pandas对象的reindex方法以及插值问题

1、调用reindex方法将会根据新索引进行重排。如果某个索引值（对应索引的值）当前不存在，就引入缺失值，比如：

>> obj = Series([4.5,7,-7,4.3],index = ['d','b','a','c'])
>> obj 
d  4.5
b  7
a  -7
c  4.3
>> obj.reindex(['a','b','c','d','e'])
a  -7
b  7
c  4.3
d  4.5
e  NaN   #索引值不存在引入索引值，not a number
>> obj.reindex(['a','b','c','d','e'],fill_value=0) #索引值不存在时进行插值处理

2、reindex方法中的method选项也可以做一些插值处理

>> obj2 = Series(['blue','purple','yellow'], index = ['0','2','4'])
>> obj2.reindex(range(6),method = 'ffill') #ffill是前向值填充，即让缺失值和前一项相同来填充，bfill是后向值填充
0  blue
1  blue
2  purple
3  purple
4  yellow
5  yellow

3、对于DataFrame,reindex可以修改行索引、列或者两个都修改。

>> frame = DataFrame(np.arange(9).reshape(3,3),index=['a','b','c'],columns=['him','her','it'])
    him  her  it
a   0     1     2
b   3     4     5
c   6     7     8
>>  frame.reindex(['a','b','c','d'])
    him  her  it
a   0     1     2
b   3     4     5
c   6     7     8
d  NaN  NaN  NaN  
### 使用columns关键字即可重新索引列
>> states = ['him','her','them']
>> frame.reindex(columns = states)
    him  her  them  
a   0     1     NaN
b   3     4     NaN
c   6     7     NaN
### 也可以同时对行和列进行重新索引，而插值只能按行应用（即0轴）
>> frame.reindex(index = ['a','b','c','d'],method = 'ffill',columns = states)
    him  her  them
a   0     1     NaN  
b   3     4     NaN  
c   6     7     NaN  
d   6     7     NaN  
###利用ix的标签索引功能，重新索引任务可以变得很简洁
>> frame.ix[['a','b','c','d'],states]
    him  her  them
a   0     1     NaN  
b   3     4     NaN  
c   6     7     NaN  
d  NaN    NaN   NaN