Agent skill · data analytics · sickn33
polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
Why this skill is useful
Provides domain-specific knowledge on using Polars for efficient data manipulation and ETL pipelines that the AI wouldn't reliably generate on its own.
What it needs
Requires polars installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Bundled upstream support Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes. Polars When to Use You need a faster in-memory DataFrame workflow than pandas for data that still fits in RAM. You are building ETL, analytics, or transformation pipelines that benefit from lazy evaluation and parallel execution. You want expression-based tabular operations on top of Apache Arrow semantics. 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: Basic DataFrame creation and operations: 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("columnname") to reference columns Chain methods to build complex transformations Expressions are lazy and only execute within contexts (select, withcolumns, filter, groupby) Example: Lazy vs Eager Evaluation Eager (DataFrame): Operations execute immediately Lazy (LazyFrame): Operations build a query plan, optimized before execution 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/coreconcepts.md. Common Operations Select Select and manipulate columns: Filter Filter rows by conditions: With Columns Add or modify columns while preserving existing ones: Group By and Aggregations Group data and compute aggregations: For detailed operation patterns, load references/operations.md. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/polars--98ea51