Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
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Supported Python Versions Documentation Slack

What is Vaex?

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).


With pip:

$ pip install vaex

Or conda:

$ conda install -c conda-forge vaex

For more details, see the documentation

Key features

Instant opening of Huge data files (memory mapping)

HDF5 and Apache Arrow supported.



Read the documentation on how to efficiently convert your data from CSV files, Pandas DataFrames, or other sources.

Lazy streaming from S3 supported in combination with memory mapping.


Expression system

Don't waste memory or time with feature engineering, we (lazily) transform your data when needed.


Out-of-core DataFrame

Filtering and evaluating expressions will not waste memory by making copies; the data is kept untouched on disk, and will be streamed only when needed. Delay the time before you need a cluster.


Fast groupby / aggregations

Vaex implements parallelized, highly performant groupby operations, especially when using categories (>1 billion/second).


Fast and efficient join

Vaex doesn't copy/materialize the 'right' table when joining, saving gigabytes of memory. With subsecond joining on a billion rows, it's pretty fast!


More features


See contributing page.


Join the discussion in our Slack channel!

Learn more about Vaex

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