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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.