numpy.ma.masked_object#

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)