Polars DataFrames

Wrangle data at speed with Polars. Loads, filters, joins, groups, and reshapes large datasets using expressions and lazy frames, and migrates pandas code to faster Polars equivalents.

This skill handles all data wrangling, cleaning, transformation, and EDA with Polars. It writes idiomatic expression chains, builds lazy frames for out-of-core performance, groups and aggregates, joins and reshapes datasets, and translates pandas code into faster Polars — ideal for large CSVs and Parquet that strain pandas.

When to use

Use for cleaning and transforming data with Polars, writing expression-based pipelines, building lazy frames for large datasets, or migrating pandas code to Polars.

Examples

Group and aggregate

Summarize a dataset

Using Polars, read sales.parquet, group by region and month, and compute total and average revenue

Lazy pipeline

Process a large file

Write a Polars lazy frame pipeline that filters, joins, and aggregates a 10GB CSV without loading it all into memory

Migrate from pandas

Convert existing code

Rewrite this pandas data-cleaning script in Polars expressions for a 5x speedup