# --------------
# 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(banks.isnull().sum())
bank_mode = banks.mode
banks = banks.fillna(value = bank_mode)


#code ends here


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

avg_loan_amount = banks.pivot_table(index = ['Gender', 'Married', 'Self_Employed'] ,values = 'LoanAmount',aggfunc=np.mean)

# code ends herez


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

loan_approved_se = ((banks['Self_Employed'] == 'Yes') & (banks['Loan_Status'] == 'Y')).sum()
loan_approved_nse = ((banks['Self_Employed'] == 'No') & (banks['Loan_Status'] == 'Y')).sum()
Loan_Status = banks['Loan_Status'].count()

percentage_se = loan_approved_se/Loan_Status *100
percentage_nse = loan_approved_nse/Loan_Status *100

# code ends here


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

# code starts here

loan_term = banks['Loan_Amount_Term'].apply(lambda x: x/12)
big_loan_term = (loan_term>=25).sum()

# code ends here


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# code starts here

loan_groupby = banks.groupby(['Loan_Status'])
loan_groupby = loan_groupby[['ApplicantIncome', 'Credit_History']]
mean_values = loan_groupby.mean()

# code ends here
