Question

In Matlab, this type of algorithm (“growing arrays”) is advised against

mine = []
for i=1:100,
    mine = [mine,randn(1)]
end

whereas it seems that many examples for Python show this kind of algorithm (this is a really bad example though):

import numpy.random as rand

mine = []
for i in range(100):
    mine.append(rand.random(1)[0])

I wonder why that is — what is the difference?

Answer 1

The difference is that:

• In MATLAB, every iteration of your loop re-allocates the matrix to increase the size by one and copies the entire contents into the newly allocated space.
• Python lists don’t work like that. More space is allocated than is needed at any given point and this allocated space grows in a manner that guarantees that appends are done in amortized constant time.

That said, I think the difference is largely cultural:

• It is common to have large numeric matrices in MATLAB, and growing such matrices one element (or one row/column) at a time would indeed be expensive.
• On the other hand, no one would use a Python list (or a list of lists) to represent a large matrix: that would be very slow and would make very poor use of memory. Numerical Python‘s ndarray would be used instead, and ndarray would offer exactly the same tradeoffs as a MATLAB matrix.