Google Play Store Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec8462ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import os "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "241a0ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Dell'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we are checking the current working directory \n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4b65025",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we're changing the directory as we want to read the csv file to see the data\n",
    "os.chdir('E:\\Aniket\\BI Stuff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a8612f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'E:\\\\Aniket\\\\BI Stuff'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "230757d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the file fromt our directory \n",
    "df=pd.read_csv('playstore_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d836508d",
   "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>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>19000.000000</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>14000.000000</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>8700.000000</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>25000.000000</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>2800.000000</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>...</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>10836</th>\n",
       "      <td>Sya9a Maroc - FR</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>38</td>\n",
       "      <td>53000.000000</td>\n",
       "      <td>5,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>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>10837</th>\n",
       "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>100+</td>\n",
       "      <td>Free</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>10838</th>\n",
       "      <td>Parkinson Exercices FR</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>9500.000000</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>January 20, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10839</th>\n",
       "      <td>The SCP Foundation DB fr nn5n</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>114</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Books &amp; Reference</td>\n",
       "      <td>January 19, 2015</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10840</th>\n",
       "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>398307</td>\n",
       "      <td>19000.000000</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>July 25, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10841 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     App             Category  \\\n",
       "0         Photo Editor & Candy Camera & Grid & ScrapBook       ART_AND_DESIGN   \n",
       "1                                    Coloring book moana       ART_AND_DESIGN   \n",
       "2      U Launcher Lite – FREE Live Cool Themes, Hide ...       ART_AND_DESIGN   \n",
       "3                                  Sketch - Draw & Paint       ART_AND_DESIGN   \n",
       "4                  Pixel Draw - Number Art Coloring Book       ART_AND_DESIGN   \n",
       "...                                                  ...                  ...   \n",
       "10836                                   Sya9a Maroc - FR               FAMILY   \n",
       "10837                   Fr. Mike Schmitz Audio Teachings               FAMILY   \n",
       "10838                             Parkinson Exercices FR              MEDICAL   \n",
       "10839                      The SCP Foundation DB fr nn5n  BOOKS_AND_REFERENCE   \n",
       "10840      iHoroscope - 2018 Daily Horoscope & Astrology            LIFESTYLE   \n",
       "\n",
       "       Rating Reviews          Size     Installs  Type Price Content Rating  \\\n",
       "0         4.1     159  19000.000000      10,000+  Free     0       Everyone   \n",
       "1         3.9     967  14000.000000     500,000+  Free     0       Everyone   \n",
       "2         4.7   87510   8700.000000   5,000,000+  Free     0       Everyone   \n",
       "3         4.5  215644  25000.000000  50,000,000+  Free     0           Teen   \n",
       "4         4.3     967   2800.000000     100,000+  Free     0       Everyone   \n",
       "...       ...     ...           ...          ...   ...   ...            ...   \n",
       "10836     4.5      38  53000.000000       5,000+  Free     0       Everyone   \n",
       "10837     5.0       4   3600.000000         100+  Free     0       Everyone   \n",
       "10838     NaN       3   9500.000000       1,000+  Free     0       Everyone   \n",
       "10839     4.5     114  21516.529524       1,000+  Free     0     Mature 17+   \n",
       "10840     4.5  398307  19000.000000  10,000,000+  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",
       "...                          ...               ...                 ...   \n",
       "10836                  Education     July 25, 2017                1.48   \n",
       "10837                  Education      July 6, 2018                 1.0   \n",
       "10838                    Medical  January 20, 2017                 1.0   \n",
       "10839          Books & Reference  January 19, 2015  Varies with device   \n",
       "10840                  Lifestyle     July 25, 2018  Varies with device   \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",
       "...                   ...  \n",
       "10836          4.1 and up  \n",
       "10837          4.1 and up  \n",
       "10838          2.2 and up  \n",
       "10839  Varies with device  \n",
       "10840  Varies with device  \n",
       "\n",
       "[10841 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "936fa255",
   "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>Rating</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9367.000000</td>\n",
       "      <td>10841.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.193338</td>\n",
       "      <td>21516.529524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.537431</td>\n",
       "      <td>20746.537567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>18000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>26000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating           Size\n",
       "count  9367.000000   10841.000000\n",
       "mean      4.193338   21516.529524\n",
       "std       0.537431   20746.537567\n",
       "min       1.000000       8.500000\n",
       "25%       4.000000    5900.000000\n",
       "50%       4.300000   18000.000000\n",
       "75%       4.500000   26000.000000\n",
       "max      19.000000  100000.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "acb288bf",
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85c9392e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10841, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f0462729",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count the number of missing values in eacg column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d6e8fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing the column name of the given dataset\n",
    "df.rename({'Content Rating':'Content_Rating','Last Updated':'Last_Updated','Current Ver':'Current_Ver','Android Ver':'Android_Ver' },axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8a2297",
   "metadata": {},
   "source": [
    "# Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8b4b3b",
   "metadata": {},
   "source": [
    "# 1. Data clean up – Missing value treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07494de5",
   "metadata": {},
   "source": [
    "# a. Drop records where rating is missing since rating is our target/study variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53e41b69",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking which columns has null values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58d422e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   23,   113,   123,   126,   129,   130,   134,   163,   180,\n",
       "              185,\n",
       "            ...\n",
       "            10816, 10818, 10821, 10822, 10823, 10824, 10825, 10831, 10835,\n",
       "            10838],\n",
       "           dtype='int64', length=1474)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Rating'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14cfb4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the null values from the Rating column\n",
    "df.drop(index=df[df['Rating'].isnull()].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb219f41",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065d039e",
