numpy.broadcast#

class numpy.broadcast(*arrays)[source]#

Produce an object that mimics broadcasting.

Parameters:

in1, in2, array_like

Input parameters.

Attributes:

index

current index in broadcasted result

iters

tuple of iterators along selfs components.

nd

Number of dimensions of broadcasted result.

ndim

Number of dimensions of broadcasted result.

numiter

Number of iterators possessed by the broadcasted result.

shape

Shape of broadcasted result.

size

Total size of broadcasted result.

Methods

reset()

Reset the broadcasted result's iterator(s).

Returns:

bbroadcast object

Broadcast the input parameters against one another, and return an object that encapsulates the result. Amongst others, it has shape and nd properties, and may be used as an iterator.

See also

broadcast_arrays

broadcast_to

broadcast_shapes

Examples

Try it in your browser!

Manually adding two vectors, using broadcasting:

>>> import numpy as np
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)

>>> out = np.empty(b.shape)
>>> out.flat = [u+v for (u,v) in b]
>>> out
array([[5.,  6.,  7.],
       [6.,  7.,  8.],
       [7.,  8.,  9.]])

Compare against built-in broadcasting:

>>> x + y
array([[5, 6, 7],
       [6, 7, 8],
       [7, 8, 9]])

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