Exploratory_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import calmap\n",
    "from pandas_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('file:///C:/Users/rajee/Downloads/supermarket_sales%20-%20Sheet1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Invoice ID</th>\n",
       "      <th>Branch</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Product line</th>\n",
       "      <th>Unit price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Tax 5%</th>\n",
       "      <th>Total</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Payment</th>\n",
       "      <th>cogs</th>\n",
       "      <th>gross margin percentage</th>\n",
       "      <th>gross income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>750-67-8428</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>74.69</td>\n",
       "      <td>7</td>\n",
       "      <td>26.1415</td>\n",
       "      <td>548.9715</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>13:08</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>522.83</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>26.1415</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>226-31-3081</td>\n",
       "      <td>C</td>\n",
       "      <td>Naypyitaw</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Female</td>\n",
       "      <td>Electronic accessories</td>\n",
       "      <td>15.28</td>\n",
       "      <td>5</td>\n",
       "      <td>3.8200</td>\n",
       "      <td>80.2200</td>\n",
       "      <td>3/8/2019</td>\n",
       "      <td>10:29</td>\n",
       "      <td>Cash</td>\n",
       "      <td>76.40</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.8200</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>631-41-3108</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Home and lifestyle</td>\n",
       "      <td>46.33</td>\n",
       "      <td>7</td>\n",
       "      <td>16.2155</td>\n",
       "      <td>340.5255</td>\n",
       "      <td>3/3/2019</td>\n",
       "      <td>13:23</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>324.31</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>16.2155</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123-19-1176</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>58.22</td>\n",
       "      <td>8</td>\n",
       "      <td>23.2880</td>\n",
       "      <td>489.0480</td>\n",
       "      <td>1/27/2019</td>\n",
       "      <td>20:33</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>465.76</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>23.2880</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>373-73-7910</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sports and travel</td>\n",
       "      <td>86.31</td>\n",
       "      <td>7</td>\n",
       "      <td>30.2085</td>\n",
       "      <td>634.3785</td>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>10:37</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>604.17</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>30.2085</td>\n",
       "      <td>5.3</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>233-67-5758</td>\n",
       "      <td>C</td>\n",
       "      <td>Naypyitaw</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>40.35</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0175</td>\n",
       "      <td>42.3675</td>\n",
       "      <td>1/29/2019</td>\n",
       "      <td>13:46</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>40.35</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>2.0175</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>303-96-2227</td>\n",
       "      <td>B</td>\n",
       "      <td>Mandalay</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Female</td>\n",
       "      <td>Home and lifestyle</td>\n",
       "      <td>97.38</td>\n",
       "      <td>10</td>\n",
       "      <td>48.6900</td>\n",
       "      <td>1022.4900</td>\n",
       "      <td>3/2/2019</td>\n",
       "      <td>17:16</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>973.80</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>48.6900</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>727-02-1313</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Male</td>\n",
       "      <td>Food and beverages</td>\n",
       "      <td>31.84</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5920</td>\n",
       "      <td>33.4320</td>\n",
       "      <td>2/9/2019</td>\n",
       "      <td>13:22</td>\n",
       "      <td>Cash</td>\n",
       "      <td>31.84</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>1.5920</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>347-56-2442</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Home and lifestyle</td>\n",
       "      <td>65.82</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2910</td>\n",
