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import pandas as pd

# Define your primary query
primary_query = f"SELECT * FROM {schema_ppm}.etl_ppm_replication_master WHERE eprm_catalog='SC' AND eprm_enabled_flg='Y'"

# Execute the primary query and get the primary dataframe
primary_df = pd.read_sql(primary_query, con=connection_ppm)

# Iterate through the primary dataframe
for index, row in primary_df.iterrows():
    # Get the table name from the 'eprm_table_name' column
    table_name = row['eprm_table_name']
    
    # Create source and target queries
    source_query = f"SELECT * FROM {schema_source}.{table_name}"
    target_query = f"SELECT * FROM {schema_ext}.{table_name}"
    
    # Execute source and target queries to get dataframes
    source_df = pd.read_sql(source_query, connection_source)
    target_df = pd.read_sql(target_query, connection_ext)
    
    # Find differences between source and target dataframes
    differences = source_df.compare(target_df, keep_shape=True)
    
    # Iterate through the differences
    for diff_index, diff_row in differences.iterrows():
        # Construct the dynamic update statement
        update_columns = []
        for column_name, diff_value in diff_row.items():
            update_columns.append(f"{column_name} = '{diff_value}'")
        
        # Get the primary key and its value
        pk_column_name = row['eprm_table_col_pk']
        pk_value = diff_row[pk_column_name]
        
        # Construct the WHERE condition
        where_condition = f"WHERE {pk_column_name} = '{pk_value}'"
        
        # Construct the dynamic update query
        update_query = f"UPDATE {schema_ext}.{table_name} SET {', '.join(update_columns)} {where_condition}"
        
        # Execute the update query to update the differing record in the target
        # Be sure to execute this query using your database connection

# End of loop