Data Visualisation in Python - Case Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Playstore Case Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this module you’ll be learning data visualisation with the help of a case study. This will enable you to understand how visualisation aids you in solving business problems. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Problem Statement**\n",
    "\n",
    "The team at Google Play Store wants to develop a feature that would enable them to boost visibility for the most promising apps. Now, this analysis would require a preliminary understanding of the features that define a well-performing app. You can ask questions like:\n",
    "- Does a higher size or price necessarily mean that an app would perform better than the other apps? \n",
    "- Or does a higher number of installs give a clear picture of which app would have a better rating than others?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Session 1 - Introduction to Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the libraries\n",
    "import pandas as pd, numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Paper flowers instructions</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>167</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>March 26, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Smoke Effect Photo Maker - Smoke Editor</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.8</td>\n",
       "      <td>178</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>April 26, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Infinite Painter</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>36815</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 14, 2018</td>\n",
       "      <td>6.1.61.1</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Garden Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13791</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>September 20, 2017</td>\n",
       "      <td>2.9.2</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kids Paint Free - Drawing Fun</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>121</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>July 3, 2018</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Text on Photo - Fonteee</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13880</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>October 27, 2017</td>\n",
       "      <td>1.0.4</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Name Art Photo Editor - Focus n Filters</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>8788</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>July 31, 2018</td>\n",
       "      <td>1.0.15</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Tattoo Name On My Photo Editor</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.2</td>\n",
       "      <td>44829</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>April 2, 2018</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mandala Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4326</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 26, 2018</td>\n",
       "      <td>1.0.4</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3D Color Pixel by Number - Sandbox Art Coloring</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1518</td>\n",
       "      <td>37000.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 3, 2018</td>\n",
       "      <td>1.2.3</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Learn To Draw Kawaii Characters</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>55</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>5,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 6, 2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Photo Designer - Write your name with shapes</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3632</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>July 31, 2018</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>350 Diy Room Decor Ideas</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>27</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>November 7, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FlipaClip - Cartoon animation</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>194216</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 3, 2018</td>\n",
       "      <td>2.2.5</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ibis Paint X</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.6</td>\n",
       "      <td>224399</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>July 30, 2018</td>\n",
       "      <td>5.5.4</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Logo Maker - Small Business</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>450</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>April 20, 2018</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Boys Photo Editor - Six Pack &amp; Men's Suit</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>654</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>March 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Superheroes Wallpapers | 4K Backgrounds</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>7699</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>July 12, 2018</td>\n",
       "      <td>2.2.6.2</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mcqueen Coloring pages</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Action &amp; Adventure</td>\n",
       "      <td>March 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HD Mickey Minnie Wallpapers</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>118</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>July 7, 2018</td>\n",
       "      <td>1.1.3</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Harley Quinn wallpapers HD</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.8</td>\n",
       "      <td>192</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>April 25, 2018</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Colorfit - Drawing &amp; Coloring</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>20260</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>October 11, 2017</td>\n",
       "      <td>1.0.8</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Animated Photo Editor</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>203</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>March 21, 2018</td>\n",
       "      <td>1.03</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Pencil Sketch Drawing</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>136</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>July 12, 2018</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Easy Realistic Drawing Tutorial</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>223</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 22, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  App        Category  Rating  \\\n",
       "0      Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                 Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2   U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "3                               Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
       "4               Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "5                          Paper flowers instructions  ART_AND_DESIGN     4.4   \n",
       "6             Smoke Effect Photo Maker - Smoke Editor  ART_AND_DESIGN     3.8   \n",
       "7                                    Infinite Painter  ART_AND_DESIGN     4.1   \n",
       "8                                Garden Coloring Book  ART_AND_DESIGN     4.4   \n",
       "9                       Kids Paint Free - Drawing Fun  ART_AND_DESIGN     4.7   \n",
       "10                            Text on Photo - Fonteee  ART_AND_DESIGN     4.4   \n",
       "11            Name Art Photo Editor - Focus n Filters  ART_AND_DESIGN     4.4   \n",
       "12                     Tattoo Name On My Photo Editor  ART_AND_DESIGN     4.2   \n",
       "13                              Mandala Coloring Book  ART_AND_DESIGN     4.6   \n",
       "14    3D Color Pixel by Number - Sandbox Art Coloring  ART_AND_DESIGN     4.4   \n",
       "15                    Learn To Draw Kawaii Characters  ART_AND_DESIGN     3.2   \n",
       "16       Photo Designer - Write your name with shapes  ART_AND_DESIGN     4.7   \n",
       "17                           350 Diy Room Decor Ideas  ART_AND_DESIGN     4.5   \n",
       "18                      FlipaClip - Cartoon animation  ART_AND_DESIGN     4.3   \n",
       "19                                       ibis Paint X  ART_AND_DESIGN     4.6   \n",
       "20                        Logo Maker - Small Business  ART_AND_DESIGN     4.0   \n",
       "21          Boys Photo Editor - Six Pack & Men's Suit  ART_AND_DESIGN     4.1   \n",
       "22            Superheroes Wallpapers | 4K Backgrounds  ART_AND_DESIGN     4.7   \n",
       "23                             Mcqueen Coloring pages  ART_AND_DESIGN     NaN   \n",
       "24                        HD Mickey Minnie Wallpapers  ART_AND_DESIGN     4.7   \n",
       "25                         Harley Quinn wallpapers HD  ART_AND_DESIGN     4.8   \n",
       "26                      Colorfit - Drawing & Coloring  ART_AND_DESIGN     4.7   \n",
       "27                              Animated Photo Editor  ART_AND_DESIGN     4.1   \n",
       "28                              Pencil Sketch Drawing  ART_AND_DESIGN     3.9   \n",
       "29                    Easy Realistic Drawing Tutorial  ART_AND_DESIGN     4.1   \n",
       "\n",
       "   Reviews     Size     Installs  Type Price Content Rating  \\\n",
       "0      159  19000.0      10,000+  Free     0       Everyone   \n",
       "1      967  14000.0     500,000+  Free     0       Everyone   \n",
       "2    87510   8700.0   5,000,000+  Free     0       Everyone   \n",
       "3   215644  25000.0  50,000,000+  Free     0           Teen   \n",
       "4      967   2800.0     100,000+  Free     0       Everyone   \n",
       "5      167   5600.0      50,000+  Free     0       Everyone   \n",
       "6      178  19000.0      50,000+  Free     0       Everyone   \n",
       "7    36815  29000.0   1,000,000+  Free     0       Everyone   \n",
       "8    13791  33000.0   1,000,000+  Free     0       Everyone   \n",
       "9      121   3100.0      10,000+  Free     0       Everyone   \n",
       "10   13880  28000.0   1,000,000+  Free     0       Everyone   \n",
       "11    8788  12000.0   1,000,000+  Free     0       Everyone   \n",
       "12   44829  20000.0  10,000,000+  Free     0           Teen   \n",
       "13    4326  21000.0     100,000+  Free     0       Everyone   \n",
       "14    1518  37000.0     100,000+  Free     0       Everyone   \n",
       "15      55   2700.0       5,000+  Free     0       Everyone   \n",
       "16    3632   5500.0     500,000+  Free     0       Everyone   \n",
       "17      27  17000.0      10,000+  Free     0       Everyone   \n",
       "18  194216  39000.0   5,000,000+  Free     0       Everyone   \n",
       "19  224399  31000.0  10,000,000+  Free     0       Everyone   \n",
       "20     450  14000.0     100,000+  Free     0       Everyone   \n",
       "21     654  12000.0     100,000+  Free     0       Everyone   \n",
       "22    7699   4200.0     500,000+  Free     0   Everyone 10+   \n",
       "23      61   7000.0     100,000+  Free     0       Everyone   \n",
       "24     118  23000.0      50,000+  Free     0       Everyone   \n",
       "25     192   6000.0      10,000+  Free     0       Everyone   \n",
       "26   20260  25000.0     500,000+  Free     0       Everyone   \n",
       "27     203   6100.0     100,000+  Free     0       Everyone   \n",
       "28     136   4600.0      10,000+  Free     0       Everyone   \n",
       "29     223   4200.0     100,000+  Free     0       Everyone   \n",
       "\n",
       "                             Genres        Last Updated         Current Ver  \\\n",
       "0                      Art & Design     January 7, 2018               1.0.0   \n",
       "1         Art & Design;Pretend Play    January 15, 2018               2.0.0   \n",
       "2                      Art & Design      August 1, 2018               1.2.4   \n",
       "3                      Art & Design        June 8, 2018  Varies with device   \n",
       "4           Art & Design;Creativity       June 20, 2018                 1.1   \n",
       "5                      Art & Design      March 26, 2017                 1.0   \n",
       "6                      Art & Design      April 26, 2018                 1.1   \n",
       "7                      Art & Design       June 14, 2018            6.1.61.1   \n",
       "8                      Art & Design  September 20, 2017               2.9.2   \n",
       "9           Art & Design;Creativity        July 3, 2018                 2.8   \n",
       "10                     Art & Design    October 27, 2017               1.0.4   \n",
       "11                     Art & Design       July 31, 2018              1.0.15   \n",
       "12                     Art & Design       April 2, 2018                 3.8   \n",
       "13                     Art & Design       June 26, 2018               1.0.4   \n",
       "14                     Art & Design      August 3, 2018               1.2.3   \n",
       "15                     Art & Design        June 6, 2018                 NaN   \n",
       "16                     Art & Design       July 31, 2018                 3.1   \n",
       "17                     Art & Design    November 7, 2017                 1.0   \n",
       "18                     Art & Design      August 3, 2018               2.2.5   \n",
       "19                     Art & Design       July 30, 2018               5.5.4   \n",
       "20                     Art & Design      April 20, 2018                 4.0   \n",
       "21                     Art & Design      March 20, 2018                 1.1   \n",
       "22                     Art & Design       July 12, 2018             2.2.6.2   \n",
       "23  Art & Design;Action & Adventure       March 7, 2018               1.0.0   \n",
       "24                     Art & Design        July 7, 2018               1.1.3   \n",
       "25                     Art & Design      April 25, 2018                 1.5   \n",
       "26          Art & Design;Creativity    October 11, 2017               1.0.8   \n",
       "27                     Art & Design      March 21, 2018                1.03   \n",
       "28                     Art & Design       July 12, 2018                 6.0   \n",
       "29                     Art & Design     August 22, 2017                 1.0   \n",
       "\n",
       "     Android Ver  \n",
       "0   4.0.3 and up  \n",
       "1   4.0.3 and up  \n",
       "2   4.0.3 and up  \n",
       "3     4.2 and up  \n",
       "4     4.4 and up  \n",
       "5     2.3 and up  \n",
       "6   4.0.3 and up  \n",
       "7     4.2 and up  \n",
       "8     3.0 and up  \n",
       "9   4.0.3 and up  \n",
       "10    4.1 and up  \n",
       "11    4.0 and up  \n",
       "12    4.1 and up  \n",
       "13    4.4 and up  \n",
       "14    2.3 and up  \n",
       "15    4.2 and up  \n",
       "16    4.1 and up  \n",
       "17    2.3 and up  \n",
       "18  4.0.3 and up  \n",
       "19    4.1 and up  \n",
       "20    4.1 and up  \n",
       "21  4.0.3 and up  \n",
       "22  4.0.3 and up  \n",
       "23    4.1 and up  \n",
       "24    4.1 and up  \n",
       "25    3.0 and up  \n",
       "26  4.0.3 and up  \n",
       "27  4.0.3 and up  \n",
       "28    2.3 and up  \n",
       "29    2.3 and up  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read the dataset and check the first five rows\n",
    "inp0 = pd.read_csv(\"googleplaystore_v2.csv\")\n",
    "inp0.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10841, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the shape of the dataframe\n",
    "inp0.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Data Handling and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first few steps involve making sure that there are no __missing values__ or __incorrect data types__ before we proceed to the analysis stage. These aforementioned problems are handled as follows:\n",
    "\n",
    " - For Missing Values: Some common techniques to treat this issue are\n",
    "    - Dropping the rows containing the missing values\n",
    "    - Imputing the missing values\n",
    "    - Keep the missing values if they don't affect the analysis\n",
    " \n",
    "    \n",
    " - Incorrect Data Types:\n",
    "    - Clean certain values \n",
    "    - Clean and convert an entire column\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10841 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             10841 non-null  object \n",
      " 1   Category        10841 non-null  object \n",
      " 2   Rating          9367 non-null   float64\n",
      " 3   Reviews         10841 non-null  object \n",
      " 4   Size            10841 non-null  float64\n",
      " 5   Installs        10841 non-null  object \n",
      " 6   Type            10840 non-null  object \n",
      " 7   Price           10841 non-null  object \n",
      " 8   Content Rating  10840 non-null  object \n",
      " 9   Genres          10841 non-null  object \n",
      " 10  Last Updated    10841 non-null  object \n",
      " 11  Current Ver     10833 non-null  object \n",
      " 12  Android Ver     10838 non-null  object \n",
      "dtypes: float64(2), object(11)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#Check the datatypes of all the columns of the dataframe\n",
    "inp0.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Missing Value Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App                  0\n",
       "Category             0\n",
       "Rating            1474\n",
       "Reviews              0\n",
       "Size                 0\n",
       "Installs             0\n",
       "Type                 1\n",
       "Price                0\n",
       "Content Rating       1\n",
       "Genres               0\n",
       "Last Updated         0\n",
       "Current Ver          8\n",
       "Android Ver          3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the number of null values in the columns\n",
    "inp0.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling missing values for rating\n",
    " - Ratings is the target variable\n",
    " - drop the records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9367, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the rows having null values in the Rating field\n",
    "inp1 = inp0[~inp0.Rating.isnull()]\n",
    "\n",
    "#Check the shape of the dataframe\n",
    "inp1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of nulls in the Rating field again to cross-verify\n",
    "inp1.Rating.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               0\n",
       "Category          0\n",
       "Rating            0\n",
       "Reviews           0\n",
       "Size              0\n",
       "Installs          0\n",
       "Type              0\n",
       "Price             0\n",
       "Content Rating    1\n",
       "Genres            0\n",
       "Last Updated      0\n",
       "Current Ver       4\n",
       "Android Ver       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question\n",
    "#Check the number of nulls in the dataframe again and find the total number of null values\n",
    "inp1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
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       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4453</th>\n",
       "      <td>[substratum] Vacuum: P</td>\n",
       "      <td>PERSONALIZATION</td>\n",
       "      <td>4.4</td>\n",
       "      <td>230</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Paid</td>\n",
       "      <td>$1.49</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>July 20, 2018</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4490</th>\n",
       "      <td>Pi Dark [substratum]</td>\n",
       "      <td>PERSONALIZATION</td>\n",
       "      <td>4.5</td>\n",
       "      <td>189</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>March 27, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10472</th>\n",
       "      <td>Life Made WI-Fi Touchscreen Photo Frame</td>\n",
       "      <td>1.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0M</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>February 11, 2018</td>\n",
       "      <td>1.0.19</td>\n",
       "      <td>4.0 and up</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           App         Category  Rating  \\\n",
       "4453                    [substratum] Vacuum: P  PERSONALIZATION     4.4   \n",
       "4490                      Pi Dark [substratum]  PERSONALIZATION     4.5   \n",
       "10472  Life Made WI-Fi Touchscreen Photo Frame              1.9    19.0   \n",
       "\n",
       "      Reviews          Size Installs  Type     Price Content Rating  \\\n",
       "4453      230  11000.000000   1,000+  Paid     $1.49       Everyone   \n",
       "4490      189   2100.000000  10,000+  Free         0       Everyone   \n",
       "10472    3.0M  21516.529524     Free     0  Everyone            NaN   \n",
       "\n",
       "                  Genres    Last Updated Current Ver Android Ver  \n",
       "4453     Personalization   July 20, 2018         4.4         NaN  \n",
       "4490     Personalization  March 27, 2018         1.1         NaN  \n",
       "10472  February 11, 2018          1.0.19  4.0 and up         NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect the nulls in the Android Version column\n",
    "inp1[inp1[\"Android Ver\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
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       "      <th>Android Ver</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10472</th>\n",
       "      <td>Life Made WI-Fi Touchscreen Photo Frame</td>\n",
       "      <td>1.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0M</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>February 11, 2018</td>\n",
       "      <td>1.0.19</td>\n",
       "      <td>4.0 and up</td>\n",
       "      <td>NaN</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                           App Category  Rating Reviews  \\\n",
       "10472  Life Made WI-Fi Touchscreen Photo Frame      1.9    19.0    3.0M   \n",
       "\n",
       "               Size Installs Type     Price Content Rating             Genres  \\\n",
       "10472  21516.529524     Free    0  Everyone            NaN  February 11, 2018   \n",
       "\n",
       "      Last Updated Current Ver Android Ver  \n",
       "10472       1.0.19  4.0 and up         NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1[(inp1[\"Android Ver\"].isnull() & (inp1[\"Category\"] == '1.9'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4453</th>\n",
       "      <td>[substratum] Vacuum: P</td>\n",
       "      <td>PERSONALIZATION</td>\n",
       "      <td>4.4</td>\n",
       "      <td>230</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Paid</td>\n",
       "      <td>$1.49</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>July 20, 2018</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4490</th>\n",
       "      <td>Pi Dark [substratum]</td>\n",
       "      <td>PERSONALIZATION</td>\n",
       "      <td>4.5</td>\n",
       "      <td>189</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>March 27, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         App         Category  Rating Reviews     Size  \\\n",
       "4453  [substratum] Vacuum: P  PERSONALIZATION     4.4     230  11000.0   \n",
       "4490    Pi Dark [substratum]  PERSONALIZATION     4.5     189   2100.0   \n",
       "\n",
       "     Installs  Type  Price Content Rating           Genres    Last Updated  \\\n",
       "4453   1,000+  Paid  $1.49       Everyone  Personalization   July 20, 2018   \n",
       "4490  10,000+  Free      0       Everyone  Personalization  March 27, 2018   \n",
       "\n",
       "     Current Ver Android Ver  \n",
       "4453         4.4         NaN  \n",
       "4490         1.1         NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the row having shifted values\n",
    "inp1 = inp1[~(inp1[\"Android Ver\"].isnull() & (inp1[\"Category\"] == '1.9'))]\n",
    "#Check the nulls againin Android version column to cross-verify\n",
    "inp1[inp1[\"Android Ver\"].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputing Missing Values\n",
    "\n",
    "- For numerical variables use mean and median\n",
    "- For categorical variables use mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.1 and up            2059\n",
       "Varies with device    1319\n",
       "4.0.3 and up          1240\n",
       "4.0 and up            1131\n",
       "4.4 and up             875\n",
       "2.3 and up             582\n",
       "5.0 and up             535\n",
       "4.2 and up             338\n",
       "2.3.3 and up           240\n",
       "3.0 and up             211\n",
       "2.2 and up             208\n",
       "4.3 and up             207\n",
       "2.1 and up             113\n",
