
Vaex
Vaex provides out-of-core DataFrames for Python to explore and visualize large tabular datasets, with support for machine learning workflows.

About Vaex
Vaex is a Python DataFrame library for exploring, visualizing, and building machine learning models on large tabular datasets. It uses memory mapping, an expression system, and out-of-core algorithms, so data can be processed without loading it fully into memory. The homepage says it builds on the Python data science stack, including pandas, scikit-learn, Arrow, XGBoost, and LightGBM, and is tailored for Jupyter notebooks.
The homepage reports benchmark results: visualizing 1 billion samples per second, and a PCA transformation that is 10 times faster than standard implementations, processing 1 billion samples in 2 minutes. These figures are the vendor's own and the homepage does not describe the test setup. Vaex.io, based in the Netherlands, also offers consultancy and training, and one hour of free consulting for people with a serious interest in using Vaex. The homepage does not state software pricing, and the directory lists the tool as not open source.
Key features
- Out-of-core DataFrames for Python on large tabular datasets
- Memory mapping and an expression system for working with big data
- Visualization and exploration of large datasets on a single machine
- Machine learning model building, including PCA transformation
- Serializable expressions and models that can move from prototype to pipeline
- Single-command deployment to Amazon Web Services or Google Cloud Platform
- The homepage states that no computing cluster is required to process large datasets on a laptop.
- It works with common Python data science libraries such as pandas, scikit-learn, and XGBoost.
- Consultancy and training services are offered alongside the software.
- The homepage does not state software pricing.
- The directory lists the tool as not open source.
Vaex at a glance
- Website
- vaex.io
- Category
- Development › Developer Tools
- Type
- Online tool
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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