title: Nested Python dictionary to Pandas dataframes
body: <p>I have a nested Python dictionary:</p>
  <pre><code>d={'CON-2': {'gene-ODF3': [2.0, 44474],'gene-SCGB1C1': [0.184937, 36615], 'gene-TRNAN-GUU-19': [32.0, 443]},'CON-1':{'gene-ODF3': [10.00, 44474], 'gene-SCGB1C1': [0.184937, 36615], 'gene-TRNAN-GUU-19': [30.0, 443], 'gene-LOC103247846': [20.0, 22111]}}
  </code></pre>
  <p>I would like to plot the FPKM of each gene (the first value) against its DNA transcript abundance (the second value) on a scatterplot. I have tried a few different things, such as:</p>
  <pre><code>CON_1=pd.DataFrame(d['CON-1'].items(),columns=['FPKM','Fraction-0'])
  CON_2=pd.DataFrame(d['CON-2'].items(),columns=['FPKM','Fraction-0'])
  
  df=pd.DataFrame.from_dict({(i,j): d[i][j]
                             for i in d.keys()
                             for j in d[i].keys()},
                             orient='index')
  </code></pre>
  <p>But I cannot separate the two values from each other. I would like to generate a separate data frame for each condition (CON-1 and CON-2), like this:</p>
  <pre><code>gene       FPKM    DNA-abundance
  gene-ODF3  2.0     44474
  </code></pre>