Extract method to reuse and simplify.

features/esm.py

@@ -316,3 +316,34 @@ def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
         )
     )
     return df_esm_clean
+
+
+def increment_answers(df_esm_clean: pd.DataFrame, increment_by=1):
+    """
+    Increment answers to keep in line with original scoring.
+
+    We always used 0 for the lowest value of user answer.
+    Some scales originally used other scoring, such as starting from 1.
+    This restores original scoring so that the values are comparable to references.
+
+    Parameters
+    ----------
+    df_esm_clean: pd.DataFrame
+        A cleaned ESM dataframe, which must also include esm_user_answer_numeric.
+    increment_by:
+        A number to add to the user answer.
+
+    Returns
+    -------
+    df_esm_clean: pd.DataFrame
+        The same df with addition of a column 'esm_user_answer_numeric'.
+
+    """
+    try:
+        df_esm_clean = df_esm_clean.assign(
+            esm_user_score=lambda x: x.esm_user_answer_numeric + increment_by
+        )
+    except AttributeError as e:
+        print("Please, clean the dataframe first using features.esm.clean_up_esm.")
+        print(e)
+    return df_esm_clean

features/esm_COPE.py

@@ -1,5 +1,7 @@
 import pandas as pd
 
+from features.esm import increment_answers
+
 COPE_ORIGINAL_MAX = 4
 COPE_ORIGINAL_MIN = 1
 
@@ -187,15 +189,6 @@ def reassign_question_ids(df_cope_cleaned: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Finally, increment numeric answers.
-    try:
-        df_cope_fixed = df_cope_fixed.assign(
-            esm_user_score=lambda x: x.esm_user_answer_numeric + 1
-        )
-        # Increment the original answer by 1
-        # to keep in line with traditional scoring
-        # (from COPE_ORIGINAL_MIN - COPE_ORIGINAL_MAX).
-    except AttributeError as e:
-        print("Please, clean the dataframe first using features.esm.clean_up_esm.")
-        print(e)
+    df_cope_fixed = increment_answers(df_cope_fixed)
 
     return df_cope_fixed

features/esm_JCQ.py

@@ -1,5 +1,7 @@
 import pandas as pd
 
+from features.esm import increment_answers
+
 JCQ_ORIGINAL_MAX = 4
 JCQ_ORIGINAL_MIN = 1
 
@@ -87,11 +89,9 @@ def reverse_jcq_demand_control_scoring(
             # In case there is an unexpected answer, raise an exception.
 
     try:
-        df_esm_jcq_demand_control = df_esm_jcq_demand_control.assign(
-            esm_user_score=lambda x: x.esm_user_answer_numeric + 1
-        )
+        df_esm_jcq_demand_control = increment_answers(df_esm_jcq_demand_control)
         # Increment the original answer by 1 to keep in line
-        # with traditional scoring (JCQ_ORIGINAL_MIN - JCQ_ORIGINAL_MAX).
+        # with traditional scoring (from JCQ_ORIGINAL_MIN to JCQ_ORIGINAL_MAX).
         df_esm_jcq_demand_control[
             df_esm_jcq_demand_control["question_id"].isin(
                 DICT_JCQ_DEMAND_CONTROL_REVERSE.keys()

features/esm_SAM.py

@@ -2,6 +2,7 @@ import numpy as np
 import pandas as pd
 
 import features.esm
+from features.esm import increment_answers
 
 SAM_ORIGINAL_MAX = 5
 SAM_ORIGINAL_MIN = 1
@@ -506,15 +507,6 @@ def reassign_question_ids(df_sam_cleaned: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Finally, increment numeric answers.
-    try:
-        df_sam_fixed = df_sam_fixed.assign(
-            esm_user_score=lambda x: x.esm_user_answer_numeric + 1
-        )
-        # Increment the original answer by 1
-        # to keep in line with traditional scoring
-        # (from SAM_ORIGINAL_MIN - SAM_ORIGINAL_MAX).
-    except AttributeError as e:
-        print("Please, clean the dataframe first using features.esm.clean_up_esm.")
-        print(e)
+    df_sam_fixed = increment_answers(df_sam_fixed)
 
     return df_sam_fixed