Task 2 Exploratory Data Analysis - Retail.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE SPARKS FOUNDATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science & Business Analytics Internship GRIP Feb 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author : Udita Goswami"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK 3 : Exploratory Data Analysis - Retail"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset -'SampleSuperstore'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query - As a business manager, try to find out the weak areas where you can\n",
    "        work to make more profit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user2\\Documents\\PG Notes & Books\\Semester 1\\Internship\n"
     ]
    }
   ],
   "source": [
    "cd C:\\Users\\user2\\Documents\\PG Notes & Books\\Semester 1\\Internship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'SampleSuperstore.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33180</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>25.2480</td>\n",
       "      <td>3</td>\n",
       "      <td>0.20</td>\n",
       "      <td>4.1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>91.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.6332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>258.5760</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>19.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>29.6000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>California</td>\n",
       "      <td>92683</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>243.1600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.9480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9994 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ship Mode    Segment        Country             City       State  \\\n",
       "0       Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "1       Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "2       Second Class  Corporate  United States      Los Angeles  California   \n",
       "3     Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4     Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "...              ...        ...            ...              ...         ...   \n",
       "9989    Second Class   Consumer  United States            Miami     Florida   \n",
       "9990  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9991  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9992  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9993    Second Class   Consumer  United States      Westminster  California   \n",
       "\n",
       "      Postal Code Region         Category Sub-Category     Sales  Quantity  \\\n",
       "0           42420  South        Furniture    Bookcases  261.9600         2   \n",
       "1           42420  South        Furniture       Chairs  731.9400         3   \n",
       "2           90036   West  Office Supplies       Labels   14.6200         2   \n",
       "3           33311  South        Furniture       Tables  957.5775         5   \n",
       "4           33311  South  Office Supplies      Storage   22.3680         2   \n",
       "...           ...    ...              ...          ...       ...       ...   \n",
       "9989        33180  South        Furniture  Furnishings   25.2480         3   \n",
       "9990        92627   West        Furniture  Furnishings   91.9600         2   \n",
       "9991        92627   West       Technology       Phones  258.5760         2   \n",
       "9992        92627   West  Office Supplies        Paper   29.6000         4   \n",
       "9993        92683   West  Office Supplies   Appliances  243.1600         2   \n",
       "\n",
       "      Discount    Profit  \n",
       "0         0.00   41.9136  \n",
       "1         0.00  219.5820  \n",
       "2         0.00    6.8714  \n",
       "3         0.45 -383.0310  \n",
       "4         0.20    2.5164  \n",
       "...        ...       ...  \n",
       "9989      0.20    4.1028  \n",
       "9990      0.00   15.6332  \n",
       "9991      0.20   19.3932  \n",
       "9992      0.00   13.3200  \n",
       "9993      0.00   72.9480  \n",
       "\n",
       "[9994 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Ship Mode    Segment        Country             City       State  \\\n",
