Google_Play_Store_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Apps 商店的数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 简介\n",
    "\n",
    "相信大家对移动应用商店都不陌生吧。Google Play Store（Google Play商店）是谷歌官方的软件应用商店，拥有上架软件数十万款，下载量更是突破了20亿次，为了手机用户提供了极为广泛的应用选择，很受大家的欢迎。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本数据集(googleplaystore.csv)包含了 Google Play 商店中 App 的数据。该数据是Kaggle中 [Google Play Store Apps](https://www.kaggle.com/lava18/google-play-store-apps) 的一部分，其中包含 Google Play 商店中 10k+ 应用软件的信息。\n",
    "\n",
    "数据中的变量含义解释：\n",
    "```\n",
    "App: 应用的名称，字符变量。\n",
    "Category: 应用所属的分类，字符变量。\n",
    "Rating: 某应用的用户评分，数值变量。\n",
    "Reviews: 某应用获得的用户评论数量，数值变量。\n",
    "Size: 某应用的所占存储空间的大小，字符变量。\n",
    "Installs: 用户安装和下载某应用的次数，字符变量。\n",
    "Type: 付费或免费，分类变量。\n",
    "Price: 价格，字符变量。\n",
    "Content Rating: 应用商店针对内容给出的年龄评级组 - Children / Mature 21+ / Adult，分类变量。\n",
    "Genres: 类型/流派，一个应用可以属于多个流派，比如音乐、游戏、家庭等，字符变量。\n",
    "Last Updated: 应用最新更新的日期，字符变量。\n",
    "Current Ver: 当前应用的版本，字符变量。\n",
    "Android Ver: 安装该应用所需要的最低安卓版本，字符变量。\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提出问题\n",
    "\n",
    "在此项目中，你将以一名数据分析师的身份执行数据的探索性分析。你将了解数据分析过程的基本流程。在你分析数据之前，请先思考几个你需要了解的关于 Google 商店中应用的问题，例如，最受欢迎（下载量最高）的 Apps 有什么特征？哪些 App 的评分更高？\n",
    "\n",
    "**问题**：请写下你感兴趣的问题，请确保这些问题能够由现有的数据进行回答。\n",
    "（为了确保学习的效果，请确保你的数据分析报告中能够包含2幅可视化和1个相关性分析。）\n",
    "#### 问题：最受欢迎的app是什么类型？\n",
    " \n",
    "\n",
    "**答案**："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据评估和清理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-12-03 10:35:19--  http://d.xiazaiziti.com/en_fonts/fonts/s/SimHei.ttf\n",
      "Resolving d.xiazaiziti.com (d.xiazaiziti.com)... 67.198.189.58\n",
      "Connecting to d.xiazaiziti.com (d.xiazaiziti.com)|67.198.189.58|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 10050868 (9.6M) [application/octet-stream]\n",
      "Saving to: ‘/opt/conda/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/SimHei.ttf’\n",
      "\n",
      "/opt/conda/lib/pyth 100%[===================>]   9.58M  3.55MB/s    in 2.7s    \n",
      "\n",
      "2019-12-03 10:35:22 (3.55 MB/s) - ‘/opt/conda/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/SimHei.ttf’ saved [10050868/10050868]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 请先运行此代码块，以确保在可视化中可以显示中文\n",
    "!rm -rf ~/.cache/matplotlib/fontList.json\n",
    "!wget http://d.xiazaiziti.com/en_fonts/fonts/s/SimHei.ttf -O /opt/conda/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/SimHei.ttf\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pacakges\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset\n",
    "df_google = pd.read_csv('googleplaystore.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10841 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      "App               10841 non-null object\n",
      "Category          10841 non-null object\n",
      "Rating            9367 non-null float64\n",
      "Reviews           10841 non-null object\n",
      "Size              10841 non-null object\n",
      "Installs          10841 non-null object\n",
      "Type              10840 non-null object\n",
      "Price             10841 non-null object\n",
      "Content Rating    10840 non-null object\n",
      "Genres            10841 non-null object\n",
      "Last Updated      10841 non-null object\n",
      "Current Ver       10833 non-null object\n",
      "Android Ver       10838 non-null object\n",
      "dtypes: float64(1), object(12)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# check the dataset general info\n",
    "df_google.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_google.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type',\n",
       "       'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver',\n",
       "       'Android Ver'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_google.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ App -----------------\n",
      "['Photo Editor & Candy Camera & Grid & ScrapBook' 'Coloring book moana'\n",
      " 'U Launcher Lite – FREE Live Cool Themes, Hide Apps' ...,\n",
      " 'Parkinson Exercices FR' 'The SCP Foundation DB fr nn5n'\n",
      " 'iHoroscope - 2018 Daily Horoscope & Astrology']\n",
      "------------ Category -----------------\n",
      "['ART_AND_DESIGN' 'AUTO_AND_VEHICLES' 'BEAUTY' 'BOOKS_AND_REFERENCE'\n",
      " 'BUSINESS' 'COMICS' 'COMMUNICATION' 'DATING' 'EDUCATION' 'ENTERTAINMENT'\n",
