add processed data

data/processed/f_327_jenny_w_doc.py

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+import random
+import matplotlib.pyplot as plt
+
+
+def f_327(points: int):
+    """
+    Generate a plot of random numbers such that indices are on the x-axis and generated numbers are on the y-axis.
+
+    Parameters:
+    - points (int): Number of random points to generate.
+
+    Returns:
+    - Returns a tuple containing:
+        - A list of generated random numbers.
+        - A matplotlib Axes object representing the plot.
+
+    Requirements:
+    - random
+    - matplotlib.pyplot
+
+    Example:
+    >>> import random
+    >>> random.seed(0)
+    >>> f_327(5)
+    ([0.8444218515250481, 0.7579544029403025, 0.420571580830845, 0.25891675029296335, 0.5112747213686085], <Axes: >)
+    >>> f_327(3)
+    ([0.4049341374504143, 0.7837985890347726, 0.30331272607892745], <Axes: >)
+    """
+    x = list(range(points))
+    y = [random.random() for _ in range(points)]
+
+    _, ax = plt.subplots()
+    ax.plot(x, y)
+
+    return y, ax
+
+import unittest
+import random
+class TestCases(unittest.TestCase):
+    def test_case_1(self):
+        random.seed(0)
+        y, _ = f_327(5)
+        # Test correct number of points are generated
+        self.assertEqual(len(y), 5)
+    def test_case_2(self):
+        random.seed(0)
+        y, _ = f_327(5)
+        # Test expected values
+        self.assertTrue(all(0 <= num <= 1 for num in y))
+        self.assertAlmostEqual(
+            y,
+            [
+                0.8444218515250481,
+                0.7579544029403025,
+                0.420571580830845,
+                0.25891675029296335,
+                0.5112747213686085,
+            ],
+        )
+    def test_case_3(self):
+        random.seed(0)
+        # Test incorrect data types
+        with self.assertRaises(TypeError):
+            f_327("5")
+        with self.assertRaises(TypeError):
+            f_327([])
+        with self.assertRaises(TypeError):
+            f_327(None)
+    def test_case_4(self):
+        random.seed(0)
+        # Test handling 1 number
+        y, ax = f_327(1)
+        # Assert that 1 random number is generated
+        self.assertEqual(len(y), 1)
+        # Assert that the plot has the correct x and y data
+        self.assertEqual(list(ax.lines[0].get_xdata()), [0])
+        self.assertEqual(list(ax.lines[0].get_ydata()), y)
+    def test_case_5(self):
+        random.seed(0)
+        # Test handling no random numbers
+        y, ax = f_327(0)
+        self.assertEqual(len(y), 0)
+        # Assert that the plot has no data
+        self.assertEqual(list(ax.lines[0].get_xdata()), [])
+        self.assertEqual(list(ax.lines[0].get_ydata()), [])
+    def tearDown(self):
+        plt.close("all")

