Python 合并具有重复日期时间索引的两个熊猫系列

我有两个Pandas系列（d1和d2），按datetime索引，每个系列包含一列数据，其中包含float和NaN。这两个指数都是以一天为间隔的，尽管时间条目与许多缺失天数的时段不一致。d1范围为1974-12-16至2002-01-30。d2范围为1997-12-19至2017-07-06。从1997年12月19日到2002年1月30日期间，这两个系列之间存在许多重复指数。重复索引的数据有时是相同的值、不同的值或一个值加一个值

我想将这两个系列合并为一个系列，在存在重复索引时对d2中的数据进行优先级排序（即，在存在重复索引时将所有d1数据替换为d2数据）。在众多可用工具（合并、连接、连接等）中，最有效的方法是什么

以下是我的数据示例：

In [7]: print d1
fldDate
1974-12-16    19.0
1974-12-17    28.0
1974-12-18    24.0
1974-12-19    18.0
1974-12-20    17.0
1974-12-21    28.0
1974-12-22    28.0
1974-12-23    10.0
1974-12-24     6.0
1974-12-25     5.0
1974-12-26    12.0
1974-12-27    19.0
1974-12-28    22.0
1974-12-29    20.0
1974-12-30    16.0
1974-12-31    12.0
1975-01-01    12.0
1975-01-02    15.0
1975-01-03    14.0
1975-01-04    15.0
1975-01-05    18.0
1975-01-06    21.0
1975-01-07    22.0
1975-01-08    18.0
1975-01-09    20.0
1975-01-10    12.0
1975-01-11     8.0
1975-01-12    -2.0
1975-01-13    13.0
1975-01-14    24.0
              ... 
2002-01-01    18.0
2002-01-02    16.0
2002-01-03     NaN
2002-01-04    24.0
2002-01-05    23.0
2002-01-06    15.0
2002-01-07    22.0
2002-01-08    34.0
2002-01-09    35.0
2002-01-10    29.0
2002-01-11    21.0
2002-01-12    24.0
2002-01-13     NaN
2002-01-14    18.0
2002-01-15    14.0
2002-01-16    10.0
2002-01-17     5.0
2002-01-18     7.0
2002-01-19     7.0
2002-01-20     7.0
2002-01-21    11.0
2002-01-22     NaN
2002-01-23     9.0
2002-01-24     8.0
2002-01-25    15.0
2002-01-26     NaN
2002-01-27     NaN
2002-01-28    18.0
2002-01-29    13.0
2002-01-30    13.0
Name: MaxTempMid, dtype: float64

In [8]: print d2
fldDate
1997-12-19    22.0
1997-12-20    14.0
1997-12-21    18.0
1997-12-22    16.0
1997-12-23    16.0
1997-12-24    10.0
1997-12-25    12.0
1997-12-26    12.0
1997-12-27     9.0
1997-12-28    12.0
1997-12-29    18.0
1997-12-30    23.0
1997-12-31    28.0
1998-01-01    26.0
1998-01-02    29.0
1998-01-03    27.0
1998-01-04    22.0
1998-01-05    19.0
1998-01-06    17.0
1998-01-07    14.0
1998-01-08    14.0
1998-01-09    14.0
1998-01-10    16.0
1998-01-11    20.0
1998-01-12    21.0
1998-01-13    19.0
1998-01-14    20.0
1998-01-15    16.0
1998-01-16    17.0
1998-01-17    20.0
              ... 
2017-06-07    68.0
2017-06-08    71.0
2017-06-09    71.0
2017-06-10    59.0
2017-06-11    41.0
2017-06-12    57.0
2017-06-13    58.0
2017-06-14    36.0
2017-06-15    50.0
2017-06-16    58.0
2017-06-17    54.0
2017-06-18    53.0
2017-06-19    58.0
2017-06-20    68.0
2017-06-21    71.0
2017-06-22    71.0
2017-06-23    59.0
2017-06-24    61.0
2017-06-25    65.0
2017-06-26    68.0
2017-06-27    71.0
2017-06-28    60.0
2017-06-29    54.0
2017-06-30    48.0
2017-07-01    60.0
2017-07-02    68.0
2017-07-03    65.0
2017-07-04    73.0
2017-07-05    74.0
2017-07-06    77.0
Name: MaxTempMid, dtype: float64

