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

print("\nData: \n\n", data)

print("\nType of data: \n\n", type(data))

#New record
new_record=[[50,  9,  4,  1,  0,  0, 40,  0]]

census = np.concatenate([data,new_record])

#Code starts here



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

age = census[:,0]


max_age = age.max()
print(max_age)
min_age = age.min()
print(min_age)

age_mean = age.mean()

age_std = age.std()


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

#Creating new subsets based on '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]


#Finding the length of the above created subsets
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)

#Printing the length of the above created subsets
print('Race_0: ', len_0)
print('Race_1: ', len_1)
print('Race_2: ', len_2)
print('Race_3: ', len_3)
print('Race_4: ', len_4)

#Storing the different race lengths with appropriate indexes
race_list=[len_0, len_1,len_2, len_3, len_4]

#Storing the race with minimum length into a variable 
minority_race=race_list.index(min(race_list))
minority_race

#Code ends here


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

w = census.copy()

senior_citizens = w[(w[:, 0] > 60).ravel(), :]
# senior_citizens

working_hours_sum=senior_citizens.sum(axis=0)[6]

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()