      " 'EVENTS' 'FINANCE' 'FOOD_AND_DRINK' 'HEALTH_AND_FITNESS' 'HOUSE_AND_HOME'\n",
      " 'LIBRARIES_AND_DEMO' 'LIFESTYLE' 'GAME' 'FAMILY' 'MEDICAL' 'SOCIAL'\n",
      " 'SHOPPING' 'PHOTOGRAPHY' 'SPORTS' 'TRAVEL_AND_LOCAL' 'TOOLS'\n",
      " 'PERSONALIZATION' 'PRODUCTIVITY' 'PARENTING' 'WEATHER' 'VIDEO_PLAYERS'\n",
      " 'NEWS_AND_MAGAZINES' 'MAPS_AND_NAVIGATION' '1.9']\n",
      "------------ Rating -----------------\n",
      "[  4.1   3.9   4.7   4.5   4.3   4.4   3.8   4.2   4.6   3.2   4.    nan\n",
      "   4.8   4.9   3.6   3.7   3.3   3.4   3.5   3.1   5.    2.6   3.    1.9\n",
      "   2.5   2.8   2.7   1.    2.9   2.3   2.2   1.7   2.    1.8   2.4   1.6\n",
      "   2.1   1.4   1.5   1.2  19. ]\n",
      "------------ Reviews -----------------\n",
      "['159' '967' '87510' ..., '603' '1195' '398307']\n",
      "------------ Size -----------------\n",
      "['19M' '14M' '8.7M' '25M' '2.8M' '5.6M' '29M' '33M' '3.1M' '28M' '12M'\n",
      " '20M' '21M' '37M' '2.7M' '5.5M' '17M' '39M' '31M' '4.2M' '7.0M' '23M'\n",
      " '6.0M' '6.1M' '4.6M' '9.2M' '5.2M' '11M' '24M' 'Varies with device' '9.4M'\n",
      " '15M' '10M' '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' '8.9M' '3.9M' '2.9M'\n",
      " '38M' '32M' '5.4M' '18M' '1.1M' '2.2M' '4.5M' '9.8M' '52M' '9.0M' '6.7M'\n",
      " '30M' '2.6M' '7.1M' '3.7M' '22M' '7.4M' '6.4M' '3.2M' '8.2M' '9.9M' '4.9M'\n",
      " '9.5M' '5.0M' '5.9M' '13M' '73M' '6.8M' '3.5M' '4.0M' '2.3M' '7.2M' '2.1M'\n",
      " '42M' '7.3M' '9.1M' '55M' '23k' '6.5M' '1.5M' '7.5M' '51M' '41M' '48M'\n",
      " '8.5M' '46M' '8.3M' '4.3M' '4.7M' '3.3M' '40M' '7.8M' '8.8M' '6.6M' '5.1M'\n",
      " '61M' '66M' '79k' '8.4M' '118k' '44M' '695k' '1.6M' '6.2M' '18k' '53M'\n",
      " '1.4M' '3.0M' '5.8M' '3.8M' '9.6M' '45M' '63M' '49M' '77M' '4.4M' '4.8M'\n",
      " '70M' '6.9M' '9.3M' '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' '72M' '43M'\n",
      " '7.7M' '6.3M' '334k' '34M' '93M' '65M' '79M' '100M' '58M' '50M' '68M'\n",
      " '64M' '67M' '60M' '94M' '232k' '99M' '624k' '95M' '8.5k' '41k' '292k'\n",
      " '11k' '80M' '1.7M' '74M' '62M' '69M' '75M' '98M' '85M' '82M' '96M' '87M'\n",
      " '71M' '86M' '91M' '81M' '92M' '83M' '88M' '704k' '862k' '899k' '378k'\n",
      " '266k' '375k' '1.3M' '975k' '980k' '4.1M' '89M' '696k' '544k' '525k'\n",
      " '920k' '779k' '853k' '720k' '713k' '772k' '318k' '58k' '241k' '196k'\n",
      " '857k' '51k' '953k' '865k' '251k' '930k' '540k' '313k' '746k' '203k' '26k'\n",
      " '314k' '239k' '371k' '220k' '730k' '756k' '91k' '293k' '17k' '74k' '14k'\n",
      " '317k' '78k' '924k' '902k' '818k' '81k' '939k' '169k' '45k' '475k' '965k'\n",
      " '90M' '545k' '61k' '283k' '655k' '714k' '93k' '872k' '121k' '322k' '1.0M'\n",
      " '976k' '172k' '238k' '549k' '206k' '954k' '444k' '717k' '210k' '609k'\n",
      " '308k' '705k' '306k' '904k' '473k' '175k' '350k' '383k' '454k' '421k'\n",
      " '70k' '812k' '442k' '842k' '417k' '412k' '459k' '478k' '335k' '782k'\n",
      " '721k' '430k' '429k' '192k' '200k' '460k' '728k' '496k' '816k' '414k'\n",
      " '506k' '887k' '613k' '243k' '569k' '778k' '683k' '592k' '319k' '186k'\n",
      " '840k' '647k' '191k' '373k' '437k' '598k' '716k' '585k' '982k' '222k'\n",
      " '219k' '55k' '948k' '323k' '691k' '511k' '951k' '963k' '25k' '554k' '351k'\n",
      " '27k' '82k' '208k' '913k' '514k' '551k' '29k' '103k' '898k' '743k' '116k'\n",
      " '153k' '209k' '353k' '499k' '173k' '597k' '809k' '122k' '411k' '400k'\n",
      " '801k' '787k' '237k' '50k' '643k' '986k' '97k' '516k' '837k' '780k' '961k'\n",
      " '269k' '20k' '498k' '600k' '749k' '642k' '881k' '72k' '656k' '601k' '221k'\n",
      " '228k' '108k' '940k' '176k' '33k' '663k' '34k' '942k' '259k' '164k' '458k'\n",
      " '245k' '629k' '28k' '288k' '775k' '785k' '636k' '916k' '994k' '309k'\n",
      " '485k' '914k' '903k' '608k' '500k' '54k' '562k' '847k' '957k' '688k'\n",
      " '811k' '270k' '48k' '329k' '523k' '921k' '874k' '981k' '784k' '280k' '24k'\n",
      " '518k' '754k' '892k' '154k' '860k' '364k' '387k' '626k' '161k' '879k'\n",
      " '39k' '970k' '170k' '141k' '160k' '144k' '143k' '190k' '376k' '193k'\n",
      " '246k' '73k' '658k' '992k' '253k' '420k' '404k' '1,000+' '470k' '226k'\n",
      " '240k' '89k' '234k' '257k' '861k' '467k' '157k' '44k' '676k' '67k' '552k'\n",
      " '885k' '1020k' '582k' '619k']\n",
      "------------ Installs -----------------\n",
