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An opinionated list of awesome Python frameworks, libraries, software and resources.
Magnificent app which corrects your previous console command.
The Web framework for perfectionists with deadlines.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
🥧 HTTPie CLI — modern, user-friendly command-line HTTP client for the API era. JSON support, colors, sessions, downloads, plugins & more.
The fundamental package for scientific computing with Python.
Distributed Task Queue (development branch)
☁️ Build multimodal AI applications with cloud-native stack
Official inference repo for FLUX.1 models
A Django content management system focused on flexibility and user experience
Accelerate your web app development | Build fast. Run fast.
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Asynchronous HTTP client/server framework for asyncio and Python
AWX provides a web-based user interface, REST API, and task engine built on top of Ansible. It is one of the upstream projects for Red Hat Ansible Automation Platform.
match command-line arguments to their help text
A graph-relational database with declarative schema, built-in migration system, and a next-generation query language
A framework for managing and maintaining multi-language pre-commit hooks.
Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4-3.13. You feed it your Python app, it does a lot of clever things, and spits out an executable or exte…
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
Cross-platform lib for process and system monitoring in Python
dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.