title: dictionary with counter dictionary (multiple keys) as a 2 column dataframe
body: <p>I have a dictionary of bigrams, which was created as</p>
  
  <pre><code>self.bigram_counts = defaultdict(lambda: Counter())
  </code></pre>
  
  <p>2 sample rows from self.bigram_counts:</p>
  
  <pre><code> [(None, Counter({'de': 1})),
   ('de', Counter({'la': 7839,filtradojardin': 1,'cantera': 236})))]
  </code></pre>
  
  <p>Trying to load it into a dataframe as follows seems inefficient:</p>
  
  <pre><code>bigrams2 = pd.DataFrame.from_dict(list((vocab.bigram_counts.keys(), 
                                         vocab.bigram_counts.values().keys()), 
                                         vocab.bigram_counts.values().values()))
  </code></pre>
  
  <p>I would like 2 columns out of this dataframe, one with the full bigram, and one with the count. What's the best way of calling pd.DataFrame in this case?</p>
  
  <p>Previously, I used:</p>
  
  <pre><code>bigrams = pd.DataFrame.from_dict(list(vocab.bigram_counts.items()))
  bigrams.columns = [['word(s)', 'count(s)']]
  bigrams.head()
  </code></pre>
  
  <p>Which works but provides a counter object for the count column. I don't think .melt() is right here, but maybe something similar can be used?</p>
  
  <p>Desired output:</p>
  
  <pre><code>     0    1
  0    None, de            1
  1    de, la           7839
  2    de, filtradojardin  1
  3    de, cantera       236
  </code></pre>