运行Python脚本怎样查看脚本的执行时间 运行Python脚本的执行计时实用方法

time

time()

timeit

time.time()

import time

start_time = time.time()
# 这里放你的Python代码，比如一个耗时操作
for _ in range(1000000):
    pass
end_time = time.time()
print(f"脚本执行耗时: {end_time - start_time:.4f} 秒")

timeit

import timeit

# 定义要测试的代码
code_list_comp = "[i for i in range(1000)]"
code_loop = """
my_list = []
for i in range(1000):
    my_list.append(i)
"""

# 使用timeit.timeit()进行测试
# number参数指定代码执行的次数
# setup参数用于设置代码运行前的环境
time_list_comp = timeit.timeit(code_list_comp, number=10000)
time_loop = timeit.timeit(code_loop, number=10000)

print(f"列表推导耗时: {time_list_comp:.6f} 秒")
print(f"普通循环耗时: {time_loop:.6f} 秒")

timeit

time.time()

timeit

time.time()

__enter__

__exit__

with

import time

class Timer:
    def __enter__(self):
        self.start_time = time.time()
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.end_time = time.time()
        self.duration = self.end_time - self.start_time
        print(f"代码块执行耗时: {self.duration:.4f} 秒")

# 使用方法
with Timer():
    # 这里放你的代码
    sum(range(10000000))

print("脚本其他部分继续执行...")

start_time

end_time

import time

def time_it(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"函数 '{func.__name__}' 执行耗时: {end_time - start_time:.4f} 秒")
        return result
    return wrapper

@time_it
def my_complex_function(n):
    total = 0
    for i in range(n):
        total += i * i
    return total

my_complex_function(5000000)

@time_it

print

cProfile

profile

cProfile

import cProfile
import time

def func_a():
    sum(range(1000000))

def func_b():
    time.sleep(0.1) # 模拟I/O或耗时操作
    [i*i for i in range(500000)]

def main_script():
    func_a()
    func_b()
    func_a()

cProfile.run('main_script()')

ncalls

tottime

percall

tottime

cumtime

cProfile