fix whitespace in pandas_converter

pyltr/data/pandas_converter.py

@@ -1,54 +1,54 @@
 import pandas as pd
 
 
-class Pandas_Converter(object):    
+class Pandas_Converter(object):
     '''
     Class Converter implements parsing from original letor txt files to
     pandas data frame representation.
     '''
-    
+
     def __init__(self, path):
         '''
         Arguments:
             path: path to letor txt file
         '''
         self._path = path
-        
+
     @property
     def path(self):
         return self._path
-    
+
     @path.setter
     def path(self, p):
         self._path = p
-        
+
     def _load_file(self):
-        '''        
+        '''
         Loads and parses raw letor txt file.
-        
+
         Return:
             letor txt file parsed to csv in raw format
         '''
         return pd.read_csv(str(self._path), sep=" ", header=None)
-        
+
     def _drop_col(self, df):
         '''
         Drops last column, which was added in the parsing procedure due to a
         trailing white space for each sample in the text file
-        
+
         Arguments:
             df: pandas dataframe
         Return:
             df: original df with last column dropped
         '''
         return df.drop(df.columns[-1], axis=1)
-    
+
     def _split_colon(self, df):
         '''
         Splits the data on the colon and transforms it into a tabular format
         where columns are features and rows samples. Cells represent feature
         values per sample.
-        
+
         Arguments:
             df: pandas dataframe object
         Return:
@@ -58,15 +58,14 @@ def _split_colon(self, df):
             df.loc[:,col] = df.loc[:,col].apply(lambda x: str(x).split(':')[1])
         df.columns = ['rel', 'qid'] + [str(x) for x in range(1,len(df.columns)-1)] # renaming cols
         return df
-    
+
     def convert(self):
         '''
         Performs final conversion.
-        
+
         Return:
             fully converted pandas dataframe
         '''
         df_raw = self._load_file(self._path)
         df_drop = self._drop_col(df_raw)
         return self._split_colon(df_drop)
-