我有以下数据框，由“tz-aware”＆＃39;编入索引。 Datetimeindex。
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_time的列，其中包含时间，但是作为字符串，并且在不同的时区（America/New_York）中，而不是索引中的列Europe/Paris {1}}）。
我的目标是在正确的时区将此列转换为日期时间。
我尝试过以下方法：
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]
这有效地将列转换为datetime对象。
但以下失败
In [96]: pd.to_datetime(df['last_time']).tz_localize('America/New_York')
错误
TypeError: Already tz-aware, use tz_convert to convert.
我设法通过以下
获得我想要的系列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重新索引它并将其重新插入数据帧。
但是我发现这个解决方案非常脏，我想知道是否有更好的方法。
答案 0 :(得分：8)
你几乎就在那里 - 只需添加.dt访问者...
来源DF：
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