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In this chapter, we will discuss about NumPy – broadcast. As seen earlier, NumPy has in-built support for broadcasting. This function mimics the broadcasting mechanism. It returns an object that encapsulates the result of broadcasting one array against the other.

The function takes two arrays as input parameters. The following example illustrates its use.

Example Of NumPy broadcast

import numpy as np 
x = np.array([[1], [2], [3]]) 
y = np.array([4, 5, 6])  
   
# tobroadcast x against y 
b = np.broadcast(x,y)  
# it has an iterator property, a tuple of iterators along self's "components." 

print 'Broadcast x against y:' 
r,c = b.iters 
print r.next(), c.next() 
print r.next(), c.next() 
print '\n'  
# shape attribute returns the shape of broadcast object 

print 'The shape of the broadcast object:' 
print b.shape 
print '\n'  
# to add x and y manually using broadcast 
b = np.broadcast(x,y) 
c = np.empty(b.shape) 

print 'Add x and y manually using broadcast:' 
print c.shape 
print '\n'  
c.flat = [u + v for (u,v) in b] 

print 'After applying the flat function:' 
print c 
print '\n'  
# same result obtained by NumPy's built-in broadcasting support 

print 'The summation of x and y:' 
print x + y

Its output is as follows −

Broadcast x against y:
1 4
1 5

The shape of the broadcast object:
(3, 3)

Add x and y manually using broadcast:
(3, 3)

After applying the flat function:
[[ 5. 6. 7.]
 [ 6. 7. 8.]
 [ 7. 8. 9.]]

The summation of x and y:
[[5 6 7]
 [6 7 8]
 [7 8 9]]

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