# --------------
# 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((data, new_record), axis=0)


# --------------
#Code starts here
age = census[:,0]
print(age)

max_age = max(age)
min_age = min(age)
age_mean= np.mean(age)
age_std = np.std(age)




# --------------
#Code starts here
race_0 = census[np.where(census[:,2] == 0)]
race_1 = census[np.where(census[:,2] == 1)]
race_2 = census[np.where(census[:,2] == 2)]
race_3 = census[np.where(census[:,2] == 3)]
race_4 = census[np.where(census[:,2] == 4)]

len_0 = race_0.shape[0]
len_1 = race_1.shape[0]
len_2 = race_2.shape[0]
len_3 = race_3.shape[0]
len_4 = race_4.shape[0]

minority_race = np.argmin([len_0, len_1, len_2, len_3, len_4])
print(minority_race)



# --------------
#Code starts here
senior_citizens = census[census[:,0] > 60]

working_hours_sum = np.sum(senior_citizens[:,6])

senior_citizens_len = senior_citizens.shape[0]

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('')
print(avg_pay_low)


