{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a04d510-74aa-4a4e-b585-092cf2833b27",
   "metadata": {},
   "source": [
    "# EDA\n",
    "## Exploratory data analysis using supermarket sales data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e043806-30c1-4a63-91cb-775dfe1526b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Install\n",
    "!pip3 install matplotlib\n",
    "!pip3 install seaborn\n",
    "!pip3 install missingno\n",
    "!pip3 install geopy\n",
    "!pip3 install folium\n",
    "!pip3 install dataframe_image\n",
    "!pip3 install jinja2 --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "323a0e3d-051c-42b6-b70e-55ff62ee490f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip3 install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eee582e-e39a-44c2-95be-9ed59cebf7bd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip3 install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d394d832-b999-47e0-8baa-01a0ba8796c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#import dataframe_image as dfi\n",
    "\n",
    "# Visualizing NaN values\n",
    "import missingno as msno\n",
    "\n",
    "# Visualization\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "import plotly.io as pio\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# Calendar\n",
    "import calendar\n",
    "\n",
    "# Geocode\n",
    "from geopy.geocoders import Nominatim\n",
    "from geopy.distance import geodesic\n",
    "import folium\n",
    "from folium import plugins\n",
    "import ipywidgets\n",
    "from branca.element import Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ced3d92f-ca47-4dc4-b109-732840bca1c7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Plotly Options and Config\n",
    "pio.templates.default = 'plotly_white'\n",
    "config = {'displayModeBar': False}\n",
    "colors_x = ['#E0C95A', '#CBE0CA', '#E0864E', '#CDCCE3', '#E3B8D1']\n",
    "cmaps = 'Blues'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3a19274e-123f-4731-beb5-e71cceb54319",
   "metadata": {
    "tags": []
   },
   "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",
       "  </tbody>\n",
       "</table>\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",
       "             Product line  Unit price  Quantity   Tax 5%     Total       Date   \n",
       "0       Health and beauty       74.69         7  26.1415  548.9715   1/5/2019  \\\n",
       "1  Electronic accessories       15.28         5   3.8200   80.2200   3/8/2019   \n",
       "2      Home and lifestyle       46.33         7  16.2155  340.5255   3/3/2019   \n",
       "3       Health and beauty       58.22         8  23.2880  489.0480  1/27/2019   \n",
       "4       Sports and travel       86.31         7  30.2085  634.3785   2/8/2019   \n",
       "\n",
       "    Time      Payment    cogs  gross margin percentage  gross income  Rating  \n",
       "0  13:08      Ewallet  522.83                 4.761905       26.1415     9.1  \n",
       "1  10:29         Cash   76.40                 4.761905        3.8200     9.6  \n",
       "2  13:23  Credit card  324.31                 4.761905       16.2155     7.4  \n",
       "3  20:33      Ewallet  465.76                 4.761905       23.2880     8.4  \n",
       "4  10:37      Ewallet  604.17                 4.761905       30.2085     5.3  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data - CSV file\n",
    "df = pd.read_csv('supermarket_sales.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3755d1df-21df-4c31-90dc-c20df1b08c51",
   "metadata": {},
   "source": [
    "# Location of the branches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a6ffd46-4f3b-44d3-86dc-d1d8b5978c1d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "city_data = df.City.unique()\n",
    "cities = pd.DataFrame(data = city, columns = {'City'})\n",
    "cities\n",
    "\n",
    "nom = Nominatim(user_agent='Kaggle')\n",
    "\n",
    "def geo_cities(cities):\n",
    "    lat = []\n",
    "    long = []\n",
    "    flag = 0\n",
    "    for city in cities['city_name']:\n",
    "      location = nom.geocode(city)\n",
    "      try:\n",
    "        lat.append(location.latitude)\n",
    "        long.append(location.longitude)\n",
    "        print(flag)\n",
    "      except:\n",
    "        lat.append('NA')\n",
    "        long.append('NA')\n",
    "      flag += 1\n",
    "    cities['Latitude'] = lat\n",
    "    cities['Longitude'] = long \n",
    "    \n",
    "#geo_cities(cities)\n",
    "# The geocoding doesn't seem to work in  kaggle notebook so i'll manually add  the output here.\n",
    "\n",
    "cities = pd.DataFrame({'city': ['Yangon', 'Naypyitaw', 'Mandalay'],\n",
    "                       'Latitude': [16.79671316,19.690708,21.981275],\n",
    "                       'Longitude' : [96.160992, 96.165883, 96.082375]})\n",
    "\n",
    "supermarket_geo = folium.Map(location = [19.680708,\t96.265883], zoom_start = 7, tiles = 'CartoDB Positron', control_scale = True)\n",
    "\n",
    "fig = Figure(width = 800, height = 600)\n",
    "fig.add_child(supermarket_geo)\n",
    "\n",
    "for (index, row) in cities.iterrows():\n",
    "  folium.Marker(location = [row.loc['Latitude'], row.loc['Longitude']], popup = row.loc['city_name'],tooltip = 'click',\n",
    "                icon = folium.Icon(icon = 'glyphicon-shopping-cart', prefix = 'glyphicon')).add_to(supermarket_geo)\n",
    "\n",
    "supermarket_geo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79888657-d766-4baf-810e-e35f1038e913",
   "metadata": {},
   "source": [
    "## Variable transformations\n",
    "Change the date variable to date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a24245f2-ae6f-43df-9cee-ebc0edcc4155",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "df['weekday'] = df['Date'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "35a2f00c-ea01-48bf-9edf-b3d00c65c94f",
   "metadata": {
    "tags": []
   },
   "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",
       "      <th>weekday</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>2019-01-05</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",
       "      <td>Saturday</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>2019-03-08</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",
       "      <td>Friday</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>2019-03-03</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",
       "      <td>Sunday</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>2019-01-27</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",
       "      <td>Sunday</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>2019-02-08</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",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "             Product line  Unit price  Quantity   Tax 5%     Total       Date   \n",
       "0       Health and beauty       74.69         7  26.1415  548.9715 2019-01-05  \\\n",
