How to scrape Google Play Store data in 2026

Tracking competitor apps, analyzing user reviews, or building market research dashboards all require Google Play Store data.

This guide shows you how to scrape Google Play Store data using Python, from basic app details to thousands of user reviews.

You'll learn three proven methods, plus performance benchmarks and production-ready code you can use today.

What You Can Extract From Google Play

Google Play data lets you extract app names, ratings, download counts, reviews, developer info, and pricing from any public app listing. You scrape this information using Python scripts that parse HTML or consume unofficial APIs.

This data helps you track competitors, validate product ideas, optimize app store listings, and analyze market trends at scale.

Common data points include:

• App title and description
• Star ratings (1-5 scale)
• Total downloads and installs
• Review text, dates, and ratings
• Developer name and contact info
• In-app purchase details
• Screenshots and promotional images
• Update history and version numbers
• Similar apps and recommendations

Method 1: Using google-play-scraper Library (Fastest)

The google-play-scraper Python library provides the quickest path to Google Play data. It handles all HTTP requests and parsing internally.

This approach works best for collecting data from 10-1,000 apps without complex requirements.

Install the library first:

pip install google-play-scraper

Scraping Single App Details

Extract complete app information with just the package ID. Google Play uses package IDs like com.instagram.android to identify apps.

from google_play_scraper import app

result = app(
    'com.instagram.android',
    lang='en',
    country='us'
)

print(f"App: {result['title']}")
print(f"Rating: {result['score']}")  
print(f"Downloads: {result['installs']}")
print(f"Price: {result['free']}")

The app() function returns a dictionary with 30+ fields including ratings, reviews count, developer info, and descriptions.

Set lang to target specific regions. Use country to get localized pricing and availability.

Extracting Reviews at Scale

Reviews provide sentiment data and feature requests. The library lets you pull up to 200 reviews per request with pagination support.

from google_play_scraper import reviews, Sort

result, continuation_token = reviews(
    'com.instagram.android',
    lang='en',
    country='us', 
    sort=Sort.NEWEST,
    count=200
)

for review in result:
    print(f"{review['userName']}: {review['score']} stars")
    print(f"Review: {review['content']}")
    print(f"Date: {review['at']}")

The function returns reviews plus a continuation token. Pass this token back to fetch the next batch of 200 reviews.

Set sort=Sort.MOST_RELEVANT to prioritize helpful reviews. Use Sort.NEWEST for recent feedback.

Searching Apps by Keyword

Search queries help discover apps in specific niches. You can scrape entire categories or trending apps.

from google_play_scraper import search

results = search(
    'photo editor',
    lang='en',
    country='us',
    n_hits=30
)

for app in results:
    print(f"{app['title']} - {app['score']} stars")
    print(f"Downloads: {app['installs']}")

The n_hits parameter controls result count. Maximum is around 250 apps per query.

Filter results programmatically by rating, download count, or developer.

Handling Rate Limits

Google Play throttles excessive requests from single IPs. Add delays between requests to avoid blocks.

import time
from google_play_scraper import app

app_ids = ['com.app1', 'com.app2', 'com.app3']

for app_id in app_ids:
    data = app(app_id)
    print(f"Scraped: {data['title']}")
    time.sleep(2)  # Wait 2 seconds between requests

Use random delays between 1-3 seconds. This mimics human browsing patterns.

For large-scale scraping (1,000+ apps), rotate proxies or use residential IPs.

Method 2: Direct HTTP Requests (More Control)

Raw HTTP requests give you complete control over scraping logic. This method works when the library doesn't support specific data points.

You'll parse HTML directly using BeautifulSoup and handle pagination manually.

Setting Up the Environment

Install required packages for HTTP requests and HTML parsing:

pip install requests beautifulsoup4 lxml

Create a session with realistic headers:

import requests
from bs4 import BeautifulSoup

session = requests.Session()
session.headers.update({
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/120.0.0.0',
    'Accept-Language': 'en-US,en;q=0.9',
    'Accept': 'text/html,application/xhtml+xml'
})

The User-Agent header makes requests look like they come from a real browser.

Google Play checks this header before serving content.

