# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode 
 



# code starts here
bank=pd.read_csv(path)
categorical_var=bank.select_dtypes(include = 'object')
print(categorical_var)
numerical_var= bank.select_dtypes(include = 'number')
print(numerical_var)




# code ends here


# --------------
# code starts here
banks=bank.drop(['Loan_ID'],axis=1)
print(bank.isnull().sum())
bank_mode=banks.mode()
banks=banks.fillna(bank_mode)
banks=banks.fillna('')


#code ends here


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

# check the avg_loan_amount
avg_loan_amount = banks.pivot_table(values=["LoanAmount"], index=["Gender","Married","Self_Employed"], aggfunc=np.mean)


print (avg_loan_amount)
# code ends here



# --------------




# code starts here
loan_approved_se = banks.loc[(banks["Self_Employed"]=="Yes")  & (banks["Loan_Status"]=="Y"), ["Loan_Status"]].count()[0]
print(loan_approved_se)

loan_approved_nse = banks.loc[(banks["Self_Employed"]=="No")  & (banks["Loan_Status"]=="Y"), ["Loan_Status"]].count()[0]
print(loan_approved_nse)

percentage_se= (loan_approved_se/614)*100
print(percentage_se)

percentage_nse = (loan_approved_nse * 100 / 614)
#percentage_nse=percentage_nse[0]
print ("%.2f"%percentage_nse)


# code ends here


# --------------
# code starts here
loan_term=banks['Loan_Amount_Term'].apply(lambda months : months / 12)
big_loan_term = len(loan_term[loan_term >= 25])
print(big_loan_term)



# code ends here


# --------------
# code starts here
loan_groupby= banks.groupby('Loan_Status')
loan_groupby[['ApplicantIncome','Credit_History']]
mean_values=loan_groupby.mean()


# code ends here


