Welcome to this section using Jupyter notebooks. The notebook is a web application that allows you to combine explanatory text, math equations, code, and visualizations all in one easily sharable document. You can view a few more tutorial notebooks at Gravitational Wave Open Science Center homepage.
Notebooks have quickly become an essential tool when working with data.
You'll find them being used for data cleaning and exploration, visualization, machine learning, and big data analysis.
Typically you'd be doing this work in a terminal, either the normal Python shell or with IPython. Your visualizations would be in separate windows, any documentation would be in separate documents, along with various scripts for functions and classes. However, with notebooks, all of these are in one place and easily read together.
Notebooks are also rendered automatically on GitHub. It’s a great feature that lets you easily share your work. There is also http://nbviewer.jupyter.org/ that renders the notebooks from your GitHub repo or from notebooks stored elsewhere.
Literate Programming
Notebooks are a form of literate programming proposed by Donald Knuth in 1984. With literate programming, the documentation is written as a narrative alongside the code instead of sitting off by its own. In Donald Knuth's words,
Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do.
After all, code is written for humans, not for computers. Notebooks provide exactly this capability. You are able to write documentation as narrative text, along with code. This is not only useful for the people reading your notebooks, but for your future self coming back to the analysis.
Just a small aside: recently, this idea of literate programming has been extended to a whole programming language, Eve.
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