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# numpy.ma.resize

ma.resize(x, new_shape)

  Return a new masked array with the specified size and shape.

  This is the masked equivalent of the `numpy.resize` function. The new array is filled with repeated copies of x (in the order that the data are stored in memory). If x is masked, the new array will be masked, and the new mask will be a repetition of the old one.

  See also

  `numpy.resize`

    Equivalent function in the top level NumPy module.

  Examples

      >>> import numpy as np
      >>> import numpy.ma as ma
      >>> a = ma.array([[1, 2] ,[3, 4]])
      >>> a[0, 1] = ma.masked
      >>> a
      masked_array(
        data=[[1, --],
              [3, 4]],
        mask=[[False,  True],
              [False, False]],
        fill_value=999999)
      >>> np.resize(a, (3, 3))
      masked_array(
        data=[[1, 2, 3],
              [4, 1, 2],
              [3, 4, 1]],
        mask=False,
        fill_value=999999)
      >>> ma.resize(a, (3, 3))
      masked_array(
        data=[[1, --, 3],
              [4, 1, --],
              [3, 4, 1]],
        mask=[[False,  True, False],
              [False, False,  True],
              [False, False, False]],
        fill_value=999999)

  A MaskedArray is always returned, regardless of the input type.

      >>> a = np.array([[1, 2] ,[3, 4]])
      >>> ma.resize(a, (3, 3))
      masked_array(
        data=[[1, 2, 3],
              [4, 1, 2],
              [3, 4, 1]],
        mask=False,
        fill_value=999999)