ass that defines ``__missing__`` (i.e.
        provides a method for default values), then this default is used
        rather than ``NaN``.

        Examples
        --------
        >>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
        >>> s
        0      cat
        1      dog
        2      NaN
        3   rabbit
        dtype: object

        ``map`` accepts a ``dict`` or a ``Series``. Values that are not found
        in the ``dict`` are converted to ``NaN``, unless the dict has a default
        value (e.g. ``defaultdict``):

        >>> s.map({'cat': 'kitten', 'dog': 'puppy'})
        0   kitten
        1    puppy
        2      NaN
        3      NaN
        dtype: object

        It also accepts a function:

        >>> s.map('I am a {}'.format)
        0       I am a cat
        1       I am a dog
        2       I am a nan
        3    I am a rabbit
        dtype: object

        To avoid applying the function to missing values (and keep them as
        ``NaN``) ``na_action='ignore'`` can be used:

        >>> s.map('I am a {}'.format, na_action='ignore')
        0     I am a cat
        1     I am a dog
        2            NaN
        3  I am a rabbit
        dtype: object
        )