dpnp.insert
- dpnp.insert(arr, obj, values, axis=None)[source]
Insert values along the given axis before the given indices.
For full documentation refer to
numpy.insert
.- Parameters:
arr (array_like) -- Input array.
obj ({slice, int, array-like of ints or bools}) -- Object that defines the index or indices before which values is inserted. It supports multiple insertions when obj is a single scalar or a sequence with one element (similar to calling insert multiple times). Boolean indices are treated as a mask of elements to insert.
values (array_like) -- Values to insert into arr. If the type of values is different from that of arr, values is converted to the type of arr. values should be shaped so that
arr[..., obj, ...] = values
is legal.axis ({None, int}, optional) -- Axis along which to insert values. If axis is
None
then arr is flattened first. Default:None
.
- Returns:
out -- A copy of arr with values inserted. Note that
dpnp.insert
does not occur in-place: a new array is returned. If axis isNone
, out is a flattened array.- Return type:
dpnp.ndarray
See also
dpnp.append
Append elements at the end of an array.
dpnp.concatenate
Join a sequence of arrays along an existing axis.
dpnp.delete
Delete elements from an array.
Notes
Note that for higher dimensional inserts
obj=0
behaves very different fromobj=[0]
just likearr[:, 0, :] = values
is different fromarr[:, [0], :] = values
.Examples
>>> import dpnp as np >>> a = np.array([[1, 1], [2, 2], [3, 3]]) >>> a array([[1, 1], [2, 2], [3, 3]]) >>> np.insert(a, 1, 5) array([1, 5, 1, 2, 2, 3, 3]) >>> np.insert(a, 1, 5, axis=1) array([[1, 5, 1], [2, 5, 2], [3, 5, 3]])
Difference between sequence and scalars:
>>> np.insert(a, [1], [[1],[2],[3]], axis=1) array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]) >>> np.array_equal(np.insert(a, 1, [1, 2, 3], axis=1), ... np.insert(a, [1], [[1],[2],[3]], axis=1)) array(True)
>>> b = a.flatten() >>> b array([1, 1, 2, 2, 3, 3]) >>> np.insert(b, [2, 2], [5, 6]) array([1, 1, 5, 6, 2, 2, 3, 3])
>>> np.insert(b, slice(2, 4), [5, 6]) array([1, 1, 5, 2, 6, 2, 3, 3])
>>> np.insert(b, [2, 2], [7.13, False]) # dtype casting array([1, 1, 7, 0, 2, 2, 3, 3])
>>> x = np.arange(8).reshape(2, 4) >>> idx = (1, 3) >>> np.insert(x, idx, 999, axis=1) array([[ 0, 999, 1, 2, 999, 3], [ 4, 999, 5, 6, 999, 7]])