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


age = census[:,0]
max_age = np.max(age)
print(max_age)
min_age = np.min(age)
print(min_age)
age_mean = round(np.mean(age),2)
print(age_mean)
age_std = round(np.std(age))
print(age_std)


#Step3
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 = min(len_0,len_1,len_2,len_3,len_4)
print(minority_race)

#Step4
senior_citizens = census[census[:,0]>60]
working_hours_sum = senior_citizens.sum(axis=0)[6]
senior_citizens_len = len(senior_citizens)
avg_working_hours = round(working_hours_sum/senior_citizens_len,2)
print(avg_working_hours)

#Step5
high = census[census[:,1]>10]
low = census[census[:,1]<=10]
avg_pay_high = round(np.mean(high[:,7]),2)
avg_pay_low = round(np.mean(low[:,7]),2)
print(avg_pay_high)
print(avg_pay_low)



