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# numpy.ma.masked_object¶

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

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
xarray_like

Array to mask

valueobject

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
resultMaskedArray

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')
>>> 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=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)
```