diff --git a/exploration/expl_features_groups_analysis.py b/exploration/expl_features_groups_analysis.py
index aae4bdd..a509ddb 100644
--- a/exploration/expl_features_groups_analysis.py
+++ b/exploration/expl_features_groups_analysis.py
@@ -291,15 +291,15 @@ plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, reca
 best_sensor_group = sensor_groups_importance_scores[0][0] # take the highest rated sensor group
 best_sensor_features = [col for col in model_input if col.startswith(best_sensor_group)]
 
-best_sensor_features_scores = find_sensor_group_features_importance(model_input, best_sensor_features)
+# best_sensor_features_scores = find_sensor_group_features_importance(model_input, best_sensor_features)
 
-xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(best_sensor_features_scores)
+# xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(best_sensor_features_scores)
 
 # %% [markdown]
 # ### Visualize best sensor's F1 and recall scores
-print(best_sensor_features_scores)
-plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
-                                                    title="Best sensor addition it's features with F1 and recall scores")
+# print(best_sensor_features_scores)
+# plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
+#                                                     title="Best sensor addition it's features with F1 and recall scores")
 
 # %%
 # This section iterates over all sensor groups and investigates sequential feature importance feature-by-feature
@@ -311,8 +311,8 @@ for i, sensor_group in enumerate(sensor_groups_importance_scores):
     current_sensor_features = [col for col in model_input if col.startswith(sensor_group[0])]
     current_sensor_features_scores = find_sensor_group_features_importance(model_input, current_sensor_features)
     xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(current_sensor_features_scores)
-    feature_sequence = feature_sequence.append(pd.DataFrame({"sensor_name":sensor_group[0], "feature_sequence": [xs], "recall": [y_recall], 
-                                                             "f1_score": [y_fscore], "recall_std": [recall_std], "f1_std": [fscore_std]}))
+    feature_sequence = pd.concatinate([feature_sequence, pd.DataFrame({"sensor_name":sensor_group[0], "feature_sequence": [xs], "recall": [y_recall], 
+                                                             "f1_score": [y_fscore], "recall_std": [recall_std], "f1_std": [fscore_std]})])
 
     plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std, 
     title=f"Sequential addition of features for {sensor_group[0]} and its F1, and recall scores")