title: Merge pandas dataframe, with column operation
body: <p>I searched archive, but did not find what I wanted (probably because I don't really know what key words to use)</p>
  
  <p>Here is my problem: I have a bunch of dataframes need to be merged; I also want to update the values of a subset of columns with the sum across the dataframes.</p>
  
  <p>For example, I have two dataframes, df1 and df2:</p>
  
  <pre><code>df1=pd.DataFrame([ [1,2],[1,3], [0,4]], columns=["a", "b"])
  df2=pd.DataFrame([ [1,6],[1,4]], columns=["a", "b"])
  
      a   b           a   b
  0   1   2       0   1   5
  1   1   3       2   0   6
  2   0   4       
  </code></pre>
  
  <p>after merging, I'd like to have the column 'b' updated with the sum of matched records, while column 'a' should be just like df1 (or df2, don't really care) as before:</p>
  
  <pre><code>    a   b
  0   1   7
  1   1   3
  2   0   10
  </code></pre>
  
  <p>Now, expand this to merging three or more data frames.</p>
  
  <p>Are there straightforward, build-in tricks to do this? or I need to process one by one, line by line?</p>
  
  <p>===== Edit / Clarification =====</p>
  
  <p>In the real world example, each data frame may contain indexes that are not in the other data frames. In this case, the merged data frame should have all of them and update the shared entries/indexes with sum (or some other operation).</p>