Jake VanderPlas

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This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.

You can read the book in its entirety online at https://jakevdp.github.io/PythonDataScienceHandbook/

The book was written and tested with Python 3.5, though older 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:

To create a stand-alone environment named with Python 3.5 and all the required package versions, run the following:

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.

Modified on: Sep 20, 2017

Jake VanderPlas, Summer 2016

This repository contains the Jupyter Notebooks behind my O'Reilly report, A Whirlwind Tour of Python (free 100-page pdf).

A Whirlwind Tour of Python is a fast-paced introduction to essential components of the Python language for researchers and developers who are already familiar with programming in another language.

The material is particularly aimed at those who wish to use Python for data science and/or scientific programming, and in this capacity serves as an introduction to The Python Data Science Handbook (also with notebooks on github). These materials are adapted from courses and workshops I've given on these topics at University of Washington and at various conferences, meetings, and workshops around the world.

This material was written and tested using Python 3.5, and should work for any Python 3.X version. I have done my best to note places where the syntax of Python 2.X will differ.

(Note: sometimes GitHub's notebook rendering can be slow or finicky. If you're having trouble with the following links, try viewing the material on nbviewer)

Notebook Index

  1. Introduction
  2. How to Run Python Code
  3. Basic Python Syntax
  4. Python Semantics: Variables
  5. Python Semantics: Operators
  6. Built-In Scalar Types
  7. Built-In Data Structures
  8. Control Flow Statements
  9. Defining Functions
  10. Errors and Exceptions
  11. Iterators
  12. List Comprehensions
  13. Generators and Generator Expressions
  14. Modules and Packages
  15. Strings and Regular Expressions
  16. Preview of Data Science Tools
  17. Resources for Further Learning
  18. Appendix: Code To Reproduce Figures

This material is released under the "No Rights Reserved" CC0 license, and thus you are free to re-use, modify, build-on, and enhance this material for any purpose. Read more about CC0 here.

If you do use this material, I would appreciate attribution. An attribution usually includes the title, author, publisher, and ISBN. For example:

A Whirlwind Tour of Python by Jake VanderPlas (O’Reilly). Copyright 2016 O’Reilly Media, Inc., 978-1-491-96465-1.

Modified on: Sep 20, 2017
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