POS tagging in German

Natural language software does its magic by leveraging corpora and the statistics they provide. You'll need to tell nltk about some German corpus to help it tokenize German correctly. I believe the EUROPARL corpus might help get you going.

See nltk.corpus.europarl_raw and this answer for example configuration.

Also, consider tagging this question with "nlp".

The Pattern library includes a function for parsing German sentences and the result includes the part-of-speech tags. The following is copied from their documentation:

from pattern.de import parse, split
s = parse('Die Katze liegt auf der Matte.')
s = split(s)
print s.sentences[0]

>>>   Sentence('Die/DT/B-NP/O Katze/NN/I-NP/O liegt/VB/B-VP/O'
     'auf/IN/B-PP/B-PNP der/DT/B-NP/I-PNP Matte/NN/I-NP/I-PNP ././O/O')

Update: Another option is spacy, there is a quick example in this blog article:

import spacy

nlp = spacy.load('de')
doc = nlp(u'Ich bin ein Berliner.')

# show universal pos tags
print(' '.join('{word}/{tag}'.format(word=t.orth_, tag=t.pos_) for t in doc))
# output: Ich/PRON bin/AUX ein/DET Berliner/NOUN ./PUNCT