What is Vaex and why should I use it with Claude?

Vaex is a Python library for lazy, out-of-core DataFrames. Using this skill with Claude allows the AI to correctly implement memory-efficient patterns for datasets that are too large for standard libraries like Pandas.

Can I use Vaex for machine learning?

Yes, Vaex includes a machine learning system that supports feature scaling, encoding, and integration with popular libraries like scikit-learn, XGBoost, and CatBoost.

Which file formats are best for Vaex?

While Vaex supports CSV and Parquet, it is optimized for HDF5 and Apache Arrow formats, which allow for instant opening and efficient out-of-core access.

Does Vaex require loading the entire dataset into memory?

No, Vaex uses memory mapping and lazy evaluation to process data without loading it into RAM, allowing you to work with datasets of billions of rows on a standard laptop.

Vaex Big Data Processing

Name: Vaex Big Data Processing
Author: Microck

byMicrock

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Data Science & ML

Analyzes and visualizes massive tabular datasets exceeding available RAM using high-performance out-of-core DataFrame operations.

About

Vaex is a specialized Claude Code Skill designed for handling high-performance data operations on datasets containing billions of rows. It enables Claude to assist with lazy evaluation, fast statistical aggregations, and interactive visualizations of terabyte-scale data without the overhead of loading everything into memory. This skill is essential for data scientists and engineers working with astronomical, financial, or large-scale scientific datasets where standard libraries like Pandas would fail due to memory constraints, providing optimized patterns for HDF5, Arrow, and Parquet workflows.

Key Features

Interactive big data visualization including heatmaps and histograms
89 GitHub stars
Lazy evaluation and virtual columns to minimize memory usage
Out-of-core DataFrame operations for billion-row datasets
Seamless integration with ML frameworks like scikit-learn and XGBoost
High-speed statistical aggregations and complex filtering

Use Cases

Processing CSV or Parquet files that are significantly larger than available system RAM
Generating performant visualizations and heatmaps for massive scientific datasets
Building memory-efficient machine learning pipelines for big data feature engineering

About

Key Features

Interactive big data visualization including heatmaps and histograms
89 GitHub stars
Lazy evaluation and virtual columns to minimize memory usage
Out-of-core DataFrame operations for billion-row datasets
Seamless integration with ML frameworks like scikit-learn and XGBoost
High-speed statistical aggregations and complex filtering

Use Cases

Processing CSV or Parquet files that are significantly larger than available system RAM
Generating performant visualizations and heatmaps for massive scientific datasets
Building memory-efficient machine learning pipelines for big data feature engineering