data/processed/f_328_jenny_wo_doc.py

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+import sqlite3
+import pandas as pd
+
+
+def f_328(db_file: str, query: str) -> pd.DataFrame:
+    """Query an SQLite database and return the results.
+
+    This function connects to a given SQLite database, executes a given SQL query,
+    and returns the results as a pandas DataFrame.
+
+    Parameters:
+    - db_file (str): Path to the SQLite database file.
+    - query (str): SQL query to execute.
+
+    Returns:
+    - pd.DataFrame: A DataFrame containing the results of the executed query.
+
+    Requirements:
+    - sqlite3
+    - pandas
+
+    Example:
+    >>> db_file = 'sample_database.db'
+    >>> df = f_328(db_file, "SELECT * FROM users WHERE name = 'John Doe'")
+    pd.DataFrame:
+    id        name  age
+    --  ----------  ---
+    ..  John Doe   ..
+    >>> df = f_328(db_file, "SELECT age, COUNT(*) AS count FROM users GROUP BY age")
+    pd.DataFrame:
+    age  count
+    ---  -----
+    25   3
+    """
+    with sqlite3.connect(db_file) as conn:
+        return pd.read_sql_query(query, conn)
+
+import unittest
+import sqlite3
+from faker import Faker
+import os
+class TestCases(unittest.TestCase):
+    fake = Faker()
+    specific_names = [
+        "John Doe",
+        "Jane Smith",
+        "Alice Brown",
+        "Bob White",
+        "Charlie Green",
+    ]
+    specific_ages = [25, 30, 35, 40, 45]
+    @classmethod
+    def setUpClass(cls):
+        """Set up test data before running tests."""
+        cls.db_file = cls.generate_test_data_with_file()
+    @staticmethod
+    def generate_test_data_with_file() -> str:
+        """Generate test data and save it to a temporary SQLite database file."""
+        db_file = "./temp_test_db.sqlite3"
+        if os.path.exists(db_file):
+            os.remove(db_file)
+        conn = sqlite3.connect(db_file)
+        create_table_query = """
+        CREATE TABLE users (
+            id INTEGER PRIMARY KEY,
+            name TEXT NOT NULL,
+            age INTEGER NOT NULL
+        )
+        """
+        conn.execute(create_table_query)
+        for _ in range(100):
+            name = TestCases.fake.name()
+            age = TestCases.fake.random_int(min=20, max=70)
+            conn.execute("INSERT INTO users (name, age) VALUES (?, ?)", (name, age))
+        for name, age in zip(TestCases.specific_names, TestCases.specific_ages):
+            conn.execute("INSERT INTO users (name, age) VALUES (?, ?)", (name, age))
+        conn.commit()
+        conn.close()
+        return db_file
+    def test_case_1(self):
+        """Test fetching all users."""
+        df = f_328(self.db_file, "SELECT * FROM users")
+        self.assertEqual(len(df), 100 + len(self.specific_names))
+        for name in self.specific_names:
+            self.assertIn(name, df["name"].values)
+    def test_case_2(self):
+        """Test fetching specific users based on names."""
+        names_as_strings = "', '".join(self.specific_names)
+        df = f_328(
+            self.db_file,
+            f"SELECT name, age FROM users WHERE name IN ('{names_as_strings}')",
+        )
+        for name in self.specific_names:
+            self.assertIn(name, df["name"].values)
+        for age in self.specific_ages:
+            self.assertIn(age, df["age"].values)
+    def test_case_3(self):
+        """Test fetching users based on age condition."""
+        age_limit = self.fake.random_int(min=20, max=60)
+        df = f_328(self.db_file, f"SELECT * FROM users WHERE age > {age_limit}")
+        self.assertTrue(all(df["age"] > age_limit))
+    def test_case_4(self):
+        """Test fetching users and sorting by name."""
+        df = f_328(self.db_file, "SELECT * FROM users ORDER BY name")
+        sorted_names = sorted(df["name"].tolist())
+        self.assertListEqual(df["name"].tolist(), sorted_names)
+    def test_case_5(self):
+        """Test fetching users based on age and sorting by age."""
+        age_limit = self.fake.random_int(min=20, max=30)
+        df = f_328(
+            self.db_file,
+            f"SELECT * FROM users WHERE age < {age_limit} ORDER BY age DESC",
+        )
+        self.assertTrue(all(df["age"] < age_limit))
+        self.assertTrue(
+            all(df["age"].iloc[i] >= df["age"].iloc[i + 1] for i in range(len(df) - 1))
+        )
+    @classmethod
+    def tearDownClass(cls):
+        """Clean up test data after running tests."""
+        os.remove(cls.db_file)

data/processed/f_329_jenny_wo_doc.py

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+import pandas as pd
+import json
+
+
+def f_329(data: dict, output_path: str = "./default_data_output.json") -> str:
+    """Converts the given DataFrame to a dictionary, dropping the column named 'c'
+    if it exists, and then saves it as a JSON file.
+
+    Parameters:
+    - data (dict): The input data dictionary.
+    - output_path (str, optional): The path where the JSON file should be saved. Default is './default_data_output.json'.
+
+    Returns:
+    - str: Path where the JSON file was saved.
+
+    Requirements:
+    - pandas
+    - json
+
+    Example:
+    >>> f_329({'a': [1,2], 'b': [3,4], 'c': [5,6]})
+    './default_data_output.json'
+    >>> f_329({'a': [1,2], 'b': [3,4], 'c': [5,6]}, 'custom/path/results.json')
+    'custom/path/results.json'
+    """
+    df = pd.DataFrame(data)
+    # Drop column named 'c' if it exists
+    df = df.drop(columns="c", errors="ignore")
+    # Convert the DataFrame to dictionary
+    data_dict = df.to_dict(orient="dict")
+    # Save the dictionary as a JSON file
+    with open(output_path, "w") as file:
+        json.dump(data_dict, file)
+
+    return output_path
+
+import unittest
+import pandas as pd
+import json
+import os
+class TestCases(unittest.TestCase):
+    def read_json_file(self, path):
+        # Helper function to read content from a JSON file
+        with open(path, "r") as f:
+            return json.load(f)
+    def tearDown(self):
+        # Cleanup procedure after each test to remove generated files
+        files_to_remove = [
+            "./default_data_output.json",
+            "./custom_data_output_2.json",
+            "./custom_data_output_3.json",
+            "./custom_data_output_4.json",
+            "./custom_data_output_5.json",
+        ]
+        for file in files_to_remove:
+            if os.path.exists(file):
+                os.remove(file)
+    def convert_keys_to_str(self, dictionary):
+        # Convert dictionary keys to strings recursively
+        if not isinstance(dictionary, dict):
+            return dictionary
+        return {str(k): self.convert_keys_to_str(v) for k, v in dictionary.items()}
+    def test_case_1(self):
+        # Test basic DataFrame with column "c"
+        data = {"a": [1, 2], "b": [3, 4], "c": [5, 6]}
+        df = pd.DataFrame(data)
+        output_path = f_329(data)
+        self.assertTrue(os.path.exists(output_path))
+        expected_data = self.convert_keys_to_str(
+            df.drop(columns="c").to_dict(orient="dict")
+        )
+        self.assertEqual(self.read_json_file(output_path), expected_data)
+    def test_case_2(self):
+        # Test DataFrame with non-numeric data and column "c"
+        data = {"name": ["Alice", "Bob"], "country": ["USA", "Canada"], "c": ["x", "y"]}
+        df = pd.DataFrame(data)
+        custom_path = "./custom_data_output_2.json"
+        output_path = f_329(data, custom_path)
+        self.assertTrue(os.path.exists(output_path))
+        expected_data = self.convert_keys_to_str(
+            df.drop(columns="c").to_dict(orient="dict")
+        )
+        self.assertEqual(self.read_json_file(output_path), expected_data)
+    def test_case_3(self):
+        # Test DataFrame with multiple columns and no column "c"
+        data = {"age": [25, 30], "height": [170, 175]}
+        df = pd.DataFrame(data)
+        custom_path = "./custom_data_output_3.json"
+        output_path = f_329(data, custom_path)
+        self.assertTrue(os.path.exists(output_path))
+        expected_data = self.convert_keys_to_str(df.to_dict(orient="dict"))
+        self.assertEqual(self.read_json_file(output_path), expected_data)
+    def test_case_4(self):
+        # Test DataFrame with mixed data types including column "c"
+        data = {
+                "id": [1, 2],
+                "is_student": [True, False],
+                "grades": ["A", "B"],
+                "c": [0.5, 0.8],
+            }
+        df = pd.DataFrame(data)
+        output_path = f_329(data)
+        self.assertTrue(os.path.exists(output_path))
+        expected_data = self.convert_keys_to_str(
+            df.drop(columns="c").to_dict(orient="dict")
+        )
+        self.assertEqual(self.read_json_file(output_path), expected_data)
+    def test_case_5(self):
+        # Test an empty DataFrame
+        data = {}
+        df = pd.DataFrame(data)
+        custom_path = "./custom_data_output_5.json"
+        output_path = f_329(data, custom_path)
+        self.assertTrue(os.path.exists(output_path))
+        expected_data = self.convert_keys_to_str(df.to_dict(orient="dict"))
+        self.assertEqual(self.read_json_file(output_path), expected_data)