Create a double for loop that goes through a randomly generated matrix and counts (and prints) the number of threes that appear in the matrix. This code samples from a uniform distribution so think about how many threes you would expect to see. Optional (not for marks): Another way to count these (as long as your matrix is a NumPy matrix) would be (randMat == 3).sum(). If you're curious about how that works, try generating a smaller matrix like smallMat = np.random.randint(1, 11, size = (10, 10)) and then smallIMat == 3 to see what you get. You might be surprised that summing the result matrix works, but in Python True is equivalent to 1 in many cases and False equivalent to 0. There are often these sorts of 'terse' codes which can greatly reduce the typing required to achieve a result. In some cases, they can also be faster. However, for now we mostly want to emphasize how to use the for loop to loop through the matrix. n[ ]: H randMat = np.random.randint(1, 101, size = (100, 100)) for loop(for loop ) n [139]: import numpy as np randMat = np.random.randint(1, 101, size = (100, 100)) for i in range(len(randMat)): print (i)