Visualizing correlations with a heatmap

Plotting relationships between many variables using a pair plot can quickly get visually overwhelming. It is therefore often useful to compute covariances between the variables instead. The covariance matrix can then easily be visualized as a heatmap. A heatmap is effectively a pseudocolor plot with labelled rows and columns (i.e., a pseudocolor plot based on a pandas DataFrame rather than a matrix). The DataFrame does not have to be square or symmetric (but, in the context of a covariance matrix, it is both).

In this exercise, you will view the covariance matrix between the continuous variables in the auto-mpg dataset. You do not have to know here how the covariance matrix is computed; the important point is that its diagonal entries are all 1s, and the off-diagonal entries are between -1 and +1 (quantifying the degree to which variable pairs vary jointly). It is also, then, a symmetric matrix.

Print the covariance matrix cov_matrix to examine its contents and labels. This has been done for you.
Plot the covariance matrix cov_matrix using sns.heatmap().

Exercise

Visualizing correlations with a heatmap

Instructions