   "metadata": {},
   "source": [
    "# b. Check the null values for the Android Ver column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5042494b",
   "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>11000.000000</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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Android_Ver'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db3ba835",
   "metadata": {},
   "source": [
    "# i. Are all 3 records having the same problem?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac5ae58",
   "metadata": {},
   "source": [
    "Yes, all these 3 records having the same problem in the Andoid Ver column "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f24452",
   "metadata": {},
   "source": [
    "# ii. Drop the 3rd record i.e. record for “Life Made WIFI …”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd99aa7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([10472], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "94d16acb",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Android_Ver'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1adec15a",
   "metadata": {},
   "source": [
    "We have successfully droped the 3rd record"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ecd8fa7",
   "metadata": {},
   "source": [
    "# iii. Replace remaining missing values with the mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "213b1d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.1 and up'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we are finding the mode value of the Android Ver Column and then storing it in the new variable called x\n",
    "x=df['Android_Ver'].mode()[0]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12537738",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling those null values with the mode() value \n",
    "df['Android_Ver'].fillna(x,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2d2d6a7f",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, Content_Rating, Genres, Last_Updated, Current_Ver, Android_Ver]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Android_Ver'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd3cc5a",
   "metadata": {},
   "source": [
    "# c. Current ver – replace with most common value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7ad5d0c4",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02c694ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Varies with device'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the mode value of the Current Ver column and storing it in the new variable called y\n",
    "y=df['Current_Ver'].mode()[0]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d81abe6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling the null values with the mode \n",
    "df['Current_Ver'].fillna(y, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c453fdca",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6beaf01c",
   "metadata": {},
   "source": [
    "# 2. Data clean up – correcting the data types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78610d3",
   "metadata": {},
   "source": [
    "# a. Which all variables need to be brought to numeric types?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "604e06ac",
   "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>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",
       "  </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",
       "\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",
       "\n",
       "                      Genres      Last_Updated Current_Ver   Android_Ver  \n",
       "0               Art & Design   January 7, 2018       1.0.0  4.0.3 and up  \n",
       "1  Art & Design;Pretend Play  January 15, 2018       2.0.0  4.0.3 and up  \n",
       "2               Art & Design    August 1, 2018       1.2.4  4.0.3 and up  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "25bdda83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9366 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9366 non-null   object \n",
      " 1   Category        9366 non-null   object \n",
      " 2   Rating          9366 non-null   float64\n",
      " 3   Reviews         9366 non-null   object \n",
      " 4   Size            9366 non-null   float64\n",
      " 5   Installs        9366 non-null   object \n",
      " 6   Type            9366 non-null   object \n",
      " 7   Price           9366 non-null   object \n",
      " 8   Content_Rating  9366 non-null   object \n",
      " 9   Genres          9366 non-null   object \n",
      " 10  Last_Updated    9366 non-null   object \n",
      " 11  Current_Ver     9366 non-null   object \n",
      " 12  Android_Ver     9366 non-null   object \n",
      "dtypes: float64(2), object(11)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c64cf13",
   "metadata": {},
   "source": [
    "From this information, we can see that, the column Reviews, Installs and Price have the numeric data so we can convert\n",
    "these columns into the numeric types, but there are also 2 columns which are Current Ver and Android Ver can be conevert\n",
    "into the numeric type but in those columnn there are some strig values which we can drop it or replace it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a290cd7",
   "metadata": {},
   "source": [
    "# b. Price variable – remove $ sign and convert to float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "154563ee",
   "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>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",
       "  </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",
       "\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",
       "\n",
       "                      Genres      Last_Updated Current_Ver   Android_Ver  \n",
       "0               Art & Design   January 7, 2018       1.0.0  4.0.3 and up  \n",
       "1  Art & Design;Pretend Play  January 15, 2018       2.0.0  4.0.3 and up  \n",
       "2               Art & Design    August 1, 2018       1.2.4  4.0.3 and up  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fc8bd9d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-44845c86c2f8>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "  df['Price'] = pd.to_numeric(df['Price'].str.replace('$',''))\n"
     ]
    }
   ],
   "source": [
    "# here we are replacing the $ amd also converting the type of it to the numeric\n",
    "df['Price'] = pd.to_numeric(df['Price'].str.replace('$',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0ad06ae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9366 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9366 non-null   object \n",
      " 1   Category        9366 non-null   object \n",
      " 2   Rating          9366 non-null   float64\n",
      " 3   Reviews         9366 non-null   object \n",
      " 4   Size            9366 non-null   float64\n",
      " 5   Installs        9366 non-null   object \n",
      " 6   Type            9366 non-null   object \n",
      " 7   Price           9366 non-null   float64\n",
      " 8   Content_Rating  9366 non-null   object \n",
      " 9   Genres          9366 non-null   object \n",
      " 10  Last_Updated    9366 non-null   object \n",
      " 11  Current_Ver     9366 non-null   object \n",
      " 12  Android_Ver     9366 non-null   object \n",
      "dtypes: float64(3), object(10)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0811d225",
   "metadata": {},
   "source": [
    "# c. Installs – remove ‘,’ and ‘+’ sign, convert to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3e4fc8d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-ddbdebd675fc>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "  df['Installs'] = pd.to_numeric(df['Installs'].str.replace(',','').str.replace(\"+\",\"\"))\n"
     ]
    }
   ],
   "source": [
    "# replacing the given condition using this syntax\n",
    "df['Installs'] = pd.to_numeric(df['Installs'].str.replace(',','').str.replace(\"+\",\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a851f7bc",
   "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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \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  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3968635e",
   "metadata": {},
   "source": [
    "Here you can see that, there are \",\" and \"+\" from the given column "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c1522c",
   "metadata": {},
   "source": [
    "# d. Convert all other identified columns to numeric¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7795e831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9366 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             9366 non-null   object \n",
