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# pandas.DatetimeIndex.resize

`DatetimeIndex.``resize`(new_shape, refcheck=True)

  Change shape and size of array in-place.

  Parameters : | new_shape : tuple of ints, or n ints Shape of resized array. refcheck : bool, optional If False, reference count will not be checked. Default is True.
  Returns : | None
  Raises : | ValueError If a does not own its own data or references or views to it exist, and the data memory must be changed. SystemError If the order keyword argument is specified. This behaviour is a bug in NumPy.

  See also

  `resize`
    Return a new array with the specified shape.

  Notes

  This reallocates space for the data area if necessary.

  Only contiguous arrays (data elements consecutive in memory) can be resized.

  The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False.

  Examples

  Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:

      >>> a = np.array([[0, 1], [2, 3]], order='C')
      >>> a.resize((2, 1))
      >>> a
      array([[0],
             [1]])

      >>> a = np.array([[0, 1], [2, 3]], order='F')
      >>> a.resize((2, 1))
      >>> a
      array([[0],
             [2]])

  Enlarging an array: as above, but missing entries are filled with zeros:

      >>> b = np.array([[0, 1], [2, 3]])
      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
      >>> b
      array([[0, 1, 2],
             [3, 0, 0]])

  Referencing an array prevents resizing...

      >>> c = a
      >>> a.resize((1, 1))
      Traceback (most recent call last):
      ...
      ValueError: cannot resize an array that has been referenced ...

  Unless refcheck is False:

      >>> a.resize((1, 1), refcheck=False)
      >>> a
      array([[0]])
      >>> c
      array([[0]])

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