Renaming index values in multiindex dataframe

Question

Creating my dataframe:

from pandas import *
arrays = [[\'bar\', \'bar\', \'baz\', \'baz\', \'foo\', \'foo\', \'qux\', \'qux\'],
          [\'one\', \'two\', \'o         


        

Answer 1

Use the set_levels method (new in version 0.13.0):

data.index.set_levels([[u'cat', u'dog', u'foo', u'qux'], 
                       [u'one', u'two']], inplace=True)

yields

                    c1        c2
first second                    
cat   one    -0.289649 -0.870716
      two    -0.062014 -0.410274
dog   one     0.030171 -1.091150
      two     0.505408  1.531108
foo   one     1.375653 -1.377876
      two    -1.478615  1.351428
qux   one     1.075802  0.532416
      two     0.865931 -0.765292

---

To remap a level based on a dict, you could use a function such as this:

def map_level(df, dct, level=0):
    index = df.index
    index.set_levels([[dct.get(item, item) for item in names] if i==level else names
                      for i, names in enumerate(index.levels)], inplace=True)

dct = {'bar':'cat', 'baz':'dog'}
map_level(data, dct, level=0)

---

Here's a runnable example:

import numpy as np
import pandas as pd

arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = zip(*arrays)
index = pd.MultiIndex.from_tuples(tuples, names=['first','second'])
data = pd.DataFrame(np.random.randn(8,2),index=index,columns=['c1','c2'])
data2 = data.copy()

data.index.set_levels([[u'cat', u'dog', u'foo', u'qux'], 
                       [u'one', u'two']], inplace=True)
print(data)
#                     c1        c2
# first second                    
# cat   one     0.939040 -0.748100
#       two    -0.497006 -1.185966
# dog   one    -0.368161  0.050339
#       two    -2.356879 -0.291206
# foo   one    -0.556261  0.474297
#       two     0.647973  0.755983
# qux   one    -0.017722  1.364244
#       two     1.007303  0.004337

def map_level(df, dct, level=0):
    index = df.index
    index.set_levels([[dct.get(item, item) for item in names] if i==level else names
                      for i, names in enumerate(index.levels)], inplace=True)
dct = {'bar':'wolf', 'baz':'rabbit'}
map_level(data2, dct, level=0)
print(data2)
#                      c1        c2
# first  second                    
# wolf   one     0.939040 -0.748100
#        two    -0.497006 -1.185966
# rabbit one    -0.368161  0.050339
#        two    -2.356879 -0.291206
# foo    one    -0.556261  0.474297
#        two     0.647973  0.755983
# qux    one    -0.017722  1.364244
#        two     1.007303  0.004337

Answer 2

The set_levels method was causing my new column names to be out of order. So I found a different solution that isn't very clean, but works well. The method is to print df.index (or equivalently df.columns) and then copy and paste the output with the desired values changed. For example:

print data.index

MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], ['one', 'two']], labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]], names=['first', 'second'])

data.index = MultiIndex(levels=[['new_bar', 'new_baz', 'new_foo', 'new_qux'],
                                ['new_one', 'new_two']],
                        labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
                        names=['first', 'second'])

We can have full control over names by editing the labels as well. For example:

data.index = MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'],
                                ['one', 'twooo', 'three', 'four',
                                 'five', 'siz', 'seven', 'eit']],
                        labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 3, 4, 5, 6, 7]],
                        names=['first', 'second'])

Note that in this example we have already done something like from pandas import MultiIndex or from pandas import *.