Which file formats work best with this skill?

Vaex performs best with memory-mapped formats like HDF5 and Apache Arrow, though it also supports Parquet and can convert large CSV files for better performance.

Does this skill help with data visualization?

Absolutely; Vaex excels at creating 1D and 2D visualizations, such as heatmaps and histograms, even when working with billions of data points.

What is Vaex and how does it differ from Pandas?

Vaex is designed for datasets larger than RAM, using lazy evaluation and memory mapping to process billions of rows, whereas Pandas typically requires loading the entire dataset into memory.

Can I use Vaex with machine learning libraries?

Yes, Vaex integrates with scikit-learn, XGBoost, and CatBoost, allowing you to build and run ML pipelines on massive datasets without memory overhead.

Vaex Big Data Analysis

Name: Vaex Big Data Analysis
Author: pur3v4d3r

bypur3v4d3r

•

データサイエンスとML

Processes and visualizes massive tabular datasets with billions of rows using memory-efficient out-of-core DataFrames.

概要

Vaex is a high-performance Python library designed to handle datasets that exceed available RAM by leveraging lazy evaluation and memory mapping. This skill enables Claude to perform complex statistical aggregations, create interactive visualizations like heatmaps, and build machine learning pipelines on datasets containing billions of rows without materializing them in memory. It is particularly useful for scientific data, financial time series, and converting large-scale files between formats like HDF5, Parquet, and Apache Arrow.

主な機能

1 GitHub stars
High-speed statistical aggregations and groupings
Out-of-core DataFrame operations for billion-row datasets
Lazy evaluation and memory-efficient virtual columns
Seamless integration with scikit-learn and XGBoost for large-scale ML
Interactive big data visualization and heatmaps

ユースケース

Visualizing billion-point datasets with interactive heatmaps and plots
Converting massive CSV files into optimized HDF5 or Arrow formats
Analyzing terabyte-scale scientific or financial datasets on standard hardware

概要

主な機能

1 GitHub stars
High-speed statistical aggregations and groupings
Out-of-core DataFrame operations for billion-row datasets
Lazy evaluation and memory-efficient virtual columns
Seamless integration with scikit-learn and XGBoost for large-scale ML
Interactive big data visualization and heatmaps

ユースケース

Visualizing billion-point datasets with interactive heatmaps and plots
Converting massive CSV files into optimized HDF5 or Arrow formats
Analyzing terabyte-scale scientific or financial datasets on standard hardware