import numpy as np

print "Executing: 08_Standardizing_Data.py"

# 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 this country name to change what country will be printed when you
# click "Test Run". Your function will be called to determine the standardized
# score for this country for each of the given 5 Gapminder variables in 2007.
# The possible country names are available in the Downloadables section.

country_name = 'United States'

def standardize_data(values):
    '''
    Fill in this function to return a standardized version of the given values,
    which will be in a NumPy array. Each value should be translated into the
    number of standard deviations that value is away from the mean of the data.
    (A positive number indicates a value higher than the mean, and a negative
    number indicates a value lower than the mean.)
    '''
    standardized_values = (values - values.mean()) / values.std()
    return standardized_values