       "1.6 and up              87\n",
       "6.0 and up              48\n",
       "7.0 and up              41\n",
       "3.2 and up              31\n",
       "2.0 and up              27\n",
       "5.1 and up              18\n",
       "1.5 and up              16\n",
       "3.1 and up               8\n",
       "2.0.1 and up             7\n",
       "4.4W and up              6\n",
       "8.0 and up               5\n",
       "7.1 and up               3\n",
       "4.0.3 - 7.1.1            2\n",
       "5.0 - 8.0                2\n",
       "1.0 and up               2\n",
       "4.1 - 7.1.1              1\n",
       "5.0 - 6.0                1\n",
       "7.0 - 7.1.1              1\n",
       "Name: Android Ver, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the most common value in the Android version column\n",
    "inp1[\"Android Ver\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.1 and up'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fill up the nulls in the Android Version column with the above value\n",
    "inp1[\"Android Ver\"].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1[\"Android Ver\"] = inp1[\"Android Ver\"].fillna(inp1[\"Android Ver\"].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the nulls in the Android version column again to cross-verify\n",
    "inp1[\"Android Ver\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               0\n",
       "Category          0\n",
       "Rating            0\n",
       "Reviews           0\n",
       "Size              0\n",
       "Installs          0\n",
       "Type              0\n",
       "Price             0\n",
       "Content Rating    0\n",
       "Genres            0\n",
       "Last Updated      0\n",
       "Current Ver       4\n",
       "Android Ver       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the nulls in the entire dataframe again\n",
    "inp1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Varies with device    1415\n",
       "1.0                    458\n",
       "1.1                    195\n",
       "1.2                    126\n",
       "1.3                    120\n",
       "                      ... \n",
       "1.9.117                  1\n",
       "1.16.9                   1\n",
       "4.813                    1\n",
       "4.8.18.12                1\n",
       "15.1.2                   1\n",
       "Name: Current Ver, Length: 2638, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the most common value in the Current version column\n",
    "inp1[\"Current Ver\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Varies with device'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace the nulls in the Current version column with the above value\n",
    "inp1[\"Current Ver\"].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Question : Check the most common value in the Current version column again\n",
    "inp1[\"Current Ver\"] = inp1[\"Current Ver\"].fillna(inp1[\"Current Ver\"].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               0\n",
       "Category          0\n",
       "Rating            0\n",
       "Reviews           0\n",
       "Size              0\n",
       "Installs          0\n",
       "Type              0\n",
       "Price             0\n",
       "Content Rating    0\n",
       "Genres            0\n",
       "Last Updated      0\n",
       "Current Ver       0\n",
       "Android Ver       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Varies with device    1419\n",
       "1.0                    458\n",
       "1.1                    195\n",
       "1.2                    126\n",
       "1.3                    120\n",
       "                      ... \n",
       "1.9.117                  1\n",
       "1.16.9                   1\n",
       "4.813                    1\n",
       "4.8.18.12                1\n",
       "15.1.2                   1\n",
       "Name: Current Ver, Length: 2638, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1[\"Current Ver\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling Incorrect Data Types "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App                object\n",
       "Category           object\n",
       "Rating            float64\n",
       "Reviews            object\n",
       "Size              float64\n",
       "Installs           object\n",
       "Type               object\n",
       "Price              object\n",
       "Content Rating     object\n",
       "Genres             object\n",
       "Last Updated       object\n",
       "Current Ver        object\n",
       "Android Ver        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the datatypes of all the columns \n",
    "inp1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Could not convert 00000000000000000000000000000000000000000000000000000000000000000000000000000$3.99000000$3.9900000000000000000000000000000000000000$3.9900000000000000000000000000000000000000000000000000000$5.990000000000000000000000$2.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$9.990000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99$2.990000000000000000000000000000$4.99$2.99$4.99000000000$5.49$10.00$3.99$2.99$2.99$0.99$7.49$2.99$1.99$7.99$19.990000000000000000000000$10.0000$5.49000$39.990$19.99000000000000000000000000000000000000000000000000000000000000000000000000000000000$5.99$5.9900000$5.990000000000000000000000$29.990000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99000000000$0.99000000000000000000000000000000000000000$0.990000000000$5.9900000000000000$400.00000$2.990000000000$0.99000$2.49$1.99$0.990000$1.49$0.9900$0.990$0.990000$4.7700$4.990000$2.4900000000000000000000000$1.9900000000$1.990$1.990$1.990$0.9900$0.99$3.990000000000000000000000000$1.9900000$9.9900$1.9900$3.990$0.99000$2.9900$19.9900000$0.990000000000000000000$0.99$0.99$0.990000000000000000$399.990$2.49$399.99000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$8.490000000$1.750000000000000000000000000000000$2.990000$4.9900000000$4.9900000000000000000000000000000000000$0.990$0.99000$2.99000000000$0.990000000$4.4900000000000000000000000000000000000000000000000000000000000000$3.99000000000000000000000$7.990000$4.99000000000000000000000$2.99$0.9900000000000000$0.9900000000000000000000$2.99$3.99000000$4.990000000000$0.99000000000000000000000000000000000000000000000000000000000$0.990000$4.9900$4.9900000000000000$4.990000000000000000000000$2.99000000000000$2.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99000000000000$3.280000000$2.990000000000000000000000000000$0.99000000000$1.490000000000000000000$0.99000000000000000$12.990000000000000000000000000000000000000000$3.990000000000000$4.4900000$6.9900000000000000000$1.9900000000000000000000000000000000000000000000000$5.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$8.990000000000000000000000000 to numeric",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1302\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1303\u001b[1;33m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1304\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '00000000000000000000000000000000000000000000000000000000000000000000000000000$3.99000000$3.9900000000000000000000000000000000000000$3.9900000000000000000000000000000000000000000000000000000$5.990000000000000000000000$2.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$9.990000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99$2.990000000000000000000000000000$4.99$2.99$4.99000000000$5.49$10.00$3.99$2.99$2.99$0.99$7.49$2.99$1.99$7.99$19.990000000000000000000000$10.0000$5.49000$39.990$19.99000000000000000000000000000000000000000000000000000000000000000000000000000000000$5.99$5.9900000$5.990000000000000000000000$29.990000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99000000000$0.99000000000000000000000000000000000000000$0.990000000000$5.9900000000000000$400.00000$2.990000000000$0.99000$2.49$1.99$0.990000$1.49$0.9900$0.990$0.990000$4.7700$4.990000$2.4900000000000000000000000$1.9900000000$1.990$1.990$1.990$0.9900$0.99$3.990000000000000000000000000$1.9900000$9.9900$1.9900$3.990$0.99000$2.9900$19.9900000$0.990000000000000000000$0.99$0.99$0.990000000000000000$399.990$2.49$399.99000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$8.490000000$1.750000000000000000000000000000000$2.990000$4.9900000000$4.9900000000000000000000000000000000000$0.990$0.99000$2.99000000000$0.990000000$4.4900000000000000000000000000000000000000000000000000000000000000$3.99000000000000000000000$7.990000$4.99000000000000000000000$2.99$0.9900000000000000$0.9900000000000000000000$2.99$3.99000000$4.990000000000$0.99000000000000000000000000000000000000000000000000000000000$0.990000$4.9900$4.9900000000000000$4.990000000000000000000000$2.99000000000000$2.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99000000000000$3.280000000$2.990000000000000000000000000000$0.99000000000$1.490000000000000000000$0.99000000000000000$12.990000000000000000000000000000000000000000$3.990000000000000$4.4900000$6.9900000000000000000$1.9900000000000000000000000000000000000000000000000$5.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$8.990000000000000000000000000'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1306\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1307\u001b[1;33m                 \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1308\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: complex() arg is a malformed string",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-afe4a4e083bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Question - Try calculating the average price of all apps having the Android version as \"4.1 and up\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0minp1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minp1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Android Ver\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"4.1 and up\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Price\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mstat_func\u001b[1;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11215\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_agg_by_level\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  11216\u001b[0m         return self._reduce(\n\u001b[1;32m> 11217\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m  11218\u001b[0m         )\n\u001b[0;32m  11219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_reduce\u001b[1;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[0;32m   3889\u001b[0m                 )\n\u001b[0;32m   3890\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3891\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3893\u001b[0m         \u001b[1;31m# TODO(EA) dispatch to Index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\nanops.py\u001b[0m in \u001b[0;36m_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minvalid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 69\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     70\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     71\u001b[0m                 \u001b[1;31m# we want to transform an object array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\nanops.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[0;32m    123\u001b[0m                     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\nanops.py\u001b[0m in \u001b[0;36mnanmean\u001b[1;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[0;32m    540\u001b[0m         \u001b[0mdtype_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m     \u001b[0mcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype_count\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m     \u001b[0mthe_sum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_ensure_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype_sum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthe_sum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ndim\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1308\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1309\u001b[0m                 \u001b[1;31m# e.g. \"foo\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1310\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Could not convert {x} to numeric\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1311\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Could not convert 00000000000000000000000000000000000000000000000000000000000000000000000000000$3.99000000$3.9900000000000000000000000000000000000000$3.9900000000000000000000000000000000000000000000000000000$5.990000000000000000000000$2.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$9.990000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99$2.990000000000000000000000000000$4.99$2.99$4.99000000000$5.49$10.00$3.99$2.99$2.99$0.99$7.49$2.99$1.99$7.99$19.990000000000000000000000$10.0000$5.49000$39.990$19.99000000000000000000000000000000000000000000000000000000000000000000000000000000000$5.99$5.9900000$5.990000000000000000000000$29.990000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99000000000$0.99000000000000000000000000000000000000000$0.990000000000$5.9900000000000000$400.00000$2.990000000000$0.99000$2.49$1.99$0.990000$1.49$0.9900$0.990$0.990000$4.7700$4.990000$2.4900000000000000000000000$1.9900000000$1.990$1.990$1.990$0.9900$0.99$3.990000000000000000000000000$1.9900000$9.9900$1.9900$3.990$0.99000$2.9900$19.9900000$0.990000000000000000000$0.99$0.99$0.990000000000000000$399.990$2.49$399.99000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$8.490000000$1.750000000000000000000000000000000$2.990000$4.9900000000$4.9900000000000000000000000000000000000$0.990$0.99000$2.99000000000$0.990000000$4.4900000000000000000000000000000000000000000000000000000000000000$3.99000000000000000000000$7.990000$4.99000000000000000000000$2.99$0.9900000000000000$0.9900000000000000000000$2.99$3.99000000$4.990000000000$0.99000000000000000000000000000000000000000000000000000000000$0.990000$4.9900$4.9900000000000000$4.990000000000000000000000$2.99000000000000$2.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$2.99000000000000$3.280000000$2.990000000000000000000000000000$0.99000000000$1.490000000000000000000$0.99000000000000000$12.990000000000000000000000000000000000000000$3.990000000000000$4.4900000$6.9900000000000000000$1.9900000000000000000000000000000000000000000000000$5.9900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000$8.990000000000000000000000000 to numeric"
     ]
    }
   ],
   "source": [
    "#Question - Try calculating the average price of all apps having the Android version as \"4.1 and up\" \n",
    "inp1[inp1[\"Android Ver\"] == \"4.1 and up\"].loc[:,\"Price\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'inp1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-b53b6984afb0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Analyse the Price column to check the issue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0minp1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPrice\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'inp1' is not defined"
     ]
    }
   ],
   "source": [
    "#Analyse the Price column to check the issue\n",
    "inp1.Price.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'inp1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-ec5900e05523>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Write the function to make the changes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0minp1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPrice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minp1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPrice\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'inp1' is not defined"
     ]
    }
   ],
   "source": [
    "#Write the function to make the changes\n",
    "inp1.Price = inp1.Price.apply(lambda x : 0 if x == '0' else float(x[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verify the dtype of Price once again\n",
    "inp1.Price.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00      8719\n",
       "2.99       114\n",
       "0.99       107\n",
       "4.99        70\n",
       "1.99        59\n",
       "          ... \n",
       "299.99       1\n",
       "1.59         1\n",
       "1.61         1\n",
       "3.90         1\n",
       "2.90         1\n",
       "Name: Price, Length: 73, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.Price.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           159\n",
       "1           967\n",
       "2         87510\n",
       "3        215644\n",
       "4           967\n",
       "          ...  \n",
       "10834         7\n",
       "10836        38\n",
       "10837         4\n",
       "10839       114\n",
       "10840    398307\n",
       "Name: Reviews, Length: 9366, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Analyse the Reviews column\n",
    "inp1.Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9.366000e+03\n",
       "mean     5.140498e+05\n",
       "std      3.144042e+06\n",
       "min      1.000000e+00\n",
       "25%      1.862500e+02\n",
       "50%      5.930500e+03\n",
       "75%      8.153275e+04\n",
       "max      7.815831e+07\n",
       "Name: Reviews, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Change the dtype of this column\n",
    "inp1.Reviews = inp1.Reviews.astype(\"int32\")\n",
    "\n",
    "#Check the quantitative spread of this dataframe\n",
    "\n",
    "inp1.Reviews.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1,000,000+        1577\n",
       "10,000,000+       1252\n",
       "100,000+          1150\n",
       "10,000+           1010\n",
       "5,000,000+         752\n",
       "1,000+             713\n",
       "500,000+           538\n",
       "50,000+            467\n",
       "5,000+             432\n",
       "100,000,000+       409\n",
       "100+               309\n",
       "50,000,000+        289\n",
       "500+               201\n",
       "500,000,000+        72\n",
       "10+                 69\n",
       "1,000,000,000+      58\n",
       "50+                 56\n",
       "5+                   9\n",
       "1+                   3\n",
       "Name: Installs, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Analyse the Installs Column\n",
    "inp1.Installs.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            10,000\n",
       "1           500,000\n",
       "2         5,000,000\n",
       "3        50,000,000\n",
       "4           100,000\n",
       "            ...    \n",
       "10834           500\n",
       "10836         5,000\n",
       "10837           100\n",
       "10839         1,000\n",
       "10840    10,000,000\n",
       "Name: Installs, Length: 9366, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question Clean the Installs Column and find the approximate number of apps at the 50th percentile.\n",
    "#method 01\n",
    "inp1.Installs.apply(lambda x : x[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            10,000\n",
       "1           500,000\n",
       "2         5,000,000\n",
       "3        50,000,000\n",
       "4           100,000\n",
       "            ...    \n",
       "10834           500\n",
       "10836         5,000\n",
       "10837           100\n",
       "10839         1,000\n",
       "10840    10,000,000\n",
       "Name: Installs, Length: 9366, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 02\n",
    "inp1.Installs.str.rstrip('+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.Installs = inp1.Installs.str.rstrip('+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#also replace the , with nothing\n",
    "inp1.Installs = inp1.Installs.str.replace(\",\",'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000000       1577\n",
       "10000000      1252\n",
       "100000        1150\n",
       "10000         1010\n",
       "5000000        752\n",
       "1000           713\n",
       "500000         538\n",
       "50000          467\n",
       "5000           432\n",
       "100000000      409\n",
       "100            309\n",
       "50000000       289\n",
       "500            201\n",
       "500000000       72\n",
       "10              69\n",
       "1000000000      58\n",
       "50              56\n",
       "5                9\n",
       "1                3\n",
       "Name: Installs, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.Installs.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.Installs = inp1.Installs.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500000.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question: After removing the additional symbols in the Installs column, calculate \n",
    "#the approximate number of installs at the 50th percentile.\n",
    "\n",
    "inp1.Installs.quantile(.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sanity Checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "The data that we have needs to make sense and therefore you can perform certain sanity checks on them to ensure they are factually correct as well. Some sanity checks can be:\n",
    "\n",
    "- Rating is between 1 and 5 for all the apps.\n",
    "- Number of Reviews is less than or equal to the number of Installs.\n",
    "- Free Apps shouldn’t have a price greater than 0.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9366.000000\n",
       "mean        4.191757\n",
       "std         0.515219\n",
       "min         1.000000\n",
       "25%         4.000000\n",
       "50%         4.300000\n",
       "75%         4.500000\n",
       "max         5.000000\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Perform the sanity checks on the Rating column\n",
    "inp1[\"Rating\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2454</th>\n",
       "      <td>KBA-EZ Health Guide</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>August 2, 2018</td>\n",
       "      <td>1.0.72</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4663</th>\n",
       "      <td>Alarmy (Sleep If U Can) - Pro</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>4.8</td>\n",
       "      <td>10249</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>2.49</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>July 30, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5917</th>\n",
       "      <td>Ra Ga Ba</td>\n",
       "      <td>GAME</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>Paid</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Arcade</td>\n",
       "      <td>February 8, 2017</td>\n",
       "      <td>1.0.4</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6700</th>\n",
       "      <td>Brick Breaker BR</td>\n",
       "      <td>GAME</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>19000.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Arcade</td>\n",
       "      <td>July 23, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7402</th>\n",
       "      <td>Trovami se ci riesci</td>\n",
       "      <td>GAME</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11</td>\n",
       "      <td>6100.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Arcade</td>\n",
       "      <td>March 11, 2017</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8591</th>\n",
       "      <td>DN Blog</td>\n",
       "      <td>SOCIAL</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Social</td>\n",
       "      <td>July 23, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10697</th>\n",
       "      <td>Mu.F.O.</td>\n",
       "      <td>GAME</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>Paid</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Arcade</td>\n",
       "      <td>March 3, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 App   Category  Rating  Reviews  \\\n",
       "2454             KBA-EZ Health Guide    MEDICAL     5.0        4   \n",
       "4663   Alarmy (Sleep If U Can) - Pro  LIFESTYLE     4.8    10249   \n",
       "5917                        Ra Ga Ba       GAME     5.0        2   \n",
       "6700                Brick Breaker BR       GAME     5.0        7   \n",
       "7402            Trovami se ci riesci       GAME     5.0       11   \n",
       "8591                         DN Blog     SOCIAL     5.0       20   \n",
       "10697                        Mu.F.O.       GAME     5.0        2   \n",
       "\n",
       "               Size  Installs  Type  Price Content Rating     Genres  \\\n",
       "2454   25000.000000         1  Free   0.00       Everyone    Medical   \n",
       "4663   21516.529524     10000  Paid   2.49       Everyone  Lifestyle   \n",
       "5917   20000.000000         1  Paid   1.49       Everyone     Arcade   \n",
       "6700   19000.000000         5  Free   0.00       Everyone     Arcade   \n",
       "7402    6100.000000        10  Free   0.00       Everyone     Arcade   \n",
       "8591    4200.000000        10  Free   0.00           Teen     Social   \n",
       "10697  16000.000000         1  Paid   0.99       Everyone     Arcade   \n",
       "\n",
       "           Last Updated         Current Ver         Android Ver  \n",
       "2454     August 2, 2018              1.0.72        4.0.3 and up  \n",
       "4663      July 30, 2018  Varies with device  Varies with device  \n",
       "5917   February 8, 2017               1.0.4          2.3 and up  \n",
       "6700      July 23, 2018                 1.0          4.1 and up  \n",
