Learn to build a retention-rate heat map with Seaborn. The tutorial covers importing libraries, setting figure size, and using seaborn.heatmap with key arguments like annot=True for values, fmt='.1%' for percentages, and vmin/vmax to control color scaling so outliers don't skew the visualization.
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Welcome to the final lesson on cohort analysis. In this section we will learn how to build powerful cohort analysis visualizations. The most effective way to visualize and analyze cohort analysis data is through a heat map. It provides both the actual metric values and the color coding to see the differences in the numbers Visually. One of the best things about heat maps is that they are very easy to build with Pythons and Seaborn package. First we will load the retention table in a pivot format which we built in the previous lesson. It has the same format of data with the lower right triangle filled with not a number values. This is expected as the more recent cohorts had less time to be active. You will see in the next page that having NAN values helps producing a clean heat map. Lets get down to visualizing the retention rates as the heatmap. The first step is to load the packages Seaborn and Pyplot. Next we create an empty figure with a predefined width and height in inches. We can customize this view depending on the format of the data. Then we add a title to the table and then we call the seabornheatmap function. We pass the retention table to the data parameter, ensure the numbers are also printed by passing true value to the annotation argument. Then define the format as percentage with one decimal value. The vmin and vmax parameters are used to anchor the color map and make sure the outliers don't impact the visualization. Finally, we pass the green palette to the color map. You can find multiple other color palettes in Seaborn documentation. We can now run the plot show function to bring the heatmap to life. Here we go. The retention rate heatmap that we've seen through this chapter is finally built with just a few lines of code. This visualization can be used as a standalone representation of company's retention rate or as an analytical tool to get insights. Great job. Now it's your time to visualize customer cohorts metrics in a heat map.
