pandas :在不知道列名的情况下重命名单个DataFrame列

问题描述

我知道我可以使用以下方式重命名单个pandas.DataFrame列:

I know I can rename single pandas.DataFrame columns with:

drugInfo.rename(columns = {'col_1': 'col_1_new_name'}, inplace = True)

但是我想在不知道列名的情况下重命名该列(基于其索引-尽管我知道字典中没有该列).我想这样重命名第1列:

But I'd like to rename a column without knowing its name (based on its index - although I know dictionaries don't have it). I would like rename column number 1 like this:

drugInfo.rename(columns = {1: 'col_1_new_name'}, inplace = True)

但是在DataFrame.columns字典中没有'1'条目，因此没有重命名.我怎么能做到这一点?

But in the DataFrame.columns dict there is no '1' entry, so no renaming is done. How could I achieve this?

推荐答案

应该可以:

drugInfo.rename(columns = {list(drugInfo)[1]: 'col_1_new_name'}, inplace = True)

示例:

In [18]:

df = pd.DataFrame({'a':randn(5), 'b':randn(5), 'c':randn(5)})
df
Out[18]:
          a         b         c
0 -1.429509 -0.652116  0.515545
1  0.563148 -0.536554 -1.316155
2  1.310768 -3.041681 -0.704776
3 -1.403204  1.083727 -0.117787
4 -0.040952  0.108155 -0.092292
In [19]:

df.rename(columns={list(df)[1]:'col1_new_name'}, inplace=True)
df
Out[19]:
          a  col1_new_name         c
0 -1.429509      -0.652116  0.515545
1  0.563148      -0.536554 -1.316155
2  1.310768      -3.041681 -0.704776
3 -1.403204       1.083727 -0.117787
4 -0.040952       0.108155 -0.092292

索引到dataframe列属性中可能更易读:

It is probably more readable to index into the dataframe columns attribute:

df.rename(columns={df.columns[1]:'col1_new_name'}, inplace=True)

所以对你来说:

drugInfo.rename(columns = {drugInfo.columns[1]: 'col_1_new_name'}, inplace = True)

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