nums.numpy.row_stack

nums.numpy.row_stack(tup)[source]

Stack arrays in sequence vertically (row wise).

This docstring was copied from numpy.row_stack.

Some inconsistencies with the NumS version may exist.

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 BlockArrays) – The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.
Returns: stacked – The array formed by stacking the given arrays, will be at least 2-D.
Return type: BlockArray

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

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array([1, 2, 3])  
>>> b = nps.array([2, 3, 4])  
>>> nps.vstack((a,b)).get()  
array([[1, 2, 3],
       [2, 3, 4]])

>>> a = nps.array([[1], [2], [3]])  
>>> b = nps.array([[2], [3], [4]])  
>>> nps.vstack((a,b)).get()  
array([[1],
       [2],
       [3],
       [2],
       [3],
       [4]])

nums.numpy.dstack

nums.numpy.column_stack