pytrends-modern 0.2.1

Modern Google Trends API - Combining the best of pytrends, with RSS feeds, Selenium scraping, DrissionPage browser automation, and enhanced features

pytrends-modern

The Modern Google Trends API - Combining the best features from pytrends, trendspyg, and more.

🌟 Why pytrends-modern?

pytrends-modern is a next-generation Google Trends library that combines:

• ✅ All classic pytrends features - Interest over time, by region, related topics/queries
• ✅ RSS Feed Support - Fast real-time trending data with rich media (0.2s vs 10s)
• ✅ Enhanced Error Handling - Automatic retries, rate limit management, proxy rotation
• ✅ Modern Python - Full type hints, async support, dataclasses
• ✅ Selenium Integration - Advanced scraping when needed
• ✅ Multiple Export Formats - CSV, JSON, Parquet, Excel, DataFrame
• ✅ Comprehensive CLI - Easy command-line interface with rich output
• ✅ Better Rate Limiting - Smart backoff, quota management
• ✅ Active Maintenance - Modern codebase, actively maintained

🚀 Quick Start

Installation

# Basic installation
pip install pytrends-modern

# With browser mode (Camoufox for bypassing rate limits)
pip install pytrends-modern[browser]

# With Selenium support (for advanced scraping)
pip install pytrends-modern[selenium]

# With CLI support
pip install pytrends-modern[cli]

# With all features
pip install pytrends-modern[all]

Basic Usage

from pytrends_modern import TrendReq

# Initialize
pytrends = TrendReq(hl='en-US', tz=360)

# Build payload
pytrends.build_payload(
    kw_list=['Python', 'JavaScript'],
    timeframe='today 12-m',
    geo='US'
)

# Get interest over time
interest_df = pytrends.interest_over_time()
print(interest_df.head())

# Get interest by region
region_df = pytrends.interest_by_region()
print(region_df.head())

# Get related queries
related = pytrends.related_queries()
print(related['Python']['top'])

🦊 Browser Mode (Camoufox) - Bypass Rate Limits

NEW! Use Camoufox with advanced fingerprinting to bypass Google's rate limits by using your Google account:

from pytrends_modern import TrendReq, BrowserConfig
from pytrends_modern.camoufox_setup import setup_profile

# First-time setup: Configure Google account login
setup_profile()  # Opens browser - log in to Google once

# Use browser mode (persistent login, no rate limits!)
config = BrowserConfig(headless=False)
pytrends = TrendReq(browser_config=config)

# Works like normal API
pytrends.kw_list = ['Python']
df = pytrends.interest_over_time()
print(df.head())

Browser Mode Limitations:

• ⚠️ Only 1 keyword at a time (no comparisons)
• ⚠️ Only 'today 1-m' timeframe
• ⚠️ Only WORLDWIDE region
• ✅ No rate limits (uses your Google account)
• ✅ Perfect anti-detection with Camoufox fingerprinting

Setup from command line:

# Check profile status
python -m pytrends_modern.camoufox_setup status

# Run setup (opens browser for Google login)
python -m pytrends_modern.camoufox_setup

# Export profile for Docker/other machines
python -m pytrends_modern.camoufox_setup export camoufox-profile.tar.gz

# Import profile on another machine
python -m pytrends_modern.camoufox_setup import camoufox-profile.tar.gz

Docker Usage:

Yes! You can export your profile and use it in Docker containers:

# 1. Export profile locally
python -m pytrends_modern.camoufox_setup export profile.tar.gz

# 2. Use in Dockerfile
COPY profile.tar.gz /tmp/
RUN mkdir -p /root/.config && \
    cd /root/.config && \
    tar -xzf /tmp/profile.tar.gz

# 3. Use headless mode in container
config = BrowserConfig(headless=True)

See Dockerfile.example for complete Docker setup.

⚠️ Security: Profile contains Google session - keep secure, don't commit to git!

