839.66  255915.07  351333.85        NaN  \n",
       "                   A66        113094.58        NaN        NaN        NaN  \n",
       "                   A95              NaN        NaN        NaN        NaN  \n",
       "        CHENNAI    A27       1855518.11  409963.50        NaN        NaN  \n",
       "                   A67         55638.77        NaN        NaN        NaN  \n",
       "        TRIVANDRUM A28       2233253.41        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary('Gold','Monthly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e0b92ecc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide input for product and timeperiod\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Yearly</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
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       "      <th rowspan=\"38\" valign=\"top\">Gold</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">BANGALORE</th>\n",
       "      <th>A1</th>\n",
       "      <td>2359797.84</td>\n",
       "      <td>1529158.77</td>\n",
       "      <td>864735.06</td>\n",
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       "      <td>2681025.72</td>\n",
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       "      <td>978777.17</td>\n",
       "      <td>2667746.38</td>\n",
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       "      <th rowspan=\"6\" valign=\"top\">CALCUTTA</th>\n",
       "      <th>A10</th>\n",
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       "      <td>2783141.43</td>\n",
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       "      <td>1376793.94</td>\n",
       "      <td>1706779.22</td>\n",
       "      <td>3434298.00</td>\n",
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       "    <tr>\n",
       "      <th>A31</th>\n",
       "      <td>407599.13</td>\n",
       "      <td>1448641.71</td>\n",
       "      <td>1527798.41</td>\n",
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       "    <tr>\n",
       "      <th>A60</th>\n",
       "      <td>2890721.81</td>\n",
       "      <td>3742856.10</td>\n",
       "      <td>3242712.83</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">COCHIN</th>\n",
       "      <th>A11</th>\n",
       "      <td>958556.39</td>\n",
       "      <td>2045470.88</td>\n",
       "      <td>2306906.76</td>\n",
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       "    <tr>\n",
       "      <th>A16</th>\n",
       "      <td>1122211.16</td>\n",
       "      <td>1226247.16</td>\n",
       "      <td>1911369.63</td>\n",
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       "    <tr>\n",
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       "      <td>344312.67</td>\n",
       "      <td>696673.46</td>\n",
       "      <td>1517308.65</td>\n",
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       "    <tr>\n",
       "      <th>A45</th>\n",
       "      <td>2557947.39</td>\n",
       "      <td>4042447.57</td>\n",
       "      <td>1847939.91</td>\n",
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       "    <tr>\n",
       "      <th>A61</th>\n",
       "      <td>2518154.95</td>\n",
       "      <td>4276860.55</td>\n",
       "      <td>3744127.41</td>\n",
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       "    <tr>\n",
       "      <th>A65</th>\n",
       "      <td>468922.60</td>\n",
       "      <td>230667.34</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>A90</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>153465.89</td>\n",
       "      <td>NaN</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">BOMBAY</th>\n",
       "      <th>A12</th>\n",
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       "      <td>3118090.96</td>\n",
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       "      <td>247781.00</td>\n",
       "    </tr>\n",
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       "      <th rowspan=\"4\" valign=\"top\">DELHI</th>\n",
       "      <th>A44</th>\n",
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       "      <td>1430028.85</td>\n",
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       "    <tr>\n",
       "      <th>A64</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>323229.09</td>\n",
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       "    <tr>\n",
       "      <th>A82</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342200.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A93</th>\n",
       "      <td>342326.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PATNA</th>\n",
       "      <th>A26</th>\n",
       "      <td>1490427.84</td>\n",
       "      <td>2433837.95</td>\n",
       "      <td>3330421.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>477039.78</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">CHENNAI</th>\n",
       "      <th>A27</th>\n",
       "      <td>1842823.04</td>\n",
       "      <td>2782591.71</td>\n",
       "      <td>2387485.81</td>\n",
       "    </tr>\n",
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       "      <th>A67</th>\n",
       "      <td>67845.60</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRIVANDRUM</th>\n",
       "      <th>A28</th>\n",
       "      <td>965108.49</td>\n",
       "      <td>2827009.82</td>\n",
       "      <td>3376614.65</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Yearly                             2004        2005        2006\n",
       "Product City       Customer                                    \n",
       "Gold    BANGALORE  A1        2359797.84  1529158.77   864735.06\n",
       "                   A13       2337012.77  3757673.00  3477314.89\n",
       "                   A14       2770261.30  2491981.61  2681025.72\n",
       "                   A30        978777.17  2667746.38  2486958.41\n",
       "                   A43       1906497.47  4006956.60  2576417.39\n",
       "                   A63        788855.60         NaN   211117.12\n",
       "                   A81              NaN         NaN   207780.32\n",
       "                   A88              NaN         NaN   146821.30\n",
       "                   A92              NaN         NaN   459105.69\n",
       "        CALCUTTA   A10        892190.53  1246843.93  1556613.06\n",
       "                   A15       1051502.61  1720768.79  2783141.43\n",
       "                   A29       1376793.94  1706779.22  3434298.00\n",
       "                   A31        407599.13  1448641.71  1527798.41\n",
       "                   A60       2890721.81  3742856.10  3242712.83\n",
       "                   A89              NaN         NaN   497237.97\n",
       "        COCHIN     A11        958556.39  2045470.88  2306906.76\n",
       "                   A16       1122211.16  1226247.16  1911369.63\n",
       "                   A32        344312.67   696673.46  1517308.65\n",
       "                   A45       2557947.39  4042447.57  1847939.91\n",
       "                   A61       2518154.95  4276860.55  3744127.41\n",
       "                   A65        468922.60   230667.34         NaN\n",
