In [4]:

    

import numpy as np
import pandas as pd

dates = pd.date_range('20160101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A', 'B', 'C', 'D'])

df.iloc[0,1] = np.nan
df.iloc[1,2] = np.nan
df


    

    
Out[4]:






  

    

      

      
A
      
B
      
C
      
D
    
  
  

    

      
2016-01-01
      
0
      
NaN
      
2.0
      
3
    
    

      
2016-01-02
      
4
      
5.0
      
NaN
      
7
    
    

      
2016-01-03
      
8
      
9.0
      
10.0
      
11
    
    

      
2016-01-04
      
12
      
13.0
      
14.0
      
15
    
    

      
2016-01-05
      
16
      
17.0
      
18.0
      
19
    
    

      
2016-01-06
      
20
      
21.0
      
22.0
      
23
    
  

In [6]:

    

print df.dropna(axis=0, how='any') #any  all


    

    




             A     B     C   D
2016-01-03   8   9.0  10.0  11
2016-01-04  12  13.0  14.0  15
2016-01-05  16  17.0  18.0  19
2016-01-06  20  21.0  22.0  23

In [9]:

    

print(df.fillna(value=0))


    

    




             A     B     C   D
2016-01-01   0   0.0   2.0   3
2016-01-02   4   5.0   0.0   7
2016-01-03   8   9.0  10.0  11
2016-01-04  12  13.0  14.0  15
2016-01-05  16  17.0  18.0  19
2016-01-06  20  21.0  22.0  23

In [13]:

    

print(pd.isnull(df))
print np.any(df.isnull) == True


    

    




                A      B      C      D
2016-01-01  False   True  False  False
2016-01-02  False  False   True  False
2016-01-03  False  False  False  False
2016-01-04  False  False  False  False
2016-01-05  False  False  False  False
2016-01-06  False  False  False  False
False