The dstack()
method stacks the sequence of input arrays depthwise.
Example
import numpy as np
array1 = np.array([[0, 1], [2, 3]])
array2 = np.array([[4, 5], [6, 7]])
# stack the arrays
stackedArray = np.dstack((array1, array2))
print(stackedArray)
'''
Output
[[[0 4]
[1 5]]
[[2 6]
[3 7]]]
'''
dstack() Syntax
The syntax of dstack()
is:
numpy.dstack(tup)
dstack() Argument
The dstack()
method takes a single argument:
tup
- a tuple of arrays to be stacked
Note: The shape of all arrays in a given tuple must be the same, except for the third dimension because we are stacking in axis 2 (depthwise).
dstack() Return Value
The dstack()
method returns the depthwise stacked array.
Example 1: Stack Arrays of Different Shapes Depthwise
import numpy as np
# 3-D array with shape (2,2,2)
array1 = np.array([[[0, 1], [2,3]],
[[4, 5], [6, 7]]])
# 3-D array with shape (2,2,1)
array2 = np.array([[[4], [5]],
[[6], [7]]])
# stack the arrays
stackedArray = np.dstack((array1, array2))
print(stackedArray)
Output
[[[0 1 4] [2 3 5]] [[4 5 6] [6 7 7]]]
Here, we have stacked 2 arrays of different shapes.
The shape of array2 is (2, 2, 1)
, yet we could stack it with array1 of shape (2, 2, 2)
because only the third dimension of array2 is different from array1.
Example 2: Stack Arrays Depthwise With Invalid Shapes
import numpy as np
array1 = np.array([[[0, 1], [2,3]],
[[4, 5], [6, 7]]])
array2 = np.array([[[4], [5], [6]],
[[7], [8], [9]]])
# stack the arrays with invalid shapes
stackedArray = np.dstack((array1, array2))
print(stackedArray)
Output
ValueError: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 2 and the array at index 1 has size 3
Here, the shape of array2 is (2, 3, 1)
, which which results in ValueError while stacking with arrays of shape (2, 2, 2)
.