In [1]:

    

# import
import pandas as pd


    

In [2]:

    

# reading the file
data = pd.read_csv("data/train.csv")


    

In [5]:

    

# Looking the data
data.head(6)


    

    
Out[5]:






  

    

      

      
Loan_ID
      
Gender
      
Married
      
Dependents
      
Education
      
Self_Employed
      
ApplicantIncome
      
CoapplicantIncome
      
LoanAmount
      
Loan_Amount_Term
      
Credit_History
      
Property_Area
      
Loan_Status
    
  
  

    

      
0
      
LP001002
      
Male
      
No
      
0
      
Graduate
      
No
      
5849
      
0.0
      
NaN
      
360.0
      
1.0
      
Urban
      
Y
    
    

      
1
      
LP001003
      
Male
      
Yes
      
1
      
Graduate
      
No
      
4583
      
1508.0
      
128.0
      
360.0
      
1.0
      
Rural
      
N
    
    

      
2
      
LP001005
      
Male
      
Yes
      
0
      
Graduate
      
Yes
      
3000
      
0.0
      
66.0
      
360.0
      
1.0
      
Urban
      
Y
    
    

      
3
      
LP001006
      
Male
      
Yes
      
0
      
Not Graduate
      
No
      
2583
      
2358.0
      
120.0
      
360.0
      
1.0
      
Urban
      
Y
    
    

      
4
      
LP001008
      
Male
      
No
      
0
      
Graduate
      
No
      
6000
      
0.0
      
141.0
      
360.0
      
1.0
      
Urban
      
Y
    
    

      
5
      
LP001011
      
Male
      
Yes
      
2
      
Graduate
      
Yes
      
5417
      
4196.0
      
267.0
      
360.0
      
1.0
      
Urban
      
Y
    
  

In [8]:

    

# isnull or notnull
data.isnull().head(6)


    

    
Out[8]:






  

    

      

      
Loan_ID
      
Gender
      
Married
      
Dependents
      
Education
      
Self_Employed
      
ApplicantIncome
      
CoapplicantIncome
      
LoanAmount
      
Loan_Amount_Term
      
Credit_History
      
Property_Area
      
Loan_Status
    
  
  

    

      
0
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
True
      
False
      
False
      
False
      
False
    
    

      
1
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
    
    

      
2
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
    
    

      
3
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
    
    

      
4
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
    
    

      
5
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
      
False
    
  

In [9]:

    

data.notnull().head(6)


    

    
Out[9]:






  

    

      

      
Loan_ID
      
Gender
      
Married
      
Dependents
      
Education
      
Self_Employed
      
ApplicantIncome
      
CoapplicantIncome
      
LoanAmount
      
Loan_Amount_Term
      
Credit_History
      
Property_Area
      
Loan_Status
    
  
  

    

      
0
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
False
      
True
      
True
      
True
      
True
    
    

      
1
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
    
    

      
2
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
    
    

      
3
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
    
    

      
4
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
    
    

      
5
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
      
True
    
  

In [10]:

    

# use of any
data.isnull().values.any()


    

    
Out[10]:





True

In [11]:

    

# use of all
data.isnull().values.all()


    

    
Out[11]:





False

In [12]:

    

# taking the count of Null/NaN in each column of dataframe
data.isnull().sum()


    

    
Out[12]:





Loan_ID               0
Gender               13
Married               3
Dependents           15
Education             0
Self_Employed        32
ApplicantIncome       0
CoapplicantIncome     0
LoanAmount           22
Loan_Amount_Term     14
Credit_History       50
Property_Area         0
Loan_Status           0
dtype: int64

In [13]:

    

# if want to know the total
data.isnull().sum().sum()


    

    
Out[13]:





149

In [14]:

    

# if want to check in any particular column
data['Dependents'].isnull().sum()


    

    
Out[14]:





15

In [ ]: