Stack arrays in sequence depth wise (along third axis). This is equivalent to concatenation along the third axis after 2-D arrays of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape (N,) have been reshaped to (1,N,1). Rebuilds arrays divided by dsplit. This function makes most sense for
arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations. The arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape. The array formed
by stacking the given arrays, will be at least 3-D. See also Join a sequence of arrays along an existing axis. Join a sequence of arrays along a new axis. Assemble an nd-array from nested lists of blocks. Stack arrays in sequence vertically (row wise).
Stack arrays in sequence horizontally (column wise).
column_stackStack 1-D arrays as columns into a 2-D array.
dsplitSplit array along third axis.
Examples
>>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.dstack((a,b)) array([[[1, 2], [2, 3], [3, 4]]]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.dstack((a,b)) array([[[1, 2]], [[2, 3]], [[3, 4]]])View Discussion
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With the help of numpy.dstack() method, we can get the combined array index by index and store like a stack by using numpy.dstack() method.
Syntax : numpy.dstack((array1, array2))
Return : Return combined array index by index.
Example #1 :
In this example we can see that by using numpy.dstack() method, we are able to get the combined array in a stack index by index.
import numpy as np
gfg1 = np.array([1, 2, 3])
gfg2 = np.array([4, 5, 6])
print(np.dstack((gfg1, gfg2)))
Output :
[[[1 4]
[2 5]
[3 6]]]
Example #2 :
import numpy as np
gfg1 = np.array([[10], [2], [13]])
gfg2 = np.array([[41], [55], [6]])
print(np.dstack((gfg1, gfg2)))
Output :
[[[10 41]]
[[ 2 55]]
[[13 6]]]