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.
npx skills add LeonChaoX/qinyan-academic-skills --skill polars --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: ```python uv pip install polars ``` Basic DataFrame creation and operations: ```python 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:** ```python # Exp
- 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()
What does the polars skill do?
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.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill polars --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.