calculate entropy in decision tree algorithm

entropy.py

# x = np.random.rand(10,2) * 10
# y = np.random.rand(10,1) * 10

def calculate_entropy(pi):
    total = 0
    for p in pi:
        p = p / sum(pi)
        if p != 0:
            total += p * np.log2(p)
        else:
            total += 0
    total *= -1
    return total

# entropy of data in ascending order
def entropy(x, y):
    entropies = []    

    for i in x:
        entropies.append(calculate_entropy(i))
    entropies.sort()

  return entropies