python - Select only one index of multiindex DataFrame

Question

I am trying to create a new DataFrame using only one index from a multi-indexed DataFrame.

                   A         B         C
first second                              
bar   one     0.895717  0.410835 -1.413681
      two     0.805244  0.813850  1.607920
baz   one    -1.206412  0.132003  1.024180
      two     2.565646 -0.827317  0.569605
foo   one     1.431256 -0.076467  0.875906
      two     1.340309 -1.187678 -2.211372
qux   one    -1.170299  1.130127  0.974466
      two    -0.226169 -1.436737 -2.006747

Ideally, I would like something like this:

In: df.ix[level="first"]

and:

Out:

               A         B         C
first                               
bar        0.895717  0.410835 -1.413681
           0.805244  0.813850  1.607920
baz       -1.206412  0.132003  1.024180
           2.565646 -0.827317  0.569605
foo        1.431256 -0.076467  0.875906
           1.340309 -1.187678 -2.211372
qux       -1.170299  1.130127  0.974466
          -0.226169 -1.436737 -2.006747
`

Essentially I want to drop all the other indexes of the multi-index other than level first. Is there an easy way to do this?

Answer 1

One way could be to simply rebind df.index to the desired level of the MultiIndex. You can do this by specifying the label name you want to keep:

df.index = df.index.get_level_values('first')

or use the level's integer value:

df.index = df.index.get_level_values(0)

All other levels of the MultiIndex would disappear here.

Answer 2

The solution is fairly new and uses the df.xs function as

In [88]: df.xs('bar', level='first')
Out[88]:
Second  Third
one     A       -2.315312
        B        0.497769
        C        0.108523
two     A       -0.778303
        B       -1.555389
        C       -2.625022
dtype: float64

Can also do with multiple indices as

In [89]: df.xs(('bar', 'A'), level=('First', 'Third'))
Out[89]:
Second
one   -2.315312
two   -0.778303
dtype: float64

The setup for the examples is below

import pandas as pd
import numpy as np
arrays = [
    np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
    np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])
]
index = pd.MultiIndex.from_tuples(list(zip(*arrays)), names=['first', 'second'])
df = pd.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index)
df.index.names = pd.core.indexes.frozen.FrozenList(['First', 'Second', 'Third'])
df = df.unstack()

Answer 3

I used the get_level_values(0) to get the first level index in a multi index group by to build a dataframe containing the aggregate value and the description dictionary value of the encoded value. I get the index for "airline_enc" values in the group by

def getAirlineByGrouped(grouped,dictGeneric):
    mylist=[]
    for key in grouped.index.get_level_values(0):
        item=dictGeneric.get(key)
        mylist.append(item)
    return mylist

encoder=LabelEncoder()
df['airline_enc']=encoder.fit_transform(df['airline'])

dictAirline=   df[['airline_enc','airline']].set_index('airline_enc').to_dict()
grouped=results.groupby(['airline_enc','rating'])['recommended'].count()

#print(grouped)
airlines=getAirlineByGrouped(grouped, dictAirline['airline'])

result_df=pd.DataFrame({'index': grouped.index.get_level_values(0),'value':grouped.values,'airline':airlines})
result_df.plot(x='airline',y='value')
plt.xticks(rotation=90)