       "                   A90              NaN         NaN   109931.32\n",
       "                   A94        153465.89         NaN         NaN\n",
       "        BOMBAY     A12       2339080.90  2877588.30  3118090.96\n",
       "                   A17       1425448.02   731143.83  3127783.14\n",
       "                   A42       3045914.71  3125758.43  1443787.72\n",
       "                   A62       1744114.25  1541617.21  1684085.69\n",
       "                   A91              NaN         NaN   247781.00\n",
       "        DELHI      A44       2149383.50  4165317.77  1430028.85\n",
       "                   A64              NaN         NaN   323229.09\n",
       "                   A82              NaN         NaN   342200.74\n",
       "                   A93        342326.14         NaN         NaN\n",
       "        PATNA      A26       1490427.84  2433837.95  3330421.30\n",
       "                   A66        477039.78         NaN   113094.58\n",
       "                   A95        369405.96         NaN         NaN\n",
       "        CHENNAI    A27       1842823.04  2782591.71  2387485.81\n",
       "                   A67         67845.60    55638.77         NaN\n",
       "        TRIVANDRUM A28        965108.49  2827009.82  3376614.65"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary('Gold','Yearly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "id": "3676587b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide input for product and timeperiod\n"
     ]
    },
    {
     "data": {
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       "      <th>Monthly</th>\n",
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       "      <th rowspan=\"30\" valign=\"top\">Silver</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">BANGALORE</th>\n",
       "      <th>A18</th>\n",
       "      <td>100000.00</td>\n",
       "      <td>100000.00</td>\n",
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       "      <td>391592.71</td>\n",
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       "      <td>600000.00</td>\n",
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       "      <td>459884.12</td>\n",
       "      <td>100001.0</td>\n",
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       "      <td>100001.00</td>\n",
       "      <td>500005.00</td>\n",
       "      <td>300003.00</td>\n",
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       "      <td>100001.00</td>\n",
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       "      <th>A59</th>\n",
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       "      <td>200006.00</td>\n",
       "      <td>476551.50</td>\n",
       "      <td>100003.0</td>\n",
       "      <td>110585.36</td>\n",
       "      <td>400012.00</td>\n",
       "      <td>400012.00</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>A72</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>A76</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265619.05</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>A77</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">CALCUTTA</th>\n",
       "      <th>A2</th>\n",
       "      <td>200000.00</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.00</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110274.00</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A58</th>\n",
       "      <td>200004.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191585.97</td>\n",
       "      <td>333811.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>408754.94</td>\n",
       "      <td>251471.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A73</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A78</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55344.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A87</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">COCHIN</th>\n",
       "      <th>A100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69551.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A25</th>\n",
       "      <td>300009.00</td>\n",
       "      <td>1168.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>236157.25</td>\n",
       "      <td>586332.08</td>\n",
       "      <td>100003.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300009.00</td>\n",
       "      <td>572616.74</td>\n",
       "      <td>101622.24</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>100003.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100001.00</td>\n",
       "      <td>100001.00</td>\n",
       "      <td>594009.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329246.84</td>\n",
       "      <td>352813.31</td>\n",
       "      <td>55108.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A74</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A79</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193230.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">BOMBAY</th>\n",
       "      <th>A22</th>\n",
       "      <td>392140.92</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349814.89</td>\n",
       "      <td>900000.00</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>253130.91</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>41542.81</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>203604.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A75</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A80</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>481205.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DELHI</th>\n",
       "      <th>A24</th>\n",
       "      <td>222060.59</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>286593.13</td>\n",
       "      <td>484643.62</td>\n",
       "      <td>100002.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300006.00</td>\n",
       "      <td>401378.84</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>98261.85</td>\n",
       "      <td>100002.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27398.18</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>600000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.00</td>\n",
       "      <td>302639.38</td>\n",
       "      <td>153215.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PATNA</th>\n",
       "      <th>A46</th>\n",
       "      <td>327793.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>544231.79</td>\n",
       "      <td>713614.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158285.12</td>\n",
       "      <td>287541.80</td>\n",
       "      <td>514213.98</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23892.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A8</th>\n",
       "      <td>100002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300006.00</td>\n",
       "      <td>600012.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231806.40</td>\n",
       "      <td>400008.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A84</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CHENNAI</th>\n",
       "      <th>A85</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A9</th>\n",
