polars

Q: How do I install polars?

Run npx skills add https://github.com/davila7/claude-code-templates --skill polars in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/polars/ (personal) or .claude/skills/polars/ (project).

by davila7Backend & APIs

Fast DataFrame library (Apache Arrow). Select, filter, groupby, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Installs: 539
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/davila7/claude-code-templates --skill polars

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads polars automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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 Polars:

uv pip install polars

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

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:

group_to_rows (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")

JSON:

df = pl.read_json("file.json")
df.write_json("output.json")

For comprehensive I/O documentation, load references/io_guide.md.

Transformations

Joins

Combine DataFrames:

# Inner join
df1.join(df2, on="id", how="inner")

# Left join
df1.join(df2, on="id", how="left")

# Join on different column names
df1.join(df2, left_on="user_id", right_on="id")

Concatenation

Stack DataFrames:

# Vertical (stack rows)
pl.concat([df1, df2], how="vertical")

# Horizontal (add columns)
pl.concat([df1, df2], how="horizontal")

# Diagonal (union with different schemas)
pl.concat([df1, df2], how="diagonal")

Pivot and Unpivot

Reshape data:

# Pivot (wide format)
df.pivot(values="sales", index="date", columns="product")

# Unpivot (long format)
df.unpivot(index="id", on=["col1", "col2"])

For detailed transformation examples, load references/transformations.md.

Pandas Migration

Polars offers significant performance improvements over pandas with a cleaner API. Key differences:

Conceptual Differences

No index: Polars uses integer positions only
Strict typing: No silent type conversions
Lazy evaluation: Available via LazyFrame
Parallel by default: Operations parallelized automatically

Common Operation Mappings

Operation	Pandas	Polars
Select column	`df["col"]`	`df.select("col")`
Filter	`df[df["col"] > 10]`	`df.filter(pl.col("col") > 10)`
Add column	`df.assign(x=...)`	`df.with_columns(x=...)`
Group by	`df.groupby("col").agg(...)`	`df.group_by("col").agg(...)`
Window	`df.groupby("col").transform(...)`	`df.with_columns(...).over("col")`

Key Syntax Patterns

Pandas sequential (slow):

df.assign(
    col_a=lambda df_: df_.value * 10,
    col_b=lambda df_: df_.value * 100
)

Polars parallel (fast):

df.with_columns(
    col_a=pl.col("value") * 10,
    col_b=pl.col("value") * 100,
)

For comprehensive migration guide, load references/pandas_migration.md.

Best Practices

Performance Optimization

Use lazy evaluation for large datasets:

lf = pl.scan_csv("large.csv")  # Don't use read_csv
result = lf.filter(...).select(...).collect()

Avoid Python functions in hot paths:
- Stay within expression API for parallelization
- Use .map_elements() only when necessary
- Prefer native Polars operations
Use streaming for very large data:
```
lf.collect(streaming=True)
```

Select only needed columns early:

# Good: Select columns early
lf.select("col1", "col2").filter(...)

# Bad: Filter on all columns first
lf.filter(...).select("col1", "col2")

Use appropriate data types:
- Categorical for low-cardinality strings
- Appropriate integer sizes (i32 vs i64)
- Date types for temporal data

Expression Patterns

Conditional operations:

pl.when(condition).then(value).otherwise(other_value)

Column operations across multiple columns:

df.select(pl.col("^.*_value$") * 2)  # Regex pattern

Null handling:

pl.col("x").fill_null(0)
pl.col("x").is_null()
pl.col("x").drop_nulls()

For additional best practices and patterns, load references/best_practices.md.

Resources

This skill includes comprehensive reference documentation:

references/

core_concepts.md - Detailed explanations of expressions, lazy evaluation, and type system
operations.md - Comprehensive guide to all common operations with examples
pandas_migration.md - Complete migration guide from pandas to Polars
io_guide.md - Data I/O operations for all supported formats
transformations.md - Joins, concatenation, pivots, and reshaping operations
best_practices.md - Performance optimization tips and common patterns

Load these references as needed when users require detailed information about specific topics.

FAQ

What does the polars skill do?

Fast DataFrame library (Apache Arrow). Select, filter, groupby, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

How do I install polars?

Run npx skills add https://github.com/davila7/claude-code-templates --skill polars in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/polars/ (personal) or .claude/skills/polars/ (project).

Which AI agents can use polars?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is polars safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains polars?

It is published by davila7 in davila7/claude-code-templates. It has 539 recorded installs.

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polars

Install

Install

Describe the task

Review the result

SKILL.md

Polars

Overview

Quick Start

Installation and Basic Usage

Core Concepts

Expressions

Lazy vs Eager Evaluation

Common Operations

Select

Filter

With Columns

Group By and Aggregations

Aggregations and Window Functions

Aggregation Functions

Window Functions with over()

Data I/O

Supported Formats

Common I/O Operations

Transformations

Joins

Concatenation

Pivot and Unpivot

Pandas Migration

Conceptual Differences

Common Operation Mappings

Key Syntax Patterns

Best Practices

Performance Optimization

Expression Patterns

Resources

references/

FAQ

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