Update app.py

app.py

@@ -192,10 +192,13 @@ def col_to_ints(df,columns_to_convert):
 
 def fill_missing_quarters(df, lob, acc, transaction):
     print('accident',acc,'transaction',transaction)
-    # Create a list of all possible quarters between 2017 and 2022
     columns_to_convert = [acc,transaction]  # Only affect acc and transaction
+
+    print('Number of NaN values in', acc, ':', df[acc].isna().sum())
+    print('Number of NaN values in', transaction, ':', df[transaction].isna().sum())
     for col in columns_to_convert:
       df[col] = df[col].apply(lambda x: str(int(x)) if isinstance(x, (int, float)) and str(x) != 'nan' else str(x))
+        
     quarters = []
     start_year = 2017
     end_year = min(int(df[acc].max()[:4]), 2025)
@@ -222,10 +225,17 @@ def fill_missing_quarters(df, lob, acc, transaction):
           print(l,'was filled with the dates',l_quarters)
         l_missing_df[lob] = l
         missing_quarters.append(l_missing_df)
+    print('Unique values in', acc, 'for missing quarters:', l_missing_df[acc].unique())
     # Concatenate the original dataframe and the missing quarters dataframe
     filled_df = pd.concat([df] + missing_quarters, ignore_index=True)
+    print('Number of NaN values in', acc, 'after concatenation:', filled_df[acc].isna().sum())
+    
+    print('Unique values in', acc, 'before conversion:', filled_df[acc].unique())
     # Convert the 'accident_quarter_bracket' column to datetime format
     filled_df[acc] = pd.to_datetime(filled_df[acc], format='%Y%m').dt.strftime('%Y%m')
+    print('Unique values in', acc, 'after conversion:', filled_df[acc].unique())
+
+
     # Sort the dataframe by quarter
     filled_df = filled_df.sort_values(acc)
     # Reset the index
@@ -237,6 +247,7 @@ def fill_missing_quarters(df, lob, acc, transaction):
         print("No missing quarters between 2017-2022")
     else:
         pass#print(filtered_quarters)
+
     #filled_df = filled_df[[acc, transaction] + [col for col in filled_df.columns if col not in [acc, transaction]]]
     return filled_df