ma.
hstack
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 hsplit.
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.
concatenate
stack
block
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.
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.
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.
Split an array into multiple sub-arrays horizontally (column-wise).
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.hstack((a,b)) array([1, 2, 3, 2, 3, 4]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.hstack((a,b)) array([[1, 2], [2, 3], [3, 4]])