text: # --------------
  # Importing header files
  import numpy as np
  
  # Path of the file has been stored in variable called 'path'
  
  #New record
  new_record=np.array([[50,  9,  4,  1,  0,  0, 40,  0]])
  
  #Code starts here
  data = np.genfromtxt(path, delimiter=',', skip_header=1)
  census =np.concatenate((data,new_record),axis=0)
  
  
  # --------------
  #Code starts here
  age = census[:,0]
  max_age = np.amax(age)
  print (max_age)
  min_age = np.amin(age)
  print (min_age) 
  age_mean = np.mean(age)
  print (age_mean)
  age_std = np.std(age)
  print (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[:,2])
  len_1 = len(race_1[:,2])
  len_2 = len(race_2[:,2])
  len_3 = len(race_3[:,2])
  len_4 = len(race_4[:,2])
  
  print (len_0,len_1,len_2,len_3,len_4)
  
  lengths = [len_0,len_1,len_2,len_3,len_4]
  minority_race = lengths.index(min(lengths))
  
  
  # --------------
  #Code starts here
  senior_citizens = census[census[:,0]>60]
  working_hours_sum = sum(senior_citizens[:,6])
  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 = np.mean(high[:,7])
  avg_pay_low = np.mean(low[:,7])
  
  if (avg_pay_high-avg_pay_low > 0):
      print (' better education leads to better pay')
  else:
      print (" better education doesn't lead to better pay")    
  
  
  