python – 使用的更奇怪的结果：pandas中的groupby和nlargest()

这个问题是以下帖子的扩展：select largest N of a column of each groupby group using pandas

让我们使用相同的df和所选答案中提出的解决方法.基本上,我正在尝试进行2次groupby操作并选择每组的nlargest N.但是,正如您在下面看到的,我得到其中一个操作的错误.

鉴于原始帖子在代码中发现了一个错误(see here),我想知道是否有另一个错误或同一个错误的另一个表现？

不幸的是,在这些问题得到修复和解决之前,我仍处于工作中.我们能不能在这件事上得到一些关注？直到明天我才能提供赏金.

DF：

{'city1': {0: 'Chicago',
  1: 'Chicago',
  2: 'Chicago',
  3: 'Chicago',
  4: 'Miami',
  5: 'Houston',
  6: 'Austin'},
 'city2': {0: 'Toronto',
  1: 'Detroit',
  2: 'St.Louis',
  3: 'Miami',
  4: 'Dallas',
  5: 'Dallas',
  6: 'Dallas'},
 'p234_r_c': {0: 5.0, 1: 4.0, 2: 2.0, 3: 0.5, 4: 1.0, 5: 4.0, 6: 3.0},
 'plant1_type': {0: 'COMBCYCL',
  1: 'COMBCYCL',
  2: 'NUKE',
  3: 'COAL',
  4: 'NUKE',
  5: 'COMBCYCL',
  6: 'COAL'},
 'plant2_type': {0: 'COAL',
  1: 'COAL',
  2: 'COMBCYCL',
  3: 'COMBCYCL',
  4: 'COAL',
  5: 'NUKE',
  6: 'NUKE'}}

您可以使用上面的dict生成df：pd.DataFrame(dct)

First groupby：似乎生成有意义的结果

cols = ['city2','plant1_type','plant2_type']
df.set_index(cols).groupby(level=cols)['p234_r_c'].nlargest(1).reset_index()

    city2   plant1_type plant2_type p234_r_c
0   Toronto COMBCYCL    COAL        5.0
1   Detroit COMBCYCL    COAL        4.0
2   St.Louis    NUKE    COMBCYCL    2.0
3   Miami   COAL        COMBCYCL    0.5
4   Dallas  NUKE        COAL        1.0
5   Dallas  COMBCYCL    NUKE        4.0
6   Dallas  COAL        NUKE        3.0

第二组：产生错误.唯一的区别是使用city1而不是city2.

cols = ['city1','plant1_type','plant2_type']
df.set_index(cols).groupby(level=cols)['p234_r_c'].nlargest(1).reset_index()

错误结果：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-443-6426182b55e1> in <module>()
----> 1 test1.set_index(cols).groupby(level=cols)['p234_r_c'].nlargest(1).reset_index()

C:\Users\user1\Anaconda3\lib\site-packages\pandas\core\series.py in reset_index(self, level, drop, name, inplace)
    967         else:
    968             df = self.to_frame(name)
--> 969             return df.reset_index(level=level, drop=drop)
    970 
    971     def __unicode__(self):

C:\Users\user1\Anaconda3\lib\site-packages\pandas\core\frame.py in reset_index(self, level, drop, inplace, col_level, col_fill)
   2944                     level_values = _maybe_casted_values(lev, lab)
   2945                     if level is None or i in level:
-> 2946                         new_obj.insert(0, col_name, level_values)
   2947 
   2948         elif not drop:

C:\Users\user1\Anaconda3\lib\site-packages\pandas\core\frame.py in insert(self, loc, column, value, allow_duplicates)
   2447         value = self._sanitize_column(column, value)
   2448         self._data.insert(loc, column, value,
-> 2449                           allow_duplicates=allow_duplicates)
   2450 
   2451     def assign(self, **kwargs):

C:\Users\user1\Anaconda3\lib\site-packages\pandas\core\internals.py in insert(self, loc, item, value, allow_duplicates)
   3508         if not allow_duplicates and item in self.items:
   3509             # Should this be a different kind of error??
-> 3510             raise ValueError('cannot insert %s, already exists' % item)
   3511 
   3512         if not isinstance(loc, int):

ValueError: cannot insert plant2_type, already exists

最后：

如何使用[‘city1′,’plant1_type’,’plant2_type’]在groupby的结果中使用[‘city2′,’plant1_type’,’plant2_type’]和city2列获取groupby结果中的city1列？

我想知道groupby使用[‘city2′,’plant1_type’,’plant2_type’]的相应city1值以及groupby使用[‘city1′,’plant1_type’,’plant2_type’]的相应city2值.

更新：

为什么以下结果具有完全不同的结构？唯一的区别是city2用于#A,而city1用于#B.

一种)

cols = ['city2','plant1_type','plant2_type']
test1.set_index(cols).groupby(level=cols)['p234_r_c'].nlargest(1)


city2     plant1_type  plant2_type
Toronto   COMBCYCL     COAL           5.0
Detroit   COMBCYCL     COAL           4.0
St.Louis  NUKE         COMBCYCL       2.0
Miami     COAL         COMBCYCL       0.5
Dallas    NUKE         COAL           1.0
          COMBCYCL     NUKE           4.0
          COAL         NUKE           3.0
Name: p234_r_c, dtype: float64

B)

cols2 = ['city1','plant1_type','plant2_type']
test1.set_index(cols2).groupby(level=cols2)['p234_r_c'].nlargest(1)

city1    plant1_type  plant2_type  city1    plant1_type  plant2_type
Austin   COAL         NUKE         Austin   COAL         NUKE           3.0
Chicago  COAL         COMBCYCL     Chicago  COAL         COMBCYCL       0.5
         COMBCYCL     COAL         Chicago  COMBCYCL     COAL           5.0
         NUKE         COMBCYCL     Chicago  NUKE         COMBCYCL       2.0
Houston  COMBCYCL     NUKE         Houston  COMBCYCL     NUKE           4.0
Miami    NUKE         COAL         Miami    NUKE         COAL           1.0
Name: p234_r_c, dtype: float64

解决方法:

尝试这个：

In [76]: df.groupby(cols2)['p234_r_c'].nlargest(1).reset_index(level=3, drop=True).reset_index()
Out[76]:
     city1 plant1_type plant2_type  p234_r_c
0   Austin        COAL        NUKE       3.0
1  Chicago        COAL    COMBCYCL       0.5
2  Chicago    COMBCYCL        COAL       5.0
3  Chicago        NUKE    COMBCYCL       2.0
4  Houston    COMBCYCL        NUKE       4.0
5    Miami        NUKE        COAL       1.0

坦率地说,我不明白以下行为：

In [77]: df.set_index(cols2).groupby(level=cols2)['p234_r_c'].nlargest(1)
Out[77]:
city1    plant1_type  plant2_type  city1    plant1_type  plant2_type
Austin   COAL         NUKE         Austin   COAL         NUKE           3.0
Chicago  COAL         COMBCYCL     Chicago  COAL         COMBCYCL       0.5
         COMBCYCL     COAL         Chicago  COMBCYCL     COAL           5.0
         NUKE         COMBCYCL     Chicago  NUKE         COMBCYCL       2.0
Houston  COMBCYCL     NUKE         Houston  COMBCYCL     NUKE           4.0
Miami    NUKE         COAL         Miami    NUKE         COAL           1.0
Name: p234_r_c, dtype: float64

哪里：

In [78]: cols2
Out[78]: ['city1', 'plant1_type', 'plant2_type']

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