       "      <td>43323.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266926.41</td>\n",
       "      <td>380193.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400012.00</td>\n",
       "      <td>487597.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A96</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">TRIVANDRUM</th>\n",
       "      <th>A57</th>\n",
       "      <td>177378.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100001.00</td>\n",
       "      <td>200002.00</td>\n",
       "      <td>400004.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>578549.42</td>\n",
       "      <td>200002.00</td>\n",
       "      <td>200002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A68</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A86</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Monthly                          April     August   December   February  \\\n",
       "Product City       Customer                                               \n",
       "Silver  BANGALORE  A18       100000.00  100000.00        NaN   38296.41   \n",
       "                   A23       405083.59  100001.00        NaN  472921.32   \n",
       "                   A59        16652.72        NaN        NaN  200006.00   \n",
       "                   A72             NaN        NaN        NaN  100000.00   \n",
       "                   A76             NaN        NaN        NaN  265619.05   \n",
       "                   A77             NaN        NaN        NaN    3159.62   \n",
       "        CALCUTTA   A2        200000.00  100000.00        NaN  300000.00   \n",
       "                   A58       200004.00        NaN        NaN  191585.97   \n",
       "                   A73             NaN        NaN        NaN  100001.00   \n",
       "                   A78             NaN        NaN        NaN        NaN   \n",
       "                   A87             NaN        NaN        NaN        NaN   \n",
       "        COCHIN     A100            NaN        NaN        NaN        NaN   \n",
       "                   A25       300009.00    1168.46        NaN  236157.25   \n",
       "                   A7              NaN        NaN  100001.00  100001.00   \n",
       "                   A74             NaN        NaN        NaN  100002.00   \n",
       "                   A79             NaN        NaN        NaN        NaN   \n",
       "        BOMBAY     A22       392140.92  100000.00        NaN  349814.89   \n",
       "                   A75             NaN        NaN        NaN  100003.00   \n",
       "                   A80             NaN        NaN        NaN        NaN   \n",
       "        DELHI      A24       222060.59  100002.00  100002.00  286593.13   \n",
       "                   A6              NaN        NaN   27398.18  100000.00   \n",
       "        PATNA      A46       327793.48        NaN        NaN  544231.79   \n",
       "                   A8        100002.00        NaN        NaN  300006.00   \n",
       "                   A84             NaN        NaN        NaN        NaN   \n",
       "        CHENNAI    A85             NaN        NaN        NaN        NaN   \n",
       "                   A9         43323.90        NaN        NaN  266926.41   \n",
       "                   A96             NaN        NaN        NaN        NaN   \n",
       "        TRIVANDRUM A57       177378.57        NaN  100001.00  200002.00   \n",
       "                   A68             NaN        NaN        NaN        NaN   \n",
       "                   A86             NaN        NaN        NaN        NaN   \n",
       "\n",
       "Monthly                        January      July       June      March  \\\n",
       "Product City       Customer                                              \n",
       "Silver  BANGALORE  A18       391592.71       NaN  200000.00  600000.00   \n",
       "                   A23       459884.12  100001.0        NaN  100001.00   \n",
       "                   A59       476551.50  100003.0  110585.36  400012.00   \n",
       "                   A72             NaN       NaN        NaN        NaN   \n",
       "                   A76             NaN       NaN        NaN        NaN   \n",
       "                   A77             NaN       NaN        NaN        NaN   \n",
       "        CALCUTTA   A2        200000.00       NaN        NaN        NaN   \n",
       "                   A58       333811.62       NaN  100002.00  408754.94   \n",
       "                   A73             NaN       NaN        NaN        NaN   \n",
       "                   A78             NaN       NaN        NaN   55344.81   \n",
       "                   A87             NaN       NaN        NaN  100003.00   \n",
       "        COCHIN     A100       69551.19       NaN        NaN        NaN   \n",
       "                   A25       586332.08  100003.0        NaN  300009.00   \n",
       "                   A7        594009.28       NaN        NaN  329246.84   \n",
       "                   A74             NaN       NaN        NaN        NaN   \n",
       "                   A79             NaN       NaN        NaN  193230.31   \n",
       "        BOMBAY     A22       900000.00  100000.0  100000.00  253130.91   \n",
       "                   A75             NaN       NaN        NaN        NaN   \n",
       "                   A80             NaN       NaN        NaN  481205.64   \n",
       "        DELHI      A24       484643.62  100002.0        NaN  300006.00   \n",
       "                   A6        600000.00       NaN        NaN  300000.00   \n",
       "        PATNA      A46       713614.32       NaN  158285.12  287541.80   \n",
       "                   A8        600012.00       NaN        NaN  231806.40   \n",
       "                   A84             NaN       NaN        NaN  100000.00   \n",
       "        CHENNAI    A85             NaN       NaN        NaN  100001.00   \n",
       "                   A9        380193.34       NaN        NaN  400012.00   \n",
       "                   A96       100000.00       NaN        NaN        NaN   \n",
       "        TRIVANDRUM A57       400004.00       NaN        NaN  578549.42   \n",
       "                   A68       100000.00       NaN        NaN        NaN   \n",
       "                   A86             NaN       NaN        NaN  100002.00   \n",
       "\n",
       "Monthly                            May   November    October  September  \n",
       "Product City       Customer                                              \n",
       "Silver  BANGALORE  A18       200000.00  100000.00        NaN  100000.00  \n",
       "                   A23       500005.00  300003.00        NaN  100001.00  \n",
       "                   A59       400012.00        NaN        NaN        NaN  \n",
       "                   A72             NaN        NaN        NaN        NaN  \n",
       "                   A76             NaN        NaN        NaN        NaN  \n",
       "                   A77             NaN        NaN        NaN        NaN  \n",
       "        CALCUTTA   A2              NaN  110274.00  100000.00        NaN  \n",
