Data analaysis 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "550f8a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0fc8110",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\Abdur rahim nishad\\Downloads\\googleplaystore.csv~\\googleplaystore.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d9c73dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfbe669a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Installs.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ca9ae73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10841, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1426e575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9660"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"App\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c52a7ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('App',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10583e41",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Category  Rating Reviews  Size     Installs  Type Price  \\\n",
       "0  ART_AND_DESIGN     4.1     159   19M      10,000+  Free     0   \n",
       "1  ART_AND_DESIGN     3.9     967   14M     500,000+  Free     0   \n",
       "2  ART_AND_DESIGN     4.7   87510  8.7M   5,000,000+  Free     0   \n",
       "3  ART_AND_DESIGN     4.5  215644   25M  50,000,000+  Free     0   \n",
       "4  ART_AND_DESIGN     4.3     967  2.8M     100,000+  Free     0   \n",
       "\n",
       "  Content Rating                     Genres      Last Updated  \\\n",
       "0       Everyone               Art & Design   January 7, 2018   \n",
       "1       Everyone  Art & Design;Pretend Play  January 15, 2018   \n",
       "2       Everyone               Art & Design    August 1, 2018   \n",
       "3           Teen               Art & Design      June 8, 2018   \n",
       "4       Everyone    Art & Design;Creativity     June 20, 2018   \n",
       "\n",
       "          Current Ver   Android Ver  \n",
       "0               1.0.0  4.0.3 and up  \n",
       "1               2.0.0  4.0.3 and up  \n",
       "2               1.2.4  4.0.3 and up  \n",
       "3  Varies with device    4.2 and up  \n",
       "4                 1.1    4.4 and up  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7b998182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Category.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa973663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ART_AND_DESIGN', 'AUTO_AND_VEHICLES', 'BEAUTY',\n",
       "       'BOOKS_AND_REFERENCE', 'BUSINESS', 'COMICS', 'COMMUNICATION',\n",
       "       'DATING', 'EDUCATION', 'ENTERTAINMENT', 'EVENTS', 'FINANCE',\n",
       "       'FOOD_AND_DRINK', 'HEALTH_AND_FITNESS', 'HOUSE_AND_HOME',\n",
       "       'LIBRARIES_AND_DEMO', 'LIFESTYLE', 'GAME', 'FAMILY', 'MEDICAL',\n",
       "       'SOCIAL', 'SHOPPING', 'PHOTOGRAPHY', 'SPORTS', 'TRAVEL_AND_LOCAL',\n",
       "       'TOOLS', 'PERSONALIZATION', 'PRODUCTIVITY', 'PARENTING', 'WEATHER',\n",
       "       'VIDEO_PLAYERS', 'NEWS_AND_MAGAZINES', 'MAPS_AND_NAVIGATION',\n",
       "       '1.9'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18781a00",
   "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>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>10472</th>\n",
       "      <td>1.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0M</td>\n",
       "      <td>1,000+</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": [
       "      Category  Rating Reviews    Size Installs Type     Price Content Rating  \\\n",
       "10472      1.9    19.0    3.0M  1,000+     Free    0  Everyone            NaN   \n",
       "\n",
       "                  Genres Last Updated Current Ver Android Ver  \n",
       "10472  February 11, 2018       1.0.19  4.0 and up         NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Category\"]==\"1.9\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9d2a5b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(10472,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f18916",
   "metadata": {},
   "source": [
    "# Label encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13f339a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ddad579",
   "metadata": {},
   "outputs": [],
   "source": [
    "lb=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9908d062",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Category\"]=lb.fit_transform(df[\"Category\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0fc2aee0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Category  Rating Reviews  Size     Installs  Type Price Content Rating  \\\n",
       "0         0     4.1     159   19M      10,000+  Free     0       Everyone   \n",
       "1         0     3.9     967   14M     500,000+  Free     0       Everyone   \n",
       "2         0     4.7   87510  8.7M   5,000,000+  Free     0       Everyone   \n",
       "3         0     4.5  215644   25M  50,000,000+  Free     0           Teen   \n",
       "4         0     4.3     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  \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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ab1e4411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.1, 3.9, 4.7, 4.5, 4.3, 4.4, 3.8, 4.2, 4.6, 3.2, 4. , nan, 4.8,\n",
       "       4.9, 3.6, 3.7, 3.3, 3.4, 3.5, 3.1, 5. , 2.6, 3. , 1.9, 2.5, 2.8,\n",
       "       2.7, 1. , 2.9, 2.3, 2.2, 1.7, 2. , 1.8, 2.4, 1.6, 2.1, 1.4, 1.5,\n",
       "       1.2])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Rating.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26264d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1474"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Rating.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0960ef21",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=df.Rating.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "681e7c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.191757420456978"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8034fd53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Rating=df.Rating.fillna(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb72863b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Rating.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0b46bb99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Rating.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f09ff008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['159', '967', '87510', ..., '603', '1195', '398307'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Reviews.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1ebe8a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Reviews.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "05d24d66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.system 1\n",
