Python Pandas 中的多索引排序
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Multi Index Sorting in Pandas
提问by MattB
I have a dataset with multi-index columns in a pandas df that I would like to sort by values in a specific column. I have tried using sortindex and sortlevel but haven't been able get the results I am looking for. My dataset looks like:
我在 Pandas df 中有一个包含多索引列的数据集，我想按特定列中的值排序。我曾尝试使用 sortindex 和 sortlevel，但无法获得我正在寻找的结果。我的数据集看起来像：
Group1 Group2
A B C A B C
1 1 0 3 2 5 7
2 5 6 9 1 0 0
3 7 0 2 0 3 5
I want to sort all data and the index by column C in Group 1 in descending order so my results look like:
我想按第 1 组中的 C 列按降序对所有数据和索引进行排序，因此我的结果如下所示：
Group1 Group2
A B C A B C
2 5 6 9 1 0 0
1 1 0 3 2 5 7
3 7 0 2 0 3 5
Is it possible to do this sort with the structure that my data is in, or should I be swapping Group1 to the index side?
是否可以使用我的数据所在的结构进行这种排序，还是应该将 Group1 交换到索引端？
采纳答案by Andy Hayden
When sorting by a MultiIndex you need to contain the tuple describing the column inside a list*:
当按 MultiIndex 排序时，您需要包含描述列表中列的元组*：
In [11]: df.sort_values([('Group1', 'C')], ascending=False)
Out[11]:
Group1 Group2
A B C A B C
2 5 6 9 1 0 0
1 1 0 3 2 5 7
3 7 0 2 0 3 5
* so as not to confuse pandas into thinking you want to sort first by Group1 then by C.
*以免让大熊猫误以为您想先按 Group1 然后按 C 排序。
Note: Originally used .sortsince deprecated then removed in 0.20, in favor of .sort_values.
注意：最初使用.sort自弃用，然后在 0.20 中删除，支持.sort_values.
