# numpy.insert#

numpy.insert(arr, obj, values, axis=None)[source]#

Insert values along the given axis before the given indices.

Parameters:
arrarray_like

Input array.

objint, slice or sequence of ints

Object that defines the index or indices before which values is inserted.

New in version 1.8.0.

Support for multiple insertions when obj is a single scalar or a sequence with one element (similar to calling insert multiple times).

valuesarray_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.

axisint, optional

Axis along which to insert values. If axis is None then arr is flattened first.

Returns:
outndarray

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.

`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 from `obj=[0]` just like `arr[:,0,:] = values` is different from `arr[:,[0],:] = values`. This is because of the difference between basic and advanced indexing.

Examples

```>>> a = np.arange(6).reshape(3, 2)
>>> a
array([[0, 1],
[2, 3],
[4, 5]])
>>> np.insert(a, 1, 6)
array([0, 6, 1, 2, 3, 4, 5])
>>> np.insert(a, 1, 6, axis=1)
array([[0, 6, 1],
[2, 6, 3],
[4, 6, 5]])
```

Difference between sequence and scalars, showing how `obj=[1]` behaves different from `obj=1`:

```>>> np.insert(a, [1], [[7],[8],[9]], axis=1)
array([[0, 7, 1],
[2, 8, 3],
[4, 9, 5]])
>>> np.insert(a, 1, [[7],[8],[9]], axis=1)
array([[0, 7, 8, 9, 1],
[2, 7, 8, 9, 3],
[4, 7, 8, 9, 5]])
>>> np.array_equal(np.insert(a, 1, [7, 8, 9], axis=1),
...                np.insert(a, [1], [[7],[8],[9]], axis=1))
True
```
```>>> b = a.flatten()
>>> b
array([0, 1, 2, 3, 4, 5])
>>> np.insert(b, [2, 2], [6, 7])
array([0, 1, 6, 7, 2, 3, 4, 5])
```
```>>> np.insert(b, slice(2, 4), [7, 8])
array([0, 1, 7, 2, 8, 3, 4, 5])
```
```>>> np.insert(b, [2, 2], [7.13, False]) # type casting
array([0, 1, 7, 0, 2, 3, 4, 5])
```
```>>> 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]])
```