analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = \"{:.2f}\".format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"11.PNG\"  style=\"width:900px;\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions = pd.read_csv('./transactions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "products = pd.read_csv('./products.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "markets = pd.read_csv('./markets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers = pd.read_csv('./customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = pd.read_csv('./date.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing All the Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus002</td>\n",
       "      <td>Mark002</td>\n",
       "      <td>2018-05-08</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>1</td>\n",
       "      <td>875.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "1      Prod001        Cus002     Mark002  2018-05-08          3         -1.00   \n",
       "2      Prod002        Cus003     Mark003  2018-04-06          1        875.00   \n",
       "\n",
       "  currency  \n",
       "0      INR  \n",
       "1      INR  \n",
       "2      INR  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>product_code</th>\n",
       "      <th>product_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prod004</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Prod115</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_code  product_type\n",
       "3        Prod004     Own Brand\n",
       "114      Prod115  Distribution"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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_code</th>\n",
       "      <th>custmer_name</th>\n",
       "      <th>customer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Cus021</td>\n",
       "      <td>Modular</td>\n",
       "      <td>E-Commerce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cus020</td>\n",
       "      <td>Nixon</td>\n",
       "      <td>E-Commerce</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_code custmer_name customer_type\n",
       "20        Cus021      Modular    E-Commerce\n",
       "19        Cus020        Nixon    E-Commerce"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>markets_code</th>\n",
       "      <th>markets_name</th>\n",
       "      <th>zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mark007</td>\n",
       "      <td>Bhopal</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mark013</td>\n",
       "      <td>Bhopal</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   markets_code markets_name     zone\n",
       "6       Mark007       Bhopal  Central\n",
       "12      Mark013       Bhopal  Central"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>date</th>\n",
       "      <th>cy_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month_name</th>\n",
       "      <th>date_yy_mmm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>2019-05-10</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>May</td>\n",
       "      <td>19-May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2017-11-05</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>November</td>\n",
       "      <td>17-Nov</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     cy_date  year month_name date_yy_mmm\n",
       "708  2019-05-10  2019-05-01  2019        May      19-May\n",
       "157  2017-11-05  2017-11-01  2017   November      17-Nov"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_code      object\n",
       "customer_code     object\n",
       "market_code       object\n",
       "order_date        object\n",
       "sales_qty          int64\n",
       "sales_amount     float64\n",
       "currency          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_code     0\n",
       "customer_code    0\n",
       "market_code      0\n",
       "order_date       0\n",
       "sales_qty        0\n",
       "sales_amount     0\n",
       "currency         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sales_amout below 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18380</th>\n",
       "      <td>Prod065</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark006</td>\n",
       "      <td>2018-10-16</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111769</th>\n",
       "      <td>Prod292</td>\n",
       "      <td>Cus022</td>\n",
       "      <td>Mark011</td>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28500</th>\n",
       "      <td>Prod106</td>\n",
       "      <td>Cus007</td>\n",
       "      <td>Mark006</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_code customer_code market_code  order_date  sales_qty  \\\n",
       "18380       Prod065        Cus003     Mark006  2018-10-16          1   \n",
       "111769      Prod292        Cus022     Mark011  2019-07-19          1   \n",
       "28500       Prod106        Cus007     Mark006  2018-05-23          7   \n",
       "\n",
       "        sales_amount currency  \n",
       "18380           0.00      INR  \n",
       "111769          0.00      INR  \n",
       "28500           0.00      INR  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions[transactions['sales_amount'] <= 0].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1611"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(transactions[transactions['sales_amount'] <= 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150283, 7)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions.drop(transactions[transactions['sales_amount'] <= 0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 7)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USD to INR conversation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['INR', 'USD'], dtype=object)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['currency'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-20</td>\n",
       "      <td>59</td>\n",
       "      <td>500.00</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>36</td>\n",
       "      <td>250.00</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-20</td>\n",
