Skip to content
Get packs

Python Data Science Handbook

The full text of a Python data science book, delivered as runnable Jupyter notebooks.

GitHub Download source
Stars
50.3k
Forks
19.2k
Licence
MIT
Language
Jupyter Notebook
Last update
2.4 years ago
Type
Learning resource

No commits in over a year — check for forks or alternatives before adopting.

What it is

This repository holds the complete Python Data Science Handbook as Jupyter notebooks, so the explanations and the code examples live together. It introduces core Python data libraries such as IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. You can read it online, or run the notebooks yourself to try the code.

  • Read the chapters online and follow along with the matching notebook
  • Open a notebook in Google Colab or Binder to run examples without installing anything locally
  • Work through the Pandas or Scikit-Learn notebooks as a self-paced exercise
  • Use the notebooks as a reference for specific NumPy or Matplotlib techniques

Great for

  • Students learning NumPy, Pandas, Matplotlib, or Scikit-Learn through hands-on examples
  • Analysts who already know Python and want a practical tour of data science libraries
  • Readers who prefer running code next to the text rather than only reading
  • People who want to try the notebooks without local setup, using Colab or Binder

Not ideal for

  • Complete beginners to Python: the README says familiarity with Python is assumed, and points to a separate companion project for that
  • Readers who need current library versions: the book was tested with Python 3.5, and the README warns some pinned package versions may not be available on every platform
Step-by-step guide: how to use Python Data Science HandbookInstall, first run, examples, tips and FAQs
Data Science for Beginners vs Python Data Science HandbookAI Cheatsheets vs Python Data Science Handbook

How to get it

  1. 1

    Clone and build from source

    You'll need: Python (the README names 3.5 as the tested version) · conda (for the README's install commands)

    Install requirements into the current conda environment

    conda install --file requirements.txt

    Create a stand-alone environment named PDSH with Python 3.5

    conda create -n PDSH python=3.5 --file requirements.txt
  2. 2

    Run it

    • Clone the repository from GitHub
    • Install the requirements with conda, or create the PDSH environment
    • Open notebooks/Index.ipynb to see the list of notebooks
    • Open any chapter notebook in Jupyter and run its cells
  3. 3

    Get the most out of it

    • Start with Index.ipynb, which the README describes as an index of notebooks that accompany the text
    • Use the Colab or Binder links to run notebooks without a local install
    • Read the book online at the project's GitHub Pages site alongside the notebooks
    • Check the README's conda environment guidance if package versions conflict

Downloads

Source code (.zip)master branch · straight from GitHubOfficial website & docsjakevdp.github.io/PythonDataScienceHandbook

Strengths

  • Text and runnable code are combined in the same notebooks
  • Free to read online, with Colab and Binder links for running code
  • Code is released under the MIT license

Trade-offs

  • Written and tested with Python 3.5; newer library versions may behave differently, and the README admits some pinned versions may be unavailable
  • The text is licensed CC-BY-NC-ND, which limits reuse and modification of the book content, unlike the MIT-licensed code

How it compares

ProjectStarsTypeLevelUpdatedBest for
PythonDataScienceHandbook (this)50.3kLearning resourcebeginner2.4 years agoStudents learning NumPy, Pandas, Matplotlib, or Scikit-Learn through hands-on examples
cheatsheets-ai15.4kLearning resourcebeginner7.1 years agoStudents who want a one-page summary of a library's main functions
fg-data-profiling13.7kLibrarybeginner6 days agoAnalysts who want a quick first look at an unfamiliar dataset
Data-Science-For-Beginners37.9kLearning resourcebeginner16 days agoComplete beginners who want a guided, week-by-week data science course
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers28.2kLearning resourcebeginner2.4 years agoProgrammers who know some Python and want to learn Bayesian methods by doing
homemade-machine-learning24.8kLearning resourcebeginner12 months agoStudents learning the math behind machine learning algorithms

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.