Skip to content

Python modules and IPython Notebooks, for the book "Introduction to Statistics With Python"

License

Notifications You must be signed in to change notification settings

sanjiv999/statsintro_python

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Title

Python modules and IPython Notebooks, which accompany the book Introduction to Statistics With Python

Cover

This repo contains three folders: ISP, ipynb, and ipynb_slides

"ISP": Introduction to Statistics with Python

All the Python programs that go with the book:

  • Code samples (also called Quantlets)
  • Solutions for the Exercises in the book
  • Code-listings, i.e. Python programs printed in the book
  • Code to generate the Figures in the book

"ipynb": IPython Notebooks

  • These notebooks are not used explicitly in the book, and contain important samples and solutions to statistical applications of Python.
  • Also contains a folder for data used by the IPython notebooks.

"ipynb_slides": Corresponding reveal.js-Slides

reveal.js is a powerful presentation application, based on CSS and HTML5. It exists for all platforms (Windows, Linux, OSX), and has to be installed on your computer if you want to use those slides.

  • You can either create the slides yourself from the IPYNB-files, using the command

    jupyter nbconvert --to slides --reveal-prefix ".." *.ipynb

    (Note that the string after "--reveal-prefix" indicates where your reveal.js directories can be found.)

  • Or you copy this directory (i.e. ipynb_slides) to the location where your reveal.js directories are, and are ready to go right away.

Errata

The file Errata.pdf contains the a list of mistakes in the manuscript, and the corresponding corrections.

About

Python modules and IPython Notebooks, for the book "Introduction to Statistics With Python"

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • HTML 77.9%
  • Jupyter Notebook 20.1%
  • Python 2.0%