Over the years, the lack of an array data type in Python has resulted in the creation of numerous array libraries, each specializing in unique niches but still having some interoperability between each other. NumPy has become the de facto array library of Python, and the other array libraries try to keep their API close to that of NumPy. However, this often becomes infeasible, and the libraries deviate out of necessity. To make Python's array libraries shake hands with each other without any inconsistencies, the Consortium for Python Data API Standards has formalised an Array API standard for libraries offering array creation and manipulation operations.

The Array API standard allows users to write and use the same code for arrays belonging to any of the standard-conforming libraries. Through this talk, we will explore the need for such standardisation and discuss its salient features in detail. We will primarily delve into the example of using this standard to make specific parts of European Space Agency's Euclid space mission's code GPU and autodiff compatible. Besides cosmology, we will also take a look at a few other examples, mostly sourced from my experience working with and on several Python array libraries for scientific computing. Ultimately, the audience can expect to leave the room with the knowledge of both, the software engineering and the research side of the array API standard.