       "      <td>59</td>\n",
       "      <td>500.00</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>36</td>\n",
       "      <td>250.00</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_code customer_code market_code  order_date  sales_qty  \\\n",
       "5        Prod003        Cus005     Mark004  2017-11-20         59   \n",
       "6        Prod003        Cus005     Mark004  2017-11-22         36   \n",
       "286      Prod003        Cus005     Mark004  2017-11-20         59   \n",
       "287      Prod003        Cus005     Mark004  2017-11-22         36   \n",
       "\n",
       "     sales_amount currency  \n",
       "5          500.00      USD  \n",
       "6          250.00      USD  \n",
       "286        500.00      USD  \n",
       "287        250.00      USD  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions[transactions['currency'] == 'USD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     500.00\n",
       "6     250.00\n",
       "286   500.00\n",
       "287   250.00\n",
       "Name: sales_amount, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions[transactions['currency'] == 'USD']['sales_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = transactions[transactions['currency'] == 'USD'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([5, 6, 286, 287], dtype='int64')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in val:\n",
    "    transactions.at[x,'sales_amount'] = transactions.at[x,'sales_amount']*75\n",
    "    transactions.at[x,'currency'] = 'INR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "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>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-20</td>\n",
       "      <td>59</td>\n",
       "      <td>37500.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>36</td>\n",
       "      <td>18750.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-20</td>\n",
       "      <td>59</td>\n",
       "      <td>37500.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>36</td>\n",
       "      <td>18750.00</td>\n",
       "      <td>INR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_code customer_code market_code  order_date  sales_qty  \\\n",
       "5        Prod003        Cus005     Mark004  2017-11-20         59   \n",
       "6        Prod003        Cus005     Mark004  2017-11-22         36   \n",
       "286      Prod003        Cus005     Mark004  2017-11-20         59   \n",
       "287      Prod003        Cus005     Mark004  2017-11-22         36   \n",
       "\n",
       "     sales_amount currency  \n",
       "5        37500.00      INR  \n",
       "6        18750.00      INR  \n",
       "286      37500.00      INR  \n",
       "287      18750.00      INR  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.loc[val]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding column product_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['product_price'] = transactions['sales_amount'] / transactions['sales_qty']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>1</td>\n",
       "      <td>875.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>875.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>1</td>\n",
       "      <td>583.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>583.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus004</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>6</td>\n",
       "      <td>7176.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>1196.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "2      Prod002        Cus003     Mark003  2018-04-06          1        875.00   \n",
       "3      Prod002        Cus003     Mark003  2018-04-11          1        583.00   \n",
       "4      Prod002        Cus004     Mark003  2018-06-18          6       7176.00   \n",
       "\n",
       "  currency  product_price  \n",
       "0      INR         412.41  \n",
       "2      INR         875.00  \n",
       "3      INR         583.00  \n",
       "4      INR        1196.00  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_code     0\n",
       "customer_code    0\n",
       "market_code      0\n",
       "order_date       0\n",
       "sales_qty        0\n",
       "sales_amount     0\n",
       "currency         0\n",
       "product_price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding column original_price\n",
    "#### original_price refers to product price without profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_perc(y):\n",
    "    profit = 8\n",
    "    \n",
    "    if y > 4000:\n",
    "        profit = 23\n",
    "    elif y > 3000:\n",
    "        profit = 19\n",
    "    elif y > 2000:\n",
    "        profit = 17\n",
    "    elif y > 1000:\n",
    "        profit = 15\n",
    "    elif y > 500:\n",
    "        profit = 13\n",
    "    elif y > 100:\n",
    "        profit = 11\n",
    "    else:\n",
    "        profit = 8\n",
    "        \n",
    "    return profit    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['profit_percentage'] = transactions['product_price'].apply(calc_perc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>1</td>\n",
       "      <td>875.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>875.00</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>1</td>\n",
       "      <td>583.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>583.00</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus004</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>6</td>\n",
       "      <td>7176.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>1196.00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Cus005</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2017-11-20</td>\n",
       "      <td>59</td>\n",
       "      <td>37500.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>635.59</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "2      Prod002        Cus003     Mark003  2018-04-06          1        875.00   \n",
       "3      Prod002        Cus003     Mark003  2018-04-11          1        583.00   \n",
