Series as of docstring

pandas/core/generic.py

@@ -5401,40 +5401,59 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
 
     def asof(self, where, subset=None):
         """
-        The last row without any NaN is taken (or the last row without
-        NaN considering only the subset of columns in the case of a DataFrame)
-
-        .. versionadded:: 0.19.0 For DataFrame
-
-        If there is no good value, NaN is returned for a Series
-        a Series of NaN values for a DataFrame
-
+        Take the last row without any NaN. 
+
+        Or the last row without NaN considering only the subset of columns in the case of a DataFrame.
+        If there is no matched value, NaN is returned for a Series a Series of NaN values for a DataFrame.
+
+        .. versionadded:: 0.19.0 For DataFrame.
+
         Parameters
         ----------
-        where : date or array of dates
-        subset : string or list of strings, default None
-           if not None use these columns for NaN propagation
+        where : datetime
+            Date or array of dates.
+        subset : str
+            String or list of strings, default None if not None use these columns for NaN propagation.
 
         Notes
         -----
-        Dates are assumed to be sorted
-        Raises if this is not the case
+        Dates are assumed to be sorted.
+        Raises if this is not the case.
 
         Returns
         -------
         where is scalar
 
-          - value or NaN if input is Series
-          - Series if input is DataFrame
+        - value or NaN if input is Series.
+        - Series if input is DataFrame.
 
-        where is Index: same shape object as input
+        where is Index: same shape object as input.
 
         See Also
         --------
-        merge_asof
-
-        """
+        merge_asof : Perform an asof merge. This is similar to a left-join except that we match on nearest key rather than equal keys.
 
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> from datetime import datetime
+
+        >>> names = ['curupira', 'saci', 'boitata', 'tupa',  float('NaN'), 'cuca']
+        >>> dates = [datetime.strptime('20130101', '%Y%m%d'), 
+        ...          datetime.strptime('20140101', '%Y%m%d'),
+        ...          datetime.strptime('20140101', '%Y%m%d'),
+        ...          datetime.strptime('20150101', '%Y%m%d'),
+        ...          datetime.strptime('20150101', '%Y%m%d'),
+        ...          datetime.strptime('20160101', '%Y%m%d')]
+        >>> mySeries = pd.Series(data=names, index=dates)
+        >>> mySeries.asof(datetime.strptime('20150102', '%Y%m%d'))
+        'tupa'
+        >>> mySeries.asof(datetime.strptime('20190201', '%Y%m%d'))
+        'cuca'
+        >>> mySeries.asof(datetime.strptime('20120201', '%Y%m%d'))
+        nan
+        """
+
         if isinstance(where, compat.string_types):
             from pandas import to_datetime
             where = to_datetime(where)