numpy.repeat

numpy.insert

# numpy.delete¶

numpy.delete(arr, obj, axis=None)[source]

Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by arr[obj].

Parameters: arr : array_like Input array. obj : slice, int or array of ints Indicate which sub-arrays to remove. axis : int, optional The axis along which to delete the subarray defined by obj. If axis is None, obj is applied to the flattened array. out : ndarray A copy of arr with the elements specified by obj removed. Note that delete does not occur in-place. If axis is None, out is a flattened array.

insert
Insert elements into an array.
append
Append elements at the end of an array.

Notes

Often it is preferable to use a boolean mask. For example:

```>>> mask = np.ones(len(arr), dtype=bool)
```

Is equivalent to np.delete(arr, [0,2,4], axis=0), but allows further use of mask.

Examples

```>>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
>>> arr
array([[ 1,  2,  3,  4],
[ 5,  6,  7,  8],
[ 9, 10, 11, 12]])
>>> np.delete(arr, 1, 0)
array([[ 1,  2,  3,  4],
[ 9, 10, 11, 12]])
```
```>>> np.delete(arr, np.s_[::2], 1)
array([[ 2,  4],
[ 6,  8],
[10, 12]])
>>> np.delete(arr, [1,3,5], None)
array([ 1,  3,  5,  7,  8,  9, 10, 11, 12])
```