dask.array.hstack
dask.array.hstack¶
- dask.array.hstack(tup, allow_unknown_chunksizes=False)[source]¶
Stack arrays in sequence horizontally (column wise).
This docstring was copied from numpy.hstack.
Some inconsistencies with the Dask version may exist.
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 hsplit.
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
- 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.
- Returns
- stackedndarray
The array formed by stacking the given arrays.
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 nd-array from nested lists of blocks.
vstack
Stack arrays in sequence vertically (row wise).
dstack
Stack arrays in sequence depth wise (along third axis).
column_stack
Stack 1-D arrays as columns into a 2-D array.
hsplit
Split an array into multiple sub-arrays horizontally (column-wise).
Examples
>>> 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]])