夏令時偏移1小時時不正確的索引

我正試圖解決發生在美國夏令時時區的1小時時間偏移問題。夏令時偏移1小時時不正確的索引

的時間序列的一部分。這（以下剪斷）

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，當我申請使用新的日期範圍：

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')

於是，我重新索引使用新的日期範圍內的數據幀，時間序列將所有21:00的值轉換爲22:00，並且BID-CLOSE變爲NaN。我理解爲什麼，但是我不確定如何使代碼意識到按照美國夏令時間表的1小時時間步驟。

輸出重新索引的：

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]

所需的輸出將具有填充有NaN的任何日期的間隙，但是保持其已經具有日期unchnaged的BID-CLOSE值。請注意下面的輸出是虛構的，只是爲了說明理想的結果。

     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

如何讓代碼知道美國時區？

來源

2016-10-12 James

如果你傳遞一個'tz'到'''date_range']（http://pandas.pydata.org/pandas-docs/stable/generated/pandas.date_range.html）？所以如果時區匹配，那麼它應該對齊 – EdChum

我猜你的日期指數是天真的時區。

第一設置時區，我會認爲他們是UTC

eurusd = eurusd.tz_localize('UTC')

，那麼你可以將它們轉換到任何時區，你喜歡這樣有

eurusd = eurusd.tz_convert('America/New_York')

，那麼你可以重新索引你想

來源

2016-10-12 13:31:57

謝謝你對此的幫助。我也在我的idx varable上使用了時區功能，這似乎已經成功了。 – James

夏令時偏移1小時時不正確的索引

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