Stupid Python Ideas

Python has a whole hierarchy of collection-related abstract types, described in the collections.abc module in the standard library. But there are two key, prototypical kinds. Iterators are one-shot, used for a single forward traversal, and usually lazy, generating each value on the fly as requested. Sequences--like list, tuple, or range--are repeatably iterable (generating a new iterator each time), searchable with the "in" operator, and indexable with subscript expressions (including negative indices and slices).

There are a lot of novice questions on optimizing NumPy code on StackOverflow, that make a lot of the same mistakes. I'll try to cover them all here.

What does NumPy speed up?

Let's look at some Python code that does some computation element-wise on two lists of lists. I'll use operator.add because it's just about the fastest function you can call.

c = [] for ra, rb in zip(a, b): c.append([]) for ca, cb in zip(ra, rb): c[-1].append(operator.add(ca, cb)) This is slow.