Memory Efficient Deconstructed Vectorized Dataframe Interface
Project description
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.
- Rely on numpy.
- 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.
- Be opinionated. Reject extra features.
Unless you know what you miss, you should be better with regular Pandas.
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