在Python中使用哪个更好?Time.clock()还是time.time()?哪一种更准确?
例如:
start = time.clock()
... do something
elapsed = (time.clock() - start)
vs.
start = time.time()
... do something
elapsed = (time.time() - start)
在Python中使用哪个更好?Time.clock()还是time.time()?哪一种更准确?
例如:
start = time.clock()
... do something
elapsed = (time.clock() - start)
vs.
start = time.time()
... do something
elapsed = (time.time() - start)
当前回答
从3.3开始,time.clock()已弃用,建议使用time.process_time()或time.perf_counter()。
在2.7之前,根据time模块docs:
time.clock() On Unix, return the current processor time as a floating point number expressed in seconds. The precision, and in fact the very definition of the meaning of “processor time”, depends on that of the C function of the same name, but in any case, this is the function to use for benchmarking Python or timing algorithms. On Windows, this function returns wall-clock seconds elapsed since the first call to this function, as a floating point number, based on the Win32 function QueryPerformanceCounter(). The resolution is typically better than one microsecond.
此外,还有timeit模块用于对代码段进行基准测试。
其他回答
在Linux上,time()比clock()具有更好的精度。Clock()的精度小于10毫秒。而time()提供完美的精度。 我的测试用的是CentOS 6.4和python 2.6
using time():
1 requests, response time: 14.1749382019 ms
2 requests, response time: 8.01301002502 ms
3 requests, response time: 8.01491737366 ms
4 requests, response time: 8.41021537781 ms
5 requests, response time: 8.38804244995 ms
使用时钟():
1 requests, response time: 10.0 ms
2 requests, response time: 0.0 ms
3 requests, response time: 0.0 ms
4 requests, response time: 10.0 ms
5 requests, response time: 0.0 ms
6 requests, response time: 0.0 ms
7 requests, response time: 0.0 ms
8 requests, response time: 0.0 ms
正确答案:它们都是相同长度的分数。
但如果主题是时间,哪个更快?
一个小测试案例:
import timeit
import time
clock_list = []
time_list = []
test1 = """
def test(v=time.clock()):
s = time.clock() - v
"""
test2 = """
def test(v=time.time()):
s = time.time() - v
"""
def test_it(Range) :
for i in range(Range) :
clk = timeit.timeit(test1, number=10000)
clock_list.append(clk)
tml = timeit.timeit(test2, number=10000)
time_list.append(tml)
test_it(100)
print "Clock Min: %f Max: %f Average: %f" %(min(clock_list), max(clock_list), sum(clock_list)/float(len(clock_list)))
print "Time Min: %f Max: %f Average: %f" %(min(time_list), max(time_list), sum(time_list)/float(len(time_list)))
我不是在瑞士实验室工作,但我做过测试。
基于这个问题:time.clock()比time.time()更好
编辑:time.clock()是内部计数器,所以不能在外部使用,得到限制最大32位浮点数,如果不存储第一个/最后一个值,就不能继续计数。不能合并另一个计数器…
Clock() ->浮点数
返回CPU时间或进程启动后的实时时间 第一次调用clock()。这和系统的精度一样高 记录。
Time() ->浮点数
返回当前时间(以秒为单位)。 如果系统时钟提供,可能会出现几分之一秒。
通常time()更精确,因为操作系统存储进程运行时间的精度与存储系统时间(即实际时间)的精度不同。
我使用这段代码来比较2种方法。我的操作系统是windows 8,处理器核心i5, RAM 4GB
import time
def t_time():
start=time.time()
time.sleep(0.1)
return (time.time()-start)
def t_clock():
start=time.clock()
time.sleep(0.1)
return (time.clock()-start)
counter_time=0
counter_clock=0
for i in range(1,100):
counter_time += t_time()
for i in range(1,100):
counter_clock += t_clock()
print "time() =",counter_time/100
print "clock() =",counter_clock/100
输出:
time() = 0.0993799996376
clock() = 0.0993572257367
正如其他人所注意到的,time.clock()已被弃用,取而代之的是time.perf_counter()或time.process_time(),但Python 3.7通过time.perf_counter_ns()、time.process_time_ns()和time.time_ns()以及其他3个函数引入了纳秒分辨率计时。
PEP 564中详细介绍了这6个新的纳秒分辨率函数:
time.clock_gettime_ns (clock_id) 时间。clock_settime_ns (clock_id、时间:int) time.monotonic_ns () time.perf_counter_ns () time.process_time_ns () time.time_ns () 这些函数类似于没有_ns后缀的版本,但是 返回一个纳秒数作为Python int。
正如其他人也注意到的那样,使用timeit模块为函数和小代码段计时。