xorbits.numpy.vstack#

xorbits.numpy.vstack(tup)[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.

np.row_stack is an alias for vstack. They are the same function.

Parameters

tup (sequence of ndarrays) – The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.
dtype (str or dtype (Not supported yet)) – If provided, the destination array will have this dtype. Cannot be provided together with out.
versionadded: (..) – 1.24(numpy):
casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional (Not supported yet)) – Controls what kind of data casting may occur. Defaults to ‘same_kind’.
versionadded: – 1.24(numpy):

Returns

stacked – The array formed by stacking the given arrays, will be at least 2-D.

Return type

ndarray

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]])

This docstring was copied from numpy.