05_kaggle_google.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kaggle google EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "#import ydata_profiling as yd\n",
    "df=pd.read_csv('.\\data\\googleplaystore.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run automatics eda using ydata profiling\n",
    "#profile=yd.ProfileReport(df) #profile = yd.ProfileReport(df)\n",
    "#profile.to_file(output_file='./output/ydata_googleplaystore_kaggle.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </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>07-Jan-18</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>15-Jan-18</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              App        Category  Rating  \\\n",
       "0  Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
       "1                             Coloring book moana  ART_AND_DESIGN     3.9   \n",
       "\n",
       "   Reviews Size  Installs  Type Price Content Rating  \\\n",
       "0      159  19M   10,000+  Free     0       Everyone   \n",
       "1      967  14M  500,000+  Free     0       Everyone   \n",
       "\n",
       "                      Genres Last Updated Current Ver   Android Ver  \n",
       "0               Art & Design    07-Jan-18       1.0.0  4.0.3 and up  \n",
       "1  Art & Design;Pretend Play    15-Jan-18       2.0.0  4.0.3 and up  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10839</th>\n",
       "      <td>The SCP Foundation DB fr nn5n</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>114</td>\n",
       "      <td>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>19-Jan-15</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10840</th>\n",
       "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>398307</td>\n",
       "      <td>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>25-Jul-18</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App             Category  \\\n",
       "10839                  The SCP Foundation DB fr nn5n  BOOKS_AND_REFERENCE   \n",
       "10840  iHoroscope - 2018 Daily Horoscope & Astrology            LIFESTYLE   \n",
       "\n",
       "       Rating  Reviews                Size     Installs  Type Price  \\\n",
       "10839     4.5      114  Varies with device       1,000+  Free     0   \n",
       "10840     4.5   398307                 19M  10,000,000+  Free     0   \n",
       "\n",
       "      Content Rating             Genres Last Updated         Current Ver  \\\n",
       "10839     Mature 17+  Books & Reference    19-Jan-15  Varies with device   \n",
       "10840       Everyone          Lifestyle    25-Jul-18  Varies with device   \n",
       "\n",
       "              Android Ver  \n",
       "10839  Varies with device  \n",
       "10840  Varies with device  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               1362\n",
       "Category          1362\n",
       "Rating            1319\n",
       "Reviews           1362\n",
       "Size              1362\n",
       "Installs          1362\n",
       "Type              1361\n",
       "Price             1362\n",
       "Content Rating    1362\n",
       "Genres            1362\n",
       "Last Updated      1362\n",
       "Current Ver       1362\n",
       "Android Ver       1362\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Android Ver']=='Varies with device'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App\n",
       "False    9660\n",
       "True     1181\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['App'].duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9660"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['App'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "Name: App, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['App'].duplicated().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               0\n",
       "Category          0\n",
       "Rating            0\n",
       "Reviews           0\n",
       "Size              0\n",
       "Installs          0\n",
       "Type              0\n",
       "Price             0\n",
       "Content Rating    0\n",
       "Genres            0\n",
       "Last Updated      0\n",
       "Current Ver       0\n",
       "Android Ver       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['App']=='False'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10841"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Size'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1181"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['App'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['App'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 0 entries\n",
      "Empty DataFrame\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Size'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Size'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[22], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# how to convert column size into \u001b[39;00m\n\u001b[0;32m      2\u001b[0m df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mDataFrame()\n\u001b[1;32m----> 3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSize\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSize\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n",
      "File \u001b[1;32mc:\\Users\\Aaima Amir\\miniconda3\\envs\\eda_env\\lib\\site-packages\\pandas\\core\\frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4088\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   4089\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4090\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4091\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   4092\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\Users\\Aaima Amir\\miniconda3\\envs\\eda_env\\lib\\site-packages\\pandas\\core\\indexes\\range.py:417\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    415\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[1;32m--> 417\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[0;32m    418\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[0;32m    419\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Size'"
     ]
    }
   ],
   "source": [
    "# how to convert column size into \n",
    "df=pd.DataFrame()\n",
    "df['Size']=np.array(df['Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10841"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Size'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aaima Amir\\AppData\\Local\\Temp\\ipykernel_2512\\4088365378.py:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Size']=df['Size'].replace(\"Varies with device\",\"np.NaN\",inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        None\n",
       "1        None\n",
       "2        None\n",
       "3        None\n",
       "4        None\n",
       "         ... \n",
       "10836    None\n",
       "10837    None\n",
       "10838    None\n",
       "10839    None\n",
       "10840    None\n",
       "Name: Size, Length: 10841, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import pandas as pd\n",
    "\n",
    "# Read the CSV file into a pandas DataFrame\n",
    "#df = pd.read_csv('.csv')\n",
    "\n",
    "#How to convert string value to NaN in pandas\n",
    "df['Size']=df['Size'].replace(\"Varies with device\",\"np.NaN\",inplace=True)\n",
    "df['Size']\n",
    "\n",
    "# Convert 'size' values from KB to MB\n",
    "#df['Size'] = df['Size'].str.replace('KB', '').astype(float) / 1024\n",
    "df['Size'] = df['Size'].apply(lambda x: float(x.replace('KB', ''))/1024 if 'KB' in x else float(x.replace('M', '')))\n",
    "\n",
    "# Convert 'size' column to numerical values\n",
    "# df['Size'] = pd.to_numeric(df['Size'], errors='coerce')\n",
    "\n",
    "# Save the DataFrame back to a new CSV file\n",
    "# df.to_csv('converted_file.csv', index=False)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "eda_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}