sklearn CountVectorizer token_pattern -- skip token if pattern match
Found this SO post which says to use the following regex:

\b[^\d\W]+\b/g

yielding the following:

&gt;&gt;&gt; vec = CountVectorizer(token_pattern=r'\b[^\d\W]+\b')
&gt;&gt;&gt; X = vec.fit_transform(docs)
&gt;&gt;&gt; pd.DataFrame(X.toarray(), columns=vec.get_feature_names())
   aaa  is  more  some  text  this
0    0   1     0     1     1     1
1    0   0     0     0     0     0
2    1   0     1     0     0     0
3    0   0     0     0     0     0


What I needed in my regex were the \b word boundary characters of which I was not aware of. That does make this a misplaced question as it has nothing to do with data science or that discipline's tools (sklearn).