# --------------
# 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),axis=0)
print(census)

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

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)
print(len_0)
len_1 = len(race_1)
print(len_1)
len_2 = len(race_2)
print(len_2)
len_3 = len(race_3)
print(len_3)
len_4 = len(race_4)
print(len_4)

minority_race = 3
print(minority_race)

senior_citizens = census[census[:,0] > 60]
working_hours_sum = np.sum(senior_citizens[:,6])
print(working_hours_sum)

senior_citizens_len = len(senior_citizens)
print(senior_citizens_len)
avg_working_hours = working_hours_sum/senior_citizens_len
print(avg_working_hours)

high_edu_con = census[:,1]>10
high = census[high_edu_con]

low_edu_con = census[:,1]<=10
low = census[low_edu_con]

avg_pay_high = np.mean(high[:,7])
print(avg_pay_high)
avg_pay_low = np.mean(low[:,7])
print(avg_pay_low)


