bluebank.py

# -*- coding: utf-8 -*-
"""
Created on Thu Sep  1 12:11:58 2022

@author: Mandar
"""

import json 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#Method 1 to read json data. Type in variable explorer will
#be list but inside its dictionary
json_file = open('loan_data_json.json')
data = json.load(json_file)

#Method 2 to read json data. Type in variable explorer will
#be list but inside its dictionary
with open('loan_data_json.json') as json_file:
    data = json.load(json_file)
    
#Transform to dataframe
loandata = pd.DataFrame(data)

#Finding Unique values for purpose column
loandata['purpose'].unique()

#describe thr data
loandata.describe()

#describe data for a specific column
loandata['int.rate'].describe()


loandata['fico'].describe()

loandata['dti'].describe()

#using exp to get annual income
income = np.exp(loandata['log.annual.inc'])

loandata['annualincome'] = income


#Working with arrays

#1D array
arr = np.array([1,2,3,4])

#0D array

arr = np.array(43)

#2D array

arr = np.array([[1,2,3], [4,5,6]])

#Working with IF statements

a = 40
b = 500

if b > a:
    print('b is greater than a')

#Lets add more conditions
a = 40
b = 500
c = 1000

if b > a and b < c:
    print('b is greater than a & less than c')
    
#What if the condition is not met

a = 40
b = 500
c = 20

if b > a and b < c:    
    print('b is greater than a but less than c')
else:
    print('No conditions met')  
    
#Another condition different matrices

a = 40
b = 0
c = 30

if b > a and b < c:
    print('b is greater than a but less than c')
elif b > a and b > c:
    print('b is greater than a and c')    
else:
    print('No conditions met')    
    
#using or

a = 40
b = 500
c = 30

if b > a or b < c:
    print('b is greater than a or less than c')
else:
    print('No conditions met')
    
    
#FICO Score

fico = 250

if fico >=  300 and fico < 400:
    ficocat = 'Very Poor'
elif fico >= 400 and fico < 600:    
    ficocat = 'Poor'
elif fico >= 601 and fico < 660:        
    ficocat = 'Fair'
elif fico >= 660 and fico < 700:        
    ficocat = 'Good'
elif fico >= 700:
    ficocat = 'Excellent'
else:
    ficocat = 'Unknown'    
    
print(ficocat)    

#for loops

fruits = ['apple', 'pear', 'banana', 'cherry']

for x in fruits:
    print(x)
    y = x +' fruit'
    print(y)
    
for x in range(0,4):
    y = fruits[x]+' for sale'    
    print(y)
    
#applying for loop to loan data

#using first 10

length = len(loandata)
ficocat = []
for x in range(0,length):
    category = loandata['fico'][x]
    
    try:
        if category >= 300 and category < 400:
            cat = 'Very Poor'
        elif category >= 400 and category < 600:
            cat = 'Poor'
        elif category >= 601 and category < 660:
            cat = 'Fair'
        elif category >= 660 and category < 700:    
            cat = 'Good'
        elif category >= 700:
            cat = 'Excellent'
        else:  
            cat = 'Unknown'
    except:
         cat = 'Unknown'
         
    ficocat.append(cat)    
    
ficocat = pd.Series(ficocat)    

loandata['fico.category'] = ficocat

#i = 1
#while i < 10:
#    print(i)
#    i = i + 1


#testing error

#length = len(loandata)
#ficocat = []
#for x in range(0,length):
#    category = 'red'
#    try:
#        if category >= 300 and category < 400:
#            cat = 'Very Poor'
#        elif category >= 400 and category < 600:
#            cat = 'Poor'
#        elif category >= 601 and category < 660:
#            cat = 'Fair'
#        elif category >= 660 and category < 700:    
#            cat = 'Good'
#        elif category >= 700:
#            cat = 'Excellent'
#       else:  
#            cat = 'Unknown'
#    except:       
#         cat = 'Error'
    
#    ficocat.append(cat)    
    
#ficocat = pd.Series(ficocat)    

loandata['fico.category'] = ficocat

#df.loc as conditional statements
#df.loc[df[columnname] condition, newcolumnname] = 'value if the condition is met'
#for interest rates, a new column is wanted, rate > 0.12 then high, else low

loandata.loc[loandata['int.rate'] > 0.12 , 'int.rate.type'] = 'High'

loandata.loc[loandata['int.rate'] <= 0.12 , 'int.rate.type'] = 'Low'

#number of loans/rows by fico.category

catplot = loandata.groupby(['fico.category']).size()
catplot.plot.bar(color = 'green', width = 0.1)
plt.show()

purposeplot = loandata.groupby(['purpose']).size()
purposeplot.plot.bar(color = 'red', width = 0.2)
plt.show()

#scatter plots

ypoint = loandata['annualincome']
xpoint = loandata['dti']
plt.scatter(xpoint, ypoint, color = '#4caf50')
plt.show()

#writing to csv

loandata.to_csv('loancleaned.csv',index = True)