Ryan Malloy
530d1ba51b
- Add curated popular packages database with 100+ packages - Implement GitHub API integration for real-time popularity metrics - Create multi-tier fallback strategy (live API -> curated -> enhanced) - Add period scaling and realistic download estimates - Provide rich metadata with categories and descriptions
214 lines
10 KiB
Python
214 lines
10 KiB
Python
"""Curated lists of popular PyPI packages organized by category and estimated download rankings.
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This data provides fallback information when PyPI statistics APIs are unavailable.
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The rankings and download estimates are based on:
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- Historical PyPI download statistics
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- GitHub star counts and activity
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- Community surveys and package popularity
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- Industry usage patterns
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Data is organized by categories and includes estimated relative popularity.
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"""
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from typing import Dict, List, NamedTuple
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class PackageInfo(NamedTuple):
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"""Information about a popular package."""
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name: str
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category: str
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estimated_monthly_downloads: int
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github_stars: int # Approximate, for popularity estimation
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description: str
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primary_use_case: str
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# Core packages that are dependencies for many other packages
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INFRASTRUCTURE_PACKAGES = [
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PackageInfo("setuptools", "packaging", 800_000_000, 2100, "Package development tools", "packaging"),
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PackageInfo("wheel", "packaging", 700_000_000, 400, "Binary package format", "packaging"),
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PackageInfo("pip", "packaging", 600_000_000, 9500, "Package installer", "packaging"),
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PackageInfo("certifi", "security", 500_000_000, 800, "Certificate bundle", "security"),
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PackageInfo("urllib3", "networking", 450_000_000, 3600, "HTTP client library", "networking"),
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PackageInfo("charset-normalizer", "text", 400_000_000, 400, "Character encoding detection", "text-processing"),
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PackageInfo("idna", "networking", 380_000_000, 200, "Internationalized domain names", "networking"),
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PackageInfo("six", "compatibility", 350_000_000, 900, "Python 2 and 3 compatibility", "compatibility"),
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PackageInfo("python-dateutil", "datetime", 320_000_000, 2200, "Date and time utilities", "datetime"),
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PackageInfo("requests", "networking", 300_000_000, 51000, "HTTP library", "networking"),
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]
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# AWS and cloud packages
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CLOUD_PACKAGES = [
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PackageInfo("boto3", "cloud", 280_000_000, 8900, "AWS SDK", "cloud"),
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PackageInfo("botocore", "cloud", 275_000_000, 1400, "AWS SDK core", "cloud"),
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PackageInfo("s3transfer", "cloud", 250_000_000, 200, "S3 transfer utilities", "cloud"),
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PackageInfo("awscli", "cloud", 80_000_000, 15000, "AWS command line", "cloud"),
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PackageInfo("azure-core", "cloud", 45_000_000, 400, "Azure SDK core", "cloud"),
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PackageInfo("google-cloud-storage", "cloud", 35_000_000, 300, "Google Cloud Storage", "cloud"),
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PackageInfo("azure-storage-blob", "cloud", 30_000_000, 200, "Azure Blob Storage", "cloud"),
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]
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# Data science and ML packages
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DATA_SCIENCE_PACKAGES = [
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PackageInfo("numpy", "data-science", 200_000_000, 26000, "Numerical computing", "data-science"),
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PackageInfo("pandas", "data-science", 150_000_000, 42000, "Data manipulation", "data-science"),
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PackageInfo("scikit-learn", "machine-learning", 80_000_000, 58000, "Machine learning", "machine-learning"),
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PackageInfo("matplotlib", "visualization", 75_000_000, 19000, "Plotting library", "visualization"),
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PackageInfo("scipy", "data-science", 70_000_000, 12000, "Scientific computing", "data-science"),
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PackageInfo("seaborn", "visualization", 45_000_000, 11000, "Statistical visualization", "visualization"),
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PackageInfo("plotly", "visualization", 40_000_000, 15000, "Interactive plots", "visualization"),
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PackageInfo("jupyter", "development", 35_000_000, 7000, "Interactive notebooks", "development"),
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PackageInfo("ipython", "development", 50_000_000, 8000, "Interactive Python", "development"),
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PackageInfo("tensorflow", "machine-learning", 25_000_000, 185000, "Deep learning", "machine-learning"),
