Untitled

# ... (existing code above)

# Assuming 'your_primary_key_column' is the primary key column name
merged_df = pd.merge(df, target_df, on='your_primary_key_column', suffixes=('_source', '_target'), how='inner')

# Iterate through merged DataFrame to compare columns and generate update queries
for index, row in merged_df.iterrows():
    primary_key_value = row['your_primary_key_column']  # Replace with the actual column name
    
    # Initialize a list to store column updates
    column_updates = []

    # Compare columns for changes
    for column_name in df.columns:
        source_value = row[column_name + '_source']
        target_value = row[column_name + '_target']
        
        if source_value != target_value:
            column_updates.append(f"{column_name}='{source_value}'")
    
    # Generate and execute an SQL update statement if there are column updates
    if column_updates:
        update_query = f"UPDATE {schema_ext}.your_table SET {', '.join(column_updates)} WHERE your_primary_key_column='{primary_key_value}'"
        connection_ext.execute(update_query)

# ... (continue with your existing code below)