# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode 
 



# code starts here
bank = pd.read_csv(path)
df = pd.DataFrame(bank)
categorical_var = df.select_dtypes(include = 'object')
numerical_var =df.select_dtypes(include = 'number')

print(categorical_var)
print("\n", 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 = banks.pivot_table(values=["LoanAmount"], index=["Gender","Married","Self_Employed"], aggfunc=np.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_se=percentage_se[0]
print(percentage_se)
percentage_nse = (loan_approved_nse * 100 / 614)
percentage_nse=percentage_nse[0]
print (percentage_nse)
# 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])
print(big_loan_term)

# code ends here


# --------------
# code starts here
columns_to_show = ['ApplicantIncome', 'Credit_History']
loan_groupby=banks.groupby(['Loan_Status'])
loan_groupby=loan_groupby[columns_to_show]
mean_values=loan_groupby.agg([np.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)
df = pd.DataFrame(bank)
categorical_var = df.select_dtypes(include = 'object')
numerical_var =df.select_dtypes(include = 'number')

print(categorical_var)
print("\n", 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 = banks.pivot_table(values=["LoanAmount"], index=["Gender","Married","Self_Employed"], aggfunc=np.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_se=percentage_se[0]
print(percentage_se)
percentage_nse = (loan_approved_nse * 100 / 614)
percentage_nse=percentage_nse[0]
print (percentage_nse)
# 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])
print(big_loan_term)

# code ends here


# --------------
# code starts here
columns_to_show = ['ApplicantIncome', 'Credit_History']
loan_groupby=banks.groupby(['Loan_Status'])
loan_groupby=loan_groupby[columns_to_show]
mean_values=loan_groupby.agg([np.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 # code ends here