[goatlover]

Probably the built in support for vectorization, which reduces the amount of code you need to write, and allows your code to be closer to mathematical expressions. Of course there are other languages like R, Julia and Fortran that also have built-in vectorization, and Python adds that with the Numpy library. It makes certain operations much easier to express than with your normal lists/arrays. That's why the Python scientific stack is built on top of Numpy arrays, and not the builtin data structures.

You can compare it to regular expressions for manipulating text.

[evincarofautumn]

This setup often gives APL-family languages a curious performance advantage, even though they’re largely dynamic & interpreted. All of the hot loops are in the implementation layer, and the surface language just glues them together, just like how Python can “be” fast by acting as glue for C and Fortran.