delete(arr, obj, axis=None)

Notes

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

>>> arr = np.arange(12) + 1
>>> mask = np.ones(len(arr), dtype=bool)
>>> mask[[0,2,4]] = False
>>> result = arr[mask,...]

Is equivalent to :None:None:`np.delete(arr, [0,2,4], axis=0)`, but allows further use of :None:None:`mask`.

Parameters

arr : array_like

Input array.

obj : slice, int or array of ints

Indicate indices of sub-arrays to remove along the specified axis.

versionchanged

Boolean indices are now treated as a mask of elements to remove, rather than being cast to the integers 0 and 1.

axis : int, optional

The axis along which to delete the subarray defined by :None:None:`obj`. If :None:None:`axis` is None, :None:None:`obj` is applied to the flattened array.

Returns

out : ndarray

A copy of :None:None:`arr` with the elements specified by :None:None:`obj` removed. Note that delete does not occur in-place. If :None:None:`axis` is None, :None:None:`out` is a flattened array.

Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by :None:None:`arr[obj]`.

See Also

append

Append elements at the end of an array.

insert

Insert elements into an array.

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])

See :

Back References

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GitHub : /numpy/lib/function_base.py#4958
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