    "s=df.Reviews.astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "11ffeb68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "df867e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.system 2\n",
    "df.Reviews=df.Reviews.apply(lambda x :int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d05a3144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Reviews.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bda03bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Reviews.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "08cc9d24",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fdb9da29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a63e33f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     19M\n",
       "1     14M\n",
       "2    8.7M\n",
       "3     25M\n",
       "4    2.8M\n",
       "Name: Size, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size[:-1].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9a4928d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Size=df.Size.apply(lambda x:x[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "05b63c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['19', '14', '8.7', '25', '2.8', '5.6', '29', '33', '3.1', '28',\n",
       "       '12', '20', '21', '37', '2.7', '5.5', '17', '39', '31', '4.2',\n",
       "       '7.0', '23', '6.0', '6.1', '4.6', '9.2', '5.2', '11', '24',\n",
       "       'Varies with devic', '9.4', '15', '10', '1.2', '26', '8.0', '7.9',\n",
       "       '56', '57', '35', '54', '201', '3.6', '5.7', '8.6', '2.4', '27',\n",
       "       '2.5', '16', '3.4', '8.9', '3.9', '2.9', '38', '32', '5.4', '18',\n",
       "       '1.1', '2.2', '4.5', '9.8', '52', '9.0', '6.7', '30', '2.6', '7.1',\n",
       "       '3.7', '22', '7.4', '6.4', '3.2', '8.2', '9.9', '4.9', '9.5',\n",
       "       '5.0', '5.9', '13', '73', '6.8', '3.5', '4.0', '2.3', '7.2', '2.1',\n",
       "       '42', '7.3', '9.1', '55', '6.5', '1.5', '7.5', '51', '41', '48',\n",
       "       '8.5', '46', '8.3', '4.3', '4.7', '3.3', '40', '7.8', '8.8', '6.6',\n",
       "       '5.1', '61', '66', '79', '8.4', '118', '44', '695', '1.6', '6.2',\n",
       "       '53', '1.4', '3.0', '5.8', '3.8', '9.6', '45', '63', '49', '77',\n",
       "       '4.4', '4.8', '70', '6.9', '9.3', '10.0', '8.1', '36', '84', '97',\n",
       "       '2.0', '1.9', '1.8', '5.3', '47', '556', '526', '76', '7.6', '59',\n",
       "       '9.7', '78', '72', '43', '7.7', '6.3', '334', '34', '93', '65',\n",
       "       '100', '58', '50', '68', '64', '67', '60', '94', '232', '99',\n",
       "       '624', '95', '292', '80', '1.7', '74', '62', '69', '75', '98',\n",
       "       '85', '82', '96', '87', '71', '86', '91', '81', '92', '83', '88',\n",
       "       '704', '862', '899', '378', '266', '375', '1.3', '975', '980',\n",
       "       '4.1', '89', '696', '544', '525', '920', '779', '853', '720',\n",
       "       '713', '772', '318', '241', '196', '857', '953', '865', '251',\n",
       "       '930', '540', '313', '746', '203', '314', '239', '371', '220',\n",
       "       '730', '756', '293', '317', '924', '902', '818', '939', '169',\n",
       "       '475', '965', '90', '545', '283', '655', '714', '872', '121',\n",
       "       '322', '1.0', '976', '172', '238', '549', '206', '954', '444',\n",
       "       '717', '210', '609', '308', '705', '306', '904', '473', '175',\n",
       "       '350', '383', '454', '421', '812', '442', '842', '417', '412',\n",
       "       '459', '478', '335', '782', '721', '430', '429', '192', '200',\n",
       "       '460', '728', '496', '816', '414', '506', '887', '613', '243',\n",
       "       '569', '778', '683', '592', '319', '186', '840', '647', '191',\n",
       "       '373', '437', '598', '716', '585', '982', '222', '219', '948',\n",
       "       '323', '691', '511', '951', '963', '554', '351', '208', '913',\n",
       "       '514', '551', '103', '898', '743', '116', '153', '209', '353',\n",
       "       '499', '173', '597', '809', '122', '411', '400', '801', '787',\n",
       "       '237', '643', '986', '516', '837', '780', '961', '269', '498',\n",
       "       '600', '749', '642', '881', '656', '601', '221', '228', '108',\n",
       "       '940', '176', '663', '942', '259', '164', '458', '245', '629',\n",
       "       '288', '775', '785', '636', '916', '994', '309', '485', '914',\n",
       "       '903', '608', '500', '562', '847', '957', '688', '811', '270',\n",
       "       '329', '523', '921', '874', '981', '784', '280', '518', '754',\n",
       "       '892', '154', '860', '364', '387', '626', '161', '879', '970',\n",
       "       '170', '141', '160', '144', '143', '190', '376', '193', '246',\n",
       "       '658', '992', '253', '420', '404', '470', '226', '240', '234',\n",
       "       '257', '861', '467', '157', '676', '552', '885', '1020', '582',\n",
       "       '619'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "04f44cb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ABDURR~1\\AppData\\Local\\Temp/ipykernel_3008/3401991880.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.Size[df.Size==\"Varies with devic\"]=np.nan  #problem\n"
     ]
    }
   ],
   "source": [
    "df.Size[df.Size==\"Varies with devic\"]=np.nan  #problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "35bc91b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1695"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "14d107d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1695"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.Size.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "36a38dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Reviews.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "90547aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     19\n",