       "                   A58       251471.11        NaN        NaN        NaN  \n",
       "                   A73             NaN        NaN        NaN        NaN  \n",
       "                   A78             NaN        NaN        NaN        NaN  \n",
       "                   A87             NaN        NaN        NaN        NaN  \n",
       "        COCHIN     A100            NaN        NaN        NaN        NaN  \n",
       "                   A25       572616.74  101622.24  100003.00  100003.00  \n",
       "                   A7        352813.31   55108.71        NaN        NaN  \n",
       "                   A74             NaN        NaN        NaN        NaN  \n",
       "                   A79             NaN        NaN        NaN        NaN  \n",
       "        BOMBAY     A22       500000.00   41542.81  100000.00  203604.58  \n",
       "                   A75             NaN        NaN        NaN        NaN  \n",
       "                   A80             NaN        NaN        NaN        NaN  \n",
       "        DELHI      A24       401378.84  100002.00   98261.85  100002.00  \n",
       "                   A6        302639.38  153215.28        NaN        NaN  \n",
       "        PATNA      A46       514213.98  100000.00        NaN   23892.79  \n",
       "                   A8        400008.00  100002.00        NaN        NaN  \n",
       "                   A84             NaN        NaN        NaN        NaN  \n",
       "        CHENNAI    A85             NaN        NaN        NaN        NaN  \n",
       "                   A9        487597.98        NaN        NaN        NaN  \n",
       "                   A96             NaN        NaN        NaN        NaN  \n",
       "        TRIVANDRUM A57       200002.00  200002.00        NaN        NaN  \n",
       "                   A68       100000.00        NaN        NaN        NaN  \n",
       "                   A86             NaN        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 824,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary('Silver','Monthly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "id": "aa39efc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide input for product and timeperiod\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Yearly</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">Silver</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">BANGALORE</th>\n",
       "      <th>A18</th>\n",
       "      <td>491592.71</td>\n",
       "      <td>638296.41</td>\n",
       "      <td>700000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A23</th>\n",
       "      <td>664965.71</td>\n",
       "      <td>1000010.00</td>\n",
       "      <td>872925.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A59</th>\n",
       "      <td>676557.50</td>\n",
       "      <td>427247.08</td>\n",
       "      <td>600018.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A72</th>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A76</th>\n",
       "      <td>NaN</td>\n",
       "      <td>265619.05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A77</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3159.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CALCUTTA</th>\n",
       "      <th>A2</th>\n",
       "      <td>400000.00</td>\n",
       "      <td>410274.00</td>\n",
       "      <td>200000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A58</th>\n",
       "      <td>533815.62</td>\n",
       "      <td>540173.85</td>\n",
       "      <td>411640.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A73</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100001.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A78</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55344.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A87</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100003.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">COCHIN</th>\n",
       "      <th>A100</th>\n",
       "      <td>69551.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A25</th>\n",
       "      <td>786338.08</td>\n",
       "      <td>611555.69</td>\n",
       "      <td>1000030.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A7</th>\n",
       "      <td>500005.00</td>\n",
       "      <td>422177.27</td>\n",
       "      <td>608997.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A74</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A79</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193230.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">BOMBAY</th>\n",
       "      <th>A22</th>\n",
       "      <td>953419.47</td>\n",
       "      <td>1141542.81</td>\n",
       "      <td>945271.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A75</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A80</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>481205.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DELHI</th>\n",
       "      <th>A24</th>\n",
       "      <td>397014.53</td>\n",
       "      <td>1184870.98</td>\n",
       "      <td>711068.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A6</th>\n",
       "      <td>548574.15</td>\n",
       "      <td>427398.18</td>\n",
       "      <td>507280.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PATNA</th>\n",
       "      <th>A46</th>\n",
       "      <td>713614.32</td>\n",
       "      <td>1116730.89</td>\n",
       "      <td>839228.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A8</th>\n",
       "      <td>800016.00</td>\n",
       "      <td>400008.00</td>\n",
       "      <td>531812.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A84</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CHENNAI</th>\n",
       "      <th>A85</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100001.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A9</th>\n",
       "      <td>380193.34</td>\n",
       "      <td>500015.00</td>\n",
       "      <td>697845.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A96</th>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">TRIVANDRUM</th>\n",
       "      <th>A57</th>\n",
       "      <td>1000010.00</td>\n",
       "      <td>200002.00</td>\n",
       "      <td>655926.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A68</th>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A86</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100002.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Yearly                             2004        2005        2006\n",
       "Product City       Customer                                    \n",
       "Silver  BANGALORE  A18        491592.71   638296.41   700000.00\n",
       "                   A23        664965.71  1000010.00   872925.32\n",
       "                   A59        676557.50   427247.08   600018.00\n",
       "                   A72        100000.00         NaN         NaN\n",
       "                   A76              NaN   265619.05         NaN\n",
       "                   A77              NaN         NaN     3159.62\n",
       "        CALCUTTA   A2         400000.00   410274.00   200000.00\n",
