dpnp.concat

dpnp.concat(arrays, /, *, axis=0, out=None, dtype=None, casting='same_kind')

Join a sequence of arrays along an existing axis.

Note that dpnp.concat is an alias of dpnp.concatenate.

For full documentation refer to numpy.concatenate.

Parameters:

arrays ({Sequence of dpnp.ndarray or usm_ndarray}) -- The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).
axis (int, optional) -- The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default: 0.
out (dpnp.ndarray, optional) -- If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.
dtype (str or dtype) -- If provided, the destination array will have this dtype. Cannot be provided together with out.
casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) -- Controls what kind of data casting may occur. Defaults to 'same_kind'.

Returns:

out -- The concatenated array.

Return type:

dpnp.ndarray

See also

dpnp.array_split: Split an array into multiple sub-arrays of equal or near-equal size.
dpnp.split: Split array into a list of multiple sub-arrays of equal size.
dpnp.hsplit: Split array into multiple sub-arrays horizontally (column wise).
dpnp.vsplit: Split array into multiple sub-arrays vertically (row wise).
dpnp.dsplit: Split array into multiple sub-arrays along the 3rd axis (depth).
dpnp.stack: Stack a sequence of arrays along a new axis.
dpnp.block: Assemble arrays from blocks.
dpnp.hstack: Stack arrays in sequence horizontally (column wise).
dpnp.vstack: Stack arrays in sequence vertically (row wise).
dpnp.dstack: Stack arrays in sequence depth wise (along third dimension).
dpnp.column_stack: Stack 1-D arrays as columns into a 2-D array.

Examples

>>> import dpnp as np
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
       [3, 4, 6]])
>>> np.concatenate((a, b), axis=None)
array([1, 2, 3, 4, 5, 6])