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



# code ends here


# --------------
# 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', 1)
print(banks.isnull().sum())
bank_mode=banks.mode()
banks=banks.replace(np.nan, bank_mode)
print(banks)
#print(banks)
#code ends here


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


# code ends here



# --------------
# code starts here
loan_approved_se=banks[(banks['Self_Employed']=='Yes') & (banks['Loan_Status']=='Y')].shape[0]
print(loan_approved_se)

loan_approved_nse=banks[(banks['Self_Employed']=='No') & (banks['Loan_Status']=='Y')].shape[0]
print(loan_approved_nse)

percentage_se=(loan_approved_se * 100/614)

percentage_nse=(loan_approved_nse * 100/614)
# code ends here


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

loan_term=banks['Loan_Amount_Term'].apply(lambda x: x/12)
big_loan_term = banks[loan_term >=25].shape[0]



# code ends here



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



# code ends here


# --------------
# 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', 1)
print(banks.isnull().sum())
bank_mode=banks.mode()
banks=banks.replace(np.nan, bank_mode)
print(banks)
#print(banks)
#code ends here


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


# code ends here



# --------------
# code starts here
loan_approved_se=banks[(banks['Self_Employed']=='Yes') & (banks['Loan_Status']=='Y')].shape[0]
print(loan_approved_se)

loan_approved_nse=banks[(banks['Self_Employed']=='No') & (banks['Loan_Status']=='Y')].shape[0]
print(loan_approved_nse)

percentage_se=(loan_approved_se * 100/614)

percentage_nse=(loan_approved_nse * 100/614)
# code ends here


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

loan_term=banks['Loan_Amount_Term'].apply(lambda x: x/12)
big_loan_term = banks[loan_term >=25].shape[0]



# code ends here



# -------------- # code ends here # code ends here # -------------- # Import packages # code starts here # code ends here # -------------- # code starts here #print(banks) #code ends here # -------------- # Code starts here # code ends here # -------------- # code starts here # code ends here # -------------- # code starts here # code ends here