diff --git a/src/features/all_cleaning_individual/straw/main.py b/src/features/all_cleaning_individual/straw/main.py
index 0b0259f7..5557a2dc 100644
--- a/src/features/all_cleaning_individual/straw/main.py
+++ b/src/features/all_cleaning_individual/straw/main.py
@@ -17,12 +17,13 @@ def straw_cleaning(sensor_data_files, provider):
     with open('config.yaml', 'r') as stream:
         config = yaml.load(stream, Loader=yaml.FullLoader)
 
+    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
+
     # (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
     if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
         target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
-        features = features[features['phone_esm_straw_' + target].notna()].reset_index()
+        features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
 
-    # TODO: reorder the cleaning steps so it makes sense for the analysis
     # TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
     # the snakemake rules will also have to come with additional parameter (in rules/features.smk)
 
@@ -64,10 +65,10 @@ def straw_cleaning(sensor_data_files, provider):
     if provider["DATA_YIELD_RATIO_THRESHOLD"]:
         features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
 
-    # (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved)
+    # (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
     features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
 
-    # (4) REMOVE COLS WHERE VARIANCE IS 0 TODO: preveri za local_segment stolpce
+    # (4) REMOVE COLS WHERE VARIANCE IS 0
     if provider["COLS_VAR_THRESHOLD"]:
         features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
     
@@ -91,31 +92,35 @@ def straw_cleaning(sensor_data_files, provider):
 
         features.drop(to_drop, axis=1, inplace=True)
 
-    # Remove rows if threshold of NaN values is passed
+    # (6) Remove rows if threshold of NaN values is passed
     min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
     features.dropna(axis=0, thresh=min_count, inplace=True)
 
+
     sns.set(rc={"figure.figsize":(16, 8)})
     sns.heatmap(features.isna(), cbar=False)
     plt.savefig(f'features_nans_bf_knn.png', bbox_inches='tight')
         
-    ## STANDARDIZATION - should it happen before or after kNN imputation?
-    # TODO: check if there are additional columns that need to be excluded from the standardization
-    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
+    ## (7) STANDARDIZATION 
     
     if provider["STANDARDIZATION"]:
         features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
 
-    # KNN IMPUTATION
+    # (8) KNN IMPUTATION
     impute_cols = [col for col in features.columns if col not in excluded_columns]
     features[impute_cols] = impute(features[impute_cols], method="knn")
 
+    # (9) STANDARDIZATION AGAIN
+    
+    if provider["STANDARDIZATION"]:
+        features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)]) 
+
 
     sns.set(rc={"figure.figsize":(16, 8)})
     sns.heatmap(features.isna(), cbar=False)
     plt.savefig(f'features_nans_af_knn.png', bbox_inches='tight')
 
-    # VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
+    # (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
     if features.isna().any().any():
         raise ValueError
 
diff --git a/tests/scripts/missing_vals.py b/tests/scripts/missing_vals.py
index acbae0bb..41cf4709 100644
--- a/tests/scripts/missing_vals.py
+++ b/tests/scripts/missing_vals.py
@@ -3,8 +3,8 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 
 
-participant = "p031"
-all_sensors = ["eda", "bvp", "ibi", "temp", "acc"]
+participant = "p01"
+all_sensors = ["eda", "ibi", "temp", "acc"]
 
 for sensor in all_sensors: