ENH: pandas-dev#14591 - formatting

Author: nchmura4

pandas/core/generic.py

@@ -4054,7 +4054,8 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
                        sort=sort, group_keys=group_keys, squeeze=squeeze,
                        **kwargs)
 
-    def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
+    def asfreq(self, freq, method=None, how=None, normalize=False,
+               fill_value=None):
         """
         Convert TimeSeries to specified frequency.
 
@@ -4089,7 +4090,8 @@ def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
         <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
         """
         from pandas.tseries.resample import asfreq
-        return asfreq(self, freq, method=method, how=how, normalize=normalize, fill_value=fill_value)
+        return asfreq(self, freq, method=method, how=how, normalize=normalize,
+                      fill_value=fill_value)
 
     def at_time(self, time, asof=False):
         """

pandas/tseries/tests/test_resample.py

@@ -368,16 +368,14 @@ def test_fill_value(self):
         ts2 = pd.Series(np.random.rand(len(rng)), index=rng)
         df = pd.DataFrame({'one': ts, 'two': ts2})
 
-        # upsample to get NaN values
-        ts_up = ts.resample('1S').asfreq()
-        df_up = df.resample('1S').asfreq()
+        # insert pre-existing missing value
+        df.loc['2016-01-01 00:00:08', 'two'] = None
 
-        expected_ts = ts_up.fillna(2).resample('2S').sum()
-        actual_ts = ts_up.resample('2S', fill_value=2).sum()
-        assert_series_equal(expected_ts, actual_ts)
+        actual_df = df.resample('1S').asfreq(fill_value=9.0)
+
+        expected_df = df.resample('1S').asfreq().fillna(9.0)
+        expected_df.loc['2016-01-01 00:00:08', 'two'] = None
 
-        expected_df = df_up.fillna(1.5).resample('2S').sum()
-        actual_df = df_up.resample('2S', fill_value=1.5).sum()
         assert_frame_equal(expected_df, actual_df)
 
     def test_apply_without_aggregation(self):