code: # --------------
  # Importing header files
  import numpy as np
  
  #New record
  new_record=[[50,  9,  4,  1,  0,  0, 40,  0]]
  
  #Code starts here
  
  #Loading data file and saving it into a new numpy array 
  data = np.genfromtxt(path, delimiter=",", skip_header=1)
  
  print(data.shape)
  
  #Concatenating the new record to the existing numpy array
  census=np.concatenate((data, new_record),axis = 0)
  
  print(census.shape)
  
  #Code ends here
  
  
  # --------------
  #Code starts here
  import numpy as np
  age=census[:,0]
  print (age)
  print ('='*50)
  
  max_age=np.max(age)
  print (max_age)
  print ('='*50)
  
  min_age=np.min(age)
  print (min_age)
  print ('='*50)
  
  age_mean=np.mean(age)
  print (age_mean)
  print ('='*50)
  
  age_std=np.std(age)
  print (age_std)
  print('='*50)
  
  
  
  
  
  # --------------
  #Code starts here
  
  #Creating new subsets based on 'Age'
  race_0=census[census[:,2]==0]
  race_1=census[census[:,2]==1]
  race_2=census[census[:,2]==2]
  race_3=census[census[:,2]==3]
  race_4=census[census[:,2]==4]
  
  
  #Finding the length of the above created subsets
  len_0=len(race_0)
  len_1=len(race_1)
  len_2=len(race_2)
  len_3=len(race_3)
  len_4=len(race_4)
  
  #Printing the length of the above created subsets
  print('Race_0: ', len_0)
  print('Race_1: ', len_1)
  print('Race_2: ', len_2)
  print('Race_3: ', len_3)
  print('Race_4: ', len_4)
  
  #Storing the different race lengths with appropriate indexes
  race_list=[len_0, len_1,len_2, len_3, len_4]
  
  #Storing the race with minimum length into a variable 
  minority_race=race_list.index(min(race_list))
  print ('minority_race:',minority_race)
  
  #Code ends here
  
  
  # --------------
  #Code starts here
  import numpy as np
  senior_citizens=census[census[:,0 ]>60]
  working_hours_sum=senior_citizens.sum(axis=0)[6]
  senior_citizens_len=len(senior_citizens)
  avg_working_hours=(working_hours_sum/senior_citizens_len)
  print (avg_working_hours)
  
  
  # --------------
  #Code starts here
  import numpy as np
  high=census[census[:,1 ]>10]
  low=census[census[:,1]<=10]
  avg_pay_high=high.mean(axis=0)[7]
  avg_pay_low=low.mean(axis=0)[7]
  if (avg_pay_high>avg_pay_low):
      print ("Better Education leads to better pay")
  else:
      print ("Better Education does not leads to better pay")
  
  
  
apis: numpy.genfromtxt numpy.max numpy.min numpy.std numpy.concatenate numpy.mean