如何将不同时区的tz_convert应用于 pandas 数据帧中的不同行 [英] How to apply tz_convert with different timezones to different rows in pandas dataframe

问题描述

我正在尝试根据标准为Pandas数据框中的各行设置不同的时区。作为MWE，这是我尝试过的：

I am trying to set different timezones for various rows in a Pandas dataframe based on a criterion. As a MWE, here is what I have tried:

test = pd.DataFrame( data = pd.to_datetime(['2015-03-30 20:12:32','2015-03-12 00:11:11']) ,columns=['time'] )
test['new_col']=['new','old']
test.time=test.set_index('time').index.tz_localize('UTC')
test.loc[test.new_col=='new','time']=test[test.new_col=='new'].set_index('time').index.tz_convert('US/Pacific')
print test

此输出：

                        time new_col
0        1427746352000000000     new
1  2015-03-12 00:11:11+00:00     old

如您所见，具有更新时区的行将转换为整数。如何正确进行，以便更新的条目是一个日期时间？

As you can see, the row with the updated timezone is converted to an integer. How can I do this properly so that the updated entry is a datetime?

推荐答案

使用0.17.0rc2（0.17.0是release在10月9日），你可以这样做。

Using 0.17.0rc2 (0.17.0 is release on Oct 9), you can do this.

In [43]: test['new_col2'] = [Timestamp('2015-03-30 20:12:32',tz='US/Eastern'),Timestamp('2015-03-30 20:12:32',tz='US/Pacific')]

In [44]: test
Out[44]: 
                       time new_col                   new_col2
0 2015-03-30 20:12:32+00:00     new  2015-03-30 20:12:32-04:00
1 2015-03-12 00:11:11+00:00     old  2015-03-30 20:12:32-07:00

In [45]: test.dtypes
Out[45]: 
time        datetime64[ns, UTC]
new_col                  object
new_col2                 object
dtype: object

请注意，混合 / em>强制对象 dtype的列。所以可以做到，但一般不推荐。您将需要单独更改条目。

Note that mixed timezones within a column with force object dtype. So it can be done, but is generally not recommended. You would need to change entries individually.

您几乎总是单个时区的单个dtyped列。

You almost always a single dtyped column of a single timezone.

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