python - Pandas - Dataframe.set_index - how to keep the old index column

Question

I have this Dataframe:

import pandas as pd
df = pd.DataFrame({\'Hugo\' : {\'age\' : 21, \'weight\' : 75},
                   \'Bertram\': {\'age\' :         


        

Answer 1

Change the drop variable to False.

df = df.set_index("age", drop=False)

Answer 2

Use reset_index first and then set_index:

df = df.reset_index().set_index('age')
print (df)
        name  weight
age                 
45   Bertram      65
75    Donald      85
21      Hugo      75

Answer 3

Adding the append=True and with 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

Answer 4

Your DataFrame df has name (= 'Bertram', 'Donald', 'Hugo') as index

That is, your df is:

         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 df by using the .reset_index() method.

df.reset_index(inplace=True)

name becomes a column and the new index is the standard default integer index:

Your df looks like this now:

Out[1]:    
    name     age  weight

0   Bertram   45      65
1   Donald    75      85
2   Hugo      21      75

Now, you can change the index to age with the .set_index() method.

df.set_index('age',inplace=True)

dfis now:

Out[2]: 
     name  weight
age                 
45   Bertram      65
75   Donald       85
21   Hugo         75

As @jezrael points out above you can do this in a single step, instead of two steps, like this:

df = df.reset_index().set_index('age')

Answer 5

The below is the most efficient since it appends the new index of age and makes sure its inplace

df.set_index('age',append=True,inplace=True)