title: Grouping a Pandas DataFrame
body: <p>Can someone please share how they approach grouping the following dataframe?
  When I try to do this, I end up fiddling with groupbys, stacks and lambda functions but never quite get there. Its not readily apparent which methods and examples in the documentation are helpful for what I'm trying to do. </p>
  
  <p>Does this stuff get more intuitive with experience? Appreciate any pointers or advice on how to choose and get more fluent with Pandas functions. </p>
  
  <p>Here is the dummy data:</p>
  
  <pre><code>df = pd.DataFrame({'A':[11,11,11,12,12,12],'B':[4,5,6,3,11,12]})
  
     A  B
  0  11  4
  1  11  5
  2  11  6
  3  12  3
  4  12  11
  5  12  12
  </code></pre>
  
  <p>I'd like to show that each of the original values in one column, is in a group with the values from the second column. (Duplicate 12 from second column removed in Group 2 below)</p>
  
  <pre><code>Group 1: [11,4,5,6,12] 
  Group 2: [12,3,11]
  </code></pre>
  
  <p>And in the end, for the resulting dataframe to have the values from column A with the groups added as a list and numbered:</p>
  
  <pre><code>Col_A   Grouped_values  Group_name
  11      [11,4,5,6,12]     Group 1
  12      [12,3,11]         Group 2
  </code></pre>
  
  <p>Thank you for your help!</p>