Apply suggestions from code review

Co-authored-by: Jano Roelandt <48879098+ZlaTanskY@users.noreply.github.com>

Author: patrickleonardy

cobra/preprocessing/preprocessor.py

@@ -492,9 +492,9 @@ def _get_variable_list(continuous_vars: list, discrete_vars: list) -> list:
         return var_list
 
     def _check_nan_columns_and_drop_columns_containing_only_nan(data: pd.DataFrame) -> pd.DataFrame:
-        """Checkes how much missing values are in the dataframe and drops columns that contain only missing values. 
-        It also logs an error message displaying the percentage of missing values in the diffenent columns 
-        (columns are only diosplaied if they contain a missing values)
+        """Checks how much missing values are in the dataframe and drops columns that contain only missing values. 
+        It also logs an error message displaying the percentage of missing values in the different columns 
+        (columns are only displayed if they contain a missing values)
 
         Parameters
         ----------
@@ -504,7 +504,7 @@ def _check_nan_columns_and_drop_columns_containing_only_nan(data: pd.DataFrame)
         Returns
         -------
         pd.DataFrame
-            Data without columns conatining only missing values
+            Data without columns containing only missing values
         """
 
         # Check how much NaN values are in each variable 
@@ -522,6 +522,6 @@ def _check_nan_columns_and_drop_columns_containing_only_nan(data: pd.DataFrame)
 
         if to_drop:
             data = data.drop(to_drop, axis=1)
-            logging.warning(f"Following variables contain only missing values and were droped: {to_drop}")
+            logging.warning(f"Following variables contain only missing values and were dropped: {to_drop}")
 
         return data

tests/preprocessing/test_preprocessor.py

@@ -185,7 +185,8 @@ def test_mutable_train_data_fit_transform(self, mocker: MockerFixture):
     [
         # example 1
         (pd.DataFrame({
-            "a":[1,8,np.nan],
+            "a":[1, 8 , np.nan],
+
             "b":[np.nan,8,np.nan],
             "c":[np.nan,np.nan,np.nan],
             "d":[np.nan,np.nan,5],