xorbits.numpy.insert#
- xorbits.numpy.insert(arr, obj, values, axis=None)[source]#
Insert values along the given axis before the given indices.
- Parameters
arr (array_like) – Input array.
obj (int, slice or sequence of ints) –
Object that defines the index or indices before which values is inserted.
New in version 1.8.0(numpy).
Support for multiple insertions when obj is a single scalar or a sequence with one element (similar to calling insert multiple times).
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 (int, optional) – Axis along which to insert values. If axis is None then arr is flattened first.
- Returns
out – A copy of arr with values inserted. Note that insert does not occur in-place: a new array is returned. If axis is None, out is a flattened array.
- Return type
ndarray
See also
append
Append elements at the end of an array.
concatenate
Join a sequence of arrays along an existing axis.
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
>>> 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, 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)) True
>>> b = a.flatten() >>> b array([1, 1, 2, 2, 3, 3]) >>> np.insert(b, [2, 2], [5, 6]) array([1, 1, 5, ..., 2, 3, 3])
>>> np.insert(b, slice(2, 4), [5, 6]) array([1, 1, 5, ..., 2, 3, 3])
>>> np.insert(b, [2, 2], [7.13, False]) # type casting array([1, 1, 7, ..., 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]])
This docstring was copied from numpy.