       "1  Electronic accessories       15.28         5   3.8200   80.2200 2019-03-08   \n",
       "2      Home and lifestyle       46.33         7  16.2155  340.5255 2019-03-03   \n",
       "3       Health and beauty       58.22         8  23.2880  489.0480 2019-01-27   \n",
       "4       Sports and travel       86.31         7  30.2085  634.3785 2019-02-08   \n",
       "\n",
       "    Time      Payment    cogs  gross margin percentage  gross income  Rating   \n",
       "0  13:08      Ewallet  522.83                 4.761905       26.1415     9.1  \\\n",
       "1  10:29         Cash   76.40                 4.761905        3.8200     9.6   \n",
       "2  13:23  Credit card  324.31                 4.761905       16.2155     7.4   \n",
       "3  20:33      Ewallet  465.76                 4.761905       23.2880     8.4   \n",
       "4  10:37      Ewallet  604.17                 4.761905       30.2085     5.3   \n",
       "\n",
       "    weekday  \n",
       "0  Saturday  \n",
       "1    Friday  \n",
       "2    Sunday  \n",
       "3    Sunday  \n",
       "4    Friday  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782e1c9c-4786-44e9-97f0-ea698487e043",
   "metadata": {},
   "source": [
    "## Set the Date as the index of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23c87895-6eb4-4200-96aa-d0be12ae2298",
   "metadata": {
    "tags": []
   },
   "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>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",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-05</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>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",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</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>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",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</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>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",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</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>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",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-08</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>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",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Invoice ID Branch       City Customer type  Gender   \n",
       "Date                                                              \n",
       "2019-01-05  750-67-8428      A     Yangon        Member  Female  \\\n",
       "2019-03-08  226-31-3081      C  Naypyitaw        Normal  Female   \n",
       "2019-03-03  631-41-3108      A     Yangon        Normal    Male   \n",
       "2019-01-27  123-19-1176      A     Yangon        Member    Male   \n",
       "2019-02-08  373-73-7910      A     Yangon        Normal    Male   \n",
       "\n",
       "                      Product line  Unit price  Quantity   Tax 5%     Total   \n",
       "Date                                                                          \n",
       "2019-01-05       Health and beauty       74.69         7  26.1415  548.9715  \\\n",
       "2019-03-08  Electronic accessories       15.28         5   3.8200   80.2200   \n",
       "2019-03-03      Home and lifestyle       46.33         7  16.2155  340.5255   \n",
       "2019-01-27       Health and beauty       58.22         8  23.2880  489.0480   \n",
       "2019-02-08       Sports and travel       86.31         7  30.2085  634.3785   \n",
       "\n",
       "             Time      Payment    cogs  gross margin percentage  gross income   \n",
       "Date                                                                            \n",
       "2019-01-05  13:08      Ewallet  522.83                 4.761905       26.1415  \\\n",
       "2019-03-08  10:29         Cash   76.40                 4.761905        3.8200   \n",
       "2019-03-03  13:23  Credit card  324.31                 4.761905       16.2155   \n",
       "2019-01-27  20:33      Ewallet  465.76                 4.761905       23.2880   \n",
       "2019-02-08  10:37      Ewallet  604.17                 4.761905       30.2085   \n",
       "\n",
       "            Rating   weekday  \n",
       "Date                          \n",
       "2019-01-05     9.1  Saturday  \n",
       "2019-03-08     9.6    Friday  \n",
       "2019-03-03     7.4    Sunday  \n",
       "2019-01-27     8.4    Sunday  \n",
       "2019-02-08     5.3    Friday  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Date', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc0ed17-7391-45dc-85ed-b7aab755d1e5",
   "metadata": {},
   "source": [
    "# Univariate analysis\n",
    "\n",
    "## Question 1\n",
    "*What does the customer rating look like and is it skewed?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2074df9f-62f9-4597-b47c-962a1cd2f82c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2119c6d5890>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Rating'], kde=True)\n",
    "plt.axvline(x=np.mean(df['Rating']), c='red', ls='--', label='mean')\n",
    "plt.axvline(x=np.percentile(df['Rating'],25),c='green', ls='--', label = '25th percentile:Q1')\n",
    "plt.axvline(x=np.percentile(df['Rating'],75),c='orange', ls='--',label = '75th percentile:Q3' )\n",
    "plt.legend()\n",
    "#plt.savefig(\"uni1.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b06a9c5-d7be-4376-be6b-072499a3c118",
   "metadata": {},
   "source": [
    "The rating distribution looks uniform and there seems to be no skewness on the left or right side of the distribution.\n",
    "\n",
    "We can plot the mean and the percentiles to show this as well.\n",
    "\n",
    "The red dotted lime is the mean and the green and orange lines indicate the 25th and 75th quartiles respectively.\n",
    "\n",
    "The mean rating is ~7 across products."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1d592b-ab45-466c-98fb-966f3882599f",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "*Is there any difference in aggregate sales across branches?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3895f34-2fe2-42c7-9331-7773a5d40276",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Branch\n",
       "A    340\n",
       "B    332\n",
