Functionality of rnorm with numpy.random.normal

Question

For some vector m (of length N) of numbers in R we can write

rnorm(N, mean = m, sd = 1)

and this will give a vector of length N where each element will be a sample for a normal distribution centred at the different elements of m. My question is, is it possible to do the same easily with numpy? As far as I can tell numpy.random.normal() requires the loc to be the same for all the elements. The point is that I want a random vector with different means.

Also while writing this, would it work to sample from a standard normal distribution and transform this sample? That would be easier.

Answer 1

One way you can do is random sampling at center 0 then move the sample:

m, N = np.array([1,2,3]), 1000

np.random.seed(42)
samples = np.random.randn(N,len(m)) + m