numpy.vstack — NumPy v2.2 Manual (original) (raw)
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 byvsplit.
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 andblock 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
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).
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]])