       "C    328\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aNmyYHnvsMeXk5GjZsmUKCwuz4tQAAECQCKpndmprayVJ0dHRkqR9+/apqalJ48aN848ZNGiQevfurbKyMklSWVmZkpKSFBcX5x+Tlpamuro6HTx48LLv4/V6VVdXF7AAAAAzBU3stLa2av78+brjjjs0ZMgQSZLH41FYWJiioqICxsbFxcnj8fjHfDV0Lu6/uO9y8vLy5HA4/EtCQkIHnw0AAAgWQRM7WVlZ+vTTT/XGG29c9ffKzc1VbW2tfzl58uRVf08AAGANS5/ZuWju3LkqLCzU9u3b1atXL/92p9OpxsZG1dTUBFzdqaqqktPp9I/ZvXt3wOtd/LTWxTF/z263y263d/BZAACAYGTplR2fz6e5c+dqw4YN2rp1q2666aaA/cOHD1fnzp1VUlLi31ZRUaETJ07I7XZLktxutw4cOKDq6mr/mOLiYkVGRioxMfHanAgAAAhall7ZycrKUkFBgd5//311797d/4yNw+FQ165d5XA4NHPmTGVnZys6OlqRkZGaN2+e3G63Ro4cKUkaP368EhMTNW3aNK1YsUIej0eLFy9WVlYWV28AAEDbruyMGTNGNTU1l2yvq6vzf0T821i1apVqa2uVmpoql8vlX958803/mGeeeUZ33323MjMzNWrUKDmdTr377rv+/aGhoSosLFRoaKjcbremTp2q6dOna/ny5W05NQAAYJg2XdnZtm2bGhsbL9l+4cIF/fGPf/zWr+Pz+b5xTJcuXZSfn6/8/PwrjunTp48++OCDb/2+AADgxvGdYmf//v3+nw8dOhTw0e6WlhYVFRXpe9/7XsfNDgAAoJ2+U+wMGzZMNptNNpvtsrerunbtqueff77DJgcAANBe3yl2jh8/Lp/Pp759+2r37t3q2bOnf19YWJhiY2MVGhra4ZMEAABoq+8UO3369JH0t287BgAAuB60+aPnR44c0UcffaTq6upL4oe/OA4AAIJFm2LnxRdf1Jw5c9SjRw85nU7ZbDb/PpvNRuwAAICg0abYefzxx/WrX/1KOTk5HT0fAACADtWmLxX84osv9MMf/rCj5wIAANDh2hQ7P/zhD7V58+aOngsAAECHa9NtrP79++uRRx7Rzp07lZSUpM6dOwfs/9nPftYhkwMAAGivNsXO7373O0VERKi0tFSlpaUB+2w2G7EDAACCRpti5/jx4x09DwAAgKuiTc/sAAAAXC/adGXnJz/5ydfuf+mll9o0GQAAgI7Wptj54osvAtabmpr06aefqqam5rJ/IBQAAMAqbYqdDRs2XLKttbVVc+bMUb9+/do9KQAAgI7SYc/shISEKDs7W88880xHvSQAAEC7degDyseOHVNzc3NHviQAAEC7tOk2VnZ2dsC6z+dTZWWl/vCHP2jGjBkdMjEAAICO0KbY+fOf/xywHhISop49e+o3v/nNN35SCwAA4FpqU+x89NFHHT0PAACAq6JNsXPR6dOnVVFRIUkaOHCgevbs2SGTAgAA6ChtekC5oaFBP/nJT+RyuTRq1CiNGjVK8fHxmjlzps6fP9/RcwQAAGizNsVOdna2SktLtXHjRtXU1Kimpkbvv/++SktL9fDDD3f0HAEAANqsTbex3nnnHb399ttKTU31b5swYYK6du2qf/7nf9aqVas6an4AAADt0qYrO+fPn1dcXNwl22NjY7mNBQAAgkqbYsftdmvp0qW6cOGCf9uXX36pRx99VG63u8MmBwAA0F5tuo21cuVKpaenq1evXho6dKgk6S9/+Yvsdrs2b97coRMEAABojzbFTlJSko4cOaJ169bps88+kyRNnjxZU6ZMUdeuXTt0ggAAAO3RptjJy8tTXFycZs2aFbD9pZde0unTp5WTk9MhkwMAAGivNj2z8x//8R8aNGjQJdtvueUWrV69ut2TAgAA6Chtih2PxyOXy3XJ9p49e6qysrLdkwIAAOgobYqdhIQEffLJJ5ds/+STTxQfH9/uSQEAAHSUNj2zM2vWLM2fP19NTU0aM2aMJKmkpESLFi3iG5QBAEBQaVPsLFy4UGfOnNG//uu/qrGxUZLUpUsX5eTkKDc3t0MnCAAA0B5tih2bzaZf//rXeuSRR3T48GF17dpVAwYMkN1u7+j5AQAAtEubYueiiIgIjRgxoqPmAgAA0OHa9IAyAADA9YLYAQAARiN2AACA0YgdAABgNEtjZ/v27Zo0aZLi4+Nls9n03nvvBex/8MEHZbPZApb09PSAMWfPntWUKVMUGRmpqKgozZw5U/X19dfwLAAAQDCzNHYaGho0dOhQ5efnX3FMenq6Kisr/cvrr78esH/KlCk6ePCgiouLVVhYqO3bt2v27NlXe+oAAOA60a6PnrdXRkaGMjIyvnaM3W6X0+m87L7Dhw+rqKhIe/bs0W233SZJev755zVhwgQ9/fTTV/zTFV6vV16v179eV1fXxjMAAADBLuif2dm2bZtiY2M1cOBAzZkzR2fOnPHvKysrU1RUlD90JGncuHEKCQnRrl27rviaeXl5cjgc/iUhIeGqngMAALBOUMdOenq6Xn31VZWUlOjXv/61SktLlZGRoZaWFkl/++vrsbGxAcd06tRJ0dHR8ng8V3zd3Nxc1dbW+peTJ09e1fMAAADWsfQ21jd54IEH/D8nJSUpOTlZ/fr107Zt2zR27Ng2v67dbudPWwAAcIMI6is7f69v377q0aOHjh49KklyOp2qrq4OGNPc3KyzZ89e8TkfAABwY7muYufzzz/XmTNn5HK5JElut1s1NTXat2+ff8zWrVvV2tqqlJQUq6YJAACCiKW3serr6/1XaSTp+PHjKi8vV3R0tKKjo/Xoo48qMzNTTqdTx44d06JFi9S/f3+lpaVJkgYPHqz09HTNmjVLq1evVlNTk+bOnasHHnjgip/EAgAANxZLr+zs3btXt956q2699VZJUnZ2tm699VYtWbJEoaGh2r9/v/7pn/5JN998s2bOnKnhw4frj3/8Y8DzNuvWrdOgQYM0duxYTZgwQXfeead+97vfWXVKAAAgyFh6ZSc1NVU+n++K+zdt2vSNrxEdHa2CgoKOnBYAADDIdfXMDgAAwHdF7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjGZp7Gzfvl2TJk1SfHy8bDab3nvvvYD9Pp9PS5YskcvlUteuXTVu3DgdOXIkYMzZs2c1ZcoURUZGKioqSjNnzlR9ff01PAsAABDMLI2dhoYGDR06VPn5+Zfdv2LFCj333HNavXq1du3apW7duiktLU0XLlzwj5kyZYoOHjyo4uJiFRYWavv27Zo9e/a1OgUAABDkOln55hkZGcrIyLjsPp/Pp5UrV2rx4sW65557JEmvvvqq4uLi9N577+mBBx7Q4cOHVVRUpD179ui2226TJD3//POaMGGCnn76acXHx1/2tb1er7xer3+9rq6ug88MAAAEi6B9Zuf48ePyeDwaN26cf5vD4VBKSorKysokSWVlZYqKivKHjiSNGzdOISEh2rVr1xVfOy8vTw6Hw78kJCRcvRMBAACWCtrY8Xg8kqS4uLiA7XFxcf59Ho9HsbGxAfs7deqk6Oho/5jLyc3NVW1trX85efJkB88eAAAEC0tvY1nFbrfLbrdbPQ0AAHANBO2VHafTKUmqqqoK2F5VVeXf53Q6VV1dHbC/ublZZ8+e9Y8BAAA3tqCNnZtuuklOp1MlJSX+bXV1ddq1a5fcbrckye12q6amRvv27fOP2bp1q1pbW5WSknLN5wwAAIKPpbex6uvrdfToUf/68ePHVV5erujoaPXu3Vvz58/X448/rgEDBuimm27SI488ovj4eN17772SpMGDBys9PV2zZs3S6tWr1dTUpLlz5+qBBx644iexAADAjcXS2Nm7d69Gjx7tX8/OzpYkzZgxQ2vXrtWiRYvU0NCg2bNnq6amRnfeeaeKiorUpUsX/zHr1q3T3LlzNXbsWIWEhCgzM1PPPffcNT8XAAAQnCyNndTUVPl8vivut9lsWr58uZYvX37FMdHR0SooKLga0wMAAAYI2md2AAAAOgKxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaEEdO8uWLZPNZgtYBg0a5N9/4cIFZWVlKSYmRhEREcrMzFRVVZWFMwYAAMEmqGNHkm655RZVVlb6l48//ti/b8GCBdq4caPWr1+v0tJSnTp1Svfdd5+FswUAAMGmk9UT+CadOnWS0+m8ZHttba3WrFmjgoICjRkzRpL08ssva/Dgwdq5c6dGjhx5racKAACCUNBf2Tly5Iji4+PVt29fTZkyRSdOnJAk7du3T01NTRo3bpx/7KBBg9S7d2+VlZV97Wt6vV7V1dUFLAAAwExBHTspKSlau3atioqKtGrVKh0/flzf//73de7cOXk8HoWFhSkqKirgmLi4OHk8nq993by8PDkcDv+SkJBwFc8CAABYKahvY2VkZPh/Tk5OVkpKivr06aO33npLXbt2bfPr5ubmKjs7279eV1dH8AAAYKigvrLz96KionTzzTfr6NGjcjqdamxsVE1NTcCYqqqqyz7j81V2u12RkZEBCwAAMNN1FTv19fU6duyYXC6Xhg8frs6dO6ukpMS/v6KiQidOnJDb7bZwlgAAIJgE9W2sn//855o0aZL69OmjU6dOaenSpQoNDdXkyZPlcDg0c+ZMZWdnKzo6WpGRkZo3b57cbjefxAIAAH5BHTuff/65Jk+erDNnzqhnz5668847tXPnTvXs2VOS9MwzzygkJESZmZnyer1KS0vTCy+8YPGsAQBAMAnq2HnjjTe+dn+XLl2Un5+v/Pz8azQjAABwvbmuntkBAAD4rogdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRjImd/Px8/cM//IO6dOmilJQU7d692+opAQCAIGBE7Lz55pvKzs7W0qVL9ac//UlDhw5VWlqaqqurrZ4aAACwmBGx8+///u+aNWuWfvzjHysxMVGrV69WeHi4XnrpJaunBgAALNbJ6gm0V2Njo/bt26fc3Fz/tpCQEI0bN05lZWWXPcbr9crr9frXa2trJUl1dXVtnkeL98s2HwvztOd3qSOdu9Bi9RQQRILh97L5y2arp4Ag0t7fyYvH+3y+rx133cfO//7v/6qlpUVxcXEB2+Pi4vTZZ59d9pi8vDw9+uijl2xPSEi4KnPEjcfx/ENWTwG4VJ7D6hkAARw5HfM7ee7cOTkcV36t6z522iI3N1fZ2dn+9dbWVp09e1YxMTGy2WwWzuz6VldXp4SEBJ08eVKRkZFWTweQxO8lgg+/kx3H5/Pp3Llzio+P/9px133s9OjRQ6GhoaqqqgrYXlVVJafTedlj7Ha77HZ7wLaoqKirNcUbTmRkJP8CI+jwe4lgw+9kx/i6KzoXXfcPKIeFhWn48OEqKSnxb2ttbVVJSYncbreFMwMAAMHgur+yI0nZ2dmaMWOGbrvtNt1+++1auXKlGhoa9OMf/9jqqQEAAIsZETv333+/Tp8+rSVLlsjj8WjYsGEqKiq65KFlXF12u11Lly695BYhYCV+LxFs+J289my+b/q8FgAAwHXsun9mBwAA4OsQOwAAwGjEDgAAMBqxAwAAjEbsoMOUlZUpNDRUEydOtHoqgCTJ4/Fo3rx56tu3r+x2uxISEjRp0qSA7+UCrpUHH3xQNpvNv8TExCg9PV379++3emrGI3bQYdasWaN58+Zp+/btOnXqlNXTwQ3ur3/9q4YPH66tW7fqqaee0oEDB1RUVKTRo0crKyvL6unhBpWenq7KykpVVlaqpKREnTp10t133231tIzHR8/RIerr6+VyubR3714tXbpUycnJ+uUvf2n1tHADmzBhgvbv36+Kigp169YtYF9NTQ1/IgbX3IMPPqiamhq99957/m0ff/yxvv/976u6ulo9e/a0bnKG48oOOsRbb72lQYMGaeDAgZo6dapeeukl0dGwytmzZ1VUVKSsrKxLQkfib+EhONTX1+u1115T//79FRMTY/V0jGbENyjDemvWrNHUqVMl/e0ybW1trUpLS5WammrtxHBDOnr0qHw+nwYNGmT1VIAAhYWFioiIkCQ1NDTI5XKpsLBQISFce7ia+KeLdquoqNDu3bs1efJkSVKnTp10//33a82aNRbPDDcqrioiWI0ePVrl5eUqLy/X7t27lZaWpoyMDP33f/+31VMzGld20G5r1qxRc3Oz4uPj/dt8Pp/sdrt++9vfyuFwWDg73IgGDBggm82mzz77zOqpAAG6deum/v37+9d///vfy+Fw6MUXX9Tjjz9u4czMxpUdtEtzc7NeffVV/eY3v/H/30p5ebn+8pe/KD4+Xq+//rrVU8QNKDo6WmlpacrPz1dDQ8Ml+2tqaq79pIDLsNlsCgkJ0Zdffmn1VIxG7KBdCgsL9cUXX2jmzJkaMmRIwJKZmcmtLFgmPz9fLS0tuv322/XOO+/oyJEjOnz4sJ577jm53W6rp4cblNfrlcfjkcfj0eHDhzVv3jzV19dr0qRJVk/NaMQO2mXNmjUaN27cZW9VZWZmau/evXxhFizRt29f/elPf9Lo0aP18MMPa8iQIbrrrrtUUlKiVatWWT093KCKiorkcrnkcrmUkpKiPXv2aP369XyY4yrje3YAAIDRuLIDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwBfY+3atYqKirJ6GgDagdgBYLkHH3xQNpvNv8TExCg9PZ0/NQKgQxA7AIJCenq6KisrVVlZqZKSEnXq1El33333Fcc3NTVdw9kBuJ4ROwCCgt1ul9PplNPp1LBhw/SLX/xCJ0+e1OnTp/XXv/5VNptNb775pn7wgx+oS5cuWrdunc6cOaPJkyfre9/7nsLDw5WUlKTXX3894HVTU1P1s5/9TIsWLVJ0dLScTqeWLVsWMKampkY//elPFRcXpy5dumjIkCEqLCwMGLNp0yYNHjxYERER/jADcH0gdgAEnfr6er322mvq37+/YmJi/Nt/8Ytf6N/+7d90+PBhpaWl6cKFCxo+fLj+8Ic/6NNPP9Xs2bM1bdo07d69O+D1XnnlFXXr1k27du3SihUrtHz5chUXF0uSWltblZGRoU8++USvvfaaDh06pCeffFKhoaH+48+fP6+nn35a//mf/6nt27frxIkT+vnPf35t/mEAaLdOVk8AACSpsLBQERERkqSGhga5XC4VFhYqJOT//z/Z/Pnzdd999wUc99XomDdvnjZt2qS33npLt99+u397cnKyli5dKkkaMGCAfvvb36qkpER33XWXtmzZot27d+vw4cO6+eabJUl9+/YNeI+mpiatXr1a/fr1kyTNnTtXy5cv78CzB3A1ETsAgsLo0aO1atUqSdIXX3yhF154QRkZGQFXaW677baAY1paWvTEE0/orbfe0v/8z/+osbFRXq9X4eHhAeOSk5MD1l0ul6qrqyVJ5eXl6tWrlz90Lic8PNwfOn9/PIDgR+wACArdunVT//79/eu///3v5XA49OKLL+pf/uVf/GO+6qmnntKzzz6rlStXKikpSd26ddP8+fPV2NgYMK5z584B6zabTa2trZKkrl27fuPcLne8z+f79icHwFI8swMgKNlsNoWEhOjLL7+84phPPvlE99xzj6ZOnaqhQ4eqb9+++q//+q/v9D7Jycn6/PPPv/NxAK4fxA6AoOD1euXxeOTxeHT48GHNmzdP9fX1mjRp0hWPGTBggIqLi7Vjxw4dPnxYP/3pT1VVVfWd3vcHP/iBRo0apczMTBUXF+v48eP68MMPVVRU1N5TAhAkiB0AQaGoqEgul0sul0spKSnas2eP1q9fr9TU1Cses3jxYv3jP/6j0tLSlJqaKqfTqXvvvfc7v/c777yjESNGaPLkyUpMTNSiRYvU0tLS9pMBEFRsPm48AwAAg3FlBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNH+D7XChfuSDGPqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['Branch'])\n",
