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# numpy.ma.masked_object

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

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

      >>> eat
      masked_array(data=['cheese', 'ham', 'pineapple'],
                   mask=False,
             fill_value='green_eggs',
                  dtype=object)