From Python to NumPy

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

Our great sponsors
  • SonarLint - Clean code begins in your IDE with SonarLint
  • InfluxDB - Access the most powerful time series database as a service
  • SaaSHub - Software Alternatives and Reviews
  • gonum

    Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more

    Go is quite a bit cleaner than Python and its concurrency/parallelism primitives can be well suited to scientific workloads.

    You may want to have a look at Gonum (https://www.gonum.org), and the Go HEP package developed by CERN (https://go-hep.org).

    I was also surprised to see DSP and pretty sophisticated packages, although I never used them: https://awesome-go.com/science-and-data-analysis

    And of course Go has Jupyter integration, it's almost like running a script thanks to its fast compilation time.

  • hep

    hep is the mono repository holding all of go-hep.org/x/hep packages and tools

    Go is quite a bit cleaner than Python and its concurrency/parallelism primitives can be well suited to scientific workloads.

    You may want to have a look at Gonum (https://www.gonum.org), and the Go HEP package developed by CERN (https://go-hep.org).

    I was also surprised to see DSP and pretty sophisticated packages, although I never used them: https://awesome-go.com/science-and-data-analysis

    And of course Go has Jupyter integration, it's almost like running a script thanks to its fast compilation time.

  • SonarLint

    Clean code begins in your IDE with SonarLint. Up your coding game and discover issues early. SonarLint is a free plugin that helps you find & fix bugs and security issues from the moment you start writing code. Install from your favorite IDE marketplace today.

  • numpy-groupies

    Optimised tools for group-indexing operations: aggregated sum and more

    I have had the same experience and instead of Pandas have been using numpy-groupies to handle aggregate/groupby operations. It's quite performant and feels a bit cleaner to use than importing pandas for a couple operations.

    https://github.com/ml31415/numpy-groupies

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.

Suggest a related project

Related posts