# --------------
# 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')

numerical_var = bank.select_dtypes(include = 'number')

print(categorical_var)

print('====================')

print(numerical_var)



# code ends here


# --------------
# code starts here

banks = bank.drop(columns = 'Loan_ID')

print('==================================')

print(banks.isnull().sum())
print('==================================')

bank_mode = banks.mode().iloc[0]

print(bank_mode)
print('==================================')

banks.fillna(bank_mode, inplace = True)
#code ends here


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

avg_loan_amount = pd.pivot_table(banks, index=['Gender','Married','Self_Employed'], values = 'LoanAmount', aggfunc = '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()
loan_approved_nse = banks.loc[(banks["Self_Employed"]=="No")  & (banks["Loan_Status"]=="Y"), ["Loan_Status"]].count()
#loan_approved_nse = banks.loc[(bank['Self_Employed'] == 'No') & (banks['Loan_Status']== 'Y')].count()

percentage_se = (loan_approved_se * 100 / 614)
percentage_se = percentage_se[0]
percentage_nse = (loan_approved_nse * 100 / 614)
percentage_nse = percentage_nse[0]
print(loan_approved_nse)
print(percentage_se)
print('-------------------------')
print(percentage_nse)
# code ends here


# --------------
# code starts here


loan_term = banks['Loan_Amount_Term'].apply(lambda x: int(x)/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 = loan_groupby[['ApplicantIncome','Credit_History']]


mean_values = loan_groupby.mean()

# code ends here


