numpy.block¶

numpy.
block
(arrays)[source]¶ Assemble an ndarray from nested lists of blocks.
Blocks in the innermost lists are concatenated (see
concatenate
) along the last dimension (1), then these are concatenated along the secondlast 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 likenp.block([v, 1])
is valid, wherev.ndim == 1
.When the nested list is two levels deep, this allows block matrices to be constructed from their components.
New in version 1.13.0.
 Parameters
 arraysnested list of array_like or scalars (but not tuples)
If passed a single ndarray or scalar (a nested list of depth 0), this is returned unmodified (and not copied).
Elements shapes must match along the appropriate axes (without broadcasting), but leading 1s will be prepended to the shape as necessary to make the dimensions match.
 Returns
 block_arrayndarray
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], []]
See also
concatenate
Join a sequence of arrays together.
stack
Stack arrays in sequence along a new dimension.
hstack
Stack arrays in sequence horizontally (column wise).
vstack
Stack arrays in sequence vertically (row wise).
dstack
Stack arrays in sequence depth wise (along third dimension).
vsplit
Split array into a list of multiple subarrays vertically.
Notes
When called with only scalars,
np.block
is equivalent to an ndarray call. Sonp.block([[1, 2], [3, 4]])
is equivalent tonp.array([[1, 2], [3, 4]])
.This function does not enforce that the blocks lie on a fixed grid.
np.block([[a, b], [c, d]])
is not restricted to arrays of the form:AAAbb AAAbb cccDD
But is also allowed to produce, for some
a, b, c, d
:AAAbb AAAbb cDDDD
Since concatenation happens along the last axis first,
block
is _not_ capable of producing the following directly:AAAbb cccbb cccDD
Matlab’s “square bracket stacking”,
[A, B, ...; p, q, ...]
, is equivalent tonp.block([[A, B, ...], [p, q, ...]])
.Examples
The most common use of this function is to build a block matrix
>>> 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.]])
With a list of depth 1,
block
can be used ashstack
>>> np.block([1, 2, 3]) # hstack([1, 2, 3]) array([1, 2, 3])
>>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.block([a, b, 10]) # hstack([a, b, 10]) array([ 1, 2, 3, 2, 3, 4, 10])
>>> A = np.ones((2, 2), int) >>> B = 2 * A >>> np.block([A, B]) # hstack([A, B]) array([[1, 1, 2, 2], [1, 1, 2, 2]])
With a list of depth 2,
block
can be used in place ofvstack
:>>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.block([[a], [b]]) # vstack([a, b]) array([[1, 2, 3], [2, 3, 4]])
>>> A = np.ones((2, 2), int) >>> B = 2 * A >>> np.block([[A], [B]]) # vstack([A, B]) array([[1, 1], [1, 1], [2, 2], [2, 2]])
It can also be used in places of
atleast_1d
andatleast_2d
>>> a = np.array(0) >>> b = np.array([1]) >>> np.block([a]) # atleast_1d(a) array([0]) >>> np.block([b]) # atleast_1d(b) array([1])
>>> np.block([[a]]) # atleast_2d(a) array([[0]]) >>> np.block([[b]]) # atleast_2d(b) array([[1]])