       "4      Prod002        Cus004     Mark003  2018-06-18          6       7176.00   \n",
       "5      Prod003        Cus005     Mark004  2017-11-20         59      37500.00   \n",
       "\n",
       "  currency  product_price  profit_percentage  \n",
       "0      INR         412.41                 11  \n",
       "2      INR         875.00                 13  \n",
       "3      INR         583.00                 13  \n",
       "4      INR        1196.00                 15  \n",
       "5      INR         635.59                 13  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 10)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['profit_percentage'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_code         0\n",
       "customer_code        0\n",
       "market_code          0\n",
       "order_date           0\n",
       "sales_qty            0\n",
       "sales_amount         0\n",
       "currency             0\n",
       "product_price        0\n",
       "profit_percentage    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['original_price'] = (transactions['product_price'] - (transactions['product_price']*(transactions['profit_percentage'] / 100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>1</td>\n",
       "      <td>875.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>875.00</td>\n",
       "      <td>13</td>\n",
       "      <td>761.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>1</td>\n",
       "      <td>583.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>583.00</td>\n",
       "      <td>13</td>\n",
       "      <td>507.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus004</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>6</td>\n",
       "      <td>7176.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>1196.00</td>\n",
       "      <td>15</td>\n",
       "      <td>1016.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "2      Prod002        Cus003     Mark003  2018-04-06          1        875.00   \n",
       "3      Prod002        Cus003     Mark003  2018-04-11          1        583.00   \n",
       "4      Prod002        Cus004     Mark003  2018-06-18          6       7176.00   \n",
       "\n",
       "  currency  product_price  profit_percentage  original_price  \n",
       "0      INR         412.41                 11          367.04  \n",
       "2      INR         875.00                 13          761.25  \n",
       "3      INR         583.00                 13          507.21  \n",
       "4      INR        1196.00                 15         1016.60  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding column profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['profit'] = transactions['product_price']*(transactions['profit_percentage'] / 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "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>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>45.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>1</td>\n",
       "      <td>875.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>875.00</td>\n",
       "      <td>13</td>\n",
       "      <td>761.25</td>\n",
       "      <td>113.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>1</td>\n",
       "      <td>583.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>583.00</td>\n",
       "      <td>13</td>\n",
       "      <td>507.21</td>\n",
       "      <td>75.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus004</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>6</td>\n",
       "      <td>7176.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>1196.00</td>\n",
       "      <td>15</td>\n",
       "      <td>1016.60</td>\n",
       "      <td>179.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "2      Prod002        Cus003     Mark003  2018-04-06          1        875.00   \n",
       "3      Prod002        Cus003     Mark003  2018-04-11          1        583.00   \n",
       "4      Prod002        Cus004     Mark003  2018-06-18          6       7176.00   \n",
       "\n",
       "  currency  product_price  profit_percentage  original_price  profit  \n",
       "0      INR         412.41                 11          367.04   45.37  \n",
       "2      INR         875.00                 13          761.25  113.75  \n",
       "3      INR         583.00                 13          507.21   75.79  \n",
       "4      INR        1196.00                 15         1016.60  179.40  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Column total_profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['total_profit'] = (transactions['sales_qty'])*(transactions['product_price']*(transactions['profit_percentage'] / 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>profit</th>\n",
       "      <th>total_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>45.37</td>\n",
       "      <td>4536.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>1</td>\n",
       "      <td>875.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>875.00</td>\n",
       "      <td>13</td>\n",
       "      <td>761.25</td>\n",
       "      <td>113.75</td>\n",
       "      <td>113.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>1</td>\n",
       "      <td>583.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>583.00</td>\n",
       "      <td>13</td>\n",
       "      <td>507.21</td>\n",
       "      <td>75.79</td>\n",
       "      <td>75.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus004</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>6</td>\n",
       "      <td>7176.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>1196.00</td>\n",
       "      <td>15</td>\n",
       "      <td>1016.60</td>\n",
       "      <td>179.40</td>\n",
       "      <td>1076.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "2      Prod002        Cus003     Mark003  2018-04-06          1        875.00   \n",
       "3      Prod002        Cus003     Mark003  2018-04-11          1        583.00   \n",
       "4      Prod002        Cus004     Mark003  2018-06-18          6       7176.00   \n",
