重命名多索引数据框中的索引值

创建我的数据框：

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

tuples = zip(*arrays)

index = MultiIndex.from_tuples(tuples, names=['first','second'])
data = DataFrame(randn(8,2),index=index,columns=['c1','c2'])

data
Out[68]: 
                    c1        c2
first second                    
bar   one     0.833816 -1.529639
      two     0.340150 -1.818052
baz   one    -1.605051 -0.917619
      two    -0.021386 -0.222951
foo   one     0.143949 -0.406376
      two     1.208358 -2.469746
qux   one    -0.345265 -0.505282
      two     0.158928  1.088826

我想重命名“第一个”索引值，例如“bar”->“cat”、“baz”->“dog”等。但是，我读过的每个示例要么在单级索引上运行，要么在单级索引上运行。 /or 循环整个索引以有效地从头开始重新创建它。我在想这样的事情：

data = data.reindex(index={'bar':'cat','baz':'dog'})

但这不起作用，我也不真正期望它能在多个索引上起作用。我可以在不循环整个数据帧索引的情况下进行这样的替换吗？

开始编辑

Use the set_levels方法 （0.13.0版本中的新增功能 http://pandas.pydata.org/pandas-docs/dev/whatsnew.html#what-s-new):

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

要根据字典重新映射级别，您可以使用如下函数：

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)

这是一个可运行的示例：

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