    "df['Branch'].value_counts()\n",
    "#plt.savefig(\"uni2.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb45595e-6cfa-4291-b22b-9b562933104d",
   "metadata": {},
   "source": [
    "There is not much difference in sales across the 3 branches of A, B and C. \n",
    "The sales in branch A is a bit higher than the rest of the branches."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2536dfe7-ad1f-47e5-b284-5b71fe69915f",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "*Which is the most pouplar payment method used by customers?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9af57c42-fab5-419a-909a-ba58c5d0c4a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Payment', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y1KdPH0nS3Llz1alTJ61evVo9e/bUkiVLtGnTJi1btkyRkZG65JJL9MQTT2j8+PGaNGmS/P39vXFqAADAR/jUmp3y8nJJUnh4uCQpPz9f1dXV6tevnz2mY8eOatOmjfLy8iRJeXl5SkhIUGRkpD0mOTlZFRUV2rhx4zGPU1lZqYqKCo8NAACYyWfCTl1dncaMGaOrrrpKXbp0kSQVFxfL399fYWFhHmMjIyNVXFxsj/l10DnSf6TvWDIzMxUaGmpvMTExDXw2AADAV/hM2ElLS9N3332nN99884wfKyMjQ+Xl5fa2a9euM35MAADgHfUKO3369FFZWdlR7RUVFfbamt9j9OjRWrRokVauXKnWrVvb7S6XS1VVVUcdq6SkRC6Xyx7z23dnHdk/Mua3nE6nQkJCPDYAAGCmeoWdnJwcVVVVHdV++PBhff7556c8j2VZGj16tBYuXKgVK1booosu8ujv1q2bmjRpouXLl9tthYWF2rlzp9xutyTJ7XZrw4YNKi0ttccsXbpUISEhio+P/72nBgAADPO73o21fv16++dNmzZ5rImpra1Vdna2LrjgglOeLy0tTVlZWfrwww/VrFkze77Q0FAFBgYqNDRUI0aMUHp6usLDwxUSEqIHHnhAbrdbPXv2lCT1799f8fHxuuOOOzR16lQVFxdrwoQJSktLk9Pp/D2nBwAADPS7ws4ll1wih8Mhh8NxzMdVgYGBev755095vtmzZ0uSkpKSPNrnzp2rO++8U5L03HPPyc/PTykpKaqsrFRycrJeeOEFe2yjRo20aNEijRo1Sm63W8HBwUpNTdWUKVN+z6kBAABD/a6ws337dlmWpYsvvlhfffWVWrVqZff5+/srIiJCjRo1OuX5LMs66ZiAgADNmjVLs2bNOu6Y2NhYffLJJ6d8XAAAcP74XWEnNjZW0i9vEwcAADgX1PsTlLds2aKVK1eqtLT0qPDDVzUAAABfUa+w89JLL2nUqFFq2bKlXC6XHA6H3edwOAg7AADAZ9Qr7Pz1r3/Vk08+qfHjxzd0PQAAAA2qXp+z89NPP+nmm29u6FoAAAAaXL3Czs0336wlS5Y0dC0AAAANrl6PseLi4vTYY49p9erVSkhIUJMmTTz6H3zwwQYpDgAA4HTVK+y8+OKLatq0qXJzc5Wbm+vR53A4CDsAAMBn1CvsbN++vaHrAAAAOCPqtWYHAADgXFGvOzt33333CftfffXVehUDAADQ0OoVdn766SeP/erqan333XcqKys75heEAgAAeEu9ws7ChQuPaqurq9OoUaPUtm3b0y4KAACgoTTYmh0/Pz+lp6frueeea6gpAQAATluDLlDetm2bampqGnJKAACA01Kvx1jp6eke+5ZlqaioSB9//LFSU1MbpDAAAICGUK+w880333js+/n5qVWrVpo2bdpJ36kFAABwNtUr7KxcubKh6wAAADgj6hV2jtizZ48KCwslSR06dFCrVq0apCgAAICGUq8FygcPHtTdd9+tqKgo9erVS7169VJ0dLRGjBihQ4cONXSNAAAA9VavsJOenq7c3Fx99NFHKisrU1lZmT788EPl5ubqkUceaegaAQAA6q1ej7Hee+89vfvuu0pKSrLbBg0apMDAQP3xj3/U7NmzG6o+AACA01KvOzuHDh1SZGTkUe0RERE8xgIAAD6lXmHH7Xbr8ccf1+HDh+22n3/+WZMnT5bb7W6w4gAAAE5XvR5jTZ8+XQMGDFDr1q3VtWtXSdK3334rp9OpJUuWNGiBAAAAp6NeYSchIUFbtmzRggUL9P3330uSbrvtNg0bNkyBgYENWiAAAMDpqFfYyczMVGRkpEaOHOnR/uqrr2rPnj0aP358gxQHAABwuuq1Zucf//iHOnbseFR7586dNWfOnNMuCgAAoKHUK+wUFxcrKirqqPZWrVqpqKjotIsCAABoKPUKOzExMfriiy+Oav/iiy8UHR192kUBAAA0lHqt2Rk5cqTGjBmj6upq9enTR5K0fPlyjRs3jk9QBgAAPqVeYWfs2LHau3ev7r//flVVVUmSAgICNH78eGVkZDRogQAAAKejXmHH4XDomWee0WOPPabNmzcrMDBQ7dq1k9PpbOj6AAAATku9ws4RTZs21eWXX95QtQAAADS4ei1QBgAAOFcQdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGM2rYeezzz7TkCFDFB0dLYfDoQ8++MCj/84775TD4fDYBgwY4DFm3759GjZsmEJCQhQWFqYRI0bowIEDZ/EsAACAL/Nq2Dl48KC6du2qWbNmHXfMgAEDVFRUZG9vvPGGR/+wYcO0ceNGLV26VIsWLdJnn32me++990yXDgAAzhGn9aGCp2vgwIEaOHDgCcc4nU65XK5j9m3evFnZ2dlau3atunfvLkl6/vnnNWjQIP3973/nS0kBAIDvr9nJyclRRESEOnTooFGjRmnv3r12X15ensLCwuygI0n9+vWTn5+f1qxZc9w5KysrVVFR4bEBAAAz+XTYGTBggF577TUtX75czzzzjHJzczVw4EDV1tZKkoqLixUREeHxmsaNGys8PFzFxcXHnTczM1OhoaH2FhMTc0bPAwAAeI9XH2OdzK233mr/nJCQoMTERLVt21Y5OTnq27dvvefNyMhQenq6vV9RUUHgAQDAUD59Z+e3Lr74YrVs2VJbt26VJLlcLpWWlnqMqamp0b59+467zkf6ZR1QSEiIxwYAAMx0ToWdf//739q7d6+ioqIkSW63W2VlZcrPz7fHrFixQnV1derRo4e3ygQAAD7Eq4+xDhw4YN+lkaTt27eroKBA4eHhCg8P1+TJk5WSkiKXy6Vt27Zp3LhxiouLU3JysiSpU6dOGjBggEaOHKk5c+aourpao0eP1q233so7sQAAgCQv39lZt26dLr30Ul166aWSpPT0dF166aWaOHGiGjVqpPXr1+v6669X+/btNWLECHXr1k2ff/65nE6nPceCBQvUsWNH9e3bV4MGDdLVV1+tF1980VunBAAAfIxX7+wkJSXJsqzj9i9evPikc4SHhysrK6shywIAAAY5p9bsAAAA/F6EHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRvBp2PvvsMw0ZMkTR0dFyOBz64IMPPPoty9LEiRMVFRWlwMBA9evXT1u2bPEYs2/fPg0bNkwhISEKCwvTiBEjdODAgbN4FgAAwJd5NewcPHhQXbt21axZs47ZP3XqVM2cOVNz5szRmjVrFBwcrOTkZB0+fNgeM2zYMG3cuFFLly7VokWL9Nlnn+nee+89W6cAAAB8XGNvHnzgwIEaOHDgMfssy9L06dM1YcIE3XDDDZKk1157TZGRkfrggw906623avPmzcrOztbatWvVvXt3SdLzzz+vQYMG6e9//7uio6PP2rkAAADf5LNrdrZv367i4mL169fPbgsNDVWPHj2Ul5cnScrLy1NYWJgddCSpX79+8vPz05o1a447d2VlpSoqKjw2AABgJp8NO8XFxZKkyMhIj/bIyEi7r7i4WBERER79jRs3Vnh4uD3mWDIzMxUaGmpvMTExDVw9AADwFT4bds6kjIwMlZeX29uuXbu8XRIAADhDfDbsuFwuSVJJSYlHe0lJid3ncrlUWlrq0V9TU6N9+/bZY47F6XQqJCTEYwMAAGby2bBz0UUXyeVyafny5XZbRUWF1qxZI7fbLUlyu90qKytTfn6+PWbFihWqq6tTjx49znrNAADA93j13VgHDhzQ1q1b7f3t27eroKBA4eHhatOmjcaMGaO//vWvateunS666CI99thjio6O1o033ihJ6tSpkwYMGKCRI0dqzpw5qq6u1ujRo3XrrbfyTiwAACDJy2Fn3bp1uuaaa+z99PR0SVJqaqrmzZuncePG6eDBg7r33ntVVlamq6++WtnZ2QoICLBfs2DBAo0ePVp9+/aVn5+fUlJSNHPmzLN+LgAAwDd5NewkJSXJsqzj9jscDk2ZMkVTpkw57pjw8HBlZWWdifIAAIABfHbNDgAAQEMg7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACM5tNhZ9KkSXI4HB5bx44d7f7Dhw8rLS1NLVq0UNOmTZWSkqKSkhIvVgwAAHyNT4cdSercubOKiorsbdWqVXbfww8/rI8++kjvvPOOcnNztXv3bg0dOtSL1QIAAF/T2NsFnEzjxo3lcrmOai8vL9crr7yirKws9enTR5I0d+5cderUSatXr1bPnj3PdqkAAMAH+fydnS1btig6OloXX3yxhg0bpp07d0qS8vPzVV1drX79+tljO3bsqDZt2igvL++Ec1ZWVqqiosJjAwAAZvLpsNOjRw/NmzdP2dnZmj17trZv364//OEP2r9/v4qLi+Xv76+wsDCP10RGRqq4uPiE82ZmZio0NNTeYmJizuBZAAAAb/Lpx1gDBw60f05MTFSPHj0UGxurt99+W4GBgfWeNyMjQ+np6fZ+RUUFgQcAAEP59J2d3woLC1P79u21detWuVwuVVVVqayszGNMSUnJMdf4/JrT6VRISIjHBgAAzHROhZ0DBw5o27ZtioqKUrdu3dSkSRMtX77c7i8sLNTOnTvldru9WCUAAPAlPv0Y69FHH9WQIUMUGxur3bt36/HHH1ejRo102223KTQ0VCNGjFB6errCw8MVEhKiBx54QG63m3diAQAAm0+HnX//+9+67bbbtHfvXrVq1UpXX321Vq9erVatWkmSnnvuOfn5+SklJUWVlZVKTk7WCy+84OWqAQCAL/HpsPPmm2+esD8gIECzZs3SrFmzzlJFAADgXHNOrdkBAAD4vQg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMZE3ZmzZqlCy+8UAEBAerRo4e++uorb5cEAAB8gBFh56233lJ6eroef/xxff311+ratauSk5NVWlrq7dIAAICXGRF2nn32WY0cOVJ33XWX4uPjNWfOHAUFBenVV1/1dmkAAMDLGnu7gNNVVVWl/Px8ZWRk2G1+fn7q16+f8vLyjvmayspKVVZW2vvl5eWSpIqKinrXUVv5c71fC/OczrXUkPYfrvV2CfAhvnBd1vxc4+0S4ENO95o88nrLsk447pwPOz/++KNqa2sVGRnp0R4ZGanvv//+mK/JzMzU5MmTj2qPiYk5IzXi/BP6/H3eLgE4WmaotysAPISOb5hrcv/+/QoNPf5c53zYqY+MjAylp6fb+3V1ddq3b59atGghh8PhxcrObRUVFYqJidGuXbsUEhLi7XIASVyX8D1ckw3Hsizt379f0dHRJxx3zoedli1bqlGjRiopKfFoLykpkcvlOuZrnE6nnE6nR1tYWNiZKvG8ExISwn/A8Dlcl/A1XJMN40R3dI445xco+/v7q1u3blq+fLndVldXp+XLl8vtdnuxMgAA4AvO+Ts7kpSenq7U1FR1795dV1xxhaZPn66DBw/qrrvu8nZpAADAy4wIO7fccov27NmjiRMnqri4WJdccomys7OPWrSMM8vpdOrxxx8/6hEh