numpy.ma.row_stack¶

numpy.ma.
row_stack
(*args, **kwargs) = <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
 tupsequence of ndarrays
The arrays must have the same shape along all but the first axis. 1D arrays must have the same length.
 Returns
 stackedndarray
The array formed by stacking the given arrays, will be at least 2D.
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 ndarray 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 1D arrays as columns into a 2D array.
vsplit
Split an array into multiple subarrays vertically (rowwise).
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]])