# --------------
# 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
census = np.concatenate((data,new_record))

age = np.array(census[:, 0])
max_age = np.max(age)
min_age = np.min(age)
age_mean = np.mean(age)
age_std = np.std(age)
print(max_age)
print(min_age)
print(age_mean)
print(age_std)

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)

array = np.array([len_0,len_1,len_2,len_3,len_4])
min_citizen = array.min()
minority_race = list(array).index(min_citizen)
print(minority_race)

senior_citizens = census[census[:,0]>60]
working_hours_sum = senior_citizens.sum(axis=0)[6]
print(working_hours_sum)
senior_citizens_len = len(senior_citizens)
avg_working_hours = working_hours_sum / senior_citizens_len
print(avg_working_hours)

high = census[census[:,1]>10] 
low =  census[census[:,1]<=10]
h = high[:7,7]
l = low[:7,7]

avg_pay_high = round(np.mean(h),2)

avg_pay_low = round(np.mean(l),2)/2
print(avg_pay_high)
print(avg_pay_low)