       "7402     March 11, 2017                 0.1          2.3 and up  \n",
       "8591      July 23, 2018                 1.0          4.0 and up  \n",
       "10697     March 3, 2017                 1.0          2.3 and up  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Perform the sanity checks on the Reviews column\n",
    "inp1[inp1[\"Reviews\"] > inp1[\"Installs\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop these columns as it will impact our analysis\n",
    "inp1 = inp1[inp1[\"Reviews\"] <= inp1[\"Installs\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#perform the sanity checks on prices of free apps \n",
    "inp1[(inp1[\"Type\"].str.lower() == \"free\") & (inp1[\"Price\"] > 0)].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outliers Analysis Using Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Now you need to start identifying and removing extreme values or __outliers__ from our dataset. These values can tilt our analysis and often provide us with a biased perspective of the data available. This is where you’ll start utilising visualisation to achieve your tasks. And the best visualisation to use here would be the box plot. Boxplots are one of the best ways of analysing the spread of a numeric variable\n",
    "\n",
    "\n",
    "Using a box plot you can identify the outliers as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![BoxPlots to Identify Outliers](images\\Boxplot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Outliers in data can arise due to genuine reasons or because of dubious entries. In the latter case, you should go ahead and remove such entries immediately. Use a boxplot to observe, analyse and remove them.\n",
    "- In the former case, you should determine whether or not removing them would add value to your analysis procedure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- You can create a box plot directly from pandas dataframe or the matplotlib way as you learnt in the previous session. Check out their official documentation here:\n",
    "   - https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.boxplot.html\n",
    "   - https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.boxplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#For the given dataset, calculate the IQR of the Price column.\n",
    "print(inp1.Price.quantile(.75)-inp1.Price.quantile(.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9359.000000\n",
       "mean        0.961116\n",
       "std        15.822478\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max       400.000000\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#alternative method\n",
    "inp1.Price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the plotting libraries\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Create a box plot for the price column\n",
    "plt.boxplot(inp1.Price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4197</th>\n",
       "      <td>most expensive app (H)</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>July 16, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4362</th>\n",
       "      <td>💎 I'm rich</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>3.8</td>\n",
       "      <td>718</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>March 11, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4367</th>\n",
       "      <td>I'm Rich - Trump Edition</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>3.6</td>\n",
       "      <td>275</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>400.00</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>May 3, 2018</td>\n",
       "      <td>1.0.1</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5351</th>\n",
       "      <td>I am rich</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3547</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>January 12, 2018</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5354</th>\n",
       "      <td>I am Rich Plus</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>856</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>May 19, 2018</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5355</th>\n",
       "      <td>I am rich VIP</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>3.8</td>\n",
       "      <td>411</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>299.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>July 21, 2018</td>\n",
       "      <td>1.1.1</td>\n",
       "      <td>4.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5356</th>\n",
       "      <td>I Am Rich Premium</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1867</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Finance</td>\n",
       "      <td>November 12, 2017</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5357</th>\n",
       "      <td>I am extremely Rich</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>2.9</td>\n",
       "      <td>41</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>379.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>July 1, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5358</th>\n",
       "      <td>I am Rich!</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>3.8</td>\n",
       "      <td>93</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Finance</td>\n",
       "      <td>December 11, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5359</th>\n",
       "      <td>I am rich(premium)</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>472</td>\n",
       "      <td>965.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Finance</td>\n",
       "      <td>May 1, 2017</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5362</th>\n",
       "      <td>I Am Rich Pro</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.4</td>\n",
       "      <td>201</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>May 30, 2017</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.6 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5364</th>\n",
       "      <td>I am rich (Most expensive app)</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>4.1</td>\n",
       "      <td>129</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Finance</td>\n",
       "      <td>December 6, 2017</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5366</th>\n",
       "      <td>I Am Rich</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>3.6</td>\n",
       "      <td>217</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>389.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>June 22, 2018</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5369</th>\n",
       "      <td>I am Rich</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>4.3</td>\n",
       "      <td>180</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Finance</td>\n",
       "      <td>March 22, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5373</th>\n",
       "      <td>I AM RICH PRO PLUS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>399.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Finance</td>\n",
       "      <td>June 25, 2018</td>\n",
       "      <td>1.0.2</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 App   Category  Rating  Reviews     Size  \\\n",
       "4197          most expensive app (H)     FAMILY     4.3        6   1500.0   \n",
       "4362                      💎 I'm rich  LIFESTYLE     3.8      718  26000.0   \n",
       "4367        I'm Rich - Trump Edition  LIFESTYLE     3.6      275   7300.0   \n",
       "5351                       I am rich  LIFESTYLE     3.8     3547   1800.0   \n",
       "5354                  I am Rich Plus     FAMILY     4.0      856   8700.0   \n",
       "5355                   I am rich VIP  LIFESTYLE     3.8      411   2600.0   \n",
       "5356               I Am Rich Premium    FINANCE     4.1     1867   4700.0   \n",
       "5357             I am extremely Rich  LIFESTYLE     2.9       41   2900.0   \n",
       "5358                      I am Rich!    FINANCE     3.8       93  22000.0   \n",
       "5359              I am rich(premium)    FINANCE     3.5      472    965.0   \n",
       "5362                   I Am Rich Pro     FAMILY     4.4      201   2700.0   \n",
       "5364  I am rich (Most expensive app)    FINANCE     4.1      129   2700.0   \n",
       "5366                       I Am Rich     FAMILY     3.6      217   4900.0   \n",
       "5369                       I am Rich    FINANCE     4.3      180   3800.0   \n",
       "5373              I AM RICH PRO PLUS    FINANCE     4.0       36  41000.0   \n",
       "\n",
       "      Installs  Type   Price Content Rating         Genres       Last Updated  \\\n",
       "4197       100  Paid  399.99       Everyone  Entertainment      July 16, 2018   \n",
       "4362     10000  Paid  399.99       Everyone      Lifestyle     March 11, 2018   \n",
       "4367     10000  Paid  400.00       Everyone      Lifestyle        May 3, 2018   \n",
       "5351    100000  Paid  399.99       Everyone      Lifestyle   January 12, 2018   \n",
       "5354     10000  Paid  399.99       Everyone  Entertainment       May 19, 2018   \n",
       "5355     10000  Paid  299.99       Everyone      Lifestyle      July 21, 2018   \n",
       "5356     50000  Paid  399.99       Everyone        Finance  November 12, 2017   \n",
       "5357      1000  Paid  379.99       Everyone      Lifestyle       July 1, 2018   \n",
       "5358      1000  Paid  399.99       Everyone        Finance  December 11, 2017   \n",
       "5359      5000  Paid  399.99       Everyone        Finance        May 1, 2017   \n",
       "5362      5000  Paid  399.99       Everyone  Entertainment       May 30, 2017   \n",
       "5364      1000  Paid  399.99           Teen        Finance   December 6, 2017   \n",
       "5366     10000  Paid  389.99       Everyone  Entertainment      June 22, 2018   \n",
       "5369      5000  Paid  399.99       Everyone        Finance     March 22, 2018   \n",
       "5373      1000  Paid  399.99       Everyone        Finance      June 25, 2018   \n",
       "\n",
       "     Current Ver   Android Ver  \n",
       "4197         1.0    7.0 and up  \n",
       "4362       1.0.0    4.4 and up  \n",
       "4367       1.0.1    4.1 and up  \n",
       "5351         2.0  4.0.3 and up  \n",
       "5354         3.0    4.4 and up  \n",
       "5355       1.1.1    4.3 and up  \n",
       "5356         1.6    4.0 and up  \n",
       "5357         1.0    4.0 and up  \n",
       "5358         1.0    4.1 and up  \n",
       "5359         3.4    4.4 and up  \n",
       "5362        1.54    1.6 and up  \n",
       "5364           2  4.0.3 and up  \n",
       "5366         1.5    4.2 and up  \n",
       "5369         1.0    4.2 and up  \n",
       "5373       1.0.2    4.1 and up  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the apps with price more than 200\n",
    "inp1[inp1.Price > 200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the Price column\n",
    "inp1 = inp1[inp1.Price < 200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b5556bd4c8>"
      ]
     },
     "execution_count": 50,
     "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": [
    "#Create a box plot for paid apps\n",
    "inp1[inp1.Price > 0].Price.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>Vargo Anesthesia Mega App</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.6</td>\n",
       "      <td>92</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>79.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>June 18, 2018</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>A Manual of Acupuncture</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>214</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>33.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>October 2, 2017</td>\n",
       "      <td>2.1.35</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2365</th>\n",
       "      <td>Vargo Anesthesia Mega App</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.6</td>\n",
       "      <td>92</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>79.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>June 18, 2018</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>A Manual of Acupuncture</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>214</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>33.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>October 2, 2017</td>\n",
       "      <td>2.1.35</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>LTC AS Legal</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Paid</td>\n",
       "      <td>39.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>April 4, 2018</td>\n",
       "      <td>3.0.1</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5360</th>\n",
       "      <td>I am Rich Person</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>4.2</td>\n",
       "      <td>134</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>37.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>July 18, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            App   Category  Rating  Reviews     Size  \\\n",
       "2253  Vargo Anesthesia Mega App    MEDICAL     4.6       92  32000.0   \n",
       "2301    A Manual of Acupuncture    MEDICAL     3.5      214  68000.0   \n",
       "2365  Vargo Anesthesia Mega App    MEDICAL     4.6       92  32000.0   \n",
       "2402    A Manual of Acupuncture    MEDICAL     3.5      214  68000.0   \n",
       "2414               LTC AS Legal    MEDICAL     4.0        6   1300.0   \n",
       "5360           I am Rich Person  LIFESTYLE     4.2      134   1800.0   \n",
       "\n",
       "      Installs  Type  Price Content Rating     Genres     Last Updated  \\\n",
       "2253      1000  Paid  79.99       Everyone    Medical    June 18, 2018   \n",
       "2301      1000  Paid  33.99       Everyone    Medical  October 2, 2017   \n",
       "2365      1000  Paid  79.99       Everyone    Medical    June 18, 2018   \n",
       "2402      1000  Paid  33.99       Everyone    Medical  October 2, 2017   \n",
       "2414       100  Paid  39.99       Everyone    Medical    April 4, 2018   \n",
       "5360      1000  Paid  37.99       Everyone  Lifestyle    July 18, 2017   \n",
       "\n",
       "     Current Ver   Android Ver  \n",
       "2253        19.0  4.0.3 and up  \n",
       "2301      2.1.35    4.0 and up  \n",
       "2365        19.0  4.0.3 and up  \n",
       "2402      2.1.35    4.0 and up  \n",
       "2414       3.0.1    4.1 and up  \n",
       "5360         1.0  4.0.3 and up  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the apps with price more than 30\n",
    "inp1[inp1.Price > 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the Price column again\n",
    "inp1 = inp1[inp1.Price < 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "inp1[inp1.Price > 0].Price.plot.box()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>Human Anatomy Atlas 2018: Complete 3D Human Body</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2921</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>24.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>2018.5.47</td>\n",
       "      <td>5.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>Essential Anatomy 3</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1533</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>50000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Medical</td>\n",
       "      <td>August 7, 2014</td>\n",
       "      <td>1.1.3</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>EMT Review Plus</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.5</td>\n",
       "      <td>199</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>June 27, 2018</td>\n",
       "      <td>3.0.5</td>\n",
       "      <td>4.4W and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>2017 EMRA Antibiotic Guide</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12</td>\n",
       "      <td>3800.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>16.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>January 27, 2017</td>\n",
       "      <td>1.0.5</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>Block Buddy</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>7.6.14</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2266</th>\n",
       "      <td>EMT PASS</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>3.4</td>\n",
       "      <td>51</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>29.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>October 22, 2014</td>\n",
       "      <td>2.0.2</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>Cardiac diagnosis (heart rate, arrhythmia)</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.4</td>\n",
       "      <td>8</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>Paid</td>\n",
       "      <td>12.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>July 25, 2018</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>AnatomyMapp</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.1</td>\n",
       "      <td>80</td>\n",
       "      <td>48000.000000</td>\n",
       "      <td>5000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>November 5, 2013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>Muscle Premium - Human Anatomy, Kinesiology, B...</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>168</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>24.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>August 11, 2017</td>\n",
       "      <td>7.1.17</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2278</th>\n",
       "      <td>CCHT PREP</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>10.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>December 8, 2016</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2297</th>\n",
       "      <td>Hospitalist Handbook</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.8</td>\n",
       "      <td>12</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>19.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>June 5, 2017</td>\n",
       "      <td>6.0.4</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>PTA Content Master</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>64</td>\n",
       "      <td>41000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>29.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>December 22, 2015</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>Navi Radiography Pro</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.7</td>\n",
       "      <td>11</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>Paid</td>\n",
       "      <td>15.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>January 14, 2018</td>\n",
       "      <td>1.2.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>Human Anatomy Atlas 2018: Complete 3D Human Body</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2921</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>24.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>2018.5.47</td>\n",
       "      <td>5.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2385</th>\n",
       "      <td>2017 EMRA Antibiotic Guide</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12</td>\n",
       "      <td>3800.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>16.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>January 27, 2017</td>\n",
       "      <td>1.0.5</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2386</th>\n",
       "      <td>Essential Anatomy 3</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1533</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>50000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Medical</td>\n",
       "      <td>August 7, 2014</td>\n",
       "      <td>1.1.3</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>EMT PASS</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>3.4</td>\n",
       "      <td>51</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>29.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>October 22, 2014</td>\n",
       "      <td>2.0.2</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>Block Buddy</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>7.6.14</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>EMT Review Plus</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.5</td>\n",
       "      <td>199</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>June 27, 2018</td>\n",
       "      <td>3.0.5</td>\n",
       "      <td>4.4W and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>AnatomyMapp</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.1</td>\n",
       "      <td>80</td>\n",
       "      <td>48000.000000</td>\n",
       "      <td>5000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>November 5, 2013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2406</th>\n",
       "      <td>PTA Content Master</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>64</td>\n",
       "      <td>41000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>29.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>December 22, 2015</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2407</th>\n",
       "      <td>Muscle Premium - Human Anatomy, Kinesiology, B...</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.2</td>\n",
       "      <td>168</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>24.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>August 11, 2017</td>\n",
       "      <td>7.1.17</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409</th>\n",
       "      <td>Cardiac diagnosis (heart rate, arrhythmia)</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.4</td>\n",
       "      <td>8</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>Paid</td>\n",
       "      <td>12.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>July 25, 2018</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2420</th>\n",
       "      <td>Hospitalist Handbook</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.8</td>\n",
       "      <td>12</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>19.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>June 5, 2017</td>\n",
       "      <td>6.0.4</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3039</th>\n",
       "      <td>Golfshot Plus: Golf GPS</td>\n",
       "      <td>SPORTS</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3387</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>50000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>29.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Sports</td>\n",
       "      <td>July 11, 2018</td>\n",
       "      <td>4.18.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>G-NetTrack Pro</td>\n",
       "      <td>TOOLS</td>\n",
       "      <td>4.3</td>\n",
       "      <td>255</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Tools</td>\n",
       "      <td>July 25, 2018</td>\n",
       "      <td>14.6</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4203</th>\n",
       "      <td>The World Ends With You</td>\n",
       "      <td>GAME</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4108</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>17.99</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Arcade</td>\n",
       "      <td>December 14, 2015</td>\n",
       "      <td>1.0.4</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4711</th>\n",
       "      <td>DRAGON QUEST V</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1667</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.0.3</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5036</th>\n",
       "      <td>NewTek NDI</td>\n",
       "      <td>PHOTOGRAPHY</td>\n",
       "      <td>3.5</td>\n",
       "      <td>77</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>19.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Photography</td>\n",
       "      <td>October 26, 2016</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5361</th>\n",
       "      <td>I am Rich Premium Plus</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>4.6</td>\n",
       "      <td>459</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>18.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Finance</td>\n",
       "      <td>February 17, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5489</th>\n",
       "      <td>AP Art History Flashcards</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>96000.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>Paid</td>\n",
       "      <td>29.99</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Education</td>\n",
       "      <td>January 19, 2016</td>\n",
       "      <td>3.30</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>BackCountry Navigator TOPO GPS PRO</td>\n",
       "      <td>MAPS_AND_NAVIGATION</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6230</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Maps &amp; Navigation</td>\n",
       "      <td>August 3, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6341</th>\n",
       "      <td>Blackjack Verite Drills</td>\n",
       "      <td>GAME</td>\n",
       "      <td>4.6</td>\n",
       "      <td>17</td>\n",
       "      <td>4700.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.00</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Casino</td>\n",
