Python Polars: The Definitive Guide

Chapter 12. Working with Textual, Temporal, and Nested Data Types

In Chapter 4, we covered the basic data types that Polars offers and how they are used to store data in Series. Certain data types deserve special attention because they either have special methods or they are optimized for specific use cases.

These data types can be grouped into textual, temporal, and nested data types. The three textual data types are String, Categorical, and Enum. The four temporal data types are Date, Datetime, Time, and Duration. The three nested data types are List, Array, and Struct.

All these data types, except for Enum, have their own namespace. A namespace groups multiple methods into one accessor. For example, the Expr.str namespace has all the methods for String, and the Expr.dt namespaces have all the methods for temporal data types.

In this chapter, you’ll learn how to:

• Create Series with textual, temporal, and nested data types

• Work with text using the String data type

• Use Categoricals and Enums for efficiently working with textual data

• Process temporal data using Dates and Datetimes

• Store sequences and nested data using Lists, Arrays, and Structs

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

String

A String is a data type for representing text, consisting of a sequence of characters, digits, or symbols. This brings with it a unique set of operations that can be performed on Strings, such ...