Sometimes, you need some written explanations and the opportunity to test some sample code by yourself. Jupyter, an open tool like Python itself, offers you all of this via its notebooks—interactive web pages where you will find both explanations and example code that can be tested directly. We devote explanations about Jupyter and its kernels throughout the is book because it is a real data science workhorse. It allows you to easily run Python scripts and evaluate their effects on the data that you are work on.
The GitHub location of the IPython kernel (the Python kernel of Jupyter, since Jupyter can run many different programming languages) offers a complete list of example notebooks. You can check it out at github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks ...
