Python Polars: The Definitive Guide

Chapter 15. Reshaping

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This will be the 15th chapter of the final book. Please note that the GitHub repo will be made active later on.

If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the editor at [e-mail].

In the last chapter we focussed on aggregating data to create informative summaries. However, what should you do if the data is not in the right shape to perform these aggregations? Reshaping data is a crucial step in the data analysis process.

In this chapter, you’ll learn:

• how to reshape data to make it more suitable for analysis.

• how to change the dimensions of the data to make it more suitable for analysis, improve computational performance, or prepare it for visualization.

• how to use the various method Polars offers, such as df.pivot(), df.unpivot(), df.transpose(), df.explode(), and df.partition_by().

Wide Versus Long DataFrames

Wide DataFrames have many columns and few rows. The idea is that every row contains a column with an identifier, and the data is spread over many columns. This format is often used when there are multiple measurements per observation. An example of wide data would be ...