import unittest
import psycopg2
import pandas as pd
import math

class TestPostgresQuery(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.connection = psycopg2.connect(
            host="localhost",
            user="agent",
            password="qwerty",
            dbname="testdb",
            sslmode="disable"
        )
        cls.cursor = cls.connection.cursor()

    def test_columns_present(self):
        """Check that required columns are present in the result."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        expected_columns = set(["employee_id", "name", "avg_score", "performance_quartile"])
        result = set(columns)
        _dynamic_test(
            self,
            expected_columns.issubset(result),
            "All required columns are present.",
            f"Missing columns: {expected_columns - result}"
        )

    def test_all_employees_with_reviews_included(self):
        """Check that all employees with reviews are included in the result."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute("SELECT DISTINCT employee_id FROM performance")
        expected_ids = set(row[0] for row in self.cursor.fetchall())
        self.cursor.execute(query)
        result_ids = set(row[0] for row in self.cursor.fetchall())
        _dynamic_test(
            self,
            expected_ids == result_ids,
            "All employees with reviews are included.",
            f"Missing employee_ids: {expected_ids - result_ids}"
        )

    def test_avg_score_calculation(self):
        """Check that avg_score is calculated correctly for each employee."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Calculate expected averages
        self.cursor.execute("SELECT employee_id, AVG(score) FROM performance GROUP BY employee_id")
        expected_averages = {row[0]: float(row[1]) for row in self.cursor.fetchall()}
        self.cursor.execute(query)
        result = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        idx_id = columns.index("employee_id")
        idx_avg = columns.index("avg_score")
        mismatches = []
        for row in result:
            emp_id = row[idx_id]
            avg_score = float(row[idx_avg])
            if round(avg_score, 5) != round(expected_averages[emp_id], 5):
                mismatches.append((emp_id, avg_score, expected_averages[emp_id]))
        _dynamic_test(
            self,
            not mismatches,
            "Average scores are calculated correctly.",
            f"Incorrect averages for employee_ids: {mismatches}"
        )

    def test_ntile_quartile_assignment(self):
        """Check that NTILE(4) correctly assigns quartiles with 1 = highest avg_score."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        df = pd.DataFrame(rows, columns=columns)
        df = df.sort_values("avg_score", ascending=False).reset_index(drop=True)
        n = len(df)
        # Use the same logic as Postgres NTILE to assign expected quartiles
        # NTILE(4): bucket_size = ceil(n/4), assign buckets in round-robin order
        bucket_size = math.ceil(n / 4)
        expected_quartiles = [((i // bucket_size) + 1) for i in range(n)]
        # If n is not divisible by 4, NTILE distributes extra rows to lower-numbered buckets
        # Let's use the logic from Postgres docs
        # See: https://www.postgresql.org/docs/current/functions-window.html#FUNCTIONS-WINDOW-TABLE
        # The NTILE function divides the ordered rows as evenly as possible among the number of buckets.
        # If the number of rows is not divisible by the number of buckets, the first buckets get one extra row each.
        ntiles = 4
        per_bucket = n // ntiles
        remainder = n % ntiles
        bucket_limits = []
        current = 0
        for i in range(1, ntiles+1):
            add = per_bucket + (1 if i <= remainder else 0)
            bucket_limits.append(current + add)
            current += add
        expected_quartiles = []
        for i in range(n):
            for b, lim in enumerate(bucket_limits):
                if i < lim:
                    expected_quartiles.append(b+1)
                    break
        df["expected_quartile"] = expected_quartiles
        mismatches = df[df["performance_quartile"] != df["expected_quartile"]]
        _dynamic_test(
            self,
            mismatches.empty,
            "NTILE(4) assigns quartiles correctly (1 = highest avg_score).",
            f"Quartile assignment mismatches: {mismatches[['employee_id','avg_score','performance_quartile','expected_quartile']].to_dict('records')}"
        )

    def test_quartile_1_is_highest(self):
        """Check that employees with highest avg_score are assigned to quartile 1."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        df = pd.DataFrame(rows, columns=columns)
        if df.empty:
            _dynamic_test(self, False, "", "Query result is empty.")
            return
        max_avg = df["avg_score"].max()
        top_quartile = df[df["avg_score"] == max_avg]["performance_quartile"].unique()
        _dynamic_test(
            self,
            all(q == 1 for q in top_quartile),
            "Employees with highest avg_score are assigned to quartile 1.",
            f"Top scoring employees not in quartile 1: {top_quartile}"
        )

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

def normalize_text(text):
    import re
    text = text.lower()
    text = re.sub(r"\s{2,}", " ", text)
    text = re.sub(r"\s*([,:?])\s*", r"\1 ", text)
    return text.strip()

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


test_solution.py

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Challenge: Assign Performance Quartiles

Lösung

Challenge: Assign Performance Quartiles

Lösung