      "['10,000+' '500,000+' '5,000,000+' '50,000,000+' '100,000+' '50,000+'\n",
      " '1,000,000+' '10,000,000+' '5,000+' '100,000,000+' '1,000,000,000+'\n",
      " '1,000+' '500,000,000+' '50+' '100+' '500+' '10+' '1+' '5+' '0+' '0'\n",
      " 'Free']\n",
      "------------ Type -----------------\n",
      "['Free' 'Paid' nan '0']\n",
      "------------ Price -----------------\n",
      "['0' '$4.99' '$3.99' '$6.99' '$1.49' '$2.99' '$7.99' '$5.99' '$3.49'\n",
      " '$1.99' '$9.99' '$7.49' '$0.99' '$9.00' '$5.49' '$10.00' '$24.99' '$11.99'\n",
      " '$79.99' '$16.99' '$14.99' '$1.00' '$29.99' '$12.99' '$2.49' '$10.99'\n",
      " '$1.50' '$19.99' '$15.99' '$33.99' '$74.99' '$39.99' '$3.95' '$4.49'\n",
      " '$1.70' '$8.99' '$2.00' '$3.88' '$25.99' '$399.99' '$17.99' '$400.00'\n",
      " '$3.02' '$1.76' '$4.84' '$4.77' '$1.61' '$2.50' '$1.59' '$6.49' '$1.29'\n",
      " '$5.00' '$13.99' '$299.99' '$379.99' '$37.99' '$18.99' '$389.99' '$19.90'\n",
      " '$8.49' '$1.75' '$14.00' '$4.85' '$46.99' '$109.99' '$154.99' '$3.08'\n",
      " '$2.59' '$4.80' '$1.96' '$19.40' '$3.90' '$4.59' '$15.46' '$3.04' '$4.29'\n",
      " '$2.60' '$3.28' '$4.60' '$28.99' '$2.95' '$2.90' '$1.97' '$200.00'\n",
      " '$89.99' '$2.56' '$30.99' '$3.61' '$394.99' '$1.26' 'Everyone' '$1.20'\n",
      " '$1.04']\n",
      "------------ Content Rating -----------------\n",
      "['Everyone' 'Teen' 'Everyone 10+' 'Mature 17+' 'Adults only 18+' 'Unrated'\n",
      " nan]\n",
      "------------ Genres -----------------\n",
      "['Art & Design' 'Art & Design;Pretend Play' 'Art & Design;Creativity'\n",
      " 'Art & Design;Action & Adventure' 'Auto & Vehicles' 'Beauty'\n",
      " 'Books & Reference' 'Business' 'Comics' 'Comics;Creativity'\n",
      " 'Communication' 'Dating' 'Education;Education' 'Education'\n",
      " 'Education;Creativity' 'Education;Music & Video'\n",
      " 'Education;Action & Adventure' 'Education;Pretend Play'\n",
      " 'Education;Brain Games' 'Entertainment' 'Entertainment;Music & Video'\n",
      " 'Entertainment;Brain Games' 'Entertainment;Creativity' 'Events' 'Finance'\n",
      " 'Food & Drink' 'Health & Fitness' 'House & Home' 'Libraries & Demo'\n",
      " 'Lifestyle' 'Lifestyle;Pretend Play' 'Adventure;Action & Adventure'\n",
      " 'Arcade' 'Casual' 'Card' 'Casual;Pretend Play' 'Action' 'Strategy'\n",
      " 'Puzzle' 'Sports' 'Music' 'Word' 'Racing' 'Casual;Creativity'\n",
      " 'Casual;Action & Adventure' 'Simulation' 'Adventure' 'Board' 'Trivia'\n",
      " 'Role Playing' 'Simulation;Education' 'Action;Action & Adventure'\n",
      " 'Casual;Brain Games' 'Simulation;Action & Adventure'\n",
      " 'Educational;Creativity' 'Puzzle;Brain Games' 'Educational;Education'\n",
      " 'Card;Brain Games' 'Educational;Brain Games' 'Educational;Pretend Play'\n",
      " 'Entertainment;Education' 'Casual;Education' 'Music;Music & Video'\n",
      " 'Racing;Action & Adventure' 'Arcade;Pretend Play'\n",
      " 'Role Playing;Action & Adventure' 'Simulation;Pretend Play'\n",
      " 'Puzzle;Creativity' 'Sports;Action & Adventure'\n",
      " 'Educational;Action & Adventure' 'Arcade;Action & Adventure'\n",
      " 'Entertainment;Action & Adventure' 'Puzzle;Action & Adventure'\n",
      " 'Strategy;Action & Adventure' 'Music & Audio;Music & Video'\n",
      " 'Health & Fitness;Education' 'Adventure;Education' 'Board;Brain Games'\n",
      " 'Board;Action & Adventure' 'Board;Pretend Play' 'Casual;Music & Video'\n",
      " 'Role Playing;Pretend Play' 'Entertainment;Pretend Play'\n",
      " 'Video Players & Editors;Creativity' 'Card;Action & Adventure' 'Medical'\n",
      " 'Social' 'Shopping' 'Photography' 'Travel & Local'\n",
      " 'Travel & Local;Action & Adventure' 'Tools' 'Tools;Education'\n",
      " 'Personalization' 'Productivity' 'Parenting' 'Parenting;Music & Video'\n",
      " 'Parenting;Education' 'Parenting;Brain Games' 'Weather'\n",
      " 'Video Players & Editors' 'Video Players & Editors;Music & Video'\n",
      " 'News & Magazines' 'Maps & Navigation'\n",
      " 'Health & Fitness;Action & Adventure' 'Educational' 'Casino'\n",
      " 'Adventure;Brain Games' 'Trivia;Education' 'Lifestyle;Education'\n",
      " 'Books & Reference;Creativity' 'Books & Reference;Education'\n",
      " 'Puzzle;Education' 'Role Playing;Education' 'Role Playing;Brain Games'\n",
      " 'Strategy;Education' 'Racing;Pretend Play' 'Communication;Creativity'\n",
