The Jupyter project spun out of the popular IPython Notebook work of the early 2000s. These notebooks provide a visual interface with sequential text and code cells. This allows you to add some text to describe a solution, then follow it with code examples. The Jupyter Notebook also use the IPython console (similar to Spyder), so you have an interactive code interpretor that can plot images inline. Launching the notebook from the Anaconda prompt is simple:
(base) $ jupyter notebook
The Jupyter project maintains a few basic notebooks. Let's look at a screenshot from one of them, as follows (it can be found at http://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics):
The concept is self-explanatory if we look at a few examples. The following are recommendations for some relevant and helpful Jupyter Notebooks on data mining and analytics from around the web:
https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb
http://nbviewer.jupyter.org/github/amplab/datascience-sp14/blob/master/hw2/HW2.ipynb
https://github.com/TomAugspurger/PyDataSeattle/blob/master/notebooks/1.%20Basics.ipynb
