import unittest
import user_code as uc


def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)


class TestFlightsEDA(unittest.TestCase):

    def test_total_rows(self):
        _dynamic_test(self,
            isinstance(uc.total_rows, int) and uc.total_rows > 0,
            "The `total_rows` is a positive integer.",
            "Expected `total_rows` to be a positive integer.")

    def test_most_delayed_airline(self):
        _dynamic_test(self,
            isinstance(uc.most_delayed_airline, str) and len(uc.most_delayed_airline) > 0,
            "The `most_delayed_airline` is a non-empty string.",
            "Expected `most_delayed_airline` to be a non-empty string.")

    def test_delayed_count(self):
        _dynamic_test(self,
            isinstance(uc.delayed_count, int) and 0 < uc.delayed_count < uc.total_rows,
            "The `delayed_count` is a positive integer less than `total_rows`.",
            "Expected `delayed_count` to be a positive integer less than `total_rows`.")

    def test_top_routes(self):
        _dynamic_test(self,
            isinstance(uc.top_routes, list) and len(uc.top_routes) == 3 and all(len(r) == 3 for r in uc.top_routes),
            "The `top_routes` is a list of 3 tuples.",
            "Expected `top_routes` to be a list of 3 tuples.")


if __name__ == "__main__":
    unittest.main()

test_main.py

Dive into the fundamentals of big data processing with PySpark – from Spark's distributed architecture and RDDs to the DataFrame API for scalable, real-world data analysis.

Explore the foundations of PySpark, from understanding big data and Spark's architecture to hands-on practice with RDDs and DataFrames.

Challenge: Exploratory Analysis of a Flights Dataset

Solution