4E1cl/A1XJNnn8M62fu1AAAAzmHn/JodAACAEyHsAAAAoxF2AACA0Qg7OKMcDoc++OADSdKOHTvkcDhUUFDg1ZqA38rJyZHD4VBZWZm3S4EBzrW/e3feeaduvPFGb5dxRhF2zjN33nmnHA7HUduAAQO8XdoxJSUlacyYMd4uAz6guLhYDzzwgC6++GI5nU7FxMRoyJAhHp+xBRyLN6+dmJgYFRUVqUuXLpII1t5ixFvP8fsMGDBAc+fO9WjjLZDwZTt27NBVV12lsLAw/e1vf1NCQoKqq6u1ePFipaWlHfd78ID6XjvV1dVq0qTJaR+/UaNGx/00/7Otoc7pXMSdnfOQ0+mUy+Xy2Jo3b65HH31U1113nT1u+vTpcjgcys7Ottvi4uL08ssvS5LWrl2ra6+9Vi1btlRoaKh69+6tr7/++nfV8t1332ngwIFq2rSpIiMjdccdd+jHH3+U9MtdqNzcXM2YMcO+A7Vjx47T/wXgnHP//ffL4XDoq6++UkpKitq3b6/OnTsrPT1dq1evliQ9++yzSkhIUHBwsGJiYnT//ffrwIED9hz/93//pyFDhqh58+YKDg5W586d9cknn3gcJz8/X927d1dQUJCuvPJKFRYWntXzRMM7lWtH+uXR0+zZs3X99dcrODhYTz75pCTpww8/1GWXXaaAgABdfPHFmjx5smpq/v9vbt+yZYt69eqlgIAAxcfHa+nSpR7H//VjrB07duiaa66RJDVv3lwOh0N33nnncWv/4osvlJSUpKCgIDVv3lzJycn66aefJEnZ2dm6+uqrFRYWphYtWui6667Ttm3bjjruW2+9pd69eysgIEALFixQbW2t0tPT7deNGzfupN8YbgLCDmy9e/fWqlWrVFtbK0nKzc1Vy5YtlZOTI0n6z3/+o23btikpKUnSL98ym5qaqlWrVmn16tVq166dBg0apP3795/S8crKytSnTx9deumlWrdunbKzs1VSUqI//vGPkqQZM2bI7XZr5MiRKioqUlFREd9Mfx7at2+fsrOzlZaWpuDg4KP6j3yvnZ+fn2bOnKmNGzdq/vz5WrFihcaNG2ePS0tLU2VlpT777DNt2LBBzzzzjJo2beox11/+8hdNmzZN69atU+PGjXX33Xef0XPDmXWq184RkyZN0k033aQNGzbo7rvv1ueff67hw4froYce0qZNm/SPf/xD8+bNs4NQXV2dhg4dKn9/f61Zs0Zz5szR+PHjj1tPTEyM3nvvPUlSYWGhioqKNGPGjGOOLSgoUN++fRUfH6+8vDytWrVKQ4YMsf8+Hzx4UOnp6Vq3bp2WL18uPz8/3XTTTaqrq/OY589//rMeeughbd68WcnJyZo2bZrmzZunV199VatWrdK+ffu0cOHCU/6dnrMsnFdSU1OtRo0aWcHBwR7bk08+af3000+Wn5+ftXbtWquurs4KDw+3MjMzrR49eliWZVn//Oc/rQsuuOC4c9fW1lrNmjWzPvroI7tNkrVw4ULLsixr+/btliTrm2++sSzLsp544gmrf//+HnPs2rXLkmQVFhZalmVZvXv3th566KGG+wXgnLNmzRpLkvX+++//rte98847VosWLez9hIQEa9KkScccu3LlSkuStWzZMrvt448/tiRZP//8c/0Kh9f9nmtHkjVmzBiPtr59+1pPPfWUR9vrr79uRUVFWZZlWYsXL7YaN25s/ec//7H7P/300xP+3Ttyrf30008nrOe2226zrrrqqpPWfcSePXssSdaGDRs8jjt9+nSPcVFRUdbUqVPt/erqaqt169bWDTfccMrHOhexZuc8dM0112j27NkebeHh4QoLC1PXrl2Vk5Mjf39/+fv7695779Xjjz+uAwcOKDc3V71797ZfU1JSogkTJignJ0elpaWqra3VoUOHtHPnzlOq49tvv9XKlSuP+r9rSdq2bZvat29/eicKI1ineIt92bJlyszM1Pfff6+KigrV1NTo8OHDOnTokIKCgvTggw9q1KhRWrJkifr166eUlBQlJiZ6zPHr/aioKElSaWmp2rRp03AnhLPmVK+dI7p37+6x/+233+qLL76w7+RIUm1trX1dbd68WTExMYqOjrb7G+oLqAsKCnTzzTcft3/Lli2aOHGi1qxZox9//NG+o7Nz5057MfRvz6m8vFxFRUXq0aOH3da4cWN1797d+EdZhJ3zUHBwsOLi4o7Zl5SUpJycHDmdTvXu3Vvh4eHq1KmTVq1apdzcXD3yyCP22NTUVO3du1czZsxQbGysnE6n3G63qqqqTqmOAwcOaMiQIXrmmWeO6jvyDw3Qrl07ORyOEy5C3rFjh6677jqNGjVKTz75pMLDw7Vq1SqNGDFCVVVVCgoK0j333KPk5GR9/PHHWrJkiTIzMzVt2jQ98MAD9jy/XrzpcDgk6ajHAjh3nMq182u/fdR14MABTZ48WUOHDj1qbEBAQIPUeDyBgYEn7B8yZIhiY2P10ksvKTo6WnV1derSpctRf3+P9fjufMSaHXg4sm5n+fLl9tqcpKQkvfHGG/rhhx/sNumXxXMPPvigBg0apM6dO8vpdNqLi0/FZZddpo0bN+rCCy9UXFycx3bkP1B/f3/7GTXOT+Hh4UpOTtasWbN08ODBo/rLysqUn5+vuro6TZs2TT179lT79u21e/fuo8bGxMTovvvu0/vvv69HHnlEL7300tk4BXjJqVw7J3LZZZepsLDwqL9PcXFx8vPzU6dOnbRr1y4VFRXZr/n1oudj8ff3l6ST/l1LTEw87lvj9+7dq8LCQk2YMEF9+/ZVp06d7IXLJxIaGqqoqCitWbPGbqupqVF+fv5JX3uuI+ychyorK1VcXOyxHQkpvXr10v79+7Vo0SKPsLNgwQJFRUV5PFpq166dXn/9dW3evFlr1qzRsGHDTvp/I7+Wlpamffv26bbbbtPatWu1bds2LV68WHfddZf9h+DCCy/UmjVrtGPHDo9btTi/zJo1S7W1tbriiiv03nvvacuWLdq8ebNmzpwpt9utuLg4VVdX6/nnn9e//vUvvf7665ozZ47HHGPGjNHixYu1fft2ff3111q5cqU6derkpTPC2XKya+dEJk6cqNdee02TJ0/Wxo0btXnzZr355puaMGGCJKlfv35q3769UlNT9e233+rzzz/XX/7ylxPOGRsbK4fDoUWLFmnPnj0e7xj8tYyMDK1du1b333+/1q9fr++//16zZ8/Wjz/+qObNm6tFixZ68cUXtXXrVq1YsULp6emn9Pt46KGH9PTTT+uDDz7Q999/r/vvv//8+MwfL68ZwlmWmppqSTpq69Chgz2ma9eulsvlsvf37t1rORwO69Zbb/WY6+uvv7a6d+9uBQQEWO3atbPeeecdKzY21nruuefsMTrBQj3LsqwffvjBuummm6ywsDArMDDQ6tixozVmzBirrq7OsizLKiwstHr27GkFBgZakqzt27c3+O8E54bdu3dbaWlpVmxsrOXv729dcMEF1vXXX2+tXLnSsizLevbZZ62oqCgrMDDQSk5Otl577TWPhaCjR4+22rZtazmdTqtVq1bWHXfcYf3444+WZR170eg333zDNWeIk107luX5t+rXsrOzrSuvvNIKDAy0QkJCrCuuuMJ68cUX7f7CwkLr6quvtvz9/a327dtb2dnZJ/27N2XKFMvlclkOh8NKTU09bt05OTnWlVdeaTmdTissLMxKTk62r9GlS5danTp1spxOp5WYmGjl5OSc9LiW9cuC5IceesgKCQmxwsLCrPT0dGv48OHGL1B2WJbhq5IAAMB5jcdYAADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AZ9Wdd94ph8Mhh8Mhf39/xcXFacqUKaqpqfF2aadl3rx5CgsL83YZAI6hsbcLAHD+GTBggObOnavKykp98sknSktLU5MmTZSRkeHt0gAYiDs7AM46p9Mpl8ul2NhYjRo1Sv369dP//u//6tlnn1VCQoKCg4MVExOj+++/3/5W6IMHDyokJETvvvuux1wffPCBgoODtX//fu3YsUMOh0Nvv/22/vCHPygwMFCXX365fvjhB61du1bdu3dX06ZNNXDgQO3Zs8djnpdfflmdOnVSQECAOnbsqBdeeMHuOzLv+++/r2uuuUZBQUHq2rWr8vLyJEk5OTm66667VF5ebt+1mjRp0pn9JQI4ZYQdAF4XGBioqqoq+fn5aebMmdq4caPmz5+vFStWaNy4cZKk4OBg3XrrrZo7d67Ha+fOnav/+q//UrNmzey2xx9/XBMmTNDXX3+txo0b6/bbb9e4ceM0Y8YMff7559q6dasmTpxoj1+wYIEmTpyoJ598Ups3b9ZTTz2lxx57TPPnz/c41l/+8hc9+uijKigoUPv27XXbbbeppqZGV155paZPn66QkBAVFRWpqKhIjz766Bn8jQH4Xbz9tesAzi+pqanWDTfcYFmWZdXV1VlLly61nE6n9eijjx419p133rFatGhh769Zs8Zq1KiRtXv3bsuyLKukpMRq3LixlZOTY1mWZW3fvt2SZL388sv2a9544w1LkrV8+XK7LTMz0+rQoYO937ZtWysrK8vj2E888YTldruPO+/GjRstSdbmzZsty7KsuXPnWqGhofX5lQA4w1izA+CsW7RokZo2barq6mrV1dXp9ttv16RJk7Rs2TJlZmbq+++/V0VFhWpqanT48GEdOnRIQUFBuuKKK9S5c2fNnz9ff/7zn/XPf/5TsbGx6tWrl8f8iYmJ9s+RkZGSpISEBI+20tJSSb88Htu2bZtGjBihkSNH2mNqamoUGhp63HmjoqIkSaWlperYsWMD/WYAnAk8xgJw1l1zzTUqKCjQli1b9PPPP2v+/Pnas2ePrrvuOiUmJuq9995Tfn6+Zs2aJUmqqqqyX3vPPfdo3rx5kn55hHXXXXfJ4XB4zN+kSRP75yN9v22rq6uTJHtN0EsvvaSCggJ7++6777R69eqTzntkHgC+izs7AM664OBgxcXFebTl5+errq5O06ZNk5/fL/8f9vbbbx/12j/96U8aN26cZs6cqU2bNik1NfW0aomMjFR0dLT+9a9/adiwYfWex9/fX7W1tadVC4Azg7ADwCfExcWpurpazz//vIYMGaIvvvhCc+bMOWpc8+bNNXToUI0dO1b9+/dX69atT/vYkydP1oMPPqjQ0FANGDBAlZWVWrdunX766Selp6ef0hwXXnihDhw4oOXLl6tr164KCgpSUFDQadcG4PTxGAuAT+jataueffZZPfPMM+rSpYsWLFigzMzMY44dMWKEqqqqdPfddzfIse+55x69/PLLmjt3rhISEtS7d2/NmzdPF1100SnPceWVV+q+++7TLbfcolatWmnq1KkNUhuA0+ewLMvydhEA8Hu8/vrrevjhh7V79275+/t7uxwAPo7HWADOGYcOHVJRUZGefvpp/fd//zdBB8Ap4TEWgHPG1KlT1bFjR7lcLr5aAsAp4zEWAAAwGnd2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACj/T/ClE5awDVUUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['Payment'])\n",
    "#plt.savefig(\"uni3.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a6bf197-f46e-4793-86c5-f43565dfa7ae",
   "metadata": {},
   "source": [
    "The most popular payment method is E-wallet and not credit cards.\n",
    "Cash payment is also popular"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc81be2-318b-4a66-8d95-b702e415fe84",
   "metadata": {},
   "source": [
    "## Bi-variate analysis\n",
    "Let us consider two (bi) variables together and look at the interaction/relationship between them. \n",
    "\n",
    "This is bi-variate analysis that can help to draw important conclusions about the data.\n",
    "Let us consider gross income and try to answer the following questions:\n",
    "\n",
    "## Question 4\n",
    "*Does gross income affect the ratings that the customers provide?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "97b47b5b-1ca9-4713-ab15-cfe71fc87979",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Rating', ylabel='gross income'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=df['Rating'], y=df['gross income'])\n",
    "#plt.savefig(\"uni4.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60019f4f-4cbc-4d0b-9cc9-aba971bcec7e",
   "metadata": {},
   "source": [
