Sampling from a matrix of discrete probability distributions in Python without a loop

I have a numpy array P with size (N,M), where each row is a discrete probability distribution over 0, ..., M -1 (i.e. each row sums to one and all elements are non-negative). I also have an array x with length K containing elements in 0, ..., N-1. For each x[k], my goal is to generate one sample according to distribution P[x[k], :] and store them in array y with length K.

I have written a code for this task with a loop

y = np.zeros(K)
for k in range(K):
    y[k] = np.random.choice(np.arange(M), 1, p = P[x[k],:]) 

Is it possible to compute y without using a loop?