Python retry decorator implements simple retry pattern with exponential backoff algorithm. More details on blog post: https://www.favtuts.com/retry-decorator-as-python-utility-to-protect-unexpected-one-off-exceptions-while-calling-apis/

retry-decorator.py

from asyncio.log import logger
from functools import wraps
import time
import logging
import random

logger = logging.getLogger(__name__)

def log(msg, logger = None):
    if logger:
        logger.warning(msg)
    else:
        print(msg)

def retry(exceptions, total_tries=4, initial_wait=0.5, backoff_factor=2, logger=None):
    """
    calling the decorated function applying an exponential backoff.
    Args:
        exceptions: Exception(s) that trigger a retry, can be a tuple
        total_tries: Total tries
        initial_wait: Time to first retry
        backoff_factor: Backoff multiplier (e.g. value of 2 will double the delay each retry).
        logger: logger to be used, if none specified print
    """
    def retry_decorator(f):
        @wraps(f)
        def func_with_retries(*args, **kwargs):
            _tries, _delay = total_tries + 1, initial_wait
            while _tries > 1:
                try:
                    log(f'{total_tries + 2 - _tries}. try:', logger)
                    return f(*args, **kwargs)
                except exceptions as e:
                    _tries -= 1
                    print_args = args if args else 'no args'
                    if _tries == 1:
                        msg = str(f'Function: {f.__name__}\n'
                                  f'Failed despite best efforts after {total_tries} tries.\n'
                                  f'args: {print_args}, kwargs: {kwargs}')
                        log(msg, logger)
                        raise
                    msg = str(f'Function: {f.__name__}\n'
                              f'Exception: {e}\n'
                              f'Retrying in {_delay} seconds!, args: {print_args}, kwargs: {kwargs}\n')
                    log(msg, logger)
                    time.sleep(_delay)
                    _delay *= backoff_factor

        return func_with_retries
    return retry_decorator

@retry(Exception, total_tries=4, logger=logger)
def test_func(*args, **kwargs):
    rnd = random.random()
    if rnd < .2:
        raise ConnectionAbortedError('Connection was aborted :(')
    elif rnd < .4:
        raise ConnectionRefusedError('Connection was refused :/')
    elif rnd < .8:
        raise ConnectionResetError('Guess the connection was reset')
    else:
        return 'Yay!!'

@retry(Exception, total_tries=10, logger=logger)
def test_func_with_decor(*args, **kwargs):
    rnd = random.random()
    if rnd < .2:
        raise ConnectionAbortedError('Connection was aborted :(')
    elif rnd < .4:
        raise ConnectionRefusedError('Connection was refused :/')
    elif rnd < .6:
        raise ConnectionResetError('Guess the connection was reset')
    elif rnd < .8:
        raise TimeoutError('This took too long')
    else:
        return 'Yay!!'


if __name__ == '__main__':
    # TEST wrapper for specific Exception
    # wrapper = retry((ConnectionAbortedError), total_tries=3, logger=logger)
    # wrapped_test_func = wrapper(test_func)
    # print(wrapped_test_func('hi', 'bye', hi='ciao'))

    # TEST wrapper for all Exceptions
    # wrapper_all_exceptions = retry(Exception, total_tries=10, logger=logger)
    # wrapped_test_func = wrapper_all_exceptions(test_func)
    # print(wrapped_test_func('hi', 'bye', hi='ciao'))

    # TEST decorator for all Exceptions
    print(test_func_with_decor('hi', 'bye', hi='ciao'))

study_decorating_function.py

def first_func(x):
    return x**2
    
def second_func(x):
    return x - 2

# print(first_func('2'))     # output: unsupported operand type(s) for ** or pow(): 'str' and 'int'
# print(second_func('2'))    # output: unsupported operand type(s) for ** or pow(): 'str' and 'int'

def convert_to_numeric(func):

    # define a function within the outer function
    def new_func(x):
        return func(float(x))
    
    # return the newly defined function
    return new_func

#new_first_func = convert_to_numeric(first_func)
#print(new_first_func('2'))  # output: 4.0
#print(convert_to_numeric(second_func)('2')) # output: 0.0

# third function with decorator
@convert_to_numeric
def third_func(x):
    return x**2 + x - 2

print(third_func('2'))  # output : 2^2 + 2 - 2 = 4.0