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In this chapter, we will discuss about NumPy – Broadcasting. The term refers to the ability of NumPy to treat arrays of different shapes during arithmetic operations.

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NumPy – Broadcasting Examples

Example 1

import numpy as np 

a = np.array([1,2,3,4]) 
b = np.array([10,20,30,40]) 
c = a * b 
print c

Its output is as follows −

[10   40   90   160]

If the dimensions of two arrays are dissimilar, element-to-element operations are not possible. However, operations on arrays of non-similar shapes is still possible in NumPy, because of the broadcasting capability.

Broadcasting is possible if the following rules are satisfied −

• Array with smaller ndim than the other is prepended with ‘1’ in its shape.
• Size in each dimension of the output shape is maximum of the input sizes in that dimension.
• Arrays have exactly the same shape.
• Arrays have the same number of dimensions and the length of each dimension is either a common length or 1.
• Array having too few dimensions can have its shape prepended with a dimension of length 1, so that the above stated property is true.

The following program shows an example of broadcasting.

Example 2

import numpy as np 
a = np.array([[0.0,0.0,0.0],[10.0,10.0,10.0],[20.0,20.0,20.0],[30.0,30.0,30.0]]) 
b = np.array([1.0,2.0,3.0])  
   
print 'First array:' 
print a 
print '\n'  
   
print 'Second array:' 
print b 
print '\n'  
   
print 'First Array + Second Array' 
print a + b

The output of this program would be as follows −

First array:
[[ 0. 0. 0.]
 [ 10. 10. 10.]
 [ 20. 20. 20.]
 [ 30. 30. 30.]]

Second array:
[ 1. 2. 3.]

First Array + Second Array
[[ 1. 2. 3.]
 [ 11. 12. 13.]
 [ 21. 22. 23.]
 [ 31. 32. 33.]]

The following this figure –