pandas官方文档cookbook（4）中Arithmetic&Slicing&Sorting翻译

文档版本：0.20.3
这些例子是用python3.4写出来的。对于较早的python版本需要对代码做些相应的调整。
Pandas（pd）和Numpy（np）是唯一两个默认导入的包。其余的包会显示导入给新用户看。
若有翻译不当的地方，请多多指教。

这份文档中的例子都是从Stack-Overflow和Github中别人提问的比较经典的问题，作者从中进行提炼与总结。

Arithmetic

对多重索引执行算法需要进行广播

In [61]: cols = pd.MultiIndex.from_tuples([ (x,y) for x in ['A','B','C'] for y in ['O','I']])

In [62]: df = pd.DataFrame(np.random.randn(2,6),index=['n','m'],columns=cols); df
Out[62]: 
      A                   B                   C          
      O         I         O         I         O         I
n  1.920906 -0.388231 -2.314394  0.665508  0.402562  0.399555
m -1.765956  0.850423  0.388054  0.992312  0.744086 -0.739776

In [63]: df = df.div(df['C'],level=1); df
Out[63]: 
      A                   B              C     
      O         I         O         I    O    I
n  4.771702 -0.971660 -5.749162  1.665625  1.0  1.0
m -2.373321 -1.149568  0.521518 -1.341367  1.0  1.0

切片

用xs函数对多重索引进行切片

In [64]: coords = [('AA','one'),('AA','six'),('BB','one'),('BB','two'),('BB','six')]

In [65]: index = pd.MultiIndex.from_tuples(coords)

In [66]: df = pd.DataFrame([11,22,33,44,55],index,['MyData']); df
Out[66]: 
    MyData
AA one      11
   six      22
BB one      33
   two      44
   six      55

获取第一水平和第一个轴的交叉部分

In [67]: df.xs('BB',level=0,axis=0)  #Note : level and axis are optional, and default to zero
Out[67]: 
     MyData
one      33
two      44
six      55

获取第二水平和第一个轴的交叉部分

In [68]: df.xs('six',level=1,axis=0)
Out[68]: 
    MyData
AA      22
BB      55

用xs函数对多重索引进行切片方法二

In [69]: index = list(itertools.product(['Ada','Quinn','Violet'],['Comp','Math','Sci']))

In [70]: headr = list(itertools.product(['Exams','Labs'],['I','II']))

In [71]: indx = pd.MultiIndex.from_tuples(index,names=['Student','Course'])

In [72]: cols = pd.MultiIndex.from_tuples(headr) #Notice these are un-named

In [73]: data = [[70+x+y+(x*y)%3 for x in range(4)] for y in range(9)]

In [74]: df = pd.DataFrame(data,indx,cols); df
Out[74]: 
           Exams     Labs    
               I  II    I  II
Student Course                   
Ada     Comp      70  71   72  73
        Math      71  73   75  74
        Sci       72  75   75  75
Quinn   Comp      73  74   75  76
        Math      74  76   78  77
        Sci       75  78   78  78
Violet  Comp      76  77   78  79
        Math      77  79   81  80
        Sci       78  81   81  81

In [75]: All = slice(None)

In [76]: df.loc['Violet']
Out[76]: 
       Exams     Labs    
           I  II    I  II
Course                   
Comp      76  77   78  79
Math      77  79   81  80
Sci       78  81   81  81

In [77]: df.loc[(All,'Math'),All]
Out[77]: 
           Exams     Labs    
               I  II    I  II
Student Course                   
Ada     Math      71  73   75  74
Quinn   Math      74  76   78  77
Violet  Math      77  79   81  80

In [78]: df.loc[(slice('Ada','Quinn'),'Math'),All]
Out[78]: 
               Exams     Labs    
                   I  II    I  II
Student Course                   
Ada     Math      71  73   75  74
Quinn   Math      74  76   78  77

In [79]: df.loc[(All,'Math'),('Exams')]
Out[79]: 
                 I  II
Student Course        
Ada     Math    71  73
Quinn   Math    74  76
Violet  Math    77  79

In [80]: df.loc[(All,'Math'),(All,'II')]
Out[80]: 
               Exams Labs
              II   II
Student Course           
Ada     Math      73   74
Quinn   Math      76   77
Violet  Math      79   80

排序

在多重索引中用某一列进行排序

In [81]: df.sort_values(by=('Labs', 'II'), ascending=False)
Out[81]: 
               Exams     Labs    
                   I  II    I  II
Student Course                   
Violet  Sci       78  81   81  81
        Math      77  79   81  80
        Comp      76  77   78  79
Quinn   Sci       75  78   78  78
        Math      74  76   78  77
        Comp      73  74   75  76
Ada     Sci       72  75   75  75
        Math      71  73   75  74
        Comp      70  71   72  73