      " 1   Category        9366 non-null   object \n",
      " 2   Rating          9366 non-null   float64\n",
      " 3   Reviews         9366 non-null   object \n",
      " 4   Size            9366 non-null   float64\n",
      " 5   Installs        9366 non-null   int64  \n",
      " 6   Type            9366 non-null   object \n",
      " 7   Price           9366 non-null   float64\n",
      " 8   Content_Rating  9366 non-null   object \n",
      " 9   Genres          9366 non-null   object \n",
      " 10  Last_Updated    9366 non-null   object \n",
      " 11  Current_Ver     9366 non-null   object \n",
      " 12  Android_Ver     9366 non-null   object \n",
      "dtypes: float64(3), int64(1), object(9)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe43c808",
   "metadata": {},
   "source": [
    "From the given info, we have already converted the identifies columns into the numeric type so now, there are no such \n",
    "columns which we need to convert to numeric type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec645e3",
   "metadata": {},
   "source": [
    "# 3. Sanity checks – check for the following and handle accordingly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a6b86bd",
   "metadata": {},
   "source": [
    "# a. Avg. rating should be between 1 and 5, as only these values are allowed on the play store."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f95286",
   "metadata": {},
   "source": [
    "# i. Are there any such records? Drop if so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4b0a2262",
   "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>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",
       "    </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",
       "    </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",
       "    </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",
       "\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",
       "\n",
       "                      Genres      Last_Updated Current_Ver   Android_Ver  \n",
       "0               Art & Design   January 7, 2018       1.0.0  4.0.3 and up  \n",
       "1  Art & Design;Pretend Play  January 15, 2018       2.0.0  4.0.3 and up  \n",
       "2               Art & Design    August 1, 2018       1.2.4  4.0.3 and up  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4572ce77",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, Content_Rating, Genres, Last_Updated, Current_Ver, Android_Ver]\n",
       "Index: []"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Rating']>5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e94399",
   "metadata": {},
   "source": [
    "There are no such records which have rating greater than 5, so we don't need to drop anything from the given dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c177f8e3",
   "metadata": {},
   "source": [
    "# b. Reviews should not be more than installs as only those who installed can review the app."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a203e7c",
   "metadata": {},
   "source": [
    "# i. Are there any such records? Drop if so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7bf381ea",
   "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>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",
       "    </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",
       "    </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",
       "\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",
       "\n",
       "                      Genres      Last_Updated Current_Ver   Android_Ver  \n",
       "0               Art & Design   January 7, 2018       1.0.0  4.0.3 and up  \n",
       "1  Art & Design;Pretend Play  January 15, 2018       2.0.0  4.0.3 and up  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1b2177e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing the type of the Reviews column\n",
    "df['Reviews']=pd.to_numeric(df['Reviews'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b7c5704e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Reviews']>df['Installs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "db0d8828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2454, 4663, 5917, 6700, 7402, 8591, 10697], dtype='int64')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Reviews']>df['Installs']].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5ed3f485",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index=df[df['Reviews']>df['Installs']].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "679739ce",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, Content_Rating, Genres, Last_Updated, Current_Ver, Android_Ver]\n",
       "Index: []"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Reviews']>df['Installs']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a629c9",
   "metadata": {},
   "source": [
    "After performing the operations, we found 7 such records which were greater than the installs so we droped those records\n",
    "So, now there are no such records that is Review which are greater than the installs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce269ba",
   "metadata": {},
   "source": [
    "# 4. Identify and handle outliers –"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7541b4fb",
   "metadata": {},
   "source": [
    "# a. Price column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18df6ec7",
   "metadata": {},
   "source": [
    "# i. Make suitable plot to identify outliers in price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "083a1f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "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": [
    "df.boxplot('Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59ad735",
   "metadata": {},
   "source": [
    "So, after ploting the boxplot for the Price column, we can see that there are some outliers present in the price column\n",
    "And from this we can assume that, there are outliers beyond value 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c05510",
   "metadata": {},
   "source": [
    "# ii. Do you expect apps on the play store to cost $200? Check out these cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c505e9f8",
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Price']>200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b560c0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Price']>200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "82df5271",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([4197, 4362, 4367, 5351, 5354, 5355, 5356, 5357, 5358, 5359, 5362,\n",
       "            5364, 5366, 5369, 5373],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Price']>200].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "febeb891",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index=df[df['Price']>200].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "abce98cf",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, Content_Rating, Genres, Last_Updated, Current_Ver, Android_Ver]\n",
       "Index: []"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Price']>200]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce7f6a71",
   "metadata": {},
   "source": [
    "Here we have droped the records which are greater than 200 (cost more than 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2184a589",
   "metadata": {},
   "source": [
