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 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 ndarrays
The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.
Returns: stacked : ndarray
The array formed by stacking the given arrays, will be at least 2-D.
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 sub-arrays 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]])