vaex

Process and analyze large tabular datasets exceeding RAM with Vaex.

1|Updated Jan 14, 2026
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill vaex-sologa
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: vaex
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/vaex
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill vaex-sologa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill tackles the challenge of processing and analyzing datasets that are too large to fit into your computer's available RAM, enabling efficient work with billions of rows.

Core Features & Use Cases

  • Out-of-Core DataFrames: Works with datasets larger than memory using lazy evaluation.
  • Fast Aggregations & Visualizations: Perform quick statistical summaries and create visualizations on massive data.
  • Use Case: Analyze a multi-terabyte CSV file containing astronomical data, performing complex statistical calculations and generating visualizations without running out of memory.

Quick Start

Use the vaex skill to open the file 'large_dataset.hdf5' and print its description.

Frequently Asked Questions about vaex

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I process big data when the dataset is larger than my available RAM?

Out-of-core processing allows you to analyze big data exceeding available RAM by using lazy evaluation. This technique handles massive tabular datasets without loading them entirely into memory, preventing system crashes.

What file formats can I use for out-of-core data processing?

Out-of-core data processing supports various file formats including CSV, HDF5, Arrow, and Parquet. These formats enable seamless data integration when loading and analyzing massive tabular datasets.

How do I perform fast aggregations and visualizations on massive datasets?

Fast aggregations and visualizations on massive datasets are performed using out-of-core processing and lazy evaluation. This approach enables quick statistical summaries and efficient visualizations on billions of rows.

Can I run machine learning on big data that exceeds memory limits?

Machine learning on big data exceeding memory limits is possible using out-of-core DataFrames. This approach supports applying algorithms to massive tabular datasets through lazy evaluation without running out of memory.

What is the best way to analyze a multi-terabyte CSV file without running out of memory?

Analyzing a multi-terabyte CSV file without running out of memory is best achieved through out-of-core processing. This method performs complex statistical calculations and visualizations on large datasets using lazy evaluation.

Does out-of-core data processing work with astronomical data stored in HDF5?

Out-of-core data processing works with astronomical data stored in HDF5. This approach handles multi-terabyte files, performing complex statistical calculations and generating visualizations on massive datasets.