Stack arrays in sequence vertically (row wise).
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.
The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length. In the case of a single array_like input, it will be treated as a sequence of arrays; i.e., each element along the zeroth axis is treated as a separate array.
If provided, the destination array will have this dtype. Cannot be provided together with out.
New in version 1.24.
Controls what kind of data casting may occur. Defaults to ‘same_kind’.
New in version 1.24.
The array formed by stacking the given arrays, will be at least 2-D.
See also
concatenate
Join a sequence of arrays along an existing axis.
stack
Join a sequence of arrays along a new axis.
block
Assemble an nd-array from nested lists of blocks.
hstack
Stack arrays in sequence horizontally (column wise).
dstack
Stack arrays in sequence depth wise (along third axis).
column_stack
Stack 1-D arrays as columns into a 2-D array.
vsplit
Split an array into multiple sub-arrays vertically (row-wise).
unstack
Split an array into a tuple of sub-arrays along an axis.
Examples
>>> import numpy as np
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.vstack((a,b))
array([[1, 2, 3],
[4, 5, 6]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[4], [5], [6]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[4],
[5],
[6]])
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