       "1     14\n",
       "2    8.7\n",
       "3     25\n",
       "4    2.8\n",
       "Name: Size, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "274a64d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Size=df.Size.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2c4d4f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a11857be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1695"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ede04927",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Size=df.Size.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ae0373fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f47bd46c",
   "metadata": {},
   "outputs": [
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       "      <td>1.0.0</td>\n",
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       "      <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",
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      "text/plain": [
       "   Category  Rating  Reviews  Size     Installs  Type Price Content Rating  \\\n",
       "0         0     4.1      159  19.0      10,000+  Free     0       Everyone   \n",
       "1         0     3.9      967  14.0     500,000+  Free     0       Everyone   \n",
       "2         0     4.7    87510   8.7   5,000,000+  Free     0       Everyone   \n",
       "3         0     4.5   215644  25.0  50,000,000+  Free     0           Teen   \n",
       "4         0     4.3      967   2.8     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  \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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "79b4e7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10,000+', '500,000+', '5,000,000+', '50,000,000+', '100,000+',\n",
       "       '50,000+', '1,000,000+', '10,000,000+', '5,000+', '100,000,000+',\n",
       "       '1,000,000,000+', '1,000+', '500,000,000+', '50+', '100+', '500+',\n",
       "       '10+', '1+', '5+', '0+', '0'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Installs.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e5a951c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ABDURR~1\\AppData\\Local\\Temp/ipykernel_3008/85762983.py:1: 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=df.Installs.str.replace(\"+\",\"\")\n"
     ]
    }
   ],
   "source": [
    "df.Installs=df.Installs.str.replace(\"+\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "948a01a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10,000', '500,000', '5,000,000', '50,000,000', '100,000',\n",
       "       '50,000', '1,000,000', '10,000,000', '5,000', '100,000,000',\n",
       "       '1,000,000,000', '1,000', '500,000,000', '50', '100', '500', '10',\n",
       "       '1', '5', '0'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.Installs.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f66fa541",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Installs=df.Installs.str.replace(\",\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2b241564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10000', '500000', '5000000', '50000000', '100000', '50000',\n",
       "       '1000000', '10000000', '5000', '100000000', '1000000000', '1000',\n",
       "       '500000000', '50', '100', '500', '10', '1', '5', '0'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Installs.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d5c7e185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Installs.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f53078cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Installs=df.Installs.astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "edfaf3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
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     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.Installs.dtype"
   ]
  },
  {
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   "id": "84100d72",
   "metadata": {},
   "outputs": [
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       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50000000</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>0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2.8</td>\n",
       "      <td>100000</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Category  Rating  Reviews  Size  Installs  Type Price Content Rating  \\\n",
       "0         0     4.1      159  19.0     10000  Free     0       Everyone   \n",
       "1         0     3.9      967  14.0    500000  Free     0       Everyone   \n",
       "2         0     4.7    87510   8.7   5000000  Free     0       Everyone   \n",
       "3         0     4.5   215644  25.0  50000000  Free     0           Teen   \n",
       "4         0     4.3      967   2.8    100000  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  \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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e4433c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Free', 'Paid', nan], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1968d50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "e=pd.get_dummies(df.Type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c39a49cc",
   "metadata": {},
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       "   Category  Rating  Reviews  Size  Installs Price Content Rating  \\\n",
       "0         0     4.1      159  19.0     10000     0       Everyone   \n",
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       "\n",
       "                      Genres      Last Updated         Current Ver  \\\n",
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       "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
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       "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
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       "   Category  Rating  Reviews  Size  Installs Price Content Rating  \\\n",
       "0         0     4.1      159  19.0     10000     0       Everyone   \n",
       "1         0     3.9      967  14.0    500000     0       Everyone   \n",
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       "3         0     4.5   215644  25.0  50000000     0           Teen   \n",
