abhisheksharma-17/polars
skills/polars/SKILL.md
High-performance DataFrame library written in Rust with Python bindings for blazingly fast data manipulation, lazy evaluation, and analytical queries. MANDATORY TRIGGERS: polars, pl.DataFrame, pl.LazyFrame, pl.col, pl.scan_csv, polars dataframe. Also trigger when user wants to process large datasets, replace pandas with faster alternative, do data engineering in Python, work with Parquet/CSV files at scale, or perform analytical queries on structured data. When in doubt about whether to use this skill for data processing tasks, use it.
development
Updated Apr 2, 2026
$ install --global
skillsauthnpx skillsauth add abhisheksharma-17/skills-graph polarsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- polars
- description:
- High-performance DataFrame library written in Rust with Python bindings for blazingly fast data manipulation, lazy evaluation, and analytical queries. MANDATORY TRIGGERS: polars, pl.DataFrame, pl.LazyFrame, pl.col, pl.scan_csv, polars dataframe. Also trigger when user wants to process large datasets, replace pandas with faster alternative, do data engineering in Python, work with Parquet/CSV files at scale, or perform analytical queries on structured data. When in doubt about whether to use this skill for data processing tasks, use it.
- license:
- MIT
- version:
- 1.0.0
- author:
- Abhishek Sharma
- tags:
- ["polars", "dataframe", "data-engineering", "python", "rust", "lazy-evaluation", "parquet", "analytics", "etl"]
Polars — Skill Router
Blazingly fast DataFrame library for Python — Rust-powered, lazy-first, with zero dependencies.
Source: docs.pola.rs v1.39.3 | Package: polars (PyPI) | License: MIT
Reference Files
| Reference | File | Read When |
|-----------|------|-----------|
| Overview & Setup | references/00-overview.md | Getting started, installation, what Polars is, why use it, quickstart |
| DataFrames & Series | references/01-dataframes-series.md | Creating DataFrames/Series, inspection, schema, describe, head/tail |
| Expressions | references/02-expressions.md | pl.col, pl.lit, expression contexts, select/with_columns/filter, arithmetic |
| Data Types | references/03-data-types.md | Numeric, temporal, nested (List/Struct/Array), Categorical, Enum, casting |
| Lazy API | references/04-lazy-api.md | LazyFrame, scan_csv, scan_parquet, collect, explain, query optimization |
| I/O Operations | references/05-io-operations.md | CSV, Parquet, JSON, Excel, databases, cloud storage, Hive partitions |
| Filtering & Selection | references/06-filtering-selection.md | filter, select, with_columns, sort, slice, sample, unique, drop |
| Aggregation & GroupBy | references/07-aggregation-groupby.md | group_by, agg, window functions, over(), rolling, fold |
| Joins & Concatenation | references/08-joins-concat.md | Inner/left/outer/semi/anti joins, asof joins, concat, diagonal |
| String Operations | references/09-string-operations.md | str namespace, contains, replace, extract, split, regex, case conversion |
| Time Series | references/10-time-series.md | Temporal types, dt namespace, group_by_dynamic, rolling windows, parsing |
| Missing Data | references/11-missing-data.md | Null vs NaN, fill_null, interpolate, drop_nulls, null_count, strategies |
| Performance & Migration | references/12-performance.md | Threading, memory, streaming, GPU, Pandas migration, anti-patterns |
Installation
pip install polars
# With optional dependencies
pip install 'polars[all]' # Everything
pip install 'polars[numpy]' # NumPy interop
pip install 'polars[pandas]' # Pandas interop
pip install 'polars[pyarrow]' # Arrow interop
pip install 'polars[fsspec]' # Cloud storage
pip install 'polars[connectorx]' # Database support
pip install 'polars[xlsx2csv]' # Excel support
pip install 'polars[gpu]' # GPU acceleration
Quick Reference
- Polars User Guide
- API Reference
- GitHub Repository (38K+ stars)
- Polars Cheat Sheet
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