Scraping App Pages

Fetch app pages and extract data using CSS selectors. Target specific HTML elements that contain the information you need.

def scrape_app_page(app_id):
    url = f'https://play.google.com/store/apps/details?id={app_id}'
    
    response = session.get(url, timeout=10)
    soup = BeautifulSoup(response.text, 'lxml')
    
    # Extract app title
    title = soup.select_one('h1.Fd93Bb').get_text(strip=True)
    
    # Extract rating
    rating = soup.select_one('div.TT9eCd').get_text(strip=True)
    
    # Extract developer
    developer = soup.select_one('div.Vbfug a').get_text(strip=True)
    
    return {
        'title': title,
        'rating': rating,
        'developer': developer
    }

app_data = scrape_app_page('com.instagram.android')
print(app_data)

CSS selectors change when Google Play updates its layout. Always test selectors before production use.

Add error handling for missing elements:

title = soup.select_one('h1.Fd93Bb')
title = title.get_text(strip=True) if title else 'N/A'

Extracting Dynamic Content

Some Google Play Store data loads via JavaScript. Use Selenium or Playwright when static HTML doesn't contain the data.

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

def scrape_reviews_dynamic(app_id):
    options = webdriver.ChromeOptions()
    options.add_argument('--headless')
    
    driver = webdriver.Chrome(options=options)
    driver.get(f'https://play.google.com/store/apps/details?id={app_id}')
    
    # Wait for reviews to load
    wait = WebDriverWait(driver, 10)
    reviews = wait.until(
        EC.presence_of_all_elements_located((By.CLASS_NAME, 'RHo1pe'))
    )
    
    review_data = []
    for review in reviews[:10]:
        text = review.find_element(By.CLASS_NAME, 'h3YV2d').text
        rating = len(review.find_elements(By.CLASS_NAME, 'Z1Dz7b'))
        review_data.append({'text': text, 'rating': rating})
    
    driver.quit()
    return review_data

Headless browsers consume more memory than requests. Use them only when HTTP requests fail to get data.

Set explicit timeouts to prevent hanging on slow-loading pages.

Managing Proxies

Proxies prevent IP bans when scraping thousands of apps. Rotate IPs after every 50-100 requests.

proxies = [
    'http://proxy1:port',
    'http://proxy2:port',
    'http://proxy3:port'
]

current_proxy = 0

def scrape_with_proxy(app_id):
    global current_proxy
    
    proxy = {
        'http': proxies[current_proxy],
        'https': proxies[current_proxy]
    }
    
    response = requests.get(
        f'https://play.google.com/store/apps/details?id={app_id}',
        proxies=proxy,
        timeout=10
    )
    
    current_proxy = (current_proxy + 1) % len(proxies)
    return response

Residential proxies work better than datacenter IPs for Google Play. They're harder to detect and block.

Test proxy quality before scraping. Bad proxies return CAPTCHAs or timeout errors.

Method 3: Commercial APIs (Production Ready)

Commercial scraping APIs handle infrastructure, rotating proxies, and CAPTCHA solving. They cost money but save development time.

Use these for production systems that need 99%+ uptime and scale to millions of requests monthly.

ScrapingBee Integration

ScrapingBee renders JavaScript and rotates proxies automatically. It works well for dynamic Google Play Store content.

from scrapingbee import ScrapingBeeClient

client = ScrapingBeeClient(api_key='YOUR_API_KEY')

response = client.get(
    'https://play.google.com/store/apps/details?id=com.instagram.android',
    params={
        'render_js': 'true',
        'premium_proxy': 'true',
        'country_code': 'us'
    }
)

soup = BeautifulSoup(response.content, 'html.parser')
title = soup.select_one('h1').get_text()

The API charges per request. Budget around $50-200/month for moderate scraping (10K-50K requests).

Enable render_js only when needed. It's slower and costs more than static HTML requests.

Bright Data (Oxylabs Alternative)

Bright Data offers dedicated Google Play scrapers with structured JSON output. No parsing required.

import requests

api_url = 'https://api.brightdata.com/datasets/v3/trigger'

payload = {
    'dataset_id': 'gd_l9g1234567890',
    'discover_by': 'app_id',
    'app_id': ['com.instagram.android']
}

response = requests.post(
    api_url,
    json=payload,
    auth=('USERNAME', 'PASSWORD')
)

print(response.json())

Bright Data returns clean, normalized data. No CSS selector maintenance needed.

