datetime.now() 的 Python 解析

Question

from datetime import datetime
import time
for i in range(1000):
    curr_time  = datetime.now()
    print(curr_time)
    time.sleep(0.0001)

我正在测试datetime.now() 的分辨率。由于它假设以微秒为单位输出，因此我预计每次打印都会有所不同。

但是，我总是得到类似的东西。

...
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.212073
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
2015-07-10 22:38:47.213074
...

为什么会这样？有什么方法可以将准确的时间戳精确到微秒？其实我不需要微秒，但能得到 0.1ms 的分辨率就好了。

=== 更新 ====

我将它与使用 time.perf_counter() 并添加到起始 datetime 进行了比较 从日期时间导入日期时间，时间增量 进口时间

datetime0 = datetime.now()
t0 = time.perf_counter()
for i in range(1000):
    print('datetime.now(): ', datetime.now())
    print('time.perf_counter(): ', datetime0 + timedelta(0, time.perf_counter()-t0))
    print('\n')

    time.sleep(0.000001)

我不确定它到底有多“准确”，但分辨率至少更高......这似乎并不重要，因为我的电脑甚至无法以如此高的速度打印。对于我的目的，我只需要不同的时间戳来区分不同的条目，这对我来说已经足够了。

...
datetime.now():  2015-07-10 23:24:18.010377
time.perf_counter():  2015-07-10 23:24:18.010352


datetime.now():  2015-07-10 23:24:18.010377
time.perf_counter():  2015-07-10 23:24:18.010545


datetime.now():  2015-07-10 23:24:18.010377
time.perf_counter():  2015-07-10 23:24:18.010745


datetime.now():  2015-07-10 23:24:18.011377
time.perf_counter():  2015-07-10 23:24:18.010961


datetime.now():  2015-07-10 23:24:18.011377
time.perf_counter():  2015-07-10 23:24:18.011155


datetime.now():  2015-07-10 23:24:18.011377
time.perf_counter():  2015-07-10 23:24:18.011369


datetime.now():  2015-07-10 23:24:18.011377
time.perf_counter():  2015-07-10 23:24:18.011596


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.011829


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.012026


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.012232


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.012424


datetime.now():  2015-07-10 23:24:18.012379
time.perf_counter():  2015-07-10 23:24:18.012619


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.012844


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.013044


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.013242


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.013437


datetime.now():  2015-07-10 23:24:18.013380
time.perf_counter():  2015-07-10 23:24:18.013638


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.013903


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.014125


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.014328


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.014526


datetime.now():  2015-07-10 23:24:18.014379
time.perf_counter():  2015-07-10 23:24:18.014721


datetime.now():  2015-07-10 23:24:18.015381
time.perf_counter():  2015-07-10 23:24:18.014919

...

Answer 1

这可能是您系统上time.sleep 的限制，而不是datetime.now()... 或可能两者兼有。 您运行的是什么操作系统以及 Python 的哪个版本和发行版？

您的系统可能无法提供time.sleep 文档中提到的“亚秒级精度”：

sleep(...)
    sleep(seconds)

    Delay execution for a given number of seconds.  The argument may be
    a floating point number for subsecond precision.

在 Linux 3.x on amd64 和 CPython 2.7 上，我得到的结果非常接近您预期的 0.0001 时间步：

2015-07-10 19:58:24.353711
2015-07-10 19:58:24.353879
2015-07-10 19:58:24.354052
2015-07-10 19:58:24.354227
2015-07-10 19:58:24.354401
2015-07-10 19:58:24.354577
2015-07-10 19:58:24.354757
2015-07-10 19:58:24.354938