diff --git a/src/features/all_cleaning_individual/straw/main.py b/src/features/all_cleaning_individual/straw/main.py
index 27d7ff80..6ac97cf7 100644
--- a/src/features/all_cleaning_individual/straw/main.py
+++ b/src/features/all_cleaning_individual/straw/main.py
@@ -14,6 +14,7 @@ from src.features import empatica_data_yield as edy
 pd.set_option('display.max_columns', 20)
 
 def straw_cleaning(sensor_data_files, provider):
+    # TODO (maybe): reorganize the script based on the overall 
     
     features = pd.read_csv(sensor_data_files["sensor_data"][0])
     
@@ -45,6 +46,9 @@ def straw_cleaning(sensor_data_files, provider):
     # Drop rows where empatica data yield is less then given threshold
     if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
         features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
+
+    if features.empty:
+        return features
     
     # (2.2) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
     min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
@@ -90,6 +94,7 @@ def straw_cleaning(sensor_data_files, provider):
         col.startswith('phone_messages_rapids_') or
         col.startswith('phone_screen_rapids_') or
         col.startswith('phone_wifi_visible')]
+
     features[impute_zero] = impute(features[impute_zero], method="zero")
 
     ## (5) STANDARDIZATION 
@@ -98,6 +103,7 @@ def straw_cleaning(sensor_data_files, provider):
 
     # (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
     impute_cols = [col for col in features.columns if col not in excluded_columns]
+    features.reset_index(drop=True, inplace=True)
     features[impute_cols] = impute(features[impute_cols], method="knn")
 
     # (7) REMOVE COLS WHERE VARIANCE IS 0
@@ -106,9 +112,11 @@ def straw_cleaning(sensor_data_files, provider):
     if provider["COLS_VAR_THRESHOLD"]:
         features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
 
+    fe5 = features.copy()
+
     # (8) DROP HIGHLY CORRELATED FEATURES
     drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
-    if drop_corr_features["COMPUTE"] and features.shape[0] > 5: # If small amount of segments (rows) is present, do not execute correlation check
+    if drop_corr_features["COMPUTE"] and features.shape[0]: # If small amount of segments (rows) is present, do not execute correlation check
         
         numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
 
@@ -126,15 +134,18 @@ def straw_cleaning(sensor_data_files, provider):
         if esm not in features:
             features[esm] = esm_cols[esm]
 
+    fe6 = features.copy()
+
     # (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
     if features.isna().any().any():
-        raise ValueError
+        raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
 
     return features
 
 def impute(df, method='zero'):
     
     def k_nearest(df):
+        pd.set_option('display.max_columns', None)
         imputer = KNNImputer(n_neighbors=3)
         return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)