text: # --------------
  #Importing header files
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  
  
  
  
  #Code starts here
  data = pd.read_csv(path)
  
  loan_status = data['Loan_Status'].value_counts()
  
  loan_status.plot(kind = 'bar')
  plt.show()
  
  
  # --------------
  #Code starts here
  property_and_loan = data.groupby(['Property_Area','Loan_Status']).size().unstack()
  
  property_and_loan.plot(kind = 'bar',stacked =False)
  
  plt.xlabel('Property Area')
  plt.ylabel('Loan Status')
  plt.xticks(rotation ='45')
  plt.show()
  
  
  # --------------
  #Code starts here
  education_and_loan = data.groupby(['Education','Loan_Status']).size().unstack()
  education_and_loan.plot(kind = 'bar')
  plt.xlabel('Education Status')
  plt.ylabel('Loan Status')
  plt.xticks(rotation = '45')
  plt.show()
  
  
  # --------------
  #Code starts here
  
  graduate = data[data['Education'] == 'Graduate']
  
  not_graduate = data[data['Education'] == 'Not Graduate']
  
  graduate['LoanAmount'].plot(kind = 'density',label = 'Graduate')
  not_graduate['LoanAmount'].plot(kind= 'density',label = 'Not Graduate')
  
  
  
  
  
  
  
  
  
  
  
  
  #Code ends here
  
  #For automatic legend display
  plt.legend()
  
  
  # --------------
  #Code starts here
  fig,(ax_1,ax_2,ax_3) = plt.subplots(nrows = 3,ncols= 1,figsize = (30,30))
  
  
  plt.subplot(ax_1)
  plt.scatter(data['ApplicantIncome'],data['LoanAmount'],label = 'Applicant Income')
  plt.legend()
  
  plt.subplot(ax_2)
  plt.scatter(data['CoapplicantIncome'],data['LoanAmount'],label= 'Coapplicant Income')
  plt.legend()
  
  data['TotalIncome'] =data['ApplicantIncome']+ data['CoapplicantIncome']
  plt.subplot(ax_3)
  plt.scatter(data['TotalIncome'],data['LoanAmount'],label= 'Total Income')
  
  plt.legend()
  plt.show()
  
  
  
  