We present a new Python library, called vaex, intended to handle extremely large tabular datasets such as astronomical catalogues like the Gaia catalogue, N-body simulations, or other datasets which can be structured in rows and columns. Fast computations of statistics on regular N-dimensional grids allows analysis and visualization in the order of a billion rows per second, for a high-end desktop computer. We use streaming algorithms, memory mapped files, and a zero memory copy policy to allow exploration of datasets larger than memory, for example out-of-core algorithms. Vaex allows arbitrary (mathematical) transformations using normal Python expressions and (a subset of) numpy functions which are “lazily” evaluated and computed when needed in small chunks, which avoids wasting of memory. Boolean expressions (which are also lazily evaluated) can be used to explore subsets of the data, which we call selections. Vaex uses a similar DataFrame API as Pandas, a very popular library, which helps migration from Pandas. Visualization is one of the key points of vaex, and is done using binned statistics in 1d (e.g. histogram), in 2d (e.g. 2d histograms with colourmapping) and 3d (using volume rendering). Vaex is split in in several packages: vaex-core for the computational part, vaex-viz for visualization mostly based on matplotlib, vaex-jupyter for visualization in the Jupyter notebook/lab based in IPyWidgets, vaex-server for the (optional) client-server communication, vaex-ui for the Qt based interface, vaex-hdf5 for HDF5 based memory mapped storage, vaex-astro for astronomy related selections, transformations, and memory mapped (column based) FITS storage.
Corrigendum to “A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews” [J. Bus. Res. 125 (2021) 354–367]