SciPy 2025

The SciPy library provides objects representing well over 100 univariate probability distributions. These have served the scientific Python ecosystem for decades, but they are built upon an infrastructure that has not kept up with the demands of today’s users. To address its shortcomings, SciPy 1.15 includes a new infrastructure for working with probability distributions. This talk will introduce users to the new infrastructure and demonstrate its many advantages in terms of usability, flexibility, accuracy, and performance.

Masked arrays enable the performance and convenience of (rectangular) array computing where missing data would otherwise result in ragged arrays. Some array libraries, such as NumPy [1], PyTorch [2], and Dask [3], offer partial support for masked versions of their arrays/tensors, but these often lack important features, have APIs that are inconsistent with those of the parent namespace, and see only limited support in downstream libraries (e.g. SciPy [4]). Furthermore, there is no standard interface specification, some of these implementations are treated as “experimental” or are maintained less than the parent library, and some automatically mask the result of invalid numerical calculations, hiding bugs and leading to spurious results. MArray [5] offers a new way forward by adding mask support to any array backend that is compatible with the Array API Standard [6].