Python精确统计函数执行时间的多种方法

import time

start_time = time.time()  # 记录开始时间
your_function()          # 执行目标函数
end_time = time.time()   # 记录结束时间

execution_time = end_time - start_time
print(f"函数执行时间: {execution_time:.6f}秒")

import time

start = time.perf_counter()  # 高精度计时器
your_function()
end = time.perf_counter()

print(f"函数执行时间: {end - start:.6f}秒")

import timeit

# 测量单次执行
time_taken = timeit.timeit('your_function()', 
                          setup='from __main__ import your_function',
                          number=1)
print(f"执行时间: {time_taken:.6f}秒")

# 测量多次执行求平均
repeat = 1000
total_time = timeit.timeit('your_function()', 
                         setup='from __main__ import your_function',
                         number=repeat)
print(f"平均执行时间: {total_time/repeat:.6f}秒")

import time
import functools

def timer(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.perf_counter()
        result = func(*args, **kwargs)
        end_time = time.perf_counter()
        print(f"{func.__name__} 执行时间: {end_time - start_time:.6f}秒")
        return result
    return wrapper

@timer
def example_function(n):
    return sum(i*i for i in range(n))

example_function(1000000)

from contextlib import contextmanager
import time

@contextmanager
def timer_context(name):
    start = time.perf_counter()
    yield
    end = time.perf_counter()
    print(f"{name} 执行时间: {end - start:.6f}秒")

with timer_context("复杂计算"):
    # 在这里执行需要计时的代码
    result = sum(i*i for i in range(1000000))

import cProfile

def your_function():
    # 函数实现
    pass

# 运行性能分析
profiler = cProfile.Profile()
profiler.enable()
your_function()
profiler.disable()
profiler.print_stats(sort='time')

import asyncio

async def task():
    start = asyncio.get_event_loop().time()  # 事件循环内部时钟
    await asyncio.sleep(1)
    end = asyncio.get_event_loop().time()
    print(f"耗时: {end - start:.2f}秒")  # 准确记录协程生命周期

asyncio.run(task())

async def pure_cpu_work():
    start = time.perf_counter()  # 仅测量CPU计算部分
    result = sum(i*i for i in range(10**6))
    end = time.perf_counter()
    print(f"CPU计算耗时: {end - start:.2f}秒")
    return result

async def main():
    await pure_cpu_work()  # 只统计计算时间
    await asyncio.sleep(1)  # IO等待单独处理

asyncio.run(main())

async def async_task():
    await asyncio.sleep(1)

start = time.perf_counter()
asyncio.run(async_task())  # 包含事件循环启动/关闭时间
end = time.perf_counter()
print(f"总耗时: {end - start:.2f}秒")

# 两组测量结果（单位：秒）
组A = [0.48, 0.49, 0.50, 0.51, 0.52]  # 平均值0.50，σ≈0.015
组B = [0.35, 0.45, 0.50, 0.55, 0.65]  # 平均值0.50，σ≈0.118
	
# 虽然平均值相同，但：
# - 组A性能稳定
# - 组B存在偶发的严重性能下降