PythonDataScienceHandbook README

The project's README from github.com/jakevdp/PythonDataScienceHandbook. As one markdown file: README.md.

Python Data Science Handbook

Binder Colab

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

cover image

How to Use this Book

About

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.

Software

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.

License

Code

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.

Text

The text content of the book is released under the CC-BY-NC-ND license. Read more at Creative Commons.

PythonDataScienceHandbook (jakevdp/PythonDataScienceHandbook)

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50.2K ·GitHub
PythonDataScienceHandbook
What is this?This is the full text of the Python Data Science Handbook, provided as a set of free Jupyter notebooks covering the core Python data science stack.
Is this a coding library or a book?It is a book: the notebooks are the chapters of the text, mixing explanation with runnable code, not an installable package.
How do I read or run this book?You can read it online in full, open the notebooks directly in the repo's notebooks folder, launch them instantly in Google Colab or Binder, or buy the printed version from O'Reilly.
What do I need installed to run the code myself?You need Python (written for 3.5, mostly compatible with 2.7) plus IPython, NumPy, Pandas, Matplotlib and Scikit-Learn; a requirements.txt file lists exact versions and the README shows how to set up a conda environment.
Will the code definitely work on any Python version?It was tested on Python 3.5, and while most code also works on Python 2.7 and other versions, the author notes not all of it is guaranteed to.
Do I need to already know Python?Yes, basic Python familiarity is assumed; the author points beginners to his companion project 'A Whirlwind Tour of Python' first.
How is the book organized?Index.ipynb lists all chapters, structured around the core libraries: IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn.
Can I try it without installing anything?Yes, you can launch any notebook directly in Google Colab or spin up a live Binder notebook server straight from the repo links, no local setup needed.
Is there a paid version?Yes, the same content is also sold as a printed book through O'Reilly Media.
What are similar projects?pydata-book, 100-Days-Of-ML-Code, mlcourse.ai, abu, Virgilio, data-science-ipython-notebooks, 100-Days-Of-ML-Code, Ai-Learn
Which topics is it in?ipython, matplotlib, colab, textbook, scikit-learn, mit-license, pandas, jupyter-notebook, data-analysis, numpy, jupyter notebook, data-science
Creator
jakevdp
Size
42.6 MB
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50.2K
Updated
CommitMay 05
Age
10y
Commits · 90 Days