       "                   A58        533815.62   540173.85   411640.17\n",
       "                   A73              NaN   100001.00         NaN\n",
       "                   A78              NaN         NaN    55344.81\n",
       "                   A87              NaN         NaN   100003.00\n",
       "        COCHIN     A100        69551.19         NaN         NaN\n",
       "                   A25        786338.08   611555.69  1000030.00\n",
       "                   A7         500005.00   422177.27   608997.87\n",
       "                   A74              NaN   100002.00         NaN\n",
       "                   A79              NaN         NaN   193230.31\n",
       "        BOMBAY     A22        953419.47  1141542.81   945271.83\n",
       "                   A75              NaN   100003.00         NaN\n",
       "                   A80              NaN         NaN   481205.64\n",
       "        DELHI      A24        397014.53  1184870.98   711068.52\n",
       "                   A6         548574.15   427398.18   507280.51\n",
       "        PATNA      A46        713614.32  1116730.89   839228.07\n",
       "                   A8         800016.00   400008.00   531812.40\n",
       "                   A84              NaN         NaN   100000.00\n",
       "        CHENNAI    A85              NaN         NaN   100001.00\n",
       "                   A9         380193.34   500015.00   697845.29\n",
       "                   A96        100000.00         NaN         NaN\n",
       "        TRIVANDRUM A57       1000010.00   200002.00   655926.99\n",
       "                   A68        100000.00         NaN   100000.00\n",
       "                   A86              NaN         NaN   100002.00"
      ]
     },
     "execution_count": 832,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary('Silver','Yearly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "id": "08b55d41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide input for product and timeperiod\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Monthly</th>\n",
       "      <th>April</th>\n",
       "      <th>August</th>\n",
       "      <th>December</th>\n",
       "      <th>February</th>\n",
       "      <th>January</th>\n",
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       "      <th>September</th>\n",
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       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"32\" valign=\"top\">Platinum</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">BANGALORE</th>\n",
       "      <th>A19</th>\n",
       "      <td>30000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A37</th>\n",
       "      <td>255805.86</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>404735.41</td>\n",
       "      <td>500008.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200006.00</td>\n",
       "      <td>200006.00</td>\n",
       "      <td>244585.08</td>\n",
       "      <td>477623.47</td>\n",
       "      <td>86820.21</td>\n",
       "      <td>125961.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>7276.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>60012.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30006.00</td>\n",
       "      <td>28801.07</td>\n",
       "      <td>20004.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A52</th>\n",
       "      <td>200004.00</td>\n",
       "      <td>235509.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234334.70</td>\n",
       "      <td>263853.18</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300006.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">CALCUTTA</th>\n",
       "      <th>A20</th>\n",
       "      <td>40004.00</td>\n",
       "      <td>10001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20002.00</td>\n",
       "      <td>50005.00</td>\n",
       "      <td>30003.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30003.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A34</th>\n",
       "      <td>100000.00</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198933.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148053.83</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>128854.14</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>73899.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81344.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>488742.81</td>\n",
       "      <td>169786.15</td>\n",
       "      <td>937643.24</td>\n",
       "      <td>1749083.51</td>\n",
       "      <td>242107.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1176947.70</td>\n",
       "      <td>1401293.97</td>\n",
       "      <td>825144.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A49</th>\n",
       "      <td>30006.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>70014.00</td>\n",
       "      <td>50010.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>30006.00</td>\n",
       "      <td>30006.00</td>\n",
       "      <td>60012.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A69</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A98</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A99</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">COCHIN</th>\n",
       "      <th>A21</th>\n",
       "      <td>40008.00</td>\n",
       "      <td>30006.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>50010.00</td>\n",
       "      <td>80016.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>20004.00</td>\n",
       "      <td>30006.00</td>\n",
       "      <td>40008.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>20000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A35</th>\n",
       "      <td>200002.00</td>\n",
       "      <td>130191.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300003.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131812.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A41</th>\n",
       "      <td>147383.00</td>\n",
       "      <td>6865.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>509624.20</td>\n",
       "      <td>2089998.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>499724.79</td>\n",
       "      <td>1999863.05</td>\n",
       "      <td>223029.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4110.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A50</th>\n",
       "      <td>100000.00</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>229753.02</td>\n",
       "      <td>225086.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>400000.00</td>\n",
       "      <td>308304.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A54</th>\n",
       "      <td>1025491.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215719.14</td>\n",
       "      <td>1149119.64</td>\n",
       "      <td>1154398.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100719.74</td>\n",
       "      <td>494339.29</td>\n",
       "      <td>703624.89</td>\n",
       "      <td>241392.80</td>\n",
       "      <td>490775.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A83</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">BOMBAY</th>\n",
