title: Text Preprocessing for NLP but from List of Dictionaries
body: <p>I'm attempting to do an NLP project with a goodreads data set. my data set is a list of dictionaries. Each dictionary looks like so (the list is called 'reviews'):</p>
  <pre><code>&gt;&gt;&gt; reviews[0]
  {'user_id': '8842281e1d1347389f2ab93d60773d4d',
  'book_id': '23310161',
  'review_id': 'f4b4b050f4be00e9283c92a814af2670',
  'rating': 4,
  'review_text': 'Fun sequel to the original.',
  'date_added': 'Tue Nov 17 11:37:35 -0800 2015',
  'date_updated': 'Tue Nov 17 11:38:05 -0800 2015',
  'read_at': '',
  'started_at': '',
  'n_votes': 7,
  'n_comments': 0}
  </code></pre>
  <p>There are 700k+ of these dictionaries in my dataset.</p>
  <p><strong>First question</strong>: I am only interested in the elements <code>'rating'</code> and <code>'review_text'</code>. I know I can delete elements from each dictionary, but how do I do it for all of the dictionaries?</p>
  <p><strong>Second question</strong>: I am able to do sentence and word tokenization of an individual dictionary in the list by specifying the dictionary in the list, then the element <code>'review_text'</code> within the dictionary like so:</p>
  <pre><code>paragraph = reviews[0]['review_text']
  </code></pre>
  <p>And then applying <code>sent_tokenize</code> and <code>word_tokenize</code> like so:</p>
  <pre><code>print(sent_tokenize(paragraph))
  print(word_tokenize(paragraph))
  </code></pre>
  <p>But how do I apply these methods to the entire data set? I am stuck here, and cannot even attempt to do any of the text preprocessing (lower casing, removing punctuation, lemmatizing, etc).</p>
  <p>TIA</p>