[nonameiguess]

MATLAB introduced automatic broadcasting of operators over n-dimensional arrays and logical indexing nearly 40 years and it is still the primary learning language for applied mathematicians, engineers, and scientists, and also a popular prototyping language for numerical algorithm developers. And it provides a great interactive REPL with built-in plotting for exploratory data analysis.

Since doing this, the idea and basic syntax has been adopted by GNU Octave, S, R, and now NumPy and Matplotlib, which did it to make it easier for statisticians, engineers, and scientists to adopt Python. Specifically targeting these groups with familiar syntax is exactly why Python is so popular for data science, because data scientists tend to recruited from the hard engineering and science disciplines. It's a lot easier to teach basic programming to someone with a great background in applied math, experimental design, and research methods, than it is to teach all those things to programmers.

This is an area in which languages with operator overloading shine, creating DSLs that mimic the syntax and semantics of other languages. You might have a lot to learn because you're used to == only being defined for scalar data types and arrays only being indexed by natural numbers, but the people the language is designed for are used to broadcasted operators and logical array indexing.