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Have you ever been perplexed by NumPy's indexing rules? Can you explain what a[1:-1, ..., np.newaxis, 0] does?
I've written an extensive guide to NumPy indexing that goes over all the types of indices that you can use with NumPy arrays. https://quansight-labs.github.io/ndindex/indexing-guide/index.html
The array API standard (https://data-apis.org/array-api/) is a common specification for Python array libraries, such as NumPy, PyTorch, CuPy, Dask, and JAX. This standard will make it straightforward for array-consuming libraries, like scikit-learn and SciPy, to write code that uniformly supports all of these libraries. This will allow, for instance, running the same code on the CPU and GPU. This talk will covers the scope of the array API standard, supporting tooling which includes a library-independent test suite and compatibility layer, what work has been completed so far, and the plans going forward.
This talk will have the following outline: * A motivating example, adding array API standard usage to a real-world scientific data analysis script so it runs with CuPy and PyTorch in addition to NumPy. * History of the Data APIs Consortium and array API specification. * The scope and general design principles of the specification. * Current status of implementations: * Two versions of the standard have been released, 2021.12 and 2022.12. * The standard includes all important core array functionality and extensions for linear algebra and Fast Fourier Transforms. * NumPy and CuPy have complete reference implementations in submodules (numpy.array_api). * NumPy, CuPy, and PyTorch have near full compliance and have plans to approach full compliance * array-api-compat is a wrapper library designed to be vendored by consuming libraries like scikit-learn that makes NumPy, CuPy, and PyTorch use a uniform API. * The array-api-tests package is a rigorous and complete test suite for testing against the array API and can be used to determine where an array API library follows the specification and where it doesn’t. * Future work * Add full compliance to NumPy, as part of NumPy 2.0. * Focus on improving adoption by consuming libraries, such as SciPy and scikit-learn. * Reporting website that lists array API compliance by library. * Work is being done to create a similar standard for dataframe libraries. This work has already produced a common dataframe interchange API.
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