title: Using multiple dictionaries to populate a pandas dataframe
body: <p>I currently have a dataframe and wish to input values using pairs of dictionaries. </p>
  
  <pre><code># create count dataframe
  range_of_years = range(2012, 2017)
  topics = ['ecology','evolution','mathematics','biogeography','neutral theory']
  topic_count_timeline = pandas.DataFrame(index = topics, columns = range_of_years)
  
  
  # dictionary pair
  count_dict = {2012: 10, 2013: 20, 2014: 12, 2015: 8, 2016: 9}
  paper_topics_dict = {'ecology': 0.7, 'neutral theory': 0.3}
  </code></pre>
  
  <p>I would like to iterate through the dictionary keys, selecting the dataframe cell with column and index that correspond to the keys, and then add the product of the dictionary values to that cell. Such that I would have the resultant dataframe:</p>
  
  <pre><code>               2012 2013 2014 2015 2016
  ecology           7   14  8.4  5.6  7.7
  evolution       NaN  NaN  NaN  NaN  NaN
  mathematics     NaN  NaN  NaN  NaN  NaN
  biogeography    NaN  NaN  NaN  NaN  NaN
  neutral theory    3    6  3.6  2.4  3.3
  </code></pre>
  
  <p>I intend to update the <code>topic_count_timeline</code> dataframe using many pairs of dictionaries like <code>count_dict</code> and <code>paper_topic_dict</code> such that new inputs are summed with the previous incumbent of the cell instead of overwriting.</p>
  
  <p>For example, if a further pair were used update the dataframe...</p>
  
  <pre><code># Additional dictionaries
  count_dict2 = {2012: 3, 2013: 2, 2014: 15, 2015: 16, 2016: 13}
  paper_topics_dict2 = {'mathematics': 0.6, 'neutral theory': 0.4}
  </code></pre>
  
  <p>The dataframe would then look like this:</p>
  
  <pre><code>               2012 2013 2014 2015 2016
  ecology           7   14  8.4  5.6  7.7
  evolution       NaN  NaN  NaN  NaN  NaN
  mathematics     1.8  1.2    9  9.6  5.4
  biogeography    NaN  NaN  NaN  NaN  NaN
  neutral theory  4.2  6.8  9.6  8.8  8.5
  </code></pre>
  
  <p>Thank you.</p>