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# numpy.char.chararray.resize

method

char.chararray.resize(new_shape, /, *, refcheck=True) a.resize(*new_shape, refcheck=True)
char.chararray.resize(*new_shape, refcheck=True) → None

  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. See Notes below for more explanation.

  Returns:
    None
  Raises:
    ValueError

      If a does not own its own data or references or views to may exist.

  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.

  Reallocating arrays in-place can often lead to memory fragmentation and should be avoided. If the goal is to reclaim over-allocated memory, alternatives are to create a view or a copy of just the desired data, or using two passes to build the array: one to cheaply determine the shape and another to allocate and fill. Benchmark your use case to determine what is optimum. You may be surprised to find `resize` actually slows down or bloats your application.

  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.

  On Python 3.13 and older, the check allows objects with exactly one reference to be reallocated in-place. On Python 3.14 and newer, the array must be uniquely referenced. See [1] for more details.

  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.

  References

  [1]

  Python 3.14 What’s New, https://docs.python.org/3/whatsnew/3.14.html#whatsnew314-refcount

  Examples

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

      >>> import numpy as np

      >>> 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 references or is referenced ...

  Unless refcheck is False:

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