Examples#

Practical examples of using GPlay Scraper for common tasks.

App Analytics Dashboard#

Track key metrics for your app.

from gplay_scraper import GPlayScraper

scraper = GPlayScraper()
app = scraper.app_analyze('com.myapp')

print("=== App Analytics Dashboard ===")
print(f"App: {app['title']}")
print(f"Developer: {app['developer']}")
print(f"Rating: {app['score']}/5 ({app['ratings']:,} ratings)")
print(f"\nInstall Metrics:")
print(f"  Total Installs: {app['realInstalls']:,}")
print(f"  Daily Installs: {app['dailyInstalls']:,}")
print(f"  Monthly Installs: {app['monthlyInstalls']:,}")
print(f"  App Age: {app['appAgeDays']} days")
print(f"\nRating Distribution:")
hist = app['histogram']
for i, count in enumerate(hist, 1):
    print(f"  {i}★: {count:,}")

Market Research#

Analyze a market segment.

scraper = GPlayScraper()

# Search for fitness apps
results = scraper.search_analyze('fitness tracker', count=100)

# Filter by rating
high_rated = [app for app in results if app['score'] >= 4.5]
free_apps = [app for app in high_rated if app['free']]

print(f"Total fitness tracker apps: {len(results)}")
print(f"High-rated (4.5+): {len(high_rated)}")
print(f"High-rated & Free: {len(free_apps)}")

print("\nTop 5 Free High-Rated Apps:")
for app in free_apps[:5]:
    print(f"  {app['title']}: {app['score']}/5")

Competitor Monitoring#

Track your competitors.

scraper = GPlayScraper()

competitors = ['com.competitor1', 'com.competitor2', 'com.competitor3']

print("Competitor Analysis")
print("-" * 60)

for app_id in competitors:
    app = scraper.app_analyze(app_id)
    reviews = scraper.reviews_analyze(app_id, count=100, sort='NEWEST')

    avg_recent_rating = sum(r['score'] for r in reviews) / len(reviews)

    print(f"\n{app['title']}")
    print(f"  Overall Rating: {app['score']}/5")
    print(f"  Recent Rating: {avg_recent_rating:.2f}/5")
    print(f"  Daily Installs: {app['dailyInstalls']:,}")
    print(f"  Total Installs: {app['realInstalls']:,}")

Review Sentiment Analysis#

Analyze user feedback.

scraper = GPlayScraper()

reviews = scraper.reviews_analyze('com.myapp', count=500)

# Categorize by rating
positive = [r for r in reviews if r['score'] >= 4]
neutral = [r for r in reviews if r['score'] == 3]
negative = [r for r in reviews if r['score'] <= 2]

print("Review Sentiment Analysis")
print(f"Total Reviews: {len(reviews)}")
print(f"Positive (4-5★): {len(positive)} ({len(positive)/len(reviews)*100:.1f}%)")
print(f"Neutral (3★): {len(neutral)} ({len(neutral)/len(reviews)*100:.1f}%)")
print(f"Negative (1-2★): {len(negative)} ({len(negative)/len(reviews)*100:.1f}%)")

# Show recent negative reviews
print("\nRecent Negative Reviews:")
for review in negative[:5]:
    print(f"  {review['userName']}: {review['score']}/5")
    print(f"    {review['content'][:100]}...")

Top Charts Tracking#

Monitor top charts positions.

scraper = GPlayScraper()

# Track top free games
top_games = scraper.list_analyze('TOP_FREE', category='GAME', count=50)

# Find your app's position
my_app_id = 'com.mygame'
position = next((i for i, app in enumerate(top_games, 1)
                 if app['appId'] == my_app_id), None)

if position:
    print(f"Your game is ranked #{position} in top free games!")
else:
    print("Your game is not in top 50")

# Show top 10
print("\nTop 10 Free Games:")
for i, app in enumerate(top_games[:10], 1):
    print(f"{i}. {app['title']} - {app['score']}/5")

Developer Portfolio Overview#

Analyze a developer’s entire portfolio.

scraper = GPlayScraper()

apps = scraper.developer_analyze('Google LLC')

# Calculate metrics
avg_rating = sum(app['score'] for app in apps) / len(apps)
free_count = sum(1 for app in apps if app['free'])
high_rated = [app for app in apps if app['score'] >= 4.5]

print(f"Developer: Google LLC")
print(f"Total Apps: {len(apps)}")
print(f"Average Rating: {avg_rating:.2f}/5")
print(f"Free Apps: {free_count}/{len(apps)}")
print(f"High-Rated Apps (4.5+): {len(high_rated)}")

# Best rated apps
sorted_apps = sorted(apps, key=lambda x: x['score'], reverse=True)
print("\nTop 5 Highest Rated:")
for app in sorted_apps[:5]:
    print(f"  {app['title']}: {app['score']}/5")

Batch Data Collection#

Collect data for multiple apps efficiently.

import json
from gplay_scraper import GPlayScraper

scraper = GPlayScraper()

app_ids = [
    'com.whatsapp',
    'org.telegram.messenger',
    'org.thoughtcrime.securesms',
    'com.discord'
]

results = []
for app_id in app_ids:
    # Get only the fields you need
    fields = ['title', 'developer', 'score', 'realInstalls', 'dailyInstalls']
    data = scraper.app_get_fields(app_id, fields)
    results.append(data)

# Save to JSON
with open('messaging_apps.json', 'w') as f:
    json.dump(results, f, indent=2)

print(f"Collected data for {len(results)} apps")

Multi-Language Content#

Get localized app information.

scraper = GPlayScraper()

languages = {
    'en': 'English',
    'es': 'Spanish',
    'fr': 'French',
    'de': 'German',
    'ja': 'Japanese'
}

for lang_code, lang_name in languages.items():
    app = scraper.app_analyze('com.whatsapp', lang=lang_code)
    print(f"\n{lang_name} ({lang_code}):")
    print(f"  Title: {app['title']}")
    print(f"  Summary: {app['summary']}")

Trend Discovery#

Discover trending apps in a category.

scraper = GPlayScraper()

# Get top free apps
top_free = scraper.list_analyze('TOP_FREE', category='PRODUCTIVITY', count=100)

# Filter for new apps (less than 180 days old)
new_apps = [app for app in top_free if 'New' in app.get('description', '')]

# Get apps with high install velocity
trending = []
for app in top_free[:20]:
    full_data = scraper.app_analyze(app['appId'])
    if full_data['dailyInstalls'] > 10000:
        trending.append(full_data)

print("Trending Productivity Apps:")
for app in trending:
    print(f"  {app['title']}")
    print(f"    Daily Installs: {app['dailyInstalls']:,}")
    print(f"    Rating: {app['score']}/5")

Examples

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