diff --git a/cobra/preprocessing/categorical_data_processor.py b/cobra/preprocessing/categorical_data_processor.py
index 175bfb5..bc7f733 100644
--- a/cobra/preprocessing/categorical_data_processor.py
+++ b/cobra/preprocessing/categorical_data_processor.py
@@ -309,10 +309,10 @@ def _transform_column(self, data: pd.DataFrame,
         """
 
         column_name_clean = column_name + "_processed"
-        data.loc[:, column_name_clean] = data[column_name].astype(object)
+        data[column_name_clean] = data[column_name].astype(object)
 
         # Fill missings first
-        data.loc[:, column_name_clean] = (CategoricalDataProcessor
+        data[column_name_clean] = (CategoricalDataProcessor
                                           ._replace_missings(
                                               data,
                                               column_name_clean
@@ -329,14 +329,14 @@ def _transform_column(self, data: pd.DataFrame,
                                 "and will be skipped".format(column_name))
                 return data
 
-            data.loc[:, column_name_clean] = (CategoricalDataProcessor
+            data[column_name_clean] = (CategoricalDataProcessor
                                               ._replace_categories(
                                                   data[column_name_clean],
                                                   categories,
                                                   self.regroup_name))
 
         # change data to categorical
-        data.loc[:, column_name_clean] = (data[column_name_clean]
+        data[column_name_clean] = (data[column_name_clean]
                                           .astype("category"))
 
         return data
diff --git a/cobra/preprocessing/kbins_discretizer.py b/cobra/preprocessing/kbins_discretizer.py
index c30d7de..e30b834 100644
--- a/cobra/preprocessing/kbins_discretizer.py
+++ b/cobra/preprocessing/kbins_discretizer.py
@@ -315,19 +315,19 @@ def _transform_column(self, data: pd.DataFrame,
         column_name_bin = column_name + "_bin"
 
         # use pd.cut to compute bins
-        data.loc[:, column_name_bin] = pd.cut(x=data[column_name],
+        data[column_name_bin] = pd.cut(x=data[column_name],
                                               bins=interval_idx)
 
         # Rename bins so that the output has a proper format
         bin_labels = self._create_bin_labels(bins)
 
-        data.loc[:, column_name_bin] = (data[column_name_bin]
+        data[column_name_bin] = (data[column_name_bin]
                                         .cat.rename_categories(bin_labels))
 
         if data[column_name_bin].isnull().sum() > 0:
 
             # Add an additional bin for missing values
-            data[column_name_bin].cat.add_categories(["Missing"], inplace=True)
+            data[column_name_bin]=data[column_name_bin].cat.add_categories(["Missing"])
 
             # Replace NULL with "Missing"
             # Otherwise these will be ignored in groupby
diff --git a/tests/preprocessing/test_preprocessor.py b/tests/preprocessing/test_preprocessor.py
index 08f5b63..6d69bd9 100644
--- a/tests/preprocessing/test_preprocessor.py
+++ b/tests/preprocessing/test_preprocessor.py
@@ -35,7 +35,7 @@ def test_train_selection_validation_split(
     ):
         X = np.arange(100).reshape(10, 10)
         data = pd.DataFrame(X, columns=[f"c{i+1}" for i in range(10)])
-        data.loc[:, "target"] = np.array([0] * 7 + [1] * 3)
+        data["target"] = np.array([0] * 7 + [1] * 3)
 
         actual = PreProcessor.train_selection_validation_split(
             data, train_prop, selection_prop, validation_prop
diff --git a/tests/preprocessing/test_target_encoder.py b/tests/preprocessing/test_target_encoder.py
index 51ebd79..f477bad 100644
--- a/tests/preprocessing/test_target_encoder.py
+++ b/tests/preprocessing/test_target_encoder.py
@@ -260,8 +260,7 @@ def test_target_encoder_transform_new_category_binary_classification(self):
                                         'neutral'],
                            'target': [1, 1, 0, 0, 1, 0, 0, 0, 1, 1]})
 
-        df_appended = df.append({"variable": "new", "target": 1},
-                                ignore_index=True)
+        df_appended = pd.concat([df, pd.DataFrame({"variable": "new", "target": 1}, index=[len(df)])], ignore_index=True)
 
         # inputs of TargetEncoder will be of dtype category
         df["variable"] = df["variable"].astype("category")
@@ -285,8 +284,7 @@ def test_target_encoder_transform_new_category_linear_regression(self):
                                         'neutral', 'positive'],
                            'target': [5, 4, -5, 0, -4, 5, -5, 0, 1, 0, 4]})
 
-        df_appended = df.append({"variable": "new", "target": 10},
-                                ignore_index=True)
+        df_appended = pd.concat([df, pd.DataFrame({"variable": "new", "target": 10}, index=[len(df)])], ignore_index=True)
 
         # inputs of TargetEncoder will be of dtype category
         df["variable"] = df["variable"].astype("category")