如何更改 Pandas MultiIndex 列的顺序/分组/级别？
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How do I change order/grouping/level of pandas MultiIndex columns?
提问by Matt Savoie
I'm trying to reorder/swaplevel/pivot/something columns in a pandas dataframe. The columns are a MultiIndex, but I can't find the sauce to do what I want.
我正在尝试在 Pandas 数据框中重新排序/swaplevel/pivot/something 列。这些列是一个 MultiIndex，但我找不到做我想做的事。
The fastest varying column in my multiIndex is month, but I would like it to be the slowest varying column.
我的 multiIndex 中变化最快的列是月，但我希望它是变化最慢的列。
I've got a nbviewer notebook if you would like to try it out yourself: http://nbviewer.ipython.org/gist/flamingbear/4cfac24c80fe34a67474
如果您想自己尝试一下，我有一个 nbviewer 笔记本：http://nbviewer.ipython.org/gist/flamingbear/4cfac24c80fe34a67474
What I have:
我拥有的：
+-------------------------------------------------------------------+
|+-----+------+------+-----+------+-----+-----+------+-----+-----+ |
|| |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 | |
|+-----+------+------+-----+------+-----+-----+------+-----+-----+ |
+-------------------------------------------------------------------+
What I want
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 | |
|+-----+------+------+-----------+------+-----+------+------+----+ |
+------------------------------------------------------------------+
Any help would be appreciated. I can work with my original DataFrame, but writing to a CSV with the desired ordering would be fantastic.
任何帮助，将不胜感激。我可以使用我的原始 DataFrame，但是以所需的顺序写入 CSV 会很棒。
Thanks in advance, Matt
提前致谢，马特
回答by Alexander
Your columns are a MultiIndex. You need to reassign the DataFrame's columns with a new MultiIndex created from swapping levels of the existing one:
您的列是 MultiIndex。您需要使用从现有的交换级别创建的新 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
You can then export to csv:
然后您可以导出到 csv：
df.to_csv(filename)
EDIT
编辑
Per the comment from @Silas below, sortlevelhas been deprecated. Instead, use:
根据下面@Silas 的评论，sortlevel已被弃用。相反，使用：
df.sort_index(axis=1, level=0, inplace=True)
回答by Mehdi S
Since levels indices are no more mandatory you can have even more simple way to achieve the level swapping of multi-index dataframe:
由于级别索引不再是强制性的，您可以使用更简单的方法来实现多索引数据帧的级别交换：
df = df.swaplevel(axis='columns')
