A curated list of new or uncommon Python (R, etc.) libraries that are useful for Data Science analysis.
Inspired by awesome-python.
Libraries for enhanced print functions
- printy - lite and cross-platform library that extends the functionalities of the built-in functions print() and input().
- rich - Python library for rich text and beautiful formatting in the terminal.
Libraries for visual representation of data
- pandas-bokeh - Bokeh Plotting Backend for Pandas and GeoPandas
Libraries for machine learning
- yellowbrick - extends the Scikit-Learn API to make model selection and hyperparameter tuning easier
Libraries or 3rd party tools for manipulating database systems
- Beekeper Studio - IDE for databases. Similar to DataGrip, Toad, and SQL Developer.
Libraries that manipulate the data or dataframe in some useful way
- dtale - Create interactive table from Pandas dataframe.
- nbdev - Create library and PyPi package from a Jupyter notebook.
- pyp - Easily run Python at the shell! Magical, but never mysterious.
- deon - An ethics checklist for data scientists.
*Virtual machines or VM resources
- macos-virtualbox - Push-button installer of macOS Catalina, Mojave, and High Sierra guests in Virtualbox for Windows, Linux, and macOS.
Where to discover new Python libraries.
- From Python Import Podcast
- Podcast.init
- Python Bytes
- Python Testing
- Radio Free Python
- Talk Python To Me
- Test and Code
- The Real Python Podcast
- @codetengu
- @getpy
- @importpython
- @planetpython
- @pycoders
- @pypi
- @pythontrending
- @PythonWeekly
- @TalkPython
- @realpython
- /r/CoolGithubProjects
- /r/Python
- Awesome Python @LibHunt
- Django Packages
- Full Stack Python
- Python Cheatsheet
- Python ZEEF
- Python 开发社区
- Real Python
- Trending Python repositories on GitHub today
- Сообщество Python Программистов
- Pythonic News
Your contributions are always welcome!
If you have any question about this opinionated list, do not hesitate to contact me: [email protected] or open an issue on GitHub.