Are You Still Using Pandas to Process Big Data in 2021?

Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love. This includes numpy, pandas and sklearn. It is open-source and freely available. It uses existing Python APIs and data structures to make it easy to switch between Dask-powered equivalents.

Vaex is a high-performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate basic statistics for more than a billion rows per second. It supports multiple visualizations allowing interactive exploration of big data.

Dask and Vaex Dataframes are not fully compatible with Pandas Dataframes but some most common “data wrangling” operations are supported by both tools. Dask is more focused on scaling the code to compute clusters, while Vaex makes it easier to work with large datasets on a single machine.

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