vaex

Process large tabular datasets with out-of-core DataFrame operations and lazy evaluation.

3|Updated Apr 17, 2026

Search Tags:#data processing #big data #DataFrame #out-of-core #lazy evaluation

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill vaex-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vaex
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/vaex
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill vaex-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires vaex, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill allows users to process and analyze large tabular datasets that exceed available RAM, enabling out-of-core DataFrame operations, lazy evaluation, and efficient visualization of big data.

Core Features & Use Cases

Out-of-Core DataFrame Operations: Handle datasets with billions of rows.
Lazy Evaluation: Perform operations without loading the entire dataset into memory.
Efficient Visualization: Create visualizations of large datasets without sampling.
Use Case: Ideal for working with large CSV/HDF5/Arrow/Parquet files, performing fast statistics on massive datasets, or building ML pipelines on big data.

Quick Start

To start using Vaex, import the library and open a large dataset with vaex.open('large_file.hdf5').

Frequently Asked Questions about vaex

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

FAQPage Schema

How do I process large datasets that exceed available RAM?▼

Out-of-core DataFrame operations process large datasets that exceed available RAM by using lazy evaluation to handle data without loading the entire dataset into memory. This approach enables efficient processing and visualization of billions of rows.

What file formats can I open for big data processing with out-of-core DataFrames?▼

Out-of-core DataFrames support opening and processing large CSV, HDF5, Arrow, and Parquet files. These formats allow lazy evaluation operations to execute efficiently on massive tabular datasets without memory constraints.

Can I visualize big data without sampling the dataset first?▼

Efficient visualization of big data can be performed directly without sampling by utilizing out-of-core DataFrame operations. This allows you to create visualizations of large datasets containing billions of rows while maintaining performance.

Does lazy evaluation work for building ML pipelines on big datasets?▼

Lazy evaluation is suitable for building ML pipelines on big datasets because it defers computation until necessary. This mechanism allows out-of-core DataFrames to perform fast aggregations and statistical operations without loading entire datasets into memory.

What is the best way to perform fast aggregations on massive tabular datasets?▼

Out-of-core DataFrame operations provide the best way to perform fast aggregations on massive tabular datasets. By leveraging lazy evaluation, computations are executed efficiently across billions of rows stored in formats like HDF5 and Parquet.

When should I not use out-of-core DataFrame operations for data processing?▼

Out-of-core DataFrame operations are not ideal for small datasets that fit comfortably in RAM, as the lazy evaluation overhead provides no benefit. This approach targets large tabular datasets exceeding memory limits, such as files with billions of rows.