      " 'February 11, 2018' 'Strategy;Creativity']\n",
      "------------ Last Updated -----------------\n",
      "['January 7, 2018' 'January 15, 2018' 'August 1, 2018' ...,\n",
      " 'January 20, 2014' 'February 16, 2014' 'March 23, 2014']\n",
      "------------ Current Ver -----------------\n",
      "['1.0.0' '2.0.0' '1.2.4' ..., '1.0.612928' '0.3.4' '2.0.148.0']\n",
      "------------ Android Ver -----------------\n",
      "['4.0.3 and up' '4.2 and up' '4.4 and up' '2.3 and up' '3.0 and up'\n",
      " '4.1 and up' '4.0 and up' '2.3.3 and up' 'Varies with device' '2.2 and up'\n",
      " '5.0 and up' '6.0 and up' '1.6 and up' '1.5 and up' '2.1 and up'\n",
      " '7.0 and up' '5.1 and up' '4.3 and up' '4.0.3 - 7.1.1' '2.0 and up'\n",
      " '3.2 and up' '4.4W and up' '7.1 and up' '7.0 - 7.1.1' '8.0 and up'\n",
      " '5.0 - 8.0' '3.1 and up' '2.0.1 and up' '4.1 - 7.1.1' nan '5.0 - 6.0'\n",
      " '1.0 and up' '2.2 - 7.1.1' '5.0 - 7.1.1']\n"
     ]
    }
   ],
   "source": [
    "for i in df_google.columns:\n",
    "    print('------------',i,'-----------------')\n",
    "    print(df_google[i].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identified data wrangling problems:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- category, type, content rating, genres, installs 应该是 category 类型\n",
    "- reviews,size 应该是 int 类型\n",
    "- last updated 应该是 时间类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 更变量数据和类型错误"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 尝试修改列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 列名统一为小写\n",
    "df_google.rename( columns = lambda x:x.lower().replace(' ','_'),inplace = True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 查看各列空值占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "app               0.000000\n",
       "category          0.000000\n",
       "rating            0.135965\n",
       "reviews           0.000000\n",
       "size              0.000000\n",
       "installs          0.000000\n",
       "type              0.000092\n",
       "price             0.000000\n",
       "content_rating    0.000092\n",
       "genres            0.000000\n",
       "last_updated      0.000000\n",
       "current_ver       0.000738\n",
       "android_ver       0.000277\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_google.isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 尝试 fillna 将空值填充为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google.fillna(value = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "483"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看duplicated()行数\n",
    "len(df_google[df_google.duplicated()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 清除duplicated()\n",
    "df_google.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使genres和 category 相统一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0              ART_DESIGN\n",
       "1      0              ART_DESIGN\n",
       "       1             PRETENDPLAY\n",
       "2      0              ART_DESIGN\n",
       "3      0              ART_DESIGN\n",
       "4      0              ART_DESIGN\n",
       "       1              CREATIVITY\n",
       "5      0              ART_DESIGN\n",
       "6      0              ART_DESIGN\n",
       "7      0              ART_DESIGN\n",
       "8      0              ART_DESIGN\n",
       "9      0              ART_DESIGN\n",
       "       1              CREATIVITY\n",
       "10     0              ART_DESIGN\n",
       "11     0              ART_DESIGN\n",
       "12     0              ART_DESIGN\n",
       "13     0              ART_DESIGN\n",
       "14     0              ART_DESIGN\n",
       "15     0              ART_DESIGN\n",
       "16     0              ART_DESIGN\n",
       "17     0              ART_DESIGN\n",
       "18     0              ART_DESIGN\n",
       "19     0              ART_DESIGN\n",
       "20     0              ART_DESIGN\n",
       "21     0              ART_DESIGN\n",
       "22     0              ART_DESIGN\n",
       "23     0              ART_DESIGN\n",
       "       1        ACTION_ADVENTURE\n",
       "24     0              ART_DESIGN\n",
       "25     0              ART_DESIGN\n",
       "                    ...         \n",
       "10811  0           AUTO_VEHICLES\n",
       "10812  0               EDUCATION\n",
       "10813  0                BUSINESS\n",
       "10814  0           ENTERTAINMENT\n",
       "10815  0         BOOKS_REFERENCE\n",
