In [1]:

    

from pandas import DataFrame, Series


    

In [2]:

    

import pandas as pd


    

In [3]:

    

import numpy as np


    

In [4]:

    

frame = DataFrame(np.random.randn(4, 3), 
                  columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])


    

In [5]:

    

frame


    

    
Out[5]:






  

    

      

      
b
      
d
      
e
    
  
  

    

      
Utah
      
1.164044
      
-1.084572
      
2.658843
    
    

      
Ohio
      
-1.491405
      
0.874359
      
-1.058734
    
    

      
Texas
      
0.378623
      
1.174664
      
-0.743128
    
    

      
Oregon
      
0.541705
      
0.196422
      
0.223049
    
  

In [6]:

    

np.abs(frame)


    

    
Out[6]:






  

    

      

      
b
      
d
      
e
    
  
  

    

      
Utah
      
1.164044
      
1.084572
      
2.658843
    
    

      
Ohio
      
1.491405
      
0.874359
      
1.058734
    
    

      
Texas
      
0.378623
      
1.174664
      
0.743128
    
    

      
Oregon
      
0.541705
      
0.196422
      
0.223049
    
  

In [7]:

    

f = lambda x: x.max() - x.min()


    

In [8]:

    

frame.apply(f)


    

    
Out[8]:





b    2.655449
d    2.259235
e    3.717577
dtype: float64

In [9]:

    

frame.apply(f, axis=1)


    

    
Out[9]:





Utah      3.743414
Ohio      2.365764
Texas     1.917791
Oregon    0.345283
dtype: float64

In [10]:

    

def f(x):
    return Series([x.min(), x.max()], index=['min', 'max'])


    

In [11]:

    

frame.apply(f)


    

    
Out[11]:






  

    

      

      
b
      
d
      
e
    
  
  

    

      
min
      
-1.491405
      
-1.084572
      
-1.058734
    
    

      
max
      
1.164044
      
1.174664
      
2.658843
    
  

In [12]:

    

format = lambda x: '%.2f' % x


    

In [13]:

    

frame.applymap(format)


    

    
Out[13]:






  

    

      

      
b
      
d
      
e
    
  
  

    

      
Utah
      
1.16
      
-1.08
      
2.66
    
    

      
Ohio
      
-1.49
      
0.87
      
-1.06
    
    

      
Texas
      
0.38
      
1.17
      
-0.74
    
    

      
Oregon
      
0.54
      
0.20
      
0.22
    
  

In [14]:

    

frame['e'].map(format)


    

    
Out[14]:





Utah       2.66
Ohio      -1.06
Texas     -0.74
Oregon     0.22
Name: e, dtype: object