A heatmap is a method for visualizing two-dimensional data using colors to represent the magnitude of each value. Here is an example of a heatmap:

In this example we used a heatmap to represent pairwise correlation between the variables in a dataset. With such a heatmap everything is neat and clear, exactly what we need from a good visulization.

Creating a Simple Heatmap

seaborn has a function called heatmap(). Its only required parameter is data which should be a 2D (rectangle) dataset.

Perhaps the most common use case of a heatmap is with a correlation matrix like in the example above. Given a DataFrame, we should first call its corr() method to get a correlation matrix and only then pass this matrix as an argument for the heatmap() function:

Everything is pretty straightforward here, however, we didn’t use all the columns of the DataFrame to create a correlation matrix (numeric_only=True), since they have strings and are not numeric.

Annotation and Colors

This heatmap can be made more informative via writing the appropriate value (correlation coefficient in our case) in each cell . That can be done simply by setting the annot parameter to True.

It is also possible to change the colors for our heatmap via setting the cmap parameter (you can explore the palettes here).

With this in mind, let’s now modify our example:

We could also remove the color bar on the right by setting cbar=False.

In most of the cases that’s all you will need from a heatmap customization, however, you can always explore more in its documentation.

Improving Readability

The final thing that would improve the readability of our heatmap is rotating the ticks using already familiar xticks() and yticks() functions: