xorbits.numpy.insert#

xorbits.numpy.insert(arr, obj, values, axis=None)[源代码]#

Insert values along the given axis before the given indices.

参数

arr (array_like) – Input array.
obj (int, slice or sequence of ints) –
Object that defines the index or indices before which values is inserted.

1.8.0(numpy) 新版功能.

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.

返回

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.

返回类型

ndarray

参见

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.

提示

Note that for higher dimensional inserts obj=0 behaves very different from obj=[0] just like arr[:,0,:] = values is different from arr[:,[0],:] = values.

实际案例

>>> 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.