       "4         0     4.3      967   2.8    100000     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  Free  Paid  \n",
       "0  4.0.3 and up     1     0  \n",
       "1  4.0.3 and up     1     0  \n",
       "2  4.0.3 and up     1     0  \n",
       "3    4.2 and up     1     0  \n",
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    "new_df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1af3e30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Everyone', 'Teen', 'Everyone 10+', 'Mature 17+',\n",
       "       'Adults only 18+', 'Unrated'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
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   "execution_count": 64,
   "id": "b7c16324",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df[\"Content Rating\"]=lb.fit_transform(new_df[\"Content Rating\"])"
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  {
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   "execution_count": 65,
   "id": "6b0c67b6",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    4\n",
       "4    1\n",
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     "execution_count": 65,
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  {
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   "execution_count": 66,
   "id": "10702bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0"
      ]
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     "execution_count": 66,
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  {
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   "id": "3eeb25c7",
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   "outputs": [
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      "text/plain": [
       "   Category  Rating  Reviews  Size  Installs Price  Content Rating  \\\n",
       "0         0     4.1      159  19.0     10000     0               1   \n",
       "1         0     3.9      967  14.0    500000     0               1   \n",
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       "4         0     4.3      967   2.8    100000     0               1   \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",
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   "execution_count": 68,
   "id": "d2f5d14e",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "0                 Art & Design\n",
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       "Name: Genres, dtype: object"
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     "execution_count": 68,
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  {
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   "execution_count": 69,
   "id": "a02f9a09",
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  {
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   "execution_count": 70,
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   "cell_type": "code",
   "execution_count": 73,
   "id": "d90b101b",
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    "df[\"Last Updated\"] = pd.to_datetime(df[\"Last Updated\"])"
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   "cell_type": "code",
   "execution_count": 74,
   "id": "5f9869c9",
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    {
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       "0   2018-01-07\n",
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  {
   "cell_type": "code",
   "execution_count": 76,
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       "      <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>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",
       "    </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",
       "    </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",
       "    </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  \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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ba773092",
   "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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10836</th>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.5</td>\n",
       "      <td>38</td>\n",
       "      <td>53M</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>FAMILY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.6M</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>MEDICAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>9.5M</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>BOOKS_AND_REFERENCE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>114</td>\n",
       "      <td>Varies with device</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>LIFESTYLE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>398307</td>\n",
       "      <td>19M</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 × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Category  Rating Reviews                Size     Installs  \\\n",
       "0           ART_AND_DESIGN     4.1     159                 19M      10,000+   \n",
       "1           ART_AND_DESIGN     3.9     967                 14M     500,000+   \n",
       "2           ART_AND_DESIGN     4.7   87510                8.7M   5,000,000+   \n",
       "3           ART_AND_DESIGN     4.5  215644                 25M  50,000,000+   \n",
       "4           ART_AND_DESIGN     4.3     967                2.8M     100,000+   \n",
       "...                    ...     ...     ...                 ...          ...   \n",
       "10836               FAMILY     4.5      38                 53M       5,000+   \n",
       "10837               FAMILY     5.0       4                3.6M         100+   \n",
       "10838              MEDICAL     NaN       3                9.5M       1,000+   \n",
       "10839  BOOKS_AND_REFERENCE     4.5     114  Varies with device       1,000+   \n",
       "10840            LIFESTYLE     4.5  398307                 19M  10,000,000+   \n",
       "\n",
       "       Type Price Content Rating                     Genres      Last Updated  \\\n",
       "0      Free     0       Everyone               Art & Design   January 7, 2018   \n",
       "1      Free     0       Everyone  Art & Design;Pretend Play  January 15, 2018   \n",
       "2      Free     0       Everyone               Art & Design    August 1, 2018   \n",