Pricing starts at $500/month for serious usage. Good for enterprise teams.

Performance Comparison

I tested all three methods on 1,000 apps. Here's what the data shows:

The library wins for speed and simplicity. HTTP requests give more control but require maintenance.

Commercial APIs cost money but eliminate infrastructure headaches. Use them when uptime matters.

Test duration: 72 hours, residential proxies, 2-second delays between requests.

Avoiding Blocks and CAPTCHAs

Google Play uses several anti-bot measures. Following best practices keeps your scrapers running.

Randomizing Request Timing

Fixed intervals between requests look robotic. Add randomness to mimic human behavior.

import random
import time

def smart_delay():
    delay = random.uniform(1.5, 4.0)
    time.sleep(delay)

for app_id in app_list:
    data = scrape_app(app_id)
    smart_delay()

Vary delays between 1-5 seconds. Longer delays reduce throughput but improve success rates.

Don't scrape too fast. Google flags consistent patterns faster than random ones.

Rotating User Agents

Different browsers send different headers. Rotate User-Agent strings to appear as multiple users.

user_agents = [
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/120.0.0.0',
    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) Safari/605.1.15',
    'Mozilla/5.0 (X11; Linux x86_64) Firefox/121.0'
]

headers = {
    'User-Agent': random.choice(user_agents)
}

Include mobile User-Agents too. About 60% of Google Play traffic comes from mobile devices.

Update your User-Agent list quarterly. Google detects outdated browser versions.

Handling Error Responses

Network errors and rate limits happen. Implement retry logic with exponential backoff.

import time

def scrape_with_retry(app_id, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = session.get(url, timeout=10)
            
            if response.status_code == 200:
                return response
            elif response.status_code == 429:
                wait = 2 ** attempt  # 1s, 2s, 4s
                time.sleep(wait)
            else:
                break
                
        except requests.Timeout:
            time.sleep(2)
            continue
            
    return None

Status code 429 means you're rate limited. Wait 60 seconds before retrying.

Log all errors for debugging. Track which apps fail most often.

Storing Scraped Data

Raw dictionaries aren't enough for analysis. Structure your data properly from the start.

Saving to CSV

CSV files work well for smaller datasets under 100K rows.

import csv

def save_to_csv(apps, filename='google_play_data.csv'):
    with open(filename, 'w', newline='', encoding='utf-8') as f:
        writer = csv.DictWriter(f, fieldnames=[
            'app_id', 'title', 'rating', 'downloads', 
            'developer', 'price', 'scraped_at'
        ])
        writer.writeheader()
        writer.writerows(apps)

CSV loads fast in Excel and pandas. Good for quick analysis and sharing.

Escape commas and quotes in text fields. Use pandas for safer CSV writing.

Using SQLite Database

Databases handle millions of rows better than CSV files. SQLite requires no server setup.

import sqlite3
from datetime import datetime

conn = sqlite3.connect('google_play.db')
cursor = conn.cursor()

cursor.execute('''
    CREATE TABLE IF NOT EXISTS apps (
        app_id TEXT PRIMARY KEY,
        title TEXT,
        rating REAL,
        downloads TEXT,
        developer TEXT,
        price TEXT,
        scraped_at TIMESTAMP
    )
''')

def insert_app(app_data):
    cursor.execute('''
        INSERT OR REPLACE INTO apps VALUES (?, ?, ?, ?, ?, ?, ?)
    ''', (
        app_data['app_id'],
        app_data['title'],
        app_data['rating'],
        app_data['downloads'],
        app_data['developer'],
        app_data['price'],
        datetime.now()
    ))
    conn.commit()

SQLite files stay under 10GB easily. Switch to PostgreSQL or MySQL for larger datasets.

Index frequently queried columns. Add indexes on rating, downloads, and developer for fast searches.

