# --------------
# Importing header files
import numpy as np

# Path of the file has been stored in variable called 'path'
data=np.genfromtxt(path, delimiter=',', skip_header=1)
#New record
new_record=np.array([[50,  9,  4,  1,  0,  0, 40,  0]])
census=np.concatenate((data, new_record), axis=0)
#Code starts here



# --------------
#Code starts here
import numpy as np
age=np.array(census[:,0])
max_age=age.max()
min_age=age.min()
age_mean=np.mean(age)
age_std=np.std(age)


# --------------
#Code starts here
import numpy as np 
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=0
temp=np.array([len_0,len_1,len_2,len_3,len_4])
for i in range(1,5):
    if temp[minority_race]>temp[i]:
        minority_race=i


# --------------
#Code starts here
senior_citizens=census[census[:,0]>60]
working_hours_sum=senior_citizens[:,6].sum()
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=np.mean(high[:,7])
avg_pay_low=np.mean(low[:,7])



