Update the code below for me create a pie chart based on the data provided below.   import matplotlib.pyplot as plt   # Sample data referral_sources = ['Website', 'Word of Mouth', 'Advertisement', 'Social Media'] counts = [30.34%, 14.5%, 50.70%, 60.9%] plt.bar(referral_sources, counts) plt.title('Referral Sources Count') plt.xlabel('Referral Source') plt.ylabel('Count')

Please write a view  to list staff’s names, salary, ages, and their supervisors’names, salary, and ages for all staff whose supervisor’s age is younger than the staff’s age and theirsupervisor’s salary is higher than the staff’s salary. You should use Staff2 table. The output headerneeds to distinguish between staff and manger: (E_name, E_salary, E_age, M_name, M_salary, M_age)

7. Show all item num from the item table, and a matching quoted price from the order line table (show blank if there is no matching quoted price).

calculate_trip_time( iata_src: str, iata_dst: str, flight_walk: List[str], flights: Flight Dir float: def ) -> """ Return a float corresponding to the amount of time required to travel from the source airport to the destination airport to the destination airport, as outlined by the flight_walk. The start time of the trip should be considered zero. In other words, assuming we start the trip at 12:00am, this function should return the time it takes for the trip to finish, including all the waiting times before, and between the flights. If there is no path available, return -1.0 >>> calculate_trip_time("AA1", "AA2", ["AA1", "AA2"], TEST_FLIGHTS_DIR_FOUR_ 2.0 "AA7", ["AA7", "AA1"], TEST_FLIGHTS_DIR_FOUR_ "AA7", ["AA1", "AA7"], TEST_FLIGHTS_DIR_FOUR_ "AA1", ["AA1"], TEST_FLIGHTS_DIR_FOUR_CITIES) "AA2", ["AA4", "AA1", "AA2"], TEST_FLIGHTS_DI "AA3", ["AA1", "AA2", "AA3"], TEST_FLIGHTS_DI >>> calculate_trip_time("AA1", "AA4", ["AA1", "AA4"], TEST_FLIGHTS_DIR_FOUR_ 2.0 || || || >>>…