    "*As you can see from the scatter plot and the trend line which is pretty flat that there is no relationship between gross income of a customer and his rating.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e36b1e-61b7-4e4d-bf7c-cf6bbb3d6918",
   "metadata": {},
   "source": [
    "## Question 5: \n",
    "*Which branch is the most profitable?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a707f783-00b7-447c-9658-1d9af3ff2a69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Branch', ylabel='gross income'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6ulqvvvqqJk6cqI0bN17w+zmdTjmdzqDlAwAAoSvgo5amT5+uwsLCoAVo166devfurfT0dBUUFOjqq6/Ws88+q+TkZJ05c0ZVVVV+z6+srFRycnLQPh8AAJgr4C0yDzzwgEaPHq1evXqpf//+ioyM9Fv+2muvXVSg2tpaeb1epaenKzIyUqWlpb4T7e3du1cHDhxQRkbGRX0GAABoHQIuMlOnTtU777yj4cOHKzEx8aIm+M6YMUOjRo1St27ddOLECb388st69913VVJSori4OE2ePFn5+flKSEhQbGys8vLylJGR0eBEXwAA0LYEXGSWLVumNWvWaPTo0Rf94UeOHNHdd9+t8vJyxcXFKS0tTSUlJRo5cqQkae7cuQoLC1NOTo68Xq+ysrK0cOHCi/5cAADQOgRcZBISEtSrV6+gfHhRUVGjy10ulwoLC4M6JwcAALQeAU/2nTVrlmbOnKmvv/66OfIAAAA0WcBbZObPn6+//OUvSkpKUo8ePepM9t25c2fQwgEAADQm4CJz++23N0MMAACAwAVcZGbOnNkcOQAAAAJ2wVe/3rFjh/bs2SNJuvLKK/W9730vaKEAAACaIuAic+TIEd1555169913fddBqqqq0vDhw7Vy5Up16tQp2BkBAADqFfBRS3l5eTpx4oT+53/+R8ePH9fx48f10Ucfye12a+rUqc2REQAAoF4Bb5EpLi7WW2+9pX79+vnG+vfvr8LCQt1yyy1BDQcAANCYgLfI1NbW1jnkWpIiIyNVW1sblFAAAABNEXCRGTFihO677z4dPnzYN/bFF19o+vTpuvnmm4MaDgAAoDEBF5kFCxbI7XarR48e6tWrl3r16qXLL79cbrdbzz33XHNkBAAAqFfAc2RSUlK0c+dOvfXWW/r4448lSf369VNmZmbQwwEAADTmgs4j43A4NHLkSN9VqgEAAOwQ8K6lqVOnav78+XXGFyxYoGnTpgUjEwAAQJMEXGTWrFmjIUOG1Bm//vrr9eqrrwYlFAAAQFMEXGSOHTumuLi4OuOxsbE6evRoUEIBAAA0RcBFpnfv3iouLq4z/sYbb6hnz55BCQUAANAUAU/2zc/P15QpU/Tll19qxIgRkqTS0lLNmTNH8+bNC3Y+AACABgVcZH7+85/L6/XqiSee0G9+8xtJUo8ePbRo0SLdfffdQQ8IAADQkAs6/Pree+/Vvffeqy+//FJRUVGKiYkJdi4AAIDvdEFF5ludOnUKVg4AAICABTzZt7KyUnfddZe6dOmiiIgIhYeH+90AAABaSsBbZCZNmqQDBw7okUceUefOneVwOJojFwAAwHcKuMhs3rxZ//Vf/6VrrrmmGeIAAAA0XcC7llJSUmRZVnNkAQAACEjARWbevHl66KGHtG/fvmaIAwAA0HQB71q644479PXXX6tXr15q3769IiMj/ZYfP348aOEAAAAaE3CR4ey9AAAgVARcZCZOnNgcOQAAAALWpCLjdrsVGxvru9+Yb58HAADQ3JpUZC655BKVl5fr0ksvVXx8fL3njrEsSw6HQzU1NUEPCQAAUJ8mFZm3335bCQkJkqR33nmnWQMBAAA0VZOKzE033VTvfQAAADsFfB4ZAACAUHFRV78G0PIsy5LH47E7hu3O/w74Ps5xuVxc/w5tDkUGMIzH41FWVpbdMUJKdna23RFCQklJiaKiouyOAbQodi0BAABjBbxF5vTp07IsS+3bt5ck7d+/X2vXrlX//v11yy23BD0ggIYVDq2SM7xtXsTVsqQztefutwuT2uoeFW+NQ7mb4u2OAdgm4CKTnZ2tsWPH6pe//KWqqqo0ePBgRUZG6ujRo3rmmWd07733NkdOAPVwhltyhdudwj7sRJGktllkgW8FvGtp586duvHGGyVJr776qpKSkrR//369+OKLmj9/ftADAgAANCTgIvP111+rQ4cOkqT//M//1NixYxUWFqbrrrtO+/fvD3pAAACAhgRcZHr37q1169bp4MGDKikp8c2LOXLkCNdZAgAALSrgIvPoo4/qgQceUI8ePTR48GBlZGRIOrd15nvf+17QAwIAADQk4Mm+//AP/6AbbrhB5eXluvrqq33jN998s370ox8FNRwAAEBjLuiEeMnJyUpOTpYkud1uvf322+rbt69SU1ODGg4AEPo42/Q5nG26rpY423TAReYnP/mJhg4dqilTpuj06dMaOHCg9u3bJ8uytHLlSuXk5DRHTgBAiOJs03VxtulzWuJs0wEXmU2bNulf//VfJUlr166VZVmqqqrSsmXL9Pjjj7eqIsOvjL/hl0ZdXNcGAOwXcJGprq5WQkKCJKm4uFg5OTlq3769Ro8erX/5l38JekA78SujfvzSOIfr2gB11YypabtX8bMk1fz1friktvo75xspfH3Lnakz4NUtJSVFW7ZsUUJCgoqLi7Vy5UpJ0ldffSWXyxX0gAAAg0So7RYZSYq0O0DbE/DqNm3aNI0fP14xMTHq3r27hg0bJuncLqerrroq2PlCxqnvj5fC2vB/nZYl1X5z7n5YRNu9sE3tN4reucLuFACAvwr4L/M///M/a9CgQTp48KBGjhypsLBzp6Lp2bOnHn/88aAHDBlhEVJ4W6/a7ewOAACAnwvaxDBw4EANHDhQlmXJsiw5HA6NHj062NkAAAAaFfCZfSXpxRdf1FVXXaWoqChFRUUpLS1NL730UrCzAQAANCrgLTLPPPOMHnnkEU2ZMkVDhgyRJG3evFm//OUvdfToUU2fPj3oIQEAAOoTcJF57rnntGjRIt19992+sR/+8Ie68sorNWvWLIoMAABoMQHvWiovL9f1119fZ/z6669XeXl5UEIBAAA0RcBFpnfv3lq1alWd8VdeeUV9+vQJSigAAICmCHjX0mOPPaY77rhDmzZt8s2RKSsrU2lpab0FpzEFBQV67bXX9PHHHysqKkrXX3+9/u3f/k19+/b1Pcfj8ej+++/XypUr5fV6lZWVpYULFyopKSnQ6AAAoJUJeItMTk6Otm3bpo4dO2rdunVat26dOnbsqG3btulHP/pRQO+1ceNG5ebmauvWrXrzzTd19uxZ3XLLLTp16pTvOdOnT9f69eu1evVqbdy4UYcPH9bYsWMDjQ0AAFqhgLbInD17Vvfcc48eeeQRLV++/KI/vLi42O/x0qVLdemll2rHjh0aOnSoqqurVVRUpJdfflkjRoyQJC1ZskT9+vXT1q1bdd111110BgAAYK6AtshERkZqzZo1zZVF1dXVkuS7KOWOHTt09uxZZWZm+p6Tmpqqbt26acuWLfW+h9frldvt9rsBAIDWKeBdS7fffrvWrVsX9CC1tbWaNm2ahgwZogEDBkiSKioq1K5dO8XHx/s9NykpSRUVFfW+T0FBgeLi4ny3lJSUoGcFAAChIeDJvn369NHs2bNVVlam9PR0RUdH+y2fOnXqBQXJzc3VRx99pM2bN1/Q6781Y8YM5efn+x673W7KDAAArVTARaaoqEjx8fHasWOHduzY4bfM4XBcUJGZMmWKNmzYoE2bNqlr166+8eTkZJ05c0ZVVVV+W2UqKyuVnJxc73s5nU45nc6AMwAAAPMEXGQ+//zzoH24ZVnKy8vT2rVr9e677+ryyy/3W56enq7IyEiVlpYqJydHkrR3714dOHBAGRkZQcsBAADMdEFXvw6W3Nxcvfzyy3r99dfVoUMH37yXuLg4RUVFKS4uTpMnT1Z+fr4SEhIUGxurvLw8ZWRkcMQSAAAIvMicP//kfA6HQy6XS71791Z2drbvyKPGLFq0SJI0bNgwv/ElS5Zo0qRJkqS5c+cqLCxMOTk5fifEAwAACLjI/PnPf9bOnTtVU1PjOwPvJ598ovDwcKWmpmrhwoW6//77tXnzZvXv37/R97Is6zs/z+VyqbCwUIWFhYFGBQAArVzAh19nZ2crMzNThw8f9k34PXTokEaOHKlx48bpiy++0NChQ7kKNgAAaHYBF5mnn35av/nNbxQbG+sbi4uL06xZs/TUU0+pffv2evTRR+sc0QQAABBsAReZ6upqHTlypM74l19+6TuLbnx8vM6cOXPx6QAAABpxQbuWfv7zn2vt2rU6dOiQDh06pLVr12ry5Mm6/fbbJUnbtm3TFVdcEeysAAAAfgKe7Pu73/1O06dP15133qlvvvnm3JtERGjixImaO3eupHPXQ1q8eHFwkwIAAPydgItMTEyMXnjhBc2dO1f/93//J0nq2bOnYmJifM+55pprghYQAACgIRd8QryYmBilpaUFMwsAAEBAAp4jAwAAECooMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY0XYHQBAYCzL8t331tgYBCHh/HXg/HUDaCsoMoBhvF6v737upktsTIJQ4/V61b59e7tjAC2KXUsAAMBYbJEBDON0On33C4d+JWe4jWFgO2/N37bMnb9uAG0FRQYwjMPh8N13hksuigz+6vx1A2gr2LUEAACMxRYZAMBF8Tta6hv7ciBEnLcOtMSRdBQZAMBFOf9IuvD17OvE37TEkXTsWgIAAMZiiwwA4KKcf7RUzZga/rK0dd/8bctcSxxJx+rWCL99ezVn7QuC0HHeesBZVIFz/I6WihB/WeDTEkfSsbo14vz9vtF/ftnGJAhFnEUVAOzHHBkAAGAstsg04vx9e6e+91MpPNLGNAgJNWd9W+c4iyoA2I8i0wi/fXvhkRQZ+OEsqgBgP3YtAQAAY1FkAACAsSgyAADAWLYWmU2bNmnMmDHq0qWLHA6H1q1b57fcsiw9+uij6ty5s6KiopSZmalPP/3UnrAAACDk2FpkTp06pauvvlqFhYX1Ln/qqac0f/58Pf/883r//fcVHR2trKwseTyeFk4KAABCka1HLY0aNUqjRo2qd5llWZo3b54efvhhZWdnS5JefPFFJSUlad26dbrzzjtbMioAAAhBITtH5vPPP1dFRYUyMzN9Y3FxcRo8eLC2bNnS4Ou8Xq/cbrffDQAAtE4hW2QqKiokSUlJSX7jSUlJvmX1KSgoUFxcnO+WkpLSrDkBAIB9QrbIXKgZM2aourradzt48KDdkQAAQDMJ2SKTnJwsSaqsrPQbr6ys9C2rj9PpVGxsrN8NAAC0TiFbZC6//HIlJyertLTUN+Z2u/X+++8rIyPDxmQAACBU2HrU0smTJ/XZZ5/5Hn/++efatWuXEhIS1K1bN02bNk2PP/64+vTpo8svv1yPPPKIunTpottvv92+0AAAIGTYWmQ++OADDR8+3Pc4Pz9fkjRx4kQtXbpUDz74oE6dOqVf/OIXqqqq0g033KDi4mK5XC67IgMAgBBia5EZNmyYLMtqcLnD4dDs2bM1e/bsFkwFAABMEbJzZAAAAL4LRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBF2BzBG7Td2J7CXZf3tOwiLkBwOe/PYpa2vBwAQYigyTRS9c4XdEQAAwN9h1xIAADAWW2Qa4XK5VFJSYneMkODxeJSdnS1Jev311+VyuWxOZD++AwCwH0WmEQ6HQ1FRUXbHCDkul4vvBQAQEigygMG8NQ5Jlt0xbGFZ0pnac/fbhbXd+efn1oEQ0pbnw1uSav56P1xSiP1P02JaeB2gyAAGy90Ub3cEwE/4+nC7I6CNYbIvAAAwFltkAMMwCf0cJqDXZdd3wDp5DutkXS3xHVBkAMMwCb0uJqDbi3WyLtbJlsOuJQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjBVhdwAAAC6WZVnyeDy2Zjj/8+3OIkkul0sOh8PuGM2OIgMAMJ7H41FWVpbdMXyys7PtjqCSkhJFRUXZHaPZUWQMwC+NutrKLw0AQOMoMgbgl0ZdbeWXRqiiXNdFubaXy+VSSUmJrRksy5LX65UkOZ1O29cHl8tl6+e3FIoMgIBRruuiXNvL4XCExPffvn17uyO0OUYUmcLCQj399NOqqKjQ1Vdfreeee06DBg2yO1aL4ZdGXW3llwYAoHEhX2ReeeUV5efn6/nnn9fgwYM1b948ZWVlae/evbr00kvtjtci+KWBUEO5rotyDdjDYVmWZXeIxgwePFjXXnutFixYIEmqra1VSkqK8vLy9NBDD9V5vtfr9f2fmyS53W6lpKSourpasbGxLZYbAABcOLfbrbi4uO/8+x3SJ8Q7c+aMduzYoczMTN9YWFiYMjMztWXLlnpfU1BQoLi4ON8tJSWlpeICAIAWFtJF5ujRo6qpqVFSUpLfeFJSkioqKup9zYwZM1RdXe27HTx4sCWiAgAAG4T8HJlAOZ1OOZ1Ou2MAAIAWENJbZDp27Kjw8HBVVlb6jVdWVio5OdmmVAAAIFSEdJFp166d0tPTVVpa6hurra1VaWmpMjIybEwGAABCQcjvWsrPz9fEiRM1cOBADRo0SPPmzdOpU6f0s5/9zO5oAADAZiFfZO644w59+eWXevTRR1VRUaFrrrlGxcXFdSYAAwCAtifkzyNzsZp6HDoAAAgdreI8MgAAAI2hyAAAAGNRZAAAgLEoMgAAwFgUGQAAYKyQP/z6Yn17UJbb7bY5CQAAaKpv/25/18HVrb7InDhxQpK4CjYAAAY6ceKE4uLiGlze6s8jU1tbq8OHD6tDhw5yOBx2xzGa2+1WSkqKDh48yDl5EBJYJxFqWCeDx7IsnThxQl26dFFYWMMzYVr9FpmwsDB17drV7hitSmxsLP+BIqSwTiLUsE4GR2NbYr7FZF8AAGAsigwAADAWRQZN5nQ6NXPmTDmdTrujAJJYJxF6WCdbXquf7AsAAFovtsgAAABjUWQAAICxKDIAAMBYFBkAAGAsigyaZMuWLQoPD9fo0aPtjgJIkioqKpSXl6eePXvK6XQqJSVFY8aMUWlpqd3R0AZNmjRJDofDd0tMTNStt96q3bt32x2t1aPIoEmKioqUl5enTZs26fDhw3bHQRu3b98+paen6+2339bTTz+tDz/8UMXFxRo+fLhyc3Ptjoc26tZbb1V5ebnKy8tVWlqqiIgI3XbbbXbHavU4/Brf6eTJk+rcubM++OADzZw5U2lpafr1r39tdyy0YT/4wQ+0e/du7d27V9HR0X7LqqqqFB8fb08wtFmTJk1SVVWV1q1b5xvbvHmzbrzxRh05ckSdOnWyL1wrxxYZfKdVq1YpNTVVffv21YQJE/T73//+Oy+rDjSX48ePq7i4WLm5uXVKjCRKDELCyZMntXz5cvXu3VuJiYl2x2nVWv1FI3HxioqKNGHCBEnnNp1WV1dr48aNGjZsmL3B0CZ99tlnsixLqampdkcB/GzYsEExMTGSpFOnTqlz587asGFDo1duxsXj20Wj9u7dq23btmncuHGSpIiICN1xxx0qKiqyORnaKrYGIlQNHz5cu3bt0q5du7Rt2zZlZWVp1KhR2r9/v93RWjW2yKBRRUVF+uabb9SlSxffmGVZcjqdWrBgQZMusQ4EU58+feRwOPTxxx/bHQXwEx0drd69e/seL168WHFxcXrhhRf0+OOP25isdWOLDBr0zTff6MUXX9ScOXN8vzJ27dql//7v/1aXLl30hz/8we6IaIMSEhKUlZWlwsJCnTp1qs7yqqqqlg8F1MPhcCgsLEynT5+2O0qrRpFBgzZs2KCvvvpKkydP1oABA/xuOTk57F6CbQoLC1VTU6NBgwZpzZo1+vTTT7Vnzx7Nnz9fGRkZdsdDG+X1elVRUaGKigrt2bNHeXl5OnnypMaMGWN3tFaNIoMGFRUVKTMzs97dRzk5Ofrggw842RNs0bNnT+3cuVPDhw/X/fffrwEDBmjkyJEqLS3VokWL7I6HNqq4uFidO3dW586dNXjwYG3fvl2rV6/mwIhmxnlkAACAsdgiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDoE1bunSp4uPj7Y4B4AJRZAA0u0mTJsnhcPhuiYmJuvXWW7nEBYCLRpEB0CJuvfVWlZeXq7y8XKWlpYqIiNBtt93W4PPPnj3bgukAmIoiA6BFOJ1OJScnKzk5Wddcc40eeughHTx4UF9++aX27dsnh8OhV155RTfddJNcLpdWrFihY8eOady4cbrsssvUvn17XXXVVfrDH/7g977Dhg3T1KlT9eCDDyohIUHJycmaNWuW33Oqqqp0zz33KCkpSS6XSwMGDNCGDRv8nlNSUqJ+/fopJibGV7oAhD6KDIAWd/LkSS1fvly9e/dWYmKib/yhhx7Sfffdpz179igrK0sej0fp6en64x//qI8++ki/+MUvdNddd2nbtm1+77ds2TJFR0fr/fff11NPPaXZs2frzTfflCTV1tZq1KhRKisr0/Lly/W///u/evLJJxUeHu57/ddff61///d/10svvaRNmzbpwIEDeuCBB1rmywBwUSLsDgCgbdiwYYNiYmIkSadOnVLnzp21YcMGhYX97ffUtGnTNHbsWL/XnV8o8vLyVFJSolWrVmnQoEG+8bS0NM2cOVOS1KdPHy1YsEClpaUaOXKk3nrrLW3btk179uzRFVdcIUnq2bOn32ecPXtWzz//vHr16iVJmjJlimbPnh3Efz2A5kKRAdAihg8frkWLFkmSvvrqKy1cuFCjRo3y27oycOBAv9fU1NTot7/9rVatWqUvvvhCZ86ckdfrVfv27f2el5aW5ve4c+fOOnLkiCRp165d6tq1q6/E1Kd9+/a+EvP3rwcQ2igyAFpEdHS0evfu7Xu8ePFixcXF6YUXXtA//uM/+p5zvqefflrPPvus5s2bp6uuukrR0dGaNm2azpw54/e8yMhIv8cOh0O1tbWSpKioqO/MVt/rLctq+j8OgG2YIwPAFg6HQ2FhYTp9+nSDzykrK1N2drYmTJigq6++Wj179tQnn3wS0OekpaXp0KFDAb8OgBkoMgBahNfrVUVFhSoqKrRnzx7l5eXp5MmTGjNmTIOv6dOnj958802999572rNnj+655x5VVlYG9Lk33XSThg4dqpycHL355pv6/PPP9cYbb6i4uPhi/0kAQgBFBkCLKC4uVufOndW5c2cNHjxY27dv1+rVqzVs2LAGX/Pwww/r+9//vrKysjRs2DAlJyfr9ttvD/iz16xZo2uvvVbjxo1T//799eCDD6qmpubC/zEAQobDYkcwAAAwFFtkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGCs/wfXoZbOC2VEiAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Branch'], y=df['gross income'])\n",
    "#plt.savefig(\"uni5.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aced00ba-2a90-42de-9258-b9d845a362aa",
   "metadata": {},
   "source": [
    "*There is not much difference in gross income by branches at an average level. Branch C has a slightly higher income than A or B, As observed earlier, though branch A has slightly higher sales than the rest C is the most profitable branch in terms of gross income.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b50ef2f-e3b1-4ab5-9f08-1cace20aa4c7",
   "metadata": {},
   "source": [
    "## Question 6 \n",
    "*What is the relationship between Gender and Gross income?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2487c2b-cd96-4ac0-bcaa-b607b409c469",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='gross income'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Gender'], y=df['gross income'])\n",
    "#plt.savefig(\"uni6.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fada8876-444f-4e4c-b03e-d5bd69242e0e",
   "metadata": {},
   "source": [
    "*Gross income is similar for both male and female, though female customers spend a bit higher at the 75th percentile.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9574aca9-1674-48f6-9118-fba69eb31589",
   "metadata": {},
   "source": [
    "## Question 7\n",
    "*Is there any time trend in gross income?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bc08925-fccb-4b50-8f4a-0363da70e951",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.groupby(df.index).mean()\n",
    "sns.lineplot(x= df.groupby(df.index).mean().index, \n",
    "             y = df.groupby(df.index).mean()['gross income'])\n",
    "#plt.savefig(\"uni7.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df473971-fe0a-4c14-aa6f-3403d4525673",
   "metadata": {},
   "source": [
    "*No particular time trend except for some days the gross income is pretty high and some days it is pretty low. Overall it remains at a certain average level.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f59287-19a6-4e9e-9c7a-e51147a3cafe",
   "metadata": {},
   "source": [
    "## Question 8\n",
    "*Which product line generates most income?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "151c9372-6edf-4e1f-a239-9d7cc4b61ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat=df[[\"Product line\", \"gross income\"]].groupby(['Product line'], as_index=False).sum().sort_values(by='gross income', ascending=False)\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.barplot(x='Product line', y='gross income', data=cat)\n",
    "#plt.savefig(\"uni8.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2589b4fa-de25-45c8-bd1c-e490396d0509",
   "metadata": {},
   "source": [
    "*Gross income is highest in sports and travel.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366b053d-c89a-4d05-92e6-221179d6a593",
   "metadata": {},
   "source": [
    "# Correlation analysis\n",
    "## Individual correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc190923-fe58-4266-af96-5bf0da867828",
   "metadata": {},
   "outputs": [],
   "source": [
    "round(np.corrcoef(df['gross income'], df['Rating'])[1][0],2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2201a6fe-71ea-4deb-809d-9a3c7c60f6e7",
   "metadata": {},
   "source": [
    "## Pairwise correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "968523cd-c550-4f56-b971-7ae402334360",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.round(df.corr(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4965baf0-735a-4997-b509-872d8933d31e",
   "metadata": {},
   "source": [
    "# Other analysis\n",
    "## Question 10\n",
    "*What is the spending patterns of females and males and in which category do they spend a lot?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c315014-7b0b-4b05-b01d-3380d2a9bbd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.title('Total Monthly transaction by Gender')\n",
    "sns.countplot(x=df['Product line'], hue = df.Gender)\n",
    "plt.savefig(\"uni10.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1097114-a868-49da-b999-fc002fb7b6ee",