       "10816  0                BUSINESS\n",
       "10817  0                   TOOLS\n",
       "10818  0                 FINANCE\n",
       "10819  0         BOOKS_REFERENCE\n",
       "10820  0               EDUCATION\n",
       "10821  0           ENTERTAINMENT\n",
       "10822  0            PRODUCTIVITY\n",
       "10823  0    VIDEOPLAYERS_EDITORS\n",
       "10824  0                 MEDICAL\n",
       "10825  0                  SOCIAL\n",
       "10826  0                  SOCIAL\n",
       "10827  0               EDUCATION\n",
       "10828  0                  COMICS\n",
       "10829  0         BOOKS_REFERENCE\n",
       "10830  0          NEWS_MAGAZINES\n",
       "10831  0         MAPS_NAVIGATION\n",
       "10832  0                 WEATHER\n",
       "10833  0         BOOKS_REFERENCE\n",
       "10834  0               EDUCATION\n",
       "10835  0                BUSINESS\n",
       "10836  0               EDUCATION\n",
       "10837  0               EDUCATION\n",
       "10838  0                 MEDICAL\n",
       "10839  0         BOOKS_REFERENCE\n",
       "10840  0               LIFESTYLE\n",
       "Length: 10820, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_google.genres.str.upper().str.replace('&','_').str.replace(' ','').str.split(';',expand = True).stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗 installs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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', 'Free'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看installs 的 unique()\n",
    "df_google.installs.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>10472</th>\n",
       "      <td>Life Made WI-Fi Touchscreen Photo Frame</td>\n",
       "      <td>1.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0M</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0</td>\n",
       "      <td>February 11, 2018</td>\n",
       "      <td>1.0.19</td>\n",
       "      <td>4.0 and up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           app category  rating reviews  \\\n",
       "10472  Life Made WI-Fi Touchscreen Photo Frame      1.9    19.0    3.0M   \n",
       "\n",
       "         size installs type     price content_rating             genres  \\\n",
       "10472  1,000+     Free    0  Everyone              0  February 11, 2018   \n",
       "\n",
       "      last_updated current_ver android_ver  \n",
       "10472       1.0.19  4.0 and up           0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_google[df_google.installs == 'Free']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结果发现，第10472行 的数据发生了错位，没有‘category’ 值。更正这一行为正确的值。\n",
    "- 该app 名为，Life Made WIFI Touchscreen Photo Frame,\n",
    "- 推测 其 category 应该为“PHOTOGRAPHY”\n",
    "- 因此为其赋值，并修正其他列的值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把 index = 10472的 每一列 正确的 值取出来\n",
    "d10472_values = df_google.loc[10472].values[0:12]\n",
    "# 增加 photography \n",
    "d10472_values = np.insert(d10472_values,1,'PHOTOGRAPHY')\n",
    "# 给 index = 10472 赋正确的值\n",
    "df_google.loc[10472] = d10472_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 继续清洗 installs\n",
    "- 查看 installs.unique(),发现，有一个值为 0.0, 其他均为 str\n",
    "- 查找 值为 0.0的表格，发现只有一个。\n",
    "- 将 0.0 的值改为 str,赋值为‘0’"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_google.installs.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 更改 0.0 为 str 类型\n",
    "df_google.installs = df_google.installs.apply(lambda x: '0' if x==0 else x) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看更改是否正确\n",
    "df_google.installs.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转为 int 类型\n",
    "df_google.installs = df_google.installs.str.rstrip('+').str.strip(',').str.replace(',','').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     10000,     500000,    5000000,   50000000,     100000,\n",
       "            50000,    1000000,   10000000,       5000,  100000000,\n",
       "       1000000000,       1000,  500000000,         50,        100,\n",
       "              500,         10,          1,          5,          0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查 installs 清理结果\n",
    "df_google.installs.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗 size\n",
    "- 如果是‘Varies with device’,则将 size 设置为0。\n",
    "- 经检验‘VAries with device’ 共有1526个值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1526"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_google[df_google['size'] == 'Varies with device'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google['size'] = df_google['size'].apply(lambda x: float(x[:-1])*1000 if x[-1]=='M' \n",
    "                                            else 0 if x[0]=='V' else float(x[:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google['size'] = df_google['size'].astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗last_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看 last_updated. unique()\n",
    "df_google.last_updated.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google.last_updated = pd.to_datetime(df_google.last_updated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗 category <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看 category.unique()\n",
    "df_google.category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google.category = df_google.category.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗 rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a8491b390>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2a8491b978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画bar 图查看数据。\n",
    "df_google.rating.value_counts().plot(kind = 'bar')\n",
    "# 结果发现， rating  = 0 有很多值，考虑，以后的数据分析中，不考虑0 值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google.rating = df_google.rating.apply(lambda x: float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google.rating = df_google.rating.astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗 reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看 reviews.unique(),未 发现异常\n",
    "# 更改 reviews 类型为 int\n",
    "df_google.reviews = df_google.reviews.astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗 type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Free', 'Paid', 0], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_google.type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转为 category 类型\n",
    "df_google.type = df_google.type.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗 content_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google.content_rating = df_google.content_rating.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗`price`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10358 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      "app               10358 non-null object\n",
      "category          10358 non-null category\n",
      "rating            10358 non-null object\n",
      "reviews           10358 non-null int64\n",
      "size              10358 non-null float64\n",
      "installs          10358 non-null int64\n",
      "type              10358 non-null category\n",
      "price             10358 non-null object\n",
      "content_rating    10358 non-null category\n",
      "genres            10358 non-null object\n",
      "last_updated      10358 non-null datetime64[ns]\n",
      "current_ver       10358 non-null object\n",
      "android_ver       10358 non-null object\n",
      "dtypes: category(3), datetime64[ns](1), float64(1), int64(2), object(6)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_google.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9593"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  查看 price == 0 的 items数量。发现有9593个为0.\n",
    "len(df_google[df_google['price']=='0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google.price = df_google.price.astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清理gengres，设置为 category类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_google.genres = df_google.genres.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### current_ver 和 android_ver 暂时不作处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据清理完毕 ！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算各个 category 的 app的 统计数据\n",
    "df_grouped_mean = df_google.groupby(['category'],as_index = False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a81f2a0b8>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2a823a2160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画评分Rating的 频率直方图\n",
    "df_grouped_mean['rating'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rating = df_grouped_mean.sort_values('rating',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>4.342308</td>\n",
       "      <td>1.781962e+05</td>\n",
       "      <td>14826.015385</td>\n",
       "      <td>4.107323e+06</td>\n",
       "      <td>0.138154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.156923</td>\n",
