MEDVeDI

Memory Efficient Deconstructed Vectorized Dataframe Interface.

Design goals:

• Favor performance over nice syntax features. Sacrifice fool-proof for efficient zero-copy operations.
• Ensure ideal micro-performance and optimize for moderate data sizes (megabytes).
• The use-case is API server code that you write once and execute many times.
• Try to stay compatible with the Pandas interface. There is no Series, however.
• Rely on numpy.
• Friends with Arrow.
• Frequently release GIL and depend on native extensions doing unsafe things.
• Test only CPython and Linux.
• Support only x86-64 CPUs with AVX2.
• Support only Python 3.10+.
• 100% test coverage.

Otherwise, you should be way better with regular Pandas.

Medvedi is currently heavily used in production of Athenian.