python - 通过放大设置 DataFrame 值

Question

我有两个 DataFrames(带有 DatetimeIndex)，我想用第二帧(较新的)的数据更新第一帧(较旧的)。

对于旧框架中已包含的行，新框架可能包含更新的数据。在这种情况下，旧帧中的数据应该被新帧中的数据覆盖。此外，较新的框架可能比第一个框架具有更多的列/行。在这种情况下，旧框架应该被新框架中的数据放大。

Pandas docs状态，那个

“.loc/.ix/[] 操作可以在为该轴设置不存在的键时执行放大”

和

“DataFrame 可以通过 .loc 在任一轴上放大”

但是这似乎不起作用并抛出一个KeyError。示例:

In [195]: df1
Out[195]: 
                     A  B  C
2015-07-09 12:00:00  1  1  1
2015-07-09 13:00:00  1  1  1
2015-07-09 14:00:00  1  1  1
2015-07-09 15:00:00  1  1  1

In [196]: df2
Out[196]: 
                     A  B  C  D
2015-07-09 14:00:00  2  2  2  2
2015-07-09 15:00:00  2  2  2  2
2015-07-09 16:00:00  2  2  2  2
2015-07-09 17:00:00  2  2  2  2

In [197]: df1.loc[df2.index] = df2
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-197-74e630e87cf8> in <module>()
----> 1 df1.loc[df2.index] = df2

/.../pandas/core/indexing.pyc in __setitem__(self, key, value)
    112 
    113     def __setitem__(self, key, value):
--> 114         indexer = self._get_setitem_indexer(key)
    115         self._setitem_with_indexer(indexer, value)
    116 

/.../pandas/core/indexing.pyc in _get_setitem_indexer(self, key)
    107 
    108         try:
--> 109             return self._convert_to_indexer(key, is_setter=True)
    110         except TypeError:
    111             raise IndexingError(key)

/.../pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
   1110                 mask = check == -1
   1111                 if mask.any():
-> 1112                     raise KeyError('%s not in index' % objarr[mask])
   1113 
   1114                 return _values_from_object(indexer)

KeyError: "['2015-07-09T18:00:00.000000000+0200' '2015-07-09T19:00:00.000000000+0200'] not in index"

什么是最好的方法(关于性能，因为我的真实数据要大得多)两个实现所需的更新和扩大的 DataFrame。这是我希望看到的结果:

                     A  B  C    D
2015-07-09 12:00:00  1  1  1  NaN
2015-07-09 13:00:00  1  1  1  NaN
2015-07-09 14:00:00  2  2  2    2
2015-07-09 15:00:00  2  2  2    2
2015-07-09 16:00:00  2  2  2    2
2015-07-09 17:00:00  2  2  2    2

Answer 1

df2.combine_first(df1) ( documentation )似乎满足您的要求； PFB 代码片段和输出

import pandas as pd

print 'pandas-version: ', pd.__version__

df1 = pd.DataFrame.from_records([('2015-07-09 12:00:00',1,1,1),
                                 ('2015-07-09 13:00:00',1,1,1),
                                 ('2015-07-09 14:00:00',1,1,1),
                                 ('2015-07-09 15:00:00',1,1,1)],
                                columns=['Dt', 'A', 'B', 'C']).set_index('Dt')
# print df1

df2 = pd.DataFrame.from_records([('2015-07-09 14:00:00',2,2,2,2),
                                 ('2015-07-09 15:00:00',2,2,2,2),
                                 ('2015-07-09 16:00:00',2,2,2,2),
                                 ('2015-07-09 17:00:00',2,2,2,2),],
                               columns=['Dt', 'A', 'B', 'C', 'D']).set_index('Dt')
res_combine1st = df2.combine_first(df1)
print res_combine1st

输出

pandas-version:  0.15.2
                     A  B  C   D
Dt                              
2015-07-09 12:00:00  1  1  1 NaN
2015-07-09 13:00:00  1  1  1 NaN
2015-07-09 14:00:00  2  2  2   2
2015-07-09 15:00:00  2  2  2   2
2015-07-09 16:00:00  2  2  2   2
2015-07-09 17:00:00  2  2  2   2