Stack arrays in sequence vertically (row wise).
LAX-backend implementation of
vstack(). Original docstring below.
This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.
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 (sequence of ndarrays) – The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.
stacked – The array formed by stacking the given arrays, will be at least 2-D.
- Return type
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 horizontally (column wise).
Stack arrays in sequence depth wise (along third axis).
Stack 1-D arrays as columns into a 2-D array.
Split an array into multiple sub-arrays vertically (row-wise).
>>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.vstack((a,b)) array([[1, 2, 3], [2, 3, 4]])
>>> a = np.array([, , ]) >>> b = np.array([, , ]) >>> np.vstack((a,b)) array([, , , , , ])