ExploratoryDataAnalysis-Retail.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "137e9d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3eec83e6",
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the dataset\n",
    "df = pd.read_csv(\"SampleSuperstore.csv\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e263d5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there is any missing value\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58b86112",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "      <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",
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       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop duplicate values from the dataframe\n",
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b152223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ship Mode          4\n",
       "Segment            3\n",
       "Country            1\n",
       "City             531\n",
       "State             49\n",
       "Postal Code      631\n",
       "Region             4\n",
       "Category           3\n",
       "Sub-Category      17\n",
       "Sales           5825\n",
       "Quantity          14\n",
       "Discount          12\n",
       "Profit          7287\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the count of the unique values of a column\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "36778791",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.9600</td>\n",
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       "    <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>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
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       "    </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>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>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>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",
       "    </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>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>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>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>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>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 × 12 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",
       "     Region         Category Sub-Category     Sales  Quantity  Discount  \\\n",
       "0     South        Furniture    Bookcases  261.9600         2      0.00   \n",
       "1     South        Furniture       Chairs  731.9400         3      0.00   \n",
       "2      West  Office Supplies       Labels   14.6200         2      0.00   \n",
       "3     South        Furniture       Tables  957.5775         5      0.45   \n",
       "4     South  Office Supplies      Storage   22.3680         2      0.20   \n",
       "...     ...              ...          ...       ...       ...       ...   \n",
       "9989  South        Furniture  Furnishings   25.2480         3      0.20   \n",
       "9990   West        Furniture  Furnishings   91.9600         2      0.00   \n",
       "9991   West       Technology       Phones  258.5760         2      0.20   \n",
       "9992   West  Office Supplies        Paper   29.6000         4      0.00   \n",
       "9993   West  Office Supplies   Appliances  243.1600         2      0.00   \n",
       "\n",
       "        Profit  \n",
       "0      41.9136  \n",
       "1     219.5820  \n",
       "2       6.8714  \n",
       "3    -383.0310  \n",
       "4       2.5164  \n",
       "...        ...  \n",
       "9989    4.1028  \n",
       "9990   15.6332  \n",
       "9991   19.3932  \n",
       "9992   13.3200  \n",
       "9993   72.9480  \n",
       "\n",
       "[9994 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the Postal Code column as it is of no use\n",
    "df = df.drop('Postal Code', axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c27ad11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ship Mode', 'Segment', 'Country', 'City', 'State', 'Region',\n",
       "       'Category', 'Sub-Category', 'Sales', 'Quantity', 'Discount', 'Profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the column names from the dataframe\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00fa41eb",
   "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>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200795</td>\n",
       "      <td>-0.028190</td>\n",
       "      <td>0.479064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>0.200795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008623</td>\n",
       "      <td>0.066253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount</th>\n",
       "      <td>-0.028190</td>\n",
       "      <td>0.008623</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.219487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.479064</td>\n",
       "      <td>0.066253</td>\n",
       "      <td>-0.219487</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sales  Quantity  Discount    Profit\n",
       "Sales     1.000000  0.200795 -0.028190  0.479064\n",
       "Quantity  0.200795  1.000000  0.008623  0.066253\n",
       "Discount -0.028190  0.008623  1.000000 -0.219487\n",
       "Profit    0.479064  0.066253 -0.219487  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the correlation between variables\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44a25b83",
   "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>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>388434.455308</td>\n",
       "      <td>278.459923</td>\n",
       "      <td>-3.627228</td>\n",
       "      <td>69944.096586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>278.459923</td>\n",
       "      <td>4.951113</td>\n",
       "      <td>0.003961</td>\n",
       "      <td>34.534769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount</th>\n",
       "      <td>-3.627228</td>\n",
       "      <td>0.003961</td>\n",
       "      <td>0.042622</td>\n",
       "      <td>-10.615173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>69944.096586</td>\n",
       "      <td>34.534769</td>\n",
       "      <td>-10.615173</td>\n",
       "      <td>54877.798055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Sales    Quantity   Discount        Profit\n",
       "Sales     388434.455308  278.459923  -3.627228  69944.096586\n",
       "Quantity     278.459923    4.951113   0.003961     34.534769\n",
       "Discount      -3.627228    0.003961   0.042622    -10.615173\n",
       "Profit     69944.096586   34.534769 -10.615173  54877.798055"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the covariance between the variables\n",
    "df.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "086dd91a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "West       3203\n",
