I need to sample with replacement from an array A of length n. I want to know how the below two commands are different. If they both give the same result which one is better (in terms of performance etc...)
A[np.random.randint(0, n, n)]
A[np.random.choice(n, n)]
The purpose of choice is to sample an array, giving it an integer is a shortcut to giving it a range of that integer's length. So randint is likely going to be more efficient if you're misusing choice the way you are.
However the correct way to do it is np.random.choice(A, size=n). That's exactly how you spell "sample with replacement".
randint returns a random integer in the provided range.
choice returns a random element from a provided array, or if you provide an int (like u did) it functions like np.random.randint(0, n, n).
So, in this example there's no difference however I imagine randint would be slightly faster.
Both numpy.randon.randint and numpy.random.choice gives you an option to select random numbers either from a range(in case of randint) or from an array(in case of choice). When your array has items in a range, then The main difference of using numpy.random.choice is to:
outcome of one coin flip
np.random.randint(2)
outcomes of ten thousand coin flips
np.random.randint(2, size=10000)
outcome of one coin flip
np.random.choice([0, 1])
outcome of ten thousand coin flips
np.random.choice([0, 1], size=10000)
outcomes of ten thousand biased coin flips
np.random.choice([0, 1], size=10000, p=[0.8, 0.2])
n, whilechoicetook longer for smalln. They probably both use the same random number generator, butchoicehas a longer setup time - after all it accepts more parameters. But the code for both is compiled, which is hard to study.