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The Python Data Science Handbook is a comprehensive collection of Jupyter notebooks written by Jake VanderPlas covering fundamental Python libraries for data science, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and more. The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. Each chapter is a standalone Jupyter notebook, with runnable code, explanatory prose, visuals, and examples showing how to handle data-wrangling, exploratory data analysis, machine learning workflows, and visualization. The repository is freely available and the code is released under the MIT license; the textual content is released under a Creative Commons license. Users can also launch the notebooks in Google Colab or Binder directly, making it extremely accessible.
Features
- Collection of Jupyter notebooks covering IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and other data science tools
- Free and open access under MIT (code) and CC-BY-NC-ND (text) licenses
- Executable examples and visualizations so readers can run code, modify it, and learn by practice
- Compatibility with Google Colab and Binder for browser-based interactive learning
- Structured like a full textbook (table of contents, chapters, index) but organized as code + narrative
- Widely referenced in the data science community as a go-to resource for Python-based workflows
Project Samples
