SQLの**JSON関数**の基本を学んだ後、構造化データを含む`JSON`カラムを扱う実践的なチャレンジに取り組みます。`user_profiles`テーブルにはユーザー情報が格納されており、`profile_data`カラムには`JSON`オブジェクトが保存されています。各オブジェクトには`age`、`address`、そしてこの課題で特に重要な`interests`配列などのユーザー属性が含まれています。SQLのJSON関数を使用して、各ユーザーの`interests`配列を抽出することが目標です。

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_all_user_ids_returned(self):
        """Check that all user IDs are present in the result."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        user_ids = set(row[0] for row in rows)
        self.cursor.execute("SELECT user_id FROM user_profiles;")
        expected_ids = set(row[0] for row in self.cursor.fetchall())
        _dynamic_test(
            self,
            user_ids == expected_ids,
            "All user IDs are returned in the result.",
            f"Missing or extra user IDs in result. Expected: {expected_ids}, Got: {user_ids}"
        )

    def test_interests_array_extracted(self):
        """Check that the interests array is correctly extracted for each user."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        result_rows = self.cursor.fetchall()
        self.cursor.execute("SELECT user_id, profile_data->'interests' FROM user_profiles;")
        expected_rows = self.cursor.fetchall()
        result_dict = {row[0]: row[1] for row in result_rows}
        expected_dict = {row[0]: row[1] for row in expected_rows}
        _dynamic_test(
            self,
            result_dict == expected_dict,
            "Interests array is correctly extracted for every user.",
            "Interests array is not correctly extracted for every user."
        )

    def test_result_columns(self):
        """Check that the result includes both user_id and interests columns."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        _dynamic_test(
            self,
            ('user_id' in columns and 'interests' in columns),
            "Result includes both user_id and interests columns.",
            f"Result columns are: {columns}, expected: user_id and interests."
        )

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。本モジュールでは、実践的なアプローチでSQL関数の理解と応用を深め、現実のシナリオにおけるデータクエリおよび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。

チャレンジ：ユーザーの興味を抽出

解答