title: How to apply pos_tag_sents() to pandas dataframe efficiently
body: <p>In situations where you wish to POS tag a column of text stored in a pandas dataframe with 1 sentence per row the majority of implementations on SO use the apply method</p>
  
  <pre><code>dfData['POSTags']= dfData['SourceText'].apply(
                   lamda row: [pos_tag(word_tokenize(row) for item in row])
  </code></pre>
  
  <p>The NLTK documentation <a href="http://www.nltk.org/api/nltk.tag.html#nltk.tag.pos_tag">recommends using the pos_tag_sents()</a> for efficient tagging of more than one sentence.</p>
  
  <p>Does that apply to this example and if so would the code be as simple as changing <code>pso_tag</code> to <code>pos_tag_sents</code> or does NLTK mean text sources of paragraphs </p>
  
  <p>As mentioned in the comments <code>pos_tag_sents()</code> aims to reduce the loading of the preceptor each time <strong>but the issue is how to do this and still produce a column in a pandas dataframe?</strong></p>
  
  <p><a href="http://www.sharecsv.com/dl/1ec7e9ad579c3e0a4eb5fa7c16164c6d/20kSampleTextData.csv">Link to Sample Dataset 20kRows</a> </p>