title: filter the column of 3500+ book's text . How to make my code fast to work on heavy data in NLP?
body: <p>i am working on a data where every row of column has a huge text appromixately a book in every row
  so when i apply the below code of NLP for 10-50 rows it work fine  on 100 -200 raws it work but  takes 10 mins 
  when i apply on 3000 books or rows it was not giving me the output even after 2 hours so is there is any way to make fast this code or is there is another method to get the output quickly??</p>
  
  <pre><code> import re
   import nltk
   nltk.download('stopwords')
   from nltk.corpus import stopwords
   from nltk.stem.porter import PorterStemmer
   corpus = []
   for i in range(0,3500):
       review = re.sub('[^a-zA-Z0-9]', ' ', str(dt["TEXT"][i]))
       review = review.lower()
       review = review.split()
       ps=  PorterStemmer()
       review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]review = ' '.join(review)
        corpus.append(review)
  </code></pre>