There are some easy to avoid pathological cases such as concatenating numpy arrays in a loop.

Take this gem from a well known course:

   np.concatenate([x.next() for i in range(x.nb)])

That looks pretty innocent but it can eat up your memory in an eyeblink if the input is large enough. That's the sort of pitfall that a lot of python code suffers from because the abstractions are just nice enough to make you believe this will work without penalty and without knowing how it is implemented under the hood you're suddenly out a few gigs of ram.