# --------------
#Importing header files
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


#df=pd.read_csv(filepath)
#print(df)
data=pd.read_csv(path)
print(data)

plt.xlabel(" Loan status")
plt.ylabel("Total yes and no")
loan_status=data['Loan_Status'].value_counts()
loan_status.plot(kind='bar')


#Code starts here


# --------------
#Code starts here
property_and_loan=data.groupby(['Property_Area','Loan_Status'])
property_and_loan=property_and_loan.size().unstack()
property_and_loan.plot(kind='bar',stacked=False,figsize=(15,10))
plt.ylabel("Property Area")
plt.ylabel("Loan Status")
plt.xticks(rotation=45)



# --------------
#Code starts here
education_and_loan=data.groupby(['Education','Loan_Status'])
education_and_loan=education_and_loan.size().unstack()
education_and_loan.plot(kind='bar',stacked=True,figsize=(15,10))
plt.ylabel("Loan Status")
plt.xlabel("Education Status")
plt.xticks(rotation=45)


# --------------
#Code starts here

graduate=pd.DataFrame(data[data['Education']=='Graduate'])
not_graduate=pd.DataFrame(data[data['Education']=='Not Graduate'])


graduate['LoanAmount'].plot(kind='density',label='Graduate')
not_graduate['LoanAmount'].plot(kind='density',label='Not Graduate')








#Code ends here

#For automatic legend display
plt.legend()


# --------------
#Code starts here
fig,(ax_1,ax_2,ax_3)=plt.subplots(3,1,figsize=(20,20))

data.plot.scatter(['ApplicantIncome'],['LoanAmount'],ax=ax_1)
ax_1.set_title('Aplicant Income')

data.plot.scatter(['CoapplicantIncome'],['LoanAmount'],ax=ax_2)
ax_2.set_title('CoapplicantIncome')


data['TotalIncome']=(data['ApplicantIncome'] + data['CoapplicantIncome'])
data.plot.scatter(['TotalIncome'],['LoanAmount'],ax=ax_3)
ax_3.set_title('Total Income')




# --------------
#Importing header files
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


#df=pd.read_csv(filepath)
#print(df)
data=pd.read_csv(path)
print(data)

plt.xlabel(" Loan status")
plt.ylabel("Total yes and no")
loan_status=data['Loan_Status'].value_counts()
loan_status.plot(kind='bar')


#Code starts here


# --------------
#Code starts here
property_and_loan=data.groupby(['Property_Area','Loan_Status'])
property_and_loan=property_and_loan.size().unstack()
property_and_loan.plot(kind='bar',stacked=False,figsize=(15,10))
plt.ylabel("Property Area")
plt.ylabel("Loan Status")
plt.xticks(rotation=45)



# --------------
#Code starts here
education_and_loan=data.groupby(['Education','Loan_Status'])
education_and_loan=education_and_loan.size().unstack()
education_and_loan.plot(kind='bar',stacked=True,figsize=(15,10))
plt.ylabel("Loan Status")
plt.xlabel("Education Status")
plt.xticks(rotation=45)


# --------------
#Code starts here

graduate=pd.DataFrame(data[data['Education']=='Graduate'])
not_graduate=pd.DataFrame(data[data['Education']=='Not Graduate'])


graduate['LoanAmount'].plot(kind='density',label='Graduate')
not_graduate['LoanAmount'].plot(kind='density',label='Not Graduate')








#Code ends here

#For automatic legend display
plt.legend()


# --------------
#Code starts here
fig,(ax_1,ax_2,ax_3)=plt.subplots(3,1,figsize=(20,20))

data.plot.scatter(['ApplicantIncome'],['LoanAmount'],ax=ax_1)
ax_1.set_title('Aplicant Income')

data.plot.scatter(['CoapplicantIncome'],['LoanAmount'],ax=ax_2)
ax_2.set_title('CoapplicantIncome')


data['TotalIncome']=(data['ApplicantIncome'] + data['CoapplicantIncome'])
data.plot.scatter(['TotalIncome'],['LoanAmount'],ax=ax_3)
ax_3.set_title('Total Income')




# -------------- #Importing header files #df=pd.read_csv(filepath) #print(df) #Code starts here # -------------- #Code starts here # -------------- #Code starts here # -------------- #Code starts here #Code ends here #For automatic legend display # -------------- #Code starts here