import unittest
import psycopg2
import pandas as pd
import re

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_row_count(self):
        """Check that the query returns one row for each customer."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute("SELECT COUNT(*) FROM customers;")
        expected_count = self.cursor.fetchone()[0]
        self.cursor.execute(query)
        result = self.cursor.fetchall()
        _dynamic_test(
            self,
            len(result) == expected_count,
            f"Query returns {expected_count} rows as expected.",
            f"Query should return {expected_count} rows, but returned {len(result)}."
        )

    def test_uppercase_and_length(self):
        """Check that the uppercase_name and name_length columns are correct for each customer."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute("SELECT name FROM customers;")
        customer_names = [r[0] for r in self.cursor.fetchall()]
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        for i, name in enumerate(customer_names):
            if i >= len(rows):
                _dynamic_test(
                    self,
                    False,
                    "",
                    f"Missing row for customer: {name}"
                )
                continue
            uppercase_name, name_length = rows[i]
            expected_upper = name.upper()
            expected_length = len(name)
            cond_upper = uppercase_name == expected_upper
            cond_length = name_length == expected_length
            _dynamic_test(
                self,
                cond_upper,
                f"Uppercase name is correct for '{name}'.",
                f"Uppercase name incorrect for '{name}': expected '{expected_upper}', got '{uppercase_name}'."
            )
            _dynamic_test(
                self,
                cond_length,
                f"Name length is correct for '{name}'.",
                f"Length incorrect for '{name}': expected {expected_length}, got {name_length}."
            )

    def test_column_names(self):
        """Check that the column names are 'uppercase_name' and 'name_length'."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        cond = (normalize_text(columns[0]) == "uppercase_name" and normalize_text(columns[1]) == "name_length")
        _dynamic_test(
            self,
            cond,
            "Column names are correct.",
            f"Column names should be 'uppercase_name' and 'name_length', but got {columns}."
        )

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

組み込みSQL関数を活用して、文字列、日付、数値、論理値、NULL、JSONデータを操作する方法を習得します。本コースでは、実践的なアプローチでSQL関数の理解と応用を深め、データのクエリや変換スキルを向上させます。

データベース内のテキストデータを操作および分析するための、SQLの強力な文字列関数を学習します。

SQLの日時関数を習得し、時系列データを効果的に扱う。

SQLの数値関数を使用した計算およびデータ分析の習得。

SQLの論理関数を活用して、クエリのフローを制御し、データに基づいた意思決定を行うことができます。

SQLで特殊な関数を使用してNULL値の扱いを習得します。

SQLでJSONデータを扱うための、クエリおよび操作用の専用関数の学習。

チャレンジ：顧客名の標準化

解答