Whitespace

Author: cpcloud

pandas/core/frame.py

@@ -4414,12 +4414,12 @@ def mode(self, axis=0, numeric_only=False):
         """
         Gets the mode(s) of each element along the axis selected. Empty if nothing
         has 2+ occurrences. Adds a row for each mode per label, fills in gaps
-        with nan. 
-        
+        with nan.
+
         Note that there could be multiple values returned for the selected
-        axis (when more than one item share the maximum frequency), which is the 
-        reason why a dataframe is returned. If you want to impute missing values 
-        with the mode in a dataframe ``df``, you can just do this: 
+        axis (when more than one item share the maximum frequency), which is the
+        reason why a dataframe is returned. If you want to impute missing values
+        with the mode in a dataframe ``df``, you can just do this:
         ``df.fillna(df.mode().iloc[0])``
 
         Parameters

pandas/tests/test_frame.py

@@ -5970,18 +5970,18 @@ def test_boolean_comparison(self):
 
     def test_equals_different_blocks(self):
         # GH 9330
-        df0 = pd.DataFrame({"A": ["x","y"], "B": [1,2], 
+        df0 = pd.DataFrame({"A": ["x","y"], "B": [1,2],
                             "C": ["w","z"]})
         df1 = df0.reset_index()[["A","B","C"]]
-        # this assert verifies that the above operations have 
+        # this assert verifies that the above operations have
         # induced a block rearrangement
-        self.assertTrue(df0._data.blocks[0].dtype != 
+        self.assertTrue(df0._data.blocks[0].dtype !=
                         df1._data.blocks[0].dtype)
         # do the real tests
         self.assert_frame_equal(df0, df1)
         self.assertTrue(df0.equals(df1))
         self.assertTrue(df1.equals(df0))
-        
+
     def test_to_csv_from_csv(self):
 
         pname = '__tmp_to_csv_from_csv__'