Summary  
Local vs. global variables define the scope and lifetime of data in code, where global variables are accessible throughout the entire program and local variables exist only within the function or block in which they are declared.

General domain of usage  
Software development (managing variable scope in code modules)


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import sys
import io

class TestTask(unittest.TestCase):
    def test_global_x_modified(self):
        import user_code
        importlib.reload(user_code)
        x_value = getattr(user_code, 'x', None)
        _dynamic_test(
            self,
            x_value == 20,
            "Global variable x was correctly changed to 20.",
            f"Expected global x to be 20, got {x_value}",
        )

    def test_local_y_not_global(self):
        import user_code
        importlib.reload(user_code)
        has_y = hasattr(user_code, 'y')
        _dynamic_test(
            self,
            not has_y,
            "Local variable y is not accessible outside the function.",
            "Variable y should not be defined globally.",
        )

    def test_return_string_content(self):
        import user_code
        importlib.reload(user_code)
        output = getattr(user_code, 'output', None)
        expected = "x is 20, y is 5"
        _dynamic_test(
            self,
            isinstance(output, str) and output.strip() == expected,
            f"Returned string is correct: '{expected}'",
            f"Expected return string '{expected}', got '{output}'",
        )

    def test_x_printed_value(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        sys_stdout = sys.stdout
        sys.stdout = f
        importlib.reload(user_code)
        sys.stdout = sys_stdout
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "20" in output,
            "Printed output includes updated value of x.",
            f"Expected to see '20' printed in output, got: {output}",
        )

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.

Variables Locales vs. Globales

Solución