       "      <td>69.1110</td>\n",
       "      <td>2/22/2019</td>\n",
       "      <td>15:33</td>\n",
       "      <td>Cash</td>\n",
       "      <td>65.82</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.2910</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>849-09-3807</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>Fashion accessories</td>\n",
       "      <td>88.34</td>\n",
       "      <td>7</td>\n",
       "      <td>30.9190</td>\n",
       "      <td>649.2990</td>\n",
       "      <td>2/18/2019</td>\n",
       "      <td>13:28</td>\n",
       "      <td>Cash</td>\n",
       "      <td>618.38</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>30.9190</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Invoice ID Branch       City Customer type  Gender  \\\n",
       "0    750-67-8428      A     Yangon        Member  Female   \n",
       "1    226-31-3081      C  Naypyitaw        Normal  Female   \n",
       "2    631-41-3108      A     Yangon        Normal    Male   \n",
       "3    123-19-1176      A     Yangon        Member    Male   \n",
       "4    373-73-7910      A     Yangon        Normal    Male   \n",
       "..           ...    ...        ...           ...     ...   \n",
       "995  233-67-5758      C  Naypyitaw        Normal    Male   \n",
       "996  303-96-2227      B   Mandalay        Normal  Female   \n",
       "997  727-02-1313      A     Yangon        Member    Male   \n",
       "998  347-56-2442      A     Yangon        Normal    Male   \n",
       "999  849-09-3807      A     Yangon        Member  Female   \n",
       "\n",
       "               Product line  Unit price  Quantity   Tax 5%      Total  \\\n",
       "0         Health and beauty       74.69         7  26.1415   548.9715   \n",
       "1    Electronic accessories       15.28         5   3.8200    80.2200   \n",
       "2        Home and lifestyle       46.33         7  16.2155   340.5255   \n",
       "3         Health and beauty       58.22         8  23.2880   489.0480   \n",
       "4         Sports and travel       86.31         7  30.2085   634.3785   \n",
       "..                      ...         ...       ...      ...        ...   \n",
       "995       Health and beauty       40.35         1   2.0175    42.3675   \n",
       "996      Home and lifestyle       97.38        10  48.6900  1022.4900   \n",
       "997      Food and beverages       31.84         1   1.5920    33.4320   \n",
       "998      Home and lifestyle       65.82         1   3.2910    69.1110   \n",
       "999     Fashion accessories       88.34         7  30.9190   649.2990   \n",
       "\n",
       "          Date   Time      Payment    cogs  gross margin percentage  \\\n",
       "0     1/5/2019  13:08      Ewallet  522.83                 4.761905   \n",
       "1     3/8/2019  10:29         Cash   76.40                 4.761905   \n",
       "2     3/3/2019  13:23  Credit card  324.31                 4.761905   \n",
       "3    1/27/2019  20:33      Ewallet  465.76                 4.761905   \n",
       "4     2/8/2019  10:37      Ewallet  604.17                 4.761905   \n",
       "..         ...    ...          ...     ...                      ...   \n",
       "995  1/29/2019  13:46      Ewallet   40.35                 4.761905   \n",
       "996   3/2/2019  17:16      Ewallet  973.80                 4.761905   \n",
       "997   2/9/2019  13:22         Cash   31.84                 4.761905   \n",
       "998  2/22/2019  15:33         Cash   65.82                 4.761905   \n",
       "999  2/18/2019  13:28         Cash  618.38                 4.761905   \n",
       "\n",
       "     gross income  Rating  \n",
       "0         26.1415     9.1  \n",
       "1          3.8200     9.6  \n",
       "2         16.2155     7.4  \n",
       "3         23.2880     8.4  \n",
       "4         30.2085     5.3  \n",
       "..            ...     ...  \n",
       "995        2.0175     6.2  \n",
       "996       48.6900     4.4  \n",
       "997        1.5920     7.7  \n",
       "998        3.2910     4.1  \n",
       "999       30.9190     6.6  \n",
       "\n",
       "[1000 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 3,
     "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": [
    "global_yearly_life_expectancy.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual Representation of Data\n",
    "\n",
    "--> Histogram = Vertical Bars charts of frequency\n",
    "\n",
    "--> Frequency Polygon = line graph of frequencies\n",
    "\n",
    "--> Ogive = line graph of Cumulative frequencies\n",
    "\n",
    "--> Pie Chart = proportional representation for categories of a whole\n",
    "\n",
    "--> Stem and leaf plot\n",
    "\n",
    "--> Pareto chart\n",
    "\n",
    "--> Scatter Plot\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}