numpy.vstack#

numpy.vstack(tup, *, dtype=None, casting='same_kind')[source]#

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.

Parameters:

tupsequence of ndarrays: The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.
dtypestr or dtype: If provided, the destination array will have this dtype. Cannot be provided together with out.

New in version 1.24.
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional: Controls what kind of data casting may occur. Defaults to ‘same_kind’.

New in version 1.24.

Returns:

stackedndarray: 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).

Examples

>>> 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]])