python_eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GOogle eda python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "      <th>Unnamed: 13</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>19M</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",
       "      <td>NaN</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>14M</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",
       "      <td>NaN</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>8.7M</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",
       "      <td>NaN</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>25M</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",
       "      <td>NaN</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>2.8M</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",
       "      <td>NaN</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   19M      10,000+  Free     0       Everyone   \n",
       "1      967   14M     500,000+  Free     0       Everyone   \n",
       "2    87510  8.7M   5,000,000+  Free     0       Everyone   \n",
       "3   215644   25M  50,000,000+  Free     0           Teen   \n",
       "4      967  2.8M     100,000+  Free     0       Everyone   \n",
       "\n",
       "                      Genres      Last Updated         Current Ver  \\\n",
       "0               Art & Design   January 7, 2018               1.0.0   \n",
       "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
       "2               Art & Design    August 1, 2018               1.2.4   \n",
       "3               Art & Design      June 8, 2018  Varies with device   \n",
       "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
       "\n",
       "    Android Ver  Unnamed: 13  \n",
       "0  4.0.3 and up          NaN  \n",
       "1  4.0.3 and up          NaN  \n",
       "2  4.0.3 and up          NaN  \n",
       "3    4.2 and up          NaN  \n",
       "4    4.4 and up          NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set options to be maximum for rows and columns\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type',\n",
       "       'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver',\n",
       "       'Android Ver', 'Unnamed: 13'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10841, 14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows are 10841 ,and the number of columns are 14\n"
     ]
    }
   ],
   "source": [
    "print(f\"The number of rows are {df.shape[0]} ,and the number of columns are {df.shape[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10841 entries, 0 to 10840\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   App             10841 non-null  object \n",
      " 1   Category        10840 non-null  object \n",
      " 2   Rating          9367 non-null   float64\n",
      " 3   Reviews         10841 non-null  int64  \n",
      " 4   Size            10841 non-null  object \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  10841 non-null  object \n",
      " 9   Genres          10840 non-null  object \n",
      " 10  Last Updated    10841 non-null  object \n",
      " 11  Current Ver     10833 non-null  object \n",
      " 12  Android Ver     10839 non-null  object \n",
      " 13  Unnamed: 13     0 non-null      float64\n",
      "dtypes: float64(2), int64(1), object(11)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Reviews</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9367.000000</td>\n",
       "      <td>1.084100e+04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.191513</td>\n",
       "      <td>4.441119e+05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.515735</td>\n",
       "      <td>2.927629e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.800000e+01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.094000e+03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.476800e+04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.815831e+07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating       Reviews  Unnamed: 13\n",
       "count  9367.000000  1.084100e+04          0.0\n",
       "mean      4.191513  4.441119e+05          NaN\n",
       "std       0.515735  2.927629e+06          NaN\n",
       "min       1.000000  0.000000e+00          NaN\n",
       "25%       4.000000  3.800000e+01          NaN\n",
       "50%       4.300000  2.094000e+03          NaN\n",
       "75%       4.500000  5.476800e+04          NaN\n",
       "max       5.000000  7.815831e+07          NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['19M', '14M', '8.7M', '25M', '2.8M', '5.6M', '29M', '33M', '3.1M',\n",
       "       '28M', '12M', '20M', '21M', '37M', '2.7M', '5.5M', '17M', '39M',\n",
       "       '31M', '4.2M', '7.0M', '23M', '6.0M', '6.1M', '4.6M', '9.2M',\n",
       "       '5.2M', '11M', '24M', 'Varies with device', '9.4M', '15M', '10M',\n",
       "       '1.2M', '26M', '8.0M', '7.9M', '56M', '57M', '35M', '54M', '201k',\n",
       "       '3.6M', '5.7M', '8.6M', '2.4M', '27M', '2.5M', '16M', '3.4M',\n",
       "       '8.9M', '3.9M', '2.9M', '38M', '32M', '5.4M', '18M', '1.1M',\n",
