Python Polars: The Definitive Guide

Chapter 4. Data Structures and Data Types

Now that you’ve been properly introduced, it’s time to focus on how Polars works.

Data comes in many shapes and sizes, all of which need to be stored in proper structures in order to work with them. To accommodate all the data you’ll be working with, Polars implements the Arrow memory specification, which provides a vast array of data types.

In this chapter you’ll learn about:

• The structures Polars uses to store data

• The different data types that are available

• Some of the data types that aren’t so straightforward

Let’s start the beautiful journey of learning about Polars.

The instructions to get any files you might need are in Chapter 2. We assume that you have the files in the data subdirectory.

Series, DataFrames, and LazyFrames

Polars stores all of its data in a Series or a DataFrame.

A Series is a one-dimensional data structure that holds a sequence of values. All values in a Series have the same data type, like Integers, Floats, or Strings. Series can exist on their own, but they’re most commonly used as columns in a DataFrame.

An example of a Series is the following:

sales_series = pl.Series("sales", [150.00, 300.00, 250.00])

sales_series

shape: (3,)
Series: 'sales' [f64]
[
	150.0
	300.0
	250.0
]

A DataFrame is a two-dimensional data structure that organizes data in a table format, with rows and columns. Internally, it’s represented as a sequence of multiple Series, each with the same length. To dive deeper into the inner workings ...