RSS Feed (Fast Real-Time Data)

from pytrends_modern import TrendsRSS

# Get trending searches with rich media
rss = TrendsRSS()
trends = rss.get_trends(geo='US')

for trend in trends:
    print(f"Title: {trend['title']}")
    print(f"Traffic: {trend['traffic']}")
    print(f"Articles: {len(trend['articles'])}")
    print(f"Image: {trend['picture']}")
    print("---")

CLI Usage

# Get interest over time
pytrends-modern interest --keywords "Python,JavaScript" --timeframe "today 12-m"

# Get trending searches
pytrends-modern trending --geo US

# Get RSS feed
pytrends-modern rss --geo US --format json

# Export to CSV
pytrends-modern interest --keywords "AI" --output trends.csv

📊 Features Comparison

Feature	pytrends	trendspyg	pytrends-modern
Interest Over Time	✅	❌	✅
Interest by Region	✅	❌	✅
Related Topics/Queries	✅	❌	✅
RSS Feed	❌	✅	✅
Rich Media (Images/Articles)	❌	✅	✅
Selenium Support	❌	✅	✅
Type Hints	❌	✅	✅
Async Support	❌	❌	✅
CLI	❌	✅	✅
Active Maintenance	❌	✅	✅
Auto Retry	Partial	✅	✅
Multiple Export Formats	❌	✅	✅

🎯 Key Features

1. Classic Trends Data

All the beloved pytrends methods, modernized:

• interest_over_time() - Historical search interest
• interest_by_region() - Geographic distribution
• related_topics() - Related topics
• related_queries() - Related searches
• trending_searches() - Current trending searches
• today_searches() - Daily trends
• realtime_trending_searches() - Real-time trends
• suggestions() - Keyword suggestions

2. RSS Feed Support

Fast access to real-time trending data:

• 0.2 seconds vs 10+ seconds for full scraping
• Rich media: images, news articles, headlines
• Perfect for monitoring and journalism
• Multiple geo support (125+ countries)

3. Enhanced Error Handling

• Automatic retry with exponential backoff
• Rate limit detection and management
• Proxy rotation support
• Better error messages

4. Modern Python Features

• Full type hints for IDE support
• Async/await support for concurrent requests
• Dataclasses for structured data
• Modern exception handling

5. Selenium Integration

• Fallback for advanced scraping needs
• Handles JavaScript-rendered content
• Automatic driver management
• Headless mode support

6. Multiple Export Formats

# Export to various formats
df = pytrends.interest_over_time()

# CSV
df.to_csv('trends.csv')

# JSON
pytrends.to_json('trends.json')

# Parquet (requires pyarrow)
pytrends.to_parquet('trends.parquet')

# Excel (requires openpyxl)
df.to_excel('trends.xlsx')

📚 Documentation

TrendReq Class

The main class for Google Trends API access.

TrendReq(
    hl='en-US',          # Language
    tz=360,              # Timezone offset
    geo='',              # Geographic location
    timeout=(2, 5),      # (connect, read) timeouts
    proxies=None,        # Proxy list or dict
    retries=3,           # Number of retries
    backoff_factor=0.3,  # Backoff multiplier
    verify_ssl=True      # SSL verification
)

Build Payload

pytrends.build_payload(
    kw_list=['keyword1', 'keyword2'],  # Max 5 keywords
    cat=0,                              # Category (0 = all)
    timeframe='today 5-y',             # Time range
    geo='',                            # Geographic location
    gprop=''                           # Property ('', 'images', 'news', 'youtube', 'froogle')
)

Time Frames

• 'now 1-H' - Last hour
• 'now 4-H' - Last 4 hours
• 'now 1-d' - Last day
• 'now 7-d' - Last 7 days
• 'today 1-m' - Past 30 days
• 'today 3-m' - Past 90 days
• 'today 12-m' - Past 12 months
• 'today 5-y' - Past 5 years (default)
• 'all' - Since 2004
• 'YYYY-MM-DD YYYY-MM-DD' - Custom range

Geographic Codes

Use ISO 3166-1 alpha-2 country codes:

• 'US' - United States
• 'GB' - United Kingdom
• 'US-CA' - California (US states)
• 'FR' - France
• etc.

