<reponame>madhukarCA/ga-learner-dst-repo
# --------------
# 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
data.shape
census = np.concatenate((data,new_record),axis=0)
max_age = census[ :,0].max()
min_age = census[ :,0].min()
age_mean = census[ :,0].mean()
age_std = census[ :,0].std()
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= [len_0,len_1,len_2,len_3,len_4].index(min([len_0,len_1,len_2,len_3,len_4]))
senior_citizens = census[census[:,0]>60]
working_hours_sum = census[census[:,0]>60][:,6].sum()
senior_citizens_len = len(senior_citizens)
avg_working_hours = working_hours_sum/senior_citizens_len
print(working_hours_sum,round(avg_working_hours,2))
high = census[census[:,1]>10]
low = census[census[:,1]<=10]
avg_pay_high = census[census[:,1]>10][:,7].sum()/len(high)
avg_pay_low = census[census[:,1]<=10][:,7].sum()/len(low)
print(round(avg_pay_high,2),round(avg_pay_low,2))