       "0    Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "1    Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "2    Second Class  Corporate  United States      Los Angeles  California   \n",
       "3  Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4  Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region         Category Sub-Category     Sales  Quantity  \\\n",
       "0        42420  South        Furniture    Bookcases  261.9600         2   \n",
       "1        42420  South        Furniture       Chairs  731.9400         3   \n",
       "2        90036   West  Office Supplies       Labels   14.6200         2   \n",
       "3        33311  South        Furniture       Tables  957.5775         5   \n",
       "4        33311  South  Office Supplies      Storage   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9994, 13)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ship Mode        object\n",
       "Segment          object\n",
       "Country          object\n",
       "City             object\n",
       "State            object\n",
       "Postal Code       int64\n",
       "Region           object\n",
       "Category         object\n",
       "Sub-Category     object\n",
       "Sales           float64\n",
       "Quantity          int64\n",
       "Discount        float64\n",
       "Profit          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of duplicate rows: (17, 13)\n"
     ]
    }
   ],
   "source": [
    "duplicate_rows_df=df[df.duplicated()]\n",
    "print(\"number of duplicate rows:\",duplicate_rows_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33180</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>25.2480</td>\n",
       "      <td>3</td>\n",
       "      <td>0.20</td>\n",
       "      <td>4.1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>91.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.6332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>258.5760</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>19.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>29.6000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>California</td>\n",
       "      <td>92683</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>243.1600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.9480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9977 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ship Mode    Segment        Country             City       State  \\\n",
       "0       Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "1       Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "2       Second Class  Corporate  United States      Los Angeles  California   \n",
       "3     Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4     Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "...              ...        ...            ...              ...         ...   \n",
       "9989    Second Class   Consumer  United States            Miami     Florida   \n",
       "9990  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9991  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9992  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9993    Second Class   Consumer  United States      Westminster  California   \n",
       "\n",
       "      Postal Code Region         Category Sub-Category     Sales  Quantity  \\\n",
       "0           42420  South        Furniture    Bookcases  261.9600         2   \n",
       "1           42420  South        Furniture       Chairs  731.9400         3   \n",
       "2           90036   West  Office Supplies       Labels   14.6200         2   \n",
       "3           33311  South        Furniture       Tables  957.5775         5   \n",
       "4           33311  South  Office Supplies      Storage   22.3680         2   \n",
       "...           ...    ...              ...          ...       ...       ...   \n",
       "9989        33180  South        Furniture  Furnishings   25.2480         3   \n",
       "9990        92627   West        Furniture  Furnishings   91.9600         2   \n",
       "9991        92627   West       Technology       Phones  258.5760         2   \n",
