#python #markdown #python_library #python3 #syntax_highlighting #tables #terminal #terminal_color

https://github.com/willmcgugan/rich

#python #awesome #awesome_list #best_of #data_science #data_visualization #data_visualizations #deep_learning #keras #machine_learning #nlp #python_library #pytorch #scikit_learn #tensorflow

https://github.com/ml-tooling/best-of-ml-python

#python #attack_surface #automation #automation_framework #cli #command_line_tool #hacking #information_gathering #neo4j #osint #osint_framework #osint_tool #pip #pypi #python_framework #python_library #recon #reconnaissance #scanner #subdomain_enumeration

https://github.com/blacklanternsecurity/bbot

#python #framework #fullstack #infrastructure #open_source #python_library #python3 #web #webdev

https://github.com/pynecone-io/pynecone

#python #audio #docker_image #dsp #equalizer #filter #limiter #mastering #matchering #matching #music #numpy #python #python_library #python3 #scipy #sound #spectrum #vst

Matchering 2.0 is a tool that helps make your music sound like your favorite songs. Here’s how it works: you give it two audio files - one you want to master (your song) and another you want it to sound like (a reference song). Matchering then adjusts your song to match the volume, frequency response, peak amplitude, and stereo width of the reference song. This means you can quickly make your music sound professional and consistent, just like popular tracks. You can use it without installation through online services or install it on your computer for more control. It's especially useful for music producers and audio engineers who want to enhance their tracks easily.

https://github.com/sergree/matchering

#python #awesome #collections #python #python_framework #python_library #python_resources

This list provides a comprehensive collection of Python libraries, frameworks, and tools organized by category. Here’s the most important information and the benefit to the user**
- **Admin Panels** Libraries such as `pandas` and `scikit-learn` offer powerful tools for data manipulation and analysis, enhancing productivity in data science tasks.
- **Web Development** Libraries like `TensorFlow`, `PyTorch`, and `scikit-learn` enable users to build and deploy machine learning models with ease.
- **Testing** Libraries like `requests` and `FastAPI` simplify interacting with web APIs and building RESTful APIs.

**Benefit to the User:**
These libraries and tools streamline various aspects of Python development, from web development and data analysis to machine learning and testing. They save time, improve productivity, and enhance the overall quality of the code. Whether you're a beginner or an experienced developer, these resources can significantly aid in your projects by providing well-maintained, community-supported solutions for common tasks.

https://github.com/vinta/awesome-python