让我们用，：

输出：

fldDate
1974-12-16    19.0
1974-12-17    28.0
1974-12-18    24.0
1974-12-19    18.0
1974-12-20    17.0
1974-12-21    28.0
1974-12-22    28.0
1974-12-23    10.0
1974-12-24     6.0
1974-12-25     5.0
1974-12-26    12.0
1974-12-27    19.0
1974-12-28    22.0
1974-12-29    20.0
1974-12-30    16.0
1974-12-31    12.0
1975-01-01    12.0
1975-01-02    15.0
1975-01-03    14.0
1975-01-04    15.0
1975-01-05    18.0
1975-01-06    21.0
1975-01-07    22.0
1975-01-08    18.0
1975-01-09    20.0
1975-01-10    12.0
1975-01-11     8.0
1975-01-12    -2.0
1975-01-13    13.0
1975-01-14    24.0
              ... 
2017-06-07    68.0
2017-06-08    71.0
2017-06-09    71.0
2017-06-10    59.0
2017-06-11    41.0
2017-06-12    57.0
2017-06-13    58.0
2017-06-14    36.0
2017-06-15    50.0
2017-06-16    58.0
2017-06-17    54.0
2017-06-18    53.0
2017-06-19    58.0
2017-06-20    68.0
2017-06-21    71.0
2017-06-22    71.0
2017-06-23    59.0
2017-06-24    61.0
2017-06-25    65.0
2017-06-26    68.0
2017-06-27    71.0
2017-06-28    60.0
2017-06-29    54.0
2017-06-30    48.0
2017-07-01    60.0
2017-07-02    68.0
2017-07-03    65.0
2017-07-04    73.0
2017-07-05    74.0
2017-07-06    77.0

---

您可能需要使用

删除重复项

或

重复项

：使用

keep

关键字参数。出于某种原因，当我尝试执行此操作时，会出现以下错误：“ValueError:无法从重复的轴重新编制索引”看起来df2中可能有重复的日期。您想在df2中聚合这些重复的日期吗？是的，就是这样。我的数据源在任何一个系列中都不应该有任何重复的日期，但不知何故，在df2中有一个重复的日期。我使用drop_duplicates来删除该条目，现在combine_first按需要工作。非常感谢。

fldDate
1974-12-16    19.0
1974-12-17    28.0
1974-12-18    24.0
1974-12-19    18.0
1974-12-20    17.0
1974-12-21    28.0
1974-12-22    28.0
1974-12-23    10.0
1974-12-24     6.0
1974-12-25     5.0
1974-12-26    12.0
1974-12-27    19.0
1974-12-28    22.0
1974-12-29    20.0
1974-12-30    16.0
1974-12-31    12.0
1975-01-01    12.0
1975-01-02    15.0
1975-01-03    14.0
1975-01-04    15.0
1975-01-05    18.0
1975-01-06    21.0
1975-01-07    22.0
1975-01-08    18.0
1975-01-09    20.0
1975-01-10    12.0
1975-01-11     8.0
1975-01-12    -2.0
1975-01-13    13.0
1975-01-14    24.0
              ... 
2017-06-07    68.0
2017-06-08    71.0
2017-06-09    71.0
2017-06-10    59.0
2017-06-11    41.0
2017-06-12    57.0
2017-06-13    58.0
2017-06-14    36.0
2017-06-15    50.0
2017-06-16    58.0
2017-06-17    54.0
2017-06-18    53.0
2017-06-19    58.0
2017-06-20    68.0
2017-06-21    71.0
2017-06-22    71.0
2017-06-23    59.0
2017-06-24    61.0
2017-06-25    65.0
2017-06-26    68.0
2017-06-27    71.0
2017-06-28    60.0
2017-06-29    54.0
2017-06-30    48.0
2017-07-01    60.0
2017-07-02    68.0
2017-07-03    65.0
2017-07-04    73.0
2017-07-05    74.0
2017-07-06    77.0