efficient Term Document Matrix with NLTK

Question

I am trying to create a term document matrix with NLTK and pandas. I wrote the following function:

def fnDTM_Corpus(xCorpus):
    import pandas as pd
    \'         


        

Answer 1

An Alternative approach using tokens and Data Frame

import nltk
comment #nltk.download() to get toenize
from urllib import request
url = "http://www.gutenberg.org/files/2554/2554-0.txt"
response = request.urlopen(url)
raw = response.read().decode('utf8')
type(raw)

tokens = nltk.word_tokenize(raw)
type(tokens)

tokens[1:10]
['Project',
 'Gutenberg',
 'EBook',
 'of',
 'Crime',
 'and',
 'Punishment',
 ',',
 'by']

tokens2=pd.DataFrame(tokens)
tokens2.columns=['Words']
tokens2.head()


Words
0   The
1   Project
2   Gutenberg
3   EBook
4   of

    tokens2.Words.value_counts().head()
,                 16178
.                  9589
the                7436
and                6284
to                 5278