I apologize if this question is misplaced -- I'm not sure if this is more of a re question or a CountVectorizer question. I'm trying to exclude any would be token that has one or more numbers in it.

>>> from sklearn.feature_extraction.text import CountVectorizer
>>> import pandas as pd
>>> docs = ['this is some text', '0000th', 'aaa more 0stuff0', 'blahblah923']
>>> vec = CountVectorizer()
>>> X = vec.fit_transform(docs)
>>> pd.DataFrame(X.toarray(), columns=vec.get_feature_names())
0000th  0stuff0  aaa  blahblah923  is  more  some  text  this
0       0        0    0            0   1     0     1     1     1
1       1        0    0            0   0     0     0     0     0
2       0        1    1            0   0     1     0     0     0
3       0        0    0            1   0     0     0     0     0


What I want instead is this:

   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


My thought was to use CountVectorizer's token_pattern argument to supply a regex string that will match anything except one or more numbers:

>>> vec = CountVectorizer(token_pattern=r'[^0-9]+')


but the result includes the surrounding text matched by the negated class:

   aaa more   blahblah  stuff  th  this is some text
0          0         0      0   0                  1
1          0         0      0   1                  0
2          1         0      1   0                  0
3          0         1      0   0                  0


Also, replacing the default pattern (?u)\b\w\w+\b obviously messes with the tokenizer's normal function which I want to preserver.

What I really want is to use the normal token_pattern, but apply a secondary screening of those tokens to only include those that have strictly letters in them. How can this be done?

Found this SO post which says to use the following regex:

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

yielding the following:

>>> vec = CountVectorizer(token_pattern=r'\b[^\d\W]+\b')
>>> X = vec.fit_transform(docs)
>>> 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).