dpnp.append

dpnp.append(arr, values, axis=None)

Append values to the end of an array.

For full documentation refer to numpy.append.

Parameters:

arr{dpnp.ndarray, usm_ndarray}

Values are appended to a copy of this array.

values{scalar, array_like}

These values are appended to a copy of arr. It must be of the correct shape (the same shape as arr, excluding axis). If axis is not specified, values can be any shape and will be flattened before use. These values can be in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays.

axis{None, int}, optional

The axis along which values are appended. If axis is not given, both arr and values are flattened before use.

Default: None.

Returns:

outdpnp.ndarray

A copy of arr with values appended to axis. Note that append does not occur in-place: a new array is allocated and filled. If axis is None, out is a flattened array.

Warning

When axis is None, a Python numeric scalar in values is converted to an array before the result dtype is chosen, so it can upcast arr (e.g. appending 1 to an int8 array gives int64). This is deprecated: a future release will use the Python scalar promotion rules and keep int8. A FutureWarning is emitted when the result dtype will change. Wrap the scalar in a list to keep the current result, or convert it to an array of the desired dtype.

See also

dpnp.insert

Insert elements into an array.

dpnp.delete

Delete elements from an array.

Examples

>>> import dpnp as np
>>> a = np.array([1, 2, 3])
>>> np.append(a, [[4, 5, 6], [7, 8, 9]])
array([1, 2, 3, 4, 5, 6, 7, 8, 9])

When axis is specified, values must have the correct shape.

>>> b = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.append(b, [[7, 8, 9]], axis=0)
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
>>> np.append(b, [7, 8, 9], axis=0)
Traceback (most recent call last):
    ...
ValueError: all the input arrays must have same number of dimensions, but
the array at index 0 has 2 dimension(s) and the array at index 1 has 1
dimension(s)