To solve this task, you need to identify the highest sale amount and the corresponding employee for each region in the `sales` table. You will use the `ROW_NUMBER()` window function to achieve this efficiently.

### Step-by-step approach

1. **Assign a row number within each region:**
   - Use `ROW_NUMBER()` as a window function to assign a unique number to each sale within its region.
   - Partition the data by the `region` column so that numbering restarts for each region.
   - Order the rows within each region by the `amount` column in descending order; this places the highest sale at the top for each region.

2. **Select only the top sale per region:**
   - After assigning row numbers, the row with `ROW_NUMBER() = 1` in each region will represent the highest sale for that region.
   - Use a subquery or common table expression (CTE) to filter and keep only those rows where the row number is 1.

3. **Return the required columns:**
   - Select the `region`, `employee_id`, and `amount` columns for the top sale in each region.



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_one_row_per_region(self):
        """Check that the result contains one row per unique region."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute("SELECT DISTINCT region FROM sales")
        expected_regions = set(row[0] for row in self.cursor.fetchall())
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        result_regions = set(row[0] for row in rows)
        _dynamic_test(
            self,
            result_regions == expected_regions,
            "Result contains one row per unique region.",
            f"Result regions {result_regions} do not match expected regions {expected_regions}."
        )

    def test_highest_sale_amount_per_region(self):
        """Check that each row shows the highest sale amount for its region."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        self.cursor.execute("SELECT region, MAX(amount) FROM sales GROUP BY region")
        max_amounts = dict(self.cursor.fetchall())
        failed = []
        for row in rows:
            region, employee_id, amount = row
            if float(amount) != float(max_amounts[region]):
                failed.append((region, amount, max_amounts[region]))
        condition = len(failed) == 0
        if not condition:
            msg = "\\n".join([f"{r}: got {a}, expected {e}" for r, a, e in failed])
            failure_msg = f"Not all rows show the highest sale amount per region:\\n{msg}"
        else:
            failure_msg = ""
        _dynamic_test(
            self,
            condition,
            "Each row shows the highest sale amount for its region.",
            failure_msg
        )

    def test_only_one_row_per_region_even_on_tie(self):
        """Check that only one row per region is returned, even if there are ties."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        region_counts = {}
        for row in rows:
            region = row[0]
            region_counts[region] = region_counts.get(region, 0) + 1
        failed = [r for r, c in region_counts.items() if c != 1]
        condition = len(failed) == 0
        if not condition:
            failure_msg = f"Regions with more than one row: {failed}"
        else:
            failure_msg = ""
        _dynamic_test(
            self,
            condition,
            "Only one row per region is returned, even if there are ties.",
            failure_msg
        )

    def test_uses_row_number_window_function(self):
        """Check that the query uses ROW_NUMBER() OVER (PARTITION BY region ORDER BY amount DESC)."""
        with open("query.sql", "r") as file:
            query = file.read().lower().replace('\\n', ' ').replace('\\r', ' ')
        has_row_number = (
            "row_number" in query
            and "over" in query
            and "partition by" in query
            and "region" in query
            and "order by" in query
            and "amount" in query
            and "desc" in query
        )
        _dynamic_test(
            self,
            has_row_number,
            "Query uses ROW_NUMBER() OVER (PARTITION BY region ORDER BY amount DESC).",
            "Query does not use ROW_NUMBER() OVER (PARTITION BY region ORDER BY amount DESC)."
        )


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):
    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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Explore the foundational concepts of window functions, their syntax, and how they differ from traditional aggregate functions. Learn to apply basic window functions for analytics.

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Apply window functions to solve complex business problems, including advanced framing, multi-function queries, and real-world analytics.

Challenge: Top Sales per Region

Step-by-step approach

Soluzione

Challenge: Top Sales per Region

Step-by-step approach

Soluzione