hstack(tup) = <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
stackand block provide more general stacking and concatenation operations.
- tup : sequence of ndarrays
The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
- stacked : ndarray
The array formed by stacking the given arrays.
- Join a sequence of arrays along a new axis.
- Stack arrays in sequence vertically (row wise).
- Stack arrays in sequence depth wise (along third axis).
- Join a sequence of arrays along an existing axis.
- Split array along second axis.
- Assemble arrays from blocks.
The function is applied to both the _data and the _mask, if any.
>>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.hstack((a,b)) array([1, 2, 3, 2, 3, 4]) >>> a = np.array([,,]) >>> b = np.array([,,]) >>> np.hstack((a,b)) array([[1, 2], [2, 3], [3, 4]])