I thought today I would whip up a quick post regarding Jupyter Notebooks and how to download, install and use various “addons” that I like using and find more than just a little bit useful. Among other things I’ll show how to use the “jupyter-themes” module to change and manipulate the basic theme and styling of the overall notebook, I’ll show how to download and install the Jupyter Notebook extensions module giving access to a whole range of usefull goodies you can try out, and I’ll even show you how to use Jupyter widgets and how to embed URLs, PDFs, and Youtube videos directly into a notebook itself.
Hi all, this post is going to be a relatively short and to the point run through of creating an annotated heatmap for the Dow 30 stock returns using the Python Seaborn package.
Let’s start with what is a heatmap actually is; it’s defined as “a representation of data in the form of a map or diagram in which data values are represented as colours.”
This makes it a great tool to quickly visualise the magnitude of stock returns over time in a matrix/grid format, using a colour map/scale to represent the size and direction of each stock’s percentage change over that period of time.
Creating a heatmap without stock ticker labels annotated, i.e. a heatmap annotated with just the numerical value of the relevant cell is a very easy process, thanks to the power and ease of use of Seaborn.