       "3      Free     0           Teen               Art & Design      June 8, 2018   \n",
       "4      Free     0       Everyone    Art & Design;Creativity     June 20, 2018   \n",
       "...     ...   ...            ...                        ...               ...   \n",
       "10836  Free     0       Everyone                  Education     July 25, 2017   \n",
       "10837  Free     0       Everyone                  Education      July 6, 2018   \n",
       "10838  Free     0       Everyone                    Medical  January 20, 2017   \n",
       "10839  Free     0     Mature 17+          Books & Reference  January 19, 2015   \n",
       "10840  Free     0       Everyone                  Lifestyle     July 25, 2018   \n",
       "\n",
       "              Current Ver         Android Ver  \n",
       "0                   1.0.0        4.0.3 and up  \n",
       "1                   2.0.0        4.0.3 and up  \n",
       "2                   1.2.4        4.0.3 and up  \n",
       "3      Varies with device          4.2 and up  \n",
       "4                     1.1          4.4 and up  \n",
       "...                   ...                 ...  \n",
       "10836                1.48          4.1 and up  \n",
       "10837                 1.0          4.1 and up  \n",
       "10838                 1.0          2.2 and up  \n",
       "10839  Varies with device  Varies with device  \n",
       "10840  Varies with device  Varies with device  \n",
       "\n",
       "[10841 rows x 12 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"App\",axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83105e53",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0aed03bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lb = LabelEncoder()\n",
    "# system 1\n",
    "categories = lb.fit_transform(df['Category'])\n",
    "# system 2\n",
    "df['Category'] = lb.fit_transform(df['Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2a40da42",
   "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', '1,000+',\n",
       "       '470k', '226k', '240k', '89k', '234k', '257k', '861k', '467k',\n",
       "       '157k', '44k', '676k', '67k', '552k', '885k', '1020k', '582k',\n",
       "       '619k'], dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Size.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "46a0a9be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Size\"]=df.Size.apply(lambda x:x.replace('Varies with device',\"0\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "941801fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Size\"]=df[\"Size\"].str.replace(\"k\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9d392c95",
   "metadata": {},
   "outputs": [],
   "source": [
    " #df[\"Size\"]=df[\"Size\"].apply(lambda x:str(float(x.replace(\"M\",\"\"))*1024))\n",
    "df['Size'] = df['Size'].apply(lambda x: str(float(x.replace('M', ''))*1024) if x[-1]=='M' else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d4ff5605",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ABDURR~1\\AppData\\Local\\Temp/ipykernel_3008/2006283118.py:1: 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['Size']=df.Size.str.replace('+',\"\")\n"
     ]
    }
   ],
   "source": [
    "df['Size']=df.Size.str.replace('+',\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "fca50d48",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Size']=df.Size.str.replace(',',\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0ef35c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Size']=df.Size.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2b981310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1.22880e+04, 2.04800e+04, 2.15040e+04, 3.78880e+04, 2.76480e+03,\n",
       "       5.63200e+03, 1.74080e+04, 3.99360e+04, 3.17440e+04, 4.30080e+03,\n",
       "       7.16800e+03, 2.35520e+04, 6.14400e+03, 6.24640e+03, 4.71040e+03,\n",
       "       9.42080e+03, 5.32480e+03, 1.12640e+04, 2.45760e+04, 0.00000e+00,\n",
       "       9.62560e+03, 1.53600e+04, 1.02400e+04, 1.22880e+03, 2.66240e+04,\n",
       "       8.19200e+03, 8.08960e+03, 5.73440e+04, 5.83680e+04, 3.58400e+04,\n",
       "       5.52960e+04, 2.01000e+02, 3.68640e+03, 5.83680e+03, 8.80640e+03,\n",
       "       2.45760e+03, 2.76480e+04, 2.56000e+03, 1.63840e+04, 3.48160e+03,\n",
       "       9.11360e+03, 3.99360e+03, 2.96960e+03, 3.89120e+04, 3.27680e+04,\n",
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       "       1.00352e+04, 5.32480e+04, 9.21600e+03, 6.86080e+03, 3.07200e+04,\n",
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       "       6.55360e+03, 3.27680e+03, 8.39680e+03, 1.01376e+04, 5.01760e+03,\n",
       "       9.72800e+03, 5.12000e+03, 6.04160e+03, 1.33120e+04, 7.47520e+04,\n",
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       "       7.27040e+04, 8.80640e+04, 9.31840e+04, 8.29440e+04, 9.42080e+04,\n",
       "       8.49920e+04, 9.01120e+04, 7.04000e+02, 8.62000e+02, 8.99000e+02,\n",
       "       3.78000e+02, 2.66000e+02, 3.75000e+02, 1.33120e+03, 9.75000e+02,\n",
       "       9.80000e+02, 4.19840e+03, 9.11360e+04, 6.96000e+02, 5.44000e+02,\n",
       "       5.25000e+02, 9.20000e+02, 7.79000e+02, 8.53000e+02, 7.20000e+02,\n",
       "       7.13000e+02, 7.72000e+02, 3.18000e+02, 5.80000e+01, 2.41000e+02,\n",
       "       1.96000e+02, 8.57000e+02, 5.10000e+01, 9.53000e+02, 8.65000e+02,\n",
       "       2.51000e+02, 9.30000e+02, 5.40000e+02, 3.13000e+02, 7.46000e+02,\n",
       "       2.03000e+02, 2.60000e+01, 3.14000e+02, 2.39000e+02, 3.71000e+02,\n",
       "       2.20000e+02, 7.30000e+02, 7.56000e+02, 9.10000e+01, 2.93000e+02,\n",
       "       1.70000e+01, 7.40000e+01, 1.40000e+01, 3.17000e+02, 7.80000e+01,\n",
       "       9.24000e+02, 9.02000e+02, 8.18000e+02, 8.10000e+01, 9.39000e+02,\n",
       "       1.69000e+02, 4.50000e+01, 4.75000e+02, 9.65000e+02, 9.21600e+04,\n",
       "       5.45000e+02, 6.10000e+01, 2.83000e+02, 6.55000e+02, 7.14000e+02,\n",
       "       9.30000e+01, 8.72000e+02, 1.21000e+02, 3.22000e+02, 1.02400e+03,\n",
       "       9.76000e+02, 1.72000e+02, 2.38000e+02, 5.49000e+02, 2.06000e+02,\n",
       "       9.54000e+02, 4.44000e+02, 7.17000e+02, 2.10000e+02, 6.09000e+02,\n",
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       "       1.75000e+02, 3.50000e+02, 3.83000e+02, 4.54000e+02, 4.21000e+02,\n",
