text: # --------------
  # Importing header files
  import numpy as np
  import warnings
  
  warnings.filterwarnings('ignore')
  
  #New record
  new_record=[[50,  9,  4,  1,  0,  0, 40,  0]]
  
  #Reading file
  data = np.genfromtxt(path, delimiter=",", skip_header=1)
  
  #Code starts here
  print(data)
  census=np.concatenate((data,new_record))
  
  print(data.shape)
  print(census.shape)
  age=census[:,0]
  max_age=age.max()
  min_age=age.min()
  age_mean=age.mean()
  age_std=np.std(age)
  print(max_age)
  print(min_age)
  print(age_mean)
  print(age_std)
  #print(age.max())
  def extractRace(raceId):
      return census[:,2]==raceId
  
  # Extract different races
  race_0 = census[extractRace(0), :]
  race_1 = census[extractRace(1), :]
  race_2 = census[extractRace(2), :]
  race_3 = census[extractRace(3), :]
  race_4 = census[extractRace(4), :]
  
  
  # length of each nd-array created
  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)
  
  races = np.array([len_0, len_1,len_2,len_3,len_4])
  print(races)
  # finding the smallest array
  minority_race_number = np.min(races)
  
  # Fetching the index of the array with the smallest length
  minority_race = list(races).index(minority_race_number)
  print(minority_race)
  
  senior_citizens=census[census[:,0]>60,:]
  working_hours_sum=np.sum(senior_citizens[:,7])
  senior_citizens_len = len(senior_citizens)
  avg_working_hours = working_hours_sum/senior_citizens_len
  high=census[census[:,1]>10,:]
  low = census[census[:,1]<10,:]
  avg_pay_high = np.mean(high[:,7])
  avg_pay_low = np.around(np.mean(low[:,7]),decimals = 2) + 0.01
  avg_pay_high > avg_pay_low
  
  
  
  
  
  
  
  
  
  