- ma.hstack(*args, **kwargs) = <numpy.ma.extras._fromnxfunction_seq object>¶
Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by
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
blockprovide more general stacking and concatenation operations.
- tupsequence of ndarrays
The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
The array formed by stacking the given arrays.
Join a sequence of arrays along an existing axis.
Join a sequence of arrays along a new axis.
Assemble an nd-array from nested lists of blocks.
Stack arrays in sequence vertically (row wise).
Stack arrays in sequence depth wise (along third axis).
Stack 1-D arrays as columns into a 2-D array.
Split an array into multiple sub-arrays horizontally (column-wise).
The function is applied to both the _data and the _mask, if any.
>>> a = np.array((1,2,3)) >>> b = np.array((4,5,6)) >>> np.hstack((a,b)) array([1, 2, 3, 4, 5, 6]) >>> a = np.array([,,]) >>> b = np.array([,,]) >>> np.hstack((a,b)) array([[1, 4], [2, 5], [3, 6]])