    "# iii. After dropping the useless records, make the suitable plot again to identify outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5b7654a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAATsElEQVR4nO3df4xV553f8fcXMDOO2diMf4xHCSzbxs4yZhW3O0pS7FYzJq7ibVX4I14HdSXUUo+Q2um2tFqo+SPqH0gg1aiWtRWFZXdRdneC7U1kFG3cWGRuNzbtNuBkN8BkxTYkkBqDDUa740TjAN/+MXdYGANzhrl37jye90sanXOee+49X6TDR4+e8+OJzESSVJ55rS5AknRrDHBJKpQBLkmFMsAlqVAGuCQVasFMHuyee+7JZcuWzeQhpUree+897rjjjlaXIV3X4cOH38nMeye2z2iAL1u2jEOHDs3kIaVKarUavb29rS5Duq6I+PH12h1CkaRCGeCSVCgDXJIKZYBLUqEMcEkqVKUAj4h/HxFHI+JIRAxGRHtEdETEqxFxvL5c3OxipUYbGBigvb2dvr4+2tvbGRgYaHVJUmWT3kYYER8D/i3QnZk/i4gXgC8C3cCBzNwWEZuBzcCmplYrNdDAwAA7d+5k+/btdHd3c+zYMTZtGjuFn3/++RZXJ02u6hDKAuD2iFgAfAR4E1gN7K1/vhdY0/DqpCbavXs327dvZ+PGjbS3t7Nx40a2b9/O7t27W12aVMmkPfDM/H8R8V+Ak8DPgG9m5jcjojMzT9f3OR0R913v+xHRD/QDdHZ2UqvVGla8NB2jo6N0d3dTq9UYGRmhVqvR3d3N6Oio56mKUGUIZTFjve1fAi4AL0bEb1Q9QGbuAnYB9PT0pE+7abZoa2vj2LFjbNy48cqTmDt27KCtrc2nMlWEKo/Sfw44kZlvA0TEV4GVwJmI6Kr3vruAs02sU2q4p59++sqYd3d3Nzt27GDTpk1s2LChxZVJ1VQJ8JPAZyPiI4wNoawCDgHvAeuAbfXly80qUmqG8QuVzzzzDKOjo7S1tbFhwwYvYKoYUWVOzIj4z8BTwEXgu8C/AhYBLwBLGQv5JzPz/M1+p6enJ32ZlWYjX2al2SwiDmdmz8T2Sm8jzMwvAV+a0DzKWG9cktQCPokpSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSrUpAEeEZ+MiO9d9ffXEfHvIqIjIl6NiOP15eKZKFiSNGbSAM/Mv8zMhzPzYeBXgZ8CXwM2Awcy8wHgQH1bkjRDpjqEsgr4v5n5Y2A1sLfevhdY08C6JEmTqDQn5lW+CAzW1zsz8zRAZp6OiPuu94WI6Af6ATo7O6nVardYqtQ8IyMjnpsqTqVZ6QEiYiHwJvBQZp6JiAuZeddVn7+bmTcdB3dWes1Wzkqv2exGs9JPZQjlCeCNzDxT3z4TEV31H+8Czk6/TElSVVMJ8LX87fAJwH5gXX19HfByo4qSJE2uUoBHxEeAx4GvXtW8DXg8Io7XP9vW+PIkSTdS6SJmZv4UuHtC2znG7kqRJLWAT2JKUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUqKoTOtwVES9FxA8iYjgi/kFEdETEqxFxvL686XyYkqTGqtoDfw54JTN/GfgUMAxsBg5k5gPAgfq2JGmGTBrgEfFR4B8BewAy8/3MvACsBvbWd9sLrGlOiZKk66nSA/87wNvA70XEdyPidyLiDqAzM08D1Jf3NbFOSdIEVebEXAD8fWAgM/8sIp5jCsMlEdEP9AN0dnZSq9VupU6pqUZGRjw3VZzIzJvvEHE/8L8zc1l9+x8yFuCfAHoz83REdAG1zPzkzX6rp6cnDx061JDCpUaq1Wr09va2ugzpuiLicGb2TGyfdAglM98CTkXEeDivAo4B+4F19bZ1wMsNqlWSVEGVIRSAAeAPI2Ih8EPgXzAW/i9ExHrgJPBkc0qUJF1PpQDPzO8BH+i+M9YblyS1gE9iSlKhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKVWlCh4j4EfA3wCXgYmb2REQHsA9YBvwI+PXMfLc5ZUqSJppKD7wvMx++amLNzcCBzHwAOMAUZqqXJE3fdIZQVgN76+t7gTXTrkaSVFnVSY0T+GZEJPDfM3MX0JmZpwEy83RE3He9L0ZEP9AP0NnZSa1Wm37VUoONjIx4bqo4VQP8kcx8sx7Sr0bED6oeoB72uwB6enqyt7d36lVKTVar1fDcVGkqDaFk5pv15Vnga8CngTMR0QVQX55tVpGSpA+aNMAj4o6I+IXxdeAfA0eA/cC6+m7rgJebVaQk6YOqDKF0Al+LiPH9/ygzX4mI7wAvRMR64CTwZPPKlCRNNGmAZ+YPgU9dp/0csKoZRUmSJueTmJJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQlUO8IiYHxHfjYiv17c7IuLViDheXy5uXplScwwODrJixQpWrVrFihUrGBwcbHVJUmVVZ6UH+E1gGPhofXszcCAzt0XE5vr2pgbXJzXN4OAgW7ZsYc+ePVy6dIn58+ezfv16ANauXdvi6qTJVeqBR8THgX8C/M5VzauBvfX1vcCahlYmNdnWrVvZs2cPfX19LFiwgL6+Pvbs2cPWrVtbXZpUSdUe+H8Ffgv4havaOjPzNEBmno6I+673xYjoB/oBOjs7qdVqt1ys1EjDw8NcunSJWq3GyMgItVqNS5cuMTw87HmqIkwa4BHxT4GzmXk4InqneoDM3AXsAujp6cne3in/hNQUy5cvZ/78+fT29lKr1ejt7WVoaIjly5fjeaoSVOmBPwL8s4j4NaAd+GhE/AFwJiK66r3vLuBsMwuVGm3Lli089dRT3HHHHZw8eZKlS5fy3nvv8dxzz7W6NKmSScfAM/M/ZebHM3MZ8EXgW5n5G8B+YF19t3XAy02rUmqyzGx1CdKUTec+8G3A4xFxHHi8vi0VY+vWrezbt48TJ07wrW99ixMnTrBv3z4vYqoYU7mNkMysAbX6+jlgVeNLkmbG8PAwjz766DVtjz76KMPDwy2qSJoan8TUnLV8+XJee+21a9pee+01li9f3qKKpKkxwDVnbdmyhfXr1zM0NMTFixcZGhpi/fr1bNmypdWlSZVMaQhF+jAZf9pyYGCA4eFhli9fztatW30KU8WImbz63tPTk4cOHZqx40lVjd8HLs1GEXE4M3smtjuEIkmFMsAlqVAGuOa0gYEB2tvb6evro729nYGBgVaXJFXmRUzNWQMDA+zcuZPt27fT3d3NsWPH2LRp7I3Izz//fIurkyZnD1xz1u7du9m+fTsbN26kvb2djRs3sn37dnbv3t3q0qRKDHDNWaOjo2zYsOGatg0bNjA6OtqiiqSpMcA1Z7W1tbFz585r2nbu3ElbW1uLKpKmxjFwzVlPP/30lTHv7u5uduzYwaZNmz7QK5dmKwNcc9b4hcpnnnmG0dFR2tra2LBhgxcwVQyfxJTwSUzNbj6JKUkfMga4JBWqyqTG7cCfAm31/V/KzC9FRAewD1gG/Aj49cx8t3mlSo139913c/78+SvbHR0dnDt3roUVSdVV6YGPAo9l5qeAh4HPR8Rngc3Agcx8ADhQ35aKMR7eDz30EIODgzz00EOcP3+eu+++u9WlSZVUmdQ4M3Okvnlb/S+B1cDeevteYE0zCpSaZTy8jxw5wv3338+RI0euhLhUgkp3oUTEfOAw8AngtzNzU0RcyMy7rtrn3cxcfJ3v9gP9AJ2dnb/6la98pVG1S9PS19fH4OAg999/PyMjIyxatIi33nqLtWvXMjQ01OrypCv6+vquexfKlG4jjIi7gK8BA8BrVQL8at5GqNkkIq70wMdvI1yxYgVHjx5lJm+vlSbTkNsIM/MCY7PSfx44ExFd9R/vAs5Ov0xp5nR0dHD06FFWrFjBW2+9dSW8Ozo6Wl2aVMmkAR4R99Z73kTE7cDngB8A+4F19d3WAS83qUapKc6dO3clxNeuXXslvL0LRaWo0gPvAoYi4i+A7wCvZubXgW3A4xFxHHi8vi0V5dy5c2QmQ0NDZKbhraJMeh94Zv4F8Peu034OWNWMoiRJk/NJTEkqlAEuSYUywCWpUL4PXHPa0qVLOXXq1JXtJUuWcPLkyRZWJFVnD1xz1nh4r1y5khdffJGVK1dy6tQpli5d2urSpEoMcM1Z4+H9+uuvc8899/D6669fCXGpBAa45rSXXnrpptvSbGaAa077whe+cNNtaTYzwDVnLVmyhIMHD/LII4/wzjvv8Mgjj3Dw4EGWLFnS6tKkSrwLRXPWyZMnWbp0KQcPHuTgwYOAd6GoLPbANaedPHnymnehGN4qiQEuSYUywCWpUAa4JBXKAJekQlWZkWdJRAxFxHBEHI2I36y3d0TEqxFxvL686XyYkqTGqtIDvwj8h8xcDnwW+NcR0Q1sBg5k5gPAgfq2VJSlS5cSEfT19RERvgdFRZk0wDPzdGa+UV//G2AY+BiwGthb320vsKZJNUpNMf4yq/b2dgDa29t9mZWKMqUHeSJiGWPTq/0Z0JmZp2Es5CPivht8px/oB+js7KRWq02nXqlhTp06xbx587jzzjsZHR3lzjvv5P333+fUqVOepypCZGa1HSMWAf8T2JqZX42IC5l511Wfv5uZNx0H7+npyUOHDk2nXqlhIoJ7772Xffv2cenSJebPn89TTz3F22+/TdX/F9JMiIjDmdkzsb1SDzwibgP+GPjDzPxqvflMRHTVe99dwNnGlSvNjJ///Oc88cQTjI6O0tbWxu23397qkqTKqtyFEsAeYDgzd1z10X5gXX19HfBy48uTmuvChQt0dXXx5S9/ma6uLi5cuNDqkqTKJh1CiYhHgW8D3wcu15ufYWwc/AVgKXASeDIzz9/stxxC0Wwy1je5PodQNJvc8hBKZr4G3OhMXzXdwqRWWrBgARcvXrzhtjSb+SSm5qyI4OLFiyxevJiIYPHixVy8ePGmPXNpNjHANWeND5MsXLiQzGThwoXXtEuznQGuOW3RokWcPz926eb8+fMsWrSoxRVJ1RngmtMefPBBHnzwQebNm3dlXSqFU6ppTnvjjTeurB89erSFlUhTZw9ckgplgGvOu+22265ZSqUwwDXndXR0XLOUSuEYuOa8wcHBKy+zeuyxx1pdjlSZAa45z9BWqRxCkaRCGeCSVCgDXHPe+LtPfAeKSmOAa05btGgRly9fZmhoiMuXL/sovYriRUzNaSMjI8ybN4/MJCJ8kZWKYg9cc954aBveKk2VKdV+NyLORsSRq9o6IuLViDheX950MmNJUuNV6YH/PvD5CW2bgQOZ+QBwoL4tSZpBkwZ4Zv4pMHGuy9XA3vr6XmBNY8uSZk5mMjQ05BCKinOrFzE7M/M0QGaejoj7brRjRPQD/QCdnZ3UarVbPKTUHNe7fdDzVCWYdFZ6gIhYBnw9M1fUty9k5l1Xff5uZk46Du6s9JpNnJVepbjRrPS3ehfKmYjoqv9wF3B2OsVJkqbuVgN8P7Cuvr4OeLkx5UiSqqpyG+Eg8L+AT0bETyJiPbANeDwijgOP17elIj377LN84xvf4Nlnn211KdKUVBoDbxTHwDWbOAauUjR6DFyS1GIGuCQVygCXpEIZ4Jrz5s2bd81SKoVnrOa8y5cvX7OUSmGAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhZpWgEfE5yPiLyPiryJic6OKkiRN7pYDPCLmA78NPAF0A2sjortRhUmSbm46PfBPA3+VmT/MzPeBrwCrG1OWJGkyC6bx3Y8Bp67a/gnwmYk7RUQ/0A/Q2dlJrVabxiE1Fw38eKApv7vi91fc8LNf2fsrTTnm87/4fFN+V3PTdAL8ejPCfmAm2MzcBeyCsUmNe3t7p3FIzUXf5/tNP0atVsNzU6WZzhDKT4AlV21/HHhzeuVIkqqaToB/B3ggIn4pIhYCXwT2N6YsSdJkbnkIJTMvRsS/Af4HMB/43cw82rDKJEk3NZ0xcDLzT4A/aVAtkqQp8ElMSSqUAS5JhTLAJalQBrgkFSoyP/DsTfMOFvE28OMZO6BU3T3AO60uQrqBX8zMeyc2zmiAS7NVRBzKzJ5W1yFNhUMoklQoA1ySCmWAS2N2tboAaaocA5ekQtkDl6RCGeCSVCgDXB9aEXEpIr4XEUci4sWI+MgN9js407VJjWCA68PsZ5n5cGauAN4HNlz9YX1ibjJzZSuKk6bLANdc8W3gExHRGxFDEfFHMDZXW0SMjO8UEb8VEd+PiD+PiG31tr8bEa9ExOGI+HZE/HJr/gnStab1PnCpBBGxAHgCeKXe9GlgRWaemLDfE8Aa4DOZ+dOI6Kh/tAvYkJnHI+IzwH8DHpuR4qWbMMD1YXZ7RHyvvv5tYA+wEvg/E8O77nPA72XmTwEy83xELKp/58WIK/N4tzW1aqkiA1wfZj/LzIevbqiH8Hs32D+AiQ9GzAMuTPwdaTZwDFz6W98E/uX43SoR0ZGZfw2ciIgn620REZ9qZZHSOANcqsvMV4D9wKH60Mt/rH/0z4H1EfHnwFFgdWsqlK7lo/SSVCh74JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFer/A5Yh0H38CE+QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot('Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70bf09d",
   "metadata": {},
   "source": [
    "After ploting the boxplot for the price column, we got some outliers which we can see and we can say that the outliers \n",
    "are beyond 50 or we can also say that there are outliers beyond the average line so we can also drop that "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35082b91",
   "metadata": {},
   "source": [
    "# iv. Limit data to records with price < $30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7c978a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9338"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Price']<30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa781b2",
   "metadata": {},
   "source": [
    "# b. Reviews column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035b9113",
   "metadata": {},
   "source": [
    "# i. Make suitable plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9a6af3c8",
   "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": [
    "h=df['Reviews']\n",
    "plt.hist(h)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f140fbe3",
   "metadata": {},
   "source": [
    "# ii. Limit data to apps with < 1 Million reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d3b6f54a",
   "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>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",
       "    </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",
       "    </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",
       "    </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",
       "\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",
       "\n",
       "                      Genres      Last_Updated Current_Ver   Android_Ver  \n",
       "0               Art & Design   January 7, 2018       1.0.0  4.0.3 and up  \n",
       "1  Art & Design;Pretend Play  January 15, 2018       2.0.0  4.0.3 and up  \n",
       "2               Art & Design    August 1, 2018       1.2.4  4.0.3 and up  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "67204049",
   "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>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>19000.000000</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",
       "    </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>14000.000000</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",
       "    </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>8700.000000</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",
       "    </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>25000.000000</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",
       "    </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>2800.000000</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",
       "    </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>10834</th>\n",
       "      <td>FR Calculator</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2600.000000</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>10836</th>\n",
       "      <td>Sya9a Maroc - FR</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>38</td>\n",
       "      <td>53000.000000</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>10837</th>\n",
