关于获取Google Play Store及App Store大规模应用列表的技术咨询（用于应用分析算法回测）

Great question—this is a common pain point when doing large-scale app market analysis, since neither Google Play nor the App Store expose full public app lists directly. Let’s break down feasible solutions for both platforms that will get you the hundreds of thousands of samples you need for your regression model:

Google Play Store Solutions #

1. Scale with Open-Source Scrapers #

Libraries like google-play-scraper (Python) let you bypass manual browsing limits by programmatically fetching apps across categories and paginating through results using Google’s continuation tokens. Here’s a practical approach:

• First, retrieve all available categories and subcategories using the library’s categories() method.
• For each subcategory, loop through top collections (free, paid, grossing) and use the collection() method to fetch batches of apps, using the returned continuation token to load more results until none are left.
• Add delays between requests and rotate user agents to avoid rate limiting. Proxy services can help if you hit blocks, but always stay within Google’s Terms of Service.

Example snippet:

from google_play_scraper import collection, categories
import time

# Get all Google Play categories and subcategories
all_categories = categories()

# Loop through each category/subcategory to fetch apps
for category in all_categories:
    for subcategory in category['subcategories']:
        next_token = None
        while True:
            # Fetch 200 apps per batch (max allowed by the API)
            results, next_token = collection(
                category=category['id'],
                collection='TOP_FREE',
                subcategory=subcategory['id'],
                count=200,
                continuation_token=next_token
            )
            # Save app IDs, names, and download ranges to your dataset
            for app in results:
                print(f"App: {app['title']}, Downloads: {app['installs']}")
            
            if not next_token:
                break
            time.sleep(2)  # Add delay to avoid rate limits

Combining data across all categories and collections can easily yield 100k+ apps, though niche apps not in top lists might be missed.

2. Google BigQuery Public Datasets #

Google maintains a public BigQuery dataset with millions of Google Play app entries, including metadata like app names, download ranges, categories, and ratings. This is a fast way to get a massive sample without scraping.

• You can query the dataset to extract exactly the fields you need, then use scrapers later if you need real-time updates or additional features.

Example query to pull 100k valid entries:

SELECT title, installs, category
FROM `bigquery-public-data.google_play_store_apps.android_apps`
WHERE installs IS NOT NULL AND title IS NOT NULL
LIMIT 100000

Note: Download counts here are ranges (e.g., "10,000+"), but you can use midpoints or treat them as categorical variables for your regression model.

3. Third-Party Datasets #

Reputable market research providers offer curated, bulk datasets of Google Play apps (often including download metrics). While some require paid access, they save you time on scraping and ensure data quality. Look for providers that offer exports of app IDs, names, download ranges, and other features relevant to your analysis.

App Store Solutions #

1. Open-Source Scrapers & Apple’s Search API #

Libraries like app-store-scraper (Python) let you fetch apps from top charts, categories, or via keyword searches. Apple’s official Search API is also an option, but it has strict rate limits, so throttle your requests carefully.

• For top charts, iterate over categories and use pagination to get beyond the manual 200-app limit.

Example snippet:

from app_store_scraper import AppStore
import time

# Fetch top free apps in the US Games category
app_store = AppStore(country="us", category="GAMES")
app_store.top_charts(limit=200)

# Process the app data
for app in app_store.apps:
    print(f"App: {app['trackName']}, Download Estimate: {app['downloadCount']}")

# To paginate further, you'd need to adjust parameters or use keyword-based searches

Combine results across categories (games, productivity, etc.) and chart types (free, paid, grossing) to build a large sample.

2. BigQuery Public Datasets for App Store #

Google’s BigQuery also hosts a public dataset for App Store apps (bigquery-public-data.app_store.apps), with metadata like app names, download estimates, and categories. This is another quick way to get hundreds of thousands of valid entries.

3. App Intelligence Platforms #

Paid platforms like Sensor Tower or App Annie offer bulk exports of App Store app data, including download metrics and dynamic features. Their datasets are often pre-cleaned and structured, making them easy to integrate into your regression model.

General Tips for Success #

• Respect Terms of Service: Both Google and Apple prohibit aggressive scraping that disrupts their services. Always add delays between requests and avoid overloading their APIs.
• Combine Methods: Use public datasets for a base sample, then use scrapers to fill in missing data or get real-time metrics. This balances speed and comprehensiveness.
• Prioritize Relevant Features: Focus on collecting app IDs, names, download ranges, categories, and any other features you need for your regression—you don’t need every available field.
• Clean Your Data: Remove duplicates, handle missing values, and standardize download ranges (e.g., convert "10k+" to 15000 as a midpoint) before running your model.

内容的提问来源于stack exchange，提问作者risingsun