#!/usr/bin/env python
# coding: utf-8

# # References

# **The code of this presentation**: 
# 
# * GitHub: https://github.com/batterio/intro_ipython_notebook

# **Links**:
# 
# * [IPython Notebook webpage](http://ipython.org/notebook.html)
# * [IPython Notebook Viewer](http://nbviewer.ipython.org/)
# * [Interesting IPython Notebook](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks)
# * Article on [Nature](http://www.nature.com/news/interactive-notebooks-sharing-the-code-1.16261) about the Ipython Notebooks

# **Videos:**
# * [IPython in-depth: high-productivity interactive and parallel python](http://www.pyvideo.org/video/2577/ipython-in-depth-high-productivity-interactive-a-1) by Fernando Perez at PyCon US 2014.  
# * [IPython & Jupyter in depth: high productivity interactive and parallel python](https://www.youtube.com/watch?v=05fA_DXgW-Y) by Thomas Kluyver and Kyle Kelley at PyCon 2015.

# **Books:**  
# * [Learning IPython for Interactive Computing and Data Visualization](http://ipython-books.github.io/minibook/) by Cyrille Rossant.  
# This is a beginner-level introduction to IPython for data analysis and numerical computing, covering NumPy, pandas, SciPy, and matplotlib.
# <img src="figs/ipython_book_1.jpg">  
#   
#   
# 
# * [IPython Interactive Computing and Visualization Cookbook](http://ipython-books.github.io/cookbook/) by Cyrille Rossant.  
# This is an advanced-level guide to IPython for data science, and the sequel of the IPython minibook.
# <img src="figs/ipython_book_2.jpg">