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polars skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

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Install the polars skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/polars ~/.claude/skills/polars
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Polars

Overview

Polars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. 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.

Quick Start

Installation and Basic Usage

Install the current stable Polars release verified during this refresh:

uv pip install "polars==1.41.2"

Install optional integrations only when needed:

uv pip install "polars[excel,database,fsspec,pandas,numpy]==1.41.2"

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, withcolumns, filter, groupby)

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

Lazy (LazyFrame): Operations build a query plan, optimized before execution

lf = pl.scan_csv("file.csv")  # Doesn't read yet
result = lf.filter(pl.col("age") > 25).select("name", "age")
df = result.collect()  # Now executes optimized query

When to use lazy:

  • Working with large datasets
  • Complex query pipelines
  • When only some columns/rows are needed
  • Performance is critical

Benefits of lazy evaluation:

  • Automatic query optimization
  • Predicate pushdown
  • Projection pushdown
  • Parallel execution

For detailed concepts, load references/core_concepts.md.

Common Operations

Select

Select and manipulate columns:

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

# Select with expressions
df.select(
    pl.col("name"),
    (pl.col("age") * 2).alias("double_age")
)

# Select all columns matching a pattern
df.select(pl.col("^.*_id$"))

Filter

Filter rows by conditions:

# Single condition
df.filter(pl.col("age") > 25)

# Multiple conditions (cleaner than using &)
df.filter(
    pl.col("age") > 25,
    pl.col("city") == "NY"
)

# Complex conditions
df.filter(
    (pl.col("age") > 25) | (pl.col("city") == "LA")
)

With Columns

Add or modify columns while preserving existing ones:

# Add new columns
df.with_columns(
    age_plus_10=pl.col("age") + 10,
    name_upper=pl.col("name").str.to_uppercase()
)

# Parallel computation (all columns computed in parallel)
df.with_columns(
    pl.col("value") * 10,
    pl.col("value") * 100,
)

Group By and Aggregations

Group data and compute aggregations:

# Basic grouping
df.group_by("city").agg(
    pl.col("age").mean().alias("avg_age"),
    pl.len().alias("count")
)

# Multiple group keys
df.group_by("city", "department").agg(
    pl.col("salary").sum()
)

# Conditional aggregations
df.group_by("city").agg(
    (pl.col("age") > 30).sum().alias("over_30")
)

For detailed operation patterns, load references/operations.md.

Aggregations and Window Functions

Aggregation Functions

Common aggregations within group_by context:

  • pl.len() - count rows
  • pl.col("x").sum() - sum values
  • pl.col("x").mean() - average
  • pl.col("x").min() / pl.col("x").max() - extremes
  • pl.first() / pl.last() - first/last values

Window Functions with over()

Apply aggregations while preserving row count:

# Add group statistics to each row
df.with_columns(
    avg_age_by_city=pl.col("age").mean().over("city"),
    rank_in_city=pl.col("salary").rank().over("city")
)

# Multiple grouping columns
df.with_columns(
    group_avg=pl.col("value").mean().over("category", "region")
)

Mapping strategies:

  • grouptorows (default): Preserves original row order
  • explode: Faster but groups rows together
  • join: Creates list columns

Data I/O

Supported Formats

Polars supports reading and writing:

  • CSV, Parquet, JSON, Excel
  • Databases (via connectors)
  • Cloud storage (S3, Azure, GCS)
  • Google BigQuery
  • Multiple/partitioned files

Common I/O Operations

CSV:

# Eager
df = pl.read_csv("file.csv")
df.write_csv("output.csv")

# Lazy (preferred for large files)
lf = pl.scan_csv("file.csv")
result = lf.filter(...).select(...).collect()

Parquet (recommended for performance):

df = pl.read_parquet("file.parquet")
df.write_parquet("output.parquet")

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