text: # --------------
  #Importing header files
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  
  data = pd.read_csv(path, sep = ',', delimiter = None)
  loan_status = data['Loan_Status'].value_counts()
  loan_status.plot(kind="bar")
  plt.show()
  #Code starts here
  
  
  # --------------
  #Code starts here
  
  property_and_loan = data.groupby(['Property_Area','Loan_Status']).size().unstack()
  
  property_and_loan.plot(kind='bar', stacked=False, figsize=(15,10))
  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', stacked=False, figsize=(15,10))
  
  plt.xlabel('Education Status')
  plt.ylabel('Loan Status ')
  plt.xticks(rotation=45)
  plt.show()
  
  
  # --------------
  #Code starts here
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  graduate = data[data['Education'] == 'Graduate']
   #df[df['Type 1'] == 'Electric']
  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(1,3, figsize=(20,10))
  
  ax_1.scatter(data['ApplicantIncome'],data["LoanAmount"])
  ax_2.scatter(data['CoapplicantIncome'],data["LoanAmount"])
  
  data['TotalIncome'] = data['ApplicantIncome'] + data['CoapplicantIncome']
  ax_3.scatter(data['TotalIncome'],data["LoanAmount"])
  
  
  
  
  
  
  