       "9992        92627   West  Office Supplies        Paper   29.6000         4   \n",
       "9993        92683   West  Office Supplies   Appliances  243.1600         2   \n",
       "\n",
       "      Discount    Profit  \n",
       "0         0.00   41.9136  \n",
       "1         0.00  219.5820  \n",
       "2         0.00    6.8714  \n",
       "3         0.45 -383.0310  \n",
       "4         0.20    2.5164  \n",
       "...        ...       ...  \n",
       "9989      0.20    4.1028  \n",
       "9990      0.00   15.6332  \n",
       "9991      0.20   19.3932  \n",
       "9992      0.00   13.3200  \n",
       "9993      0.00   72.9480  \n",
       "\n",
       "[9977 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9977, 13)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Ship Mode    Segment        Country             City       State  \\\n",
       "0    Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "1    Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "2    Second Class  Corporate  United States      Los Angeles  California   \n",
       "3  Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4  Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region         Category Sub-Category     Sales  Quantity  \\\n",
       "0        42420  South        Furniture    Bookcases  261.9600         2   \n",
       "1        42420  South        Furniture       Chairs  731.9400         3   \n",
       "2        90036   West  Office Supplies       Labels   14.6200         2   \n",
       "3        33311  South        Furniture       Tables  957.5775         5   \n",
       "4        33311  South  Office Supplies      Storage   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ship Mode       0\n",
      "Segment         0\n",
      "Country         0\n",
      "City            0\n",
      "State           0\n",
      "Postal Code     0\n",
      "Region          0\n",
      "Category        0\n",
      "Sub-Category    0\n",
      "Sales           0\n",
      "Quantity        0\n",
      "Discount        0\n",
      "Profit          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df1.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Quantity'>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df1['Quantity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Discount'>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df1['Discount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are outliers in both the cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Profit based on Ship Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Second Class', 'Standard Class', 'First Class', 'Same Day'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Ship Mode'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Standard Class    5955\n",
       "Second Class      1943\n",
       "First Class       1537\n",
       "Same Day           542\n",
       "Name: Ship Mode, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Ship Mode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ship Mode', ylabel='count'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbvElEQVR4nO3dfbhVdZ338fdHNMWSFDkyxMEghzsTfEhOpOM9adEk3VY4JUl3JjlOTI6ZzkyWzpSjNVzZ1dOEBhNZAfk0jA/J5GXJMKJ3StIh0SMoyqgpAwE6PaApCX7vP36/E8vDPue3gbPPg+fzuq597bW/Z621f3utffZnPez124oIzMzMurJXbzfAzMz6PoeFmZkVOSzMzKzIYWFmZkUOCzMzK9q7txvQKMOGDYvRo0f3djPMzPqVFStWPB0RTR3rr9iwGD16NK2trb3dDDOzfkXSL2rVfRjKzMyKHBZmZlbksDAzsyKHhZmZFTU0LCQdKOkGSQ9LekjS8ZKGSlos6dF8f1Bl/IslrZW0RtLJlfoESW35b7MkqZHtNjOzl2v0nsU3gB9FxOHA0cBDwEXAkogYCyzJj5F0BDANGAdMBmZLGpTnMweYAYzNt8kNbreZmVU0LCwkDQHeBnwHICJ+HxG/BqYA8/No84FT8/AU4PqI2BoRjwNrgYmSRgBDImJZpC5yF1SmMTOzHtDIPYs3AJuB70m6T9JVkl4NDI+IDQD5/pA8/kjgqcr063JtZB7uWN+JpBmSWiW1bt68uXtfjZnZANbIsNgbOBaYExFvBp4jH3LqRK3zENFFfedixNyIaImIlqamnS5ANDOz3dTIK7jXAesi4t78+AZSWGyUNCIiNuRDTJsq44+qTN8MrM/15hp160Oe/PyRvd2EPuPQS9p6uwlm3a5hexYR8UvgKUlvzKVJwGpgETA916YDt+ThRcA0SftKGkM6kb08H6raIum4/C2oMyvTmJlZD2h031DnAddIehXwGHAWKaAWSjobeBKYChARqyQtJAXKNuDciNie53MOMA8YDNyWb2Zm1kMaGhYRsRJoqfGnSZ2MPxOYWaPeCozv1saZmVndfAW3mZkVOSzMzKzIYWFmZkUOCzMzK3JYmJlZkcPCzMyKHBZmZlbksDAzsyKHhZmZFTkszMysyGFhZmZFDgszMytyWJiZWZHDwszMihwWZmZW5LAwM7Mih4WZmRU5LMzMrMhhYWZmRQ4LMzMrcliYmVmRw8LMzIocFmZmVuSwMDOzIoeFmZkVOSzMzKyooWEh6QlJbZJWSmrNtaGSFkt6NN8fVBn/YklrJa2RdHKlPiHPZ62kWZLUyHabmdnL9cSexdsj4piIaMmPLwKWRMRYYEl+jKQjgGnAOGAyMFvSoDzNHGAGMDbfJvdAu83MLOuNw1BTgPl5eD5waqV+fURsjYjHgbXAREkjgCERsSwiAlhQmcbMzHpAo8MigNslrZA0I9eGR8QGgHx/SK6PBJ6qTLsu10bm4Y71nUiaIalVUuvmzZu78WWYmQ1sezd4/idExHpJhwCLJT3cxbi1zkNEF/WdixFzgbkALS0tNccxM7Nd19A9i4hYn+83ATcDE4GN+dAS+X5THn0dMKoyeTOwPteba9TNzKyHNCwsJL1a0gHtw8C7gAeBRcD0PNp04JY8vAiYJmlfSWNIJ7KX50NVWyQdl78FdWZlGjMz6wGNPAw1HLg5f8t1b+DaiPiRpJ8BCyWdDTwJTAWIiFWSFgKrgW3AuRGxPc/rHGAeMBi4Ld/MzKyHNCwsIuIx4Oga9WeASZ1MMxOYWaPeCozv7jaamVl9fAW3mZkVOSzMzKzIYWFmZkUOCzMzK3JYmJlZkcPCzMyKHBZmZlbksDAzsyKHhZmZFTkszMysyGFhZmZFDgszMytyWJiZWZHDwszMihwWZmZW5LAwM7Mih4WZmRU5LMzMrMhhYWZmRQ4LMzMrcliYmVmRw8LMzIocFmZmVuSwMDOzIoeFmZkVOSzMzKyo4WEhaZCk+yT9MD8eKmmxpEfz/UGVcS+WtFbSGkknV+oTJLXlv82SpEa328zMduiJPYvzgYcqjy8ClkTEWGBJfoykI4BpwDhgMjBb0qA8zRxgBjA23yb3QLvNzCxraFhIagZOAa6qlKcA8/PwfODUSv36iNgaEY8Da4GJkkYAQyJiWUQEsKAyjZmZ9YBG71n8M/Bp4KVKbXhEbADI94fk+kjgqcp463JtZB7uWN+JpBmSWiW1bt68uVtegJmZNTAsJL0H2BQRK+qdpEYtuqjvXIyYGxEtEdHS1NRU59OamVnJ3g2c9wnA+yT9H2A/YIikq4GNkkZExIZ8iGlTHn8dMKoyfTOwPteba9TNzKyHNGzPIiIujojmiBhNOnH9nxFxBrAImJ5Hmw7ckocXAdMk7StpDOlE9vJ8qGqLpOPyt6DOrExjZmY9oJF7Fp25HFgo6WzgSWAqQESskrQQWA1sA86NiO15mnOAecBg4LZ8MzOzHtIjYRERS4GlefgZYFIn480EZtaotwLjG9dCMzPriq/gNjOzIoeFmZkVOSzMzKzIYWFmZkUOCzMzK3JYmJlZkcPCzMyKHBZmZlbksDAzsyKHhZmZFTkszMysyGFhZmZFDgszMyuqKywkLamnZmZmr0xddlEuaT9gf2CYpIPY8ROnQ4DXNbhtZmbWR5R+z+KvgAtIwbCCHWHxW+CbjWuWmZn1JV2GRUR8A/iGpPMi4ooeapOZmfUxdf1SXkRcIelPgNHVaSJiQYPaZWZmfUhdYSHp+8BhwEqg/XexA3BYmJkNAPX+BncLcERERCMbY2ZmfVO911k8CPxRIxtiZmZ9V717FsOA1ZKWA1vbixHxvoa0yszM+pR6w+LSRjbCzMz6tnq/DXVnoxtiZmZ9V73fhtpC+vYTwKuAfYDnImJIoxpmZmZ9R10nuCPigIgYkm/7AR8AruxqGkn7SVou6X5JqyRdlutDJS2W9Gi+P6gyzcWS1kpaI+nkSn2CpLb8t1mSVOs5zcysMXar19mI+AHwjsJoW4F3RMTRwDHAZEnHARcBSyJiLLAkP0bSEcA0YBwwGZgtaVCe1xxgBjA23ybvTrvNzGz31HsY6v2Vh3uRrrvo8pqLfE3Gs/nhPvkWwBTgpFyfDywFPpPr10fEVuBxSWuBiZKeAIZExLLclgXAqcBt9bTdzMz2XL3fhnpvZXgb8ATpw71Lec9gBfDHwDcj4l5JwyNiA0BEbJB0SB59JPDTyuTrcu3FPNyxXuv5ZpD2QDj00EPLr8rMzOpS77ehztqdmUfEduAYSQcCN0sa38Xotc5DRBf1Ws83F5gL0NLS4qvNzcy6Sb0/ftQs6WZJmyRtlHSjpOZ6nyQifk063DQZ2ChpRJ7vCGBTHm0dMKoyWTOwPteba9TNzKyH1HuC+3vAItLvWowE/j3XOiWpKe9RIGkw8E7g4Tyf6Xm06cAteXgRME3SvpLGkE5kL8+HrLZIOi5/C+rMyjRmZtYD6j1n0RQR1XCYJ+mCwjQjgPn5vMVewMKI+KGkZcBCSWcDTwJTASJilaSFwGrSeZFz82EsgHOAecBg0oltn9w2M+tB9YbF05LOAK7Ljz8EPNPVBBHxAPDmGvVngEmdTDMTmFmj3gp0db7DzMwaqN7DUH8BfBD4JbABOA3YrZPeZmbW/9S7Z/EFYHpE/ArSVdjAV0ghYmZmr3D17lkc1R4UABHxP9Q4xGRmZq9M9YbFXh36cBpK/XslZmbWz9X7gf9V4B5JN5AuiPsgNU5Em5nZK1O9V3AvkNRK6jxQwPsjYnVDW2ZmZn1G3YeScjg4IMzMBqDd6qLczMwGFoeFmZkVOSzMzKzIYWFmZkUOCzMzK3JYmJlZkcPCzMyKHBZmZlbksDAzsyKHhZmZFTkszMysyGFhZmZFDgszMytyWJiZWZHDwszMihwWZmZW5LAwM7Mih4WZmRU5LMzMrKhhYSFplKQ7JD0kaZWk83N9qKTFkh7N9wdVprlY0lpJaySdXKlPkNSW/zZLkhrVbjMz21kj9yy2AX8XEW8CjgPOlXQEcBGwJCLGAkvyY/LfpgHjgMnAbEmD8rzmADOAsfk2uYHtNjOzDhoWFhGxISJ+noe3AA8BI4EpwPw82nzg1Dw8Bbg+IrZGxOPAWmCipBHAkIhYFhEBLKhMY2ZmPaBHzllIGg28GbgXGB4RGyAFCnBIHm0k8FRlsnW5NjIPd6zXep4ZkloltW7evLlbX4OZ2UDW8LCQ9BrgRuCCiPhtV6PWqEUX9Z2LEXMjoiUiWpqamna9sWZmVtPejZy5pH1IQXFNRNyUyxsljYiIDfkQ06ZcXweMqkzeDKzP9eYa9T0y4cIFezqLV4wVXz6zt5tgZn1cI78NJeA7wEMR8bXKnxYB0/PwdOCWSn2apH0ljSGdyF6eD1VtkXRcnueZlWnMzKwHNHLP4gTgI0CbpJW59vfA5cBCSWcDTwJTASJilaSFwGrSN6nOjYjtebpzgHnAYOC2fDMzsx7SsLCIiJ9Q+3wDwKROppkJzKxRbwXGd1/rzMxsV/gKbjMzK3JYmJlZkcPCzMyKHBZmZlbksDAzsyKHhZmZFTkszMysyGFhZmZFDgszMytyWJiZWVFDe501s113whUn9HYT+oy7z7u7t5tgmfcszMysyGFhZmZFDgszMytyWJiZWZHDwszMihwWZmZW5LAwM7Mih4WZmRU5LMzMrMhhYWZmRQ4LMzMrcliYmVmRw8LMzIocFmZmVuSwMDOzooaFhaTvStok6cFKbaikxZIezfcHVf52saS1ktZIOrlSnyCpLf9tliQ1qs1mZlZbI/cs5gGTO9QuApZExFhgSX6MpCOAacC4PM1sSYPyNHOAGcDYfOs4TzMza7CGhUVE3AX8T4fyFGB+Hp4PnFqpXx8RWyPicWAtMFHSCGBIRCyLiAAWVKYxM7Me0tPnLIZHxAaAfH9Iro8EnqqMty7XRubhjvWaJM2Q1CqpdfPmzd3acDOzgayvnOCudR4iuqjXFBFzI6IlIlqampq6rXFmZgNdT4fFxnxoiXy/KdfXAaMq4zUD63O9uUbdzMx6UE+HxSJgeh6eDtxSqU+TtK+kMaQT2cvzoaotko7L34I6szKNmZn1kL0bNWNJ1wEnAcMkrQP+EbgcWCjpbOBJYCpARKyStBBYDWwDzo2I7XlW55C+WTUYuC3fzMysBzUsLCLiQ538aVIn488EZtaotwLju7FpZma2i/rKCW4zM+vDHBZmZlbksDAzsyKHhZmZFTkszMysyGFhZmZFDgszMytyWJiZWZHDwszMihwWZmZW1LDuPszM+oI733Zibzehzzjxrjt3e1rvWZiZWZHDwszMihwWZmZW5LAwM7Mih4WZmRU5LMzMrMhhYWZmRQ4LMzMrcliYmVmRw8LMzIocFmZmVuSwMDOzIoeFmZkVOSzMzKzIYWFmZkX9JiwkTZa0RtJaSRf1dnvMzAaSfhEWkgYB3wTeDRwBfEjSEb3bKjOzgaNfhAUwEVgbEY9FxO+B64EpvdwmM7MBQxHR220oknQaMDki/jI//gjw1oj4RIfxZgAz8sM3Amt6tKG7ZxjwdG834hXCy7J7eXl2r/6yPF8fEU0di/3lN7hVo7ZTykXEXGBu45vTfSS1RkRLb7fjlcDLsnt5eXav/r48+8thqHXAqMrjZmB9L7XFzGzA6S9h8TNgrKQxkl4FTAMW9XKbzMwGjH5xGCoitkn6BPBjYBDw3YhY1cvN6i796rBZH+dl2b28PLtXv16e/eIEt5mZ9a7+chjKzMx6kcPCzMyKBnRYSPoHSaskPSBppaS39kIbTpL0w07+NlHSXbmbk4clXSVpf0kflXRlT7e1RvtqLj9JF0javxuf5wlJw/Zg+k6Xl6R3S2qV9FBexl/J9UslfWp3n7MnSNqel3v7bbSke3ZxHp2uK0n7SLpc0qOSHpS0XNK789/2aJ30Nb35WSBpaf4ffyC/B6+UdGBPPX+9+sUJ7kaQdDzwHuDYiNia3/iv6uVm/YGk4cC/AdMiYpkkAR8ADujdliWF5XcBcDXwu15q26CI2F7HeOOBK4FTIuJhSXuz46LO/uD5iDimQ+1POo5UWB4X0Pm6+gIwAhif1/Fw4MTdb27f1Ec+Cz4cEa35255fBG6hjy3rgbxnMQJ4OiK2AkTE0xGxHkDSBEl3Sloh6ceSRuT6H0v6D0n3S/q5pMOUfDlvebVJOj2Pe1LeYrghby1ckz/w2ztFfFjST4D3d9K+c4H5EbEsty8i4oaI2FgdSdJ7Jd0r6b7ctuG5fmJli/M+SQdIGpH3VFbm9v5pdy8/SZ8EXgfcIemO3JY5eet9laTLKm1/QtJleVm2STo81w+WdHtu97eoXJQp6Qd5vaxSumK/vf6spM9Luhc4XtJZkh6RdCdwQiev4dPAzIh4OL+GbRExu+NIkj4m6Wd5vd/YviUuaWpejvdLuivXxuUt8JV5S3Hs7i/iXSfp2Xx/kqQ7JF0LtEl6taRbc1sflHR6rXVVmc/+wMeA8yrreGNELKzxnDutE0mDJM2r/F/8Ta5/UtLqvGyub+jCqF9XnwWX5HX/oKS5lf/hpZK+nv+fHpL0Fkk3Ke2F/VP7jCWdUXk/fEupn7tO5e6MPg0cKunoPI9ay/dsSV+vPM/HJH2tm5fLTo0bkDfgNcBK4BFgNnBiru8D3AM05cenk76qC3Av8Od5eD9gf9LW/mLSV3qHA0+S3nwnAb8hXUC4F7AM+N95uqeAsaQPwYXAD2u07yZgSidt/yhwZR4+iB3favtL4Kt5+N+BEyqvdW/g74B/yLVBwAHdvfzy354AhlUeD60851LgqMp45+XhvwauysOzgEvy8Cmkq/WHdZjXYOBB4OD8OIAP5uEReT00kbYQ725fXh1ew8+Bozt5fZcCn8rDB1fq/1RpcxswMg8fmO+vIG0lkp97cAPfw9vzOlgJ3Jxrz+b7k4DngDH58QeAb1emfW2tdVX5+1HAfV089x+mq7VOgAnA4sr47ctnPbBvtdbbt8J7eWhl+PvAe/PwUuBLefj8/LpGAPuSLiI+GHgT6f9wnzzebODMGs+/FGjpUPsBcHoXy/fVwH9V5n0PcGQjl9OA3bOIiGdJb+gZwGbgXyV9lNSn1HhgsaSVwGeBZkkHkD4Ybs7TvxARvyMFwHURsT3SVv+dwFvy0yyPiHUR8RLpzTgaOBx4PCIejbSWr97Dl9IM/FhSG3AhMC7X7wa+lrceD4yIbaSLG8+SdCnpjbVld5+0i+VXywcl/Ry4L7ev2mPwTfl+BWn5ALyNvFwi4lbgV5XxPynpfuCnpKv627fctwM35uG3AksjYnOkLbV/3Y2XWDVe0v/Ly/jDvHwZz5P0MVIQQtoo+HtJnyH1sfP8Hj53V56PiGPy7c9r/H15RDyeh9uAd0r6kqQ/jYjfdGM7aq2Tx4A3SLpC0mTgt3ncB4BrJJ0BbOvGNuy2wnv57Up77m3AO9ix7mHHhcFtwKqI2BBp7+Qx0nKYlOf7s/xZMgl4Q53NqnZxtNPyjYjngP8E3pP3yPeJiLZdeNm7bMCGBUD+gF8aEf8IfIK09SXSim//JzwyIt5F7f6p6KIOsLUyvJ0d54jqubhlFemNVnIFaav5SOCvSHsuRMTlpD2NwcBPJR0eEXeRPoj/G/i+pDPrmH+nOll+LyNpDPApYFJEHAXc2t7GrH0ZVZcP1FhGkk4C3gkcHxFHk8KnfV4vxMuPy3fnMp4HfCIv48vYsYw/TtqYGAWslHRwRFwLvA94nhTi76hj/o3yXPtARDxCeq1twBclXVKYdi3pUEiX58g6WycR8SvgaNJW87nAVXmSU0g/NzABWKF0nqjX1XovS9qPtDdwWl7336b2e/clXv6//hLpvSzSoeT2z5I3RsSlpbbkQ1VHAg8V3vNXkY4ynAV8b5df9C4asGEh6Y0djicfA/yC1FNtk9JJr/ZvhIyLiN8C6ySdmuv75uO6dwGn52O0TaQP4+VdPPXDwBhJh+XHH+pkvCuB6ap8KyMf//yjDuO9lvThDzC9Mu5hEdEWEV8CWoHDJb0e2BQR3wa+AxzbRTu71MXyA9jCjhPxQ0gfWr9ROp/y7jpmfxdpCx6lb98clOuvBX4VEb/LW1PHdTL9vcBJSuc+9gGmdjLel0l7Af8rP9dekv62xngHABvyvD7cXszL+N6IuITUm+goSW8AHouIWaQtz6PqeL0NJ+l1wO8i4mrgK+xY99V19Qd5r/k7wCylk64onfM6o8OoNdeJ0knivSLiRuBzwLGS9gJGRcQdpOPyB5IOAfWqLt7L7R/KT0t6DXDaLs56CXCapEPy8wzN/4NdtWUf0gnupyLiAbp4z0fEvaQNlf8LXLeLbdtlfSLVe8lrgCuUvqK2jbQlNSMifq/UJfosSa8lLaN/Jm2FfgT4lqTPAy+SPoRuBo4H7idtzX46In6ZV+xOIuKFfJLqVklPAz8hHfbqON5GSdOAr+Q320ukD9GbOox6KfBvkv6btJs6JtcvkPR20hb7auA2Up9aF0p6EXgW2JM9i5rLL/9tLnCbpA0R8XZJ95GW32OkQzcllwHX5UNXd5LOPwD8CPi4pAdIof7TWhNHxIZ8qG0ZsIF0bmKnE4sR8YCkC/Jz7U9af7fWmOXnSAH0C9KWefuH65fzh4xIHwz3AxcBZ+Rl/Evg83W83p5wJKm9L5Heu+fk+svWVYdpPks6R7Na0guk0O+4R9LZOhkJfC8HBMDFpHVwdf6/EvD1iPh1d73APdDZZ8GvJX2btM6fIB3GrVtErJb0WeD2vBxeJO1l/aLG6NdI2ko65/Ef7Pi9ntJ7fiFwTN6Tayh392Fm1k8pXaP19YhY0ujnGrCHoczM+itJB0p6hPQlh4YHBXjPwszM6uA9CzMzK3JYmJlZkcPCzMyKHBZmFeq8J92avaxKep+ki3Zh/qMlhaQvVGrDJL2oXexJWLkfKLOeMJCvszB7Ge1G76MRsYhd/z34x/LzfC4/nkq6DsWsz/KehdkOnfY+mp2nnXvI/cNvZSj1svovSv1IPSLpPZ08z/Okrhxa8uPTSRdXkefzeklL8t7NEkmH5voYScuUekH9QnWGki7M9QdU6dnXrLs4LMx2uJ3UZccjkmZL6vh7Ak9HxLHAHFJ/V7WMJv0OwSnAvyj1L1TL9cA0Sc2kq+yroXQlsCD3pXUNqRdegG8AcyLiLaSrwwGQ9C5S530TSV1VTJD0tjper1ndHBZmWR096dbqIbejhRHxUkQ8SjrcVLPbF1I3Dn9G6husY6+4xwPX5uHvk3o2hvS7HNdV6u3elW/3kbo2OZwdvfGadQufszCryD3XLgWWKnVLPZ3U6yx03kPuy2ZReNz+PL+XtIL0GyPjgPd21azC/AR8MSK+1cU8zPaI9yzMskJPuvWamnuvPYz02wVruhj3q8BnIuKZDvV7SJ0+Qurl9id5+O4O9XY/Bv4i94yKpJHtPZ2adRfvWZjt0FVPuvVaQ+opdzjw8Yh4obMRI2IVtb8F9Ungu5IuJB0OOyvXzweulXQ+O37oiYi4XdKbgGVKv/r5LHAGsGkX227WKfcNZdZNJM0j/UTuDb3dFrPu5sNQZmZW5D0LMzMr8p6FmZkVOSzMzKzIYWFmZkUOCzMzK3JYmJlZ0f8HkqgtA9CCCp4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df1['Ship Mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard class Ship mode has the highest count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ship Mode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>First Class</th>\n",