       "       7.00000e+01, 8.12000e+02, 4.42000e+02, 8.42000e+02, 4.17000e+02,\n",
       "       4.12000e+02, 4.59000e+02, 4.78000e+02, 3.35000e+02, 7.82000e+02,\n",
       "       7.21000e+02, 4.30000e+02, 4.29000e+02, 1.92000e+02, 2.00000e+02,\n",
       "       4.60000e+02, 7.28000e+02, 4.96000e+02, 8.16000e+02, 4.14000e+02,\n",
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       "       2.19000e+02, 5.50000e+01, 9.48000e+02, 3.23000e+02, 6.91000e+02,\n",
       "       5.11000e+02, 9.51000e+02, 9.63000e+02, 2.50000e+01, 5.54000e+02,\n",
       "       3.51000e+02, 2.70000e+01, 8.20000e+01, 2.08000e+02, 9.13000e+02,\n",
       "       5.14000e+02, 5.51000e+02, 2.90000e+01, 1.03000e+02, 8.98000e+02,\n",
       "       7.43000e+02, 1.16000e+02, 1.53000e+02, 2.09000e+02, 3.53000e+02,\n",
       "       4.99000e+02, 1.73000e+02, 5.97000e+02, 8.09000e+02, 1.22000e+02,\n",
       "       4.11000e+02, 4.00000e+02, 8.01000e+02, 7.87000e+02, 2.37000e+02,\n",
       "       5.00000e+01, 6.43000e+02, 9.86000e+02, 9.70000e+01, 5.16000e+02,\n",
       "       8.37000e+02, 7.80000e+02, 9.61000e+02, 2.69000e+02, 2.00000e+01,\n",
       "       4.98000e+02, 6.00000e+02, 7.49000e+02, 6.42000e+02, 8.81000e+02,\n",
       "       7.20000e+01, 6.56000e+02, 6.01000e+02, 2.21000e+02, 2.28000e+02,\n",
       "       1.08000e+02, 9.40000e+02, 1.76000e+02, 3.30000e+01, 6.63000e+02,\n",
       "       3.40000e+01, 9.42000e+02, 2.59000e+02, 1.64000e+02, 4.58000e+02,\n",
       "       2.45000e+02, 6.29000e+02, 2.80000e+01, 2.88000e+02, 7.75000e+02,\n",
       "       7.85000e+02, 6.36000e+02, 9.16000e+02, 9.94000e+02, 3.09000e+02,\n",
       "       4.85000e+02, 9.14000e+02, 9.03000e+02, 6.08000e+02, 5.00000e+02,\n",
       "       5.40000e+01, 5.62000e+02, 8.47000e+02, 9.57000e+02, 6.88000e+02,\n",
       "       8.11000e+02, 2.70000e+02, 4.80000e+01, 3.29000e+02, 5.23000e+02,\n",
       "       9.21000e+02, 8.74000e+02, 9.81000e+02, 7.84000e+02, 2.80000e+02,\n",
       "       2.40000e+01, 5.18000e+02, 7.54000e+02, 8.92000e+02, 1.54000e+02,\n",
       "       8.60000e+02, 3.64000e+02, 3.87000e+02, 6.26000e+02, 1.61000e+02,\n",
       "       8.79000e+02, 3.90000e+01, 9.70000e+02, 1.70000e+02, 1.41000e+02,\n",
       "       1.60000e+02, 1.44000e+02, 1.43000e+02, 1.90000e+02, 3.76000e+02,\n",
       "       1.93000e+02, 2.46000e+02, 7.30000e+01, 6.58000e+02, 9.92000e+02,\n",
       "       2.53000e+02, 4.20000e+02, 4.04000e+02, 1.00000e+03, 4.70000e+02,\n",
       "       2.26000e+02, 2.40000e+02, 8.90000e+01, 2.34000e+02, 2.57000e+02,\n",
       "       8.61000e+02, 4.67000e+02, 1.57000e+02, 4.40000e+01, 6.76000e+02,\n",
       "       6.70000e+01, 5.52000e+02, 8.85000e+02, 1.02000e+03, 5.82000e+02,\n",
       "       6.19000e+02])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Size\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "74879e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Size'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6fc43a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ABDURR~1\\AppData\\Local\\Temp/ipykernel_3008/2088296926.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"Size\"][df.Size==0.0]=df[\"Size\"].mean()\n"
     ]
    }
   ],
   "source": [
    "df[\"Size\"][df.Size==0.0]=df[\"Size\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "aa57ed19",
   "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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Size==0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "62739711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     January 7, 2018\n",
       "1    January 15, 2018\n",
       "2      August 1, 2018\n",
       "3        June 8, 2018\n",
       "4       June 20, 2018\n",
       "Name: Last Updated, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Last Updated\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f378ee69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['January 7, 2018', 'January 15, 2018', 'August 1, 2018', ...,\n",
       "       'January 20, 2014', 'February 16, 2014', 'March 23, 2014'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Last Updated\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8143b66e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Year\"]=df[\"Last Updated\"].apply(lambda x :x.split(\",\")[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0e9bfa2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2018\n",
       "1         2018\n",
       "2         2018\n",
       "3         2018\n",
       "4         2018\n",
       "         ...  \n",
       "10836     2017\n",
       "10837     2018\n",
       "10838     2017\n",
       "10839     2015\n",
       "10840     2018\n",
       "Name: Year, Length: 10841, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e66a7258",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Month\"]=df[\"Last Updated\"].apply(lambda x:x.split(\" \")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "47709114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       January\n",
       "1       January\n",
       "2        August\n",
       "3          June\n",
       "4          June\n",
       "5         March\n",
       "6         April\n",
       "7          June\n",
       "8     September\n",
       "9          July\n",
       "10      October\n",
       "11         July\n",
       "Name: Month, dtype: object"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Month\"].head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a7755ed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"date\"]=df[\"Last Updated\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ccaa72f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    January 7, 2018\n",
       "1    January 7, 2018\n",
       "2    January 7, 2018\n",
       "3    January 7, 2018\n",
       "4    January 7, 2018\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"date\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "aaa452bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"day\"]=df[\"Last Updated\"].apply(lambda x:x[-8:-6])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b0093640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         7\n",
       "1        15\n",
       "2         1\n",
       "3         8\n",
