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Python 本地化并转换datetime列，而不是datetimeindex

python pandas datetime

Python 本地化并转换datetime列，而不是datetimeindex,python,pandas,datetime,timezone,Python,Pandas,Datetime,Timezone,我有以下数据帧，它由一个“tz-aware”Datetimeindex索引 In [92]: df Out[92]: last_time ts_recv 2017-02-13 07:00:01.103036+01:00 2017-02-13 16:03:23.626000 2017-02-13 07:00:03.065284+01:00 2017-02-13 16:03:23.626000

我有以下数据帧，它由一个“tz-aware”
Datetimeindex
索引

In [92]: df
Out[92]: 
                                                   last_time
ts_recv
2017-02-13 07:00:01.103036+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:03.065284+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:13.244515+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:17.562202+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:17.917565+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:21.985626+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:28.096251+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:32.087421+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:33.386040+01:00  2017-02-13 16:03:23.626000
2017-02-13 07:00:43.923534+01:00  2017-02-13 16:03:23.626000
我只有一个名为
last\u time
的列，它也包含时间，但作为字符串，并且与索引中的列（即
欧洲/巴黎
）位于不同的时区（
美国/纽约
）

我的目标是在正确的时区将此列转换为datetime。

我尝试了以下方法：

In [94]: pd.to_datetime(df['last_time'])
Out[94]: 
ts_recv
2017-02-13 07:00:01.103036+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:03.065284+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:13.244515+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:17.562202+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:17.917565+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:21.985626+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:28.096251+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:32.087421+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:33.386040+01:00   2017-02-13 16:03:23.626
2017-02-13 07:00:43.923534+01:00   2017-02-13 16:03:23.626
Name: last_time, dtype: datetime64[ns]
这将有效地将列转换为datetime对象

但以下方法失败了

In [96]: pd.to_datetime(df['last_time']).tz_localize('America/New_York')
带着错误

TypeError: Already tz-aware, use tz_convert to convert.
我通过以下内容获得了我想要的系列

In [104]: pd.Series(pd.DatetimeIndex(df['last_time'].values)
          .tz_localize('America/New_York').tz_convert('Europe/Paris'))
Out[104]: 
0   2017-02-13 22:03:23.626000+01:00
1   2017-02-13 22:03:23.626000+01:00
2   2017-02-13 22:03:23.626000+01:00
3   2017-02-13 22:03:23.626000+01:00
4   2017-02-13 22:03:23.626000+01:00
5   2017-02-13 22:03:23.626000+01:00
6   2017-02-13 22:03:23.626000+01:00
7   2017-02-13 22:03:23.626000+01:00
8   2017-02-13 22:03:23.626000+01:00
9   2017-02-13 22:03:23.626000+01:00
dtype: datetime64[ns, Europe/Paris]
然后，我可以使用原始的datetimeindex对其重新编制索引，并将其插回数据帧

然而，我发现这个解决方案相当肮脏，我想知道是否有更好的方法来实现它。

您就快到了-只需添加
.dt
访问器

资料来源：

In [86]: df
Out[86]:
                                             last_time
ts_recv
2017-02-13 06:00:01.103036  2017-02-13 16:03:23.626000
2017-02-13 06:00:03.065284  2017-02-13 16:03:23.626000
2017-02-13 06:00:13.244515  2017-02-13 16:03:23.626000
2017-02-13 06:00:17.562202  2017-02-13 16:03:23.626000
2017-02-13 06:00:17.917565  2017-02-13 16:03:23.626000
2017-02-13 06:00:21.985626  2017-02-13 16:03:23.626000
2017-02-13 06:00:28.096251  2017-02-13 16:03:23.626000
2017-02-13 06:00:32.087421  2017-02-13 16:03:23.626000
2017-02-13 06:00:33.386040  2017-02-13 16:03:23.626000
2017-02-13 06:00:43.923534  2017-02-13 16:03:23.626000

In [87]: df.dtypes
Out[87]:
last_time    object
dtype: object
转换为datetime+TZ：

In [88]: df['last_time'] = pd.to_datetime(df['last_time']) \
                             .dt.tz_localize('Europe/Paris') \
                             .dt.tz_convert('America/New_York')

In [89]: df
Out[89]:
                                                  last_time
ts_recv
2017-02-13 06:00:01.103036 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:03.065284 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:13.244515 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:17.562202 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:17.917565 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:21.985626 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:28.096251 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:32.087421 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:33.386040 2017-02-13 10:03:23.626000-05:00
2017-02-13 06:00:43.923534 2017-02-13 10:03:23.626000-05:00

In [90]: df.dtypes
Out[90]:
last_time    datetime64[ns, America/New_York]
dtype: object
啊，谢谢你，麦克斯，我不知道这个
dt
accessor！