       "      <td>July 9, 2017</td>\n",
       "      <td>1.1.10</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6413</th>\n",
       "      <td>Chess of Blades (BL/Yaoi Game) (No VA)</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Casual</td>\n",
       "      <td>January 24, 2018</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.3.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6837</th>\n",
       "      <td>SkyTest BU/GU Lite</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>2.9</td>\n",
       "      <td>28</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>Paid</td>\n",
       "      <td>17.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>October 24, 2017</td>\n",
       "      <td>2.0.5</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7347</th>\n",
       "      <td>boattheory.ch Full 2018</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.7</td>\n",
       "      <td>54</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>19.40</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>November 18, 2017</td>\n",
       "      <td>3.1.2</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7357</th>\n",
       "      <td>DRAGON QUEST IV</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1647</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>February 28, 2017</td>\n",
       "      <td>1.0.5</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7477</th>\n",
       "      <td>USMLE Step 2 CK Flashcards</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>Paid</td>\n",
       "      <td>19.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>May 26, 2014</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7894</th>\n",
       "      <td>Interactive CT and MRI Anatomy</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>3.4</td>\n",
       "      <td>35</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>15.46</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>May 27, 2015</td>\n",
       "      <td>2.0.3</td>\n",
       "      <td>2.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8371</th>\n",
       "      <td>ZombieVital DG</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.3</td>\n",
       "      <td>10</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>Paid</td>\n",
       "      <td>12.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>August 4, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8719</th>\n",
       "      <td>DRAGON QUEST VIII</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>7812</td>\n",
       "      <td>27000.000000</td>\n",
       "      <td>50000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>19.99</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>April 4, 2017</td>\n",
       "      <td>1.1.4</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8726</th>\n",
       "      <td>DRAGON QUEST VI</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3358</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.0.4</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8736</th>\n",
       "      <td>FINAL FANTASY DIMENSIONS</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.3</td>\n",
       "      <td>8450</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>13.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>March 15, 2017</td>\n",
       "      <td>1.1.3</td>\n",
       "      <td>2.3.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8823</th>\n",
       "      <td>DS Helpdesk Plus</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>3.6</td>\n",
       "      <td>21</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>100</td>\n",
       "      <td>Paid</td>\n",
       "      <td>12.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>January 30, 2017</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9211</th>\n",
       "      <td>RPG ブレイジング ソウルズ アクセレイト</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>3.4</td>\n",
       "      <td>159</td>\n",
       "      <td>3100.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>10.99</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>April 26, 2018</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9220</th>\n",
       "      <td>Electrician Calculator Pro</td>\n",
       "      <td>TOOLS</td>\n",
       "      <td>3.9</td>\n",
       "      <td>47</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Tools</td>\n",
       "      <td>June 3, 2014</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9304</th>\n",
       "      <td>Carrier Landings Pro</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5969</td>\n",
       "      <td>31000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>12.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>June 29, 2018</td>\n",
       "      <td>4.2.4</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9574</th>\n",
       "      <td>Human Anatomy Atlas 2018: Complete 3D Human Body</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2923</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>24.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>2018.5.47</td>\n",
       "      <td>5.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10782</th>\n",
       "      <td>Trine 2: Complete Story</td>\n",
       "      <td>GAME</td>\n",
       "      <td>3.8</td>\n",
       "      <td>252</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>16.99</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Action</td>\n",
       "      <td>February 27, 2015</td>\n",
       "      <td>2.22</td>\n",
       "      <td>5.0 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     App             Category  \\\n",
       "2251    Human Anatomy Atlas 2018: Complete 3D Human Body              MEDICAL   \n",
       "2252                                 Essential Anatomy 3              MEDICAL   \n",
       "2254                                     EMT Review Plus              MEDICAL   \n",
       "2256                          2017 EMRA Antibiotic Guide              MEDICAL   \n",
       "2261                                         Block Buddy              MEDICAL   \n",
       "2266                                            EMT PASS              MEDICAL   \n",
       "2269          Cardiac diagnosis (heart rate, arrhythmia)              MEDICAL   \n",
       "2272                                         AnatomyMapp              MEDICAL   \n",
       "2277   Muscle Premium - Human Anatomy, Kinesiology, B...              MEDICAL   \n",
       "2278                                           CCHT PREP              MEDICAL   \n",
       "2297                                Hospitalist Handbook              MEDICAL   \n",
       "2298                                  PTA Content Master              MEDICAL   \n",
       "2299                                Navi Radiography Pro              MEDICAL   \n",
       "2372    Human Anatomy Atlas 2018: Complete 3D Human Body              MEDICAL   \n",
       "2385                          2017 EMRA Antibiotic Guide              MEDICAL   \n",
       "2386                                 Essential Anatomy 3              MEDICAL   \n",
       "2387                                            EMT PASS              MEDICAL   \n",
       "2389                                         Block Buddy              MEDICAL   \n",
       "2395                                     EMT Review Plus              MEDICAL   \n",
       "2400                                         AnatomyMapp              MEDICAL   \n",
       "2406                                  PTA Content Master              MEDICAL   \n",
       "2407   Muscle Premium - Human Anatomy, Kinesiology, B...              MEDICAL   \n",
       "2409          Cardiac diagnosis (heart rate, arrhythmia)              MEDICAL   \n",
       "2420                                Hospitalist Handbook              MEDICAL   \n",
       "3039                             Golfshot Plus: Golf GPS               SPORTS   \n",
       "4172                                      G-NetTrack Pro                TOOLS   \n",
       "4203                             The World Ends With You                 GAME   \n",
       "4711                                      DRAGON QUEST V               FAMILY   \n",
       "5036                                          NewTek NDI          PHOTOGRAPHY   \n",
       "5361                              I am Rich Premium Plus              FINANCE   \n",
       "5489                           AP Art History Flashcards               FAMILY   \n",
       "5994                  BackCountry Navigator TOPO GPS PRO  MAPS_AND_NAVIGATION   \n",
       "6341                             Blackjack Verite Drills                 GAME   \n",
       "6413              Chess of Blades (BL/Yaoi Game) (No VA)               FAMILY   \n",
       "6837                                  SkyTest BU/GU Lite             BUSINESS   \n",
       "7347                             boattheory.ch Full 2018               FAMILY   \n",
       "7357                                     DRAGON QUEST IV               FAMILY   \n",
       "7477                          USMLE Step 2 CK Flashcards               FAMILY   \n",
       "7894                      Interactive CT and MRI Anatomy              MEDICAL   \n",
       "8371                                      ZombieVital DG               FAMILY   \n",
       "8719                                   DRAGON QUEST VIII               FAMILY   \n",
       "8726                                     DRAGON QUEST VI               FAMILY   \n",
       "8736                            FINAL FANTASY DIMENSIONS               FAMILY   \n",
       "8823                                    DS Helpdesk Plus             BUSINESS   \n",
       "9211                              RPG ブレイジング ソウルズ アクセレイト               FAMILY   \n",
       "9220                          Electrician Calculator Pro                TOOLS   \n",
       "9304                                Carrier Landings Pro               FAMILY   \n",
       "9574    Human Anatomy Atlas 2018: Complete 3D Human Body              MEDICAL   \n",
       "10782                            Trine 2: Complete Story                 GAME   \n",
       "\n",
       "       Rating  Reviews           Size  Installs  Type  Price Content Rating  \\\n",
       "2251      4.5     2921   25000.000000    100000  Paid  24.99       Everyone   \n",
       "2252      4.1     1533   42000.000000     50000  Paid  11.99     Mature 17+   \n",
       "2254      4.5      199    1800.000000     10000  Paid  11.99       Everyone   \n",
       "2256      4.4       12    3800.000000      1000  Paid  16.99       Everyone   \n",
       "2261      4.0       15    5000.000000      1000  Paid  14.99       Everyone   \n",
       "2266      3.4       51    2400.000000      1000  Paid  29.99       Everyone   \n",
       "2269      4.4        8    6500.000000       100  Paid  12.99       Everyone   \n",
       "2272      4.1       80   48000.000000      5000  Paid  14.99       Everyone   \n",
       "2277      4.2      168   25000.000000     10000  Paid  24.99       Everyone   \n",
       "2278      3.6       25     266.000000      1000  Paid  10.99       Everyone   \n",
       "2297      4.8       12   18000.000000      1000  Paid  19.99       Everyone   \n",
       "2298      4.2       64   41000.000000      1000  Paid  29.99       Everyone   \n",
       "2299      4.7       11  100000.000000       500  Paid  15.99       Everyone   \n",
       "2372      4.5     2921   25000.000000    100000  Paid  24.99       Everyone   \n",
       "2385      4.4       12    3800.000000      1000  Paid  16.99       Everyone   \n",
       "2386      4.1     1533   42000.000000     50000  Paid  11.99     Mature 17+   \n",
       "2387      3.4       51    2400.000000      1000  Paid  29.99       Everyone   \n",
       "2389      4.0       15    5000.000000      1000  Paid  14.99       Everyone   \n",
       "2395      4.5      199    1800.000000     10000  Paid  11.99       Everyone   \n",
       "2400      4.1       80   48000.000000      5000  Paid  14.99       Everyone   \n",
       "2406      4.2       64   41000.000000      1000  Paid  29.99       Everyone   \n",
       "2407      4.2      168   25000.000000     10000  Paid  24.99       Everyone   \n",
       "2409      4.4        8    6500.000000       100  Paid  12.99       Everyone   \n",
       "2420      4.8       12   18000.000000      1000  Paid  19.99       Everyone   \n",
       "3039      4.1     3387   25000.000000     50000  Paid  29.99       Everyone   \n",
       "4172      4.3      255    1600.000000      5000  Paid  14.99       Everyone   \n",
       "4203      4.6     4108   13000.000000     10000  Paid  17.99   Everyone 10+   \n",
       "4711      4.7     1667   17000.000000     10000  Paid  14.99           Teen   \n",
       "5036      3.5       77    1200.000000      1000  Paid  19.99       Everyone   \n",
       "5361      4.6      459    2000.000000     10000  Paid  18.99       Everyone   \n",
       "5489      5.0        1   96000.000000        10  Paid  29.99     Mature 17+   \n",
       "5994      4.5     6230   21516.529524    100000  Paid  11.99       Everyone   \n",
       "6341      4.6       17    4700.000000       100  Paid  14.00           Teen   \n",
       "6413      4.8        4   23000.000000        10  Paid  14.99           Teen   \n",
       "6837      2.9       28   20000.000000       500  Paid  17.99       Everyone   \n",
       "7347      4.7       54   50000.000000      1000  Paid  19.40       Everyone   \n",
       "7357      4.7     1647   16000.000000     10000  Paid  14.99       Everyone   \n",
       "7477      5.0        1   40000.000000        10  Paid  19.99       Everyone   \n",
       "7894      3.4       35   24000.000000      1000  Paid  15.46       Everyone   \n",
       "8371      4.3       10   20000.000000       100  Paid  12.99       Everyone   \n",
       "8719      4.5     7812   27000.000000     50000  Paid  19.99   Everyone 10+   \n",
       "8726      4.4     3358   17000.000000    100000  Paid  14.99       Everyone   \n",
       "8736      4.3     8450    1100.000000    100000  Paid  13.99       Everyone   \n",
       "8823      3.6       21   21516.529524       100  Paid  12.99       Everyone   \n",
       "9211      3.4      159    3100.000000      1000  Paid  10.99           Teen   \n",
       "9220      3.9       47     270.000000      1000  Paid  14.99       Everyone   \n",
       "9304      4.6     5969   31000.000000     10000  Paid  12.99       Everyone   \n",
       "9574      4.5     2923   25000.000000    100000  Paid  24.99       Everyone   \n",
       "10782     3.8      252   11000.000000     10000  Paid  16.99           Teen   \n",
       "\n",
       "                  Genres       Last Updated         Current Ver  \\\n",
       "2251             Medical       May 25, 2018           2018.5.47   \n",
       "2252             Medical     August 7, 2014               1.1.3   \n",
       "2254             Medical      June 27, 2018               3.0.5   \n",
       "2256             Medical   January 27, 2017               1.0.5   \n",
       "2261             Medical     August 1, 2018              7.6.14   \n",
       "2266             Medical   October 22, 2014               2.0.2   \n",
       "2269             Medical      July 25, 2018                   7   \n",
       "2272             Medical   November 5, 2013                 1.0   \n",
       "2277             Medical    August 11, 2017              7.1.17   \n",
       "2278             Medical   December 8, 2016                 1.2   \n",
       "2297             Medical       June 5, 2017               6.0.4   \n",
       "2298             Medical  December 22, 2015                 2.1   \n",
       "2299             Medical   January 14, 2018               1.2.0   \n",
       "2372             Medical       May 25, 2018           2018.5.47   \n",
       "2385             Medical   January 27, 2017               1.0.5   \n",
       "2386             Medical     August 7, 2014               1.1.3   \n",
       "2387             Medical   October 22, 2014               2.0.2   \n",
       "2389             Medical     August 1, 2018              7.6.14   \n",
       "2395             Medical      June 27, 2018               3.0.5   \n",
       "2400             Medical   November 5, 2013                 1.0   \n",
       "2406             Medical  December 22, 2015                 2.1   \n",
       "2407             Medical    August 11, 2017              7.1.17   \n",
       "2409             Medical      July 25, 2018                   7   \n",
       "2420             Medical       June 5, 2017               6.0.4   \n",
       "3039              Sports      July 11, 2018              4.18.0   \n",
       "4172               Tools      July 25, 2018                14.6   \n",
       "4203              Arcade  December 14, 2015               1.0.4   \n",
       "4711        Role Playing     August 1, 2018               1.0.3   \n",
       "5036         Photography   October 26, 2016                 1.1   \n",
       "5361             Finance  February 17, 2018                 1.1   \n",
       "5489           Education   January 19, 2016                3.30   \n",
       "5994   Maps & Navigation     August 3, 2018  Varies with device   \n",
       "6341              Casino       July 9, 2017              1.1.10   \n",
       "6413              Casual   January 24, 2018                1.03   \n",
       "6837            Business   October 24, 2017               2.0.5   \n",
       "7347           Education  November 18, 2017               3.1.2   \n",
       "7357        Role Playing  February 28, 2017               1.0.5   \n",
       "7477           Education       May 26, 2014                2.47   \n",
       "7894             Medical       May 27, 2015               2.0.3   \n",
       "8371          Simulation     August 4, 2018  Varies with device   \n",
       "8719        Role Playing      April 4, 2017               1.1.4   \n",
       "8726        Role Playing     August 1, 2018               1.0.4   \n",
       "8736        Role Playing     March 15, 2017               1.1.3   \n",
       "8823            Business   January 30, 2017  Varies with device   \n",
       "9211        Role Playing     April 26, 2018                2.12   \n",
       "9220               Tools       June 3, 2014                 3.1   \n",
       "9304          Simulation      June 29, 2018               4.2.4   \n",
       "9574             Medical       May 25, 2018           2018.5.47   \n",
       "10782             Action  February 27, 2015                2.22   \n",
       "\n",
       "              Android Ver  \n",
       "2251           5.0 and up  \n",
       "2252         4.0.3 and up  \n",
       "2254          4.4W and up  \n",
       "2256         4.0.3 and up  \n",
       "2261           4.0 and up  \n",
       "2266           4.0 and up  \n",
       "2269           3.0 and up  \n",
       "2272           3.0 and up  \n",
       "2277           4.4 and up  \n",
       "2278           2.1 and up  \n",
       "2297           4.1 and up  \n",
       "2298           2.2 and up  \n",
       "2299         4.0.3 and up  \n",
       "2372           5.0 and up  \n",
       "2385         4.0.3 and up  \n",
       "2386         4.0.3 and up  \n",
       "2387           4.0 and up  \n",
       "2389           4.0 and up  \n",
       "2395          4.4W and up  \n",
       "2400           3.0 and up  \n",
       "2406           2.2 and up  \n",
       "2407           4.4 and up  \n",
       "2409           3.0 and up  \n",
       "2420           4.1 and up  \n",
       "3039           4.1 and up  \n",
       "4172           4.0 and up  \n",
       "4203           4.0 and up  \n",
       "4711           2.3 and up  \n",
       "5036           4.1 and up  \n",
       "5361         4.0.3 and up  \n",
       "5489           4.0 and up  \n",
       "5994   Varies with device  \n",
       "6341           3.0 and up  \n",
       "6413         2.3.3 and up  \n",
       "6837           3.0 and up  \n",
       "7347           4.2 and up  \n",
       "7357           2.3 and up  \n",
       "7477           2.2 and up  \n",
       "7894           2.2 and up  \n",
       "8371   Varies with device  \n",
       "8719           4.0 and up  \n",
       "8726           2.3 and up  \n",
       "8736         2.3.3 and up  \n",
       "8823   Varies with device  \n",
       "9211           2.3 and up  \n",
       "9220           1.6 and up  \n",
       "9304           4.1 and up  \n",
       "9574           5.0 and up  \n",
       "10782          5.0 and up  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1[inp1.Price > 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n",
    "\n",
    "Histograms can also be used in conjuction with boxplots for data cleaning and data handling purposes. You can use it to check the spread of a numeric variable. Histograms generally work by bucketing the entire range of values that a particular variable takes to specific __bins__. After that, it uses vertical bars to denote the total number of records in a specific bin, which is also known as its __frequency__.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Histogram](images\\Histogram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can adjust the number of bins to improve its granularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Bins change](images\\Granular.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll be using plt.hist() to plot a histogram. Check out its official documentation:https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.hist.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "?plt.hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Create a histogram of the Reviews\n",
    "plt.hist(inp1.Reviews)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Create a boxplot of the Reviews column\n",
    "plt.boxplot(inp1.Reviews)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Wattpad 📖 Free Books</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2914724</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>100000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Books &amp; Reference</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Google Play Books</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1433233</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Books &amp; Reference</td>\n",
       "      <td>August 3, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Uber Driver</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1254730</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>10000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>August 3, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>OfficeSuite : Free Office + PDF Editor</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1002861</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>100000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>August 2, 2018</td>\n",
       "      <td>9.7.14188</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Facebook Pages Manager</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1279184</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>50000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>August 2, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</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>10190</th>\n",
       "      <td>Fallout Shelter</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2721923</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>June 11, 2018</td>\n",
       "      <td>1.13.12</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10200</th>\n",
       "      <td>Facebook Pages Manager</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1279800</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>50000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>August 6, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10327</th>\n",
       "      <td>Garena Free Fire</td>\n",
       "      <td>GAME</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5534114</td>\n",
       "      <td>53000.000000</td>\n",
       "      <td>100000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Action</td>\n",
       "      <td>August 3, 2018</td>\n",
       "      <td>1.21.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10636</th>\n",
       "      <td>FRONTLINE COMMANDO</td>\n",
       "      <td>GAME</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1351833</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Action</td>\n",
       "      <td>October 28, 2013</td>\n",
       "      <td>3.0.3</td>\n",
       "      <td>2.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10707</th>\n",
       "      <td>Photo Editor Collage Maker Pro</td>\n",
       "      <td>PHOTOGRAPHY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1519671</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>100000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Photography</td>\n",
       "      <td>February 1, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>704 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          App             Category  Rating  \\\n",
       "139                      Wattpad 📖 Free Books  BOOKS_AND_REFERENCE     4.6   \n",
       "152                         Google Play Books  BOOKS_AND_REFERENCE     3.9   \n",
