# --------------
# 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)

banks.isnull().sum()
bank_mode=banks.mode()
banks = banks.fillna(bank_mode.iloc[0])
banks.isnull().sum()
#code ends here


# --------------
# Code starts here
avg_loan_amount=pd.pivot_table(banks,index=['Gender', 'Married', 'Self_Employed'],values='LoanAmount',aggfunc='mean')



# code ends here



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


loan_approved_se=banks[(banks['Self_Employed'] == 'Yes')& (banks['Loan_Status'] == 'Y' )].shape[0]
loan_approved_nse=banks[(banks['Self_Employed'] == 'No') & (banks['Loan_Status'] == 'Y') ].shape[0]
Loan_Status=614
print(loan_approved_se)
percentage_se=(loan_approved_se*100/Loan_Status)
percentage_nse=(loan_approved_nse*100/Loan_Status)
# 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])
# 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