       "      <td>228.497024</td>\n",
       "      <td>0.164610</td>\n",
       "      <td>31.839948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Same Day</th>\n",
       "      <td>236.396179</td>\n",
       "      <td>0.152394</td>\n",
       "      <td>29.266591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second Class</th>\n",
       "      <td>236.089239</td>\n",
       "      <td>0.138895</td>\n",
       "      <td>29.535545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Class</th>\n",
       "      <td>227.583067</td>\n",
       "      <td>0.160023</td>\n",
       "      <td>27.494770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Sales  Discount     Profit\n",
       "Ship Mode                                      \n",
       "First Class     228.497024  0.164610  31.839948\n",
       "Same Day        236.396179  0.152394  29.266591\n",
       "Second Class    236.089239  0.138895  29.535545\n",
       "Standard Class  227.583067  0.160023  27.494770"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_shipmodes=df.groupby(['Ship Mode'])['Sales','Discount','Profit'].mean()\n",
    "df1_shipmodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163969.228"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Ship Mode']=='Standard Class'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48953.6561"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Ship Mode']=='First Class'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57446.651600000005"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Ship Mode']=='Second Class'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15871.886900000001"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Ship Mode']=='Same Day'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='Sales'>, <AxesSubplot:ylabel='Discount'>,\n",
       "       <AxesSubplot:ylabel='Profit'>], dtype=object)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1_shipmodes.plot.pie(subplots=True,autopct='%1.2f%%',figsize=(18,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Maximum profit has come from the First class Ship mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Profit based on Discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.45, 0.2 , 0.8 , 0.3 , 0.5 , 0.7 , 0.6 , 0.32, 0.1 , 0.4 ,\n",
       "       0.15])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Discount'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00    4787\n",
       "0.20    3653\n",
       "0.70     418\n",
       "0.80     299\n",
       "0.30     226\n",
       "0.40     206\n",
       "0.60     138\n",
       "0.10      94\n",
       "0.50      66\n",
       "0.15      52\n",
       "0.32      27\n",
       "0.45      11\n",
       "Name: Discount, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Discount'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>226.742074</td>\n",
       "      <td>66.900292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>578.397351</td>\n",
       "      <td>96.055074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>529.971567</td>\n",
       "      <td>27.288298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>209.076940</td>\n",
       "      <td>24.702572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>454.742974</td>\n",
       "      <td>-45.679636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.32</th>\n",
       "      <td>536.794770</td>\n",
       "      <td>-88.560656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>565.134874</td>\n",
       "      <td>-111.927429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.45</th>\n",
       "      <td>498.634000</td>\n",
       "      <td>-226.646464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>892.705152</td>\n",
       "      <td>-310.703456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.60</th>\n",
       "      <td>48.150000</td>\n",
       "      <td>-43.077212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>97.177708</td>\n",
       "      <td>-95.874060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <td>56.545853</td>\n",
       "      <td>-101.796797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales      Profit\n",
       "Discount                        \n",
       "0.00      226.742074   66.900292\n",
