我有以下数据框,它由’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_time的列,它也包含时间,但是作为字符串,并且在不同的时区(America / New_York)中,而不是索引中的那个(欧洲/巴黎).
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.
但是以下失败了
In [96]: pd.to_datetime(df['last_time']).tz_localize('America/New_York')
有错误
TypeError: Already tz-aware, use tz_convert to convert.
我设法通过以下方式获得我想要的系列
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来做到这一点.
解决方法:
你几乎就在那里 – 只需添加.dt访问器……
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
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