       "       '2.2M', '4.5M', '9.8M', '52M', '9.0M', '6.7M', '30M', '2.6M',\n",
       "       '7.1M', '3.7M', '22M', '7.4M', '6.4M', '3.2M', '8.2M', '9.9M',\n",
       "       '4.9M', '9.5M', '5.0M', '5.9M', '13M', '73M', '6.8M', '3.5M',\n",
       "       '4.0M', '2.3M', '7.2M', '2.1M', '42M', '7.3M', '9.1M', '55M',\n",
       "       '23k', '6.5M', '1.5M', '7.5M', '51M', '41M', '48M', '8.5M', '46M',\n",
       "       '8.3M', '4.3M', '4.7M', '3.3M', '40M', '7.8M', '8.8M', '6.6M',\n",
       "       '5.1M', '61M', '66M', '79k', '8.4M', '118k', '44M', '695k', '1.6M',\n",
       "       '6.2M', '18k', '53M', '1.4M', '3.0M', '5.8M', '3.8M', '9.6M',\n",
       "       '45M', '63M', '49M', '77M', '4.4M', '4.8M', '70M', '6.9M', '9.3M',\n",
       "       '10.0M', '8.1M', '36M', '84M', '97M', '2.0M', '1.9M', '1.8M',\n",
       "       '5.3M', '47M', '556k', '526k', '76M', '7.6M', '59M', '9.7M', '78M',\n",
       "       '72M', '43M', '7.7M', '6.3M', '334k', '34M', '93M', '65M', '79M',\n",
       "       '100M', '58M', '50M', '68M', '64M', '67M', '60M', '94M', '232k',\n",
       "       '99M', '624k', '95M', '8.5k', '41k', '292k', '11k', '80M', '1.7M',\n",
       "       '74M', '62M', '69M', '75M', '98M', '85M', '82M', '96M', '87M',\n",
       "       '71M', '86M', '91M', '81M', '92M', '83M', '88M', '704k', '862k',\n",
       "       '899k', '378k', '266k', '375k', '1.3M', '975k', '980k', '4.1M',\n",
       "       '89M', '696k', '544k', '525k', '920k', '779k', '853k', '720k',\n",
       "       '713k', '772k', '318k', '58k', '241k', '196k', '857k', '51k',\n",
       "       '953k', '865k', '251k', '930k', '540k', '313k', '746k', '203k',\n",
       "       '26k', '314k', '239k', '371k', '220k', '730k', '756k', '91k',\n",
       "       '293k', '17k', '74k', '14k', '317k', '78k', '924k', '902k', '818k',\n",
       "       '81k', '939k', '169k', '45k', '475k', '965k', '90M', '545k', '61k',\n",
       "       '283k', '655k', '714k', '93k', '872k', '121k', '322k', '1.0M',\n",
       "       '976k', '172k', '238k', '549k', '206k', '954k', '444k', '717k',\n",
       "       '210k', '609k', '308k', '705k', '306k', '904k', '473k', '175k',\n",
       "       '350k', '383k', '454k', '421k', '70k', '812k', '442k', '842k',\n",
       "       '417k', '412k', '459k', '478k', '335k', '782k', '721k', '430k',\n",
       "       '429k', '192k', '200k', '460k', '728k', '496k', '816k', '414k',\n",
       "       '506k', '887k', '613k', '243k', '569k', '778k', '683k', '592k',\n",
       "       '319k', '186k', '840k', '647k', '191k', '373k', '437k', '598k',\n",
       "       '716k', '585k', '982k', '222k', '219k', '55k', '948k', '323k',\n",
       "       '691k', '511k', '951k', '963k', '25k', '554k', '351k', '27k',\n",
       "       '82k', '208k', '913k', '514k', '551k', '29k', '103k', '898k',\n",
       "       '743k', '116k', '153k', '209k', '353k', '499k', '173k', '597k',\n",
       "       '809k', '122k', '411k', '400k', '801k', '787k', '237k', '50k',\n",
       "       '643k', '986k', '97k', '516k', '837k', '780k', '961k', '269k',\n",
       "       '20k', '498k', '600k', '749k', '642k', '881k', '72k', '656k',\n",
       "       '601k', '221k', '228k', '108k', '940k', '176k', '33k', '663k',\n",
       "       '34k', '942k', '259k', '164k', '458k', '245k', '629k', '28k',\n",
       "       '288k', '775k', '785k', '636k', '916k', '994k', '309k', '485k',\n",
       "       '914k', '903k', '608k', '500k', '54k', '562k', '847k', '957k',\n",
       "       '688k', '811k', '270k', '48k', '329k', '523k', '921k', '874k',\n",
       "       '981k', '784k', '280k', '24k', '518k', '754k', '892k', '154k',\n",
       "       '860k', '364k', '387k', '626k', '161k', '879k', '39k', '970k',\n",
       "       '170k', '141k', '160k', '144k', '143k', '190k', '376k', '193k',\n",
       "       '246k', '73k', '658k', '992k', '253k', '420k', '404k', '470k',\n",
       "       '226k', '240k', '89k', '234k', '257k', '861k', '467k', '157k',\n",
       "       '44k', '676k', '67k', '552k', '885k', '1020k', '582k', '619k'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # observations in size\n",
    "# 1. K, 2, M 3, Varies with device\n",
    "df['Size'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8830"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding different types of values in size column\n",
    "df['Size'].loc[df['Size'].str.contains('M')].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "316"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Size'].loc[df['Size'].str.contains('k')].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1695"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Size'].loc[df['Size'].str.contains('Varies with device')].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting size into bytes\n",
    "def convert_size(size):\n",
    "    if isinstance (size,str):\n",
    "    if \"k\" in size:\n",
    "        return float( size.replace(\"k\", \"\")) * 1024"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python_eda",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}