【发布时间】:2017-03-26 04:24:17
【问题描述】:
我有一个熊猫数据框:
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
我正在尝试过滤我的 pandas 数据框中的一些行,如下所示:
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
这似乎是时区问题。在这里忽略时区的最佳方法是什么?谢谢!
【问题讨论】:
-
你能提供你的数据框样本吗? (以及它是如何构造的)
-
my_df[my_df.my_timestamp > pd.to_datetime("2016-07-01")]
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@DennisGolomazov:添加了示例数据框。谢谢!
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@piRSquared:我尝试了你的建议,但仍然是同样的错误。任何想法?谢谢!
标签: python python-2.7 datetime pandas filter