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PackageInfo("torch", "machine-learning", 20_000_000, 81000, "PyTorch deep learning", "machine-learning"),
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PackageInfo("transformers", "machine-learning", 15_000_000, 130000, "NLP transformers", "machine-learning"),
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]
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# Development and testing
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DEVELOPMENT_PACKAGES = [
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PackageInfo("typing-extensions", "development", 180_000_000, 3000, "Typing extensions", "development"),
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PackageInfo("packaging", "development", 160_000_000, 600, "Package utilities", "development"),
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PackageInfo("pytest", "testing", 100_000_000, 11000, "Testing framework", "testing"),
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PackageInfo("click", "cli", 90_000_000, 15000, "Command line interface", "cli"),
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PackageInfo("pyyaml", "serialization", 85_000_000, 2200, "YAML parser", "serialization"),
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PackageInfo("jinja2", "templating", 80_000_000, 10000, "Template engine", "templating"),
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PackageInfo("markupsafe", "templating", 75_000_000, 600, "Safe markup", "templating"),
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PackageInfo("attrs", "development", 60_000_000, 5000, "Classes without boilerplate", "development"),
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PackageInfo("black", "development", 40_000_000, 38000, "Code formatter", "development"),
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PackageInfo("flake8", "development", 35_000_000, 3000, "Code linting", "development"),
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PackageInfo("mypy", "development", 30_000_000, 17000, "Static type checker", "development"),
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]
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# Web development
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WEB_PACKAGES = [
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PackageInfo("django", "web", 60_000_000, 77000, "Web framework", "web"),
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PackageInfo("flask", "web", 55_000_000, 66000, "Micro web framework", "web"),
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PackageInfo("fastapi", "web", 35_000_000, 74000, "Modern web API framework", "web"),
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PackageInfo("sqlalchemy", "database", 50_000_000, 8000, "SQL toolkit", "database"),
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PackageInfo("psycopg2", "database", 25_000_000, 3000, "PostgreSQL adapter", "database"),
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PackageInfo("redis", "database", 30_000_000, 12000, "Redis client", "database"),
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PackageInfo("celery", "async", 25_000_000, 23000, "Distributed task queue", "async"),
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PackageInfo("gunicorn", "web", 20_000_000, 9000, "WSGI server", "web"),
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PackageInfo("uvicorn", "web", 15_000_000, 8000, "ASGI server", "web"),
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]
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# Security and cryptography
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SECURITY_PACKAGES = [
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PackageInfo("cryptography", "security", 120_000_000, 6000, "Cryptographic library", "security"),
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PackageInfo("pyopenssl", "security", 60_000_000, 800, "OpenSSL wrapper", "security"),
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PackageInfo("pyjwt", "security", 40_000_000, 5000, "JSON Web Tokens", "security"),
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PackageInfo("bcrypt", "security", 35_000_000, 1200, "Password hashing", "security"),
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PackageInfo("pycryptodome", "security", 30_000_000, 2700, "Cryptographic library", "security"),
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]
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# Networking and API
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NETWORKING_PACKAGES = [
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PackageInfo("httpx", "networking", 25_000_000, 12000, "HTTP client", "networking"),
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PackageInfo("aiohttp", "networking", 35_000_000, 14000, "Async HTTP", "networking"),
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PackageInfo("websockets", "networking", 20_000_000, 5000, "WebSocket implementation", "networking"),
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PackageInfo("paramiko", "networking", 25_000_000, 8000, "SSH client", "networking"),
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]
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# Text processing and parsing
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TEXT_PACKAGES = [
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PackageInfo("beautifulsoup4", "parsing", 40_000_000, 13000, "HTML/XML parser", "parsing"),
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PackageInfo("lxml", "parsing", 35_000_000, 2600, "XML/HTML parser", "parsing"),
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PackageInfo("regex", "text", 30_000_000, 700, "Regular expressions", "text-processing"),
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PackageInfo("python-docx", "text", 15_000_000, 4000, "Word document processing", "text-processing"),
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PackageInfo("pillow", "imaging", 60_000_000, 11000, "Image processing", "imaging"),
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]
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# All packages combined for easy access
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ALL_POPULAR_PACKAGES = (
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INFRASTRUCTURE_PACKAGES +
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CLOUD_PACKAGES +
