Best of Python

Overview

Best of Python is a ranked list of 390 awesome open-source Python projects with a total of 2M stars, grouped into 28 categories and automatically updated weekly with quality scores.

Features

• Automated Rankings: Weekly updates based on latest metrics
• Quality Scores: Calculated from GitHub stars, forks, contributors, commits, and package downloads
• 28 Categories: Organized by functionality and use case
• Comprehensive Metrics: Stars, downloads, contributors, commit activity
• Trending Projects: Highlights fast-growing libraries
• 390+ Projects: Curated selection of high-quality Python tools
• Multiple Package Managers: Data from PyPI, conda-forge, npm where applicable

Categories

Data Processing

• Data Containers: pandas, Polars, Vaex, Modin
• Data Pipelines: Luigi, Airflow, Prefect, Dagster
• File Formats: openpyxl, python-docx, PyPDF2, Pillow
• Code Inspection: autopep8, black, pylint, mypy

Machine Learning & Data Science

• ML Frameworks: scikit-learn, XGBoost, LightGBM, CatBoost
• Deep Learning: PyTorch, TensorFlow, Keras, JAX
• Data Visualization: Matplotlib, Plotly, Seaborn, Bokeh
• Model Interpretability: SHAP, LIME, ELI5
• Hyperparameter Optimization: Optuna, Hyperopt, Ray Tune

Web Development

• Web Frameworks: Django, Flask, FastAPI, Tornado
• API Development: FastAPI, Django REST Framework, Flask-RESTful
• WebSockets: aiohttp, websockets
• Async Frameworks: aiohttp, Sanic, Tornado

Database Clients

• SQL: SQLAlchemy, peewee, psycopg2, PyMySQL
• NoSQL: pymongo, redis-py, motor
• ORMs: SQLAlchemy, Django ORM, peewee, Pony ORM

Testing

• Test Frameworks: pytest, unittest, nose2
• Test Runners: tox, nox
• Mocking: unittest.mock, responses, freezegun
• Coverage: coverage.py, pytest-cov

Command Line Interface

• CLI Development: Click, Typer, argparse, python-fire
• Terminal UI: Rich, Textual, Prompt Toolkit
• Progress Bars: tqdm, alive-progress, rich.progress

Utilities

• Date & Time: arrow, pendulum, python-dateutil
• Functional Programming: toolz, funcy, fn.py
• Caching: cachetools, diskcache, joblib
• Validation: pydantic, marshmallow, cerberus

Configuration

• Config Management: python-decouple, dynaconf, python-dotenv
• Environment Variables: python-dotenv, environs

Logging

• Logging Libraries: loguru, structlog, python-json-logger
• Log Aggregation: Sentry SDK, elastic-apm

HTTP Clients

• HTTP Libraries: requests, httpx, aiohttp
• Web Scraping: BeautifulSoup, scrapy, lxml, selenium

Serialization

• JSON: orjson, ujson, simplejson
• General: pickle, dill, marshmallow, pydantic

Code Quality

• Linters: pylint, flake8, pyflakes, pycodestyle
• Formatters: black, autopep8, yapf, isort
• Type Checkers: mypy, pyright, pyre-check

Security

• Cryptography: cryptography, PyCrypto, passlib
• Security Tools: bandit, safety, pip-audit

Documentation

• Doc Generators: Sphinx, MkDocs, pdoc
• API Docs: Swagger/OpenAPI tools

Task Scheduling

• Schedulers: APScheduler, schedule, Celery
• Background Jobs: Celery, RQ, Huey

GUI Development

• GUI Frameworks: PyQt5, Tkinter, Kivy, wxPython
• Game Development: Pygame, Panda3D

Natural Language Processing

• NLP Libraries: spaCy, NLTK, gensim, TextBlob
• Transformers: Hugging Face Transformers

Computer Vision

• CV Libraries: OpenCV, Pillow, scikit-image
• Face Recognition: face_recognition, dlib

Audio & Video

• Audio Processing: pydub, librosa, soundfile
• Video Processing: moviepy, opencv-python

Scientific Computing

• Scientific Libraries: NumPy, SciPy, SymPy
• Statistics: statsmodels, pingouin

Networking

• Network Libraries: Twisted, asyncio, socket
• Protocol Libraries: paramiko (SSH), ftplib

System & OS

• System Tools: psutil, sh, subprocess
• Path Handling: pathlib, os.path

Package Management

• Package Tools: pip, poetry, pipenv, conda
• Virtual Environments: venv, virtualenv, pyenv

Metrics Tracked

For each project:

• GitHub stars and trending status
• PyPI download statistics (monthly)
• Number of contributors
• Commit frequency and last update
• Number of dependent projects
• License information
• Programming language percentages

Update Frequency

Automatically updated every week with:

• Latest star counts
• Recent download statistics
• New releases and versions
• Contributor counts
• Recalculated quality scores

Quality Score

Calculated based on:

• GitHub stars (popularity)
• Forks (community engagement)
• Contributors (active development)
• Commit frequency (maintenance)
• Download statistics (real usage)
• Project age and stability

Use Cases

Ideal for:

• Python developers discovering new libraries
• Project managers evaluating dependencies
• Developers making technology choices
• Learners exploring the Python ecosystem
• Researchers comparing similar tools
• Teams establishing coding standards