make generic for index and series

Author: jorisvandenbossche

pandas/core/indexes/datetimes.py

@@ -1966,30 +1966,35 @@ def normalize(self):
         """
         Convert times to midnight.
 
-        When using DatetimeIndex, the time can be converted to midnight i.e.
+        The time component of the date-timeise converted to midnight i.e.
         00:00:00. This is useful in cases, when the time does not matter.
         Length is unaltered. The timezones are unaffected.
 
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
         Returns
         -------
-        normalized : DatetimeIndex
+        DatetimeIndex or Series
+            The same type as the original data. Series will have the same
+            name and index. DatetimeIndex will have the same name.
 
         See Also
         --------
-        DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
-        DatetimeIndex.ceil : Ceil the DatetimeIndex to the specified freq.
-        DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
+        floor : Floor the datetimes to the specified freq.
+        ceil : Ceil the datetimes to the specified freq.
+        round : Round the datetimes to the specified freq.
 
         Examples
         --------
-        >>> df = pd.DatetimeIndex(start='2014-08-01 10:00', freq='H',
-        ...                       periods=3, tz='Asia/Calcutta')
-        >>> df
+        >>> idx = pd.DatetimeIndex(start='2014-08-01 10:00', freq='H',
+        ...                        periods=3, tz='Asia/Calcutta')
+        >>> idx
         DatetimeIndex(['2014-08-01 10:00:00+05:30',
                        '2014-08-01 11:00:00+05:30',
                        '2014-08-01 12:00:00+05:30'],
                         dtype='datetime64[ns, Asia/Calcutta]', freq='H')
-        >>> df.normalize()
+        >>> idx.normalize()
         DatetimeIndex(['2014-08-01 00:00:00+05:30',
                        '2014-08-01 00:00:00+05:30',
                        '2014-08-01 00:00:00+05:30'],