Probability distribution in np.random.choice
It is an optional array of probabilities p with which respective elements of sampled array A are sampled. So A and p should have the same number of elements and all elements of p should add up to 1 (as all discrete probabilities should).
The numpy function np.random.choice takes in an optional parameter called 'p' that denotes the probability distribution of the values it is sampling . So , my question is , even though the function generates random values , do values that have higher probability more likely to be sampled ? Meaning , does it randomly choose a value from a subset of " Most probable values " ?
Am I right in perceiving it that way  ? Or can someone please correct my assumption  ?