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main.py

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+import re
+import pandas as pd
+
+def parse_sql_to_excel(sql_file_path, excel_file_path):
+    with open(sql_file_path, 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    # Find all table structures to get column names
+    table_defs = re.findall(r"CREATE TABLE `(\w+)` \((.*?)\) ENGINE", content, re.DOTALL)
+    table_columns = {}
+    for table_name, columns_str in table_defs:
+        # Extract column names from the CREATE TABLE statement
+        cols = re.findall(r"`(\w+)`", columns_str.split('PRIMARY KEY')[0])
+        table_columns[table_name] = cols
+
+    # Find all INSERT statements
+    insert_matches = re.findall(r"INSERT INTO `(\w+)` VALUES (.*?);", content, re.DOTALL)
+    
+    data_frames = {}
+    for table_name, values_str in insert_matches:
+        if table_name not in table_columns:
+            continue
+            
+        # Clean up the values string to parse it as a list of tuples
+        # Note: This is a simplified parser for standard SQL values
+        rows = []
+        # Splitting rows by ),(
+        raw_rows = re.findall(r"\((.*?)\)(?:,|$)", values_str.replace('\n', ''))
+        
+        for row in raw_rows:
+            # Split values by comma, but respect strings inside single quotes
+            parsed_row = re.findall(r"'[^']*'|[^,]+", row)
+            # Clean quotes and whitespace
+            cleaned_row = [val.strip().strip("'") if val.strip() != 'NULL' else None for val in parsed_row]
+            
+            # Ensure the row matches the column length (handles trailing commas/empty values)
+            if len(cleaned_row) > len(table_columns[table_name]):
+                cleaned_row = cleaned_row[:len(table_columns[table_name])]
+            elif len(cleaned_row) < len(table_columns[table_name]):
+                cleaned_row.extend([None] * (len(table_columns[table_name]) - len(cleaned_row)))
+                
+            rows.append(cleaned_row)
+
+        if rows:
+            df = pd.DataFrame(rows, columns=table_columns[table_name])
+            data_frames[table_name] = df
+
+    # Save to Excel with each table on a different sheet
+    with pd.ExcelWriter(excel_file_path, engine='openpyxl') as writer:
+        for table_name, df in data_frames.items():
+            # Excel sheet names have a 31-character limit
+            df.to_excel(writer, sheet_name=table_name[:31], index=False)
+
+    print(f"Successfully exported {len(data_frames)} tables to {excel_file_path}")
+
+# Run the script
+parse_sql_to_excel('1088316_TeamworkPM_Backup_20260325.sql', 'Teamwork_Data.xlsx')