Summary  
The chapter explains how to use Python’s built-in `sorted` higher-order function to produce a new sorted list from any iterable—by default in ascending order or with `reverse=True`—and how to customize the sort order using the `key` parameter with functions like `len` or lambdas.  

General domain of usage  
Data processing and organization

Sorting data is a common operation when working with lists in Python. The `sorted` function is a built-in higher-order function that allows you to sort any iterable and returns a new sorted list, leaving the original data unchanged. You can use `sorted` with or without custom sorting logic. By default, `sorted` arranges elements in ascending order, but you can control the sort order and criteria using its optional parameters.

The most important parameters for `sorted` are:
- `iterable`: the data you want to sort;
- `key`: an optional function that determines the value to sort by for each element;
- `reverse`: a Boolean value that, if set to `True`, sorts the data in descending order.

Suppose you have a list of numbers and want to sort them in ascending order. You can simply call `sorted(numbers)`. If you want to sort the numbers in descending order, you can pass `reverse=True`.



numbers = [9, 5, 2, 1]

# Sorting in reverse order
sorted_desc = sorted(numbers, reverse=True)
print(sorted_desc)  

You can also use the `key` parameter to sort more complex data structures, such as lists of tuples or dictionaries, by specifying a function that extracts the comparison value from each element.

words = ["apple", "kiwi", "banana"]

# Sorting by the length of each string
sorted_words = sorted(words, key=len)
print(sorted_words)  

You can pass any function to the `key` parameter, including a **lambda function** or a previously defined function. This makes `sorted` a **higher-order function**, as it accepts another function as an argument. Sorting is not limited to numbers or strings; you can sort complex objects as long as you provide a suitable `key` function.

The original iterable is not changed by `sorted`, it always returns a new list. If you want to sort a list in place, use the `list.sort()` method instead.

Note

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

class TestTask(unittest.TestCase):
    def test_sorted_pairs_exists_and_is_list(self):
        import user_code
        importlib.reload(user_code)
        result = getattr(user_code, 'sorted_pairs', None)
        _dynamic_test(
            self,
            isinstance(result, list),
            "sorted_pairs is a list.",
            f"sorted_pairs should be a list, got {type(result)}."
        )
    def test_sorted_pairs_correct_order(self):
        import user_code
        importlib.reload(user_code)
        result = getattr(user_code, 'sorted_pairs', None)
        expected = [(4, 1), (2, 2), (1, 3)]
        _dynamic_test(
            self,
            result == expected,
            "sorted_pairs is sorted by the second tuple element.",
            f"Expected {expected}, got {result}."
        )
    def test_sorted_pairs_with_other_data(self):
        code = ""
        with open("user_code.py", "r") as f:
            code = f.read()
        test_pairs = [(5, 10), (2, -1), (7, 5)]
        code = change_var(code, "pairs", repr(test_pairs))
        ns = {}
        exec(code, ns)
        actual = ns.get('sorted_pairs', None)
        expected = [(2, -1), (7, 5), (5, 10)]
        _dynamic_test(
            self,
            isinstance(actual, list) and actual == expected,
            "sorted_pairs sorts different input correctly.",
            f"Expected {expected}, got {actual}."
        )
    def test_get_second_element_function(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'get_second_element', None)
        _dynamic_test(
            self,
            callable(func),
            "get_second_element is defined and callable.",
            "get_second_element function is not defined or not callable."
        )
        if callable(func):
            result = func((99, 123))
            _dynamic_test(
                self,
                result == 123,
                "get_second_element returns the second element of the tuple.",
                f"Expected 123, 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()
    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

Master packing/unpacking, flexible function arguments, higher-order functions, lambdas, variable scope, closures, iterators and decorators. Designed for learners who have completed an introductory Python course and want to write more efficient, expressive, and powerful code.

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.

Sorted Fundamentals

Solution