High-performance Python library for out-of-core DataFrames on big tabular data.
High-performance lazy Out-of-Core DataFrames for big tabular data exploration and visualization.
Vaex is a high-performance Python library designed for lazy out-of-core DataFrames to visualize and explore big tabular datasets. It targets data scientists and engineers who need to process billion-row datasets without wasting memory or immediately needing a cluster.
It calculates statistics on N-dimensional grids for over a billion samples per second using memory mapping, zero memory copy policy, and lazy computations. It supports fast groupbys, subsecond joins, and expression transformations without materializing intermediate data on disk.
It suits data professionals working with massive tabular files in HDF5 or Apache Arrow formats on local hardware. It does not suit teams looking for traditional in-memory Pandas workflows without adapting to lazy evaluation paradigms.
Open huge HDF5 and Apache Arrow files instantly via memory mapping.
Transform data lazily on the fly without wasting memory or time on feature engineering.
Keep data untouched on disk, streaming only when needed to delay cluster requirements.
Join massive tables quickly without copying or materializing the right table.
Free under its MIT licence, with commercial support and data science solutions available through vaex.io.
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Vaex natively supports HDF5 and Apache Arrow formats for memory-mapped, instant file opening.
Vaex provides a similar API to Pandas for out-of-core data, but relies on lazy evaluation and specific file formats for performance.
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