Outside of pandas, the seaborn library is one of the most popular in the Python data science community to create visualizations. Like pandas, it does not do any actual plotting itself and is completely reliant on matplotlib for the heavy lifting. Seaborn plotting functions work directly with pandas DataFrames to create aesthetically pleasing visualizations.

While seaborn and pandas both reduce the overhead of matplotlib, the way they approach data is completely different. Nearly all of the seaborn plotting functions require tidy (or long) data. When data is in tidy form, it is not ready for consumption or interpretation until some function is applied to it to yield a result. Tidy data is the raw building blocks that makes all other analysis possible. Processing tidy data during data analysis often creates aggregated or wide data. This data, in wide format, is what pandas uses to make its plots.