<gh_stars>0
# --------------
# 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(columns='Loan_ID')
print(banks.isnull().sum())
bank_mode = banks.mode().iloc[0]
banks.fillna(bank_mode,inplace=True)
print(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')
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()
print(loan_approved_se)
loan_approved_nse = banks.loc[(banks['Self_Employed']=='No') & (banks['Loan_Status']=='Y'),['Loan_Status']].count()
print(loan_approved_nse)

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



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


big_loan_term = len(loan_term[loan_term>=25])
print(big_loan_term)


# code ends here


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

#Grouping bank on the basis of loan tatus



loan_groupby=banks.groupby('Loan_Status')

loan_groupby=loan_groupby[['ApplicantIncome', 'Credit_History']]

mean_values=loan_groupby.mean()

print(mean_values)


# 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(columns='Loan_ID')
print(banks.isnull().sum())
bank_mode = banks.mode().iloc[0]
banks.fillna(bank_mode,inplace=True)
print(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')
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()
print(loan_approved_se)
loan_approved_nse = banks.loc[(banks['Self_Employed']=='No') & (banks['Loan_Status']=='Y'),['Loan_Status']].count()
print(loan_approved_nse)

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



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


big_loan_term = len(loan_term[loan_term>=25])
print(big_loan_term)


# code ends here


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

#Grouping bank on the basis of loan tatus



loan_groupby=banks.groupby('Loan_Status')

loan_groupby=loan_groupby[['ApplicantIncome', 'Credit_History']]

mean_values=loan_groupby.mean()

print(mean_values)


# code ends here
# -------------- # Import packages # code starts here # code ends here # -------------- # code starts here #code ends here # -------------- # Code starts here # code ends here # -------------- # code starts here # code ends here # -------------- # code starts here # code ends here # -------------- # code starts here #Grouping bank on the basis of loan tatus # code ends here