Creating multiple new columns in a multiindexed pandas dataframe

Question

Is it possible to create more than one new column in Pandas when a dataframe is multiindexed? I would like to add a two new columns one and two under the bar2 supercolumn. Like so...

import pandas as pd
import numpy as np
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo'], 
          ['one', 'two', 'one', 'two', 'one', 'two']]   

index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(3, 6), index=[1, 2, 3], columns=index)

df["bar2", ["one", "two"]] = np.random.randn(3, 2)

I know I can create them one by one using

df["bar2", "one"] = np.random.randn(3,1)
df["bar2", "two"] = np.random.randn(3,1)

Is there a quicker way of doing both at the same time?

Nader Hisham · Accepted Answer · 2015-11-05 09:22:12Z

1

In [270]:
df_to_add = pd.DataFrame(np.random.randn(3,2) , columns=[['bar2' , 'bar2'] , ['one' , 'two']] , index = [1 , 2 , 3])
df_to_add
Out[270]:
           bar2
         one    two
1   0.119730    -0.265579
2   1.777329    -1.178128
3   -2.700409   0.457430

In [271]:    
pd.concat([df , df_to_add] , axis = 1)

answered Nov 5, 2015 at 9:22

Nader Hisham

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1 Comment

tlnagy Over a year ago

This is convoluted, but it works, which was all I needed. Hopefully they make this simpler in the future.

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