NumPy: block() function

numpy.block() function

The block() function assembles an nd-array from nested lists of blocks.
Blocks in the innermost lists are concatenated along the last dimension (-1), then these are concatenated along the second-last dimension (-2), and so on until the outermost list is reached.
Blocks can be of any dimension, but will not be broadcasted using the normal rules. Instead, leading axes of size 1 are inserted, to make block.ndim the same for all blocks. This is primarily useful for working with scalars, and means that code like np.block([v, 1]) is valid, where v.ndim == 1.
When the nested list is two levels deep, this allows block matrices to be constructed from their components.

Syntax:

numpy.block(arrays)

Version: 1.15.0

Parameter:

Return value:

block_array : ndarray The array assembled from the given blocks.
The dimensionality of the output is equal to the greatest of: * the dimensionality of all the inputs * the depth to which the input list is nested

Raises: ValueError -

• If list depths are mismatched - for instance, [[a, b], c] is illegal, and should be spelt [[a, b], [c]]
• If lists are empty - for instance, [[a, b], []]

Example-1: numpy.block()

import numpy as np 
>>> A = np.eye(2) * 2
>>> B = np.eye(3) * 3
>>> np.block([
...     [A,               np.zeros((2, 3))],
...     [np.ones((3, 2)), B               ]
... ])
array([[ 2.,  0.,  0.,  0.,  0.],
       [ 0.,  2.,  0.,  0.,  0.],
       [ 1.,  1.,  3.,  0.,  0.],
       [ 1.,  1.,  0.,  3.,  0.],
       [ 1.,  1.,  0.,  0.,  3.]])

Example-2: numpy.block()

import numpy as np   
	   
>>> np.block([1, 2, 3])
array([1, 2, 3])
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.block([a, b, 10])
array([1, 2, 3, 2, 3, 4, 10])

Pictorial Presentation:

Python - NumPy Code Editor:

Previous: vstack()
Next: Splitting arrays split()