   "metadata": {},
   "source": [
    "*Females spend on 'fashion accessories' the most and for males surprisingly it is 'Health and beauty'. Females also spend more on 'Sports and travel'.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f4bb8d1-e9e2-4a61-9905-b2f60ce0fde4",
   "metadata": {},
   "source": [
    "# Dummy method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b23c9c00-ca47-4e4a-b411-832bd55b47f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_dummies  = pd.get_dummies(df['Gender'])\n",
    "gender_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aeaaac6-fa0b-470d-90bb-11a2dacc76c3",
   "metadata": {},
   "source": [
    "Join the Gender dummies with the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d42d9866-f5e7-4343-aa7d-28e2347b5ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.concat([df, gender_dummies], axis = 1)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5b5d23-cbd3-48ce-a104-cdf7472371de",
   "metadata": {},
   "source": [
    "## Spending pattern of Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7913cb8-7abb-47d7-b280-e9d69250a191",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "sns.barplot(x = 'Product line', y = 'Female', data = df1)\n",
    "plt.title('Female')\n",
    "#plt.savefig(\"uni13.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dca0be9-2480-40a3-ae1a-f269618860d8",
   "metadata": {},
   "source": [
    "## Spending pattern of Male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e52dab91-0818-4461-814e-b632f446bebd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m12\u001b[39m,\u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m sns\u001b[38;5;241m.\u001b[39mbarplot(x \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProduct line\u001b[39m\u001b[38;5;124m'\u001b[39m, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMale\u001b[39m\u001b[38;5;124m'\u001b[39m, data \u001b[38;5;241m=\u001b[39m \u001b[43mdf1\u001b[49m)\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMale\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#plt.savefig(\"uni13.png\")\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df1' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "sns.barplot(x = 'Product line', y = 'Male', data = df1)\n",
    "plt.title('Male')\n",
    "#plt.savefig(\"uni13.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31503b74-30c1-4bc7-addb-d71fa6cfc1ea",
   "metadata": {},
   "source": [
    "## Question 10\n",
    "*How many products are bought by customers?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6413ff3e-624c-4eb9-b7be-e6111414daa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "xdata = [1,2,3,4,5,6,7,8,9,10]\n",
    "plt.figure(figsize = (12,6))\n",
    "sns.histplot(df['Quantity'], kde=True)\n",
    "plt.xticks(xdata)\n",
    "#plt.savefig(\"uni14.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cccabc-2006-4db9-b639-024720a524c0",
   "metadata": {},
   "source": [
    "## Question 11\n",
    "*Which day of the week has maximum sales?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d91f21b5-8b79-4c7a-a2ab-1af0d8be28c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.title('Daily Sales by Day of the Week')\n",
    "sns.countplot(x=df['weekday'])\n",
    "plt.savefig(\"uni15.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af8fc06-f1a2-480a-816c-5d7c71d75fae",
   "metadata": {},
   "source": [
    "Sales is highest on Saturdays probably because it is the weekend. \n",
    "\n",
    "Tuesdays is a close second. Mondays is the lowest in sales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "648bfa8a-425c-4aaa-9d1f-42b3405c492a",
   "metadata": {},
   "source": [
    "## Question 12 \n",
    "*Which hour of the day is the busiest?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49768a5d-6ee9-42e6-8f77-2a6a406248d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Time'] = pd.to_datetime(df['Time'])\n",
    "df['Hour'] = (df['Time']).dt.hour\n",
    "\n",
    "df.groupby(['Hour']).size().plot(kind = \"bar\")\n",
    "plt.savefig(\"uni16.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe808902-4f2a-451a-914a-5be19b3c21bc",
   "metadata": {},
   "source": [
    "## Question 13\n",
    "*Which product line should the supermarket focus on?*\n",
    "\n",
    "### Rating of products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8846772d-2c1e-44d7-b839-08a2b973c1f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "xdata = [0,1,2,3,4,5,6,7,8,9,10]\n",
    "plt.figure(figsize = (12,6))\n",
    "sns.barplot(y = df['Product line'], x = df['Rating'])\n",
    "plt.xticks(xdata)\n",
    "#plt.savefig(\"uni17.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9abce24-35e1-47f7-8dde-714b58aaee07",
   "metadata": {},
   "source": [
    "### Quantity purchased by product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f3db00b-707f-4070-a601-d1029df286ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxenplot(y='Product line', x='Quantity', data=df )\n",
    "plt.savefig(\"uni18.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e810a4a-2efe-4ea6-b917-b4e1ef2b2923",
   "metadata": {},
   "source": [
    "Though the rating for 'fashion accessories' and 'food and beverages' is high but quantity purchased is low. Hence, supply for these products need to be increased."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767d97fd-48be-4f34-b510-1da49e7cd903",
   "metadata": {},
   "source": [
    "# Question 14\n",
    "*Which city should be chosen for expansion and which products should it focus on?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7b7fa81-e697-4e45-8b32-4a06b2c55f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "sns.barplot(x=df['City'], y=df['gross income'], palette='Set1')\n",
    "plt.xlabel('City name', fontsize='16')\n",
    "plt.xticks(fontsize='16')\n",
    "plt.ylabel('Gross income', fontsize='16')\n",
    "plt.yticks(fontsize='16')\n",
    "plt.savefig(\"uni19.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cab513-4fc6-4f95-a5b9-6b2ebe0565ec",
   "metadata": {},
   "source": [
    "Naypyitaw is the most profitable city, hence the expansion plan should be based on this city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23cc1516-9464-4066-9b5b-9122d918e61f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(dpi=125)\n",
    "sns.countplot(y='Product line', hue = \"City\", data = df) \n",
    "plt.xlabel('Count')\n",
    "plt.show()\n",
    "plt.savefig(\"uni20.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e57c33-b3f6-4ef9-a8c1-d0a7044dd189",
   "metadata": {},
   "source": [
    "Fashion accessories and food and beverages are the most sold product in Naypyitaw and these products should be focused on for expansion along with electronic accessories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "163c4c9c-4d11-4479-9811-f8167be4fcc2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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