Polars (DataFrame Library) Cheat Sheet

Manipulate large tabular datasets fast with Polars' expression API, lazy execution, and multi-threaded query engine in Python and Rust.

2 PagesIntermediateJan 8, 2026

Read, Select, and Filter

Load a Parquet file and perform basic column selection and row filtering.

python

import polars as pldf = pl.read_parquet("orders.parquet")result = df.select(["order_id", "amount", "status"]).filter(    (pl.col("amount") > 100) & (pl.col("status") == "completed"))print(result.head())

Lazy Query with the Expression API

Build a query plan with LazyFrame and let the optimizer fuse operations before execution.

python

lf = (    pl.scan_parquet("orders.parquet")    .filter(pl.col("status") == "completed")    .group_by("customer_id")    .agg([        pl.col("amount").sum().alias("total_spent"),        pl.col("order_id").count().alias("n_orders"),    ])    .sort("total_spent", descending=True))result = lf.collect()  # optimizer runs hereprint(lf.explain())    # inspect the query plan

Joins and Window Functions

Join two frames and compute a per-group rolling calculation.

python

joined = orders.join(customers, on="customer_id", how="left")with_rank = joined.with_columns(    pl.col("amount").rank(descending=True).over("customer_id").alias("spend_rank"),    pl.col("amount").rolling_mean(window_size=3).over("customer_id").alias("rolling_avg"),)

Interop with pandas and Arrow

Convert between Polars, pandas, and Arrow without copying more than necessary.

python

pandas_df = df.to_pandas()polars_df = pl.from_pandas(pandas_df)# zero-copy-ish conversion via Arrowarrow_table = df.to_arrow()df2 = pl.from_arrow(arrow_table)

Common Expressions

Frequently used building blocks inside select/filter/with_columns.

pl.col("x")- references a column inside an expression
pl.lit(value)- inserts a literal scalar into an expression
.over("group_col")- turns an expression into a windowed/group-wise calculation
.alias("name")- renames the result of an expression
pl.when(cond).then(a).otherwise(b)- vectorized conditional logic
df.lazy() / lf.collect()- switch between eager and lazy execution modes

Pro Tip

Default to scan_parquet + LazyFrame chains instead of read_parquet + eager DataFrame — the query optimizer can push filters and column projections down into the file scan, often cutting memory use by an order of magnitude on wide files.

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