title: Merge rows with same index and prioritize column values
body: <p>I have a dataframe with some duplicate index values with columns containing values for two different experiments. I want to prioritize Col_A if values are present across both index instances. I am working to solve this solution using the following algorithm.</p>
  <ol>
  <li>Merge rows with the same index</li>
  </ol>
  <p><a href="https://stackoverflow.com/questions/39646345/pandas-merging-rows-with-the-same-value-and-same-index">Pandas merging rows with the same value and same index</a></p>
  <ol start="2">
  <li>Create new column with combine_first function. to select Col_A if present otherwise select Col_B</li>
  </ol>
  <p>Example Data</p>
  <pre><code>data = {'id':['id3', 'id3', 'id6'],
         'Col_A':[11,NaN,3],
         'Col_B':[NaN,5,NaN]}
  
  ## Insert SO Magic Here
  
  ##
  
  output = {'id':['id3', 'id6'],
         'Col_Score':[11,3]}
  
  </code></pre>
  <p>If there is a &quot;better&quot; solution (shorter) than my proposed algorithm, please let me know.</p>