我正在尝试解决美国夏令时区发生的1小时时间变换。
这部分时间序列（剪下）
In [3] eurusd
Out[3]:
BID-CLOSE
TIME
1994-03-28 22:00:00 1.15981
1994-03-29 22:00:00 1.16681
1994-03-30 22:00:00 1.15021
1994-03-31 22:00:00 1.14851
1994-04-03 21:00:00 1.14081
1994-04-04 21:00:00 1.13921
1994-04-05 21:00:00 1.13881
1994-04-06 21:00:00 1.14351
1994-04-07 21:00:00 1.14411
1994-04-10 21:00:00 1.14011
1994-04-11 21:00:00 1.14391
1994-04-12 21:00:00 1.14451
1994-04-13 21:00:00 1.14201
1994-04-14 21:00:00 1.13911
1994-04-17 21:00:00 1.14821
1994-04-18 21:00:00 1.15181
1994-04-19 21:00:00 1.15621
1994-04-20 21:00:00 1.15381
1994-04-21 21:00:00 1.16201
1994-04-24 21:00:00 1.16251
1994-04-25 21:00:00 1.16721
1994-04-26 21:00:00 1.17101
1994-04-27 21:00:00 1.17721
1994-04-28 21:00:00 1.18421
1994-05-01 21:00:00 1.18751
1994-05-02 21:00:00 1.17331
1994-05-03 21:00:00 1.16801
1994-05-04 21:00:00 1.17141
1994-05-05 21:00:00 1.17691
1994-05-08 21:00:00 1.16541
...
1994-09-26 21:00:00 1.25501
1994-09-27 21:00:00 1.25761
1994-09-28 21:00:00 1.25541
1994-09-29 21:00:00 1.25421
1994-10-02 21:00:00 1.25721
1994-10-03 21:00:00 1.26131
1994-10-04 21:00:00 1.26121
1994-10-05 21:00:00 1.26101
1994-10-06 21:00:00 1.25761
1994-10-10 21:00:00 1.26161
1994-10-11 21:00:00 1.26341
1994-10-12 21:00:00 1.27821
1994-10-13 21:00:00 1.29411
1994-10-16 21:00:00 1.29401
1994-10-17 21:00:00 1.29371
1994-10-18 21:00:00 1.29531
1994-10-19 21:00:00 1.29681
1994-10-20 21:00:00 1.29971
1994-10-23 21:00:00 1.30411
1994-10-24 21:00:00 1.30311
1994-10-25 21:00:00 1.30091
1994-10-26 21:00:00 1.28921
1994-10-27 21:00:00 1.29341
1994-10-30 22:00:00 1.29931
1994-10-31 22:00:00 1.29281
1994-11-01 22:00:00 1.27771
1994-11-02 22:00:00 1.27821
1994-11-03 22:00:00 1.28321
1994-11-06 22:00:00 1.28751
1994-11-07 22:00:00 1.27091
目前，我使用以下方式申请新的日期范围：
idx = pd.date_range('1994-03-28 22:00:00', '1994-11-07 22:00:00', freq= 'D')
In [4] idx
Out[4]:
DatetimeIndex(['1994-03-28 22:00:00', '1994-03-29 22:00:00',
'1994-03-30 22:00:00', '1994-03-31 22:00:00',
'1994-04-01 22:00:00', '1994-04-02 22:00:00',
'1994-04-03 22:00:00', '1994-04-04 22:00:00',
'1994-04-05 22:00:00', '1994-04-06 22:00:00',
...