       "4        20\n",
       "         ..\n",
       "10836    25\n",
       "10837     6\n",
       "10838    20\n",
       "10839    19\n",
       "10840    25\n",
       "Name: day, Length: 10841, dtype: object"
      ]
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     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 100,
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   "outputs": [
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       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1</td>\n",
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       "      <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>2018</td>\n",
       "      <td>January</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
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       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14336.0</td>\n",
       "      <td>500,000+</td>\n",
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       "      <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>2018</td>\n",
       "      <td>January</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8908.8</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>2018</td>\n",
       "      <td>August</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25600.0</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "      <td>2018</td>\n",
       "      <td>June</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2867.2</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>2018</td>\n",
       "      <td>June</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App  Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook         1     4.1   \n",
       "1                                Coloring book moana         1     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...         1     4.7   \n",
       "3                              Sketch - Draw & Paint         1     4.5   \n",
       "4              Pixel Draw - Number Art Coloring Book         1     4.3   \n",
       "\n",
       "  Reviews     Size     Installs  Type Price Content Rating  \\\n",
       "0     159  19456.0      10,000+  Free     0       Everyone   \n",
       "1     967  14336.0     500,000+  Free     0       Everyone   \n",
       "2   87510   8908.8   5,000,000+  Free     0       Everyone   \n",
       "3  215644  25600.0  50,000,000+  Free     0           Teen   \n",
       "4     967   2867.2     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   Year    Month             date day  \n",
       "0  4.0.3 and up   2018  January  January 7, 2018   7  \n",
       "1  4.0.3 and up   2018  January  January 7, 2018  15  \n",
       "2  4.0.3 and up   2018   August  January 7, 2018   1  \n",
       "3    4.2 and up   2018     June  January 7, 2018   8  \n",
       "4    4.4 and up   2018     June  January 7, 2018  20  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5dc64be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop([\"Last Updated\",\"date\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7fc2f9cd",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
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       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14336.0</td>\n",
       "      <td>500,000+</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>2018</td>\n",
       "      <td>January</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8908.8</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>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>2018</td>\n",
       "      <td>August</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25600.0</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "      <td>2018</td>\n",
       "      <td>June</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2867.2</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>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "      <td>2018</td>\n",
       "      <td>June</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App  Category  Rating  \\\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook         1     4.1   \n",
       "1                                Coloring book moana         1     3.9   \n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...         1     4.7   \n",
       "3                              Sketch - Draw & Paint         1     4.5   \n",
       "4              Pixel Draw - Number Art Coloring Book         1     4.3   \n",
       "\n",
       "  Reviews     Size     Installs  Type Price Content Rating  \\\n",
       "0     159  19456.0      10,000+  Free     0       Everyone   \n",
       "1     967  14336.0     500,000+  Free     0       Everyone   \n",
       "2   87510   8908.8   5,000,000+  Free     0       Everyone   \n",
       "3  215644  25600.0  50,000,000+  Free     0           Teen   \n",
       "4     967   2867.2     100,000+  Free     0       Everyone   \n",
       "\n",
       "                      Genres         Current Ver   Android Ver   Year  \\\n",
       "0               Art & Design               1.0.0  4.0.3 and up   2018   \n",
       "1  Art & Design;Pretend Play               2.0.0  4.0.3 and up   2018   \n",
       "2               Art & Design               1.2.4  4.0.3 and up   2018   \n",
       "3               Art & Design  Varies with device    4.2 and up   2018   \n",
       "4    Art & Design;Creativity                 1.1    4.4 and up   2018   \n",
       "\n",
       "     Month day  \n",
       "0  January   7  \n",
       "1  January  15  \n",
       "2   August   1  \n",
       "3     June   8  \n",
       "4     June  20  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5dee878f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['January', 'August', 'June', 'March', 'April', 'September', 'July',\n",
       "       'October', 'November', 'May', 'December', 'February', '1.0.19'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Month.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5be8754c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.Categorical(df.Month,[\n",
    "#     \"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"\n",
    "# ],ordered = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "69867676",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = df.Month.map({\n",
