title: ColumnTransformer with TfidfVectorizer produces "empty vocabulary" error
body: <p>I am running a very simple experiment with <code>ColumnTransformer</code> with an intent to transform an array of columns, ["a"] in this example:</p>
  
  <pre><code>from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.compose import ColumnTransformer
  dataset = pd.DataFrame({"a":["word gone wild","gone with wind"],"c":[1,2]})
  tfidf = TfidfVectorizer(min_df=0)
  clmn = ColumnTransformer([("tfidf", tfidf, ["a"])],remainder="passthrough")
  clmn.fit_transform(dataset)
  </code></pre>
  
  <p>Which gives me:</p>
  
  <pre><code>ValueError: empty vocabulary; perhaps the documents only contain stop words
  </code></pre>
  
  <p>Obviously, <code>TfidfVectorizer</code> can do <code>fit_transform()</code> on its own:</p>
  
  <pre><code>tfidf.fit_transform(dataset.a)
  &lt;2x5 sparse matrix of type '&lt;class 'numpy.float64'&gt;'
      with 6 stored elements in Compressed Sparse Row format&gt;
  </code></pre>
  
  <p>What could be a reason for such an error and how to correct for it?</p>