import pandas as pd

sessions = pd.read_csv('sessions.csv')
sessions['session_start'] = pd.to_datetime(sessions['session_start'])

orders = pd.read_csv('book_orders.csv')
orders['event_dt'] = pd.to_datetime(orders['event_dt'])

# добавили второй аргумент
def get_profiles(sessions, orders):

    profiles = (
        sessions.sort_values(by=['user_id', 'session_start'])
        .groupby('user_id')
        .agg(
            {
                'session_start': 'first',
                'channel': 'first',
                'device': 'first',
                'region': 'first',
            }
        )
        .rename(columns={'session_start': 'first_ts'})
        .reset_index()
    )

    profiles['dt'] = profiles['first_ts'].dt.date
    profiles['month'] = profiles['first_ts'].astype('datetime64[M]')
    
    # проверьте, есть ли ID пользователей из profiles в orders
    isin_to_filter = orders['user_id'].agg({'user_id': 'nunique'})
    profiles['payer'] =  profiles['user_id'].isin(isin_to_filter)
   # ваш код здесь

    return profiles

# строим профили по двум наборам данных 
profiles = get_profiles(sessions, orders)

print(
    profiles.groupby('region')
    .agg(
            {
                'payer': 'mean'
               }
        )
    .sort_values(by = 'payer', ascending = False)# допишите код
)