       "\n",
       "  currency  product_price  profit_percentage  original_price  profit  \\\n",
       "0      INR         412.41                 11          367.04   45.37   \n",
       "2      INR         875.00                 13          761.25  113.75   \n",
       "3      INR         583.00                 13          507.21   75.79   \n",
       "4      INR        1196.00                 15         1016.60  179.40   \n",
       "\n",
       "   total_profit  \n",
       "0       4536.51  \n",
       "2        113.75  \n",
       "3         75.79  \n",
       "4       1076.40  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding column total_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['total_cost'] = (transactions['sales_amount'])-(transactions['total_profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>product_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>profit</th>\n",
       "      <th>total_profit</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>45.37</td>\n",
       "      <td>4536.51</td>\n",
       "      <td>36704.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>1</td>\n",
       "      <td>875.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>875.00</td>\n",
       "      <td>13</td>\n",
       "      <td>761.25</td>\n",
       "      <td>113.75</td>\n",
       "      <td>113.75</td>\n",
       "      <td>761.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus003</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>1</td>\n",
       "      <td>583.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>583.00</td>\n",
       "      <td>13</td>\n",
       "      <td>507.21</td>\n",
       "      <td>75.79</td>\n",
       "      <td>75.79</td>\n",
       "      <td>507.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Cus004</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>6</td>\n",
       "      <td>7176.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>1196.00</td>\n",
       "      <td>15</td>\n",
       "      <td>1016.60</td>\n",
       "      <td>179.40</td>\n",
       "      <td>1076.40</td>\n",
       "      <td>6099.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "2      Prod002        Cus003     Mark003  2018-04-06          1        875.00   \n",
       "3      Prod002        Cus003     Mark003  2018-04-11          1        583.00   \n",
       "4      Prod002        Cus004     Mark003  2018-06-18          6       7176.00   \n",
       "\n",
       "  currency  product_price  profit_percentage  original_price  profit  \\\n",
       "0      INR         412.41                 11          367.04   45.37   \n",
       "2      INR         875.00                 13          761.25  113.75   \n",
       "3      INR         583.00                 13          507.21   75.79   \n",
       "4      INR        1196.00                 15         1016.60  179.40   \n",
       "\n",
       "   total_profit  total_cost  \n",
       "0       4536.51    36704.49  \n",
       "2        113.75      761.25  \n",
       "3         75.79      507.21  \n",
       "4       1076.40     6099.60  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### verifying values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "986676768.0"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['sales_amount'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "849375728.9"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['total_cost'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137301039.1"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['total_profit'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(transactions['total_cost'].sum() + transactions['total_profit'].sum()) - transactions['sales_amount'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 13)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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",
       "    .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>product_code</th>\n",
       "      <th>product_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prod002</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod003</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code product_type\n",
       "0      Prod001    Own Brand\n",
       "1      Prod002    Own Brand\n",
       "2      Prod003    Own Brand"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Own Brand', 'Distribution'], dtype=object)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products['product_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "279"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products['product_code'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "191"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(products[products['product_type'] == 'Own Brand'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(products[products['product_type'] == 'Distribution'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "338"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['product_code'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "prod_code1 = pd.Series(products['product_code'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "prod_code2 = pd.Series(transactions['product_code'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "274    Prod275\n",
       "275    Prod276\n",
       "276    Prod277\n",
       "277    Prod278\n",
       "278    Prod279\n",
       "dtype: object"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_code1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333    Prod335\n",
       "334    Prod336\n",
       "335    Prod337\n",
       "336    Prod338\n",
       "337    Prod339\n",
       "dtype: object"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_code2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(279, 338, 1):\n",
    "    products = products.append({'product_code' : prod_code2[x] , 'product_type' : 'Own Brand'}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "338"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products['product_code'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "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_code</th>\n",
