import numpy as np

# First 20 countries with employment data
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 data in 2007 for those 20 countries
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
])

# Change False to True for each block of code to see what it does


# Element types
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)

# Looping
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))

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


def max_employment(countries, employment):
    """
    Fill in this function to return the name of the country
    with the highest employment in the given employment
    data, and the employment in that country.
    """
    i = np.argmax(employment)
    max_value = employment[i]
    max_country = countries[i]

    return max_country, max_value
