分层索引允许在一个轴向上拥有多个索引层级，也就是提供了一种在低纬度处理高纬度数据的方式

data = pd.Series(np.random.randn(9),index=[['a','a','a','b','b','c','c','d','d'],
                 [1,2,3,1,3,1,2,2,3]])
print(data)	==>
a  1    0.814410
   2    0.259285
   3   -0.020882
b  1    0.205790
   3    0.116076
c  1    2.032459
   2    1.170321
d  2   -1.397431
   3    1.657013
dtype: float64
print(data.index)	==>
MultiIndex(levels=[['a', 'b', 'c', 'd'], [1, 2, 3]],
           codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1, 1, 2]])

上面这个例子就是一个以MultiIndex作为索引的Series美化视图

同时通过分层索引对象，允许简介地选出数据的子集：

data = pd.Series(np.random.randn(9),index=[['a','a','a','b','b','c','c','d','d'],
                 [1,2,3,1,3,1,2,2,3]])
print(data['b'])	==>
1   -1.630889
3   -0.347437
dtype: float64
print(data['b':'c'])	==>
b  1   -1.630889
   3   -0.347437
c  1   -0.542486
   2   -1.166458
dtype: float64
print(data.loc[['b','d']])	==>
b  1   -1.630889
   3   -0.347437
d  2   -0.297936
   3    0.156680
dtype: float64
#choose in the inner level
print(data.loc[:,2])	==>
a    0.102767
c   -1.166458
d   -0.297936
dtype: float64

可以使用unstack方法将数据在DataFrame中重新排列（stack是其反操作）

data = pd.Series(np.random.randn(9),index=[['a','a','a','b','b','c','c','d','d'],
                 [1,2,3,1,3,1,2,2,3]])
print(data)	==>
a  1   -1.542656
   2    0.228203
   3   -1.725217
b  1    0.055736
   3   -0.596109
c  1    0.994262
   2   -0.538161
d  2    2.523848
   3   -0.929541
dtype: float64
print(data.unstack())	==>
          1         2         3
a  1.445621  0.714303  1.091814
b -0.583806       NaN -1.468077
c -0.145544  0.930694       NaN
d       NaN  0.302969 -0.427452

DataFrame中每个轴都可以拥有分层索引

frame = pd.DataFrame(np.arange(12).reshape((4,3)),
                     index=[['a','a','b','b'],[1,2,1,2]],
                     columns=[['Ohio','Ohio','Colorado'],['Green','Red','Green']])
print(frame)	==>
     Ohio     Colorado
    Green Red    Green
a 1     0   1        2
  2     3   4        5
b 1     6   7        8
  2     9  10       11
#接下来的操作是对每个索引进行命名
frame.index.names = ['key1','key2']
frame.columns.names = ['state','color']
print(frame)	==>
state      Ohio     Colorado
color     Green Red    Green
key1 key2                   
a    1        0   1        2
     2        3   4        5
b    1        6   7        8
     2        9  10       11