DOC: update the pandas.Series.map docstring

pandas/core/series.py

@@ -2951,74 +2951,25 @@ def unstack(self, level=-1, fill_value=None):
 
     def map(self, arg, na_action=None):
         """
-        Map values of Series using input correspondence (a dict, Series, or
-        function).
+        Map values of Series according to input correspondence.
+
+        Used for substituting each value in a Series with another value,
+        that may be derived from a function, a ``dict`` or
+        a :class:`Series`.
 
         Parameters
         ----------
         arg : function, dict, or Series
             Mapping correspondence.
-        na_action : {None, 'ignore'}
-            If 'ignore', propagate NA values, without passing them to the
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to the
             mapping correspondence.
 
         Returns
         -------
-        y : Series
+        Series
             Same index as caller.
 
-        Examples
-        --------
-
-        Map inputs to outputs (both of type `Series`):
-
-        >>> x = pd.Series([1,2,3], index=['one', 'two', 'three'])
-        >>> x
-        one      1
-        two      2
-        three    3
-        dtype: int64
-
-        >>> y = pd.Series(['foo', 'bar', 'baz'], index=[1,2,3])
-        >>> y
-        1    foo
-        2    bar
-        3    baz
-
-        >>> x.map(y)
-        one   foo
-        two   bar
-        three baz
-
-        If `arg` is a dictionary, return a new Series with values converted
-        according to the dictionary's mapping:
-
-        >>> z = {1: 'A', 2: 'B', 3: 'C'}
-
-        >>> x.map(z)
-        one   A
-        two   B
-        three C
-
-        Use na_action to control whether NA values are affected by the mapping
-        function.
-
-        >>> s = pd.Series([1, 2, 3, np.nan])
-
-        >>> s2 = s.map('this is a string {}'.format, na_action=None)
-        0    this is a string 1.0
-        1    this is a string 2.0
-        2    this is a string 3.0
-        3    this is a string nan
-        dtype: object
-
-        >>> s3 = s.map('this is a string {}'.format, na_action='ignore')
-        0    this is a string 1.0
-        1    this is a string 2.0
-        2    this is a string 3.0
-        3                     NaN
-        dtype: object
-
         See Also
         --------
         Series.apply : For applying more complex functions on a Series.
@@ -3027,20 +2978,51 @@ def map(self, arg, na_action=None):
 
         Notes
         -----
-        When `arg` is a dictionary, values in Series that are not in the
+        When ``arg`` is a dictionary, values in Series that are not in the
         dictionary (as keys) are converted to ``NaN``. However, if the
         dictionary is a ``dict`` subclass that defines ``__missing__`` (i.e.
         provides a method for default values), then this default is used
-        rather than ``NaN``:
-
-        >>> from collections import Counter
-        >>> counter = Counter()
-        >>> counter['bar'] += 1
-        >>> y.map(counter)
-        1    0
-        2    1
-        3    0
-        dtype: int64
+        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
         """
         new_values = super(Series, self)._map_values(
             arg, na_action=na_action)