Summary  
The chapter explains how to use the `filter` higher-order function to select elements from an iterable by applying a predicate function that returns True or False, yielding only the items that satisfy the condition.

General domain of usage  
Data processing

You have already seen how higher-order functions like `map` allow you to apply a function to each element in a sequence. Another essential higher-order function in Python is **`filter`**. 

The `filter` function is used to select elements from a sequence based on whether they satisfy a certain condition. It takes two arguments: a function that returns `True` or `False` for each element, and the sequence to filter. The result is an iterator containing only those elements for which the function returns `True`. This makes `filter` especially useful when you want to extract specific items from a list, tuple, or other iterable based on a criterion, such as even numbers, positive values, or strings of a certain length.

def is_even(n):
    return n % 2 == 0

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = list(filter(is_even, numbers))
print(even_numbers)

This code uses the `filter()` function to create a new list containing only the even integers from an existing collection. It works by defining a predicate function, `is_even(n)`. The `filter` function iterates through the numbers list, applying this check to every element and discarding any that fail the condition. Finally, the resulting filter object is converted back into a list, producing the output.

When you use the `filter` function, it returns a **filter object**, which is an iterator — not a list. If you print the result of `filter` directly, you will see output similar to:

```
<filter object at 0xfffec885eb30>
```

To access the filtered values as a list, you must convert the filter object using `list()`.

Note


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

class TestTask(unittest.TestCase):
    def test_returns_only_positive(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.filter_positive([-3, -1, 0, 2, 4, -2])
        expected = [2, 4]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Returns only positive numbers from mixed list",
            f"Expected {expected}, got {result}",
        )

    def test_returns_empty_when_no_positive(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.filter_positive([-5, -1, 0, -2])
        expected = []
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Returns empty list when no positives present",
            f"Expected {expected}, got {result}",
        )

    def test_all_positive(self):
        import user_code
        importlib.reload(user_code)
        numbers = [1, 2, 3, 4, 5]
        result = user_code.filter_positive(numbers)
        expected = [1, 2, 3, 4, 5]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Returns all numbers when all are positive",
            f"Expected {expected}, got {result}",
        )

    def test_all_non_positive(self):
        import user_code
        importlib.reload(user_code)
        numbers = [0, -1, -2, -3]
        result = user_code.filter_positive(numbers)
        expected = []
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Returns empty list for all non-positive input",
            f"Expected {expected}, got {result}",
        )

    def test_does_not_modify_original(self):
        import user_code
        importlib.reload(user_code)
        numbers = [-3, 0, 1, 5]
        numbers_copy = numbers[:]
        user_code.filter_positive(numbers)
        _dynamic_test(
            self,
            numbers == numbers_copy,
            "Original input list is not modified",
            f"Input list was modified: expected {numbers_copy}, got {numbers}",
        )

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.

Filter Function in use

Oplossing