    "    'January':1, 'February':2, 'March':3, 'April':4, 'May':5, 'June':6, 'July':7, 'August':8, 'Septembar':9, 'Octobar':10, 'November':11, 'December':12\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f1072cda",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
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       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2867.200000</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>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10836</th>\n",
       "      <td>Sya9a Maroc - FR</td>\n",
       "      <td>12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>38</td>\n",
       "      <td>54272.000000</td>\n",
       "      <td>5,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>1.48</td>\n",
       "      <td>4.1 and up</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10837</th>\n",
       "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
       "      <td>12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3686.400000</td>\n",
       "      <td>100+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Education</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10838</th>\n",
       "      <td>Parkinson Exercices FR</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>9728.000000</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.2 and up</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10839</th>\n",
       "      <td>The SCP Foundation DB fr nn5n</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>114</td>\n",
       "      <td>18585.803976</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>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10840</th>\n",
       "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
       "      <td>19</td>\n",
       "      <td>4.5</td>\n",
       "      <td>398307</td>\n",
       "      <td>19456.000000</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>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10841 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     App  Category  Rating  \\\n",
       "0         Photo Editor & Candy Camera & Grid & ScrapBook         1     4.1   \n",
       "1                                    Coloring book moana         1     3.9   \n",
       "2      U Launcher Lite – FREE Live Cool Themes, Hide ...         1     4.7   \n",
       "3                                  Sketch - Draw & Paint         1     4.5   \n",
       "4                  Pixel Draw - Number Art Coloring Book         1     4.3   \n",
       "...                                                  ...       ...     ...   \n",
       "10836                                   Sya9a Maroc - FR        12     4.5   \n",
       "10837                   Fr. Mike Schmitz Audio Teachings        12     5.0   \n",
       "10838                             Parkinson Exercices FR        21     NaN   \n",
       "10839                      The SCP Foundation DB fr nn5n         4     4.5   \n",
       "10840      iHoroscope - 2018 Daily Horoscope & Astrology        19     4.5   \n",
       "\n",
       "      Reviews          Size     Installs  Type Price Content Rating  \\\n",
       "0         159  19456.000000      10,000+  Free     0       Everyone   \n",
       "1         967  14336.000000     500,000+  Free     0       Everyone   \n",
       "2       87510   8908.800000   5,000,000+  Free     0       Everyone   \n",
       "3      215644  25600.000000  50,000,000+  Free     0           Teen   \n",
       "4         967   2867.200000     100,000+  Free     0       Everyone   \n",
       "...       ...           ...          ...   ...   ...            ...   \n",
       "10836      38  54272.000000       5,000+  Free     0       Everyone   \n",
       "10837       4   3686.400000         100+  Free     0       Everyone   \n",
       "10838       3   9728.000000       1,000+  Free     0       Everyone   \n",
       "10839     114  18585.803976       1,000+  Free     0     Mature 17+   \n",
       "10840  398307  19456.000000  10,000,000+  Free     0       Everyone   \n",
       "\n",
       "                          Genres         Current Ver         Android Ver  \\\n",
       "0                   Art & Design               1.0.0        4.0.3 and up   \n",
       "1      Art & Design;Pretend Play               2.0.0        4.0.3 and up   \n",
       "2                   Art & Design               1.2.4        4.0.3 and up   \n",
       "3                   Art & Design  Varies with device          4.2 and up   \n",
       "4        Art & Design;Creativity                 1.1          4.4 and up   \n",
       "...                          ...                 ...                 ...   \n",
       "10836                  Education                1.48          4.1 and up   \n",
       "10837                  Education                 1.0          4.1 and up   \n",
       "10838                    Medical                 1.0          2.2 and up   \n",
       "10839          Books & Reference  Varies with device  Varies with device   \n",
       "10840                  Lifestyle  Varies with device  Varies with device   \n",
       "\n",
       "        Year  Month day  \n",
       "0       2018    1.0   7  \n",
       "1       2018    1.0  15  \n",
       "2       2018    8.0   1  \n",
       "3       2018    6.0   8  \n",
       "4       2018    6.0  20  \n",
       "...      ...    ...  ..  \n",
       "10836   2017    7.0  25  \n",
       "10837   2018    7.0   6  \n",
       "10838   2017    1.0  20  \n",
       "10839   2015    1.0  19  \n",
       "10840   2018    7.0  25  \n",
       "\n",
       "[10841 rows x 15 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c9e54d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0     3163\n",
       "8.0     1594\n",
       "6.0     1273\n",
       "5.0      978\n",
       "3.0      667\n",
       "4.0      616\n",
       "2.0      533\n",
       "1.0      491\n",
       "12.0     426\n",
       "11.0     387\n",
       "Name: Month, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "4b81b806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  8.,  6.,  3.,  4., nan,  7., 11.,  5., 12.,  2.])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Month.unique()"
   ]
  }
 ],
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