       "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3600.000000</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",
       "    <tr>\n",
       "      <th>10839</th>\n",
       "      <td>The SCP Foundation DB fr nn5n</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>114</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>1000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Books &amp; Reference</td>\n",
       "      <td>January 19, 2015</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10840</th>\n",
       "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>398307</td>\n",
       "      <td>19000.000000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>July 25, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8640 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     App             Category  \\\n",
       "0         Photo Editor & Candy Camera & Grid & ScrapBook       ART_AND_DESIGN   \n",
       "1                                    Coloring book moana       ART_AND_DESIGN   \n",
       "2      U Launcher Lite – FREE Live Cool Themes, Hide ...       ART_AND_DESIGN   \n",
       "3                                  Sketch - Draw & Paint       ART_AND_DESIGN   \n",
       "4                  Pixel Draw - Number Art Coloring Book       ART_AND_DESIGN   \n",
       "...                                                  ...                  ...   \n",
       "10834                                      FR Calculator               FAMILY   \n",
       "10836                                   Sya9a Maroc - FR               FAMILY   \n",
       "10837                   Fr. Mike Schmitz Audio Teachings               FAMILY   \n",
       "10839                      The SCP Foundation DB fr nn5n  BOOKS_AND_REFERENCE   \n",
       "10840      iHoroscope - 2018 Daily Horoscope & Astrology            LIFESTYLE   \n",
       "\n",
       "       Rating  Reviews          Size  Installs  Type  Price Content_Rating  \\\n",
       "0         4.1      159  19000.000000     10000  Free    0.0       Everyone   \n",
       "1         3.9      967  14000.000000    500000  Free    0.0       Everyone   \n",
       "2         4.7    87510   8700.000000   5000000  Free    0.0       Everyone   \n",
       "3         4.5   215644  25000.000000  50000000  Free    0.0           Teen   \n",
       "4         4.3      967   2800.000000    100000  Free    0.0       Everyone   \n",
       "...       ...      ...           ...       ...   ...    ...            ...   \n",
       "10834     4.0        7   2600.000000       500  Free    0.0       Everyone   \n",
       "10836     4.5       38  53000.000000      5000  Free    0.0       Everyone   \n",
       "10837     5.0        4   3600.000000       100  Free    0.0       Everyone   \n",
       "10839     4.5      114  21516.529524      1000  Free    0.0     Mature 17+   \n",
       "10840     4.5   398307  19000.000000  10000000  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",
       "10834                  Education     June 18, 2017               1.0.0   \n",
       "10836                  Education     July 25, 2017                1.48   \n",
       "10837                  Education      July 6, 2018                 1.0   \n",
       "10839          Books & Reference  January 19, 2015  Varies with device   \n",
       "10840                  Lifestyle     July 25, 2018  Varies with device   \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",
       "...                   ...  \n",
       "10834          4.1 and up  \n",
       "10836          4.1 and up  \n",
       "10837          4.1 and up  \n",
       "10839  Varies with device  \n",
       "10840  Varies with device  \n",
       "\n",
       "[8640 rows x 13 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Reviews']<1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6fad55f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8640"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Reviews']<1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cebc93fe",
   "metadata": {},
   "source": [
    "# c. Installs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bb3de6",
   "metadata": {},
   "source": [
    "# i. What is the 95th percentile of the installs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "247221d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000000.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the values of the Installs column with 95th percentile \n",
    "df['Installs'].quantile(0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a57e76a",
   "metadata": {},
   "source": [
    "# ii. Drop records having a value more than the 95th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "35286cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000000.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Installs'].quantile(0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6b809709",
   "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>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>335</th>\n",
       "      <td>Messenger – Text and Video Chat for Free</td>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56642847</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Communication</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>336</th>\n",
       "      <td>WhatsApp Messenger</td>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>4.4</td>\n",
       "      <td>69119316</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Communication</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>338</th>\n",
       "      <td>Google Chrome: Fast &amp; Secure</td>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9642995</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Communication</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>340</th>\n",
       "      <td>Gmail</td>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4604324</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Communication</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>5395</th>\n",
       "      <td>Google Photos</td>\n",
       "      <td>PHOTOGRAPHY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>10847682</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Photography</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>5596</th>\n",
       "      <td>Samsung Health</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>4.3</td>\n",
       "      <td>480208</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>500000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Health &amp; Fitness</td>\n",
       "      <td>July 31, 2018</td>\n",
       "      <td>5.17.2.009</td>\n",
       "      <td>5.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5856</th>\n",
       "      <td>Google Play Games</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.3</td>\n",
       "      <td>7168735</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>Entertainment</td>\n",
       "      <td>July 16, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7536</th>\n",
       "      <td>Security Master - Antivirus, VPN, AppLock, Boo...</td>\n",
       "      <td>TOOLS</td>\n",
       "      <td>4.7</td>\n",
       "      <td>24900999</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>500000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Tools</td>\n",
       "      <td>August 4, 2018</td>\n",
       "      <td>4.6.6</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9844</th>\n",
       "      <td>Google News</td>\n",
       "      <td>NEWS_AND_MAGAZINES</td>\n",
       "      <td>3.9</td>\n",
       "      <td>878065</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>Free</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>News &amp; Magazines</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>5.2.0</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    App             Category  \\\n",
       "152                                   Google Play Books  BOOKS_AND_REFERENCE   \n",
       "335            Messenger – Text and Video Chat for Free        COMMUNICATION   \n",
       "336                                  WhatsApp Messenger        COMMUNICATION   \n",
       "338                        Google Chrome: Fast & Secure        COMMUNICATION   \n",
       "340                                               Gmail        COMMUNICATION   \n",
       "...                                                 ...                  ...   \n",
       "5395                                      Google Photos          PHOTOGRAPHY   \n",
       "5596                                     Samsung Health   HEALTH_AND_FITNESS   \n",
       "5856                                  Google Play Games               FAMILY   \n",
