Bears firing arrows

If you’ve done any data analysis in Python, you’ve likely at least heard of the pandas library. It is an open source, BSD-licensed library for performing data analysis in Python and one of the most popular tools used by data scientists and engineers to do their jobs. Given the ubiquity of its use, it only makes sense that Arrow’s Python library has integration for converting to and from pandas DataFrames quickly and efficiently. More recently, the Polars library was developed using an underlying implementation in Rust with Arrow directly as its internal memory model.

This section is going to first dive into the specifics and gotchas for using Arrow with pandas and how you can speed up your workflows by using them together. Following that, we’ll also cover the basics of using Polars and sharing Arrow data with it.

Before we start, though, make sure you’ve installed pandas and Polars locally so that you can follow along. Of...