title: CountVectorizer method get_feature_names() produces codes but not words
body: <p>I'm trying to vectorize some text with sklearn CountVectorizer. After, I want to look at features, which generate vectorizer. But instead, I got a list of codes, not words. What does this mean and how to deal with the problem? Here is my code:</p>
  
  <pre><code>vectorizer = CountVectorizer(min_df=1, stop_words='english')
  X = vectorizer.fit_transform(df['message_encoding'])
  vectorizer.get_feature_names()
  </code></pre>
  
  <p>And I got the following output:</p>
  
  <pre><code>[u'00',
  u'000',
  u'0000',
  u'00000',
  u'000000000000000000',
  u'00001',
  u'000017',
  u'00001_copy_1',
  u'00002',
  u'000044392000001',
  u'0001',
  u'00012',
  u'0004',
  u'0005',
  u'00077d3',
  </code></pre>
  
  <p>and so on.</p>
  
  <p>I need real feature names (words), not these codes. Can anybody help me please?</p>
  
  <p>UPDATE:
  I managed to deal with this problem, but now when I want to look at my words I see many words that actually are not words, but senseless sets of letters (see screenshot attached). Anybody knows how to filter this words before I use CountVectorizer? </p>
  
  <p><a href="https://i.stack.imgur.com/iykVC.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/iykVC.png" alt="Screenshot"></a></p>
  
  <p><a href="https://i.stack.imgur.com/flvvK.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/flvvK.png" alt="Screensot of the data.head()"></a></p>