在以下系列中:
0 1411161507178
1 1411138436009
2 1411123732180
3 1411167606146
4 1411124780140
5 1411159331327
6 1411131745474
7 1411151831454
8 1411152487758
9 1411137160544
Name: my_series, dtype: int64
此命令(转换为时间戳，本地化并转换为EST)有效:
pd.to_datetime(my_series, unit='ms').apply(lambda x: x.tz_localize('UTC').tz_convert('US/Eastern'))
但是这个失败了:
pd.to_datetime(my_series, unit='ms').tz_localize('UTC').tz_convert('US/Eastern')
和:
TypeError Traceback (most recent call last)
<ipython-input-3-58187a4b60f8> in <module>()
----> 1 lua = pd.to_datetime(df[column], unit='ms').tz_localize('UTC').tz_convert('US/Eastern')
/Users/josh/anaconda/envs/py34/lib/python3.4/site-packages/pandas/core/generic.py in tz_localize(self, tz, axis, copy, infer_dst)
3492 ax_name = self._get_axis_name(axis)
3493 raise TypeError('%s is not a valid DatetimeIndex or PeriodIndex' %
-> 3494 ax_name)
3495 else:
3496 ax = DatetimeIndex([],tz=tz)
TypeError: index is not a valid DatetimeIndex or PeriodIndex
这个也是如此:
my_series.tz_localize('UTC').tz_convert('US/Eastern')
和:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-0a7cb1e94e1e> in <module>()
----> 1 lua = df[column].tz_localize('UTC').tz_convert('US/Eastern')
/Users/josh/anaconda/envs/py34/lib/python3.4/site-packages/pandas/core/generic.py in tz_localize(self, tz, axis, copy, infer_dst)
3492 ax_name = self._get_axis_name(axis)
3493 raise TypeError('%s is not a valid DatetimeIndex or PeriodIndex' %
-> 3494 ax_name)
3495 else:
3496 ax = DatetimeIndex([],tz=tz)
TypeError: index is not a valid DatetimeIndex or PeriodIndex
据我了解，以上第二种方法(第一种失败)应该起作用。为什么会失败？
最佳答案
tz_localize/tz_convert作用于对象的INDEX，而不作用于值。最简单地将其转换为索引然后进行本地化和转换。如果您随后要回系列赛，可以使用to_series()
In [47]: pd.DatetimeIndex(pd.to_datetime(s,unit='ms')).tz_localize('UTC').tz_convert('US/Eastern')
Out[47]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-09-19 17:18:27.178000-04:00, ..., 2014-09-19 10:32:40.544000-04:00]
Length: 10, Freq: None, Timezone: US/Eastern
关于python - 无法使用内置的Series在时间戳上应用方法，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/26089670/