       "      <th>product_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>Prod335</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>Prod336</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Prod337</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>Prod338</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>Prod339</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_code product_type\n",
       "333      Prod335    Own Brand\n",
       "334      Prod336    Own Brand\n",
       "335      Prod337    Own Brand\n",
       "336      Prod338    Own Brand\n",
       "337      Prod339    Own Brand"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 13)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions2 = pd.merge(transactions, products, how='inner', on='product_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "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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       "        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_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>profit</th>\n",
       "      <th>total_profit</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>product_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18453</th>\n",
       "      <td>Prod065</td>\n",
       "      <td>Cus019</td>\n",
       "      <td>Mark011</td>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>1</td>\n",
       "      <td>551.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>551.00</td>\n",
       "      <td>13</td>\n",
       "      <td>479.37</td>\n",
       "      <td>71.63</td>\n",
       "      <td>71.63</td>\n",
       "      <td>479.37</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145763</th>\n",
       "      <td>Prod334</td>\n",
       "      <td>Cus014</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>34</td>\n",
       "      <td>36079.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>1061.15</td>\n",
       "      <td>15</td>\n",
       "      <td>901.98</td>\n",
       "      <td>159.17</td>\n",
       "      <td>5411.85</td>\n",
       "      <td>30667.15</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48770</th>\n",
       "      <td>Prod133</td>\n",
       "      <td>Cus006</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2019-04-17</td>\n",
       "      <td>1</td>\n",
       "      <td>208.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>208.00</td>\n",
       "      <td>11</td>\n",
       "      <td>185.12</td>\n",
       "      <td>22.88</td>\n",
       "      <td>22.88</td>\n",
       "      <td>185.12</td>\n",
       "      <td>Own Brand</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_code customer_code market_code  order_date  sales_qty  \\\n",
       "18453       Prod065        Cus019     Mark011  2019-01-23          1   \n",
       "145763      Prod334        Cus014     Mark003  2020-04-27         34   \n",
       "48770       Prod133        Cus006     Mark004  2019-04-17          1   \n",
       "\n",
       "        sales_amount currency  product_price  profit_percentage  \\\n",
       "18453         551.00      INR         551.00                 13   \n",
       "145763      36079.00      INR        1061.15                 15   \n",
       "48770         208.00      INR         208.00                 11   \n",
       "\n",
       "        original_price  profit  total_profit  total_cost product_type  \n",
       "18453           479.37   71.63         71.63      479.37    Own Brand  \n",
       "145763          901.98  159.17       5411.85    30667.15    Own Brand  \n",
       "48770           185.12   22.88         22.88      185.12    Own Brand  "
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions2.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 14)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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_code</th>\n",
       "      <th>custmer_name</th>\n",
       "      <th>customer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cus001</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cus002</td>\n",
       "      <td>Nomad Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cus003</td>\n",
       "      <td>Excel Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_code  custmer_name   customer_type\n",
       "0        Cus001  Surge Stores  Brick & Mortar\n",
       "1        Cus002  Nomad Stores  Brick & Mortar\n",
       "2        Cus003  Excel Stores  Brick & Mortar"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brick & Mortar', 'E-Commerce'], dtype=object)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['customer_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['customer_type'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['customer_code'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions2['customer_code'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 14)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions3 = pd.merge(transactions2, customers, how='inner', on='customer_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "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",
       "    .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_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>profit</th>\n",
       "      <th>total_profit</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>product_type</th>\n",
       "      <th>custmer_name</th>\n",
       "      <th>customer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65305</th>\n",
       "      <td>Prod318</td>\n",
       "      <td>Cus012</td>\n",
       "      <td>Mark003</td>\n",
       "      <td>2019-09-13</td>\n",
       "      <td>13</td>\n",
       "      <td>16037.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>1233.62</td>\n",
       "      <td>15</td>\n",
       "      <td>1048.57</td>\n",
       "      <td>185.04</td>\n",
       "      <td>2405.55</td>\n",
       "      <td>13631.45</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Integration Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145364</th>\n",
