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问题描述
我有一个大熊猫数据框：
I have a pandas dataframe:
name my_timestamp
------------------------------------------
0 a1 2016-07-28 09:27:07.536963-07:00
1 a2 2016-07-28 09:27:07.536963-07:00
2 a3 2016-08-15 13:05:54.924185-07:00
3 a4 2016-08-30 04:04:18.971667-07:00
4 a5 2016-03-22 14:36:22.999825-07:00
5 a6 2016-08-30 04:04:18.971667-07:00
我正在尝试过滤大熊猫数据框中的一些行，如下所示：
I am trying to filter some rows in my pandas data frame like below:
import datetime
my_df[my_df.my_timestamp > datetime.datetime(2016, 7, 1)]
但是得到以下错误：
TypeErrorTraceback (most recent call last)
<ipython-input-21-35be746f191d> in <module>()
1 import datetime
----> 2 my_df[my_df.my_timestamp > datetime.datetime(2016, 7, 1)]
/usr/local/lib/python2.7/dist-packages/pandas/core/ops.pyc in wrapper(self, other, axis)
761 other = np.asarray(other)
762
--> 763 res = na_op(values, other)
764 if isscalar(res):
765 raise TypeError('Could not compare %s type with Series' %
/usr/local/lib/python2.7/dist-packages/pandas/core/ops.pyc in na_op(x, y)
681 result = lib.vec_compare(x, y, op)
682 else:
--> 683 result = lib.scalar_compare(x, y, op)
684 else:
685
pandas/lib.pyx in pandas.lib.scalar_compare (pandas/lib.c:14261)()
TypeError: can't compare offset-naive and offset-aware date times
似乎是时区问题，在这里忽略时区的最好方法是什么？谢谢！
It seems to be the timezone issue. What would be the best way to ignore time-zone here? Thanks!
推荐答案
假设数据帧中的所有时间戳都在相同的时区：
Assuming that all timestamps in the dataframe are in the same timezone:
tz_info = my_df.iloc[0].my_timestamp.tzinfo
my_df[my_df.my_timestamp > datetime.datetime(2016, 7, 1, tzinfo=tz_info)]
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