High-performance Python

This page summarizes the projects mentioned and recommended in the original post on /r/Python

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  • Numba

    NumPy aware dynamic Python compiler using LLVM

    There are various techniques and modules/APIs that I already know/that I have used already that can be used (this talk is quite to the point and very enlightening: - NumPy’s own functions / Pandas’ own functions that are row-wise/column-wise - vectorization - Numba (although it has some limitations with “special functions”, and with the implementation with Pandas, as far as I know) - Cython - Dask - Apache Spark (with a focus in PySpark )

  • Cython

    The most widely used Python to C compiler

    There are various techniques and modules/APIs that I already know/that I have used already that can be used (this talk is quite to the point and very enlightening: - NumPy’s own functions / Pandas’ own functions that are row-wise/column-wise - vectorization - Numba (although it has some limitations with “special functions”, and with the implementation with Pandas, as far as I know) - Cython - Dask - Apache Spark (with a focus in PySpark )

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  • polars

    Dataframes powered by a multithreaded, vectorized query engine, written in Rust

    I'd like to add Polars as a library (https://github.com/pola-rs/polars).

  • db-benchmark

    reproducible benchmark of database-like ops

    You can also compare benchmarks: https://h2oai.github.io/db-benchmark

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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