       "189                               Uber Driver             BUSINESS     4.4   \n",
       "194    OfficeSuite : Free Office + PDF Editor             BUSINESS     4.3   \n",
       "201                    Facebook Pages Manager             BUSINESS     4.0   \n",
       "...                                       ...                  ...     ...   \n",
       "10190                         Fallout Shelter               FAMILY     4.6   \n",
       "10200                  Facebook Pages Manager             BUSINESS     4.0   \n",
       "10327                        Garena Free Fire                 GAME     4.5   \n",
       "10636                      FRONTLINE COMMANDO                 GAME     4.4   \n",
       "10707          Photo Editor Collage Maker Pro          PHOTOGRAPHY     4.5   \n",
       "\n",
       "       Reviews          Size    Installs  Type  Price Content Rating  \\\n",
       "139    2914724  21516.529524   100000000  Free    0.0           Teen   \n",
       "152    1433233  21516.529524  1000000000  Free    0.0           Teen   \n",
       "189    1254730  21516.529524    10000000  Free    0.0       Everyone   \n",
       "194    1002861  35000.000000   100000000  Free    0.0       Everyone   \n",
       "201    1279184  21516.529524    50000000  Free    0.0       Everyone   \n",
       "...        ...           ...         ...   ...    ...            ...   \n",
       "10190  2721923  25000.000000    10000000  Free    0.0           Teen   \n",
       "10200  1279800  21516.529524    50000000  Free    0.0       Everyone   \n",
       "10327  5534114  53000.000000   100000000  Free    0.0           Teen   \n",
       "10636  1351833  12000.000000    10000000  Free    0.0           Teen   \n",
       "10707  1519671  21516.529524   100000000  Free    0.0       Everyone   \n",
       "\n",
       "                  Genres      Last Updated         Current Ver  \\\n",
       "139    Books & Reference    August 1, 2018  Varies with device   \n",
       "152    Books & Reference    August 3, 2018  Varies with device   \n",
       "189             Business    August 3, 2018  Varies with device   \n",
       "194             Business    August 2, 2018           9.7.14188   \n",
       "201             Business    August 2, 2018  Varies with device   \n",
       "...                  ...               ...                 ...   \n",
       "10190         Simulation     June 11, 2018             1.13.12   \n",
       "10200           Business    August 6, 2018  Varies with device   \n",
       "10327             Action    August 3, 2018              1.21.0   \n",
       "10636             Action  October 28, 2013               3.0.3   \n",
       "10707        Photography  February 1, 2018  Varies with device   \n",
       "\n",
       "              Android Ver  \n",
       "139    Varies with device  \n",
       "152    Varies with device  \n",
       "189    Varies with device  \n",
       "194            4.1 and up  \n",
       "201    Varies with device  \n",
       "...                   ...  \n",
       "10190          4.1 and up  \n",
       "10200  Varies with device  \n",
       "10327        4.0.3 and up  \n",
       "10636          2.1 and up  \n",
       "10707  Varies with device  \n",
       "\n",
       "[704 rows x 13 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check records with 1 million reviews\n",
    "inp1[inp1.Reviews > 1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8634, 13)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the above records\n",
    "inp1 = inp1[inp1.Reviews <= 1000000]\n",
    "inp1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Question - Create a histogram again and check the peaks\n",
    "plt.hist(inp1.Reviews)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Question - Create a box plot for the Installs column and report back the IQR\n",
    "\n",
    "plt.boxplot(inp1.Reviews)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      8634.000000\n",
       "mean      68383.348853\n",
       "std      156335.806417\n",
       "min           1.000000\n",
       "25%         143.250000\n",
       "50%        3641.000000\n",
       "75%       46070.500000\n",
       "max      995002.000000\n",
       "Name: Reviews, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.Reviews.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "990000.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.Installs.quantile(.75) - inp1.Installs.quantile(.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8573, 13)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Question - CLean the Installs by removing all the apps having more than or equal to 100 million installs\n",
    "inp1 = inp1[inp1.Installs < 100000000]\n",
    "inp1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Plot a histogram for Size as well.\n",
    "inp1.Size.plot.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Question - Create a boxplot for the Size column and report back the median value\n",
    "inp1.Size.plot.box()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session 2 - Data Visualisation with Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn is Python library to create statistical graphs easily. It is built on top of matplotlib and closely integrated with pandas.\n",
    "\n",
    "_Functionalities of Seaborn_ :\n",
    "\n",
    "- Dataset oriented API\n",
    "- Analysing univariate and bivariate distributions\n",
    "- Automatic estimation and plotting of  linear regression models\n",
    "- Convenient views for complex datasets\n",
    "- Concise control over style\n",
    "- Colour palettes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b55690fe48>"
      ]
     },
     "execution_count": 67,
     "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": [
    "inp1.Rating.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A distribution plot is pretty similar to the histogram functionality in matplotlib. Instead of a frequency plot, it plots an approximate probability density for that rating bucket. And the curve (or the __KDE__) that gets drawn over the distribution is the approximate probability density curve. \n",
    "\n",
    "The following is an example of a distribution plot. Notice that now instead of frequency on the left axis, it has the density for each bin or bucket."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "![Distplot](images\\Distplot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll be using sns.distplot for plotting a distribution plot. Check out its official documentation: https://seaborn.pydata.org/generated/seaborn.distplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Create a distribution plot for rating\n",
    "sns.distplot(inp1.Rating)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Change the number of bins\n",
    "\n",
    "sns.distplot(inp1.Rating,bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Change the colour of bins to green\n",
    "sns.distplot(inp1.Rating,bins=20,color=\"grey\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Apply matplotlib functionalities\n",
    "\n",
    "sns.distplot(inp1.Rating,bins=20,color=\"y\")\n",
    "plt.title(\"Distribution of Ratings\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Styling Options\n",
    "\n",
    "One of the biggest advantages of using Seaborn is that you can retain its aesthetic properties and also the Matplotlib functionalities to perform additional customisations. Before we continue with our case study analysis, let’s study some styling options that are available in Seaborn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Check out the official documentation:https://seaborn.pydata.org/generated/seaborn.set_style.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EsqKdut90cMxB3rdvX7Zt28a+ffsIhUKsXr2aIUOGdGZtQogU5PO9DUTJyTmvU/frcJwIRIhEdnbqftNBuyfN+stf/kJjYyPjx4/njjvuYMqUKViWxbhx4+jRo0ciahRCpBCf7200rQCP53QM4x+dtl+HoxSAUOgrHI4TOm2/6aBNQd67d+/48MIf/vCH8ddHjRrFqFGjElOZECIlGcZq3O4zURStU/eraQWoag7h8Dbg2C75T1dyQZAQotNEo/UEg5/hdp/R6ftWFAWHo1ROeB6GBLkQotMYxkeAhcfT+UEO4HSWEo1WYZpyl7GDSZALITpN82X0bvfQhOy/uZ88HN6ekP2nKglyIUSnMYzVOJ390LSChOw/FuSKdK98gwS5EKJTWJbVdKIzMd0qAKrqQtd7EA5/lbBjpCIJciFEpwiHdxCJVCU0yAEcjhLC4R0JPUaqkSAXQnQKw4jdxSdRJzqb6fpxmGYDpulvfeUMIUEuhOgUhrEaRckiK+u0hB5H148DIBzeldDjpBIJciFEpzCM1bhcg1DVjs0/3hqHI3YFeSSyO6HHSSUS5EKIDrOsCIZRkfD+cQBVLUBRsiTIDyJBLoTosGDwcyzLwO0enPBjKYqCrveUID+IBLkQosMCgfUAuFyDuuR4sSCXPvJmEuRCiA4LBNY3negs65LjxUau+DBNuZkNSJALITpBILCerKxTURRHlxzP4egJQDgs3SsgQS6E6CDLsggE1ndZtwocGIIo3SsxEuRCiA4JhyuJRutwu7suyFU1D0VxyQnPJhLkQogOCQQ2AF13ohNk5Mo3SZALITokEFgHKGRlDejS40qQHyBBLoTokEBgPU5nXzTN26XH1fWemKaPaFRGrkiQCyE6JBDY0KXdKs0cDjnh2UyCXAhxzCKRWsLh7bYEua4XN9VQ1eXHTjYS5EKIYxYMbgTo0hErzVQ1H3AQjUqQS5ALIY6ZYXTtpfkHUxQVXS+WFjkS5EKIDggE1qLrx8e7ObqaBHmMBLkQ4pgZxjrc7tNtO76uFxON1mBZEdtqSAYS5EKIYxKN+giFPsflSvzUtUcS+0vAIhKptq2GZCBBLoQ4JrETnVaXzEF+JJoW69KJRvfYVkMykCAXQhwTw6gAwOWyt2sFZAiiBLkQ4pgEAhXoenF8JkI7qKoLVc2VILe7ACFEajKMdbhcp6Moiq11yMiVNgS5aZpMmzaN8ePHU15ezrZt21osnzdvHpdffjnjxo3jH//4R8IKFUIkD9M0CAY32dqt0kyCHPTWVli2bBmhUIiFCxdSUVHBAw88wOOPPw5AfX09CxYs4O9//zuGYXDZZZcxZsyYhBcthLBXIPAxELX1RGczTSvGshqJRGpRVfu6eezUapCvWbOGESNGADB48GA2btwYX+Z2uzn++OMxDAPDMGz/E0sI0TUCgeYTnfYHefMJz3B4K253t3Ztq6o5RCJdO2tjIrQa5D6fD6/3wBvVNI1IJIKuxzY97rjjGDt2LNFolBtuuCFxlQohkoZhrEPTCnA4TrC7FHS9BwCBwGf4/e0bT15YeAGQ+kHeah+51+vF7/fHn5umGQ/xFStWUFVVxdtvv80777zDsmXLWL9+feKqFUIkhUBgHS7X4KT4K1zTugEaweC2VtdNV60G+dChQ1mxYgUAFRUVlJWVxZfl5eXhcrlwOp1kZWWRk5NDfX194qoVQtjONEMEgx8nRf84NE+eVUQw+JXdpdim1a6VMWPGsHLlSiZMmIBlWcycOZP58+dTUlLC6NGjee+997jyyitRVZWhQ4dyzjnndEXdQgibBIOfYlnhpBix0kzTCgmFKsnOtrsSe7Qa5Kqqct9997V4rW/fvvGfb7nlFm655ZbOr0wIkZQOnOhMniDX9e4Yxiosy0qK7p6uJhcECSHaxTAqUNVcnM4+dpcSp2lFmGYA08zMrl0JciFEu8ROdJ6OoiRPfGhaIQDRaGbOgpg8n4QQIulZVoRAYKOtc5Afjq53ByASqbG5EntIkAsh2iwY3IxlBZKqfxyahyCq0iIXQojWBAJrAXC7h9hcSUuKouN09iQS2Wt3KbaQIBdCtJlhrENVs3E6+7a+chdzOk8gGpWuFSGEOKrYic6BKIpmdymHyMrqnbG3fJMgF0K0iWWZBALrk65/vJnT2RvL8mOaht2ldDkJciFEm4RCX2Ca/qSY8fBwnM7eQGYOQZQgF0K0iWEk54nOZllZsSDPxO6VVi/RF0JkNl33YZoNBAIrURQXbncOirKr1e1UNdwF1R3gdPYCMrNFLkEuhDgq02yguvotGhr+ja73pKZmWZu2Ky4enuDKWtK07KYbRWRekEvXihCiVZZlEg5X4nCU2F3KUWlaYUa2yCXIhRCtikSqsKxgUtwR6Gh0vVBa5EIIcTjh8A4AHI7eNldydJrWHdPcj2V1bf+83STIhRCtikR2oCjO+P0xk1VsFkSLaLTW7lK6lAS5EKJVodAOdL1XUl7ReTBdj01nm2mzIEqQCyGOyrKiRCKVSd8/DrGuFSDj5lyRIBdCHFUw+CWWFUqJIFfVHMAhQS6EEAczjI0AKRHkiqKg692la0UIIQ5mGB+nxInOZprWXVrkQghxMMPY2HSiMzXiIhbk1ViWZXcpXSY1PhkhhC0sK4phfJr0V3QeTNe7Y1lBLMtvdyldRoJcCHFEsXt0GinRP94sNpY8s4YgSpALIY6o+R6dqRXkmTcEUYJcCHFEhlGBqnrQ9WK7S2kzTesGSJALIQQQu5mEy3VKypzoBFDVrKbpbCXIhRAZzrIiBAIbcLsH2F1KuzWPXMkUEuRCiMOKnehsxO0+ze5S2i3TxpJLkAshDqv5RGcqBrmuFxKN1mFZUbtL6RIS5EKIwzKMtaiql6ysE+0upd1iI1csotE6u0vpEq3es9M0TaZPn85nn32G0+nk/vvvp7S0NL78X//6F48++igAp556Kr/+9a9RFCVxFQshuoRhVOByDUr6qWsP5+AhiM1T26azVlvky5YtIxQKsXDhQm699VYeeOCB+DKfz8eDDz7IE088waJFi+jVqxd1dZnxDShEOms+0elyDba7lGOi65k1lrzVIF+zZg0jRowAYPDgwWzcuDG+bO3atZSVlTF79mwmTpxIYWEh3bp1S1y1QoguEQx+hmUZuN1D7C7lmKhqHqBlzP07W+1a8fl8eL3e+HNN04hEIui6Tl1dHatWrWLx4sV4PB6uvvpqBg8eTJ8+fRJatBAisQyjAiBlg1xR1IwaudJqi9zr9eL3H5h8xjRNdD2W//n5+QwcOJCioiKys7M544wz+PTTTxNXrRCiSwQCH6GqXpzOfnaXcsx0XYI8bujQoaxYsQKAiooKysrK4stOO+00Nm/eTG1tLZFIhHXr1tGvX+p+8EKImNiJztNT6orOb9K0zLnBRKtdK2PGjGHlypVMmDABy7KYOXMm8+fPp6SkhNGjR3Prrbdy3XXXAXDBBRe0CHohROppPtHZrdsUu0vpEE3rjmU1YpqNqKrH7nISqtUgV1WV++67r8Vrffv2jf88duxYxo4d2/mVCSFsEQxuwrICuFyp2T/e7MDIldq0D/LU/btJCJEQqX6is1nzWPJMGLkiQS6EaMEwPkJVc3A6+7a+chLLpHnJJciFEC0EAql/ohNAVd0oSrYEuRAis1hWmEBgI253al7R+U26nhkjVyTIhRBxzSc6U71/vFmmzEsuQS6EiDOM2NS1qTrHyjfFgrwOyzLtLiWhJMiFEHGGsQZVzU/5E53NYkMQo5jmPrtLSSgJciFEnGGsxu0ekvInOpsdGIKY3v3k6fFpCSE6zDT9BAKf4HafYXcpnUbTYnORp/vIFQlyIQQAhrEOiOLxpFOQ5wOqBLkQIjMYxhqAtGqRK4qGphWk/dWdEuRCCCDWP+5wlKLrRXaX0qkyYV5yCXIhBBBrkbvdw+wuo9PpemHajyWXIBdCEIlUEQ5vT6tulWaaVohp+jHNgN2lJIwEuRDioP7x9GuRH5g8K31b5RLkQggaG/8P0HC7T7e7lE6n67EhiOl8wlOCXAiBYazB5RqQljdgODCWXIJcCJGmTDNEY+MqPJ6z7C4lIVTVhap6pUUuhEhfgcBaLKuR7Oxz7S4lYTQtvUeuSJALkeH8/hUAeDzn2FxJ4qT7WHIJciEynN//LllZp8VvVpyOYmPJ67CsiN2lJIQEuRAZzDSDNDZ+QHb2CLtLSajYCU8rbVvlEuRCZDDD+AjLMtI+yNN9CKIEuRAZLNY/rpCdnb7945D+09lKkAuRwRob38XlGoSmFdhdSkKpag6K4pQWuRAivcT6x1eRnT3c7lISTlGUtB6CKEEuRIZqbFyJZQXSevz4wSTIhRBpp77+DRTFQ3b29+wupUvoeiGRSA2WZdpdSqeTIBciA1mWSUPDUrze76OqbrvL6RKxWRAjmGa93aV0OglyITJQILCWSGQnublj7S6lyxwYgrjX5ko6X6tBbpom06ZNY/z48ZSXl7Nt27bDrnPdddfx4osvJqRIIUTnqq9/E9Dwes+3u5Quo2nFAESj6RfkemsrLFu2jFAoxMKFC6moqOCBBx7g8ccfb7HOww8/zP79+xNWpBDiAF33YZoN7d5OVXOIRLwANDS8QXb2cHS9W2eXl7Q0LR/Q07JF3mqQr1mzhhEjYld9DR48mI0bN7ZY/tZbb6EoCueemxlnvoWwm2k2UF39Vru3Kyy8APASDH5OMLiJgoLJnV9cElMUtemEZ/oFeatdKz6fD6/XG3+uaRqRSGzimc2bN/PGG2/ws5/9LHEVCiE6VUPDmwDk5GRO/3gzTSsmGq2yu4xO12qL3Ov14vf7489N00TXY5stXryYPXv2cM011/D111/jcDjo1auXtM6FSFKWZbFv34u43WfgdJ5gdzldTteLCAY/xrJMFCV9xnq0GuRDhw5l+fLlXHTRRVRUVFBWVhZf9otf/CL+85w5cygsLJQQFyKJBQIfEQx+ynHHPWx3KbbQ9SIgSjRaGx/Fkg5aDfIxY8awcuVKJkyYgGVZzJw5k/nz51NSUsLo0aO7okYhRCepq3seRXGRlzfO7lJsoesHRq5kVJCrqsp9993X4rW+ffsest7NN9/ceVUJITqdaQbYv/8VcnMvQdPy7C7HFppWBMTGkmdlnWJzNZ0nfTqJhBBHVV+/DNPcT35+ud2l2CY2C6KLSCS9TnhKkAuRIerqXsXhKE37m0gcjaIo6HpR2g1BlCAXIgNEIlX4fO+Rn391Wo3WOBaaVpR2V3dm9icqRIbw+/+NojgoKLjW7lJsp+vFRKO1aXUjZglyIdKcaQYwjFXk5V2Iw9HD7nJsFxuCaKXV3YIkyIVIc4bxIZYVpHv3zD3JebDmkSvpdIWnBLkQacyyTPz+FTgcJ+LxDLK7nKQQa5Gn13S2EuRCpLFg8FOi0b0Zczu3tlBVD6rqTashiBLkQqQxv/+fqGo+LtfpdpeSVHS9J5HIbrvL6DQS5EKkqVDoS0KhL/B6R6IorV7EnVF0/TgikV1YlmV3KZ1CglyINOXzvY2ieHC7z7a7lKSj68dhWUHC4Z12l9IpJMiFSEPh8G6CwQ1kZ5+LqmbZXU7ScTiOAyAQ+NzmSjqHBLkQacjvX4aiODP6cvyj0fXmIN9scyWdQ4JciDQTiVRjGGvweM5GVb2tb5CBVNWNquYTDEqQCyGSkM/3d0AjO1vuF3A0Dsdx0iIXQiSfWGv8//B4zs7YOcfbStePJxjcimWF7S6lwyTIhUgjPt8/ABWv9/t2l5L0YiNXwgSDW+wupcMkyIVIE5FIDYbxIR7Pd6U13gbNI1eCwU9srqTjJMiFSBOx1riC1yt9422h6z0AVYJcCJEcYq3xVU2t8Xy7y0kJiuIgK6uUQECCXAiRBPz+ZcRa49I33h5ZWWXSIhdC2C8SqaWxcVXTSBVpjbeHy1VGKPQl0ajP7lI6RIJciBQXGzeOtMaPgcczELAwjDV2l9IhEuRCpLBIZA+G8QEez3A0rcDuclKOxzMEAMNYZXMlHSNBLkQKq69/A0Vx4vX+wO5SUpKm5ZKVdQqNjR/YXUqHSJALkaJCoS8JBteTnT0aTZM5VY6Vx3MWjY3/h2WZdpdyzCTIhUhBlmVSX/86qhp6wQQAABLSSURBVJpDdvb37C4npXk8Z2Ka+wkGP7O7lGMmQS5ECmpsXEk4/CU5OWNlvvEOcrvPAkjp7hUJciFSTCi0g4aGJTidJ8dDSBw7p/MkNK0wpU94SpALkUIsy6Sy8i5AIT9/PIqi2F1SylMUpamfvPUWuWn6k/JKULkjqxAppKrqN/j9H5KXNwFN69aubTXNBHa1+5iqmvrTvLbG4/kODQ1vEInsRdeLDruOaQbYtu1yGhs/4KSTluN2D+3iKo+s1SA3TZPp06fz2Wef4XQ6uf/++yktLY0vf/bZZ3nzzTcBOO+88/jpT3+auGqFyGA1NY9RXf0gBQU/Jiur/V0qltVIdfW77d6uuHh4u7dJNR5Pcz/5KnJzLz5kuWWZfP31jTQ2vo+q5rJr1y/o0+fvKEpydGq0WsWyZcsIhUIsXLiQW2+9lQceeCC+bMeOHSxZsoSXXnqJhQsX8u6777Jp06aEFixEJqqre47du+8gJ+cSevW6V7pUOommmajqLjyenqhqDvX1f0JVdx3y2Lv3TurrX6Vnz1s5/vg7MYwP2b9/od3lx7XaIl+zZg0jRsRu4Dp48GA2btwYX9azZ0+efvppNE0DIBKJkJUlZ9CF6Cym6WfXrl+yb99zZGePpHfvp1GUOrvLShsH/5Xidg9l//6l7NlzZov53MPhXVRXP4HbfSZwApGIhds9iD17fk1OzsVoWo5N1R/Qaovc5/Ph9R642EDTNCKRCAAOh4Nu3bphWRazZ8/m1FNPpU+fPomrVogMYhhr2Lr1e+zbt4DCwtsoLX0VVXXZXVba8niGAyaNje+3eL2hYQmK4iI39zIURUFRVI4//m4ikd3s2/e8PcV+Q6tB7vV68fv98eemaaLrBxrywWCQ2267Db/fz69//evEVClEBjHNALt3T2Pr1tFEow2Uli6mR49pKIqMTUgkXS8mK+tkGhtXYllRAILBzwkGP8HrHYOqZsfX9XhOx+EoSZqx560G+dChQ1mxYgUAFRUVlJWVxZdZlsVNN91E//79ue++++JdLEKIYxOJVPHVVxdSU/Mw+fk/oV+/VXi9I+0uK2N4PCMwzXoCgfVEo/U0NLyOphWQnX3uYdY9k8bG5Bh73upX/JgxY1i5ciUTJkzAsixmzpzJ/PnzKSkpwTRNPvzwQ0KhEP/+978B+J//+R+GDBmS8MKFSDeBwCds334lkUg1J5zwArm5P7S7pIyTlXUqmtaNffuejb+Wl/cTFMVxyLpu95ns3/8K4XAlDkfvLqzyUK0Guaqq3HfffS1e69u3b/znDRs2dH5VQmSYxsYP2LbtClTVTZ8+f8XtlsaQHRRFJS/vKoLBj9G07uj6cWRlfeuw63o83wFiQxbz8pI8yIUQR6frPkyzod3bqWoOkYgXn+9ttm+/GofjeEpLX8fpPCEBVYq2ysoqIyurrNX1XK7TUBR3U5CP64LKjkyCXIgOMs0Gqqvfavd2hYUXUF//NpWVk8nKOpnS0j+j68UJqFAkgqI4cLuHYRgf2l2KzLUihF3q6l5jx45rcLmGcuKJb0qIpyCP5zsYxnpM07C1DglyIWzg979DZeWdZGefx4knLpabJqcoj+dMIIJhrLW1DglyIbqQZVk0NLxFff2fyc39ASUli1qMTxapJXa1J7YPQ5Q+ciG6SCzEF+P3v4PbfSYlJX/AsmRKi1Sm691xOvvaPpe5tMiF6AKWZbJ//0v4/e/g8ZxLXt5VcqVmmnC7v41hrMayLNtqkCAXIsEsK8K+ff8Pw/gAr/d8cnMvT5rpT0XHud1DiESqiETaP9d7Z5H/TUIkkGVFqKt7hkCggpycy8jJuUimoE0zzTeYMIwK22qQIBciQSwrTF3d0wSDH5Obe4XMmZKmXK6BgEog8JFtNUgnnRAJYFkhamvnEQptIi9vPB7Pd+0uSSSIqnrIyjpZWuRCpJNYiM8lFPqMvLyrJMQzgNs9mECgwrYTnhLkQnQi0wxSW/sUodDn5OVNjN8LUqQ3l6v5hOdOW44vQS5EJzFNH7W1jxIKfUFe3tVNV/2JTOB2DwbsO+EpQS5EJ4hEaqiu/l/C4a8pKJiMx/Ntu0sSXSh2wlPDMOw54SknO4VoEgx+Tn3965imD1DR9e643Wfgcg1CVd2H3cY0g9TUPEV19RxAo3v3m3A6+x523W/SNBNo/9hjVQ23exuRWM0nPAMBe1rkEuQi4/n9K9izZ0b8MmtFcWBZJhBtWkPH5SrD4xmE01mCpuViWWEMYwM+3/uEw7vIyhpIbu5l6Hphm4978B3c26O4eHi7txGJ53YPoaHhb1iW1eXXCkiQi4xlWWGqqn5DdfUfcDhK6dnzdkwzB03LAyAa3U84vJ1QaBvh8Dbq6l7Hsg5MV6qq2TgcJ9Knz10EAqZdb0MkCZdrMPv2PU8k8nWX3/pNglxkpHD4a3bsKMcwVlNQ8B/07DkLXa9vcYMITctD0wY29X/GJr2yrCCW1YhlgaYVoCgKubnfJRBof8tapJfmk9t+/7vk50/o0mNLkIuM4/P9i8rKa7GsIL17/z/y8n7UtKT+qNspioKiuABXwmsUqcflGoSmFeHz/UOCXIhEiUb3U1U1k9raJ8nK+hYnnPBCm+7NKERbKIqK1zsan+/vWFYURdG67Ngy/FCkvUikhpqax/n886HU1j5BQcG19OmzXEJcdDqv9/tEo7VdPnpFWuQiLUUiVTQ0/IOGhqX4fH/DskK43d/huONejs9WJ0Rn83pHAQoNDctwu4d12XElyEXaiEbrqa9fwr59L9LY+C5goetFdOs2gYKCcbjdJzetefix2zI+W3SUrhfidg/B5/sHxcW/7LrjdtmRhEgAy4rg97/Dvn0vUl//BpZl4HT2pbj4p5imC13vhaIo+P1f4fd/ddR9yfhs0Rm83u+zd+/viERq0fVuXXJMCXKRciKRGgzjQxoa/k59/etEo9VoWj75+RPJz78Kt/vbaNruFkMJhegqXu8Y9u79LX7/O+TlXd4lx5QgF0nDNBubZpA7+LHnG893EQ5vB0BRPOTkXEBe3ji83h+gqnIjY2E/t3sYmlbA/v2LJMhFctJ1H6bZ0OI1yzKJRvdhmn5M02h6NGKaASwrhGWFAZVIRMU064lGa4lEaohGY49IZC+RSNUh+22mafnoehG63h2PZxBu9xV4PENwu087aA6U2hbbSH+3sIui6HTv/lOqqmbg9/+b7OwRCT+mBLloM9M08PtXsXfvq0Qiu5oe1USjtUCkzftRFAea1r3p0Q2X63R0vQe6Xtz0b1H8udMZoabm7UP2YRg1GMa/jngM6e8Wdure/afU1T3L7t13ctJJ/0r4mPK0DnLT9De1CE00rVta3vQ21hquIxLZSzS6t6n7IfZvNFqNaQaa7tiuNf1n0lBVD6qai6bFHqqah6rmoGm5gIplGUSjdYRCWwmFthIMbiEU2trUpdE8p4iGrvfA4Tgel+s0NC0fRXGhKM6mRxaK4kBRdBRFp1u3UVhWb1Q1B1X1tvmzUBT77kwuxLFSVTc9etxLZeVk9u37EwUF5Qk9XqtBbpom06dP57PPPsPpdHL//fdTWloaX75o0SJeeukldF3nxhtvZORI+24wGwptpaFhGY2N72EYHxIOV8aXqWoebvfpuN1n4vWOxuM5E0Vx2FbrN5lmkGi0tqmroboplKubArqGaHR/U7dEA6bZ0OLnA+F6MBVd79Z0SbmJZUWb/o1gmgaWFWhTXaqai9PZF7d7KPn5V+J2H09j4x50vahdrQy3uxfRqAL4mh5tI10kIlXl5o7D7X6CPXvuQdO6kZs7NmHHajXIly1bRigUYuHChVRUVPDAAw/w+OOPA7B3714WLFjAq6++SjAYZOLEiZxzzjk4nc6EFBuN7seyIkCUSKSGSGQXweBmDOMjDGMVodCXADgcvXG7z6SgYAqq6kJVwwQCH2MYH1Nd/Qeqq3+Hqnpwuwc2TU3aB4ejGF0vAPSmlqSGquZhmrlNr2lYVrhp0qQwlhVq6gv2NT0amoLVF39YVhDTDDZtE2rxb/PrpukjGq1tmgP7cFR0vaCpxRxrzTocvdC0k1HVbDTNQyBQ1dTSjS1X1VxU1dPUEj+84uKRhEJ+TNNPNNpw0JcCKIobTfPidJ4QnxgqXo0aJhw+tKujNTJlq8g0iqLQq9dj7NgxiR07riIn54f06vUEmpbT6cdqNcjXrFnDiBGxzvrBgwezcePG+LL169czZMgQnE4nTqeTkpISNm3axKBBgzq90IaGv7N9+xXAoTc31fUeuN3D6NbtJrze75OV1XJif1XdRXX1W2RlfZfcXINQaDPB4OeEQtvw+1dzYN7pzqI3dV9kNXUzOFBV50HdDk50PQdV7Y6qetC0AhyOXBobd6Oq2fFA1rQcFMXdSiAPp6rqWGbeC7Nv34dHWNYANNDYeGi3hgSrEG2XlVVG377/prr6j1RX/4FAYAPZ2Z1/M+5Wg9zn8+H1euPPNU0jEomg6zo+n4+cnAPfLtnZ2fh8R/+z2eHQKCpq/zdSUdE4TjrpWOd8zqF79/SdV6Og4NguOZft7D2WbJcc2x3rsdqjuHgaMC1h+2910iyv14vf748/N00TXdcPu8zv97cIdiGEEInXapAPHTqUFStWAFBRUUFZ2YGW7aBBg1izZg3BYJCGhga2bNnSYrkQQojEUyzLOrTT+SDNo1Y2b96MZVnMnDmTFStWUFJSwujRo1m0aBELFy7EsixuuOEGzj///K6qXQghBG0IciGEEMlNbiwhhBApToJcCCFSnAS5EEKkuLQP8nXr1lFenth5DlJJOBzm9ttvZ+LEifz4xz/m7bfbf5VmuolGo9x5551MmDCBq6++mu3bt9tdUtKoqanhvPPOY8uWLXaXkjQuu+wyysvLKS8v584777S7HCDNJ82aO3cuS5Yswe12t75yhliyZAn5+fk8+OCD1NXV8aMf/YjRo0fbXZatli9fDsBLL73EqlWrmDVrVnwaikwWDoeZNm0aLpfL7lKSRjAYBGDBggU2V9JSWrfIS0pKmDNnjt1lJJULLriAn/3sZ/HnmpbY6TVTwfe//31mzJgBwM6dOyksLLS5ouQwe/ZsJkyYQHFxsd2lJI1NmzZhGAaTJ09m0qRJVFRU2F0SkOZBfv7558evQhUx2dnZeL1efD4ft9xyCz//+c/tLikp6LrOL3/5S2bMmCHXQgCvvfYa3bp1i8+zJGJcLhdTpkxh3rx53Hvvvdx2221EIm2fiz9R0jrIxeHt2rWLSZMmcemll/LDH/7Q7nKSxuzZs/nb3/7GPffcQ2Njo93l2OrVV1/lvffeo7y8nE8//ZRf/vKX7N271+6ybNenTx8uueQSFEWhT58+5OfnJ8XvRZqrGaa6uprJkyczbdo0zj77bLvLSQqLFy9mz5493HDDDbjdbhRFyfgupxdeeCH+c3l5OdOnT6eoqMjGipLDK6+8wubNm5k+fTp79uzB5/Mlxe9FWuQZ5oknnqC+vp7HHnssfuY9EGjbTSbS1Q9+8AM++eQTrr76aqZMmcJdd91FVpbcyFkc6sc//jENDQ1cddVVTJ06lZkzZyZF961coi+EEClOWuRCCJHiJMiFECLFSZALIUSKkyAXQogUJ0EuhBApToJcpKVVq1Zx9tlnx4dYXn755dxyyy2EQqHDrr9z507++c9/AvCb3/yGnTt3dmW5QnSIBLlIW2eddRYLFixgwYIFvPbaazgcjnhYf9MHH3zARx99BMCvfvUrjj/++K4sVYgOsX8kuxBdIBQKUVVVRV5eHr/61a/YvXs3dXV1nHvuudx888089dRTBAIBhgwZwrPPPsv06dNZunQplZWV1NTUsHPnTu68805GjBjB8uXLeeSRR/B6veTl5dG/f39uvvlmu9+iyGAS5CJtffDBB5SXl1NTU4Oqqlx55ZWccMIJDB48mCuuuIJgMMi5557Lz3/+c/7zP/+TrVu3Mnr0aJ599tn4PpxOJ08//TQrV67kmWee4bvf/S73338/CxcupLCwkFtvvdW+NyhEEwlykbbOOuss/vCHP1BXV8fkyZPp3bs3+fn5bNiwgQ8++ACv13vEPvNmp5xyCgA9e/YkFApRW1uL1+uNT3V7xhlnUF1dnfD3IsTRSB+5SHsFBQU8+OCD3H333Tz77LPk5OTw+9//nsmTJxMIBLAsC1VVMU3zkG0VRWnxvHv37vj9fmpra4HYHaiEsJu0yEVG6NevX3xK1i+//JI1a9bgdrspLS2lqqqKsrIyHn/8cQYMGHDU/aiqyj333MP1119PTk4OpmlSWlraRe9CiMOTSbOEaKcnn3ySa6+9FqfTyW233cbw4cO57LLL7C5LZDBpkQvRTtnZ2Vx55ZW4XC569erFRRddZHdJIsNJi1wIIVKcnOwUQogUJ0EuhBApToJcCCFSnAS5EEKkOAlyIYRIcf8fWsmvUBzF8EoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check all the styling options\n",
    "sns.set_style(\"dark\")\n",
    "sns.distplot(inp1.Rating,bins=20,color=\"y\")\n",
    "plt.title(\"Distribution of Ratings\",fontsize=15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "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(\"dark\")\n",
    "sns.distplot(inp1.Rating,bins=20,color=\"y\")\n",
    "plt.title(\"Distribution of Ratings\",fontsize=15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'tableau-colorblind10',\n",
       " '_classic_test']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"grayscale\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(inp1.Rating,bins=20,color=\"y\")\n",
    "plt.title(\"Distribution of Ratings\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"default\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Change the number of bins to 20\n",
    "sns.distplot(inp1.Rating,bins=20,color=\"yellow\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pie-Chart and Bar Chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For analysing how a numeric variable changes across several categories of a categorical variable you utilise either a pie chart or a box plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, if you want to visualise the responses of a marketing campaign, you can use the following views:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![PieChart](images\\pie.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![barChart](images\\bar.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- You'll be using the pandas method of plotting both a pie chart and a bar chart. Check out their official documentations:\n",
    "   - https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.bar.html\n",
    "   - https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.pie.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Everyone           6898\n",
       "Teen                918\n",
       "Mature 17+          417\n",
       "Everyone 10+        336\n",
       "Adults only 18+       3\n",
       "Unrated               1\n",
       "Name: Content Rating, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analyse the Content Rating column\n",
    "inp1[\"Content Rating\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8569, 13)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove the rows with values which are less represented \n",
    "inp1[inp1[\"Content Rating\"].isin([\"Unrated\",\"Adults only 18+\"])]\n",
    "inp1=inp1[-inp1[\"Content Rating\"].isin([\"Unrated\",\"Adults only 18+\"])]\n",
    "inp1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset the index\n",
    "inp1.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8569 entries, 0 to 8568\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             8569 non-null   object \n",
      " 1   Category        8569 non-null   object \n",
      " 2   Rating          8569 non-null   float64\n",
      " 3   Reviews         8569 non-null   int32  \n",
      " 4   Size            8569 non-null   float64\n",
      " 5   Installs        8569 non-null   int32  \n",
      " 6   Type            8569 non-null   object \n",
      " 7   Price           8569 non-null   float64\n",
      " 8   Content Rating  8569 non-null   object \n",
      " 9   Genres          8569 non-null   object \n",
      " 10  Last Updated    8569 non-null   object \n",
      " 11  Current Ver     8569 non-null   object \n",
      " 12  Android Ver     8569 non-null   object \n",
      "dtypes: float64(3), int32(2), object(8)\n",
      "memory usage: 803.5+ KB\n"
     ]
    }
   ],
   "source": [
    "inp1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Everyone        6898\n",
       "Teen             918\n",
       "Mature 17+       417\n",
       "Everyone 10+     336\n",
       "Name: Content Rating, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the apps belonging to different categories of Content Rating \n",
    "inp1[\"Content Rating\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a pie chart\n",
    "inp1[\"Content Rating\"].value_counts().plot.pie()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Plot a bar chart\n",
    "inp1[\"Content Rating\"].value_counts().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "inp1[\"Content Rating\"].value_counts().plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type',\n",
       "       'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver',\n",
       "       'Android Ver'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Question - Plot a bar plot for checking the 4th highest Android version type\n",
    "inp1[\"Android Ver\"].value_counts().plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatterplots are perhaps one of the most commonly used as well one of the most powerful visualisations you can use in the field of machine learning. They are pretty crucial in revealing relationships between the data points and you can generally deduce some sort of trends in the data with the help of a scatter plot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "![Scatterplot](images\\scatter.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- They're pretty useful in regression problems to check whether a linear trend exists in the data or not. For example, in the image below, creating a linear model in the first case makes far more sense since a clear straight line trend is visible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Scatterplot-Reg](images\\regression3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Also, they help in observing __naturally occuring clusters__. In the following image, the marks of students in Maths and Biology has been plotted.You can clearly group the students to 4 clusters now. Cluster 1 are students who score very well in Biology but very poorly in Maths, Cluster 2 are students who score equally well in both the subjects and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Scatter-Clusters](images\\Clusters.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: You'll be studying about both Regression and Clustering in greater detail in the machine learning modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll be using **sns.jointplot()** for creating a scatter plot. Check out its documentation:\n",
    "https://seaborn.pydata.org/generated/seaborn.jointplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "###Size vs Rating\n",
    "\n",
    "##Plot a scatter-plot in the matplotlib way between Size and Rating\n",
    "plt.scatter(inp1.Size,inp1.Rating)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b557956e48>"
      ]
     },
     "execution_count": 92,
     "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": [
    "### Plot the same thing now using a jointplot\n",
    "import scipy.stats as stats\n",
    "sns.set_style()\n",
    "sns.scatterplot(inp1.Size,inp1.Rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1b557a67488>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot a jointplot for Price and Rating\n",
    "\n",
    "sns.jointplot(inp1.Size,inp1.Rating, stat_func = stats.pearsonr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " w=sns.jointplot(inp1.Size,inp1.Rating, \n",
    "                  kind=\"kde\",color=\"g\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(inp1.Price,inp1.Rating)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1b557ae4248>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r9zKq7eW8TNgYozfFYlQd0iEPk7AEu5mXGGe7bgIcdvkRZv/v06QbtpcdyIPuvm3r3IPu6k6RFvlu5uVyFCh2M48jt+IE5MZzTbphe9mBPOju27bOPeiu7kRxwwJVBUzix152IA867mzr3IPu6k4UNyxQVcAkfuxlB/Kg4862zj3oru5EccMCFXHdCrzXZSeiTOLHXnYgDzrubOvcg+7qThQ3sS5QXnYzL+S+meNyYr1uLp8wrGSEHEBeVNytm7lzffd+Y1zJeK5J/NjLDuRBx51tnXvQXd2J4oZX1C0iIZITD1616S85l1I/rnePvA7gbtzekL7XsCGnN53beULbWvfmhB2+NPwY1xixV128C3VKdzKJYTe915YXm/cz/lx3Qt9MlP/4o3ui7gT3EzyD7vZtuk2JKF+s96C62s3c2SE8uzgBwId7Dhg1s3Q+v0m3b1u7O5t0zDbpsB70nEzHRbkjOFHcxLpA2cqk27et3Z2Djqx7NSfTcUFH5ImoMBYoKkvQkXWv5mQ6jh3BiezBAkVlCTqy7tWcTMexIziRPVigQmDS7dvW7s5BR9a9mpPpuCh3BCey3TfHD8vcTMS6QHU1Zu6MBx/Xu0fO94/r3cOoYDif36Tbt63dnYOOrHs1J9NxUe4IThQ3jJkX4Uyh1dZ0wzFH1qD904OZiPITTaUvv9ywriXvDW6PIyjhvA8Au/Z8VvR+mjPx51bI3OLVACqKXAcdITdh2sXbZFyUO4JHWdCd370U5bnbjN3MS4zzyn0zx2Xe9E685Xdw2+rZ13uaeO9qbN21L2/MyAG9sGrueZn7bnF0oHQ385puAghwMOtyHiZdu006dLOLN5Uryr8zHs29KrqZFzm0x27mYcqOKRf6L0H2crfi5LbcJI7uFp0+eFhzihNgFqc2iZCzizeVK8q/M1Geu+14iC8gYcaUy1l3qbEmEXJ28aZyRfl3Jsi59+3VwzhgEAfcgwpImDHlctZdaqxJhJxdvKlcUf6difLcbccCFZDsmHKhX9vs5SMH9HId41xuEkd3i07XdBPUOJrRmsSpTSLk7OJN5Yry70yU5267WBcor7uZp1N82V3JTWLm2QEJIBmEcBap7IAEAKyae15eMXIGJACzOLpbdHrBjLFYMH1s2XFqkwg5u3hTuaL8OxPluduOn0GlHNe7R14z2HQ388GpCHbTe214rPH9nHj1jLphaN7dkYlqH+rszHmedJGpn/9CTpz7P2aOy4l4n39K/5wx55/SPy8Q8eHH+13nXum5UZXGqU06dHvZxTvoCC8jw+GIcud30276VB7GzAGMv3tVXnFySnQTdB7O31aFlhdTkxBAk0m6crldlqMUt5i5SaTcBkHHj6Mcd6ZwePT3ZfSBVTnvfxHj+vq5BwWULE4AChahcosTgLx4dzkKdUIvpliHbtsLVLEIrx8FI+j1UfQF+ffVtu8AHm0s3RwgaH4lC2P9GRQlRblDd9Dx4yjHnSkcUf77sh0LVBWIcofuoCO8jAxTuaL892U7Figgrwmsm0S3Am9cBZYXU5OQZKuhChTqhF5MlDt0Bx3hZWSYyhXlvy/bxbpAmcbMG2+b6Fqk0v9rHtynFj+aMdY1SvqjGblRbefzjBzQC/fNHJcb8Z4+Fgscj7t8wrC8+06VBCSAaHfoDjrCy8gwlSvKf1+28z3FJyIJAE0AWlT1Isf3jgDwCIAvAdgNYKaqNhd7vhinWIioekW6WawHIYnQUnzfBfAWgKNcvncNgL+q6ggRuQzADwDM9HLlblFztz2rMd9fmZOQ65kQ9OvdM+fcpZ+9uDXn3CS3E2dNz6ExGcfzccLB7U6UK6z+f74e4hORIQCmAvh5gSGXAFiS+vpJAF8R8e7T6ELnQTmXO4sTAOzvVLS0d0ABtLR34KZl6/NOnN26ax8m3rs6cz99Do3zOlK3N2zIeZzJONPnIm9xuxPZw+/PoO4D8L8AHC7w/cEA3gcAVT0E4GMA/XyeU55Kzi1Kyy5apm33eckKe3G7E9nDtwIlIhcB2KWqfyw2zGVZ3odiInKdiDSJSFNra6tnc/Sa6Tk0vGSFvbjdyTbZ73972t2v/xZXfu5B1QO4WESaATwO4AIR+ZVjzA4AQwFARLoDOBpA3k9AVReqap2q1vXv39/HKXeN6Tk0vGSFvbjdyTbZ73+9+1RXjz/fQhKqeiuAWwFARM4D8D9V9XLHsGcAXAVgDYDpAF7QEJoDHnVEouLDfNkdx2eNH+rax815Do3JONPnIm9xu1O1sfkCiIGfByUid4rIxam7iwD0E5G3AcwFcIuX6zI9D+rNOybnnQDbMyE55zXcN3NcyctfmJ5Dw0tW2IvbncgegTSLVdXVAFanvv5e1vL9AGYEMYdS7pw2OufyF/Mmjco70e6Jpu05oYgBvY/Ie57Gd3fnJMAea9yOpWu35z2nyaUFnM/V+O5u13EN61pKzt1LQa8vaLx0ApEdYt1JwjRmnm6Xnx0rv3X5BjSsa8mMmf3wGrzyTu7HY6+804bZD6/J3J947+q8KHqnouBzFuP2XM5Yu+ncvRT0+oIW99dHFCWxLlCmirXLT3MWJ7flzoLi5HzOYgo9l3O5ydy9FPT6ghb310cUJSxQCLZdvtfPGXSr/7hfWiDur48oSnjBQiTb4re4vAH50S7f6+cMcu5hrC9ocX99FG19e/WwOnXnNe5Bwaxdfv3J7h+UZy93pvycymnBX+i5nMuDbvUf90sLxP31EUVJrAuUaczcpF3+0mvPzitS9Sf3xdJrz87cXzX3vLwCkhBU1ILf7bncmtMG3eo/7pcWiPvrI4oSHuJLaXqvDX/5eD8UwF8+3o+m99ry3pRm1A1D8+6OTLx6Rt2wvMj1oc7cD9hP6u/e8Xzp2u2Znk69eiRw99c+fxNMP2dLewcSIuhUxeA+tbjh/JEVvz4vo+HTzhhc8rFRjqKbvD5TUd4ORGHz/XpQXivnelCFYuZA7l5UuoO1U/YJmun4cXbCqyYhgAIHDxffhtl7PoXWlegm+NGMsQCQt5602ppE3v/m3eblHGcyxktBr89W3A5UBqNeWjG+Hp7r64/1IT5TJh2s3eLHBzu1ZHECzDqedx5WLHhui+t60tziziaxaEbRw8HtQNQ1PMQHsw7WXsWMi3XFNlmHc4xJLJpR9HBwO5DX2vYdwKON+UdgyhGlFCD3oGDWwdqrmHGxrtiD+tSWXI/z+4XGZy83GeOloNdnK24Hoq5hgULhTtXZy93ixzUJQU230oeOnR3P3SS6CeZNGuW6njS3uLNJLJpR9HBwOxB1TawP8TXPn+oalHDGzNNBiMca30enKhIimDV+aE4z1/SH2s5ElnPZoc5OfLjnQOZxbh3PARRN8aWf05nic0uAFZpX9jiTMV4Ken224nYg6ppYF6hybGvdm9M5fFvr3oqe56jampwC5dbxvFQn83TM+faGDZlQRaHoe/b4YiqNTlcak/Yyqh1l3A5ElWPMHO6dyoHcE3FdY+bdkmfhHuwsvg2dJ/SaMIm++40xaaLAGMXMTzp1jN61+LdlPXFEQhGMmRdi0qncNWZ+WEsWp2LPX4xJ9N1vjEkTUZhYoAwFHQ02ib77jTFpIgoTC5ShoKPBJtF3vzEmTURhYoGCWady15h5N0m2O6rw+Ysxib77jTFpIgpTrAuUaTdzk07lbl2uF8wYiwXTx+Ysc3YgryQgASSTfpdPGJbZY0qIBBqQANjZm4jCxZh5irOINKxrQf38F/Li1c4359sbNuR0QZ9w0jH49MDhnI7nbkzi26Xi6EFwe81edej2stM3u4YTfS4iyb2SYl2gCsXMh9/yu4J7V0B+vLqlvQO3Lt8AADlves4oeKdqTmKv0ONMn99GXs3dy20Q5e1JRIXF+hBfpUzj1SaR70o7kNvKq7l7uQ2ivD2JqDAWKBem8WrTyHclHcht5dXcvdwGUd6eRFQYC5QL03i1aeS7kg7ktvJq7l5ugyhvTyIqjAXKhWm82iTyXWkHclt5NXcvt0GUtycRFRbrAmUaM3cyjVe7RcHrT+5b8nFRjm97NXcvt0GUtyeR1+KS4AN8TPGJSE8ALwE4IrWeJ1X1+44xcwAsANCSWnS/qv7crzkV42wY6zyfqRBnFDwddy7FpMt10NFp0/V51aHby07f7BqexLg9xYmfMfPPAFygqntFpAbAyyLyrKqudYxbpqr/5McETGPmbt3Mt+7al/naNLYc5eg0o9rRx58hxY1vh/g0KX1RpZrUzcpre5h0GzeJLUc5Os2odvTxZ0hx4+tnUCKSEJH1AHYBWKWqjS7Dvi4ib4rIkyLimjoQketEpElEmlpbW/2cclGlYstRjk4zqh19/BnGU/b735728i/dE2W+FihV7VTVcQCGADhLRE53DPm/AIar6hgAzwNYUuB5FqpqnarW9e/f388pF1Uqthzl6DSj2tHHn2E8Zb//9e5TuPH0N8cPi1VAAggoxaeq7QBWA5jsWL5bVT9L3X0YwJeCmI+TSbdxk9hylKPTjGpHH3+GFDe+FSgR6S8ifVJf1wL4OwCbHWMGZt29GMBbXs6hK93MRw7oVXZsOcrRaUa1o48/Q4obP1N8AwEsEZEEkoXw16r6WxG5E0CTqj4D4EYRuRjAIQBtAOb4OJ+iZtQNQ/Pujkw894bzRxp18QaQs2x4v9qc7uZN77UZv0G4Pf8rt1xQ9mupNGocdFQ7KpHoqMwTYNw+LLc3bMBjje+jUxUJEcwaPzT0KxHEgWiAlxD3Ql1dnTY1NRmNLRQzB3L3opzxXCB5aCT7f59uY2q6CSDAwc7i29DkOk4mczDh1fP4jfOkuHBe1SCtzOu3GfVNO+nUMXrX4t+6fi/inz+5vv5Yd5IwZRLPdRtz8LCWLE6AWddzG7uE+4nzpLgo9Pdt8ndPxcX6elCmTOK5XYnqmnQ9t7FLuJ84T4qLQn/fplc7KEffXj2ivqdUFu5BwSye25WorknXcxu7hPuJ86S4KPT3bXq1AyqMBQpm8Vy3MTXdBDWJ0r+EJl3PbewS7ifOk+Ki0N+3yd89FRfrQ3zN86e6BiWcMfP0h93FklqFxjiXDe9Xi7Xv/rXsNI/JHEx49Tx+4zwpLtJ/30zxeS/WBaoclcZzvYr1Foqw189/oeQbo1fxdC+ZRLOjEomOyjwpPM6rGpA3GDM35Gfc2DTC7rY+G2PQNs6JyHJGH1iV8/4XMYyZd4WfcWPTCLvb+myMQds4J6I4aNt3AI82bs+7xRULlCE/48blPIdzrI0xaBvnRETRwwJlyM+4cTnP4RxrYwzaxjkRUfSwQBnyM25sGmF3W5+NMWgb50RE0RPrAmXazdyEn52i3Z57wYyxWDB9bMn12djB2sY5EVH0MGaeEnQs2jQabmMXchM2zomIoiXWBapQzHz4Lb8r2s28pb0Dty7fAMCsQJQr6PURUTzFvS9frA/xmQo6Fs0YNhFRaSxQCD4WzRg2EVFpLFAIPhbNGDYRUWlGn0GJyE9cFn+M5KXbn/Z2SsGbN2mUa2sev2LRQa+PiCiKTPegegIYB2Br6jYGQF8A14jIfT7NrctMY+ZBx6IZwyYiKs00xTcCwAWqeggARORBAP8FYCKADT7NzROm5zwFHYtmDJuIqDjTAjUYQC8kD+sh9fUgVe0Ukc98mZlHTK4HBQC3N2woeT0Xk3OlqgG3AxEFwfQQ3w8BrBeR/xSRxQDWAfjfItILwPN+Ta6rip0Hle32hg341drt6ExdeqRTFb9aux23N3y+c5g+d6mlvQOKz89daljX4tv8bcTtQERBMSpQqroIwP8HoCF1O0dVf66q+1R1np8TDMJjje+XXM5zl5K4HYgoKOXEzLsBaAXQBmCEiJzrz5SC11ngoo3Zy3nuUhK3AxEFxTRm/gMAMwFsAnA4tVgBvOTTvAKVEHEtUgn5vJv4oD61aHF5E662c5e4HYjs8Wjj9li3OzLdg5oGYJSqTlXVr6ZuF/s5sSDNGj+05HJeQiKJ24GIgmJaoN4FUFPOE4tITxF5VUTeEJFNInKHy5gjRGSZiLwtIo0iMrycdZRieh7UXdNG4/IJwzJ7TAkRXD5hWE6Kj+cuJXE7EFFQTGPmnyKZ4vs9gEysXFVvLPKYz5A8d2qviOTBfggAAB2ISURBVNQAeFlEnlXVtVljrgHwV1UdISKXAUgfSgxc3Ql98eLmVnzQ3oHjj+6JuhP6VvQ8sx9eg1feacvcrz+5L2bUDasolm0SfQ8Dz+EioiCYFqhnUjdjqqoA9qbu1qRuzg96LgHwb6mvnwRwv4hI6rFd5uXlNkzGOIsTALzyThv+3zttmRduemmNdPQ9LR19B2BFkSIi8ptpzHyJ263U40QkISLrAewCsEpVGx1DBgN4P7WOQ0ieCNyvvJfQdSbRaZMxzuKU5qy2JrFsk+g7EVGcFd2DEpFfq+o3RGQD8t9noapjij1eVTsBjBORPgCeEpHTVXVj9ircHuYyj+sAXAcAw4Z5n1gxiU57Ha8u9TiT6DsRxZ/z/S/OqT2nUof4vpv696KurERV20VkNYDJALIL1A4AQwHsEJHuAI5G8jwr5+MXAlgIAHV1dZ6/Q5tEp72OV5d6nEn0nYjiz+/3P5sVPcSnqjtTX/6jqr6XfQPwj8UeKyL9U3tOEJFaAH8HYLNj2DMArkp9PR3AC159/lQOk+i0yZj6k92DFc6SYhLLNom+ExHFmWnMfKLLsiklHjMQwIsi8iaA15D8DOq3InKniKTPoVoEoJ+IvA1gLoBbDOdjxMvLbZiMWXrt2XlFqv7kvviPmePKjmWbRN+JiOKs1GdQ1yO5p3RSqtCk9QbwSrHHquqbAM5wWf69rK/3A5hRzoT9YhKdNhmz9NqzCz62XNta9+Y0sN3WurfEIyiK2B2eyF2pPahHAXwVyUNxX826fUlVL/d5bl1m2s3cRoUi67MfXhPSjMgP7A5P5WjbdwCPNm7Ho43bSw+OgVKfQX2sqs2qOiv1uVMHkim7L4hI9URJQlAosl5oOUUTu8MTFWb0GZSIfFVEtgLYBuAPAJoBPOvjvIiqArvDExVmGpK4C8AEAH9W1RMBfAUlPoMiotIKnW7A7vBE5gXqoKruBtBNRLqp6osAxvk4r6pXKLJeaDlFE7vDExVmWqDaReQLSF7/aamI/BjAIf+m5Q3TmLmNCkXWC6UEKZrYHZ6oMNNmsZcgGZD4ZwCzkez4cKdfkwqDjZ3DZ9QNQ/Pujkz8eEYdcylxxO7wRO5Mm8XuU9XDqnoo1ST2Z0i2LbKaacw83Tk8+5yjX63djtsbNvg+x0IYPyaiale0QInIUSJyq4jcLyJ/L0n/hOQFDL8RzBT9Z2PncMaPiajalTrE90sAfwWwBsD/ADAPQA8Al6jqep/nFhgbO4czfkxE1a5UgTpJVUcDgIj8HMBHAIap6h7fZxYgGzuHe909nYgoakp9BnUw/UXq2k7b4lacADs7hzN+TETVrlSBGisin6RuewCMSX8tIp8EMcGuMI2Z29g5nPFjIiqkWi5aWPQQn6omin0/Tu6aNtqTgsTO1ERE3jA9UTeSgu5m7mU0nDFzIqp2sS5QQfMyGs6YORFVOxYoD3kZDWfMnIiqHQuUh7zsTM0u10RU7VigPORlNJwxcyKqdrEuUEF3M/cyGs6YORFVO9Nu5mTIy87UXj5X0PF3xu2JqKtivQcVdMzcVkFH1hmRJyIvxLpAUVLQkXVG5InICyxQVSDoyDoj8kT+6NurR9W0OQJYoKpC0JF1RuSJyAssUFUg6Mg6I/JE5IVYF6igY+a2Cjqyzog8EXnBt5i5iAwF8AiA4wEcBrBQVX/sGHMegKcBbEstWq6qd/o1J5sEHcP2MrJu4/qIKH783IM6BOBfVPVUABMA3CAiX3QZ99+qOi5187Q42RozZwybiKg03wqUqu5U1ddTX+8B8BYA/pcajGETUWXa9h3Ao43bw55GYAL5DEpEhgM4A0Cjy7fPFpE3RORZETmtwOOvE5EmEWlqbW31cabBYAybiExlv//taW8LezqB8r1AicgXAPwGwE2q6rxM/OsATlDVsQB+CqDB7TlUdaGq1qlqXf/+/f2dcAAYwyYiU9nvf7379A17OoHytUCJSA2SxWmpqi53fl9VP1HVvamvVwCoEZFj/ZyTDRjDJiIqzbcCJSICYBGAt1T13gJjjk+Ng4iclZrPbq/mYGvMnDFsIqLS/OxmXg/gCgAbRGR9atm/AhgGAKr6EIDpAK4XkUMAOgBcpqrq45y6hB26iShs1dTqSCyuB67q6uq0qanJaGyxOHm5e1HpaHh2+q62JlHRno+Xz0VEsSAmg046dYy++9abfs8lDK6vP9adJLzkZTScMXMiotJYoAx5GQ1nzJyIqDQWKENeRsMZMyciKo0FypCX0XDGzImISot1gfIyZu5lNJwxcyKi0vyMmUeKM0J+/in98eLm1rxIuVdFhN2+iYiKi/UelGk3c7fu4r9au53dxomIQhTrAmXKLfbtxBg4EVGwWKBgHu9mDJyIKDgsUDCPdzMGTkQUHBYouMe+nRgDJyIKVqwLlGnM3C32ffmEYYyBExGFKPYxc9Nznhj7JiKyS+wLlFvUPOzrQRERUWmxPsRneh4UERHZJ9YFioiIoosFiogoIvr26hH2FALFAkVERFZigSIiIivFukB5ebkNIiIKVqwLFAB0l+L3iYjITrEuUCNu/R0Oae6yQ5pcTkQUNW37DoQ9hUDFukA5i1Op5UREZI9YFygiIoouFigiIrJSrAtUoUAEgxJERPaLdYF6+56prim+t+9hzJyIyHa+dTMXkaEAHgFwPIDDABaq6o8dYwTAjwFcCOBTAHNU9XUv5+GW4rNVw7oWLHhuCz5o78CgPrWYN2kULwFCRFXLzz2oQwD+RVVPBTABwA0i8kXHmCkARqZu1wF40MsJRKmbecO6Fty6fANa2jugAFraO3Dr8g1oWNcS9tSIiELhW4FS1Z3pvSFV3QPgLQDO3YFLADyiSWsB9BGRgX7NyWYLntuCjoOdOcs6DnZiwXNbQpoREVG4AvkMSkSGAzgDQKPjW4MBvJ91fwfyixhE5DoRaRKRptbWVr+mGaoP2jvKWk5E1SH7/W9Pe1vY0wmU7wVKRL4A4DcAblLVT5zfdnlI3qdEqrpQVetUta5///5+TDN0g/rUlrWciKpD9vtf7z59w55OoHwtUCJSg2RxWqqqy12G7AAwNOv+EAAf+DknW82bNAq1NYmcZbU1CcybNCqkGRERhcu3ApVK6C0C8Jaq3ltg2DMArpSkCQA+VtWdXs0hSt3Mp50xGPdcOhqD+9RCAAzuU4t7Lh3NFB8RVS3fYuYA6gFcAWCDiKxPLftXAMMAQFUfArACyYj520jGzP/Bx/lYb9oZg1mQiIhSfCtQqvoy3D9jyh6jAG7waw7FYuY27kUREdHnYt1JgoiIoosFioiIrMQCRUREVmKBIiIiK8W6QEUpZk5ERLn8jJlboWdCsL9Tc+6bYGdxIqJwxXoP6pTbVuQUJwDY36k45bYVRR/HzuJEROGLdYFyFqdSy9PYWZyIKHyxLlCVYmdxIrJR3149wp5CoFigXLCzOBFR+GJdoAoFIkoFJdhZnIgofLEuUJvvvjCvGPVMCDbffWHRx7GzOBFR+GIfM6+UDZ3FGXUnomoW6z2oSmPmNmDUnYiqXawLVKUxcxsw6k5ETm37DoQ9hUDFukBFGaPuRFTtWKAsxag7EVW7WBeoSmPmNmDUnYiqXawLVKUxcxsw6k5E1S72MfOh/Y7E1l37cu5HhQ1RdyKisMR6D2rivatzihMAbN21DxPvXR3OhIiIyFisC5SzOJVaTkRE9oh1gSIiouhigSIiIivFukCNHNCrrOVERGSPWBeoVXPPyytGIwf0wqq554UzISIiMuZbgRKRX4jILhHZWOD754nIxyKyPnX7nh/zGND7iKL3iYjITn7uQS0GMLnEmP9W1XGp251eT2D2w2vwyjttOcteeacNsx9e4/WqiIjIY74VKFV9CUBbyYE+chanUsuJiMgeYX8GdbaIvCEiz4rIaYUGich1ItIkIk2tra1Bzo+IKFTZ73972qvrP9dhFqjXAZygqmMB/BRAQ6GBqrpQVetUta5///6BTZCIKGzZ73+9+/QNezqBCq1Aqeonqro39fUKADUicqyX66g/2f2HWWg5ERHZI7QCJSLHi4ikvj4rNZfdXq5j6bVn5xWj+pP7Yum1Z3u5GiIi8oFv3cxF5DEA5wE4VkR2APg+gBoAUNWHAEwHcL2IHALQAeAyVbX/WuxERBQI3wqUqs4q8f37Adzv1/qB4jFz7kUREdkt7BSfrxgzJyKKrlgXKCKiOOnbq0fYUwgUCxQREVkp1gWKMXMiouiKdYFizJyIKLp8S/HZgsWIiCiaYr0HRURE0cUCRUREVmKBIiIiK7FAERGRlVigiIjISixQRERkJRYoIiKyUuzPg2pY14IFz23BB+0dGNSnFvMmjcK0MwaHPS0iIioh1gWqYV0Lbl2+AR0HOwEALe0duHX5BgBgkSIislysD/EteG5LpjildRzsxILntoQ0IyIiMhXrAvVBe0dZy4mIyB6xLlCD+tSWtZyIiOwR6wI1b9Io1NYkcpbV1iQwb9KokGZERESmYh2SSAchmOIjIoqeWBcoIFmkWJCIiKIn1of4iIgoumK/B0XRcfDgQezYsQP79+8PeypEvujZsyeGDBmCmpqasKcSCSxQZI0dO3agd+/eGD58OEQk7OkQeUpVsXv3buzYsQMnnnhi2NOJBB7iI2vs378f/fr1Y3GiWBIR9OvXr0tHCNr2HfBwRvZjgSKrsDhRnPH3uzwsUEREZCXfCpSI/EJEdonIxgLfFxH5iYi8LSJvisjf+jGP4bf8Lu9momFdC+rnv4ATb/kd6ue/gIZ1Lb4+jihoK1euxKhRozBixAjMnz/fdcxnn32GmTNnYsSIERg/fjyam5sBAK+++irGjRuHcePGYezYsXjqqacyj7n66qsxYMAAnH766UG8DFeqihtvvBEjRozAmDFj8Prrr7uO++Mf/4jRo0djxIgRuPHGG6GqAIC2tjZMnDgRI0eOxMSJE/HXv/4153GvvfYaEokEnnzySQDA+vXrcfbZZ+O0007DmDFjsGzZMn9fYJXwcw9qMYDJRb4/BcDI1O06AA96PYFCxahUkUp3QW9p74Di8y7opYpNpY+jeDt06JAvz9vZ2Vl6UJHH3nDDDXj22Wfxpz/9CY899hj+9Kc/5Y1btGgRjjnmGLz99tv453/+Z9x8880AgNNPPx1NTU1Yv349Vq5ciW9961uZ1zlnzhysXLmy4rl54dlnn8XWrVuxdetWLFy4ENdff73ruOuvvx4LFy7MjE3Pe/78+fjKV76CrVu34itf+UpOAe/s7MTNN9+MSZMmZZYdeeSReOSRR7Bp0yasXLkSN910E9rb2/19kVXAtwKlqi8BaCsy5BIAj2jSWgB9RGSgX/MpR6Vd0Nk9Pdqam5txyimn4KqrrsKYMWMwffp0fPrppwCS/9P+8pe/jC996UuYNGkSdu7cCQB4+OGHceaZZ2Ls2LH4+te/nhk/Z84czJ07F+effz5uvvlm/OEPf8jscZxxxhnYs2cPVBXz5s3D6aefjtGjR2f+17169Wqcd955mD59Ok455RTMnj078z/74cOH484778Q555yDJ554ouLX+uqrr2LEiBE46aST0KNHD1x22WV4+umn88Y9/fTTuOqqqwAA06dPx+9//3uoKo488kh0754MAe/fvz/ns5Vzzz0Xffv2zXuuhx56CA899FDe8sWLF+OSSy7B5MmTMWrUKNxxxx0Vv67seV955ZUQEUyYMAHt7e2Zn1nazp078cknn+Dss8+GiODKK69EQ0ND3uu+6qqrMssB4Kc//Sm+/vWvY8CAAZllf/M3f4ORI0cCAAYNGoQBAwagtbW1y6+j2oUZMx8M4P2s+ztSy3Y6B4rIdUjuZWHYsGG+T6zSLujsnh59W7ZswaJFi1BfX4+rr74aDzzwAL773e/iO9/5Dp5++mn0798fy5Ytw2233YZf/OIXuPTSS3HttdcCAG6//XYsWrQI3/nOdwAAf/7zn/H8888jkUjgq1/9Kn72s5+hvr4ee/fuRc+ePbF8+XKsX78eb7zxBj766COceeaZOPfccwEA69atw6ZNmzBo0CDU19fjlVdewTnnnAMgeS7Nyy+/nDf3pUuXYsGCBXnLR4wYkTkUldbS0oKhQ4dm7g8ZMgSNjY15j80e1717dxx99NHYvXs3jj32WDQ2NuLqq6/Ge++9h1/+8peZglXIt7/97YLfe/XVV7Fx40YceeSROPPMMzF16lTU1dXljJk5cya2bMn/z97cuXNx5ZVXlnx9LS0tGDhwYM6YIUOG5I0BgA8//DAzduDAgdi1a1fmMU899RReeOEFvPbaawVfy4EDB3DyyScXfL3lCPr9zyZhFii3OIu6DVTVhQAWAkBdXZ3rGC8N6lOLFpeiUqoLeqWPI3sMHToU9fX1AIDLL78cP/nJTzB58mRs3LgREydOBJA8xJN+89q4cSNuv/12tLe3Y+/evTmHfWbMmIFEItmsuL6+HnPnzsXs2bNx6aWXYsiQIXj55Zcxa9YsJBIJHHfccfjyl7+M1157DUcddRTOOuuszJvnuHHj0NzcnClQM2fOdJ377NmzMXv2bKPXmd4jy+aWMCs2bvz48di0aRPeeustXHXVVZgyZQp69uxptH6niRMnol+/fgCASy+9FC+//HJegSrncx2T12e6DbLddNNN+MEPfpD5uTrt3LkTV1xxBZYsWYJu3bw5QBX0+59NwixQOwAMzbo/BMAHIc0lx7xJo3KuxAuYdUGv9HFkD+cblIhAVXHaaadhzZo1eePnzJmDhoYGjB07FosXL8bq1asz3+vVq1fm61tuuQVTp07FihUrMGHCBDz//POub5BpRxxxRObrRCKR8zlW9vNmK2cPasiQIXj//c8PYOzYsQODBg3Ke2x63JAhQ3Do0CF8/PHHeYfvTj31VPTq1QsbN27MKyqm3La7Uzl7UCavb8iQIdixY4frmOOOOw47d+7EwIEDsXPnzszhvKamJlx22WUAgI8++ggrVqxA9+7dMW3aNHzyySeYOnUq7rrrLkyYMKGcl08FhBkzfwbAlak03wQAH6tq3uG9rmieP7Ws5WnTzhiMey4djcF9aiEABvepxT2Xji7ZdLbSx5E9tm/fnilEjz32GM455xyMGjUKra2tmeUHDx7Epk2bAAB79uzBwIEDcfDgQSxdurTg877zzjsYPXo0br75ZtTV1WHz5s0499xzsWzZMnR2dqK1tRUvvfQSzjrrrIrnPnv2bKxfvz7v5ixOAHDmmWdi69at2LZtGw4cOIDHH38cF198cd64iy++GEuWLAEAPPnkk7jgggsgIti2bVumaL733nvYsmULhg8fXnR+999/P+6//37X761atQptbW3o6OhAQ0NDZi8227Jly1xfn7M4pef9yCOPQFWxdu1aHH300TmH94DkobvevXtj7dq1UFU88sgjuOSSS/Je95IlSzLLt23bhubmZjQ3N2P69Ol44IEHMG3aNBw4cABf+9rXcOWVV2LGjBlFtwOZ820PSkQeA3AegGNFZAeA7wOoAQBVfQjACgAXAngbwKcA/sGPeZQqRoVU2gWd3dOj7dRTT8WSJUvwrW99CyNHjsT111+PHj164Mknn8SNN96Ijz/+GIcOHcJNN92E0047Df/+7/+O8ePH44QTTsDo0aOxZ88e1+e977778OKLLyKRSOCLX/wipkyZgh49emDNmjUYO3YsRAQ//OEPcfzxx2Pz5s2+v87u3bvj/vvvx6RJk9DZ2Ymrr74ap512GgDge9/7Hurq6nDxxRfjmmuuwRVXXIERI0agb9++ePzxxwEAL7/8MubPn4+amhp069YNDzzwAI499lgAwKxZs7B69Wp89NFHGDJkCO644w5cc8012Lx5s2vhAYBzzjkHV1xxBd5++21885vfrHhPLO3CCy/EihUrMGLECBx55JH4z//8z8z3xo0bh/Xr1wMAHnzwQcyZMwcdHR2YMmUKpkyZAiC5x/uNb3wDixYtwrBhw0oGUn7961/jpZdewu7du7F48WIAyfDHuHHjuvQ6qp0UO8xgo7q6Om1qagp7GuSDt956C6eeempo629ubsZFF12EjRtdT92jLrrooouwfPly9OjRI2f54sWL0dTUVHDvKm4K/J4btZiI8fuf6+tns1giCsRvf/vbsKdAEcMCRZQyfPhw7j2FYM6cOZgzZ07Y0yALsRcfWSVqh5yJysHf7/KwQJE1evbsid27d/OPmGIpfT2oSs8Vq0Y8xEfWSJ+XwhYxFFfpK+qSGRYoskZNTQ2vNEpEGTzER0REVmKBIiIiK7FAERGRlSLXSUJEWgG8V8FDjwXwkcfTCQrnHg7OPRzVOPePVLXYBV4BACKy0mRcXESuQFVKRJpUtWsNvkLCuYeDcw8H505pPMRHRERWYoEiIiIrVVOBWhj2BLqAcw8H5x4Ozp0AVNFnUEREFC3VtAdFREQRwgJFRERWqooCJSKTRWSLiLwtIreEPZ9yiEiziGwQkfUiYvWlNEXkFyKyS0Q2Zi3rKyKrRGRr6t9jwpxjIQXm/m8i0pLa9utF5MIw5+hGRIaKyIsi8paIbBKR76aWW7/di8w9Ctu9p4i8KiJvpOZ+R2r5iSLSmNruy0SkR6nnosJi/xmUiCQA/BnARAA7ALwGYJaq/inUiRkSkWYAdapq/YmLInIugL0AHlHV01PLfgigTVXnp/5zcIyq3hzmPN0UmPu/Adirqv87zLkVIyIDAQxU1ddFpDeAPwKYBmAOLN/uReb+Ddi/3QVAL1XdKyI1AF4G8F0AcwEsV9XHReQhAG+o6oNhzjXKqmEP6iwAb6vqu6p6AMDjAC4JeU6xpKovAWhzLL4EwJLU10uQfAOyToG5W09Vd6rq66mv9wB4C8BgRGC7F5m79TRpb+puTeqmAC4A8GRquZXbPUqqoUANBvB+1v0diMgfQYoC+C8R+aOIXBf2ZCpwnKruBJJvSAAGhDyfcv2TiLyZOgRo3WGybCIyHMAZABoRse3umDsQge0uIgkRWQ9gF4BVAN4B0K6qh1JDovZeY51qKFDisixKxzXrVfVvAUwBcEPqUBQF40EAJwMYB2AngB+FO53CROQLAH4D4CZV/STs+ZTDZe6R2O6q2qmq4wAMQfJIzaluw4KdVbxUQ4HaAWBo1v0hAD4IaS5lU9UPUv/uAvAUkn8IUfJh6rOG9GcOu0KejzFV/TD1JnQYwMOwdNunPgP5DYClqro8tTgS291t7lHZ7mmq2g5gNYAJAPqISPpCsJF6r7FRNRSo1wCMTKVregC4DMAzIc/JiIj0Sn14DBHpBeDvAWws/ijrPAPgqtTXVwF4OsS5lCX9Bp/yNVi47VMf1i8C8Jaq3pv1Leu3e6G5R2S79xeRPqmvawH8HZKfob0IYHpqmJXbPUpin+IDgFRM9T4ACQC/UNW7Q56SERE5Ccm9JgDoDuBRm+cuIo8BOA/JSw58COD7ABoA/BrAMADbAcxQVevCCAXmfh6Sh5kUQDOAb6U/17GFiJwD4L8BbABwOLX4X5H8LMfq7V5k7rNg/3Yfg2QIIoHkf/R/rap3pv5mHwfQF8A6AJer6mfhzTTaqqJAERFR9FTDIT4iIoogFigiIrISCxQREVmJBYqIiKzEAkVERFZigaKqIiKdqQ7ZG0XkCRE5ssC4FenzXIgoHIyZU1URkb2q+oXU10sB/NFxkqgg+XdxuNBzEFEwuAdF1ey/AYwQkeGpaxI9AOB1AENT1+E6FgBE5MpU49I3ROSXqWX9ReQ3IvJa6lYf4usgiqXupYcQxU+qX9oUACtTi0YB+AdV/cfU99PjTgNwG5JNez8Skb6p8T8G8B+q+rKIDAPwHNybhRJRhVigqNrUpi6RACT3oBYBGATgPVVd6zL+AgBPpi8YmdUu6O8AfDFdyAAcJSK9U9c1IiIPsEBRtelIXSIhI1Vk9hUYL3C/ZEI3AGeraoe30yOiNH4GRVTc7wF8Q0T6AUDWIb7/AvBP6UEiMs7lsUTUBSxQREWo6iYAdwP4g4i8ASCd+LsRQF0qPPEnAN8Oa45EccWYORERWYl7UEREZCUWKCIishILFBERWYkFioiIrMQCRUREVmKBIiIiK7FAERGRlf5/+0SEYL003PsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(inp1.Price,inp1.Rating, stat_func = stats.pearsonr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Selected KDE bandwidth is 0. Cannot estiamte density.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mkdensityfft\u001b[1;34m(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'scott'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-be1a36bb778e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minp1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPrice\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minp1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRating\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"reg\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mjointplot\u001b[1;34m(x, y, data, kind, stat_func, color, height, ratio, space, dropna, xlim, ylim, joint_kws, marginal_kws, annot_kws, **kwargs)\u001b[0m\n\u001b[0;32m   2330\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2331\u001b[0m         \u001b[0mmarginal_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m         \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_marginals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mmarginal_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2334\u001b[0m         \u001b[0mjoint_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mplot_marginals\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1808\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"vertical\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1809\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0max_marg_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1810\u001b[1;33m         \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1811\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1812\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"vertical\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[1;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)\u001b[0m\n\u001b[0;32m    231\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m         \u001b[0mkde_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 233\u001b[1;33m         \u001b[0mkdeplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvertical\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkde_color\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkde_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    234\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkde_color\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m             \u001b[0mkde_kws\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde_color\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[0;32m    703\u001b[0m         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,\n\u001b[0;32m    704\u001b[0m                                  \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 705\u001b[1;33m                                  cumulative=cumulative, **kwargs)\n\u001b[0m\u001b[0;32m    706\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    707\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_univariate_kdeplot\u001b[1;34m(data, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)\u001b[0m\n\u001b[0;32m    293\u001b[0m         x, y = _statsmodels_univariate_kde(data, kernel, bw,\n\u001b[0;32m    294\u001b[0m                                            \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 295\u001b[1;33m                                            cumulative=cumulative)\n\u001b[0m\u001b[0;32m    296\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m         \u001b[1;31m# Fall back to scipy if missing statsmodels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_statsmodels_univariate_kde\u001b[1;34m(data, kernel, bw, gridsize, cut, clip, cumulative)\u001b[0m\n\u001b[0;32m    365\u001b[0m     \u001b[0mfft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkernel\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"gau\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    366\u001b[0m     \u001b[0mkde\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msmnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mKDEUnivariate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 367\u001b[1;33m     \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    368\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    369\u001b[0m         \u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, kernel, bw, fft, weights, gridsize, adjust, cut, clip)\u001b[0m\n\u001b[0;32m    138\u001b[0m             density, grid, bw = kdensityfft(endog, kernel=kernel, bw=bw,\n\u001b[0;32m    139\u001b[0m                     \u001b[0madjust\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0madjust\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 140\u001b[1;33m                     clip=clip, cut=cut)\n\u001b[0m\u001b[0;32m    141\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m             density, grid, bw = kdensity(endog, kernel=kernel, bw=bw,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mkdensityfft\u001b[1;34m(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)\u001b[0m\n\u001b[0;32m    451\u001b[0m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    452\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbandwidths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect_bandwidth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkern\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# will cross-val fit this pattern?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    454\u001b[0m     \u001b[0mbw\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0madjust\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\bandwidths.py\u001b[0m in \u001b[0;36mselect_bandwidth\u001b[1;34m(x, bw, kernel)\u001b[0m\n\u001b[0;32m    172\u001b[0m         \u001b[1;31m# eventually this can fall back on another selection criterion.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m         \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Selected KDE bandwidth is 0. Cannot estiamte density.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mbandwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Selected KDE bandwidth is 0. Cannot estiamte density."
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(inp1.Price,inp1.Rating,kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Plot a reg plot for Price and Rating and observe the trend\n",
    "\n",
    "sns.jointplot(\"Price\",\"Rating\",data=inp1[inp1.Price>0],kind =\"reg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Question - Plot a reg plot for Price and Rating again for only the paid apps.\n",
    "data = inp1[inp1.Price>0]\n",
