Numpy: Subtract 2 numpy arrays row wise

Question

I have 2 numpy arrays a and b as below:

a = np.random.randint(0,10,(3,2))
Out[124]: 
array([[0, 2],
       [6, 8],
       [0, 4]])
b = np.random.randint(0,10         


        

Answer 1

You can shave a little time off using np.subtract(), and a good bit more using np.concatenate()

import numpy as np
import time

start = time.time()
for i in range(100000):

    a = np.random.randint(0,10,(3,2))
    b = np.random.randint(0,10,(2,2))
    c = np.c_[(a - b[0]),(a - b[1])].reshape(3,2,2)

print time.time() - start

start = time.time()
for i in range(100000):

    a = np.random.randint(0,10,(3,2))
    b = np.random.randint(0,10,(2,2))
    #c = np.c_[(a - b[0]),(a - b[1])].reshape(3,2,2)
    c = np.c_[np.subtract(a,b[0]),np.subtract(a,b[1])].reshape(3,2,2)

print time.time() - start

start = time.time()
for i in range(100000):

    a = np.random.randint(0,10,(3,2))
    b = np.random.randint(0,10,(2,2))
    #c = np.c_[(a - b[0]),(a - b[1])].reshape(3,2,2)
    c = np.concatenate([np.subtract(a,b[0]),np.subtract(a,b[1])],axis=1).reshape(3,2,2)

print time.time() - start

>>>

3.14023900032
3.00368094444
1.16146492958

reference:

confused about numpy.c_ document and sample code

np.c_ is another way of doing array concatenate