import unittest
import psycopg2
import pandas as pd

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_column_names(self):
        """Check that all required columns are present in the query 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 = ["sale_id", "sale_date", "employee_id", "region", "amount", "cumulative_sales"]
        missing_columns = [col for col in expected_columns if col not in columns]
        condition = len(missing_columns) == 0
        _dynamic_test(
            self,
            condition,
            "All required columns are present in the result.",
            f"Missing columns: {', '.join(missing_columns)}"
        )

    def test_cumulative_sales_correctness(self):
        """Check that cumulative_sales is calculated correctly per region and ordering."""
        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]
        result_df = pd.DataFrame(rows, columns=columns)
        # Prepare expected cumulative sum per region, ordered by sale_date, sale_id
        self.cursor.execute("SELECT sale_id, sale_date, employee_id, region, amount FROM sales")
        all_rows = self.cursor.fetchall()
        all_columns = [desc[0] for desc in self.cursor.description]
        sales_df = pd.DataFrame(all_rows, columns=all_columns)
        # Ensure 'amount' is numeric
        sales_df['amount'] = pd.to_numeric(sales_df['amount'])
        sales_df = sales_df.sort_values(["region", "sale_date", "sale_id"]).reset_index(drop=True)
        sales_df["cumulative_sales_expected"] = sales_df.groupby("region")['amount'].cumsum()
        # Compare result rows to expected
        merged = pd.merge(result_df, sales_df, on=["sale_id", "sale_date", "employee_id", "region", "amount"], how="left")
        # Check cumulative_sales matches
        condition = True
        mismatches = []
        for idx, row in merged.iterrows():
            user_val = float(row["cumulative_sales"])
            expected_val = float(row["cumulative_sales_expected"])
            if abs(user_val - expected_val) > 0.01:
                mismatches.append({
                    "sale_id": row["sale_id"],
                    "region": row["region"],
                    "expected": expected_val,
                    "user": user_val
                })
                condition = False
        if not condition:
            msg = "Cumulative sales mismatches:\\n" + "\\n".join(
                f"sale_id {m['sale_id']} (region {m['region']}): expected {m['expected']}, got {m['user']}" for m in mismatches)
        else:
            msg = ""
        _dynamic_test(
            self,
            condition,
            "Cumulative sales values are correct for each region.",
            msg
        )

    def test_ordering(self):
        """Check that results are ordered by region, sale_date, sale_id."""
        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]
        result_df = pd.DataFrame(rows, columns=columns)
        sorted_df = result_df.sort_values(["region", "sale_date", "sale_id"]).reset_index(drop=True)
        # Compare row by row
        condition = result_df.reset_index(drop=True).equals(sorted_df)
        _dynamic_test(
            self,
            condition,
            "Result is ordered by region, sale_date, and sale_id.",
            "Result is not ordered by region, sale_date, and sale_id."
        )

    def test_cumulative_sales_resets_per_region(self):
        """Check that cumulative_sales restarts from the first row for each region."""
        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]
        result_df = pd.DataFrame(rows, columns=columns)
        # Ensure 'amount' is numeric
        result_df['amount'] = pd.to_numeric(result_df['amount'])
        result_df['cumulative_sales'] = pd.to_numeric(result_df['cumulative_sales'])
        # For each region, first row cumulative_sales == amount
        group = result_df.groupby("region")
        resets = []
        for region, g in group:
            g_sorted = g.sort_values(["sale_date", "sale_id"]).reset_index(drop=True)
            if abs(float(g_sorted.iloc[0]["cumulative_sales"]) - float(g_sorted.iloc[0]["amount"])) > 0.01:
                resets.append(region)
        condition = len(resets) == 0
        _dynamic_test(
            self,
            condition,
            "Cumulative sales resets correctly for each region.",
            f"Cumulative sales does not reset for regions: {', '.join(resets)}"
        )

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: Cumulative Sales by Region

Lösung

Challenge: Cumulative Sales by Region

Lösung