与Series对象类似，轴索引也有一个map方法：
In [83]: df = pd.DataFrame(np.arange(12).reshape((3, 4)),
...: index=['Ohio', 'Colorado', 'New York'],
...: columns=['one', 'two', 'three', 'four'])
...:
In [84]: transform = lambda x: x[:4].upper() # 截取前4个字符并大写
In [85]: df.index.map(transform) # map的结果
Out[85]: Index(['OHIO', 'COLO', 'NEW '], dtype='object')
In [86]: df.index = df.index.map(transform) #用结果修改原来的index
In [87]: df
Out[87]:
one two three four
OHIO 0 1 2 3
COLO 4 5 6 7
NEW 8 9 10 11
还可以使用rename方法修改索引，且不修改原数据：
# 参数的值是对索引进行修改的处理函数，比如str.title
In [88]: df.rename(index=str.title, columns=str.upper)
Out[88]:
ONE TWO THREE FOUR
Ohio 0 1 2 3
Colo 4 5 6 7
New 8 9 10 11
In [89]: df # 原值未变
Out[89]:
one two three four
OHIO 0 1 2 3
COLO 4 5 6 7
NEW 8 9 10 11
或者使用字典的方式，将指定的索引重命名为新值：
In [90]: df.rename(index={'OHIO': 'INDIANA'},
...: columns={'three': 'peekaboo'})
...:
Out[90]:
one two peekaboo four
INDIANA 0 1 2 3
COLO 4 5 6 7
NEW 8 9 10 11
In [91]: df
Out[91]:
one two three four
OHIO 0 1 2 3
COLO 4 5 6 7
NEW 8 9 10 11
使用inplace=True可以原地修改数据集。