如何简单地向 pandas 数据框添加列级别 [英] How to simply add a column level to a pandas dataframe

问题描述

假设我有一个看起来像这样的数据框:

let say I have a dataframe that looks like this:

df = pd.DataFrame(index=list('abcde'), data={'A': range(5), 'B': range(5)})
 df
Out[92]: 
   A  B
a  0  0
b  1  1
c  2  2
d  3  3
e  4  4

假设此数据框已经存在，我如何简单地在列索引中添加一个级别'C'，以便得到此信息:

Asumming that this dataframe already exist, how can I simply add a level 'C' to the column index so I get this:

 df
Out[92]: 
   A  B
   C  C
a  0  0
b  1  1
c  2  2
d  3  3
e  4  4

我看到这样的答案

I saw SO anwser like this python/pandas: how to combine two dataframes into one with hierarchical column index? but this concat different dataframe instead of adding a column level to an already existing dataframe.

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推荐答案

如@StevenG本人所建议的，一个更好的答案:

As suggested by @StevenG himself, a better answer:

df.columns = pd.MultiIndex.from_product([df.columns, ['C']])

print(df)
#    A  B
#    C  C
# a  0  0
# b  1  1
# c  2  2
# d  3  3
# e  4  4

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