Can it improve the execution speed of my existing scripts?

Yes, it specializes in replacing slow '.iterrows()' loops and '.apply()' functions with optimized vectorized operations that run at C-speed.

How does this skill help with memory-intensive data tasks?

It provides patterns for chunked reading, categorical data types, and numeric downcasting, which can reduce the memory footprint of a DataFrame by up to 90%.

What version of pandas is this optimized for?

The patterns focus on modern pandas (2.0+) standards, including named aggregations and improved categorical handling.

Does it support modern data formats like Parquet?

Absolutely. It promotes using Parquet with Snappy compression and predicate pushdown over traditional CSVs for faster I/O and smaller storage.

Pandas Data Optimizer

Name: Pandas Data Optimizer
Author: majesticlabs-dev

bymajesticlabs-dev

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Data Science & ML

Optimizes Python data processing using production-grade pandas patterns for large-scale DataFrame manipulation and memory efficiency.

About

The Pandas Data Optimizer skill equips Claude with advanced techniques for high-performance data manipulation and engineering. It focuses on replacing inefficient row-by-row iteration with vectorized operations, implementing memory-saving strategies like categorical dtypes and numeric downcasting, and handling massive datasets through chunked processing and Parquet integration. This skill is essential for data scientists and engineers building scalable, production-ready data pipelines where memory footprint and execution speed are critical factors.

Key Features

Automated dtype optimization and memory reduction recipes
High-performance I/O patterns using Parquet and optimized CSV reading
18 GitHub stars
Vectorized operations to eliminate slow Python loops
Memory-efficient chunked processing for datasets exceeding RAM
Advanced GroupBy aggregations and data transformations

Use Cases

Processing and cleaning multi-gigabyte datasets without memory overflow
Refactoring legacy data scripts to use memory-efficient production patterns
Building high-speed feature engineering pipelines for machine learning models

About

Key Features

Automated dtype optimization and memory reduction recipes
High-performance I/O patterns using Parquet and optimized CSV reading
18 GitHub stars
Vectorized operations to eliminate slow Python loops
Memory-efficient chunked processing for datasets exceeding RAM
Advanced GroupBy aggregations and data transformations

Use Cases

Processing and cleaning multi-gigabyte datasets without memory overflow
Refactoring legacy data scripts to use memory-efficient production patterns
Building high-speed feature engineering pipelines for machine learning models