bundles / numpy 2.4.3 / numpy / ma / extras / vstack
function
numpy.ma.extras:vstack
source: /numpy/_core/shape_base.py :218
Signature
def vstack ( tup , * , dtype = None , casting = same_kind ) Summary
Stack arrays in sequence vertically (row wise).
Extended Summary
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
tup: sequence of ndarraysThe 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.
dtype: str or dtypeIf provided, the destination array will have this dtype. Cannot be provided together with
out.casting: {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optionalControls what kind of data casting may occur. Defaults to 'same_kind'.
Returns
stacked: ndarrayThe array formed by stacking the given arrays, will be at least 2-D.
Notes
The function is applied to both the _data and the _mask, if any.
Examples
import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) np.vstack((a,b))✓
a = np.array([[1], [2], [3]]) b = np.array([[4], [5], [6]]) np.vstack((a,b))✓
See also
- block
Assemble an nd-array from nested lists of blocks.
- column_stack
Stack 1-D arrays as columns into a 2-D array.
- concatenate
Join a sequence of arrays along an existing axis.
- dstack
Stack arrays in sequence depth wise (along third axis).
- hstack
Stack arrays in sequence horizontally (column wise).
- stack
Join a sequence of arrays along a new axis.
- unstack
Split an array into a tuple of sub-arrays along an axis.
- vsplit
Split an array into multiple sub-arrays vertically (row-wise).
Aliases
-
numpy.ma.row_stack