If it is a broader project that uses machine learning tools, choosing Python maximises your chances of success. If it is a novel implementation of an existing machine-learning method, then Rust is great. Ndarray it seems has experimental support to delegate to native BLAS which may help.įor your purposes, you need to consider what your project needs to deliver. Some of the BLAS implementations it wraps like GotoBLAS are super-mature and optimised, with chunks of handcrafted assembly. I would expect Numpy to be as fast or faster than ndarry. As soon as you get into any decent sort of math – machine learning in my case – you need some of the features in Lapack which Scipy wraps and (significantly) augments. The features it offers are fairly bare-bones. Numpy wraps whichever BLAS library it finds on your machine. There are also many open-source implementations, like GotoBLAS and Atlas. Vendors make their own compatible implementations of these library APIs: Intel has the MKL, and even NVidia has CuBLAS. Between them these are to maths what OpenGL is to graphics. There are two standards for math API libraries – BLAS and Lapack. We'll do our best to keep these links up to date, but if we fall behind please don't hesitate to shoot us a modmail. This is not an official Rust forum, and cannot fulfill feature requests. Err on the side of giving others the benefit of the doubt.Īvoid re-treading topics that have been long-settled or utterly exhausted. Please create a read-only mirror and link that instead.Ī programming language is rarely worth getting worked up over.īe charitable in intent.
If criticizing a project on GitHub, you may not link directly to the project's issue tracker. Post titles should include useful context.įor Rust questions, use the stickied Q&A thread.Īrts-and-crafts posts are permitted on weekends.Ĭriticism is encouraged, though it must be constructive, useful and actionable. For content that does not, use a text post to explain its relevance. Posts must reference Rust or relate to things using Rust.
We observe the Rust Project Code of Conduct. Strive to treat others with respect, patience, kindness, and empathy. It seems that NumPy with 11.1K GitHub stars and 3.67K forks on GitHub has more adoption than SciPy with 6.01K GitHub stars and 2.85K GitHub forks.Īccording to the StackShare community, NumPy has a broader approval, being mentioned in 63 company stacks & 34 developers stacks compared to SciPy, which is listed in 12 company stacks and 4 developer stacks.Please read The Rust Community Code of Conduct The Rust Programming LanguageĪ place for all things related to the Rust programming language-an open-source systems language that emphasizes performance, reliability, and productivity. NumPy and SciPy are both open source tools. NumPy and SciPy can be primarily classified as "Data Science" tools. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
Python-based ecosystem of open-source software for mathematics, science, and engineering. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases SciPy: Scientific Computing Tools for Python.
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data.
NumPy: Fundamental package for scientific computing with Python. NumPy vs SciPy: What are the differences?