numpy.ma.vstack#
- ma.vstack = <numpy.ma.extras._fromnxfunction_seq object>#
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
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.
- 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).
Notes
The function is applied to both the _data and the _mask, if any.
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]])