NumPy - divide() function

The NumPy divide() function returns a true division of the inputs, element-wise. The syntax for using this function is given below:

Note: It is equivalent to x1 / x2 in terms of array broadcasting.

Syntax

numpy.divide(x1, x2, out=None)

Parameters

`x1, x2`	`Required.` Specify arrays to be divided: x1 as dividend and x2 as divisor. If x1.shape != x2.shape, they must be broadcastable to a common shape.
`out`	`Optional.` Specify a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.

Return Value

Returns true division of x1 and x2, element-wise.

Example:

The example below shows the usage of divide() function.

import numpy as np
Arr1 = np.array([[10,20],[30,40]])
Arr2 = np.array([[2,3]])
Arr3 = np.array([[2],[3]])
Arr4 = np.array([[2,3],[4,5]])

#divide each element of Arr1 by 5
print("divide(Arr1, 5) returns:")
print(np.divide(Arr1, 5))

#divideing elements of Arr1 by Arr2
#Arr1 and Arr2 are broadcastable
print("\ndivide(Arr1, Arr2) returns:")
print(np.divide(Arr1, Arr2))

#divideing elements of Arr1 by Arr3
#Arr1 and Arr3 are broadcastable
print("\ndivide(Arr1, Arr3) returns:")
print(np.divide(Arr1, Arr3))

#divideing elements of Arr1 by Arr4
print("\ndivide(Arr1, Arr4) returns:")
print(np.divide(Arr1, Arr4))

The output of the above code will be:

divide(Arr1, 5) returns:
[[2. 4.]
 [6. 8.]]

divide(Arr1, Arr2) returns:
[[ 5.          6.66666667]
 [15.         13.33333333]]

divide(Arr1, Arr3) returns:
[[ 5.         10.        ]
 [10.         13.33333333]]

divide(Arr1, Arr4) returns:
[[5.         6.66666667]
 [7.5        8.        ]]

❮ NumPy - Functions