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# numpy.ma.masked_object

`numpy.ma.``masked_object`(x, value, copy=True, shrink=True)

  Mask the array x where the data are exactly equal to value.

  This function is similar to masked_values, but only suitable for object arrays: for floating point, use masked_values instead.

  Parameters : | x : array_like Array to mask value : object Comparison value copy : {True, False}, optional Whether to return a copy of x. shrink : {True, False}, optional Whether to collapse a mask full of False to nomask
  Returns : | result : MaskedArray The result of masking x where equal to value.

  See also

  `masked_where`
    Mask where a condition is met.
  `masked_equal`
    Mask where equal to a given value (integers).
  `masked_values`
    Mask using floating point equality.

  Examples

      >>> import numpy.ma as ma
      >>> food = np.array(['green_eggs', 'ham'], dtype=object)
      >>> # don't eat spoiled food
      >>> eat = ma.masked_object(food, 'green_eggs')
      >>> print eat
      [-- ham]
      >>> # plain ol` ham is boring
      >>> fresh_food = np.array(['cheese', 'ham', 'pineapple'], dtype=object)
      >>> eat = ma.masked_object(fresh_food, 'green_eggs')
      >>> print eat
      [cheese ham pineapple]

  Note that mask is set to `nomask` if possible.

      >>> eat
      masked_array(data = [cheese ham pineapple],
            mask = False,
            fill_value=?)

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