numpy.vstack

numpy.vstack(tup, *, dtype=None, casting='same_kind')

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. 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.

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).

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]])