# --------------
# 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
#bank = pd.read_csv(path)
#print(bank)
print(bank.columns)
banks = bank.drop(['Loan_ID'], axis=1)

print(banks.isnull().sum())

bank_mode = banks.mode()

print(bank_mode)

banks = banks.fillna(0)

#print(banks)

print(banks.isnull().values.any())
#code ends here


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


# code ends here



# --------------
# code starts here
loan_approved_se = len(banks[(banks['Self_Employed'] == 'Yes') & (banks['Loan_Status'] == 'Y')])

loan_approved_nse = len(banks[(banks['Self_Employed'] == 'No') & (banks['Loan_Status'] == 'Y')])

percentage_se = (loan_approved_se / len(banks['Loan_Status'])) * 100

print(percentage_se)

percentage_nse = (loan_approved_nse / len(banks['Loan_Status'])) * 100

print(percentage_nse)
# code ends here


# --------------
# code starts here
loan_term = banks['Loan_Amount_Term'].apply(lambda x: x / 12)
print(type(loan_term))

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()
print(mean_values)

# code ends here


