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

# load the dataset and drop the Loan_ID
banks= bank.drop(columns='Loan_ID')


# check  all the missing values filled.

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

# apply mode 

bank_mode = banks.mode().iloc[0]

# Fill the missing values with 

banks.fillna(bank_mode, inplace=True)

# check again all the missing values filled.

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





#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

# code for loan aprroved for self employed
loan_approved_se = banks.loc[(banks["Self_Employed"]=="Yes")  & (banks["Loan_Status"]=="Y"), ["Loan_Status"]].count()
print(loan_approved_se)

# code for loan approved for non self employed
loan_approved_nse = banks.loc[(banks["Self_Employed"]=="No")  & (banks["Loan_Status"]=="Y"), ["Loan_Status"]].count()
print(loan_approved_nse)

# percentage of loan approved for self employed
percentage_se = (loan_approved_se * 100 / 614)
percentage_se=percentage_se[0]
# print percentage of loan approved for self employed
print(percentage_se)

#percentage of loan for non self employed
percentage_nse = (loan_approved_nse * 100 / 614)
percentage_nse=percentage_nse[0]
#print percentage of loan for non self employed
print (percentage_nse)

# code ends here


# --------------
# code starts here
loan_term=banks['Loan_Amount_Term'].apply(lambda x: int(x)/12)
N=[i for i in loan_term if i<25]
n=len(N)
print(n)
m=loan_term.count()
print(m)
big_loan_term=m-n
print(big_loan_term)
# code ends here


# --------------
# code starts here
loan_groupby = banks.groupby('Loan_Status')
loan_groupby=loan_groupby['ApplicantIncome','Credit_History']
print(loan_groupby)
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

# load the dataset and drop the Loan_ID
banks= bank.drop(columns='Loan_ID')


# check  all the missing values filled.

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

# apply mode 

bank_mode = banks.mode().iloc[0]

# Fill the missing values with 

banks.fillna(bank_mode, inplace=True)

# check again all the missing values filled.

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





#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

# code for loan aprroved for self employed
loan_approved_se = banks.loc[(banks["Self_Employed"]=="Yes")  & (banks["Loan_Status"]=="Y"), ["Loan_Status"]].count()
print(loan_approved_se)

# code for loan approved for non self employed
loan_approved_nse = banks.loc[(banks["Self_Employed"]=="No")  & (banks["Loan_Status"]=="Y"), ["Loan_Status"]].count()
print(loan_approved_nse)

# percentage of loan approved for self employed
percentage_se = (loan_approved_se * 100 / 614)
percentage_se=percentage_se[0]
# print percentage of loan approved for self employed
print(percentage_se)

#percentage of loan for non self employed
percentage_nse = (loan_approved_nse * 100 / 614)
percentage_nse=percentage_nse[0]
#print percentage of loan for non self employed
print (percentage_nse)

# code ends here


# --------------
# code starts here
loan_term=banks['Loan_Amount_Term'].apply(lambda x: int(x)/12)
N=[i for i in loan_term if i<25]
n=len(N)
print(n)
m=loan_term.count()
print(m)
big_loan_term=m-n
print(big_loan_term)
# code ends here


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



# -------------- # Import packages # code starts here # code ends here # -------------- # code starts here # load the dataset and drop the Loan_ID # check  all the missing values filled. # apply mode # Fill the missing values with # check again all the missing values filled. #code ends here # -------------- # Code starts here # code ends here # -------------- # code starts here # code for loan aprroved for self employed # code for loan approved for non self employed # percentage of loan approved for self employed # print percentage of loan approved for self employed #percentage of loan for non self employed #print percentage of loan for non self employed # code ends here # -------------- # code starts here # code ends here # -------------- # code starts here # code ends here