Python Best Practices

Wrap a Generator with a Context Manager

Wrapping a generator with the contextmanager decorator helps clean up resources when the context manager is exited. To convert a generator function into a context manager, use contextmanager from contextlib.

from contextlib import contextmanager

@contextmanager
def file_reader(filename):
    try:
        # Open the file
        f = open(filename, 'r')
        yield f  # Yield the file object to the caller
    finally:
        # Cleanup: Close the file
        f.close()

# Example usage
with file_reader('example.txt') as f:
    for line in f:
        print(line.strip())

Receive input arguments from the console, split them using space as the delimiter, and convert each element to an integer in a single line.

The input() function treats all characters as str by default.

arr = map(int, input().split())

from None in raise

def dequeue(self):
    try:
        return self._items.popleft()
    except IndexError:
        raise IndexError("dequeue from an empty queue") from None

from None in the raise statement is used in situations where the originally raised exception and the exception raised by the raise statement are of the same exception class (e.g., IndexError).

When an IndexError is raised and caught, and another IndexError is raised, both the user-defined message and the original exception message are shown. Using from None suppresses the original exception message, so only the user-defined message is displayed.

The three basic built-in sequence types in Python are lists, tuples, and strings.

list_iterator

In the code snippet below, number_iterator is a list_iterator and is used to save memory by storing only one element at a time in memory.

The list_iterator allows you to access the elements of a list (or any iterable) one at a time, rather than loading all elements into memory at once. This is useful when working with large datasets or when you only need to process elements sequentially, saving memory.

numbers = [random.randint(1, 20) for _ in range(20)]
numbers_iterator = iter(numbers)