Polars Sorting Data

last modified March 1, 2025

Polars is a fast and efficient DataFrame library in Python. Sorting data is a common operation in data analysis. This tutorial covers how to sort data in Polars with practical examples.

Sorting helps organize data for better analysis and visualization. Polars provides methods like sort and sort_by for sorting DataFrames.

Basic Sorting

This example shows how to sort a DataFrame by a single column.

basic_sort.py

import polars as pl

data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’], ‘Age’: [25, 30, 22, 35] }

df = pl.DataFrame(data) sorted_df = df.sort(‘Age’)

print(sorted_df)

The sort(‘Age’) sorts the DataFrame by the ‘Age’ column in ascending order. This is useful for organizing data by a specific attribute.

Sorting in Descending Order

This example demonstrates sorting a DataFrame in descending order.

descending_sort.py

import polars as pl

data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’], ‘Age’: [25, 30, 22, 35] }

df = pl.DataFrame(data) sorted_df = df.sort(‘Age’, reverse=True)

print(sorted_df)

The sort(‘Age’, reverse=True) sorts the DataFrame by the ‘Age’ column in descending order. This is useful for ranking data.

Sorting by Multiple Columns

This example shows how to sort a DataFrame by multiple columns.

multi_column_sort.py

import polars as pl

data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’], ‘Age’: [25, 30, 22, 25], ‘Salary’: [50000, 60000, 45000, 55000] }

df = pl.DataFrame(data) sorted_df = df.sort([‘Age’, ‘Salary’])

print(sorted_df)

The sort([‘Age’, ‘Salary’]) sorts the DataFrame first by ‘Age’ and then by ‘Salary’. This is useful for hierarchical sorting.

Sorting with Null Values

This example demonstrates sorting a DataFrame with null values.

null_sort.py

import polars as pl

data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’], ‘Age’: [25, None, 22, 35] }

df = pl.DataFrame(data) sorted_df = df.sort(‘Age’, nulls_last=True)

print(sorted_df)

The sort(‘Age’, nulls_last=True) sorts the DataFrame with null values placed at the end. This is useful for handling missing data.

Sorting by Expression

This example shows how to sort a DataFrame using an expression.

expression_sort.py

import polars as pl

data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’], ‘Age’: [25, 30, 22, 35] }

df = pl.DataFrame(data) sorted_df = df.sort(pl.col(‘Age’) * -1)

print(sorted_df)

The sort(pl.col(‘Age’) * -1) sorts the DataFrame by ‘Age’ in descending order using an expression. This is useful for custom sorting logic.

Sorting with Custom Order

This example demonstrates sorting a DataFrame with a custom order.

custom_order_sort.py

import polars as pl

data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’], ‘Age’: [25, 30, 22, 35] }

df = pl.DataFrame(data) sorted_df = df.sort(‘Name’, descending=[False, True])

print(sorted_df)

The sort(‘Name’, descending=[False, True]) sorts the DataFrame by ‘Name’ with a custom order. This is useful for specific sorting requirements.

Sorting by Index

This example shows how to sort a DataFrame by its index.

index_sort.py

import polars as pl

data = { ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’], ‘Age’: [25, 30, 22, 35] }

df = pl.DataFrame(data) sorted_df = df.sort(by=‘index’, reverse=True)

print(sorted_df)

The sort(by=‘index’, reverse=True) sorts the DataFrame by its index in descending order. This is useful for reordering rows.

Best Practices for Sorting Data

• Understand Data: Analyze data structure before sorting.

• Choose Appropriate Columns: Select columns that align with your analysis goals.

• Handle Null Values: Use nulls_last to manage missing data.

• Validate Results: Check sorted data for accuracy and consistency.

Source

Polars Documentation

In this article, we have explored how to sort data in Polars.

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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