The compound score is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence.
You can then set thresholds for negative[-1, 0), neutral[0, 0.5) and positive[0.5, 1] to classify them into categories. For example:
def get_category(compounded):
if compounded < 0:
return 'negative'
elif compounded < 0.5:
return 'neutral'
else:
return 'positive'
Choose thresholds that would work best for your data and use case.