How to divide an array by an other array element wise in numpy?

Question

I have two arrays, and I want all the elements of one to be divided by the second. For example,

In [24]: a = np.array([1,2,3])                                                      

In [25]: b = np.array([1,2,3])                                                      

In [26]: a/b                                                                        
Out[26]: array([1., 1., 1.])

In [27]: 1/b                                                                        
Out[27]: array([1.        , 0.5       , 0.33333333])

This is not the answer I want, the output I want is like (we can see all of the elements of a are divided by b)

In [28]: c = []                                                                     

In [29]: for i in a: 
    ...:     c.append(i/b) 
    ...:                                                                            

In [30]: c                                                                          
Out[30]: 
[array([1.        , 0.5       , 0.33333333]),
 array([2.        , 1.        , 0.66666667]),
In [34]: np.array(c)                                                                
Out[34]: 
array([[1.        , 0.5       , 0.33333333],
       [2.        , 1.        , 0.66666667],
       [3.        , 1.5       , 1.        ]])

But I don't like for loop, it's too slow for big data, so is there a function that included in numpy package or any good (faster) way to solve this problem?

Answer 1

It is simple to do in pure numpy, you can use broadcasting to calculate the outer product (or any other outer operation) of two vectors:

import numpy as np

a = np.arange(1, 4)
b = np.arange(1, 4)

c = a[:,np.newaxis] / b

# array([[1.        , 0.5       , 0.33333333],
#        [2.        , 1.        , 0.66666667],
#        [3.        , 1.5       , 1.        ]])

This works, since a[:,np.newaxis] increases the dimension of the (3,) shaped array a into a (3, 1) shaped array, which can be used for the desired broadcasting operation.

Answer 2

First you need to cast a into a 2D array (same shape as the output), then repeat for the dimension you want to loop over. Then vectorized division will work.

>>> a.reshape(-1,1)
array([[1],
       [2],
       [3]])

>>> a.reshape(-1,1).repeat(b.shape[0], axis=1) 
array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])


>>> a.reshape(-1,1).repeat(b.shape[0], axis=1) / b
array([[1.        , 0.5       , 0.33333333],
       [2.        , 1.        , 0.66666667],
       [3.        , 1.5       , 1.        ]])


# Transpose will let you do it the other way around, but then you just get 1 for everything
>>> a.reshape(-1,1).repeat(b.shape[0], axis=1).T
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

>>> a.reshape(-1,1).repeat(b.shape[0], axis=1).T / b
array([[1., 1., 1.],
       [1., 1., 1.],
       [1., 1., 1.]])

Answer 3

This should do the job:

import numpy as np

a = np.array([1, 2, 3])
b = np.array([1, 2, 3])

print(a.reshape(-1, 1) / b)

Output:

[[ 1.          0.5         0.33333333]
 [ 2.          1.          0.66666667]
 [ 3.          1.5         1.        ]]