       "0.10      578.397351   96.055074\n",
       "0.15      529.971567   27.288298\n",
       "0.20      209.076940   24.702572\n",
       "0.30      454.742974  -45.679636\n",
       "0.32      536.794770  -88.560656\n",
       "0.40      565.134874 -111.927429\n",
       "0.45      498.634000 -226.646464\n",
       "0.50      892.705152 -310.703456\n",
       "0.60       48.150000  -43.077212\n",
       "0.70       97.177708  -95.874060\n",
       "0.80       56.545853 -101.796797"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_discnt=df.groupby(['Discount'])['Sales','Profit'].mean()\n",
    "df1_discnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320844.4073"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.00].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90306.607"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.20].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-40075.3569"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.70].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-30532.8022"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.80].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-10357.2186"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.30].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-23057.0504"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.40].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5944.655199999999"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.60].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9029.176999999998"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.10].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-20506.4281"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.50].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1418.9915000000003"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.15].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2391.1376999999993"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.32].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2493.1111"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Discount']==0.45].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xb4d9880>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1_discnt['Profit'].plot(kind='bar',figsize=(15,8))\n",
    "plt.ylabel('Profit')\n",
    "plt.title('Profit vs Discount')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Maximum profit is at discount 0.1 and maximum negative profit is at discount 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discount based on Quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.45, 0.2 , 0.8 , 0.3 , 0.5 , 0.7 , 0.6 , 0.32, 0.1 , 0.4 ,\n",
       "       0.15])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Discount'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  5,  7,  4,  6,  9,  1,  8, 14, 11, 13, 10, 12],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Quantity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xc21d370>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df1['Discount'],df1['Quantity'],marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Discount and Quantity are independent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Profit based on Category\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Ship Mode    Segment        Country             City       State  \\\n",
       "0    Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "1    Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "2    Second Class  Corporate  United States      Los Angeles  California   \n",
       "3  Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4  Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region         Category Sub-Category     Sales  Quantity  \\\n",
       "0        42420  South        Furniture    Bookcases  261.9600         2   \n",
       "1        42420  South        Furniture       Chairs  731.9400         3   \n",
       "2        90036   West  Office Supplies       Labels   14.6200         2   \n",
       "3        33311  South        Furniture       Tables  957.5775         5   \n",
       "4        33311  South  Office Supplies      Storage   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Furniture', 'Office Supplies', 'Technology'], dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Office Supplies    6012\n",
