Python Polars Cheatsheet (based on our O'Reilly book)

Great Tables plotnine | Python Python Polars: The Definitive Cheatsheet Quick reference guide for transforming, analyzing, and visualizing data with Python Polars Jeroen Janssens , Thijs Nieuwdorp Download PDF Polars is a library for transforming, analyzing, and visualizing data with a fast and expressive DataFrame API. It was first released by Ritchie Vink in 2020. Install Polars with all of its optional dependencies from the terminal: uv pip install "polars[all]" Import Polars in Python, and confirm which versions of Polars and its dependencies you have installed: import polars as pl pl.show_versions() Polars queries typically read data, transform it, and write the result back out. A complete query is often a single chain of method calls: fruit = pl.read_csv("fruit.csv") fruit.filter( (pl.col("weight") > 1000) & pl.col("is_round") ).write_parquet("fruit.parquet") Throughout this cheatsheet, df is a DataFrame , lf is a LazyFrame , o is a second DataFrame to combine with df , and e stands for any expression. So e.abs() means “call .abs() on an expression”, as in pl.col("x").abs() . Data Structures # Polars stores all of its data in either a Series or a DataFrame. Structure Description Series One-dimensional. Holds a sequence of values of the same data type. DataFrame Two-dimensional. Has rows and columns. One or more Series, all of the same length. LazyFrame Resembles a DataFrame but holds no data. A bluepri…