# --------------
# 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))


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


# --------------
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]
# calculate length
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)
r = np.array([len_0, len_1, len_2, len_3, len_4])
print(r)
minimum = r.min()

#checking the min race
for i in range(0,5):
    if r[i] == minimum:
        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
high = census[census[:,1] > 10]
low = census[census[:,1] <= 10]
avg_pay_high = high[:,7].mean()
avg_pay_low = low[:,7].mean()
if avg_pay_high>avg_pay_low:
    print("True")


