
import unittest
import user_code
import ast
import re   
import unittest
import importlib
import io
from contextlib import redirect_stdout

class TestTask(unittest.TestCase):
    def test_average_multiple_rows(self):
        import user_code
        importlib.reload(user_code)
        csv_data = "name,age\\nAlice,30\\nBob,25\\nCharlie,35"
        result = user_code.average_age(csv_data)
        _dynamic_test(
            self,
            isinstance(result, float) and abs(result - 30.0) < 1e-7,
            "Returns correct average for multiple rows.",
            f"Expected 30.0, got {result}"
        )

    def test_average_single_row(self):
        import user_code
        importlib.reload(user_code)
        csv_data = "name,age\\nAlice,42"
        result = user_code.average_age(csv_data)
        _dynamic_test(
            self,
            isinstance(result, float) and abs(result - 42.0) < 1e-7,
            "Returns correct average for single row.",
            f"Expected 42.0, got {result}"
        )

    def test_ages_are_integers(self):
        import user_code
        importlib.reload(user_code)
        csv_data = "name,age\\nTom,10\\nAnn,20\\nSue,30"
        result = user_code.average_age(csv_data)
        _dynamic_test(
            self,
            isinstance(result, float) and abs(result - 20.0) < 1e-7,
            "Handles integer ages.",
            f"Expected 20.0, got {result}"
        )

    def test_returns_float(self):
        import user_code
        importlib.reload(user_code)
        csv_data = "name,age\\nBob,51\\nSam,49"
        result = user_code.average_age(csv_data)
        _dynamic_test(
            self,
            isinstance(result, float),
            "Returns a float value.",
            f"Expected float, got {type(result)}"
        )

    def test_empty_data(self):
        import user_code
        importlib.reload(user_code)
        csv_data = "name,age"
        result = user_code.average_age(csv_data)
        _dynamic_test(
            self,
            isinstance(result, float) and result == 0.0,
            "Returns 0.0 for no data rows.",
            f"Expected 0.0, got {result}"
        )

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()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    for i, node in enumerate(tree.body):
        if isinstance(node, ast.Assign):
            for target in node.targets:
                if isinstance(target, ast.Name) and target.id == var_name:
                    start_line = node.lineno - 1
                    line = lines[start_line]
                    indent = ' ' * (len(line) - len(line.lstrip()))
                    lines[start_line] = f"{indent}{var_name} = {value}"
                    next_line = len(lines)
                    for next_node in tree.body[i+1:]:
                        if hasattr(next_node, 'lineno'):
                            next_line = next_node.lineno - 1
                            break
                    if next_line > start_line + 1:
                        lines[start_line+1:next_line] = []
                    
                    return '\\n'.join(lines)
    return code

if __name__ == "__main__":
    unittest.main()


test_main.py

A beginner-friendly course focused on mastering string manipulation and handling various data formats in Python. Learn essential techniques for processing, formatting, and converting data, with hands-on practice and quizzes to reinforce your understanding.

Learn the core techniques for working with strings in Python, including slicing, searching, and modifying text.

Develop skills for formatting strings and converting between data types for effective data presentation and processing.

Gain practical experience handling structured data formats such as CSV and JSON using Python's built-in capabilities.

Challenge: CSV Data Processing

解答