Pandas中的索引对象Index用于存储轴标签和其它元数据。索引对象是不可变的，用户无法修改它。
In [73]: obj = pd.Series(range(3),index = ['a','b','c'])
In [74]: index = obj.index
In [75]: index
Out[75]: Index(['a', 'b', 'c'], dtype='object')
In [76]: index[1:]
Out[76]: Index(['b', 'c'], dtype='object')
In [77]: index[1] = 'f' # TypeError
In [8]: index.size
Out[8]: 3
In [9]: index.shape
Out[9]: (3,)
In [10]: index.ndim
Out[10]: 1
In [11]: index.dtype
Out[11]: dtype('O')
索引对象的不可变特性，使得在多种数据结构中分享索引对象更安全：
In [78]: labels = pd.Index(np.arange(3)) In [79]: labels Out[79]: Int64Index([0, 1, 2], dtype='int64') In [80]: obj2 = pd.Series([2,3.5,0], index=labels) In [81]: obj2 Out[81]: 0 2.0 1 3.5 2 0.0 dtype: float64 In [82]: obj2.index is labels Out[82]: True
索引对象，本质上也是一个容器对象，所以可以使用Python的in操作：
In [84]: f2 Out[84]: key year state pop debt order a 2000 beijing 1.5 NaN b 2001 beijing 1.7 NaN c 2002 beijing 3.6 1.0 d 2001 shanghai 2.4 2.0 e 2002 shanghai 2.9 NaN f 2003 shanghai 3.2 3.0 In [86]: 'c' in f2.index Out[86]: True In [88]: 'pop' in f2.columns Out[88]: True
而且最关键的是，pandas的索引对象可以包含重复的标签：
In [89]: dup_lables = pd.Index(['foo','foo','bar','bar']) In [90]: dup_lables Out[90]: Index(['foo', 'foo', 'bar', 'bar'], dtype='object')
那么思考一下，DataFrame对象可不可以有重复的columns或者index呢？
可以的！但是请尽量不要这么做！：
In [91]: f2.index = ['a']*6 In [92]: f2 Out[92]: key year state pop debt a 2000 beijing 1.5 NaN a 2001 beijing 1.7 NaN a 2002 beijing 3.6 1.0 a 2001 shanghai 2.4 2.0 a 2002 shanghai 2.9 NaN a 2003 shanghai 3.2 3.0 In [93]: f2.loc['a'] Out[93]: key year state pop debt a 2000 beijing 1.5 NaN a 2001 beijing 1.7 NaN a 2002 beijing 3.6 1.0 a 2001 shanghai 2.4 2.0 a 2002 shanghai 2.9 NaN a 2003 shanghai 3.2 3.0 In [94]: f2.columns = ['year']*4 In [95]: f2 Out[95]: year year year year a 2000 beijing 1.5 NaN a 2001 beijing 1.7 NaN a 2002 beijing 3.6 1.0 a 2001 shanghai 2.4 2.0 a 2002 shanghai 2.9 NaN a 2003 shanghai 3.2 3.0 In [96]: f2.index.is_unique # 可以使用这个属性来判断是否存在重复的索引 Out[96]: False
index对象也可以进行集合的交、并、差和异或运算，类似Python的标准set数据结构。