# --------------
# 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]]

#Code starts here
data = np.genfromtxt(path, delimiter = ",", skip_header=1)
census = np.concatenate((new_record, data), axis=0)


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


# --------------
#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)
len_list = [len_0,len_1,len_2,len_3,len_4]
minority_race = len_list.index(min(len_list))



# --------------
#Code starts here
senior_citizens = census[census[:,0]>60,:]
working_hours_sum = np.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])
print(avg_pay_high)
print(avg_pay_low)


