NumPy Array

import numpy as np

countries = np.array([
    'Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',
    'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas',
    'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',
    'Belize', 'Benin', 'Bhutan', 'Bolivia',
    'Bosnia and Herzegovina'
])

employment = np.array([
    55.70000076,  51.40000153,  50.5       ,  75.69999695,
    58.40000153,  40.09999847,  61.5       ,  57.09999847,
    60.90000153,  66.59999847,  60.40000153,  68.09999847,
    66.90000153,  53.40000153,  48.59999847,  56.79999924,
    71.59999847,  58.40000153,  70.40000153,  41.20000076
])

if False:
	print countries[0]
	print countries[3]

if False:
	print countries[0:3]
	print countries[:3]
	print countries[17:]
	print countries[:]

if False:
	print countries.dtype
	print employment.dtype
	print np.array([0, 1, 2, 3]).dtype
	print np.array([1.0, 1.5, 2.0, 2.5]).dtype
	print np.array([True, False, True]).dtype
	print np.array(['AL', 'AK', 'AZ', 'AR', 'CA']).dtype

if False:
	for country in countries:
		print 'Examining country {}'.format(country)

	for i in range(len(countries)):
		country = countries[i]
		country_employment = employment[i]
		print 'Country {} has employment {}'.format(country,
			country_employment)

if False:
	print employment.mean()
	print employment.std()
	print employment.max()
	print employment.sum()

def max_employment(countries, employment):
	max_country = None
	max_value = None

	return (max_country, max_value)