Exporting to JSON

JSON preserves nested structures like reviews and metadata. Use it when structure matters more than size.

import json

def save_to_json(apps, filename='google_play_data.json'):
    with open(filename, 'w', encoding='utf-8') as f:
        json.dump(apps, f, indent=2, ensure_ascii=False)

JSON files grow large quickly. Compress with gzip for storage.

Load JSON incrementally for huge files. Don't load everything into memory at once.

Legal and Ethical Considerations

Scraping public data is generally legal, but terms of service matter. Google's Terms prohibit automated access to Google Play Store without permission.

They rarely enforce this for small-scale research. Commercial scraping at scale carries more risk.

Check your local laws before scraping. GDPR and CCPA affect how you can store user data.

Only scrape public information. Don't attempt to access protected areas or user accounts.

Respect robots.txt directives. Google Play's robots.txt blocks certain paths.

Add reasonable delays between requests. Don't overload Google's servers.

Common Pitfalls to Avoid

Missing developer field errors happen when apps get delisted. Always check if elements exist before accessing.

developer_elem = soup.select_one('div.Vbfug a')
developer = developer_elem.get_text() if developer_elem else 'Unknown'

Hardcoded selectors break when Google Play updates its HTML. Store selectors in config files for easy updates.

Ignoring rate limits gets your IP banned for 24+ hours. Always add delays and monitor response codes.

Not handling unicode characters crashes parsers. Use UTF-8 encoding for all file operations.

Scraping during peak hours (9am-5pm EST) faces more CAPTCHAs. Schedule scrapers for off-peak times.

Advanced Techniques

Parallel Scraping

Process multiple apps simultaneously to increase throughput. Use thread pools for I/O-bound scraping.

from concurrent.futures import ThreadPoolExecutor

def scrape_parallel(app_ids, max_workers=5):
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        results = executor.map(scrape_app, app_ids)
    return list(results)

apps = scrape_parallel(['com.app1', 'com.app2', 'com.app3'])

Don't use more than 10 workers. Higher concurrency triggers rate limits faster.

Add per-worker delays. Each thread should wait 2-3 seconds between requests.

Monitoring Changes

Track app updates by scraping periodically. Store historical data to detect changes.

def detect_changes(old_data, new_data):
    changes = {}
    
    if old_data['rating'] != new_data['rating']:
        changes['rating'] = {
            'old': old_data['rating'],
            'new': new_data['rating']
        }
    
    if old_data['downloads'] != new_data['downloads']:
        changes['downloads'] = {
            'old': old_data['downloads'],
            'new': new_data['downloads']
        }
    
    return changes

Schedule daily scrapes for active monitoring. Weekly works for less critical apps.

Alert on significant changes like rating drops below 3.5 or sudden download spikes.

Conclusion

Scraping Google Play Store data requires choosing the right method for your scale. Use google-play-scraper for quick scripts and prototypes.

Switch to HTTP requests when you need custom logic or specific data points.

Invest in commercial APIs when reliability and speed matter more than cost.

Always add delays, rotate IPs, and handle errors gracefully for production systems.

Start small, test thoroughly, and scale gradually based on your results.

Frequently Asked Questions

Is scraping Google Play Store legal?

Scraping public Google Play data is legal in most jurisdictions. Google's Terms of Service technically prohibit it, but enforcement is rare for research purposes.

Commercial use carries more risk. Consult a lawyer for large-scale commercial scraping operations.

How many requests can I make per day?

Without proxies, expect to scrape 500-1,000 apps per IP per day before hitting rate limits.

With residential proxies, scale to 10,000+ apps daily across multiple IPs.

Why do I get CAPTCHA challenges?

CAPTCHAs appear when Google Play detects automated behavior. Common triggers include fixed request intervals, datacenter IPs, and missing headers.

Add random delays, rotate User-Agents, and use residential proxies to reduce CAPTCHAs.

Can I scrape app reviews in bulk?

Yes, but review scraping requires more requests. Each app page shows 20-40 reviews initially.

Use pagination or the google-play-scraper library to fetch hundreds of reviews per app.

What's the best Python library for this?

The google-play-scraper library works best for 90% of use cases. It's actively maintained and handles pagination automatically.

Use Selenium or Playwright only when you need JavaScript-heavy pages or visual verification.