       "      <th>A33</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30006.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20004.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>30006.00</td>\n",
       "      <td>30006.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2273.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A36</th>\n",
       "      <td>511685.20</td>\n",
       "      <td>117379.12</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>427759.59</td>\n",
       "      <td>400008.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>472431.76</td>\n",
       "      <td>100002.00</td>\n",
       "      <td>200004.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <td>10001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10001.00</td>\n",
       "      <td>20002.00</td>\n",
       "      <td>20002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10001.00</td>\n",
       "      <td>30003.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10001.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A51</th>\n",
       "      <td>100001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206147.92</td>\n",
       "      <td>282084.48</td>\n",
       "      <td>300003.00</td>\n",
       "      <td>100001.00</td>\n",
       "      <td>167366.29</td>\n",
       "      <td>74206.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78996.70</td>\n",
       "      <td>100001.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A70</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A71</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELHI</th>\n",
       "      <th>A53</th>\n",
       "      <td>300009.00</td>\n",
       "      <td>50308.75</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>279929.09</td>\n",
       "      <td>140309.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>200006.00</td>\n",
       "      <td>100003.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300009.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATNA</th>\n",
       "      <th>A55</th>\n",
       "      <td>619774.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1124130.33</td>\n",
       "      <td>781731.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>483681.85</td>\n",
       "      <td>409413.63</td>\n",
       "      <td>297365.80</td>\n",
       "      <td>201404.18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CHENNAI</th>\n",
       "      <th>A38</th>\n",
       "      <td>369151.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>492660.82</td>\n",
       "      <td>1365681.76</td>\n",
       "      <td>1655584.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215272.04</td>\n",
       "      <td>775994.68</td>\n",
       "      <td>981011.33</td>\n",
       "      <td>952412.81</td>\n",
       "      <td>317309.23</td>\n",
       "      <td>345877.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A47</th>\n",
       "      <td>50000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A56</th>\n",
       "      <td>770261.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162103.09</td>\n",
       "      <td>413912.92</td>\n",
       "      <td>36537.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1500854.63</td>\n",
       "      <td>708576.18</td>\n",
       "      <td>706827.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">TRIVANDRUM</th>\n",
       "      <th>A39</th>\n",
       "      <td>472407.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278687.47</td>\n",
       "      <td>1212860.65</td>\n",
       "      <td>1565119.57</td>\n",
       "      <td>343050.41</td>\n",
       "      <td>235033.71</td>\n",
       "      <td>1141202.46</td>\n",
       "      <td>1668976.58</td>\n",
       "      <td>705144.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A48</th>\n",
       "      <td>50005.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70007.00</td>\n",
       "      <td>40004.00</td>\n",
       "      <td>10001.00</td>\n",
       "      <td>30003.00</td>\n",
       "      <td>40004.00</td>\n",
       "      <td>50005.00</td>\n",
       "      <td>20002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A97</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Monthly                            April     August   December    February  \\\n",
       "Product  City       Customer                                                 \n",
       "Platinum BANGALORE  A19         30000.00        NaN   10000.00    10000.00   \n",
       "                    A37        255805.86  100003.00  100003.00   404735.41   \n",
       "                    A5           7276.60        NaN        NaN    10002.00   \n",
       "                    A52        200004.00  235509.11        NaN   234334.70   \n",
       "         CALCUTTA   A20         40004.00   10001.00        NaN    20002.00   \n",
       "                    A34        100000.00  200000.00        NaN   198933.36   \n",
       "                    A40              NaN  488742.81  169786.15   937643.24   \n",
       "                    A49         30006.00   10002.00   10002.00    70014.00   \n",
       "                    A69              NaN        NaN        NaN         NaN   \n",
       "                    A98              NaN        NaN        NaN         NaN   \n",
       "                    A99              NaN        NaN        NaN         NaN   \n",
       "         COCHIN     A21         40008.00   30006.00   10002.00    50010.00   \n",
       "                    A3          20000.00        NaN        NaN    30000.00   \n",
       "                    A35        200002.00  130191.67        NaN   300003.00   \n",
       "                    A41        147383.00    6865.68        NaN   509624.20   \n",
       "                    A50        100000.00  100000.00        NaN   229753.02   \n",
       "                    A54       1025491.68        NaN  215719.14  1149119.64   \n",
       "                    A83              NaN        NaN        NaN         NaN   \n",
       "         BOMBAY     A33              NaN   10002.00        NaN    30006.00   \n",
       "                    A36        511685.20  117379.12  100002.00   427759.59   \n",
       "                    A4          10001.00        NaN   10001.00    20002.00   \n",
       "                    A51        100001.00        NaN        NaN   206147.92   \n",
       "                    A70              NaN   10001.00        NaN    10001.00   \n",
       "                    A71              NaN        NaN        NaN    10002.00   \n",
       "         DELHI      A53        300009.00   50308.75  100003.00   279929.09   \n",
       "         PATNA      A55        619774.41        NaN        NaN  1124130.33   \n",
       "         CHENNAI    A38        369151.51        NaN  492660.82  1365681.76   \n",
       "                    A47         50000.00        NaN        NaN    80000.00   \n",
       "                    A56        770261.09        NaN  162103.09   413912.92   \n",
       "         TRIVANDRUM A39        472407.74        NaN  278687.47  1212860.65   \n",
       "                    A48         50005.00        NaN        NaN    70007.00   \n",
