Parsed document content is iterable, and each element has numerous attributes produced by the processing pipeline. The following sample illustrates how to access the following attributes:

• .text: Original word text
• .lemma_: Word root
• .pos_: Basic POS tag
• .tag_: Detailed POS tag
• .dep_: Syntactic relationship or dependency between tokens
• .shape_: The shape of the word regarding capitalization, punctuation, or digits
• .is alpha: Check whether the token is alphanumeric
•  .is stop: Check whether the token is on a list of common words for the given language

We iterate over each token and assign its attributes to a pd.DataFrame:

pd.DataFrame([[t.text, t.lemma_, t.pos_, t.tag_, t.dep_, t.shape_, t.is_alpha, t.is_stop] for t in doc],
             columns=['text', 'lemma', 'pos', 'tag', 'dep', 'shape', 'is_alpha', 'is_stop'])

Which produces the following output:

We can visualize syntactic dependency in a browser or notebook using the following:

displacy.render(doc, style='dep', options=options, jupyter=True)

The result is a dependency tree:

We can get additional insights into the meaning of attributes using spacy.explain(), as here:

spacy.explain("VBZ")
verb, 3rd person singular present