 Please follow the instructions below to compare the computational time while using Python array and Numpy array. Based on your observations which one is faster?

INSTRUCTIONS

### Time comparison of a NumPy operation and a corresponding Python code

Let us create the below function (`multiply_loops`) which takes two arrays as input and computes their multiplication using normal Python way.

``````def multiply_loops(A, B):
c=np.zeros((A.shape, B.shape))
for i in range(A.shape):
for k in range(B.shape):
c[i,k] = 0
for j in range(B.shape):
n = A[i,j] * B[j,k]
c[i,k] += n
return c
``````

Now, let us create the below function (`multiply_vector`) which takes two arrays as input and computes their multiplication using NumPy's vector multiplication way.

``````def multiply_vector(A, B):
return A @ B
``````

Let us create two randomly generated 100x100 matrices - `X` and `Y` - to test the above functions

``````X = np.random.random((100, 100))
Y = np.random.random((100, 100))
``````

Now execute the below command (`timeit`) in Jupyter, which will output you the time taken by each of these functions

``````%timeit multiply_loops(X, Y)
%timeit multiply_vector(X, Y)
``````

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