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# numpy.delete

`numpy.``delete`(arr, obj, axis=None)

  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.
  Returns: | 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.

  See also

  `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)
      >>> mask[[0,2,4]] = False
      >>> result = arr[mask,...]

  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])