如何更改 pandas MultiIndex 列的順序/分組/級別？

Question

我正在嘗試對 pandas dataframe 中的列重新排序/swaplevel/pivot/something 列。這些列是一個 MultiIndex，但我找不到做我想做的事情的醬汁。

我的 multiIndex 中變化最快的列是月份，但我希望它成為變化最慢的列。

如果您想自己嘗試一下，我有一個 nbviewer 筆記本： http://nbviewer.ipython.org/gist/flamingbear/4cfac24c80fe34a67474

我擁有的：

+-------------------------------------------------------------------+
|+-----+------+------+-----+------+-----+-----+------+-----+-----+  |
||     |weight             |extent            |rank                ||
|+-----+------+------+-----+------+-----+-----+------+-----+-----+  |
||month|'1Jan'|'Feb' |'Mar'|'1Jan'|'Feb'|'Mar'|'1Jan'|'Feb'|'Mar'|  |
|+-----+------+------+-----+------+-----+-----+------+-----+-----+  |
||year |      |      |     |      |     |     |      |     |     |  |
|+-----+------+------+-----+------+-----+-----+------+-----+-----+  |
||2000 |45.1  |46.1  |25.1 |13.442|14.94|15.02|13    |17   |14   |  |
|+-----+------+------+-----+------+-----+-----+------+-----+-----+  |
||2001 |85.0  |16.0  |49.0 |13.380|14.81|15.14|12    |15   |17   |  |
|+-----+------+------+-----+------+-----+-----+------+-----+-----+  |
||2002 |90.0  |33.0  |82.0 |13.590|15.13|14.88|15    |22   |10   |  |
|+-----+------+------+-----+------+-----+-----+------+-----+-----+  |
||2003 |47.0  |34.0  |78.0 |13.640|14.83|15.27|17    |16   |22   |  |
|+-----+------+------+-----+------+-----+-----+------+-----+-----+  |
+-------------------------------------------------------------------+

我想要的是

+------------------------------------------------------------------+
|+-----+------+------+----+------+------+-----+------+------+----+ |
||month|1Jan              |Feb                |Mar                ||
|+-----+------+------+----+------+------+-----+------+------+----+ |
||     |weight|extent|rank|weight|extent|rank |weight|extent|rank| |
|+-----+------+------+----+------+------+-----+------+------+----+ |
||year |      |      |    |      |      |     |      |      |    | |
|+-----+------+------+----+------+------+-----+------+------+----+ |
||2000 |45.1  |13.442|13  |46.1  |14.94 |17   | 25.1 |15.02 |14  | |
|+-----+------+------+----+------+------+-----+------+------+----+ |
||2001 |85.0  |13.380|12  |16.0  |14.81 |15   | 49.0 |15.14 |17  | |
|+-----+------+------+----+------+------+-----+------+------+----+ |
||2002 |90.0  |13.590|15  |33.0  |15.13 |22   | 82.0 |14.88 |10  | |
|+-----+------+------+----+------+------+-----+------+------+----+ |
||2003 |47.0  |13.640|17  |34.0  |14.83 |16   | 78.0 |15.27 |22  | |
|+-----+------+------+-----------+------+-----+------+------+----+ |
+------------------------------------------------------------------+

任何幫助，將不勝感激。 我可以使用我原來的 DataFrame，但是按照所需的順序寫入 CSV 會很棒。

提前致謝， 馬特

Answer 1

您的列是 MultiIndex。 您需要使用從現有的交換級別創建的新 MultiIndex 重新分配 DataFrame 的列：

df.columns = df.columns.swaplevel(0, 1)
df.sortlevel(0, axis=1, inplace=True)
>>> df

month   '1Jan'                 'Feb'                 'Mar'              
        weight  extent  rank  weight  extent  rank  weight  extent  rank
year                                                                    
2000      45.1  13.442    13    46.1   14.94    17    25.1   15.02    14
2001      85.0  13.380    12    16.0   14.81    15    49.0   15.14    17
2002      90.0  13.590    15    33.0   15.13    22    82.0   14.88    10
2003      47.0  13.640    17    34.0   14.83    16    78.0   15.27    22

然后您可以導出到 csv：

df.to_csv(filename)

編輯

根據下面sortlevel的評論，不推薦使用sortlevel 。 相反，使用：

df.sort_index(axis=1, level=0, inplace=True)

Answer 2

由於級別索引不再是強制性的，因此您可以使用更簡單的方法來實現多索引數據幀的級別交換：

df = df.swaplevel(axis='columns')

Answer 3

另一種不需要顯式索引排序的方法是

df.stack(0).unstack()