In [2]:

    

import pandas as pd


    

In [3]:

    

df = pd.read_csv('loan.csv')


    

    




/home/pierre/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (19,55) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

In [4]:

    

len(df)


    

    
Out[4]:





887379

In [5]:

    

df.head()


    

    
Out[5]:







  

    

      

      
id
      
member_id
      
loan_amnt
      
funded_amnt
      
funded_amnt_inv
      
term
      
int_rate
      
installment
      
grade
      
sub_grade
      
...
      
total_bal_il
      
il_util
      
open_rv_12m
      
open_rv_24m
      
max_bal_bc
      
all_util
      
total_rev_hi_lim
      
inq_fi
      
total_cu_tl
      
inq_last_12m
    
  
  

    

      
0
      
1077501
      
1296599
      
5000.0
      
5000.0
      
4975.0
      
36 months
      
10.65
      
162.87
      
B
      
B2
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
1
      
1077430
      
1314167
      
2500.0
      
2500.0
      
2500.0
      
60 months
      
15.27
      
59.83
      
C
      
C4
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
2
      
1077175
      
1313524
      
2400.0
      
2400.0
      
2400.0
      
36 months
      
15.96
      
84.33
      
C
      
C5
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
3
      
1076863
      
1277178
      
10000.0
      
10000.0
      
10000.0
      
36 months
      
13.49
      
339.31
      
C
      
C1
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
4
      
1075358
      
1311748
      
3000.0
      
3000.0
      
3000.0
      
60 months
      
12.69
      
67.79
      
B
      
B5
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
  

5 rows × 74 columns

In [6]:

    

df.columns


    

    
Out[6]:





Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',
       'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title',
       'emp_length', 'home_ownership', 'annual_inc', 'verification_status',
       'issue_d', 'loan_status', 'pymnt_plan', 'url', 'desc', 'purpose',
       'title', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs',
       'earliest_cr_line', 'inq_last_6mths', 'mths_since_last_delinq',
       'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal',
       'revol_util', 'total_acc', 'initial_list_status', 'out_prncp',
       'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp',
       'total_rec_int', 'total_rec_late_fee', 'recoveries',
       'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt',
       'next_pymnt_d', 'last_credit_pull_d', 'collections_12_mths_ex_med',
       'mths_since_last_major_derog', 'policy_code', 'application_type',
       'annual_inc_joint', 'dti_joint', 'verification_status_joint',
       'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m',
       'open_il_6m', 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il',
       'total_bal_il', 'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc',
       'all_util', 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl',
       'inq_last_12m'],
      dtype='object')

In [8]:

    

df[df.index<10].to_csv('sample.csv')


    

In [9]:

    

df.loan_status.unique()


    

    
Out[9]:





array(['Fully Paid', 'Charged Off', 'Current', 'Default',
       'Late (31-120 days)', 'In Grace Period', 'Late (16-30 days)',
       'Does not meet the credit policy. Status:Fully Paid',
       'Does not meet the credit policy. Status:Charged Off', 'Issued'], dtype=object)

In [10]:

    

in_default = ['Charged Off', 'Default', 'Late (31-120 days)', 'Late (16-30 days)', 'Does not meet the credit policy. Status:Charged Off']


    

In [11]:

    

len(in_default)


    

    
Out[11]:





5

In [27]:

    

df['in_default'] = df.loan_status.map(lambda x: 1 if x in in_default else 0)


    

In [28]:

    

df.in_default.unique()


    

    
Out[28]:





array([0, 1])

In [30]:

    

len(df[df.in_default==1]) / len(df)


    

    
Out[30]:





0.06894010338310913

In [31]:

    

cols = ['loan_amnt', 'funded_amnt', 'funded_amnt_inv',
       'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title',
       'emp_length', 'home_ownership', 'annual_inc', 'verification_status',
       'issue_d', 'loan_status', 'pymnt_plan', 'desc', 'purpose',
       'title', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs',
       'earliest_cr_line', 'inq_last_6mths', 
       'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal',
       'revol_util', 'total_acc', 'initial_list_status', 'out_prncp',
       'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'last_pymnt_amnt',
       'next_pymnt_d', 'last_credit_pull_d', 'collections_12_mths_ex_med',
       'mths_since_last_major_derog', 'policy_code', 'application_type',
       'annual_inc_joint', 'dti_joint', 'verification_status_joint', 'in_default']


    

In [32]:

    

df[cols].to_csv('default.csv', index=False)


    

In [20]:

    

# df1 = pd.DataFrame(cols)


    

In [22]:

    

# df1.to_csv('desc.csv', index=False)


    

In [ ]: