Scrape google play reviews.ipynb

{
 "cells": [
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   "execution_count": 24,
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    "'''\n",
    "Inspired from the following:\n",
    "https://github.com/JoMingyu/google-play-scraper\n",
    "https://towardsdatascience.com/very-simple-python-script-for-extracting-most-common-words-from-a-story-1e3570d0b9d0\n",
    "'''\n",
    "\n",
    "import json\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pygments import highlight\n",
    "from pygments.lexers import JsonLexer\n",
    "from pygments.formatters import TerminalFormatter\n",
    "\n",
    "from google_play_scraper import Sort, reviews, app\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "app_packages = ['com.redcarpetup.rewardpay'] #one or more App IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:42<00:00, 42.93s/it]\n"
     ]
    }
   ],
   "source": [
    "app_reviews = []\n",
    "\n",
    "for ap in tqdm(app_packages):\n",
    "    for score in list(range(1, 6)):\n",
    "        for sort_order in [Sort.NEWEST, Sort.MOST_RELEVANT]:\n",
    "            rvs, _ = reviews(\n",
    "            ap,\n",
    "            lang='en',\n",
    "            country='us',\n",
    "            sort=sort_order,\n",
    "            count= 2500,\n",
    "            filter_score_with= 5 #Filter and scrape by rating to be scrapped\n",
    "          )\n",
    "        for r in rvs:\n",
    "            r['sortOrder'] = 'most_relevant' if sort_order == Sort.MOST_RELEVANT else 'newest'\n",
    "            r['appId'] = ap\n",
    "        app_reviews.extend(rvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12500"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#app_reviews[499]\n",
    "len(app_reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame()\n",
    "df['Year'] = ' '\n",
    "df['Month'] = ' '\n",
    "df['Score'] = ' '\n",
    "df['Review'] = ' '\n",
    "for i in range(len(app_reviews)):\n",
    "    if (app_reviews[i]['at']).year >= 2019 : #Filter by year\n",
    "        df.loc[i, 'Year'] = (app_reviews[i]['at']).year\n",
    "        df.loc[i, 'Month'] = (app_reviews[i]['at']).month\n",
    "        df.loc[i, 'Score'] = app_reviews[i]['score']\n",
    "        df.loc[i, 'Review'] = app_reviews[i]['content']\n",
    "df.to_excel('Rating_5.xlsx', index=None, header=True) #File name to export the dataframe values\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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