python - 合并两个具有重复日期时间索引的 Pandas 系列

Question

我有两个按日期时间索引的 Pandas 系列(d1 和 d2)，每个系列都包含一列包含 float 和 NaN 的数据。尽管时间条目与许多缺失天数的时期不一致，但这两个指数均以一天为间隔。 d1 的范围为 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，两个系列之间存在许多重复的索引。重复索引的数据有时是相同的值、不同的值或一个值和 NaN。

我想将这两个系列合并为一个，在存在重复索引时优先考虑 d2 中的数据(即，在存在重复索引时将所有 d1 数据替换为 d2 数据)。在众多可用的 Pandas 工具(合并、联接、连接等)中，最有效的方法是什么？

这是我的数据示例:

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

Answer 1

让我们使用，combine_first :

df2.combine_first(df1)

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