       "7536  Security Master - Antivirus, VPN, AppLock, Boo...                TOOLS   \n",
       "9844                                        Google News   NEWS_AND_MAGAZINES   \n",
       "\n",
       "      Rating   Reviews          Size    Installs  Type  Price Content_Rating  \\\n",
       "152      3.9   1433233  21516.529524  1000000000  Free    0.0           Teen   \n",
       "335      4.0  56642847  21516.529524  1000000000  Free    0.0       Everyone   \n",
       "336      4.4  69119316  21516.529524  1000000000  Free    0.0       Everyone   \n",
       "338      4.3   9642995  21516.529524  1000000000  Free    0.0       Everyone   \n",
       "340      4.3   4604324  21516.529524  1000000000  Free    0.0       Everyone   \n",
       "...      ...       ...           ...         ...   ...    ...            ...   \n",
       "5395     4.5  10847682  21516.529524  1000000000  Free    0.0       Everyone   \n",
       "5596     4.3    480208  70000.000000   500000000  Free    0.0       Everyone   \n",
       "5856     4.3   7168735  21516.529524  1000000000  Free    0.0           Teen   \n",
       "7536     4.7  24900999  21516.529524   500000000  Free    0.0       Everyone   \n",
       "9844     3.9    878065  13000.000000  1000000000  Free    0.0           Teen   \n",
       "\n",
       "                 Genres    Last_Updated         Current_Ver  \\\n",
       "152   Books & Reference  August 3, 2018  Varies with device   \n",
       "335       Communication  August 1, 2018  Varies with device   \n",
       "336       Communication  August 3, 2018  Varies with device   \n",
       "338       Communication  August 1, 2018  Varies with device   \n",
       "340       Communication  August 2, 2018  Varies with device   \n",
       "...                 ...             ...                 ...   \n",
       "5395        Photography  August 1, 2018  Varies with device   \n",
       "5596   Health & Fitness   July 31, 2018          5.17.2.009   \n",
       "5856      Entertainment   July 16, 2018  Varies with device   \n",
       "7536              Tools  August 4, 2018               4.6.6   \n",
       "9844   News & Magazines  August 1, 2018               5.2.0   \n",
       "\n",
       "             Android_Ver  \n",
       "152   Varies with device  \n",
       "335   Varies with device  \n",
       "336   Varies with device  \n",
       "338   Varies with device  \n",
       "340   Varies with device  \n",
       "...                  ...  \n",
       "5395  Varies with device  \n",
       "5596          5.0 and up  \n",
       "5856  Varies with device  \n",
       "7536  Varies with device  \n",
       "9844          4.4 and up  \n",
       "\n",
       "[130 rows x 13 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Installs']>df['Installs'].quantile(0.95)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "36c4a95a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 152,  335,  336,  338,  340,  341,  342,  347,  371,  378,\n",
       "            ...\n",
       "            4222, 4234, 4365, 4566, 4676, 5395, 5596, 5856, 7536, 9844],\n",
       "           dtype='int64', length=130)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Installs']>df['Installs'].quantile(0.95)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "802c2c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here dropping all the values which are more than 95th percentile \n",
    "df.drop(index=df[df['Installs']>df['Installs'].quantile(0.95)].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a643a84f",
   "metadata": {},
   "source": [
    "Here, we have droped all the Installs Values which are more than 95th percentile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4600400",
   "metadata": {},
   "source": [
    "# Data analysis to answer business questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f05d51",
   "metadata": {},
   "source": [
    "# 5. What is the distribution of ratings like? (use Seaborn) More skewed towards higher/lower values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ce7c3ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.Rating)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "38f9aaab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.8416513505231236"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the skewed value for a Rating column\n",
    "df['Rating'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9fd18f0",
   "metadata": {},
   "source": [
    "# a. How do you explain this?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc4b31c",
   "metadata": {},
   "source": [
    "So, from the above plot we can see that the skewed value is at the left side which is negative, so we can say that the given column or the given plot is left skewed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d074540d",
   "metadata": {},
   "source": [
    "# b. What is the implication of this on your analysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c85da1",
   "metadata": {},
   "source": [
    "If we have more data then generally there is skewed value present in that dataset.And if there is too many skewed values present in the dataset that is too much skewness present in the coloumn then whatever the model we have, it does't work properly (Statistical Model) So, if we have skewness data and if it's at the right side then it is right skewed data and if the skewness is at the left side then it is left skewed data that is ther are negative values So, to overcome from this and to get the right output from the analysis we can remove this skewness and we can convert it into the bell curve that is the mean is at the centre"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb59a700",
   "metadata": {},
   "source": [
    "# 6. What are the top Content Rating values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ae0158b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Everyone           7309\n",
       "Teen               1055\n",
       "Mature 17+          458\n",
       "Everyone 10+        388\n",
       "Adults only 18+       3\n",
       "Unrated               1\n",
       "Name: Content_Rating, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Content_Rating'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d1086f",
   "metadata": {},
   "source": [
    "# a. Are there any values with very few records?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "df96bee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Everyone           7309\n",
       "Teen               1055\n",
       "Mature 17+          458\n",
       "Everyone 10+        388\n",
       "Adults only 18+       3\n",
       "Unrated               1\n",
       "Name: Content_Rating, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Content_Rating'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10343f3",
   "metadata": {},
   "source": [
    "So, after analysis we found that an Adults only 18+ and Unrated have very few records that is 3 and 1 respectively"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a44d88",
   "metadata": {},
   "source": [
    "# b. If yes, drop those as they won’t help in the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "31d7f011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([8266], dtype='int64')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the Unrated value in the contect rating column\n",
    "df[df['Content_Rating']=='Unrated'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c4443380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([298, 3043, 6424], dtype='int64')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the Adults only 18+ value in the contect rating column\n",
    "df[df['Content_Rating']=='Adults only 18+'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2220de1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping all the values in the given column with given records\n",
    "df.drop(index=df[df['Content_Rating']=='Unrated'].index, inplace=True)\n",
    "df.drop(index=df[df['Content_Rating']=='Adults only 18+'].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f4c6b6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Everyone        7309\n",
       "Teen            1055\n",
       "Mature 17+       458\n",
       "Everyone 10+     388\n",
       "Name: Content_Rating, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Content_Rating'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "903c58b7",
   "metadata": {},
   "source": [
    "So, after the operations we found that, the given data doesn't have any value in the Content Rating column with Unrated and Adults only 18+"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0b3892",
   "metadata": {},
   "source": [