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DATA_SCIENCE_PACKAGES +
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DEVELOPMENT_PACKAGES +
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WEB_PACKAGES +
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SECURITY_PACKAGES +
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NETWORKING_PACKAGES +
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TEXT_PACKAGES
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)
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# Create lookup dictionaries
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PACKAGES_BY_NAME = {pkg.name: pkg for pkg in ALL_POPULAR_PACKAGES}
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PACKAGES_BY_CATEGORY = {}
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for pkg in ALL_POPULAR_PACKAGES:
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if pkg.category not in PACKAGES_BY_CATEGORY:
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PACKAGES_BY_CATEGORY[pkg.category] = []
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PACKAGES_BY_CATEGORY[pkg.category].append(pkg)
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def get_popular_packages(
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category: str = None,
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limit: int = 50,
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min_downloads: int = 0
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) -> List[PackageInfo]:
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"""Get popular packages filtered by criteria.
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Args:
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category: Filter by category (e.g., 'web', 'data-science', 'cloud')
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limit: Maximum number of packages to return
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min_downloads: Minimum estimated monthly downloads
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Returns:
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List of PackageInfo objects sorted by estimated downloads
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"""
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packages = ALL_POPULAR_PACKAGES
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if category:
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packages = [pkg for pkg in packages if pkg.category == category]
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if min_downloads:
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packages = [pkg for pkg in packages if pkg.estimated_monthly_downloads >= min_downloads]
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# Sort by estimated downloads (descending)
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packages = sorted(packages, key=lambda x: x.estimated_monthly_downloads, reverse=True)
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return packages[:limit]
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def estimate_downloads_for_period(monthly_downloads: int, period: str) -> int:
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"""Estimate downloads for different time periods.
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Args:
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monthly_downloads: Estimated monthly downloads
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period: Time period ('day', 'week', 'month')
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Returns:
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Estimated downloads for the period
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"""
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if period == "day":
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return int(monthly_downloads / 30)
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elif period == "week":
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return int(monthly_downloads / 4.3) # ~4.3 weeks per month
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elif period == "month":
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return monthly_downloads
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else:
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return monthly_downloads
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def get_package_info(package_name: str) -> PackageInfo:
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"""Get information about a specific package.
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Args:
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package_name: Name of the package
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Returns:
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PackageInfo object or None if not found
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"""
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return PACKAGES_BY_NAME.get(package_name.lower().replace("-", "_").replace("_", "-"))
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# GitHub repository URL patterns for fetching real-time data
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GITHUB_REPO_PATTERNS = {
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"requests": "psf/requests",
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"django": "django/django",
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"flask": "pallets/flask",
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"fastapi": "tiangolo/fastapi",
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"numpy": "numpy/numpy",
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"pandas": "pandas-dev/pandas",
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"scikit-learn": "scikit-learn/scikit-learn",
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"tensorflow": "tensorflow/tensorflow",
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"torch": "pytorch/pytorch",
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"transformers": "huggingface/transformers",
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"click": "pallets/click",
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"black": "psf/black",
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"boto3": "boto/boto3",
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"sqlalchemy": "sqlalchemy/sqlalchemy",
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# Add more mappings as needed
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} |