How to select columns from groupby object in pandas?

You can also reset_index() on your groupby result to get back a dataframe with the name column now accessible.

import pandas as pd
df = pd.DataFrame({"a":[1,1,3], "b":[4,5.5,6], "c":[7,8,9], "name":["hello","hello","foo"]})
df_grouped = df.groupby(["a", "name"]).median().reset_index()
df_grouped.name
 0    hello
 1      foo
 Name: name, dtype: object

If you perform an operation on a single column the return will be a series with multiindex and you can simply apply pd.DataFrame to it and then reset_index.

Set as_index = False during groupby

df = pandas.DataFrame({"a":[1,1,3], "b":[4,5.5,6], "c":[7,8,9], "name":["hello","hello","foo"]})
df.groupby(["a", "name"] , as_index = False).median()

You need to get the index values, they are not columns. In this case level 1

df.groupby(["a", "name"]).median().index.get_level_values(1)

Out[2]:

Index([u'hello', u'foo'], dtype=object)

You can also pass the index name

df.groupby(["a", "name"]).median().index.get_level_values('name')

as this will be more intuitive than passing integer values.

You can convert the index values to a list by calling tolist()

df.groupby(["a", "name"]).median().index.get_level_values(1).tolist()

Out[5]:

['hello', 'foo']