pandas 熊猫合并并保留索引
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时间：2020-09-13 20:30:29 来源:igfitidea点击:
Pandas merge and retain the index
提问by dmvianna
A similar question was asked in How to keep index when using pandas merge, but it will not work with MultiIndexes, i.e,
在How to keep index when using pandas merge中提出了类似的问题，但它不适用于 MultiIndexes，即，
a = DataFrame(np.array([1,2,3,4,1,2,3,3]).reshape((4,2)), columns=['col1','to_merge_on'], index=['a','b','a','b'])
id = pd.MultiIndex.from_arrays([[1,1,2,2],['a','b','a','b']], names =['id1','id2'])
a.index = id
In [207]: a
Out[207]:
col1 to_merge_on
id1 id2
1 a 1 2
b 3 4
2 a 1 2
b 3 4
b=DataFrame(data={"col2": [1,2,3], 'to_merge_on' : [1,3,5]})
In [209]: b
Out[209]:
col2 to_merge_on
0 1 1
1 2 3
2 3 5
a.reset_index().merge(b, how="left").set_index('index')
In [208]: a.reset_index().merge(b, how="left").set_index('index')
------------------------------------------------------------
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 2054, in set_index
level = frame[col]
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1458, in __getitem__
return self._get_item_cache(key)
File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 294, in _get_item_cache
values = self._data.get(item)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 625, in get
_, block = self._find_block(item)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 715, in _find_block
self._check_have(item)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 722, in _check_have
raise KeyError('no item named %s' % str(item))
KeyError: 'no item named index'
How can one make the merge while preserving the MultiIndex in the left dataframe?
如何在保留左侧数据框中的 MultiIndex 的同时进行合并？
采纳答案by dmvianna
Provisional solution:
临时解决方案：
In [255]: a = a.reset_index()
In [256]: a
Out[256]:
id1 id2 col1 to_merge_on
0 1 a 1 2
1 1 b 3 4
2 2 a 1 2
3 2 b 3 4
In [271]: c = pd.merge(a, b, how="left")
In [272]: c
Out[272]:
id1 id2 col1 to_merge_on col2
0 1 a 1 2 NaN
1 2 a 1 2 NaN
2 2 b 3 3 2
3 1 b 3 4 NaN
In [273]: c = c.set_index(['id1','id2'])
In [274]: c
Out[274]:
col1 to_merge_on col2
id1 id2
1 a 1 2 NaN
2 a 1 2 NaN
b 3 3 2
1 b 3 4 NaN
