Why should I use Polars instead of Pandas for ML pipelines?

Polars offers significant speedups (up to 13x) and lower memory usage (up to 4x) compared to Pandas, especially for datasets exceeding 1 million rows, thanks to its multithreaded query engine and lazy evaluation.

What is zero-copy architecture in this context?

It refers to using the Apache Arrow memory format to move data between systems—such as from ClickHouse to Polars or Polars to PyTorch—without the need for expensive serialization or memory re-allocation.

How does this skill handle ClickHouse integration?

The skill provides three patterns: Arrow Streams for zero-copy transfers, Polars native database URI reading for simplicity, and Parquet exports for reproducible batch processing jobs.

Can I still use Pandas if my project requires it?

Yes. While the skill defaults to Polars for efficiency, you can use Pandas by adding a '# polars-exception: ' comment at the top of your file to bypass the automated preference hook.

ML Data Pipeline Architecture

Name: ML Data Pipeline Architecture
Author: terrylica

byterrylica

•

데이터 과학 및 ML

Optimizes ML data workflows using Polars, Arrow, and ClickHouse for high-performance, memory-efficient pipeline development.

소개

This skill provides a specialized framework for building production-grade ML data pipelines by leveraging the high-performance capabilities of Polars and the zero-copy efficiency of Apache Arrow. It guides developers through critical architectural decisions, such as selecting between Polars and Pandas based on dataset size, implementing optimized ClickHouse integration patterns, and configuring PyTorch data loaders to minimize memory overhead. By enforcing lazy evaluation and streaming processing, this skill helps teams handle multi-gigabyte datasets on standard hardware while maintaining code maintainability and schema validation.

주요 기능

Automated Polars vs. Pandas decision framework
High-performance ClickHouse integration strategies
Lazy evaluation and streaming pipeline composition
Zero-copy data transfer patterns using Apache Arrow
Memory-efficient PyTorch DataLoader implementations
9 GitHub stars

사용 사례

Building high-throughput financial data pipelines with ClickHouse and Polars
Implementing large-scale deep learning data loaders that avoid redundant memory copies
Migrating memory-intensive Pandas workflows to efficient Polars/Arrow architectures

소개

주요 기능

Automated Polars vs. Pandas decision framework
High-performance ClickHouse integration strategies
Lazy evaluation and streaming pipeline composition
Zero-copy data transfer patterns using Apache Arrow
Memory-efficient PyTorch DataLoader implementations
9 GitHub stars

사용 사례

Building high-throughput financial data pipelines with ClickHouse and Polars
Implementing large-scale deep learning data loaders that avoid redundant memory copies
Migrating memory-intensive Pandas workflows to efficient Polars/Arrow architectures