How much memory in numpy array? Is RAM a limiting factor?

Question

I\'m using numpy to create a cube array with sides of length 100, thus containing 1 million entries total. For each of the million entries, I am inserting a 100x100 matrix w

Answer 1

A couple points:

• The size in memory of numpy arrays is easy to calculate. It's simply the number of elements times the data size, plus a small constant overhead. For example, if your cube.dtype is int64, and it has 1,000,000 elements, it will require 1000000 * 64 / 8 = 8,000,000 bytes (8Mb).
• However, as @Gabe notes, 100 * 100 * 1,000,000 doubles will require about 80 Gb.
• This will not cause anything to "break", per-se, but operations will be ridiculously slow because of all the swapping your computer will need to do.
• Your loops will not do what you expect. Instead of replacing the element in cube, element = matrix will simply overwrite the element variable, leaving the cube unchanged. The same goes for the entry = random.rand() * 100.
• Instead, see: http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#modifying-array-values

Answer 2

for the "inner" part of your function, look at the numpy.random module

import numpy as np
matrix = np.random.random((100,100))*100