In [1]:

    

import matplotlib

import os
import sqlite3
import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm

import matplotlib.pyplot as plt

%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (16.0, 6.0)
plt.rcParams['legend.markerscale'] = 3
matplotlib.rcParams['font.size'] = 16.0


    

    




/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))

In [2]:

    

DIR = os.getcwd() + "/../data/"
df = pd.read_csv(DIR + 'raw/loan.csv')
df.head()


    

    




/usr/local/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2902: DtypeWarning: Columns (19,55) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)






    
Out[2]:






  

    

      

      
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
      
5000
      
4975
      
36 months
      
10.65
      
162.87
      
B
      
B2
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
1
      
1077430
      
1314167
      
2500
      
2500
      
2500
      
60 months
      
15.27
      
59.83
      
C
      
C4
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
2
      
1077175
      
1313524
      
2400
      
2400
      
2400
      
36 months
      
15.96
      
84.33
      
C
      
C5
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
3
      
1076863
      
1277178
      
10000
      
10000
      
10000
      
36 months
      
13.49
      
339.31
      
C
      
C1
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
4
      
1075358
      
1311748
      
3000
      
3000
      
3000
      
60 months
      
12.69
      
67.79
      
B
      
B5
      
...
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
  

5 rows × 74 columns

In [3]:

    

ax = df['int_rate'].hist(bins=25)
ax.set_xlabel('Int. Rate')
ax.set_ylabel('Count')
ax.set_title('Interest Rates')
plt.show()


    

In [4]:

    

ax = df['loan_amnt'].hist(bins=20)
ax.set_xlabel('S')
ax.set_ylabel('Count')
ax.set_title('Loan Amount')
plt.show()


    

In [5]:

    

ax = df[['grade','id']].groupby('grade').count()['id'].plot.pie(figsize=(6, 6))
ax.set_ylabel('Loan Grades')
plt.show()


    

In [6]:

    

print 'Number of rows: ',len(df)
print 'Number of rows with at least 1 NA value: ', len(df.dropna())


    

    




Number of rows:  887379
Number of rows with at least 1 NA value:  0

In [7]:

    

c = df.corr().abs()
print 'Most uncorrelated Variables'
s = c.unstack()
so = pd.DataFrame(s.sort_values(kind="quicksort", ascending=True)).reset_index().dropna()
so.drop_duplicates(subset=[0]).head(10)


    

    




Most uncorrelated Variables






    
Out[7]:






  

    

      

      
level_0
      
level_1
      
0
    
  
  

    

      
0
      
open_rv_12m
      
total_cu_tl
      
0.000015
    
    

      
2
      
total_rev_hi_lim
      
recoveries
      
0.000103
    
    

      
4
      
open_acc
      
tot_coll_amt
      
0.000124
    
    

      
6
      
funded_amnt
      
delinq_2yrs
      
0.000151
    
    

      
8
      
delinq_2yrs
      
loan_amnt
      
0.000159
    
    

      
10
      
installment
      
open_il_12m
      
0.000167
    
    

      
12
      
delinq_2yrs
      
tot_coll_amt
      
0.000207
    
    

      
14
      
acc_now_delinq
      
tot_coll_amt
      
0.000237
    
    

      
16
      
collections_12_mths_ex_med
      
dti
      
0.000292
    
    

      
18
      
pub_rec
      
acc_now_delinq
      
0.000349
    
  

In [8]:

    

df.columns


    

    
Out[8]:





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

In [9]:

    

partial_df = df[['grade','funded_amnt', 'installment', 'int_rate','term', 'pymnt_plan']]
partial_df.columns = ['Grade', 'Funding Amount', 'Installment', 'Interest Rate','Term', 'Payment Plan']


    

In [10]:

    

ax = sns.pairplot(partial_df[['Grade', 'Funding Amount', 'Installment', 'Interest Rate']], hue="Grade", size=4)
matplotlib.rc("legend", fontsize=20)
plt.xticks(rotation=45)

for _ in ax.axes.flat:
    for label in _.get_xticklabels():
        label.set_rotation(30)
        
plt.show()


    

    




/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))






    

In [ ]:

    

sns.boxplot(x="Grade", y="Interest Rate", hue='Term', data=partial_df)
plt
sns.despine(offset=0, trim=False)


    

In [ ]:

    

sns.boxplot(x="Payment Plan", y="Interest Rate", hue='Term', data=partial_df)
sns.despine(offset=10, trim=True)


    

In [ ]:

    

sns.boxplot(x="term", y="int_rate", data=df)
sns.despine(offset=10, trim=True)


    

In [ ]:

    

from scipy.stats import kendalltau
matplotlib.rc("legend", fontsize=14)
sns.jointplot(df['int_rate'], df['funded_amnt'], kind="hex", stat_func=kendalltau, size=10)


    

In [ ]: