
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib

class TestTask(unittest.TestCase):
    def test_merge_all_keys(self):
        import user_code
        importlib.reload(user_code)
        d1 = {'a': 1, 'b': 2}
        d2 = {'b': 3, 'c': 4}
        result = user_code.merge_dicts(d1, d2)
        expected = {'a': 1, 'b': 3, 'c': 4}
        _dynamic_test(
            self,
            isinstance(result, dict) and result == expected,
            "Merged dictionary contains all keys and correct values when keys overlap.",
            f"Expected {expected}, got {result}"
        )

    def test_merge_with_empty_first(self):
        import user_code
        importlib.reload(user_code)
        d1 = {}
        d2 = {'x': 10}
        result = user_code.merge_dicts(d1, d2)
        expected = {'x': 10}
        _dynamic_test(
            self,
            isinstance(result, dict) and result == expected,
            "Merging with an empty first dictionary works.",
            f"Expected {expected}, got {result}"
        )

    def test_merge_with_empty_second(self):
        import user_code
        importlib.reload(user_code)
        d1 = {'foo': 9}
        d2 = {}
        result = user_code.merge_dicts(d1, d2)
        expected = {'foo': 9}
        _dynamic_test(
            self,
            isinstance(result, dict) and result == expected,
            "Merging with an empty second dictionary works.",
            f"Expected {expected}, got {result}"
        )

    def test_no_modification_of_inputs(self):
        import user_code
        importlib.reload(user_code)
        d1 = {'a': 1, 'b': 2}
        d2 = {'b': 3, 'c': 4}
        d1_copy = d1.copy()
        d2_copy = d2.copy()
        result = user_code.merge_dicts(d1, d2)
        _dynamic_test(
            self,
            d1 == d1_copy and d2 == d2_copy,
            "Input dictionaries are not modified after merge.",
            f"Input dictionaries were modified. d1: {d1}, d2: {d2}"
        )

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()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        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] = []
        changed = True

    return '\\n'.join(lines) if changed else code

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


test_main.py

Domina el empaquetado/desempaquetado, argumentos flexibles en funciones, funciones de orden superior, lambdas, alcance de variables, closures, iteradores y decoradores. Diseñado para estudiantes que han completado un curso introductorio de Python y desean escribir código más eficiente, expresivo y potente.

Explora el arte de empaquetar y desempaquetar listas, tuplas y diccionarios para escribir código Python más expresivo y eficiente.

Adentrarse en el mundo de las funciones de orden superior, el paso de funciones como argumentos y la escritura de funciones anónimas concisas con lambda.

Descubre los misterios del alcance de las variables y aprende cómo las funciones anidadas y los closures permiten una encapsulación poderosa.

Profundización en el protocolo de iteración de Python, iteradores personalizados y funciones generadoras para el procesamiento eficiente de datos.

Challenge: Merging Dictionaries With Unpacking

Solución