Python has become the dominant language in scientific computing—even in domains that demand high performance. This is largely due to the power of array-oriented programming, which separates complex problems into two parts: lightweight bookkeeping and heavy numerical computation. The latter is handled efficiently by vectorized operations that rely on fast, precompiled libraries.

This tutorial introduces array-oriented programming as a distinct mindset that encourages new ways of structuring problems. Rather than focusing on any one library, we’ll cover general techniques that apply across ecosystems like NumPy, Pandas, xarray, CuPy, and Awkward Array.

Array-oriented programming has its roots in languages like APL and remains central to scientific data analysis and simulation today. This session is designed to deepen your understanding of the paradigm and improve your ability to write efficient, expressive scientific code.