       "Furniture          2118\n",
       "Technology         1847\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>Profit</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Furniture</th>\n",
       "      <td>8.697740</td>\n",
       "      <td>350.002981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Supplies</th>\n",
       "      <td>20.353403</td>\n",
       "      <td>119.550107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>78.752002</td>\n",
       "      <td>452.709276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Profit       Sales\n",
       "Category                              \n",
       "Furniture         8.697740  350.002981\n",
       "Office Supplies  20.353403  119.550107\n",
       "Technology       78.752002  452.709276"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_ctgry=df1.groupby(['Category'])['Profit','Sales'].mean()\n",
    "df1_ctgry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18421.813700000002"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Category']=='Furniture'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145454.9481"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Category']=='Technology'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "122364.66080000001"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Category']=='Office Supplies'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='Profit'>, <AxesSubplot:ylabel='Sales'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1_ctgry.plot.pie(subplots=True,autopct='%1.1f%%',figsize=(14,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Maximum profit comes from Technology category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Profit based on Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['South', 'West', 'Central', 'East'], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "West       3193\n",
       "East       2845\n",
       "Central    2319\n",
       "South      1620\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108329.80789999999"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Region']=='West'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91506.3092"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Region']=='East'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39655.8752"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Region']=='Central'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46749.4303"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['Region']=='South'].Profit.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>Profit</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>17.100421</td>\n",
       "      <td>215.947759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>32.163905</td>\n",
       "      <td>238.465798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>28.857673</td>\n",
       "      <td>241.803645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>33.927281</td>\n",
       "      <td>227.139254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Profit       Sales\n",
       "Region                        \n",
       "Central  17.100421  215.947759\n",
       "East     32.163905  238.465798\n",
       "South    28.857673  241.803645\n",
       "West     33.927281  227.139254"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_rgn=df1.groupby(['Region'])['Profit','Sales'].mean()\n",
    "df1_rgn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='Profit'>, <AxesSubplot:ylabel='Sales'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1368 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1_rgn.plot.pie(subplots=True,autopct='%1.1f%%',figsize=(16,19))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Maximum profit comes from West region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Profit vs Region')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1_rgn['Profit'].plot(kind='bar',figsize=(9,5))\n",
    "plt.ylabel('Profit')\n",
    "plt.title('Profit vs Region')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ----------------------------------------------THANK YOU-----------------------------------------------"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}