       "East       2848\n",
       "Central    2323\n",
       "South      1620\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the count of unique values from the Region column\n",
    "df['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5453dee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhagy_000\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Region', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid') \n",
    "sns.countplot(df['Region'],order = df['Region'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "70e0bc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California              2001\n",
       "New York                1128\n",
       "Texas                    985\n",
       "Pennsylvania             587\n",
       "Washington               506\n",
       "Illinois                 492\n",
       "Ohio                     469\n",
       "Florida                  383\n",
       "Michigan                 255\n",
       "North Carolina           249\n",
       "Arizona                  224\n",
       "Virginia                 224\n",
       "Georgia                  184\n",
       "Tennessee                183\n",
       "Colorado                 182\n",
       "Indiana                  149\n",
       "Kentucky                 139\n",
       "Massachusetts            135\n",
       "New Jersey               130\n",
       "Oregon                   124\n",
       "Wisconsin                110\n",
       "Maryland                 105\n",
       "Delaware                  96\n",
       "Minnesota                 89\n",
       "Connecticut               82\n",
       "Oklahoma                  66\n",
       "Missouri                  66\n",
       "Alabama                   61\n",
       "Arkansas                  60\n",
       "Rhode Island              56\n",
       "Utah                      53\n",
       "Mississippi               53\n",
       "Louisiana                 42\n",
       "South Carolina            42\n",
       "Nevada                    39\n",
       "Nebraska                  38\n",
       "New Mexico                37\n",
       "Iowa                      30\n",
       "New Hampshire             27\n",
       "Kansas                    24\n",
       "Idaho                     21\n",
       "Montana                   15\n",
       "South Dakota              12\n",
       "Vermont                   11\n",
       "District of Columbia      10\n",
       "Maine                      8\n",
       "North Dakota               7\n",
       "West Virginia              4\n",
       "Wyoming                    1\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the count of unique values from the State column\n",
    "df['State'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9635d0ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhagy_000\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,9))\n",
    "plt.xticks(rotation=90)\n",
    "sns.set_style('whitegrid') \n",
    "sns.countplot(df['State'],order = df['State'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f525c9e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York City    915\n",
       "Los Angeles      747\n",
       "Philadelphia     537\n",
       "San Francisco    510\n",
       "Seattle          428\n",
       "Houston          377\n",
       "Chicago          314\n",
       "Columbus         222\n",
       "San Diego        170\n",
       "Springfield      163\n",
       "Name: City, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the count of unique values from the City column\n",
    "city = df['City'].value_counts()\n",
    "city.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1e7a6e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Category', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid') \n",
    "sns.countplot(x = df['Category'], order = df['Category'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6f711ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhagy_000\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sub-Category', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=90) \n",
    "sns.set_style('whitegrid') \n",
    "sns.countplot(df['Sub-Category'],order = df['Sub-Category'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "31231086",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhagy_000\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Segment', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid') \n",
    "sns.countplot(df['Segment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d8e9e71c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sub-Category', ylabel='Sales'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=90) \n",
    "sns.set_style('whitegrid') \n",
    "sns.barplot(x=df['Sub-Category'], y=df['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4c064cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State', ylabel='Profit'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=90) \n",
    "sns.set_style('whitegrid') \n",
    "sns.barplot(x=df['State'], y=df['Profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2bfd5f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sales of Top 10 Cities'}, xlabel='City', ylabel='Total sum of Sales'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10 = df.groupby('City')['Sales'].sum().sort_values(ascending = False).iloc[0:10]\n",
    "plt.title('Sales of Top 10 Cities')\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Total sum of Sales')\n",
    "top10.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b29525db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City           Discount\n",
       "New York City  0.0         650\n",
       "Los Angeles    0.0         466\n",
       "San Francisco  0.0         336\n",
       "Philadelphia   0.2         326\n",
       "Seattle        0.0         289\n",
       "Los Angeles    0.2         267\n",
       "Houston        0.2         210\n",
       "New York City  0.2         180\n",
       "Chicago        0.2         178\n",
       "San Francisco  0.2         158\n",
       "Name: Discount, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#discount = df['Discount'].value_counts()\n",
    "discount = df.groupby('City')['Discount'].value_counts().sort_values(ascending = False).iloc[0:10]\n",
    "discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "656c5a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BHAGY_~1\\AppData\\Local\\Temp/ipykernel_6188/4045359982.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  overview = df.groupby('State')['Profit','Sales'].sum()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#statewise sales and profit overview\n",
    "overview = df.groupby('State')['Profit','Sales'].sum()\n",
    "overview.plot(kind='bar',figsize=(25,8))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}