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

Answer 2

Just use np.newaxis (which is just an alias for None) to add a singleton dimension to a, and let broadcasting do the rest:

In [45]: a[:, np.newaxis] - b
Out[45]: 
array([[[-5, -7],
        [-2, -2]],

       [[ 1, -1],
        [ 4,  4]],

       [[-5, -5],
        [-2,  0]]])

Answer 3

I'm not sure what means a fully factorized solution, but may be this will help:

np.append(a, a, axis=1).reshape(3, 2, 2) - b

Answer 4

Reading from the doc on broadcasting, it says:

When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when

they are equal, or
one of them is 1

Back to your case, you want result to be of shape (3, 2, 2), following these rules, you have to play around with your dimensions. Here's now the code to do it:

In [1]: a_ = np.expand_dims(a, axis=0)

In [2]: b_ = np.expand_dims(b, axis=1)

In [3]: c = a_ - b_

In [4]: c
Out[4]: 
array([[[-5, -7],
        [ 1, -1],
        [-5, -5]],

       [[-2, -2],
        [ 4,  4],
        [-2,  0]]])

In [5]: result = c.swapaxes(1, 0)

In [6]: result
Out[6]: 
array([[[-5, -7],
        [-2, -2]],

       [[ 1, -1],
        [ 4,  4]],

       [[-5, -5],
        [-2,  0]]])

In [7]: result.shape
Out[7]: (3, 2, 2)