       "      <td>Prod053</td>\n",
       "      <td>Cus018</td>\n",
       "      <td>Mark011</td>\n",
       "      <td>2019-05-22</td>\n",
       "      <td>1</td>\n",
       "      <td>176.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>176.00</td>\n",
       "      <td>11</td>\n",
       "      <td>156.64</td>\n",
       "      <td>19.36</td>\n",
       "      <td>19.36</td>\n",
       "      <td>156.64</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Electricalslance Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97605</th>\n",
       "      <td>Prod278</td>\n",
       "      <td>Cus020</td>\n",
       "      <td>Mark004</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>1</td>\n",
       "      <td>204.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>204.00</td>\n",
       "      <td>11</td>\n",
       "      <td>181.56</td>\n",
       "      <td>22.44</td>\n",
       "      <td>22.44</td>\n",
       "      <td>181.56</td>\n",
       "      <td>Distribution</td>\n",
       "      <td>Nixon</td>\n",
       "      <td>E-Commerce</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_code customer_code market_code  order_date  sales_qty  \\\n",
       "65305       Prod318        Cus012     Mark003  2019-09-13         13   \n",
       "145364      Prod053        Cus018     Mark011  2019-05-22          1   \n",
       "97605       Prod278        Cus020     Mark004  2020-02-24          1   \n",
       "\n",
       "        sales_amount currency  product_price  profit_percentage  \\\n",
       "65305       16037.00      INR        1233.62                 15   \n",
       "145364        176.00      INR         176.00                 11   \n",
       "97605         204.00      INR         204.00                 11   \n",
       "\n",
       "        original_price  profit  total_profit  total_cost  product_type  \\\n",
       "65305          1048.57  185.04       2405.55    13631.45     Own Brand   \n",
       "145364          156.64   19.36         19.36      156.64     Own Brand   \n",
       "97605           181.56   22.44         22.44      181.56  Distribution   \n",
       "\n",
       "                   custmer_name   customer_type  \n",
       "65305        Integration Stores  Brick & Mortar  \n",
       "145364  Electricalslance Stores  Brick & Mortar  \n",
       "97605                     Nixon      E-Commerce  "
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions3.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 16)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Markets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['markets_code', 'markets_name', 'zone'], dtype='object')"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "markets = markets.rename(columns={\"markets_code\": \"market_code\", \"markets_name\": \"market_name\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>market_code</th>\n",
       "      <th>market_name</th>\n",
       "      <th>zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mark001</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mark002</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mark003</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  market_code market_name     zone\n",
       "0     Mark001     Chennai    South\n",
       "1     Mark002      Mumbai  Central\n",
       "2     Mark003   Ahmedabad    North"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chennai', 'Mumbai', 'Ahmedabad', 'Delhi NCR', 'Kanpur',\n",
       "       'Bengaluru', 'Bhopal', 'Lucknow', 'Patna', 'Kochi', 'Nagpur',\n",
       "       'Surat', 'Hyderabad', 'Bhubaneshwar'], dtype=object)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets['market_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets['market_name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['South', 'Central', 'North'], dtype=object)"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets['zone'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets['zone'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets['market_code'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions3['market_code'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 16)"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions4 = pd.merge(transactions3, markets, how='inner', on='market_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>profit</th>\n",
       "      <th>total_profit</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>product_type</th>\n",
       "      <th>custmer_name</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>market_name</th>\n",
       "      <th>zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>45.37</td>\n",
       "      <td>4536.51</td>\n",
       "      <td>36704.49</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>45.37</td>\n",
       "      <td>4536.51</td>\n",
       "      <td>36704.49</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod013</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>240</td>\n",
       "      <td>143560.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>598.17</td>\n",
       "      <td>13</td>\n",
       "      <td>520.40</td>\n",
       "      <td>77.76</td>\n",
       "      <td>18662.80</td>\n",
       "      <td>124897.20</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "1      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "2      Prod013        Cus001     Mark001  2017-10-10        240     143560.00   \n",
       "\n",
       "  currency  product_price  profit_percentage  original_price  profit  \\\n",
       "0      INR         412.41                 11          367.04   45.37   \n",
       "1      INR         412.41                 11          367.04   45.37   \n",
       "2      INR         598.17                 13          520.40   77.76   \n",
       "\n",
       "   total_profit  total_cost product_type  custmer_name   customer_type  \\\n",
       "0       4536.51    36704.49    Own Brand  Surge Stores  Brick & Mortar   \n",
       "1       4536.51    36704.49    Own Brand  Surge Stores  Brick & Mortar   \n",
