pandas DatetimeIndex与to_datetime的差异 [英] Pandas DatetimeIndex vs to_datetime discrepancies

问题描述

我正在尝试将Pandas系列的纪元时间戳转换为人类可以理解的时间.至少有两种明显的方法可以执行此操作:pd.DatetimeIndex和pd.to_datetime().它们似乎以完全不同的方式工作:

I'm trying to convert a Pandas Series of epoch timestamps to human-readable times. There are at least two obvious ways to do this: pd.DatetimeIndex and pd.to_datetime(). They seem to work in quite different ways:

In [1]: import pandas as pd

In [3]: nanos = pd.Series([1462282258000000000, 1462282258100000000, 1462282258200000000])

In [4]: pd.to_datetime(nanos)
Out[4]: 
0   2016-05-03 13:30:58.000
1   2016-05-03 13:30:58.100
2   2016-05-03 13:30:58.200
dtype: datetime64[ns]

In [5]: pd.DatetimeIndex(nanos)
Out[5]: 
DatetimeIndex([       '2016-05-03 13:30:58', '2016-05-03 13:30:58.100000',
               '2016-05-03 13:30:58.200000'],
              dtype='datetime64[ns]', freq=None)

对于to_datetime()，显示分辨率为毫秒，并且.000整秒打印.使用DatetimeIndex时，显示分辨率为微秒(我喜欢)，但是整秒完全忽略了小数部分.

With to_datetime(), the display resolution is milliseconds, and .000 is printed on whole seconds. With DatetimeIndex, the display resolution is microseconds (which I like), but the decimal part is completely omitted on whole seconds.

然后，尝试转换时区:

In [12]: pd.DatetimeIndex(nanos).tz_localize('UTC')                   
Out[12]: 
DatetimeIndex([       '2016-05-03 13:30:58+00:00',
               '2016-05-03 13:30:58.100000+00:00',
               '2016-05-03 13:30:58.200000+00:00'],
              dtype='datetime64[ns, UTC]', freq=None)

In [13]: pd.to_datetime(nanos).tz_localize('UTC')  
TypeError: index is not a valid DatetimeIndex or PeriodIndex

这很奇怪:时区函数不适用于普通的datetime系列，而只能用于DatetimeIndex.为什么会这样呢? tz_localize()方法存在并在此处记录: http: //pandas.pydata.org/pandas-docs/stable/generation/pandas.Series.tz_localize.html

This is strange: the timezone functions don't work with a plain datetime Series, only with a DatetimeIndex. Why would that be? The tz_localize() method exists and is documented here: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.tz_localize.html

我尝试使用Pandas 0.17.0和0.18.1获得相同的结果.

I've tried Pandas 0.17.0 and 0.18.1 with the same results.

我不是要创建一个实际的索引，因此在其他条件相同的情况下，我本来应该使用to_datetime()-我只是无法使用时区方法来工作.

I'm not trying to make an actual index, so all else being equal I would have expected to use to_datetime() - I just can't get time zone methods to work with it.

推荐答案

有1种方法可以转换事物，pd.to_datetime()，是的，您可以直接构造DatetimeIndex，但是它是有限制的，而to_datetime非常灵活.

There is 1 way to convert things, pd.to_datetime(), yes you can directly construct a DatetimeIndex, but it is restrictive on purpose, while to_datetime is quite flexible.

因此，to_datetime将为您提供与您输入的对象类似的对象，如果您输入类似数组的内容，则将得到DatetimeIndex，输入Series您将得到Series.

So to_datetime will give you a similar object to what you input, if you input an array-like, then you will get a DatetimeIndex, input a Series you will get a Series.

In [5]: nanos = [1462282258000000000, 1462282258100000000, 1462282258200000000]

默认情况下，它将转换为unit='ns'并在此处排列

By default it will convert with a unit='ns' which lines up here

In [7]: pd.to_datetime(nanos)
Out[7]: DatetimeIndex(['2016-05-03 13:30:58', '2016-05-03 13:30:58.100000', '2016-05-03 13:30:58.200000'], dtype='datetime64[ns]', freq=None)

所以我们可以做的一件事就是从中制作一个系列. 索引在这里是INTEGER，值是日期时间.

So one thing we could do is make a Series out of this. The index is INTEGER here, the values are Datetimes.

In [10]: s = Series(pd.to_datetime(nanos))

In [11]: s
Out[11]: 
0   2016-05-03 13:30:58.000
1   2016-05-03 13:30:58.100
2   2016-05-03 13:30:58.200
dtype: datetime64[ns]

然后，您可以使用.dt访问器对值进行操作. Series.tz_localize对 index 进行操作.

You then can use the .dt accessor to operate on the values. Series.tz_localize operates on the index.

In [12]: s.dt.tz_localize('US/Eastern')
Out[12]: 
0          2016-05-03 13:30:58-04:00
1   2016-05-03 13:30:58.100000-04:00
2   2016-05-03 13:30:58.200000-04:00
dtype: datetime64[ns, US/Eastern]

这篇关于 pandas DatetimeIndex与to_datetime的差异的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！