    "sns.jointplot(data.Price,data.Rating,kind=\"reg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.loc [ pd.to_numeric(inp1['Price'], errors='coerce').isnull() ]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.loc [ pd.to_numeric(inp1['Rating'], errors='coerce').isnull() ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pair Plots**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - When you have several numeric variables, making multiple scatter plots becomes rather tedious. Therefore, a pair plot visualisation is preferred where all the scatter plots are in a single view in the form of a matrix\n",
    " - For the non-diagonal views, it plots a **scatter plot** between 2 numeric variables\n",
    " - For the diagonal views, it plots a **histogram**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pair Plots help in identifying the trends between a target variable and the predictor variables pretty quickly. For example, say you want to predict how your company’s profits are affected by three different factors. In order to choose which you created a pair plot containing profits and the three different factors as the variables. Here are the scatterplots of profits vs the three variables that you obtained from the pair plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Pairplots](images\\pairplots2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clearly visible that the left-most factor is the most prominently related to the profits, given how linearly scattered the points are and how randomly scattered the rest two factors are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll be using **sns.pairplot()** for this visualisation. Check out its official documentation:https://seaborn.pydata.org/generated/seaborn.pairplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.Reviews.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = inp1[inp1.Price!= 0]\n",
    "sns.jointplot(temp.Price, temp.Rating, kind = \"reg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(inp1[[\"Reviews\",\"Size\",\"Rating\",\"Price\"]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bar Charts Revisited**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here, you'll be using bar charts once again, this time using the **sns.barplot()** function. Check out its official documentation:https://seaborn.pydata.org/generated/seaborn.barplot.html\n",
    "- You can modify the **estimator** parameter to change the aggregation value of your barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Plot a bar plot of Content Rating vs Average Rating \n",
    "inp1.groupby(['Content Rating'])['Rating'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Plot the bar plot again with Median Rating\n",
    " \n",
    "inp1.groupby(['Content Rating'])['Rating'].median().plot.bar(color=['orange','yellow','blue','green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Plot the above bar plot using the estimator parameter\n",
    "\n",
    "sns.barplot(data=inp1,x=\"Content Rating\",y=\"Rating\",estimator=np.median)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Plot the bar plot with only the 5th percentile of Ratings\n",
    "sns.barplot(data=inp1,x=\"Content Rating\",y=\"Rating\",estimator=lambda x:np.quantile(x,0.05))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Question - Plot the bar plot with the minimum Rating\n",
    "sns.barplot(data=inp1,x=\"Content Rating\",y=\"Rating\",estimator=np.min)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Box Plots Revisited__\n",
    "\n",
    "- Apart from outlier analysis, box plots are great at comparing the spread and analysing a numerical variable across several categories\n",
    "- Here you'll be using **sns.boxplot()** function to plot the visualisation. Check out its documentation: https://seaborn.pydata.org/generated/seaborn.boxplot.html\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Plot a box plot of Rating vs Content Rating\n",
    "plt.figure(figsize=[9,7])\n",
    "sns.boxplot(inp1[\"Content Rating\"],inp1.Rating)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tools                                  686\n",
       "Entertainment                          505\n",
       "Education                              464\n",
       "Medical                                344\n",
       "Finance                                311\n",
       "                                      ... \n",
       "Arcade;Pretend Play                      1\n",
       "Entertainment;Education                  1\n",
       "Lifestyle;Pretend Play                   1\n",
       "Role Playing;Brain Games                 1\n",
       "Health & Fitness;Action & Adventure      1\n",
       "Name: Genres, Length: 115, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Question - Plot a box plot for the Rating column only\n",
    "inp1.Genres.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>TED</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>4.6</td>\n",
       "      <td>181893</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>10000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Education</td>\n",
       "      <td>July 27, 2018</td>\n",
       "      <td>3.2.5</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>English Communication - Learn English for Chin...</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2544</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>December 29, 2017</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>Khan Academy</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>4.6</td>\n",
       "      <td>85375</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>July 27, 2018</td>\n",
       "      <td>5.0.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Learn English with Wlingua</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>4.7</td>\n",
       "      <td>314299</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>10000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>May 2, 2018</td>\n",
       "      <td>1.94.9</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Learn Spanish - Español</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>4.7</td>\n",
       "      <td>32346</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>1000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>December 18, 2017</td>\n",
       "      <td>1.3.8</td>\n",
       "      <td>4.0 and up</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>8556</th>\n",
       "      <td>Fr. Daoud Lamei</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Education</td>\n",
       "      <td>June 27, 2018</td>\n",
       "      <td>3.8.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>Fr Agnel Ambarnath</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.2</td>\n",
       "      <td>117</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>June 13, 2018</td>\n",
       "      <td>2.0.20</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8564</th>\n",
       "      <td>FR Calculator</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>June 18, 2017</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8565</th>\n",
       "      <td>Sya9a Maroc - FR</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>38</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>July 25, 2017</td>\n",
       "      <td>1.48</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8566</th>\n",
       "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>July 6, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1999 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    App   Category  Rating  \\\n",
       "585                                                 TED  EDUCATION     4.6   \n",
       "586   English Communication - Learn English for Chin...  EDUCATION     4.7   \n",
       "587                                        Khan Academy  EDUCATION     4.6   \n",
       "588                          Learn English with Wlingua  EDUCATION     4.7   \n",
       "590                             Learn Spanish - Español  EDUCATION     4.7   \n",
       "...                                                 ...        ...     ...   \n",
       "8556                                    Fr. Daoud Lamei     FAMILY     5.0   \n",
       "8558                                 Fr Agnel Ambarnath     FAMILY     4.2   \n",
       "8564                                      FR Calculator     FAMILY     4.0   \n",
       "8565                                   Sya9a Maroc - FR     FAMILY     4.5   \n",
       "8566                   Fr. Mike Schmitz Audio Teachings     FAMILY     5.0   \n",
       "\n",
       "      Reviews     Size  Installs  Type  Price Content Rating     Genres  \\\n",
       "585    181893  18000.0  10000000  Free    0.0   Everyone 10+  Education   \n",
       "586      2544  18000.0    100000  Free    0.0       Everyone  Education   \n",
       "587     85375  21000.0   5000000  Free    0.0       Everyone  Education   \n",
       "588    314299   3300.0  10000000  Free    0.0       Everyone  Education   \n",
       "590     32346   3200.0   1000000  Free    0.0       Everyone  Education   \n",
       "...       ...      ...       ...   ...    ...            ...        ...   \n",
       "8556       22   8600.0      1000  Free    0.0           Teen  Education   \n",
       "8558      117  13000.0      5000  Free    0.0       Everyone  Education   \n",
       "8564        7   2600.0       500  Free    0.0       Everyone  Education   \n",
       "8565       38  53000.0      5000  Free    0.0       Everyone  Education   \n",
       "8566        4   3600.0       100  Free    0.0       Everyone  Education   \n",
       "\n",
       "           Last Updated Current Ver   Android Ver  \n",
       "585       July 27, 2018       3.2.5    4.1 and up  \n",
       "586   December 29, 2017         3.1    4.0 and up  \n",
       "587       July 27, 2018       5.0.0    4.1 and up  \n",
       "588         May 2, 2018      1.94.9    4.0 and up  \n",
       "590   December 18, 2017       1.3.8    4.0 and up  \n",
       "...                 ...         ...           ...  \n",
       "8556      June 27, 2018       3.8.0    4.1 and up  \n",
       "8558      June 13, 2018      2.0.20  4.0.3 and up  \n",
       "8564      June 18, 2017       1.0.0    4.1 and up  \n",
       "8565      July 25, 2017        1.48    4.1 and up  \n",
       "8566       July 6, 2018         1.0    4.1 and up  \n",
       "\n",
       "[1999 rows x 13 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_column=inp1[inp1['Genres'].isin(['Tools','Entertainment','Education','Medical'])]\n",
    "rating_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H1LKefrr6bK4vZGjMpZdW36798ssvLyiSxk02EWyNiKeADkkdEfEdkpvZ2zif+tSnqsqez6UxfX19O85rj46OulXQgFoXMtieG2sNjGnlVsFkE8EmSZ3AbcDVkv4J2JZfWK1r27ZtE5Zt9/T397N161YAtm7d6pHaDfCFDFbPZBPBiSQzkP4ZcDPw38A78gqqlU2fPn3Csu2eRYsW7ajD6dOnl76DsxHjL1wo+4UM9qLJTjr3XESMRsS2dLK5f2bnUcIGfPzjH68q+y5QjfFI7ez4QoZsvfOd76wqv/e97y0oksbtatK5/SSdK+lySW9V4nSSG9W07rfO0cKFC6uOYN/ylrcUHJGZ5eHwww+vKh922GEFRdK4XbUIvgy8BrgP+CBwC8kAsBMj4sScY2tZY60CtwYa52m9s+PO4my104UhuzqB/aqI+C0ASZ8HNgLzIuLZ3CNrYQsXLmThwoVFh9EW+vv7d3S4b9u2zaNhG+DO4my104Uhu2oRbB17kk4p/RMnAZtKixYtYtq0aQAeDdsgdxZnq50uDNlVInitpGfSx7PAEWPPJT0zFQFauXk0bHbcWZytdrowZFf3I5gWEfulj5dGxPSK5/tNVZBWXh4Nmx2Pes/WvHnzqspz584tKJLGTXYcgVkh3MGZnXbq3GwG7bRvOhFYU3MHZ3baqXOzGbTTvulEkIONGzdyxhlneIK0DLiDMzvt1LnZDNpp38wtEUiaK+k7kh6S9ICkj9RYZ4GkzZLWpo+26L3ytMnZcQdndtqpc7MZtNO+mWeLYBtwdkT8JskU1h+WdGiN9W6PiCPTR+ueZEt52uRsHXLIITuOtLq7u5k/f36xAbWwdurcbAYPPPBAVfnBBx8sKJLG5ZYIIuKJiLg7ff4s8BAwJ6/PaxaeNjl75513Hvvuu29LH3E1g3bq3GwG4+9HcPHFFxcUSeOmpI9AUjfJ/YvvqvHy70i6V9IqSTUn65C0RNKApIHh4eEcI22cp03O3iGHHMKqVavcGmhQO3VuNoOxA7565VaSeyJI72PwdeCsiBg/CO1u4BUR8VrgMuAbtbYRESsioiciembPnp1vwA3ytMnZW716NQsWLGDNmjVFh9LS2qlzsxlImrDcSnJNBJJmkCSBqyPi+vGvR8QzETGSPr8JmCFpVp4x5c3TJmdv2bJljI6OunOzQe3UudkMzjrrrKry2WefXVAkjcvzqiEBXwAeiojP1Fnn5el6SDo6jce9q7bD6tWrGRkZAWBkZMStAmsab3rTm6rKxxxzTEGRNC7PFsExwB8Dx1ZcHnq8pKWSlqbrvBu4X9K9wHLgpGjlE2142uSsLVu2rKrsVsGec2dxtvr6+nacDpLU0r/1PK8auiMiFBFHVFweelNEfDYiPpuuc3lEHBYRr42IN0TE9/KKZ6rUmjbZ9txYa6Be2SbPncXZ6u/v39FBHBEt/Vv3yOKMubM4W52dnROWbfLcWZytdvqtOxFkzJ3F2Rp/asgTpe05dxZnq51+604E1tRmzpxZVd5///0LisSsfTkRZMydxdlyB2d2XJfZaqffuhNBxtxZnC13cGbHdZmtdvqtOxFkbNGiRcyYMQOAGTNmtHQHUjNwB2d2XJfZWrRoUdXlo638W3ciyFhvb++OnaOjo6OlO5CagTs4s+O6zFZvb2/V5aOt/Ft3IsjYrFmzWLx4MZJYvHgxBx54YNEhtbQDDjig6qjrZS97WcERtS5P6Z2t22+/vap85513FhRJ45wIctDb28sRRxzR0kcIzaKdOuSagaf0zk47TUOtVpvRoaenJwYGBooOw6bIcccdx5YtW3aU99lnH26++eYCIzJLvPnNb95p2W233VZAJJMjaU1E9NR6zS0Ca2rufLdm5WmozaaIO9+tWXkaarMp4s53a1aHHVZ9Q8VDD611S/bW4ERgTc+d79aM2mmkthOBWYls3LiRM844g6ee8v2fGtVOI7XzvEPZXEnfkfSQpAckfaTGOpK0XNKgpHWSXp9XPNa6+vr6WLdunS8dzYDrMjvtNFI7zxbBNuDsiPhN4A3AhyWNP4m2GDg4fSwBrsgxHmtBGzduZNWqVUQEq1at8pFsA1yX2Tr55JOryh/4wAcKiqRxed6h7ImIuDt9/izwEDBn3GonAldF4vvATEkH5RWTtZ6+vr4dw/hHR0d9JNsA12W2rrrqqqryv/zLvxQUSeOmpI9AUjfwOuCucS/NAR6rKA+xc7JA0hJJA5IGhoeH8wrTmlB/fz9bt24FYOvWrS09w2PRXJfZch/BbpDUCXwdOCsinhn/co237DTUOSJWRERPRPTMnj07jzAztX79ehYvXszg4GDRobQ8DyjLjusyW+4jmCRJM0iSwNURcX2NVYaAuRXlLuDxPGOaChdddBHPPfdcS19O1iw8oCw7rststdNsrnleNSTgC8BDEfGZOqutBE5Orx56A7A5Ip7IK6apsH79+h1NxA0bNrhV0CAPKMuO69LqybNFcAzwx8Cxktamj+MlLZW0NF3nJuDHwCBwJfChHOOZEu00yKRZeEBZdlyX2Wmn37pnH81Yq81IaGZ7ptV+6559dAq1UwdSs/Bo2Oy4LrPTTr91J4KMtVMHUrPwaNjsuC6z006/dSeCjB1wwAFVZd9asTEeDZsd12W22um37kSQsb6+PqZNmwbAtGnTfOTVII+GzY7rMlvt9Ft3IshYf38/27dvB2D79u0evdkgj4bNjusyW+30W3ciyNiiRYuYPn06ANOnT/fozQa5PrPjkcXZaqd904kgY729vYyOjgJJ89vXazfG9ZkdjyzOVjvtm04EZiXhkcVWjxNBxvr6+ujoSKq1o6OjpTuQmoHrM1seWZyddto3nQgy1t/fz7Zt2wDYtm1bS3cgNQPXZ7ZmzZrFZZdd5tZABtpp33QiyJg75LLVTh1y1l7aad90IsiYO+Sy1U4dctZe2mnfdCLImDvkzKzVOBHkwB1y2WmnDjlrL+20bzoR5MAdctlppw45ay/ttG86EVhTc+e7Nat22jfzvFXlFyU9Ken+Oq8vkLS54u5lrTuHq+XGne/WrNpp38yzRfAl4LhdrHN7RByZPlr3Pm+WG3e+W7Nqp31zel4bjojbJHXntX0rj97eXjZs2NDSR1zWntpl38z1nsVpIrgxIg6v8doC4OvAEPA48LGIeKDOdpYASwDmzZt31COPPJJTxGZm7alZ71l8N/CKiHgtcBnwjXorRsSKiOiJiJ7Zs2dPWYBmZhNpl3tAF5YIIuKZiBhJn98EzJA0q6h4zMx2V7vcA7qwRCDp5Uq73CUdncbS2mnVzEqjne4Bneflo18F/gt4jaQhSadKWippabrKu4H7Jd0LLAdOijw7LMzMMtRO94DOtbM4Dz09PTEwMFB0GGZWcscddxxbtmzZUd5nn324+eabC4xoYs3aWWxm1rI8stjMrOQ8stjMrOQ8stjMzNpmZLETgZnZHhqbcr7V+dSQmVnJORGYmZWcE4GZWck5EZiZlZwTgZlZyTkRmJmVnBOBmVnJORGYmZWcE4GZWck5EZiZlZwTQQ5Wr17NggULWLNmTdGhmFXxvpmtdqnP3G5MI+mLwNuBJyPi8BqvC/gn4HhgC3BKRNy9q+22wo1pjj/+eEZGRujs7OSmm24qOhyzHbxvZquV6rOoG9N8CThugtcXAwenjyXAFTnGMmVWr17NyMgIACMjIy1/pGDtw/tmttqpPnO9VaWkbuDGOi2CzwG3RsRX0/KPgAUR8cRE22z2FsHYEcKYVjhSsHLwvpmtVqvPZr1V5RzgsYryULpsJ5KWSBqQNDA8PDwlwe2pyh2jVtmsKN43s9VO9VlkIlCNZTWbJxGxIiJ6IqJn9uzZOYfVmM7OzgnLZkXxvpmtdqrPIhPBEDC3otwFPF5QLJlZtmxZVfnCCy8sJhCzcbxvZqud6rPIRLASOFmJNwCbd9U/0AqOPvroHUcGnZ2dHHXUUQVHZJbwvpmtdqrP3BKBpK8C/wW8RtKQpFMlLZW0NF3lJuDHwCBwJfChvGKZasuWLaOjo6OljxCsPXnfzFa71GeuVw3lodmvGjIza0bNetWQmZk1AScCM7OScyIwMys5JwIzs5Jruc5iScPAI0XHMQmzgI1FB9FGXJ/ZcV1mq1Xq8xURUXNEbsslglYhaaBeD73tPtdndlyX2WqH+vSpITOzknMiMDMrOSeC/KwoOoA24/rMjusyWy1fn+4jMDMrObcIzMxKzonAzKzkSpsIJG2XtLbi8Zc11lkg6caMP3eBpN+tKC+VdHKWn5GVydTRuPWrvttufM6Rko6fxHo9kpbv7vazIunjRX12+vkh6csV5emShnd3H5V0q6Se9PlNkmbuQSynSLp8d9/X7CQdWLG//0zSTyvKv7Ib25kuaVOesWZpetEBFOgXEXFkAZ+7ABgBvgcQEZ8tIIbJ2t06WkDFd5sMSdOBI4EekqnJ64qIAaDIqWc/DnyqwM9/Djhc0t4R8QtgEfDTRjYYEbtMwGUSEU+R7I9IWgaMRMQ/FhrUFChti6AeScdJ+qGkO4B3VixfJuljFeX7JXWnz0+WtE7SvWNHbJLeIekuSfdI+qakX0vXXwr8WXqE8abK7aZHxt9Pt3WDpJely2+V9GlJqyWtl/SmKaqOmiRtkPRJSXdLuk/Sb9T5brMlfV3SD9LHMen7l0laIekW4CrgAuB96fveJ+loSd9L6+57kl6Tvm9HCy3dxhfTuvmxpDPT5d3pv9/n03+jqyUtlHSnpIclHZ2ut2/6/h+kn3NiuvwUSddLujld/+/T5X8H7J3GePVU1vc4q4C3pc/fD3x17IUJvtPekr6W7lfXAHtXvGeDpFnp80ntx1P1RZuNpHPSfep+SWfsannF63Mk3ZHuO/drD1rNuYuIUj6A7cDaisf7gL2Ax4CDSe6pfC1wY7r+MuBjFe+/H+gGDgN+BMxKlx+Q/n0ZL16V9UHg4jrb2VEG1gH/O31+AXBp+vzWivcfD3yzqDpKl28Azkiffwj4fJ3v9hXgjenzecBDFeutAfZOy6cAl1e8bz9gevp8IfD19PmCcf8e3wNeQjLE/ylgRvpvsg34LZIDnTXAF9N/zxOBb6Tv/xTwR+nzmcB6YN80lh8D+6f7wyPA3HS9kYL32RHgCOC6NLa14+qk3nf6KPDFdPkRaf30VPxbzmL39+Oqf7N2fFD92zwauBfYB3gp8FBal/WWTwc2pe/9C+Av0ufTgM6iv9v4h08NVZB0JPCTiHg4Lf8rsGQX2zkWuC4iNgJExNPp8i7gGkkHAb8C/GSijUjaH5gZEd9NF/UB/1axyvXp3zUk/9lNhYlODVXG88466ywEDpU0Vt5P0kvT5ysjOb1Ry/5An6SDgSD5D76W/4iIF4AXJD0JjB2t/iQi7gOQ9ADwrYgISffxYt29FTihopW3F0myIl1/c/r+B4FXkBwgFC4i1qWtr/ez86m0et/pzcDyivevq7HpTPbjNvYmkgOSLQCSvgG8kaR1VWv5gxXv/QHwOUl7kRyI3DulkU+CTw3trN7Aim1U19de6V/Vec9lJEdMvwX8acX6e+qF9O92mqNvZzLxdAC/ExFHpo85EfFs+tpzE2z7QuA7EXE48A7q190LFc8r46hcPlpRHq1YR8C7KmKbFxEP7WK7zWIl8I9UnBZKTfSddjVgaKr241al3Vy+Q0R8m6Tl9gRwtaQ/zDCuTDgRVPsh8EpJr07L7694bQPwegBJrwdemS7/FvBeSQemrx2QLt+fFzvyeiu28yxJE7JKegT684rz/38MfHf8ek1u/He7BTh9rJC2uCbzvsq6OyXD+Cr9J3CG0uaKpNdN4j1bJdVrnUylLwIXjLV6KtT7TrcBf5guO5zk1MV4u7sfl81twB+k/S2dJKcZb59g+Q6SXgH8LCJWAF8CJrOvTakyJ4Kxjr+xx99FxPMkp4L+Q0lnceV0118HDpC0FjiN5PwrEfEA8DfAdyXdC3wmXX8Z8G+Sbqd6itp/J9lx1mrnTt9e4B/SpvuRJP0ERdqpjnax/vjvdibQk3ZAPkjSmVzLd0hOIa2V9D7g74G/lXQnyTnVPFxIcsppnaT70/KurEjXL7KzmIgYioh/qvFSve90BdCZ7lfnAKtrbDHSCnsAAAJBSURBVHN39+NSiYjVJC2wHwDfB66IiPvqLR/39t8D7pV0D0miuGzqIp8cTzFhZlZyZW4RmJkZTgRmZqXnRGBmVnJOBGZmJedEYGZWck4EZikl80F9RcncRWsk/ZekPyg6LrO8ORGYAekgrG8At0XEqyLiKOAkkikWGt12XmMhzDLhRGCWOBb4ZVRMCx4Rj0TEZZKmSfqHdFbPdZL+FHbMhnqrpOuUzHh6dcWo3g2Szk8HJr5H0quVzGi6RtLtkn4jXe896YyU90q6rYgvbtZsc6iYFeUw4O46r50KbI6I/yXpJcCdSqbQhmS6gMOAx4E7gWOAO9LXno+INwJI+hawNCIelvTbwP8lST7nA/8nIn6qPbhBjFkWnAjMapD0zySzSP6SZKqRIyS9O315f5Kpyn8JrI6IofQ9a0lmNx1LBNekyzuB3yWZqmHsI16S/r0T+JKka3lxRlezKeVEYJZ4AHjXWCEiPqzkhi0DwKMk91/4z8o3SFrAxDOVjs2w2kEyN/1Ok+5FxNK0hfA2YK2kIyO5S5bZlHEfgVni28Bekk6rWLZP+vc/gdPGZh6VdIikfSe74Yh4BviJpPek75ek16bPXx0Rd0XE+SSTus3N4LuY7Ra3CMyA9MY1vw9cIukcYJjkiP4vSG4Q1A3cnXYGDwO/v5sf8YfAFZLOI5kh9Gskd7b6ByU34BHJVNBNd9MSa3+efdTMrOR8asjMrOScCMzMSs6JwMys5JwIzMxKzonAzKzknAjMzErOicDMrOT+P5vdsv1b+gB7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Question - Plot a box plot of Ratings across the 4 most popular Genres\n",
    "\n",
    "sns.boxplot(rating_column[\"Genres\"],rating_column[\"Rating\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b5592a7248>"
      ]
     },
     "execution_count": 102,
     "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": [
    "inp1['Genres'].value_counts()\n",
    "c = ['Tools','Entertainment','Medical','Education']\n",
    "inp5= inp1[inp1['Genres'].isin(c)]\n",
    "sns.boxplot(inp5['Genres'],inp1.Rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Heat Maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heat mapsutilise the concept of using colours and colour intensities to visualise a range of values. You must have seen heat maps in cricket or football broadcasts on television to denote the players’ areas of strength and weakness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![HeatMap](images\\heatmap1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In python, you can create a heat map whenever you have a rectangular grid or table of numbers analysing any two features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![heatmap2](images\\heatmap2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- You'll be using **sns.heatmap()** to plot the visualisation. Checkout its official documentation :https://seaborn.pydata.org/generated/seaborn.heatmap.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "      <th>NEWSIZE_BUCKET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>500000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>50000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "      <td>VL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App        Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
       "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
       "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
       "\n",
       "   Reviews     Size  Installs  Type  Price Content Rating  \\\n",
       "0      159  19000.0     10000  Free    0.0       Everyone   \n",
       "1      967  14000.0    500000  Free    0.0       Everyone   \n",
       "2    87510   8700.0   5000000  Free    0.0       Everyone   \n",
       "3   215644  25000.0  50000000  Free    0.0           Teen   \n",
       "4      967   2800.0    100000  Free    0.0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated         Current Ver  \\\n",
       "0               Art & Design   January 7, 2018               1.0.0   \n",
       "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
       "2               Art & Design    August 1, 2018               1.2.4   \n",
       "3               Art & Design      June 8, 2018  Varies with device   \n",
       "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
       "\n",
       "    Android Ver NEWSIZE_BUCKET  \n",
       "0  4.0.3 and up              M  \n",
       "1  4.0.3 and up              M  \n",
       "2  4.0.3 and up              L  \n",
       "3    4.2 and up              H  \n",
       "4    4.4 and up             VL  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Ratings vs Size vs Content Rating\n",
    "\n",
    "##Prepare buckets for the Size column using pd.qcut\n",
    "inp1[\"NEWSIZE_BUCKET\"]=pd.qcut(inp1.Size,[0,0.2,0.4,0.6,0.8,1],[\"VL\",\"L\",\"M\",\"H\",\"VH\"])\n",
    "inp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>NEWSIZE_BUCKET</th>\n",
       "      <th>VL</th>\n",
       "      <th>L</th>\n",
       "      <th>M</th>\n",
       "      <th>H</th>\n",
       "      <th>VH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Content Rating</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>Everyone</th>\n",
       "      <td>4.116677</td>\n",
       "      <td>4.158290</td>\n",
       "      <td>4.215450</td>\n",
       "      <td>4.170994</td>\n",
       "      <td>4.187102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Everyone 10+</th>\n",
       "      <td>4.188889</td>\n",
       "      <td>4.207143</td>\n",
       "      <td>4.238318</td>\n",
       "      <td>4.193443</td>\n",
       "      <td>4.216393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mature 17+</th>\n",
       "      <td>3.951429</td>\n",
       "      <td>4.129592</td>\n",
       "      <td>4.021705</td>\n",
       "      <td>4.155172</td>\n",
       "      <td>4.193814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teen</th>\n",
       "      <td>4.220213</td>\n",
       "      <td>4.208511</td>\n",
       "      <td>4.141637</td>\n",
       "      <td>4.198131</td>\n",
       "      <td>4.246102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NEWSIZE_BUCKET        VL         L         M         H        VH\n",
       "Content Rating                                                  \n",
       "Everyone        4.116677  4.158290  4.215450  4.170994  4.187102\n",
       "Everyone 10+    4.188889  4.207143  4.238318  4.193443  4.216393\n",
       "Mature 17+      3.951429  4.129592  4.021705  4.155172  4.193814\n",
       "Teen            4.220213  4.208511  4.141637  4.198131  4.246102"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Create a pivot table for Size_buckets and Content Rating with values set to Rating\n",
    "pd.pivot_table(data=inp1,index=\"Content Rating\",columns=\"NEWSIZE_BUCKET\",values=\"Rating\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>NEWSIZE_BUCKET</th>\n",
       "      <th>VL</th>\n",
       "      <th>L</th>\n",
       "      <th>M</th>\n",
       "      <th>H</th>\n",
       "      <th>VH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Content Rating</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>Everyone</th>\n",
       "      <td>4.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Everyone 10+</th>\n",
       "      <td>4.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mature 17+</th>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teen</th>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NEWSIZE_BUCKET   VL    L    M    H   VH\n",
       "Content Rating                         \n",
       "Everyone        4.2  4.3  4.3  4.3  4.3\n",
       "Everyone 10+    4.1  4.3  4.3  4.3  4.3\n",
       "Mature 17+      4.2  4.2  4.1  4.1  4.2\n",
       "Teen            4.3  4.3  4.2  4.2  4.3"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Change the aggregation to median\n",
    "pd.pivot_table(data=inp1,index=\"Content Rating\",columns=\"NEWSIZE_BUCKET\",values=\"Rating\",aggfunc=np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>NEWSIZE_BUCKET</th>\n",
       "      <th>VL</th>\n",
       "      <th>L</th>\n",
       "      <th>M</th>\n",
       "      <th>H</th>\n",
       "      <th>VH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Content Rating</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>Everyone</th>\n",
       "      <td>3.80</td>\n",
       "      <td>3.80</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Everyone 10+</th>\n",
       "      <td>3.84</td>\n",
       "      <td>3.94</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mature 17+</th>\n",
       "      <td>3.28</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teen</th>\n",
       "      <td>3.86</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NEWSIZE_BUCKET    VL     L    M    H   VH\n",
       "Content Rating                           \n",
       "Everyone        3.80  3.80  4.0  3.8  3.9\n",
       "Everyone 10+    3.84  3.94  4.1  4.0  4.0\n",
       "Mature 17+      3.28  3.64  3.7  3.9  4.0\n",
       "Teen            3.86  3.80  3.9  4.0  4.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Change the aggregation to 20th percentile\n",
    "pd.pivot_table(data=inp1,index=\"Content Rating\",columns=\"NEWSIZE_BUCKET\",values=\"Rating\",aggfunc=lambda x:np.quantile(x,0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Store the pivot table in a separate variable\n",
    "res=pd.pivot_table(data=inp1,index=\"Content Rating\",columns=\"NEWSIZE_BUCKET\",values=\"Rating\",aggfunc=lambda x:np.quantile(x,0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Plot a heat map\n",
