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_groups_by_sale_date(self):
        """Check if the query groups by sale_date."""
        with open("query.sql", "r") as file:
            query = file.read()
        lowered = normalize_text(query)
        has_group_by = "group by" in lowered and "sale_date" in lowered
        _dynamic_test(
            self,
            has_group_by,
            "Query groups by sale_date.",
            "Query does not group by sale_date."
        )

    def test_counts_sales_per_day(self):
        """Check if the query counts sales per day."""
        with open("query.sql", "r") as file:
            query = file.read()
        lowered = normalize_text(query)
        has_count = "count(" in lowered
        _dynamic_test(
            self,
            has_count,
            "Query counts number of sales per day.",
            "Query does not count number of sales per day."
        )

    def test_filters_days_with_more_than_2_sales(self):
        """Check if only days with more than 2 sales are included in the result."""
        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)
        # All sales_count values should be greater than 2
        if not result_df.empty and "sales_count" in result_df.columns:
            all_gt_2 = (result_df["sales_count"] > 2).all()
        else:
            all_gt_2 = result_df.empty  # If empty, that's okay
        _dynamic_test(
            self,
            all_gt_2,
            "All days in result have more than 2 sales.",
            "Result contains days with 2 or fewer sales."
        )

    def test_column_names(self):
        """Check that result columns are sale_date and sales_count."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        has_sale_date = "sale_date" in columns
        has_sales_count = "sales_count" in columns
        _dynamic_test(
            self,
            has_sale_date and has_sales_count,
            "Result columns are sale_date and sales_count.",
            "Result columns do not include sale_date and sales_count."
        )

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

実践的なデータ分析スキルに焦点を当て、SQLを基礎から学べる包括的で初心者向けのコースです。分かりやすい解説、実際の事例、実践的な課題を通じて、SQLを用いたデータのクエリ、変換、分析を習得します。

リレーショナルデータベースとSQLの基礎概念を学習します。テーブルからデータを取得、フィルタリング、並べ替える方法を探求し、より高度なデータ分析の基礎を築きます。

SQLの強力な集計機能を深く学びます。データを要約し、グループ化し、より深い洞察のために分析する方法を習得します。

複数のテーブルからデータを組み合わせる力を引き出します。さまざまな種類の結合を実行し、複雑なビジネス上の質問に答える方法を学びます。

サブクエリ、ウィンドウ関数、データ変換などを含む高度なSQL技術を習得し、強力なデータ分析を実現します。

チャレンジ：デイリーセールスサマリー

解答