kermitting soon

util/helper.py

@@ -901,12 +901,12 @@ def calculate_role_specialization(df):
     return df
 
 def calculate_champion_loyalty(df):
-    print("========================== Inside: calculate_champion_loyalty ====================\n")
     df = df.copy()
     print("df.dtypes: \n", df.dtypes, "\n")
 
     
     def get_loyalty_scores(row):
+        print("========================== Inside: get_loyalty_scores() ====================\n")
         try:
 
             # Get champions lists, handle potential NaN/None values (only top 2)
@@ -944,20 +944,13 @@ def calculate_champion_loyalty(df):
                     **champ_loyalty_flags
                 }
             
-            # Calculate games played for recent champions (only top 2)
-            #print("START calculate games played for recent champions (only top 2)...\n")
-            # print("row['W_1'] type: ", type(row['W_1']), "\n row['W_1']: ", row['W_1'], "\n ") 
-            # print("row['L_1'] type: ", type(row['L_1']), "\n row['L_1']: ", row['L_1'], "\n")
-            #print(f"row['W_1'] value: {repr(row['W_1'])}, type: {type(row['W_1'])}")
-            #print(f"row['L_1'] value: {repr(row['L_1'])}, type: {type(row['L_1'])}")
-
-            #print(".... END calculate games played for recent champions (only top 2)\n \n \n")
-
             recent_games = [
                 (int(row['W_1']) + int(row['L_1'])) if pd.notna(row['most_champ_1']) else 0,
                 (int(row['W_2']) + int(row['L_2'])) if pd.notna(row['most_champ_2']) else 0
             ]
 
+            season_games = [int(x) if isinstance(x, str) and x.isdigit() else 0 for x in season_games]
+
             print(f"recent_games was: {recent_games}, types: {[type(x) for x in recent_games]}")
             print(f"season_games was: {season_games}, types: {[type(x) for x in season_games]}")