Aggregate code timing in Python

Consolidated stats on code timing in a function

There are many ways to get how much time a function takes in Python. Here is an easy decorator implementation to check how much time a function takes.

from functools import wraps
import time

def timeit(func):
    @wraps(func)
    def timeit_wrapper(*args, **kwargs):
        start_time = time.perf_counter()
        result = func(*args, **kwargs)
        end_time = time.perf_counter()
        total_time = end_time - start_time
        print(f'Function {func.__name__}{args} {kwargs} took {total_time:.4f} seconds')
        return result
    return timeit_wrapper
@timeit
def my_func():
    # do stuff

This decorator can be used by decorating a function to get the time spent. What if you want to aggregate this timing data in a timeframe, like how many times this function has been called, and what the maximum time is taken, for that, we need to use a library called codetiming. Here is a sample use case:

# pip install codetiming
# pip install humanfriendly
from codetiming import Timer
from humanfriendly import format_timespan
from loguru import logger

@Timer(name="my_func", text=lambda secs: f"my_func elapsed time: {format_timespan(secs)}")
def my_func():
    ...