ma.hstack(tup, *, dtype=None, casting='same_kind')
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
,
and
provide more general stacking and concatenation operations.
Parameters:tupsequence of ndarraysThe arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length. In the case of a single array_like input, it will be treated as a sequence of arrays; i.e., each element along the zeroth axis is treated as a separate array.
dtypestr or dtypeIf provided, the destination array will have this dtype.
Added in version 1.24.
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optionalControls what kind of data casting may occur. Defaults to ‘same_kind’.
Added in version 1.24.
Returns:stackedndarrayThe array formed by stacking the given arrays.
See also
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).
Split an array into a tuple of sub-arrays along an axis.
Notes
The function is applied to both the _data and the _mask, if any.
Examples
>>> importnumpyasnp>>> 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([[1],[2],[3]])>>> b=np.array([[4],[5],[6]])>>> np.hstack((a,b))array([[1, 4], [2, 5], [3, 6]])