python - Pandas - Dataframe.set_index - 如何保留旧的索引列
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python - Pandas - Dataframe.set_index - how to keep the old index column
提问by Egirus Ornila
I have this Dataframe:
我有这个Dataframe：
import pandas as pd
df = pd.DataFrame({'Hugo' : {'age' : 21, 'weight' : 75},
'Bertram': {'age' : 45, 'weight' : 65},
'Donald' : {'age' : 75, 'weight' : 85}}).T
df.index.names = ['name']
age weight
name
Bertram 45 65
Donald 75 85
Hugo 21 75
I want to change the index to the column 'age':
我想将索引更改为列'age'：
df.set_index('age', inplace=True)
weight
age
45 65
75 85
21 75
The old index-column name gets lost. Is there a way to change the index without losing the original index-column and getting the old column as 'normal' column again, so that it looks like this?
旧的索引列名称丢失。有没有办法在不丢失原始索引列的情况下更改索引并将旧列重新设为“正常”列，使其看起来像这样？
name weight
age
45 Bertram 65
75 Donald 85
21 Hugo 75
回答by jezrael
Use reset_indexfirst and then set_index:
reset_index先使用，然后set_index：
df = df.reset_index().set_index('age')
print (df)
name weight
age
45 Bertram 65
75 Donald 85
21 Hugo 75
回答by YOBEN_S
Adding the append=Trueand with reset_index
添加append=True和reset_index
df.set_index('age', append=True).reset_index(level=0)
Out[80]:
name weight
age
45 Bertram 65
75 Donald 85
21 Hugo 75
回答by ricky_hehe
The below is the most efficient since it appends the new index of ageand makes sure its inplace
下面是最有效的，因为它附加了新的索引age并确保它就位
df.set_index('age',append=True,inplace=True)
回答by pink.slash
Your DataFrame dfhas name(= 'Bertram', 'Donald', 'Hugo') as index
您的 DataFramedf有name(= 'Bertram', 'Donald', 'Hugo') 作为索引
That is, your dfis:
也就是说，你df是：
age weight
name
Bertram 45 65
Donald 75 85
Hugo 21 75
You can convert the index (name) into a new column inside your DataFrame dfby using the .reset_index()method.
您可以将指数（转换name）为您的数据帧中的新列df使用.reset_index()方法。
df.reset_index(inplace=True)
namebecomes a column and the new index is the standard default integer index:
name成为一列，新索引是标准的默认整数索引：
Your df looks like this now:
你的 df 现在看起来像这样：
Out[1]:
name age weight
0 Bertram 45 65
1 Donald 75 85
2 Hugo 21 75
Now, you can change the index to agewith the .set_index()method.
现在，您可以age使用该.set_index()方法更改索引。
df.set_index('age',inplace=True)
dfis now:
df就是现在：
Out[2]:
name weight
age
45 Bertram 65
75 Donald 85
21 Hugo 75
As @jezraelpoints out above you can do this in a single step, instead of two steps, like this:
正如@jezrael在上面指出的，你可以一步完成，而不是两步，像这样：
df = df.reset_index().set_index('age')
