Summary  
This chapter explains the distinction between local variables, which exist only within the function or block where they’re defined, and global variables, which are accessible throughout the entire program, including rules for accessing and modifying each.

General domain of usage  
Software development (managing variable scope and state across functions)


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

In this course, you will learn advanced topics such as function arguments, iterators, generators, closures, and decorators. Dozens of practice exercises will help you grasp these topics.

Explore the art of packing and unpacking lists, tuples, and dictionaries to write more expressive and efficient Python code.

Delve into the world of higher-order functions, passing functions as arguments, and writing concise anonymous functions with lambda.

Unravel the mysteries of variable scope, and learn how nested functions and closures enable powerful encapsulation.

Delve into Python's iteration protocol, custom iterators, and generator functions for efficient data processing.

Local vs. Global Variables

Oplossing