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jakevdp/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
The complete text of Jake VanderPlas's O'Reilly book, free in Jupyter Notebook form. Covers NumPy, Pandas, Matplotlib, Scikit-Learn, and IPython for people who already know Python and want to work with data. This is a book, not a library — you read it, you don't install it.
The notebook format is the right call here: every code example is runnable, and Colab/Binder links mean zero setup friction for someone who just wants to try something. Coverage of NumPy internals (ufuncs, broadcasting, structured arrays) is unusually thorough for an introductory resource — most tutorials skip straight to Pandas and leave readers confused about why array operations behave the way they do. The Scikit-Learn chapter does a genuinely good job explaining the estimator API and model validation concepts rather than just calling fit() and predict(). Having both v1 and v2 notebooks in the same repo lets you see what changed between editions.
The code was written for Python 3.5 and the library versions pinned in requirements.txt are ancient — pandas 0.23, scikit-learn 0.19. Most of it still runs on current versions, but some things silently behave differently and a few will break outright. The Matplotlib chapter teaches the stateful pyplot interface heavily, which is the one people now advise against; the object-oriented axes approach gets mentioned but not emphasized. Deep learning gets one passing reference in the further-reading section — reasonable for when it was written, but jarring in 2024 when that's what half the people picking up data science actually want to do. The repo hasn't been touched since mid-2024 and there's no indication of a v3.