polars

Polars

Overview

Polars is a columnar DataFrame library for Python and Rust with Arrow interoperability. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.

Reviewed against the official stable documentation and native Polars 1.44.2. Local regression tests cover the corrected APIs and scientific failure cases; cloud, GPU, BigQuery, and remote database recipes are illustrative and require provider setup. Fragments using undefined columns, paths, or ... require adaptation to the dataset.

Quick Start

Installation and Basic Usage

Install the current stable Polars release verified during this refresh:

uv pip install "polars==1.44.2"

Install optional integrations only when needed:

uv pip install "polars[excel,database,fsspec,pandas,numpy]==1.44.2"

Basic DataFrame creation and operations:

import polars as pl

# Create DataFrame
df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["NY", "LA", "SF"]
})

# Select columns
df.select("name", "age")

# Filter rows
df.filter(pl.col("age") > 25)

# Add computed columns
df.with_columns(
    age_plus_10=pl.col("age") + 10
)

Core Concepts

Expressions

Expressions are the fundamental building blocks of Polars operations. They describe transformations on data and can be composed, reused, and optimized.

Key principles:

• Use pl.col("column_name") to reference columns
• Chain methods to build complex transformations
• Expressions are lazy and only execute within contexts (select, with_columns, filter, group_by)

Example:

# Expression-based computation
df.select(
    pl.col("name"),
    (pl.col("age") * 12).alias("age_in_months")
)

Lazy vs Eager Evaluation

Eager (DataFrame): Operations execute immediately

df = pl.read_csv("file.csv")  # Reads immediately
result = df.filter(pl.col("age") > 25)  # Executes immediately