title: Python Dictionary keys with nested list to pandas DataFrame
body: <p>I have a dictionary as follows:</p>
  
  <pre><code>D = {
      'd1': [[a1, a1, a1], [a2, a2, a2], [a3, a3, a3]], 
      'd2': [[b1, b1, b1], [b2, b2, b2], [b3, b3, b3]], 
      'd3': [[c1, c1, c1], [c2, c2, c2], [c3, c3, c3]], 
      'd4': [[d1, d1, d1], [d2, d2, d2], [d3, d3, d3]]
  }
  </code></pre>
  
  <p>How do I convert it to a dataframe such that </p>
  
  <ul>
  <li><p>The columns from the lists for a key are paired up; the nested lists are time values, temperatures and damage values, respectively, and the dataframe needs to have these in separate columns. S for <code>[[a1, a1, a1], [a2, a2, a2], [a3, a3, a3]]</code>, you'd get a row with <code>a1, a2, a3</code> (first column), followed by a row for the 2nd column, etc.</p></li>
  <li><p>The dataframe rows are grouped by combining keys with the next key, <code>d1</code> combined with <code>d2</code> make 6 rows (3 from <code>d1</code> and 3 from <code>d2</code>), then <code>d2</code> is combined with <code>d3</code> to make 6 more rows, etc. So for the 4 keys with 3 rows each, you get 3 combinations of 6 rows == 18 rows.</p></li>
  </ul>
  
  <p>I tried converting to a dataframe before concatenating:</p>
  
  <pre><code>new_df = pd.DataFrame(list(D.values()), columns=['Time_sec', 'Temperature', 'Damage'])
  </code></pre>
  
  <p>but I am still stuck with concatenating part.</p>
  
  <p><img src="https://i.stack.imgur.com/BnrN4.jpg" alt=""></p>
  
  <p>Sample expected output:</p>
  
  <p><img src="https://i.stack.imgur.com/iLda8.jpg" alt="Expected output DataFrame"></p>