NumPy Array manipulation: dstack() function
The dstack() is used to 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.
|tup||The arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape.||Required|
stacked : ndarray The array formed by stacking the given arrays, will be at least 3-D.
>>> import numpy as np >>> x = np.array((3, 5, 7)) >>> y = np.array((5, 7, 9)) >>> np.dstack((x,y)) array([[[3, 5], [5, 7], [7, 9]]])
>>> import numpy as np >>> x = np.array([, , ]) >>> y = np.array([, , ]) >>> np.dstack((x,y)) array([[[3, 5]], [[5, 7]], [[7, 9]]])
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