FacetGrid, factor plot, and pair plot may take up a lot of space when we need to visualize more variables or samples. There are two special plot types that come in handy if you want the maximize space efficiency--Heatmaps and Candlestick plots.
A heatmap is an extremely compact way to display a large amount of data. In the finance world, color-coded blocks can give investors a quick glance at which stocks are up or down. In the scientific world, heatmaps allow researchers to visualize the expression level of thousands of genes.
The seaborn.heatmap() function expects a 2D list, 2D Numpy array, or pandas DataFrame as input. If a list or array is supplied, we can supply column and row labels via xticklabels and yticklabels respectively. On the other hand, if a DataFrame is supplied, the column labels and index values will be used to label the columns and rows respectively.
To get started, we will plot an overview of the performance of...