The Limitations Of Heatmap That Are Slowing Down Your Data Analysis

...And what to replace your heatmaps with.

Heatmaps often make data analysis much easier. Yet, they do have some limitations.

A traditional heatmap does not group rows (and features). Instead, its orientation is the same as the input. This makes it difficult to visually determine the similarity between rows (and features).

Clustered heatmaps can be a better choice in such cases. It clusters the rows and features together to help you make better sense of the data.

They can be especially useful when dealing with large datasets. While a traditional heatmap will be visually daunting to look at.

However, the groups in a clustered heatmap make it easier to visualize similarities and identify which rows (and features) go with one another.

To create a clustered heatmap, you can use the 𝐬𝐧𝐬.𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐦𝐚𝐩() method from Seaborn. More info here: Seaborn docs.

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Check out Sourcery, an automated code refactoring tool for Python to make your code more elegant, concise, and pythonic.

Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn.