将多级索引的一个级别拆分为多个列

本文介绍了将多级索引的一个级别拆分为多个列的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

所以我有一个数据框:

df = pd.DataFrame([["foo","fizz",1],["foo","fizz",2],["foo","buzz",3],["foo","buzz",4],["bar","fizz",6],["bar","buzz",8]],columns=["a","b","c"]) a b c 0 foo fizz 1 1 foo fizz 2 2 foo buzz 3 3 foo buzz 4 4 bar fizz 6 5 bar buzz 8

我可以分组:

df2 = df.groupby(["a","b"]).sum() c a b bar buzz 8 fizz 6 foo buzz 7 fizz 3

哪个很棒！但是我真正需要的是两列，而不是"c"列:"foo"和"bar":

Which is awesome! But what I really need, instead of the "c" column is two columns, "foo" and "bar":

foo bar b buzz 7 8 fizz 3 6

有人可以建议一种方法吗?我尝试搜索，但是我想我没有正确的术语，所以我什么也找不到.

Can someone suggest a way to do this? I tried searching, but I guess I don't have the correct terminology for this so I couldn't find anything.

推荐答案

您可以为此使用unstack:

df2.unstack(level='a')

示例:

In [146]: df2.unstack(level='a') Out[146]: c a bar foo b buzz 8 7 fizz 6 3

之后，您将获得多索引列.如果需要获取平面数据框，则可以使用multiindex的droplevel:

After that you'll get multiindexed columns. If you need to get flat dataframe you could use droplevel of multiindex:

df3 = df2.unstack(level='a') df3.columns = df3.columns.droplevel() In [177]: df3 Out[177]: a bar foo b buzz 8 7 fizz 6 3

编辑

droplevel从MultiIndex降低级别，该列在unstack之后变为.默认情况下，它删除级别0，这是该数据帧所需的级别.

droplevel drops level from MultiIndex which your columns become after unstack. By default it drops level 0 which is what you need for that dataframe.

从help(pd.core.index.MultiIndex.droplevel)复制:

关于pandas.core.index模块中的功能 droplevel 的帮助:

下降级别(自身，级别= 0) 返回索引，删除了请求的级别.如果MultiIndex只有2 级别，结果将是索引类型而不是MultiIndex.

droplevel(self, level=0) Return Index with requested level removed. If MultiIndex has only 2 levels, the result will be of Index type not MultiIndex.

Parameters ---------- level : int/level name or list thereof Notes ----- Does not check if result index is unique or not Returns ------- index : Index or MultiIndex