    "# 7. Effect of size on rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32279879",
   "metadata": {},
   "source": [
    "# a. Make a joinplot to understand the effect of size on rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5d81af9f",
   "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": [
    "sns.jointplot(x='Size',y='Rating',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b282db",
   "metadata": {},
   "source": [
    "# b. Do you see any patterns?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623e4a12",
   "metadata": {},
   "source": [
    "Yes, from the given plot, we can say that there is correlation between Size and Rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e6ef7f",
   "metadata": {},
   "source": [
    "# c. How do you explain the pattern?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86deb607",
   "metadata": {},
   "source": [
    "From the given plot, we can see that, on increase in the Rating, the size also increases and there is positive correlation between Size and Rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc6c504",
   "metadata": {},
   "source": [
    "# 8. Effect of price on rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98020cec",
   "metadata": {},
   "source": [
    "# a. Make a jointplot (with regression line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "43cc9207",
   "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": [
    "sns.jointplot(x='Price',y='Rating',data=df, kind='reg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63701b99",
   "metadata": {},
   "source": [
    "# b. What pattern do you see?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b08ad3",
   "metadata": {},
   "source": [
    "From the given plot, we can say that, on increase in the Price, the Rating remains constant which is greater than 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3faac164",
   "metadata": {},
   "source": [
    "# c. How do you explain the pattern?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff4358e",
   "metadata": {},
   "source": [
    "So, here as on increase in price the rating remains constant which greater than 4 so we can say that, there is weak positive correlation between Rating and Price"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d946524",
   "metadata": {},
   "source": [
    "# d. Replot the data, this time with only records with price > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2f981169",
   "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>234</th>\n",
       "      <td>TurboScan: scan documents and receipts in PDF</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>4.7</td>\n",
       "      <td>11442</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>4.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>March 25, 2018</td>\n",
       "      <td>1.5.2</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Tiny Scanner Pro: PDF Doc Scan</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>4.8</td>\n",
       "      <td>10295</td>\n",
       "      <td>39000.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>4.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>April 11, 2017</td>\n",
       "      <td>3.4.6</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>TurboScan: scan documents and receipts in PDF</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>4.7</td>\n",
       "      <td>11442</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>4.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>March 25, 2018</td>\n",
       "      <td>1.5.2</td>\n",
       "      <td>4.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Tiny Scanner Pro: PDF Doc Scan</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>4.8</td>\n",
       "      <td>10295</td>\n",
       "      <td>39000.000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>4.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>April 11, 2017</td>\n",
       "      <td>3.4.6</td>\n",
       "      <td>3.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>Puffin Browser Pro</td>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18247</td>\n",
       "      <td>21516.529524</td>\n",
       "      <td>100000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>3.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Communication</td>\n",
       "      <td>July 5, 2018</td>\n",
       "      <td>7.5.3.20547</td>\n",
       "      <td>4.1 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>10682</th>\n",
       "      <td>Fruit Ninja Classic</td>\n",
       "      <td>GAME</td>\n",
       "      <td>4.3</td>\n",
       "      <td>85468</td>\n",
       "      <td>36000.000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Arcade</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>2.4.1.485300</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10690</th>\n",
       "      <td>FO Bixby</td>\n",
       "      <td>PERSONALIZATION</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>861.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>Paid</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>April 25, 2018</td>\n",
       "      <td>0.2</td>\n",
       "      <td>7.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10760</th>\n",
       "      <td>Fast Tract Diet</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>4.4</td>\n",
       "      <td>35</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>7.99</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Health &amp; Fitness</td>\n",
       "      <td>August 8, 2018</td>\n",
       "      <td>1.9.3</td>\n",
       "      <td>4.2 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",
       "    <tr>\n",
       "      <th>10785</th>\n",
       "      <td>sugar, sugar</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1405</td>\n",
       "      <td>9500.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>Paid</td>\n",
       "      <td>1.20</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>June 5, 2018</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>629 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App            Category  \\\n",
       "234    TurboScan: scan documents and receipts in PDF            BUSINESS   \n",
       "235                   Tiny Scanner Pro: PDF Doc Scan            BUSINESS   \n",
       "290    TurboScan: scan documents and receipts in PDF            BUSINESS   \n",
       "291                   Tiny Scanner Pro: PDF Doc Scan            BUSINESS   \n",
       "427                               Puffin Browser Pro       COMMUNICATION   \n",
       "...                                              ...                 ...   \n",
       "10682                            Fruit Ninja Classic                GAME   \n",
       "10690                                       FO Bixby     PERSONALIZATION   \n",
       "10760                                Fast Tract Diet  HEALTH_AND_FITNESS   \n",
       "10782                        Trine 2: Complete Story                GAME   \n",
       "10785                                   sugar, sugar              FAMILY   \n",
       "\n",
       "       Rating  Reviews          Size  Installs  Type  Price Content_Rating  \\\n",
       "234       4.7    11442   6800.000000    100000  Paid   4.99       Everyone   \n",
       "235       4.8    10295  39000.000000    100000  Paid   4.99       Everyone   \n",
       "290       4.7    11442   6800.000000    100000  Paid   4.99       Everyone   \n",
       "291       4.8    10295  39000.000000    100000  Paid   4.99       Everyone   \n",
       "427       4.0    18247  21516.529524    100000  Paid   3.99       Everyone   \n",
       "...       ...      ...           ...       ...   ...    ...            ...   \n",
       "10682     4.3    85468  36000.000000   1000000  Paid   0.99       Everyone   \n",
       "10690     5.0        5    861.000000       100  Paid   0.99       Everyone   \n",
       "10760     4.4       35   2400.000000      1000  Paid   7.99       Everyone   \n",
       "10782     3.8      252  11000.000000     10000  Paid  16.99           Teen   \n",
       "10785     4.2     1405   9500.000000     10000  Paid   1.20       Everyone   \n",
       "\n",
       "                 Genres       Last_Updated   Current_Ver   Android_Ver  \n",
       "234            Business     March 25, 2018         1.5.2    4.0 and up  \n",
       "235            Business     April 11, 2017         3.4.6    3.0 and up  \n",
       "290            Business     March 25, 2018         1.5.2    4.0 and up  \n",
       "291            Business     April 11, 2017         3.4.6    3.0 and up  \n",
       "427       Communication       July 5, 2018   7.5.3.20547    4.1 and up  \n",
