# --------------
# 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(data)
#New record
new_record=[[50,  9,  4,  1,  0,  0, 40,  0]]

census=np.concatenate((data, new_record), axis=0)

print(census)

#Code starts here



# --------------
#Code starts here
age=census[:,0]
print(age)
max_age=age.max()

min_age=age.min()

age_mean=age.mean()

age_std=age.std()


# --------------
#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)
print(len_0,len_1,len_2,len_3,len_4)
race_list=[len_0,len_1,len_2,len_3,len_4]
minority_race=race_list.index(min(race_list))
#minority_race=min(race_list)
print(minority_race)




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

#Creating an array based on 'education' column
high=census[census[:,1]>10]

#Finding the average pay
avg_pay_high=high[:,7].mean()

#Printing the average pay
print(avg_pay_high)

#Creating an array based on 'education' column
low=census[census[:,1]<=10]

#Finding the average pay
avg_pay_low=low[:,7].mean()

#Printing the average pay
print(avg_pay_low)
#Code ends here




# --------------
# 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(data)
#New record
new_record=[[50,  9,  4,  1,  0,  0, 40,  0]]

census=np.concatenate((data, new_record), axis=0)

print(census)

#Code starts here



# --------------
#Code starts here
age=census[:,0]
print(age)
max_age=age.max()

min_age=age.min()

age_mean=age.mean()

age_std=age.std()


# --------------
#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)
print(len_0,len_1,len_2,len_3,len_4)
race_list=[len_0,len_1,len_2,len_3,len_4]
minority_race=race_list.index(min(race_list))
#minority_race=min(race_list)
print(minority_race)




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

#Creating an array based on 'education' column
high=census[census[:,1]>10]

#Finding the average pay
avg_pay_high=high[:,7].mean()

#Printing the average pay
print(avg_pay_high)

#Creating an array based on 'education' column
low=census[census[:,1]<=10]

#Finding the average pay
avg_pay_low=low[:,7].mean()

#Printing the average pay
print(avg_pay_low)
#Code ends here




# -------------- # Importing header files # Path of the file has been stored in variable called 'path' #New record #Code starts here # -------------- #Code starts here # -------------- #Code starts here #minority_race=min(race_list) # -------------- #Code starts here # -------------- #Code starts here #Creating an array based on 'education' column #Finding the average pay #Printing the average pay #Creating an array based on 'education' column #Finding the average pay #Printing the average pay #Code ends here