       "2      18662.80   124897.20    Own Brand  Surge Stores  Brick & Mortar   \n",
       "\n",
       "  market_name   zone  \n",
       "0     Chennai  South  \n",
       "1     Chennai  South  \n",
       "2     Chennai  South  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions4.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 18)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'cy_date', 'year', 'month_name', 'date_yy_mmm'], dtype='object')"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = date.rename(columns={\"date\": \"order_date\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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>order_date</th>\n",
       "      <th>cy_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month_name</th>\n",
       "      <th>date_yy_mmm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>June</td>\n",
       "      <td>17-Jun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>June</td>\n",
       "      <td>17-Jun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>June</td>\n",
       "      <td>17-Jun</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_date     cy_date  year month_name date_yy_mmm\n",
       "0  2017-06-01  2017-06-01  2017       June      17-Jun\n",
       "1  2017-06-02  2017-06-01  2017       June      17-Jun\n",
       "2  2017-06-03  2017-06-01  2017       June      17-Jun"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1126"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date['order_date'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "804"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions4['order_date'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 18)"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions5 = pd.merge(transactions4, date, how='inner', on='order_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "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_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>...</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>product_type</th>\n",
       "      <th>custmer_name</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>market_name</th>\n",
       "      <th>zone</th>\n",
       "      <th>cy_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month_name</th>\n",
       "      <th>date_yy_mmm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>...</td>\n",
       "      <td>36704.49</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>October</td>\n",
       "      <td>17-Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>...</td>\n",
       "      <td>36704.49</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>October</td>\n",
       "      <td>17-Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prod013</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>240</td>\n",
       "      <td>143560.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>598.17</td>\n",
       "      <td>13</td>\n",
       "      <td>520.40</td>\n",
       "      <td>...</td>\n",
       "      <td>124897.20</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>October</td>\n",
       "      <td>17-Oct</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "1      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "2      Prod013        Cus001     Mark001  2017-10-10        240     143560.00   \n",
       "\n",
       "  currency  product_price  profit_percentage  original_price  ...  total_cost  \\\n",
       "0      INR         412.41                 11          367.04  ...    36704.49   \n",
       "1      INR         412.41                 11          367.04  ...    36704.49   \n",
       "2      INR         598.17                 13          520.40  ...   124897.20   \n",
       "\n",
       "   product_type  custmer_name   customer_type market_name   zone     cy_date  \\\n",
       "0     Own Brand  Surge Stores  Brick & Mortar     Chennai  South  2017-10-01   \n",
       "1     Own Brand  Surge Stores  Brick & Mortar     Chennai  South  2017-10-01   \n",
       "2     Own Brand  Surge Stores  Brick & Mortar     Chennai  South  2017-10-01   \n",
       "\n",
       "   year month_name  date_yy_mmm  \n",
       "0  2017    October       17-Oct  \n",
       "1  2017    October       17-Oct  \n",
       "2  2017    October       17-Oct  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions5.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 22)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions5.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the file for Power BI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions5.to_csv('transactions_all.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['product_code', 'customer_code', 'market_code', 'order_date',\n",
       "       'sales_qty', 'sales_amount', 'currency', 'product_price',\n",
       "       'profit_percentage', 'original_price', 'profit', 'total_profit',\n",
       "       'total_cost', 'product_type', 'custmer_name', 'customer_type',\n",
       "       'market_name', 'zone', 'cy_date', 'year', 'month_name', 'date_yy_mmm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions5.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(transactions5.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_code          object\n",
       "customer_code         object\n",
       "market_code           object\n",
       "order_date            object\n",
       "sales_qty              int64\n",
       "sales_amount         float64\n",
       "currency              object\n",
       "product_price        float64\n",
       "profit_percentage      int64\n",
       "original_price       float64\n",
       "profit               float64\n",
       "total_profit         float64\n",
       "total_cost           float64\n",
       "product_type          object\n",
       "custmer_name          object\n",
       "customer_type         object\n",
       "market_name           object\n",
       "zone                  object\n",
       "cy_date               object\n",
       "year                   int64\n",
       "month_name            object\n",
