Polars 2 Cheatsheet

Basics

• Install Polars — Install the Polars library.
• Create a DataFrame — Build a DataFrame from a dictionary of columns.
• Read a CSV file — Load a CSV into a DataFrame.
• Look at the data — Preview rows, check the schema and summarize numeric columns.
• Select columns — Choose columns by name or expression.
• Filter rows — Keep rows that match a condition. Combine conditions with & and |.
• Add or change columns — Create new columns from expressions. Existing columns with the same name are replaced.
• Sort rows — Sort by one or more columns.

Expressions, grouping and joins

• Group and aggregate — Compute several aggregates per group.
• Conditional columns — Set a value by condition, like CASE WHEN in SQL.
• Join two DataFrames — Combine DataFrames on a key. how can be inner, left, full, semi or anti.
• Handle missing values — Replace nulls, or drop rows that contain them.
• Work with strings — Use the str namespace for text operations.
• Work with dates — Parse text to dates and extract parts with the dt namespace.
• Window functions — Compute a value per group while keeping every row.
• Pivot a DataFrame — Turn values of one column into columns.

Lazy queries and files

• Scan a file lazily — Build a query without loading the file. Polars optimizes it and reads only what it needs.
• Write and run a lazy query — Chain operations on a LazyFrame, then collect the result.
• See the query plan — Print the optimized plan of a lazy query.
• Read and write Parquet — Parquet is compact and fast to read.
• Stack DataFrames — Append DataFrames that have the same columns.
• Convert to and from pandas — Convert for libraries that expect pandas. This needs pyarrow installed.
• Process data larger than memory — Collect with the streaming engine so data is handled in batches.

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