Update app.py

app.py

@@ -295,12 +295,25 @@ def show_dimensions_selector(search_type):
     )
 
 def show_paginated_dataframe(report, rows_per_page=20):
-    # Convert 'position' column to integer
+    # Convert 'position' column to integer and 'impressions' to numeric
     report['position'] = report['position'].astype(int)
+    report['impressions'] = pd.to_numeric(report['impressions'], errors='coerce')
     
     # Format CTR as percentage and relevancy_score with two decimal places
-    report['ctr'] = report['ctr'].apply(lambda x: f"{x:.2%}")
-    report['relevancy_score'] = report['relevancy_score'].apply(lambda x: f"{x:.2f}")
+    def format_ctr(x):
+        try:
+            return f"{float(x):.2%}"
+        except ValueError:
+            return x  # Return the original value if it can't be converted to float
+    
+    def format_relevancy_score(x):
+        try:
+            return f"{float(x):.2f}"
+        except ValueError:
+            return x  # Return the original value if it can't be converted to float
+    
+    report['ctr'] = report['ctr'].apply(format_ctr)
+    report['relevancy_score'] = report['relevancy_score'].apply(format_relevancy_score)
     
     # Create a clickable URL column
     def make_clickable(url):
@@ -319,14 +332,21 @@ def show_paginated_dataframe(report, rows_per_page=20):
     ascending = sort_order == "Ascending"
     
     # Convert back to numeric for sorting
-    report['ctr'] = report['ctr'].str.rstrip('%').astype('float') / 100
-    report['relevancy_score'] = report['relevancy_score'].astype('float')
+    def safe_float_convert(x):
+        try:
+            return float(x.rstrip('%')) / 100 if isinstance(x, str) and x.endswith('%') else float(x)
+        except ValueError:
+            return 0  # Return 0 or another default value if conversion fails
+    
+    report['ctr_numeric'] = report['ctr'].apply(safe_float_convert)
+    report['relevancy_score_numeric'] = report['relevancy_score'].apply(safe_float_convert)
     
-    report = report.sort_values(by=sort_column, ascending=ascending)
+    # Sort using the numeric columns
+    sort_column_numeric = sort_column + '_numeric' if sort_column in ['ctr', 'relevancy_score'] else sort_column
+    report = report.sort_values(by=sort_column_numeric, ascending=ascending)
     
-    # Convert back to formatted strings for display
-    report['ctr'] = report['ctr'].apply(lambda x: f"{x:.2%}")
-    report['relevancy_score'] = report['relevancy_score'].apply(lambda x: f"{x:.2f}")
+    # Remove the temporary numeric columns
+    report = report.drop(columns=['ctr_numeric', 'relevancy_score_numeric'])
 
     total_rows = len(report)
     total_pages = (total_rows - 1) // rows_per_page + 1