# --------------
# Importing header files
import numpy as np

# Path of the file has been stored in variable called 'path'
data  = np.genfromtxt(path,delimiter=",",skip_header=1)
data = np.array(data)
#New record
new_record = [[50,9,4,1,0,0,40,0]]

#Code starts here
census = np.concatenate((new_record,data))


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

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




# --------------
#Code starts here
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)

val = [len_0,len_1,len_2,len_3,len_4]
min = val[0]
for i in range(1,len(val)):
    if val[i] < min:
        min = val[i]
        minority_race = i

print(minority_race)

        




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

working_hours_sum = 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)



# --------------
#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])

if avg_pay_high > avg_pay_low:
    print("true")
else:
    print("false")





132ac94b47b9d53752fdec9d92dea7d7f9d99ebe
<mask token>

<mask token>
for i in range(1, len(val)):
    if val[i] < min:
        min = val[i]
        minority_race = i
print(minority_race)
<mask token>
print(working_hours_sum)
<mask token>
print(senior_citizens_len)
<mask token>
print(avg_working_hours)
<mask token>
if avg_pay_high > avg_pay_low:
    print('true')
else:
    print('false')

<mask token>
data = np.genfromtxt(path, delimiter=',', skip_header=1)
data = np.array(data)
new_record = [[50, 9, 4, 1, 0, 0, 40, 0]]
census = np.concatenate((new_record, data))
age = census[:, 0]
max_age = np.max(age)
min_age = np.min(age)
age_mean = np.mean(age)
age_std = np.std(age)
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)
val = [len_0, len_1, len_2, len_3, len_4]
min = val[0]
for i in range(1, len(val)):
    if val[i] < min:
        min = val[i]
        minority_race = i
print(minority_race)
senior_citizens = census[census[:, 0] > 60]
working_hours_sum = 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 = census[census[:, 1] > 10]
low = census[census[:, 1] <= 10]
avg_pay_high = np.mean(high[:, 7])
avg_pay_low = np.mean(low[:, 7])
if avg_pay_high > avg_pay_low:
    print('true')
else:
    print('false')

import numpy as np
data = np.genfromtxt(path, delimiter=',', skip_header=1)
data = np.array(data)
new_record = [[50, 9, 4, 1, 0, 0, 40, 0]]
census = np.concatenate((new_record, data))
age = census[:, 0]
max_age = np.max(age)
min_age = np.min(age)
age_mean = np.mean(age)
age_std = np.std(age)
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)
val = [len_0, len_1, len_2, len_3, len_4]
min = val[0]
for i in range(1, len(val)):
    if val[i] < min:
        min = val[i]
        minority_race = i
print(minority_race)
senior_citizens = census[census[:, 0] > 60]
working_hours_sum = 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 = census[census[:, 1] > 10]
low = census[census[:, 1] <= 10]
avg_pay_high = np.mean(high[:, 7])
avg_pay_low = np.mean(low[:, 7])
if avg_pay_high > avg_pay_low:
    print('true')
else:
    print('false')

# --------------
# Importing header files
import numpy as np

# Path of the file has been stored in variable called 'path'
data  = np.genfromtxt(path,delimiter=",",skip_header=1)
data = np.array(data)
#New record
new_record = [[50,9,4,1,0,0,40,0]]

#Code starts here
census = np.concatenate((new_record,data))


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

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




# --------------
#Code starts here
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)

val = [len_0,len_1,len_2,len_3,len_4]
min = val[0]
for i in range(1,len(val)):
    if val[i] < min:
        min = val[i]
        minority_race = i

print(minority_race)

        




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

working_hours_sum = 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)



# --------------
#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])

if avg_pay_high > avg_pay_low:
    print("true")
else:
    print("false")