       "      <td>2.637600e+04</td>\n",
       "      <td>11800.000000</td>\n",
       "      <td>1.912894e+06</td>\n",
       "      <td>0.091846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>4.136036</td>\n",
       "      <td>4.285650e+05</td>\n",
       "      <td>13664.864865</td>\n",
       "      <td>2.212306e+07</td>\n",
       "      <td>0.071892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GAME</td>\n",
       "      <td>4.101784</td>\n",
       "      <td>1.262745e+06</td>\n",
       "      <td>39363.826940</td>\n",
       "      <td>2.813918e+07</td>\n",
       "      <td>0.256289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>COMICS</td>\n",
       "      <td>4.016667</td>\n",
       "      <td>5.638793e+04</td>\n",
       "      <td>11462.550000</td>\n",
       "      <td>9.347692e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          category    rating       reviews          size      installs  \\\n",
       "8        EDUCATION  4.342308  1.781962e+05  14826.015385  4.107323e+06   \n",
       "0   ART_AND_DESIGN  4.156923  2.637600e+04  11800.000000  1.912894e+06   \n",
       "9    ENTERTAINMENT  4.136036  4.285650e+05  13664.864865  2.212306e+07   \n",
       "14            GAME  4.101784  1.262745e+06  39363.826940  2.813918e+07   \n",
       "5           COMICS  4.016667  5.638793e+04  11462.550000  9.347692e+05   \n",
       "\n",
       "       price  \n",
       "8   0.138154  \n",
       "0   0.091846  \n",
       "9   0.071892  \n",
       "14  0.256289  \n",
       "5   0.000000  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 直方图中，可以看出，共有5个categories 的平均评分超过了4分，找出这5类category\n",
    "- 前5类分别为： Education, art and design, entertainment, game, comics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a8210b5c0>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2a82199e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画 下载量 installs 的 直方图\n",
    "df_grouped_mean['installs'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_installs = df_grouped_mean.sort_values('installs',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>rating</th>\n",
       "      <th>reviews</th>\n",
       "      <th>size</th>\n",
       "      <th>installs</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>3.482240</td>\n",
       "      <td>1.642824e+06</td>\n",
       "      <td>8281.904372</td>\n",
       "      <td>6.598983e+07</td>\n",
       "      <td>0.227158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SOCIAL</td>\n",
       "      <td>3.707857</td>\n",
       "      <td>1.905632e+06</td>\n",
       "      <td>12234.975000</td>\n",
       "      <td>4.469239e+07</td>\n",
       "      <td>0.057036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>VIDEO_PLAYERS</td>\n",
       "      <td>3.715429</td>\n",
       "      <td>6.307439e+05</td>\n",
       "      <td>12040.457143</td>\n",
       "      <td>3.555430e+07</td>\n",
       "      <td>0.059771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PRODUCTIVITY</td>\n",
       "      <td>3.448157</td>\n",
       "      <td>2.519767e+05</td>\n",
       "      <td>9456.434889</td>\n",
       "      <td>3.062185e+07</td>\n",
       "      <td>0.616536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PHOTOGRAPHY</td>\n",
       "      <td>3.942724</td>\n",
       "      <td>6.324998e+05</td>\n",
       "      <td>12672.405573</td>\n",
       "      <td>3.009675e+07</td>\n",
       "      <td>0.378421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         category    rating       reviews          size      installs  \\\n",
       "6   COMMUNICATION  3.482240  1.642824e+06   8281.904372  6.598983e+07   \n",
       "27         SOCIAL  3.707857  1.905632e+06  12234.975000  4.469239e+07   \n",
       "31  VIDEO_PLAYERS  3.715429  6.307439e+05  12040.457143  3.555430e+07   \n",
       "25   PRODUCTIVITY  3.448157  2.519767e+05   9456.434889  3.062185e+07   \n",
       "24    PHOTOGRAPHY  3.942724  6.324998e+05  12672.405573  3.009675e+07   \n",
       "\n",
       "       price  \n",
       "6   0.227158  \n",
       "27  0.057036  \n",
       "31  0.059771  \n",
       "25  0.616536  \n",
       "24  0.378421  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_installs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 下载量前5名类别分别是： communication, social, video_players, productivity, photography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a848e0a90>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2a81f09630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画 评论数 reviews 的频率直方图\n",