'1994-10-29 22:00:00', '1994-10-30 22:00:00',
'1994-10-31 22:00:00', '1994-11-01 22:00:00',
'1994-11-02 22:00:00', '1994-11-03 22:00:00',
'1994-11-04 22:00:00', '1994-11-05 22:00:00',
'1994-11-06 22:00:00', '1994-11-07 22:00:00'],
dtype='datetime64[ns]', length=225, freq='D')
然后，我使用新的日期范围重新索引数据帧，时间序列将所有21:00值转换为22:00，BID-CLOSE变为NaN。我理解为什么，但是我不确定如何根据美国夏令时计划让代码知道1小时的时间步。
reindex的输出：
In[5]: eurusd_copy1 = eurusd.reindex(idx, fill_value=None)
In[6]: eurusd_copy1
Out[6]:
BID-CLOSE
1994-03-28 22:00:00 1.15981
1994-03-29 22:00:00 1.16681
1994-03-30 22:00:00 1.15021
1994-03-31 22:00:00 1.14851
1994-04-01 22:00:00 NaN
1994-04-02 22:00:00 NaN
1994-04-03 22:00:00 NaN
1994-04-04 22:00:00 NaN
1994-04-05 22:00:00 NaN
1994-04-06 22:00:00 NaN
1994-04-07 22:00:00 NaN
1994-04-08 22:00:00 NaN
1994-04-09 22:00:00 NaN
1994-04-10 22:00:00 NaN
1994-04-11 22:00:00 NaN
1994-04-12 22:00:00 NaN
1994-04-13 22:00:00 NaN
1994-04-14 22:00:00 NaN
1994-04-15 22:00:00 NaN
1994-04-16 22:00:00 NaN
1994-04-17 22:00:00 NaN
1994-04-18 22:00:00 NaN
1994-04-19 22:00:00 NaN
1994-04-20 22:00:00 NaN
1994-04-21 22:00:00 NaN
1994-04-22 22:00:00 NaN
1994-04-23 22:00:00 NaN
1994-04-24 22:00:00 NaN
1994-04-25 22:00:00 NaN
1994-04-26 22:00:00 NaN
...
1994-10-09 22:00:00 NaN
1994-10-10 22:00:00 NaN
1994-10-11 22:00:00 NaN
1994-10-12 22:00:00 NaN
1994-10-13 22:00:00 NaN
1994-10-14 22:00:00 NaN
1994-10-15 22:00:00 NaN
1994-10-16 22:00:00 NaN
1994-10-17 22:00:00 NaN
1994-10-18 22:00:00 NaN
1994-10-19 22:00:00 NaN
1994-10-20 22:00:00 NaN
1994-10-21 22:00:00 NaN
1994-10-22 22:00:00 NaN
1994-10-23 22:00:00 NaN
1994-10-24 22:00:00 NaN
1994-10-25 22:00:00 NaN
1994-10-26 22:00:00 NaN
1994-10-27 22:00:00 NaN
1994-10-28 22:00:00 NaN
1994-10-29 22:00:00 NaN
1994-10-30 22:00:00 1.29931
1994-10-31 22:00:00 1.29281
1994-11-01 22:00:00 1.27771
1994-11-02 22:00:00 1.27821
1994-11-03 22:00:00 1.28321
1994-11-04 22:00:00 NaN
1994-11-05 22:00:00 NaN
1994-11-06 22:00:00 1.28751
1994-11-07 22:00:00 1.27091
[225 rows x 1 columns]
所需的输出将具有填充NaN的任何日期间隙，但是保持已经具有日期的BID-CLOSE值。请注意下面的输出是虚构的，只是为了说明预期的结果。
BID-CLOSE
28/03/1994 22:00:00 1.15981
29/03/1994 22:00:00 1.16681
30/03/1994 22:00:00 1.15021
31/03/1994 22:00:00 1.14851
01/04/1994 21:00:00 NaN
02/04/1994 21:00:00 NaN
03/04/1994 21:00:00 1.13881
04/04/1994 21:00:00 1.14351
05/04/1994 21:00:00 1.14411
06/04/1994 21:00:00 1.14011
07/04/1994 21:00:00 1.14391
08/04/1994 21:00:00 NaN
09/04/1994 21:00:00 NaN
10/04/1994 21:00:00 1.14451
11/04/1994 21:00:00 1.14201
12/04/1994 21:00:00 1.13911
13/04/1994 21:00:00 1.14821
…
25/10/1994 21:00:00 1.29371
26/10/1994 21:00:00 NaN
27/10/1994 21:00:00 1.29681
28/10/1994 21:00:00 1.29971
29/10/1994 21:00:00 1.30411
30/10/1994 22:00:00 1.30311
31/10/1994 22:00:00 NaN
01/11/1994 22:00:00 NaN
02/11/1994 22:00:00 1.29341
如何让代码了解美国时区？
答案 0 :(得分：2)
我猜你的日期索引是天真的时区。
首先设置时区，我假设它们是UTC
validate然后您可以将它们转换为您喜欢的任何时区
eurusd = eurusd.tz_localize('UTC')
然后你可以根据需要重新编制索引