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jakevdp PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
Stars
49,780
Forks
19,101
Watchers: 49,780
Language
License: MIT License
Repository Radar Score
46 / 100
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Score breakdown
- popularity 89
- growth 0
- activity 15
- freshness 100
- community 66
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Get a quote →This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.
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Read the book in its entirety online at https://jakevdp.github.io/PythonDataScienceHandbook/
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Run the code using the Jupyter notebooks available in this repository's notebooks directory.
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Launch executable versions of these notebooks using Google Colab:
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Launch a live notebook server with these notebooks using binder:
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Buy the printed book through O'Reilly Media
The book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should work in nearly all cases.
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python: it's a fast-paced introduction to the Python language aimed at researchers and scientists.
See Index.ipynb for an index of the notebooks available to accompany the text.
The code in the book was tested with Python 3.5, though most (but not all) will also work correctly with Python 2.7 and other older Python versions.
The packages I used to run the code in the book are listed in requirements.txt (Note that some of these exact version numbers may not be available on your platform: you may have to tweak them for your own use). To install the requirements using conda, run the following at the command-line:
$ conda install --file requirements.txt
To create a stand-alone environment named PDSH with Python 3.5 and all the required package versions, run the following:
$ conda create -n PDSH python=3.5 --file requirements.txt
You can read more about using conda environments in the Managing Environments section of the conda documentation.
The code in this repository, including all code samples in the notebooks listed above, is released under the MIT license. Read more at the Open Source Initiative.
The text content of the book is released under the CC-BY-NC-ND license. Read more at Creative Commons.
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- High absolute popularity (49,780 stars) signals broad adoption.
Who should use it
- Developers working primarily with Jupyter Notebook
- Platform and DevOps engineers
Potential use cases
- Reference or evaluate Jupyter Notebook open-source approaches in this domain
- Evaluate automation, CI/CD, or infrastructure patterns
Strengths
- README present in our index
- Declared license: MIT License
- Substantial fork count (19,101) suggests reuse and contribution interest
Limitations / considerations
- Insights are derived from public metadata and our observations — not a substitute for code review
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Strong signals: Strong community interest
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.