code: # --------------
  # Importing header files
  import numpy as np
  
  # Path of the file has been stored in variable called 'path'
  
  #New record
  new_record=[[50,  9,  4,  1,  0,  0, 40,  0]]
  
  data = np.genfromtxt(path, delimiter=",", skip_header=1)
  
  print("\nData: \n\n", data)
  
  print("\nType of data: \n\n", type(data))
  
  census = np.concatenate((data, new_record), axis=0)
  
  print(census)
  #Code starts here
  
  
  
  # --------------
  #Code starts here
  age=census[:,0]
  
  print(age)
  
  max_age = np.max(age)
  min_age = np.min(age)
  age_mean = np.mean(age)
  age_std = np.std(age)
  
  print("max of age : ", max_age)
  print("min of age : ", min_age)
  print("mean of age : ", age_mean)
  print("standard deviation of age : ", age_std)
  
  
  # --------------
  #Code starts here
  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]
  
  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)
  
  minority_race = 3
  print(race_0)
  
  
  
  # --------------
  #Code starts here
  senior_citizens = census[census[:, 0] > 60]
  working_hours = senior_citizens[:,6]
  working_hours_sum = working_hours.sum()
  senior_citizens_len = len(senior_citizens)
  avg_working_hours = working_hours_sum / senior_citizens_len
  print(avg_working_hours)
  
  
  # --------------
  #Code starts here
  high = census[census[:,1] > 10]
  low = census[census[:,1] <= 10]
  
  avg_pay_high = high[:, 7].mean()
  avg_pay_low = low[:, 7].mean()
  
  if avg_pay_high > avg_pay_low:
      print("Better education leads to better pay")
  else:
      print("Better education does not lead to better pay")    
  
  
  
apis: numpy.genfromtxt numpy.max numpy.min numpy.std numpy.concatenate numpy.mean