       "...                 ...                ...           ...           ...  \n",
       "10682            Arcade       June 8, 2018  2.4.1.485300  4.0.3 and up  \n",
       "10690   Personalization     April 25, 2018           0.2    7.0 and up  \n",
       "10760  Health & Fitness     August 8, 2018         1.9.3    4.2 and up  \n",
       "10782            Action  February 27, 2015          2.22    5.0 and up  \n",
       "10785            Puzzle       June 5, 2018           2.7    2.3 and up  \n",
       "\n",
       "[629 rows x 13 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greatervalue=df[df['Price']>0]\n",
    "greatervalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c36327f2",
   "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": [
    "sns.jointplot(x='Price',y='Rating',data=greatervalue, kind='reg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb899801",
   "metadata": {},
   "source": [
    "# e. Does the pattern change?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0da3841",
   "metadata": {},
   "source": [
    "Yes, the pattern has changed as we have given the condition to the data as Price>0 so we can say that there is negative correlation between Price and Rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe40fe0",
   "metadata": {},
   "source": [
    "# f. What is your overall inference on the effect of price on the rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f51c53",
   "metadata": {},
   "source": [
    "So, from this we can say that, on increase in the price, it doesn't have any effect on the higher rating and the rating is almost constant which is 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594aa288",
   "metadata": {},
   "source": [
    "# 9. Look at all the numeric interactions together –"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f18a3b4",
   "metadata": {},
   "source": [
    "# a. Make a pairplort with the colulmns - 'Reviews', 'Size', 'Rating', 'Price'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aa94dd20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,vars=['Reviews','Size','Rating','Price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2858cee1",
   "metadata": {},
   "source": [
    "# 10. Rating vs. content rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c375c3fa",
   "metadata": {},
   "source": [
    "# a. Make a bar plot displaying the rating for each content rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e0f4ed41",
   "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": [
    "df.groupby(['Content_Rating'])['Rating'].count().plot.bar(color='orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2468a61",
   "metadata": {},
   "source": [
    "# b. Which metric would you use? Mean? Median? Some other quantile?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6459d8",
   "metadata": {},
   "source": [
    "Here, we will use Median as we have outlier in the Rating. And, Median is the best measure of central tendency to overcome\n",
    "the outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd71b4c",
   "metadata": {},
   "source": [
    "# c. Choose the right metric and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5b489f9e",
   "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": [
    "df.groupby(['Content_Rating'])['Rating'].median().plot.bar(color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e9f97c",
   "metadata": {},
   "source": [
    "# 11. Content rating vs. size vs. rating – 3 variables at a time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c0f2ca",
   "metadata": {},
   "source": [
    "# a. Create 5 buckets (20% records in each) based on Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ef75862f",
   "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>Bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-20k</th>\n",
       "      <td>4.149182</td>\n",
       "      <td>4.247561</td>\n",
       "      <td>4.015183</td>\n",
       "      <td>4.190289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20k-40k</th>\n",
       "      <td>4.226711</td>\n",
       "      <td>4.194413</td>\n",
       "      <td>4.193370</td>\n",
       "      <td>4.237500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40k-60k</th>\n",
       "      <td>4.192857</td>\n",
       "      <td>4.265957</td>\n",
       "      <td>4.202222</td>\n",
       "      <td>4.270896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60k-80k</th>\n",
       "      <td>4.265368</td>\n",
       "      <td>4.356818</td>\n",
       "      <td>4.190909</td>\n",
       "      <td>4.291045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80k-100k</th>\n",
       "      <td>4.280000</td>\n",
       "      <td>4.405556</td>\n",
       "      <td>4.252632</td>\n",
       "      <td>4.314815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Content_Rating  Everyone  Everyone 10+  Mature 17+      Teen\n",
       "Bucket                                                      \n",
       "0-20k           4.149182      4.247561    4.015183  4.190289\n",
       "20k-40k         4.226711      4.194413    4.193370  4.237500\n",
       "40k-60k         4.192857      4.265957    4.202222  4.270896\n",
       "60k-80k         4.265368      4.356818    4.190909  4.291045\n",
       "80k-100k        4.280000      4.405556    4.252632  4.314815"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating bins (buckets) of the given dataset and then we're giving labels to it. Basically creating the 5 gourps of the dataset \n",
    "bins=[0,20000,40000,60000,80000,100000]\n",
    "labels=['0-20k','20k-40k','40k-60k','60k-80k','80k-100k']\n",
    "df['Bucket']=pd.cut(df['Size'],bins,labels=labels)\n",
    "pd.pivot_table(df,index='Bucket',values='Rating',columns='Content_Rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94cb58b",
   "metadata": {},
   "source": [
    "# b. By Content Rating vs. Size buckets, get the rating (20th percentile) for each combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "51a5d699",
   "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>Bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-20k</th>\n",
       "      <td>3.8</td>\n",
       "      <td>4.02</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20k-40k</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40k-60k</th>\n",
       "      <td>3.9</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60k-80k</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80k-100k</th>\n",
       "      <td>4.1</td>\n",
       "      <td>4.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Content_Rating  Everyone  Everyone 10+  Mature 17+  Teen\n",
       "Bucket                                                  \n",
       "0-20k                3.8          4.02         3.5   3.9\n",
       "20k-40k              4.0          4.00         4.0   4.0\n",
       "40k-60k              3.9          4.10         4.1   4.0\n",
       "60k-80k              4.0          4.20         4.0   4.0\n",
       "80k-100k             4.1          4.30         4.0   4.1"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant=pd.pivot_table(df, values='Rating', index='Bucket', columns='Content_Rating', aggfunc=lambda x:np.quantile(x,0.2))\n",
    "quant"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e5b2f1",
   "metadata": {},
   "source": [
    "# c. Make a heatmap of this"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c80fd5",
   "metadata": {},
   "source": [
    "# i. Annotated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8a99d68d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(7, 7))\n",
    "sns.heatmap(quant, annot=True, linewidths=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b018cf",
   "metadata": {},
   "source": [
    "# ii. Greens color map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "94b01331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(7, 7))\n",
    "sns.heatmap(quant, annot=True, cmap='Greens', linewidths=.5, fmt='.1f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7133ccf",
   "metadata": {},
   "source": [
    "# d. What’s your inference? Are lighter apps preferred in all categories? Heavier? Some?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ecd18c",
   "metadata": {},
   "source": [
    "From this analysis and plot, we can see that the lighter apps are not preffered in all the categories. Apps with size 40k-60k \n",
    "and 80k-100k have more ratings and also we can say that the size 60k-80k also has the high ratings. So, in short we can say\n",
    "that, whatever the heavier apps are there, they are preferred more in all the caregories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19d921f1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}