       "                    A97              NaN        NaN        NaN         NaN   \n",
       "\n",
       "Monthly                          January       July       June       March  \\\n",
       "Product  City       Customer                                                 \n",
       "Platinum BANGALORE  A19         50000.00   10000.00   10000.00    40000.00   \n",
       "                    A37        500008.58        NaN  200006.00   200006.00   \n",
       "                    A5          60012.00        NaN        NaN    30006.00   \n",
       "                    A52        263853.18  100002.00        NaN   300006.00   \n",
       "         CALCUTTA   A20         50005.00   30003.00        NaN    30003.00   \n",
       "                    A34              NaN  148053.83  100000.00   128854.14   \n",
       "                    A40       1749083.51  242107.49        NaN  1176947.70   \n",
       "                    A49         50010.00   10002.00   30006.00    30006.00   \n",
       "                    A69         10000.00   10000.00        NaN         NaN   \n",
       "                    A98         10001.00        NaN        NaN         NaN   \n",
       "                    A99         10002.00        NaN        NaN         NaN   \n",
       "         COCHIN     A21         80016.00   10002.00   20004.00    30006.00   \n",
       "                    A3          20000.00        NaN        NaN         NaN   \n",
       "                    A35              NaN  200002.00        NaN   100001.00   \n",
       "                    A41       2089998.54        NaN        NaN   499724.79   \n",
       "                    A50        225086.07        NaN  100000.00   400000.00   \n",
       "                    A54       1154398.99        NaN        NaN   100719.74   \n",
       "                    A83              NaN        NaN        NaN    10002.00   \n",
       "         BOMBAY     A33              NaN   20004.00   10002.00    30006.00   \n",
       "                    A36        400008.00  100002.00  100002.00   100002.00   \n",
       "                    A4          20002.00        NaN        NaN    10001.00   \n",
       "                    A51        282084.48  300003.00  100001.00   167366.29   \n",
       "                    A70              NaN        NaN        NaN         NaN   \n",
       "                    A71              NaN        NaN        NaN         NaN   \n",
       "         DELHI      A53        140309.31        NaN        NaN   100003.00   \n",
       "         PATNA      A55        781731.00        NaN        NaN   483681.85   \n",
       "         CHENNAI    A38       1655584.31        NaN  215272.04   775994.68   \n",
       "                    A47         50000.00        NaN   30000.00    30000.00   \n",
       "                    A56         36537.90        NaN        NaN  1500854.63   \n",
       "         TRIVANDRUM A39       1565119.57  343050.41  235033.71  1141202.46   \n",
       "                    A48         40004.00   10001.00   30003.00    40004.00   \n",
       "                    A97         10000.00        NaN        NaN         NaN   \n",
       "\n",
       "Monthly                              May   November    October  September  \n",
       "Product  City       Customer                                               \n",
       "Platinum BANGALORE  A19         20000.00        NaN   10000.00   10000.00  \n",
       "                    A37        244585.08  477623.47   86820.21  125961.96  \n",
       "                    A5          28801.07   20004.00   10002.00        NaN  \n",
       "                    A52        100002.00  100002.00  100002.00        NaN  \n",
       "         CALCUTTA   A20              NaN   20002.00        NaN        NaN  \n",
       "                    A34        100000.00   73899.28        NaN   81344.52  \n",
       "                    A40       1401293.97  825144.40        NaN        NaN  \n",
       "                    A49         60012.00   10002.00        NaN        NaN  \n",
       "                    A69              NaN        NaN        NaN        NaN  \n",
       "                    A98              NaN        NaN        NaN        NaN  \n",
       "                    A99              NaN        NaN        NaN        NaN  \n",
       "         COCHIN     A21         40008.00   10002.00        NaN   10002.00  \n",
       "                    A3          10000.00   20000.00        NaN   10000.00  \n",
       "                    A35              NaN  200002.00        NaN  131812.80  \n",
       "                    A41       1999863.05  223029.28        NaN    4110.00  \n",
       "                    A50        308304.43        NaN        NaN  100000.00  \n",
       "                    A54        494339.29  703624.89  241392.80  490775.95  \n",
       "                    A83              NaN        NaN        NaN        NaN  \n",
       "         BOMBAY     A33         30006.00        NaN        NaN    2273.98  \n",
       "                    A36        100002.00  472431.76  100002.00  200004.00  \n",
       "                    A4          30003.00        NaN   10001.00        NaN  \n",
       "                    A51         74206.01        NaN   78996.70  100001.00  \n",
       "                    A70              NaN        NaN        NaN        NaN  \n",
       "                    A71              NaN        NaN        NaN   10002.00  \n",
       "         DELHI      A53        200006.00  100003.00        NaN  300009.00  \n",
       "         PATNA      A55        409413.63  297365.80  201404.18        NaN  \n",
       "         CHENNAI    A38        981011.33  952412.81  317309.23  345877.86  \n",
       "                    A47         50000.00   10000.00   10000.00        NaN  \n",
       "                    A56        708576.18  706827.65        NaN        NaN  \n",
       "         TRIVANDRUM A39       1668976.58  705144.71        NaN        NaN  \n",
       "                    A48         50005.00   20002.00        NaN        NaN  \n",
       "                    A97              NaN        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 831,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary('Platinum','Monthly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "f5b2a872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide input for product and timeperiod\n"
     ]
    },
    {
     "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Yearly</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"32\" valign=\"top\">Platinum</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">BANGALORE</th>\n",
       "      <th>A19</th>\n",
       "      <td>50000.00</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>80000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A37</th>\n",
       "      <td>770552.62</td>\n",
       "      <td>1031641.04</td>\n",
       "      <td>893364.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>68809.07</td>\n",
       "      <td>57286.60</td>\n",
       "      <td>40008.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A52</th>\n",
