关于windows：datetime.now()的Python解析

Python resolution of datetime.now()

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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，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。

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...
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？有什么方法可以让我得到一个精确的时间戳到微秒？实际上我不需要微秒，但获得0.1ms的分辨率会很不错。

===更新====

我将它与使用time.perf_counter()并添加到起始datetime进行了比较
从datetime import datetime，timedelta
进口时间

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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('
')

time.sleep(0.000001)

我不确定它到底有多"准确"，但分辨率至少要高......这似乎并不重要，因为我的电脑甚至无法以高速打印。出于我的目的，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。

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...
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

...