numpy.ma.row_stack¶

numpy.ma.
row_stack
(tup) = <numpy.ma.extras._fromnxfunction_seq object>¶ Stack arrays in sequence vertically (row wise).
This is equivalent to concatenation along the first axis after 1D 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 pixeldata 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 ndarrays
The arrays must have the same shape along all but the first axis. 1D arrays must have the same length.
Returns: stacked : ndarray
The array formed by stacking the given arrays, will be at least 2D.
See also
stack
 Join a sequence of arrays along a new axis.
hstack
 Stack arrays in sequence horizontally (column wise).
dstack
 Stack arrays in sequence depth wise (along third dimension).
concatenate
 Join a sequence of arrays along an existing axis.
vsplit
 Split array into a list of multiple subarrays vertically.
block
 Assemble arrays from blocks.
Notes
The function is applied to both the _data and the _mask, if any.
Examples
>>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.vstack((a,b)) array([[1, 2, 3], [2, 3, 4]])
>>> a = np.array([[1], [2], [3]]) >>> b = np.array([[2], [3], [4]]) >>> np.vstack((a,b)) array([[1], [2], [3], [2], [3], [4]])