       "      <td>263853.18</td>\n",
       "      <td>769849.81</td>\n",
       "      <td>600012.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">CALCUTTA</th>\n",
       "      <th>A20</th>\n",
       "      <td>70007.00</td>\n",
       "      <td>60006.00</td>\n",
       "      <td>70007.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A34</th>\n",
       "      <td>81344.52</td>\n",
       "      <td>372832.64</td>\n",
       "      <td>676907.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A40</th>\n",
       "      <td>2576916.73</td>\n",
       "      <td>2527720.55</td>\n",
       "      <td>1886111.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A49</th>\n",
       "      <td>100020.00</td>\n",
       "      <td>130026.00</td>\n",
       "      <td>80016.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A69</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A98</th>\n",
       "      <td>10001.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A99</th>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">COCHIN</th>\n",
       "      <th>A21</th>\n",
       "      <td>110022.00</td>\n",
       "      <td>140028.00</td>\n",
       "      <td>80016.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>30000.00</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A35</th>\n",
       "      <td>31811.80</td>\n",
       "      <td>430194.67</td>\n",
       "      <td>800008.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A41</th>\n",
       "      <td>1752953.17</td>\n",
       "      <td>2486187.34</td>\n",
       "      <td>1241458.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A50</th>\n",
       "      <td>325086.07</td>\n",
       "      <td>338057.45</td>\n",
       "      <td>900000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A54</th>\n",
       "      <td>490081.00</td>\n",
       "      <td>1829813.48</td>\n",
       "      <td>3255687.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A83</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">BOMBAY</th>\n",
       "      <th>A33</th>\n",
       "      <td>12275.98</td>\n",
       "      <td>40008.00</td>\n",
       "      <td>80016.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A36</th>\n",
       "      <td>772335.72</td>\n",
       "      <td>856921.95</td>\n",
       "      <td>1100022.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <td>30003.00</td>\n",
       "      <td>40004.00</td>\n",
       "      <td>40004.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A51</th>\n",
       "      <td>423029.79</td>\n",
       "      <td>459351.63</td>\n",
       "      <td>526425.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A70</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20002.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A71</th>\n",
       "      <td>10002.00</td>\n",
       "      <td>10002.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELHI</th>\n",
       "      <th>A53</th>\n",
       "      <td>440318.31</td>\n",
       "      <td>679941.09</td>\n",
       "      <td>450320.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATNA</th>\n",
       "      <th>A55</th>\n",
       "      <td>335081.18</td>\n",
       "      <td>708035.12</td>\n",
       "      <td>2874384.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CHENNAI</th>\n",
       "      <th>A38</th>\n",
       "      <td>1951311.01</td>\n",
       "      <td>2408626.65</td>\n",
       "      <td>3111018.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A47</th>\n",
       "      <td>60000.00</td>\n",
       "      <td>160000.00</td>\n",
       "      <td>90000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A56</th>\n",
       "      <td>36537.90</td>\n",
       "      <td>966666.69</td>\n",
       "      <td>3295868.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">TRIVANDRUM</th>\n",
       "      <th>A39</th>\n",
       "      <td>1342503.16</td>\n",
       "      <td>3699578.97</td>\n",
       "      <td>2580401.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A48</th>\n",
       "      <td>70007.00</td>\n",
       "      <td>140014.00</td>\n",
       "      <td>100010.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A97</th>\n",
       "      <td>10000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Yearly                              2004        2005        2006\n",
       "Product  City       Customer                                    \n",
       "Platinum BANGALORE  A19         50000.00    70000.00    80000.00\n",
       "                    A37        770552.62  1031641.04   893364.91\n",
       "                    A5          68809.07    57286.60    40008.00\n",
       "                    A52        263853.18   769849.81   600012.00\n",
       "         CALCUTTA   A20         70007.00    60006.00    70007.00\n",
       "                    A34         81344.52   372832.64   676907.97\n",
       "                    A40       2576916.73  2527720.55  1886111.99\n",
       "                    A49        100020.00   130026.00    80016.00\n",
       "                    A69              NaN    10000.00    10000.00\n",
       "                    A98         10001.00         NaN         NaN\n",
       "                    A99         10002.00         NaN         NaN\n",
       "         COCHIN     A21        110022.00   140028.00    80016.00\n",
       "                    A3          30000.00    50000.00    30000.00\n",
       "                    A35         31811.80   430194.67   800008.00\n",
       "                    A41       1752953.17  2486187.34  1241458.03\n",
       "                    A50        325086.07   338057.45   900000.00\n",
       "                    A54        490081.00  1829813.48  3255687.64\n",
       "                    A83              NaN         NaN    10002.00\n",
       "         BOMBAY     A33         12275.98    40008.00    80016.00\n",
       "                    A36        772335.72   856921.95  1100022.00\n",
       "                    A4          30003.00    40004.00    40004.00\n",
       "                    A51        423029.79   459351.63   526425.98\n",
       "                    A70              NaN    20002.00         NaN\n",
       "                    A71         10002.00    10002.00         NaN\n",
       "         DELHI      A53        440318.31   679941.09   450320.75\n",
       "         PATNA      A55        335081.18   708035.12  2874384.90\n",
       "         CHENNAI    A38       1951311.01  2408626.65  3111018.69\n",
       "                    A47         60000.00   160000.00    90000.00\n",
       "                    A56         36537.90   966666.69  3295868.87\n",
       "         TRIVANDRUM A39       1342503.16  3699578.97  2580401.17\n",
       "                    A48         70007.00   140014.00   100010.00\n",
       "                    A97         10000.00         NaN         NaN"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary('Platinum','Yearly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f27ab7e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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