       "date_yy_mmm           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions5.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./transactions_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "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_code</th>\n",
       "      <th>customer_code</th>\n",
       "      <th>market_code</th>\n",
       "      <th>order_date</th>\n",
       "      <th>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>currency</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>...</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>product_type</th>\n",
       "      <th>custmer_name</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>market_name</th>\n",
       "      <th>zone</th>\n",
       "      <th>cy_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month_name</th>\n",
       "      <th>date_yy_mmm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>...</td>\n",
       "      <td>36704.49</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>October</td>\n",
       "      <td>17-Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prod001</td>\n",
       "      <td>Cus001</td>\n",
       "      <td>Mark001</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>100</td>\n",
       "      <td>41241.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>412.41</td>\n",
       "      <td>11</td>\n",
       "      <td>367.04</td>\n",
       "      <td>...</td>\n",
       "      <td>36704.49</td>\n",
       "      <td>Own Brand</td>\n",
       "      <td>Surge Stores</td>\n",
       "      <td>Brick &amp; Mortar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>South</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>October</td>\n",
       "      <td>17-Oct</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_code customer_code market_code  order_date  sales_qty  sales_amount  \\\n",
       "0      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "1      Prod001        Cus001     Mark001  2017-10-10        100      41241.00   \n",
       "\n",
       "  currency  product_price  profit_percentage  original_price  ...  total_cost  \\\n",
       "0      INR         412.41                 11          367.04  ...    36704.49   \n",
       "1      INR         412.41                 11          367.04  ...    36704.49   \n",
       "\n",
       "   product_type  custmer_name   customer_type market_name   zone     cy_date  \\\n",
       "0     Own Brand  Surge Stores  Brick & Mortar     Chennai  South  2017-10-01   \n",
       "1     Own Brand  Surge Stores  Brick & Mortar     Chennai  South  2017-10-01   \n",
       "\n",
       "   year month_name  date_yy_mmm  \n",
       "0  2017    October       17-Oct  \n",
       "1  2017    October       17-Oct  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148672, 22)"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "986676768.0"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sales_amount'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "849375728.9"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_cost'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137301039.10000002"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_profit'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sales_amount'].sum() - (df['total_cost'].sum() + df['total_profit'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "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>sales_qty</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>product_price</th>\n",
       "      <th>profit_percentage</th>\n",
       "      <th>original_price</th>\n",
       "      <th>profit</th>\n",
       "      <th>total_profit</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sales_qty</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales_amount</th>\n",
       "      <td>0.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_price</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit_percentage</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_price</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_profit</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_cost</th>\n",
       "      <td>0.36</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sales_qty  sales_amount  product_price  profit_percentage  \\\n",
       "sales_qty               1.00          0.34          -0.01              -0.05   \n",
       "sales_amount            0.34          1.00           0.30               0.25   \n",
       "product_price          -0.01          0.30           1.00               0.88   \n",
       "profit_percentage      -0.05          0.25           0.88               1.00   \n",
       "original_price         -0.02          0.30           1.00               0.88   \n",
       "profit                 -0.01          0.31           0.99               0.84   \n",
       "total_profit            0.24          0.98           0.32               0.27   \n",
       "total_cost              0.36          1.00           0.30               0.25   \n",
       "year                    0.00         -0.00           0.01               0.01   \n",
       "\n",
       "                   original_price  profit  total_profit  total_cost  year  \n",
       "sales_qty                   -0.02   -0.01          0.24        0.36  0.00  \n",
       "sales_amount                 0.30    0.31          0.98        1.00 -0.00  \n",
       "product_price                1.00    0.99          0.32        0.30  0.01  \n",
       "profit_percentage            0.88    0.84          0.27        0.25  0.01  \n",
       "original_price               1.00    0.98          0.32        0.29  0.01  \n",
       "profit                       0.98    1.00          0.34        0.30  0.00  \n",
       "total_profit                 0.32    0.34          1.00        0.97 -0.00  \n",
       "total_cost                   0.29    0.30          0.97        1.00 -0.00  \n",
       "year                         0.01    0.00         -0.00       -0.00  1.00  "
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a4c92c1640>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}