🔥💯[Pandas] Easy to Understand (properly commented)💯🔥
\n\n# Code\n\nimport pandas as pd\n\ndef sales_person(sales_person: pd.DataFrame, company: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFrame:\n    # Merge Sal
\n\n# Code\n```\nimport pandas as pd\n\ndef sales_person(sales_person: pd.DataFrame, company: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFrame:\n    # Merge SalesPerson, Orders, and Company tables\n    merged_data = sales_person.merge(orders, on=\'sales_id\', how=\'left\').merge(company, on=\'com_id\', how=\'left\')\n    \n    # Filter rows where the company name is not "RED" or is null\n    filtered_data = merged_data[(merged_data[\'name_y\'] != \'RED\') | (merged_data[\'name_y\'].isna())]\n    \n    # Select the \'name_x\' column, which represents the salesperson\'s name\n    result = filtered_data[[\'name_x\']].rename(columns={\'name_x\': \'name\'})\n    \n    # Drop duplicates (in case a salesperson had multiple orders not related to "RED")\n    result = result.drop_duplicates()\n    \n    # Find the salespersons who had orders related to "RED"\n    sales_with_red_orders = merged_data[merged_data[\'name_y\'] == \'RED\'][\'name_x\'].unique()\n    \n    # Filter out salespersons with "RED" orders from the result\n    result = result[~result[\'name\'].isin(sales_with_red_orders)]\n    \n    return result\n\n```
['Pandas']