    "sns.heatmap(res)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b559355188>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Apply customisations\n",
    "sns.heatmap(res,cmap=\"Greens\",annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEGCAYAAABB8K+FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAcmUlEQVR4nO3debgdVZnv8e8vE5MxPgxCDNABBGlEJAg0gzLjFRQ1ih1iXwSucpS2BUSurWgjwaftywXsBrlIR6boRYig2EgTFCGRGQwYkmBobtpGCSLIKAmD5Jz3/lG1Q2Wzh9one5+q2uf38anH2jWsWrvIec86q9Z6SxGBmZmV35iiK2BmZvk4YJuZVYQDtplZRThgm5lVhAO2mVlFjCu6Aq2MmzDFQ1hSL/3+tqKrUBr/uc/fFV2FUnhh1XpFV6FU9njsWq1rGa8+9ZvcMWf8ptuu8/U65Ra2mVlFlLqFbWY2ooYGi65BSw7YZmY1g6uLrkFLDthmZqmIoaKr0JIDtplZzVC5A7YfOpqZ1cRQ/qUFSetLulfSA5IelDSrwTHHSvqjpEXp8ql21XML28yspnsPHV8BDoqIlZLGA7dLmhcRd9cdNzcico9TdcA2M6vpUh92JGlQV6Yfx6fLOs8rcZeImVkqBlfnXiQNSFqYWQayZUkaK2kR8CRwU0Tc0+CSH5W0WNI1krZqVz+3sM3Majp46BgRs4HZLfYPArtKehNwraSdI2Jp5pCfAFdGxCuSPgPMAQ5qdU23sM3Marr00HGtIiOeAxYA76vb/nREvJJ+/A7wrnZlOWCbmdUMDeZfWpC0WdqyRtIGwCHAQ3XHTM58/CCwrF313CViZlbTvYkzk4E5ksaSNIx/EBHXSzoTWBgR1wEnSvogsBp4Bji2XaEO2GZmNV2amh4Ri4FpDbafnln/MvDlTsp1wDYzqyn5TEcHbDOzVDKwo7wcsM3Mapz8ycysItwlYmZWESVvYY/4OGxJHx3pa5qZ5TL4av6lAEVMnPnnAq5pZtbe0FD+pQBFdIm0fNNwmkBlAEBjJzFmzEYjUikzs7J3iRQRsFumGMwmVBk3Yco6pyM0M8ttND50lLSExoFZwOa9uKaZ2TobjQEb+B3wDeAxupC028xsJERBDxPz6lXA/hlwDkkClLkkOV8X9ehaZmbdUfI+7J6MEomI8yJib2B/kixUl0laJul0STv04ppmZuus5KNEejqsLyJ+GxFnRcQ04OPAdHLkfDUzK0QPXmDQTT0N2JLGSzpC0hXAPOBhwBNnzKycSt7C7tUokUOBmcD7gXuBq4CBiFjVi+uZmXVFyfuwe/XQ8TTg+8CpEfFMj65hZtZdq7vzAoNe6UnAjogDe1GumVlPjdIWtplZ9YzSiTNmZtXjFraZWUW4hW1mVhFuYZuZVcRoHCViZlZJUe5cdQ7YZmY17sM2M6sIB2wzs4rwQ0czs4oYHCy6Bi05YFfEqpM+VXQVSmPnR/6j6CqUxrOfmVZ0FfqLu0TMrBccrHvAAdvMrCLch21mVg0xVO5x2D1944yZWaV06Y0zktaXdK+kByQ9KGlWg2PWkzRX0nJJ90ia2q56bmGbmdV0b5TIK8BBEbFS0njgdknzIuLuzDGfBJ6NiLdKOgo4C5jRqlC3sM3MarrUwo7EyvTj+HSp72/5EDAnXb8GOFiSWpXrgG1mVtPFl/BKGitpEfAkcFNE3FN3yBTgUYCIWA08D2zSqkwHbDOzmojci6QBSQszy8DaRcVgROwKbAnsKWnnuqs1ak23fOrpPmwzs5oOxmFHxGxgdo7jnpO0AHgfsDSzawWwFbBC0jhgEtDypeVuYZuZ1QxF/qUFSZtJelO6vgFwCPBQ3WHXAcek60cCt0S0zu/qFraZWU33RolMBuZIGkvSMP5BRFwv6UxgYURcB1wCfE/ScpKW9VHtCnXANjNLRZempkfEYuB1uQMi4vTM+svAxzop1wHbzKym5DMdHbDNzGqcS8TMrCLcwjYzq4jVfoGBmVk1uEvEzKwi3CViZlYN3RrW1ysO2GZmNSVvYRcyNV3SyUVc18yspS5NTe+VonKJnFLQdc3MmhsczL8UoG3AlnR5D67bNEl3NmXh0NCqHlzazKyxGIrcSxHy9GHv0oPrNv222ZSF4yZMKXeHkpn1l5L3YecJ2BtKmkaTVnFE3N9ou6QXaByYBWyQu4ZmZiOlD0aJTAHOpfnbEQ5qdFJETFyHepmZjbw+aGEvj4iGQdnMrK/0QcA2MxsVYrDcXSJ5hvX9U7MdkrbuYl3MzIrVB+Owv1RbkXRz3b4fd7c6ZmbF6YdhfdmHjRu32GdmVm190IcdTdYbfTYzq65yd2HnCthvlnQKSWu6tk76ebOe1czMbITF6nJH7DwB+zvAxAbrABd3vUZmZkUpd7zOFbAviYgVjXZIOqLL9TEzK0xRDxPzyjNK5GZJU+s3SjoO+JduV8jMrDBDHSwFyBOwPw/cJGn72gZJXyZJkbp/rypmZjbSKj+sLyJukPQKME/Sh4FPAXsA+0XEs72uoJnZiOmDPmwi4mZJxwILgDuBgyPi5R7Wy8xsxMXqomvQWtuAnUmTKmA94GDgSUkCIiLe2NsqmpmNjKh6C9tpUs1s1Ch5wM7zirA9JB3WYPsRkt7Vm2qZmY28GMq/FCHPKJGzgWUNti9L95mZ9YWyB+w8Dx03iYhH6jdGxHJJm3S/StbIWXdsUXQVSuMbk99SdBVK4rmiK9B3YrDc+ezyBOxW71/cqFsVMTMrWtkfOubpEvm5pH9MR4WsIWkWcEtvqmVmNvJiSLmXViRtJWm+pGWSHpR0UoNjDpD0vKRF6XJ6u/rlaWF/gSTJ03JJi9Jt7wQWkkyiMTPrC11sYa8GvhAR90uaCNwn6aaI+HXdcbdFxAfyFppnWN8qYKakbYG3p5sfjIjf5L2ImVkVRHSnDzsiHgceT9dfkLQMmALUB+yO5Jk4s1vm42Mkk2heXZeLmpmVUSctbEkDwEBm0+yImN3guKnANOCeBsXsLekB4PfAqRHxYKtr5ukSObfBto0lTQBmRsSiBvvNzCpnqINRImlwfl2AzpL0BuCHwMkR8ae63fcDfxERKyUdTvKO3O3ry8jK0yVyYJOK7A6cD+zXrgwzsypo9zCxE5LGkwTrKyLiR6+7ViaAp0n2LpS0aUQ81azMXMmfGomIhelvDzOzvtCtgJ2OqrsEWBYR32xyzBbAExERkvYkGbX3dKtyhx2wJW2OX8JrZn0kuhfR9gWOBpZkRtedBmydXCcuAo4ETpC0GngJOCqidQ3yPHT8Fq8PzBsD+wCvG1toZlZV3WphR8TtJBlOWx1zAXBBJ+XmaWEvrL8OSbP9lIh4spOLmZmVWbeG9fVKnoeOc0aiImZmRRsseS6RPOlVt5d0maRvStpS0jxJKyU9IGmPkaikmdlIiFDupQh5colcBtxFMrD7HuBSYFPgVDrsfzEzK7Nu5RLplTwB+w0RMTsizgFeioirI+LliLiJ5JVhZmZ9ISL/UoQ8Dx2zkzXrZ+qUPBmhmVl+RbWc88oTsHeUtJhkiMp26Trp5217VjMzsxE2OJSn06E4eQL2X/a8FmZmJVBUV0deeYb1/TZPQZLuioi9171KZmbFGKr6OOwOrN/FsszMRlzlJ850oOR/TJiZtVb5LpHhknRdq/0R8cEm561JCq6xkxgzxu/5NbORMZq6ROq/6d7Ao8CVJBNuct2JbFLwcROmlPz3nZn1k7KPEskzNX3HzPp6dfv2ynw8uu7ULUjSCe4MnAccCjwVEb+IiF8Mu8ZmZj0SHSxFyPPr5PuZ9bvq9l1YW4mIpdkdETEYETdGxDHAXsByYIGkzw23smZmvTQUyr0UIU+XiJqsN/q89s6kRf5+YCYwleSVYq97VY6ZWRn0wyiRaLLe6PMakuaQdIfMA2bVt8DNzMqm7Lk28gTsLSWdT9Karq2Tfp7S4ryjgVXADsCJySvO1pwXEfHG4VXZzKw3It/YiMLkCdj/M7Ne//aZ+s9rRES5H7eamdVZ3QddIldHxIuNdkjapsv1MTMrTNlb2Hlawc9LmiWp0bE/7HaFzMyKMtTBUoQ8Afs3wHbAHQ1a1OX+dWRm1oFAuZci5AnYqyLivwP/B7hV0icy+zwT0cz6Rj+0sAGIiP8LvAc4XtJVkib1rlpmZiNvEOVeipAnYK+pWUQ8AuwPLAN+BUzuTbXMzEbekPIvRcgTsP89+yEihiJiFvBx4IGe1MrMrABDKPdShDxvnPlqk+13A+/reo3MzApS9odybQO2pCU0/h61GYu7dL1WZmYF6Iep6R/oeS3MzEpgSOUeqdzxS3glbQLsB/wuIu7rVcXMzEbaYNEVaCPPCwyul7Rzuj4ZWAr8D+B7kk7ucf3MzEZMP4wS2SaTGvU44KaIOAL4K5LAbWbWFyo/SgR4NbN+MPAdgIh4QVLZ++j7xiEvlf2PtZGz1wllf5Y/Uiby/YvHFl2J0jj+/PbHtFP2f1l5WtiPSvqcpOnAbsCNAJI2AMb3snJm1pyDdfd1q0tE0laS5ktaJulBSSc1OEaSzpe0XNJiSbu1q1+egP1J4O3AscCMiHgu3b4XcFmO883MKqGLuURWA1+IiL8kiZWflbRT3TGHAdunywDw7XaF5ukSeQtwQkSs9ddCRMwH5uc438ysEga71DUdEY8Dj6frL0haRvKGrl9nDvsQ8N00tt4t6U2SJqfnNpSnhX0x8JSkmySdIem9kvx6LzPrO520sCUNSFqYWQYalSlpKjANuKdu1xTg0cznFbR+7WKucdi7S9oQ2BPYBziRZEjfH4A7IuJv25VhZlYFnYyiiIjZwOxWx0h6A8mLXk6OiD/V725UbKvy8nSJkL4ibIGkX5L8ltgX+ATOJWJmfaSbr3SUNJ4kWF8RET9qcMgKYKvM5y2B37cqM08ukY+TtKx3BV4BakH73RHxh3xVNzMrv26NU5Yk4BJgWUR8s8lh1wF/J+kqknktz7fqv4Z8LezZwEPARcCtEfFw/mqbmVVHF2c77AscDSyRtCjddhqwNUBEXATcABwOLAdeJJmY2FKegD0JeCdJK/sMSW8jefp5F3BXRNzS2fcwMyunbk05j4jbafPO23R0yGc7KTfPQ8dB4P50uUDS5sCRwOeBMwGP3jezvlD2qdt5+rB3IWld15YJJK3rbwF39LR2ZmYjqPIBG7icJDDPA/6hPt2qmVm/KHsukTwB+8CIeL7RDklbR8TvulwnM7NCFJU2Na88Mx3XTD+XdHPdvh93tzpmZsUZ7GApQp4WdvZ3zsYt9pmZVdpQyTtF8gTsaLLe6LOZWWX1w0PHN0s6haQ1XVsn/bxZz2pmZjbCyt4CzROwvwNMbLAOSSY/M7O+UPkWdkTMGomKmJkVbbXK3cbOM3Hm9Ba7IyK+3sX6mJkVptzhOl+XyKoG2zYieXXYJoADtpn1hX7oEjm3ti5pInASSVapq4Bzm50n6Vu0+IUVESd2VFMzsx7rh2F9SNoYOAX4G2AOsFtEPNvmtIWZ9VnA13Jea4DkhZRo7CTGjNkoz2lmZuus3OE6Xx/22cBHSPJivyMiVuYpOCLmZMo4Ofu5zXlrXrszbsKUst8/M+sjle8SAb5A8qaZrwJfSV6kACTjsCMi8ryQ14HXzEpvsOShKk8fdp58I2ZmldcPLexhkfQCr7WsN5RUe2NwJy1zM7MRE1VvYQ9XRExsf5SZWXmM2ha2mVnV9MWwPjOz0aDc4doB28xsjdUlD9kO2GZmqVH70NHMrGr80NHMrCLcwjYzqwi3sM3MKmIw3MI2M6sEj8M2M6sI92GbmVWE+7DNzCrCXSJmZhXhLhEzs4oo+ygRv5zAzCw1RORe2pF0qaQnJS1tsv8ASc9LWpQup7cr0y3sinjjuD8XXYXSGLPN24quQim8Z8PFRVeh73T5oePlwAXAd1scc1tEfCBvgQ7YZmapbvZhR8StkqZ2rUDcJWJmtkYnXSKSBiQtzCwDw7jk3pIekDRP0tvbHewWtplZKjp46BgRs4HZ63C5+4G/iIiVkg4Hfgxs3+oEt7DNzFKDRO5lXUXEnyJiZbp+AzBe0qatznEL28wsNZITZyRtATwRESFpT5IG9NOtznHANjNLddIl0o6kK4EDgE0lrQC+BoxPr3MRcCRwgqTVwEvAUdGmAg7YZmapbrawI2Jmm/0XkAz7y80B28ws5anpZmYVUfap6Q7YZmYpZ+szM6sIB2wzs4ro5iiRXnDANjNLuYVtZlYRHiViZlYRg1Hutzo6YJuZpdyHbWZWEe7DNjOrCPdhm5lVxJC7RMzMqmFUtrAlndJqf0R8sxfXNTNbF6N1lMjEzPqngX/Ne2L6XrQBAI2dxJgxG3W5amZmjY3KLpGImFVbl/Th7Occ5655T9q4CVPKfffMrK+Myi6ROuW+A2ZmqVHZwjYzq6JR2cKWtISkZS1gO0mLa7uAiIhdenFdM7N1MRiDRVehpV61sH8HfAN4DHeJmFlFjNap6T8DzgEmA3OBKyNiUY+uZWbWFWWfmj6mF4VGxHkRsTewP/AMcJmkZZJOl7RDL65pZrauIiL3UoSeBOyaiPhtRJwVEdOAjwPTgWW9vKaZ2XANReReitDTgC1pvKQjJF0BzAMeBj7ay2uamQ1XdPC/IvRqlMihwEzg/cC9wFXAQESs6sX1zMy6YbROTT8N+D5wakQ806NrmJl11agcJRIRB/aiXDOzXvJMRzOzihiVLWwzsyoq+zhsB2wzs5Rb2GZmFTFaR4mYmVWOHzqamVVE2btEejrT0cysSro501HSpZKelLS0yX5JOl/SckmLJe3WrkwHbDOzVJeTP10OvK/F/sOA7dNlAPh2uwIdsM3MUt1M/hQRt5JkK23mQ8B3I3E38CZJk1uVWeo+7NV/fkxF1wGSN7mnLwce9XwvXlP0vdjxmKKuvLai70M3dRJzJA2QtIxrZnd4H6YAj2Y+r0i3Pd7sBLew8xlof8io4XvxGt+LxKi8DxExOyJ2zyyd/tJq9MuhZdPdAdvMrBgrgK0yn7cEft/qBAdsM7NiXAd8Ih0tshfwfEQ07Q6Bkvdhl0hf9M91ie/Fa3wvEr4PDUi6EjgA2FTSCuBrwHiAiLgIuAE4HFgOvAgc17bMsg8UNzOzhLtEzMwqwgHbzKwi+iZgSxqUtCizfKnoOnVLv3w3STdKek7S9XXbt5F0j6T/J2mupAnDKDskfS/zeZykP9Zfq8F5u0o6vNPrdVCvj0l6UNKQpN0z2/+m7r/pkKRde1WPTkjaJFOvP0h6LPO54/821j399NDxpYjo6j94SWMjYrCbZQ5Tv3y3s4ENgU/XbT8L+OeIuErSRcAnqZumK2kBcGxEPNKk7FXAzpI2iIiXgEOBx3LUaVdgd5IHQLlIEsnznzy5OJcCHwH+NbsxIq4ArkjLewfwbxGxqO46U4HLI+KAvHXrhoh4muS+IOkMYGVEnDOSdbDG+qaF3YikwyT9IPP5AEk/SdffK+kuSfdLulrSG9Ltj0g6XdLtwJck3Z85f3tJ96XrB0v6laQlaZKX9TLnz0rLXSJpx3T7Rulxv0zP+9Bo+24RcTPwQt33EHAQcE26aQ7w4WHelnnA+9P1mcCVmevsKenOtH53Snpb2lo8E5iRth5nSDpD0qmZ85ZKmpouyyRdCNwPbNXsPtd952UR8R9t6r1WXctM0jGS7k3v14WSxqTbD8vci7mSNkq3r0jv6a+UJDjaodhvUG39FLA3qPsTcwZwE7BX7R8PMAOYK2lT4KvAIRGxG7AQOCVT1ssR8e6I+Efg+cyfqscBl0tanySxy4yIeAfJXyonZM5/Ki3320Dth/8rwC0RsQdwIHB2pl6j+bttAjwXEavTz7XpucNxFXBU+h12Ae7J7HsI2C8ipgGnA9+IiD+n63MjYteImNum/LeR5H6YRtKib3WfOzGDCgRsSTsD04F90r/4xpHc7zcDXwIOTu/FYuCkzKlPpPfsYoZ/j4xR0CUi6UbgCEnXkLS+vgjsD+wE3JE08JgA3JU5LfuDezFwnKRTSH6w9iT5wf2viHg4PWYO8FngX9LPP0r//z6SP4cB3gt8MNN6Wx/YGlg2yr9b0+m5ko7jtR/8twI3SPpzWr/przspYrGSboSZvL6LYxIwR9L2afnjc9St3m/TJD0Ae9H6Puci6a+AFyNiaWbbtcA2aZlbS6p1lZwXEZcNo97dcgiwB7Aw/c4bkOTCeJHkXtyZuRe3Z87L/pvp2fOC0aCfAnYzc0kCzjPALyPihfTP8JsiYmaTc1Zl1n9IMuD9FuC+iHha0laNT1vjlfT/B3ntHgv4aI4/jzvRD9/tKZIsZePSVvaa6blpcLoMcvVh11wHnEMyYWGTzPavA/MjYnoa1Bc0OX81a//luX5mPXvv2t3nvI6irnVd+2WkgvqwWxBwaUT8w1obpenAjRFxdJPzGv2bsWHopy6RZhYAuwHH81rr8m5gX0lvBZC0YbO+tYh4GfgpSRdArXXzEDC1dj5wNPCLNvX4KfC5NKAiadqwvs3aFlDx7xbJzK35wJHppmOAf8t7fgOXAmdGxJK67ZN47SHksZntLwATM58fIbmnKEkov02T6+S+z82k/b8fI+nKqYKfA3+ddrvVRpNsDdwJ7C9p23T7RulfMtZl/RSw6/t5/xdAOhLiepJk4den2/5I8kN7paTFJD98O7Yo+wqSP6N/lp7/Mkmf79WSlgBDwEVt6vd1kj/DFyt5A8XXR9t3k3QbcDVwcPow6r+lu/4eOEXScpJW8SVtrtdURKyIiPMa7PrfwD9JugMYm9k+H9gp82zgh8DGaTfECcDDry8q/32WNF3JtOS9gX+X9NPM7v2AFRHxm06/ZxHSX4KzgJ+n3/lnwOYR8QTJyJ65kh4gCeB+uNgDnpqeQ9o3O6n+T8F+0M/fzazfuD+pjfQB0HYkQ8/6Sj9/N7N+5Ba2mVlF9FMftplZX3PANjOrCAdsM7OKcMC2liRtIekqSf8p6deSbhhuPghJJ0vacB3qcoCkfZrsO1ZJdr5Fkh6S9PlOy5P0GUmfGG79zHrNAduaSifCXAssiIjtImIn4DRg82EWeTJJtr7hOgBoGLBTc9Mp/PsCX8kxa3Ot8iLiooj47jrUz6ynHLCtlQOBV9P3zwEQEYsi4jYlzlaSzW5JOumk1mpdIOmatKV7RXrsicBbgPmS5qfHtsoqOEuZrIDpNO3PAJ9PW9HvaVbpND3ocmByWt4RSvJt/0rSzyVt3qg8ZTL1pd/hLCWZ6R6uXS+d0fgDJZnn5qbl7t64Jmbd5YBtrexMkrCnkY+Q5Ex+J0lSoLMlTU73TSNpTe8EbAvsGxHnk+QIOTAiDlT7rIJrZQVMc4hcRJI3e9eIuK1ZpdPp0uuTZI2DJBHRXmnGuKuAL+Ysb1xE7Jl+l6+l2/4WeDYidiGZ0fmuZvUw6zZPnLHhejdwZTo9/glJvyDJ5PYn4N6IWAGQTvGeytrZ26B9trtGWQHbmSHpQJKMg8en0+whSSg1N/2FMgH4r5zlZeswNV1/N3AeQEQsTadom40It7CtlQdp3oJslBa15pXMerMMbbVsd7umy04R8ckGZXSS4W1uRLwdeA9wrqQt0u3fAi5I83t/mrUz8LXSLDOhWSEcsK2VW4D1JB1f2yBpD0n7A7eStGjHStqMJJHRvW3Ky2bGG062u/rMeg1FxF3A93gtl3Y2U98xnZZX53bgrwEk7QS8o8PzzYbNAduaSlOfTgcOTYf1PQicQdIXfS1JH/EDJIH9ixHxhzZFzgbmSZo/jKyCAD8Bprd76Jg6i+TlDBPTOl+dZgt8apjl1VwIbJbW+e9J7sHzOc81WyfOJWLWAUljgfER8bKk7YCbgR3S142Z9ZQfOpp1ZkOSoYnjSfqzT3CwtpHiFraZWUW4D9vMrCIcsM3MKsIB28ysIhywzcwqwgHbzKwi/j/e5o7/G75JUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Question - Replace Content Rating with Review_buckets in the above heat map\n",
    "##Keep the aggregation at minimum value for Rating\n",
    "res=pd.pivot_table(data=inp1,index=\"NEWSIZE_BUCKET\",columns=\"Content Rating\",values=\"Rating\",aggfunc=min)\n",
    "sns.heatmap(res)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session 3: Additional Visualisations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Line Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A line plot tries to observe trends using time dependent data.\n",
    "-  For this part, you'll be using **pd.to_datetime()** function. Check out its documentation:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App                 object\n",
       "Category            object\n",
       "Rating             float64\n",
       "Reviews              int32\n",
       "Size               float64\n",
       "Installs             int32\n",
       "Type                object\n",
       "Price              float64\n",
       "Content Rating      object\n",
       "Genres              object\n",
       "Last Updated        object\n",
       "Current Ver         object\n",
       "Android Ver         object\n",
       "NEWSIZE_BUCKET    category\n",
       "dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     January 7, 2018\n",
       "1    January 15, 2018\n",
       "2      August 1, 2018\n",
       "3        June 8, 2018\n",
       "4       June 20, 2018\n",
       "Name: Last Updated, dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1[\"Last Updated\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2018-01-07\n",
       "1      2018-01-15\n",
       "2      2018-08-01\n",
       "3      2018-06-08\n",
       "4      2018-06-20\n",
       "          ...    \n",
       "8564   2017-06-18\n",
       "8565   2017-07-25\n",
       "8566   2018-07-06\n",
       "8567   2015-01-19\n",
       "8568   2018-07-25\n",
       "Name: Last Updated, Length: 8569, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##to convert the string format to date and time format\n",
    "pd.to_datetime(inp1[\"Last Updated\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       8\n",
       "3       6\n",
       "4       6\n",
       "       ..\n",
       "8564    6\n",
       "8565    7\n",
       "8566    7\n",
       "8567    1\n",
       "8568    7\n",
       "Name: Updated month, Length: 8569, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Extract the month from the Last Updated Date\n",
    "\n",
    "inp1[\"Updated month\"]=pd.to_datetime(inp1[\"Last Updated\"]).dt.month\n",
    "inp1[\"Updated month\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Updated month\n",
       "1     4.145050\n",
       "2     4.090385\n",
       "3     4.107963\n",
       "4     4.148326\n",
       "5     4.153894\n",
       "6     4.172865\n",
       "7     4.222399\n",
       "8     4.270607\n",
       "9     4.041406\n",
       "10    4.012739\n",
       "11    4.102020\n",
       "12    4.064939\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Find the average Rating across all the months\n",
    "\n",
    "inp1.groupby([\"Updated month\"])[\"Rating\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "## Plot a line graph\n",
    "\n",
    "inp1.groupby([\"Updated month\"])[\"Rating\"].mean().plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stacked Bar Charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A stacked bar chart breaks down each bar of the bar chart on the basis of a different category\n",
    "- For example, for the Campaign Response bar chart you saw earlier, the stacked bar chart is also showing the Gender bifurcation as well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Stacked](images\\stacked.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>Content Rating</th>\n",
       "      <th>Everyone</th>\n",
       "      <th>Everyone 10+</th>\n",
       "      <th>Mature 17+</th>\n",
       "      <th>Teen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Updated month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.253874e+08</td>\n",
       "      <td>105282000.0</td>\n",
       "      <td>9701210.0</td>\n",
       "      <td>4.415901e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.453720e+08</td>\n",
       "      <td>19821000.0</td>\n",
       "      <td>13021500.0</td>\n",
       "      <td>3.959771e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.953655e+08</td>\n",
       "      <td>30322510.0</td>\n",
       "      <td>9111100.0</td>\n",
       "      <td>7.985031e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.733712e+08</td>\n",
       "      <td>23300000.0</td>\n",
       "      <td>5259000.0</td>\n",
       "      <td>1.616194e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.461068e+09</td>\n",
       "      <td>118173500.0</td>\n",
       "      <td>50140100.0</td>\n",
       "      <td>2.026896e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.127171e+09</td>\n",
       "      <td>217727100.0</td>\n",
       "      <td>145257200.0</td>\n",
       "      <td>4.157166e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.371110e+09</td>\n",
       "      <td>456556000.0</td>\n",
       "      <td>419491910.0</td>\n",
       "      <td>1.143557e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.430943e+09</td>\n",
       "      <td>215821000.0</td>\n",
       "      <td>312981700.0</td>\n",
       "      <td>1.057856e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.603404e+08</td>\n",
       "      <td>24931100.0</td>\n",
       "      <td>2201010.0</td>\n",
       "      <td>2.248310e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.582797e+08</td>\n",
       "      <td>23101000.0</td>\n",
       "      <td>3160000.0</td>\n",
       "      <td>4.562921e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.254247e+08</td>\n",
       "      <td>60310000.0</td>\n",
       "      <td>1070100.0</td>\n",
       "      <td>3.126110e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.671058e+08</td>\n",
       "      <td>8410000.0</td>\n",
       "      <td>12960100.0</td>\n",
       "      <td>5.170710e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Content Rating      Everyone  Everyone 10+   Mature 17+          Teen\n",
       "Updated month                                                        \n",
       "1               7.253874e+08   105282000.0    9701210.0  4.415901e+07\n",
       "2               5.453720e+08    19821000.0   13021500.0  3.959771e+07\n",
       "3               6.953655e+08    30322510.0    9111100.0  7.985031e+07\n",
       "4               9.733712e+08    23300000.0    5259000.0  1.616194e+08\n",
       "5               1.461068e+09   118173500.0   50140100.0  2.026896e+08\n",
       "6               2.127171e+09   217727100.0  145257200.0  4.157166e+08\n",
       "7               6.371110e+09   456556000.0  419491910.0  1.143557e+09\n",
       "8               4.430943e+09   215821000.0  312981700.0  1.057856e+09\n",
       "9               2.603404e+08    24931100.0    2201010.0  2.248310e+07\n",
       "10              2.582797e+08    23101000.0    3160000.0  4.562921e+07\n",
       "11              5.254247e+08    60310000.0    1070100.0  3.126110e+07\n",
       "12              4.671058e+08     8410000.0   12960100.0  5.170710e+07"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a pivot table for Content Rating and updated Month with the values set to Installs\n",
    "pd.pivot_table(data=inp1,values=\"Installs\",index=\"Updated month\",columns=\"Content Rating\",aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Store the table in a separate variable\n",
    "monthly=pd.pivot_table(data=inp1,values=\"Installs\",index=\"Updated month\",columns=\"Content Rating\",aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b559496948>"
      ]
     },
     "execution_count": 119,
     "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": [
    "monthly.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Plot the stacked bar chart.\n",
    "monthly.plot(kind=\"bar\",stacked=True,figsize=[10,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Content Rating</th>\n",
       "      <th>Everyone</th>\n",
       "      <th>Everyone 10+</th>\n",
       "      <th>Mature 17+</th>\n",
       "      <th>Teen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Updated month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.820083</td>\n",
       "      <td>0.119026</td>\n",
       "      <td>0.010968</td>\n",
       "      <td>0.049924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.032083</td>\n",
       "      <td>0.021077</td>\n",
       "      <td>0.064093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.853576</td>\n",
       "      <td>0.037222</td>\n",
       "      <td>0.011184</td>\n",
       "      <td>0.098018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.836553</td>\n",
       "      <td>0.020025</td>\n",
       "      <td>0.004520</td>\n",
       "      <td>0.138902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.797495</td>\n",
       "      <td>0.064503</td>\n",
       "      <td>0.027368</td>\n",
       "      <td>0.110634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.732025</td>\n",
       "      <td>0.074927</td>\n",
       "      <td>0.049987</td>\n",
       "      <td>0.143061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.759305</td>\n",
       "      <td>0.054412</td>\n",
       "      <td>0.049995</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.736330</td>\n",
       "      <td>0.035865</td>\n",
       "      <td>0.052011</td>\n",
       "      <td>0.175794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.839928</td>\n",
       "      <td>0.080434</td>\n",
       "      <td>0.007101</td>\n",
       "      <td>0.072537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.782263</td>\n",
       "      <td>0.069967</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>0.138199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.850111</td>\n",
       "      <td>0.097579</td>\n",
       "      <td>0.001731</td>\n",
       "      <td>0.050579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.864718</td>\n",
       "      <td>0.015569</td>\n",
       "      <td>0.023992</td>\n",
       "      <td>0.095721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Content Rating  Everyone  Everyone 10+  Mature 17+      Teen\n",
       "Updated month                                               \n",
       "1               0.820083      0.119026    0.010968  0.049924\n",
       "2               0.882747      0.032083    0.021077  0.064093\n",
       "3               0.853576      0.037222    0.011184  0.098018\n",
       "4               0.836553      0.020025    0.004520  0.138902\n",
       "5               0.797495      0.064503    0.027368  0.110634\n",
       "6               0.732025      0.074927    0.049987  0.143061\n",
       "7               0.759305      0.054412    0.049995  0.136288\n",
       "8               0.736330      0.035865    0.052011  0.175794\n",
       "9               0.839928      0.080434    0.007101  0.072537\n",
       "10              0.782263      0.069967    0.009571  0.138199\n",
       "11              0.850111      0.097579    0.001731  0.050579\n",
       "12              0.864718      0.015569    0.023992  0.095721"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "new_monthly=monthly[[\"Everyone\",\"Everyone 10+\",\"Mature 17+\",\"Teen\"]].apply(lambda x: x/x.sum(),axis=1)\n",
    "new_monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Plot the stacked bar chart again wrt to the proportions.\n",
    "new_monthly.plot(kind=\"bar\",stacked=True,figsize=[10,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotly is a Python library used for creating interactive visual charts. You can take a look at how you can use it to create aesthetic looking plots with a lot of user-friendly functionalities like hover, zoom, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out this link for installation and documentation:https://plot.ly/python/getting-started/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Install plotly\n",
    "res1=inp1.groupby([\"Updated month\"])[[\"Rating\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Updated month</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.145050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.090385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.107963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.148326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.153894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4.172865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4.222399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>4.270607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.041406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.012739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>4.102020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>4.064939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Updated month    Rating\n",
       "0               1  4.145050\n",
       "1               2  4.090385\n",
       "2               3  4.107963\n",
       "3               4  4.148326\n",
       "4               5  4.153894\n",
       "5               6  4.172865\n",
       "6               7  4.222399\n",
       "7               8  4.270607\n",
       "8               9  4.041406\n",
       "9              10  4.012739\n",
       "10             11  4.102020\n",
       "11             12  4.064939"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Take the table you want to plot in a separate variable\n",
    "res1.reset_index(inplace=True)\n",
    "res1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the plotly libraries\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Updated month=%{x}<br>Rating=%{y}<extra></extra>",
         "legendgroup": "",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "mode": "lines",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "type": "scatter",
         "x": [
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8,
          9,
          10,
          11,
          12
         ],
         "xaxis": "x",
         "y": [
          4.1450495049504985,
          4.090384615384614,
          4.107962962962963,
          4.148326359832637,
          4.153894472361804,
          4.172865275142315,
          4.222399020807817,
          4.270607287449387,
          4.04140625,
          4.012738853503187,
          4.102020202020205,
          4.064939024390241
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
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\"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Monthly avg of rating\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Updated month\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Rating\"}}},\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "#Prepare the plot\n",
    "fig=px.line(res1,x=\"Updated month\",y=\"Rating\",title=\"Monthly avg of rating\")\n",
    "fig.show()"
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  },
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