Categories

Common category codes:

• 0 - All categories
• 3 - Arts & Entertainment
• 7 - Business & Industrial
• 16 - News
• 20 - Sports
• 32 - Science
• More at: https://github.com/pat310/google-trends-api/wiki/Google-Trends-Categories

🔧 Advanced Usage

Proxy Support

# List of proxies
pytrends = TrendReq(
    proxies=['https://proxy1.com:8080', 'https://proxy2.com:8080'],
    retries=3
)

# Dict format
pytrends = TrendReq(
    proxies={
        'http': 'http://proxy.com:8080',
        'https': 'https://proxy.com:8080'
    }
)

Async Support

import asyncio
from pytrends_modern import AsyncTrendReq

async def get_trends():
    pytrends = AsyncTrendReq(hl='en-US')
    await pytrends.build_payload(['Python', 'JavaScript'])
    df = await pytrends.interest_over_time()
    return df

df = asyncio.run(get_trends())

Rate Limit Handling

from pytrends_modern import TrendReq
from pytrends_modern.exceptions import TooManyRequestsError

pytrends = TrendReq(retries=5, backoff_factor=0.5)

try:
    pytrends.build_payload(['keyword'])
    df = pytrends.interest_over_time()
except TooManyRequestsError:
    print("Rate limited. Wait before retrying.")

Batch Processing

from pytrends_modern import TrendReq
import time

keywords = ['Python', 'JavaScript', 'Rust', 'Go', 'Java']
pytrends = TrendReq()

results = {}
for kw in keywords:
    pytrends.build_payload([kw], timeframe='today 12-m')
    results[kw] = pytrends.interest_over_time()
    time.sleep(2)  # Avoid rate limits

🧪 Testing

# Run tests
pytest

# With coverage
pytest --cov=pytrends_modern

# Specific test
pytest tests/test_request.py::test_interest_over_time

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Ensure all tests pass
5. Submit a pull request

📝 License

MIT License - see LICENSE file for details

🙏 Credits

This project builds upon and combines features from:

• pytrends - Original Google Trends API
• trendspyg - RSS feed support and modern features
• google-trends Flask app - Visualization and retry logic

📊 Changelog

Version 1.0.0 (2025-12-26)

• Initial release
• Combined pytrends, trendspyg, and google-trends features
• Added async support
• Full type hints
• Enhanced error handling
• CLI interface
• Multiple export formats

📌 Important Notes

Google API Changes

Google has deprecated several trending search API endpoints. pytrends-modern provides two working alternatives:

Option 1: Fast RSS Feed (Recommended for most use cases)

from pytrends_modern import TrendsRSS

rss = TrendsRSS()
trends = rss.get_trends(geo='US')  # ~0.7s, returns 10 trends with images/articles

Pros: Lightning fast, includes rich media, no browser needed
Cons: Limited to 10 trends, no filtering options

Option 2: Selenium Web Scraper (For complete data)

from pytrends_modern import TrendsScraper

scraper = TrendsScraper(headless=True)
df = scraper.trending_searches(geo='US', hours=24)  # ~15s, returns 400+ trends
scraper.close()

Pros: Complete data (400+ trends), supports categories/filters
Cons: Slower, requires Chrome browser

Working Features

✅ All core API methods work perfectly:

• interest_over_time() - Historical search trends
• interest_by_region() - Geographic distribution
• related_queries() / related_topics() - Related searches
• suggestions() - Keyword suggestions
• And more!

✅ RSS feeds for 125+ countries
✅ Selenium scraper for comprehensive trending data

⚠️ Disclaimer

This is an unofficial library and is not affiliated with or endorsed by Google. Use responsibly and in accordance with Google's Terms of Service.