The proposed patch add two methods to the Random class and two module level functions: weighted_choice() and weighted_choice_generator().

weighted_choice(data) accepts either mapping or sequence and returns a key or index x with probability which is proportional to data[x].

If you need several elements with same distribution, use weighted_choice_generator(data) which returns an iterator which produces random keys or indices of the data. It is more faster than calling weighted_choice(data) repeatedly and is more flexible than generating a list of random values at specified size (as in NumPy).