    "df_grouped_mean['reviews'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- reviews 和 installs 貌似成正相关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reviews = df_grouped_mean.sort_values('reviews',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>rating</th>\n",
       "      <th>reviews</th>\n",
       "      <th>size</th>\n",
       "      <th>installs</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SOCIAL</td>\n",
       "      <td>3.707857</td>\n",
       "      <td>1.905632e+06</td>\n",
       "      <td>12234.975000</td>\n",
       "      <td>4.469239e+07</td>\n",
       "      <td>0.057036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>3.482240</td>\n",
       "      <td>1.642824e+06</td>\n",
       "      <td>8281.904372</td>\n",
       "      <td>6.598983e+07</td>\n",
       "      <td>0.227158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GAME</td>\n",
       "      <td>4.101784</td>\n",
       "      <td>1.262745e+06</td>\n",
       "      <td>39363.826940</td>\n",
       "      <td>2.813918e+07</td>\n",
       "      <td>0.256289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PHOTOGRAPHY</td>\n",
       "      <td>3.942724</td>\n",
       "      <td>6.324998e+05</td>\n",
       "      <td>12672.405573</td>\n",
       "      <td>3.009675e+07</td>\n",
       "      <td>0.378421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>VIDEO_PLAYERS</td>\n",
       "      <td>3.715429</td>\n",
       "      <td>6.307439e+05</td>\n",
       "      <td>12040.457143</td>\n",
       "      <td>3.555430e+07</td>\n",
       "      <td>0.059771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         category    rating       reviews          size      installs  \\\n",
       "27         SOCIAL  3.707857  1.905632e+06  12234.975000  4.469239e+07   \n",
       "6   COMMUNICATION  3.482240  1.642824e+06   8281.904372  6.598983e+07   \n",
       "14           GAME  4.101784  1.262745e+06  39363.826940  2.813918e+07   \n",
       "24    PHOTOGRAPHY  3.942724  6.324998e+05  12672.405573  3.009675e+07   \n",
       "31  VIDEO_PLAYERS  3.715429  6.307439e+05  12040.457143  3.555430e+07   \n",
       "\n",
       "       price  \n",
       "27  0.057036  \n",
       "6   0.227158  \n",
       "14  0.256289  \n",
       "24  0.378421  \n",
       "31  0.059771  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 评论数前5名类别分别是： social, communication, game, photography, video_players"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 观察结果\n",
    "- 前5名 `评分`、`下载量`、`评论数` 三个指标，没有一个是三者共同的类别\n",
    "- 其中 `game` 是 评分前5名中 唯一 和 评论数前5名重复的类别, 再次查看installs的排名，发现game的下载量`installs`排在第6位。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 得出结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题**：上面的分析能够回答你提出的问题？通过这些分析你能够得出哪些结论？\n",
    "\n",
    "**答案**：\n",
    "1. 评分前5名的类别是 `Education`, `art and design`, `entertainment`, `game`, `comics`\n",
    "2. 下载量前5名的类别是：`communication`, `social`, `video_players`, `productivity`, `photography`\n",
    "   另外，`game`排第6位\n",
    "3. 评论数前5名类别分别是： `social`, `communication`, `game`, `photography`, `video_players`\n",
    "4. 结论：\n",
    "    - 评分、下载量和评论数 均靠前的 类别是： `Game`，是最受欢迎的APP类型\n",
    "    - 下载量 和 评论数 多的 APP类别 并没有对应很高的评分，但是两者的前5名有4项重合。说明用户下载量和评论数有可能是相关的。可后续进一步分析。\n",
    "    - 评分前5名的类别中 只有4类下载量和评论数也很高。说明高评分不代表受众广。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 反思"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题**：在你的分析和总结过程中是否存在逻辑严谨。是否有改进的空间? 你可以从下面的一些角度进行思考：\n",
    "1. 数据集是否完整，包含所有想要分析的数据？\n",
    "2. 在对数据进行处理的时候，你的操作（例如删除/填充缺失值）是否可能影响结论？\n",
    "3. 是否还有其他变量（本数据中没有）能够对你的分析有帮助？\n",
    "4. 在得出结论时，你是否混淆了相关性和因果性？\n",
    "\n",
    "**答案**：\n",
    "- 分析中，尝试根据 评分、下载量和评论数 来找出最受欢迎的APP类别。该分析中，抽取了各指标的前5名来进行考虑，可以做更多的比如前10名。\n",
    "- 如果数据集再包含用户使用频次，将有助于更好地找出最受欢迎app类别。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "恭喜你完成了此项目！这只是数据分析过程的一个样本：从生成问题、整理数据、探索数据到得出结论。在数据分析(进阶)纳米学位中，你将会学到更多高级的数据分析方法和技术，如果你感兴趣的话，我们鼓励你继续学习后续的课程，掌握更多的数据分析的高级技能！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 若想与他人分享我们的分析结果，除了向他们提供 jupyter Notebook (.ipynb) 文件的副本外，我们还可以将 Notebook 输出导出为一种甚至那些未安装 Python 的人都能打开的形式。从左上方的“文件”菜单，前往“下载为”子菜单。然后你可以选择一个可以更普遍查看的格式，例如 HTML (.html) 。你可能需要额外软件包或软件来执行这些导出。"
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