Python Polars: The Definitive Guide

Chapter 9. Combining Expressions

Now that you understand the fundamentals of expressions and know various methods to continue them, it’s time to learn how to combine them.

Combining expressions is necessary whenever the Series you want to construct is based on more than one value or column. This happens to be the case more often than you might think: for example, when you want to compute the ratio between two Float columns, filter rows based on multiple conditions, or concatenate multiple String columns into one.

In fact, you’ve already combined expressions several times in the previous chapters. Let’s look at an example from Chapter 7 to refresh your memory:

fruit = pl.read_csv("data/fruit.csv")
fruit.filter(pl.col("is_round") & (pl.col("weight") > 1000))

shape: (2, 5)
┌────────────┬────────┬────────┬──────────┬────────┐
│ name       │ weight │ color  │ is_round │ origin │
│ ---        │ ---    │ ---    │ ---      │ ---    │
│ str        │ i64    │ str    │ bool     │ str    │
╞════════════╪════════╪════════╪══════════╪════════╡
│ Cantaloupe │ 2500   │ orange │ true     │ Africa │
│ Watermelon │ 5000   │ green  │ true     │ Africa │
└────────────┴────────┴────────┴──────────┴────────┘

This code combines, in two steps, two existing columns (is_round and weight), and one value (1000) into one expression. The df.filter() method then uses this expression to filter rows.

Because of how the parentheses are organized, the comparison greater than operator (>) combines the weight column and the value 1000. When the value is larger, it produces ...