title: pandas dataframe update column values based on other columns of the corresponding row
body: <p>I have a pandas <em>dataframe</em> as:</p>
  
  <pre><code>Name    abc_1   abc_2   abc_3   abc_4   abc_5   abc_6   cde_1   cde_2   cde_3   cde_4   cde_5   cde_6
   xyz    True    True    False   False   False   True    True    True    False   False   False   False
   stu    False   False   False   False   False   False   False   False   False   False   False   True
   vwx    False   True    False   False   False   False   True    True    False   False   False   False
  </code></pre>
  
  <p>For a given <em>row</em>, any <em>abc_x</em> column should be updated to <em>True</em> if corresponding cde column is <em>True</em> <strong>AND</strong> <strong><em>at least</em></strong> one of the <em>abc</em> column of the corresponding row is <em>True</em>. </p>
  
  <p>Result: <em>abc_1</em> of <em>vwx</em> row becomes <em>True</em> but <em>abc_6</em> of <em>stu</em> row remains <em>False</em> as below:</p>
  
  <pre><code>Name    abc_1   abc_2   abc_3   abc_4   abc_5   abc_6   cde_1   cde_2   cde_3   cde_4   cde_5   cde_6
   xyz    True    True    False   False   False   False   True    True    False   False   False   False
   stu    False   False   False   False   False   False   False   False   False   False   False   True
   vwx    True    True    False   False   False   False   True    True    False   False   False   False
  </code></pre>
  
  <p>Seems, using <em>iterrows</em> and then by iterating each column is the option. But I have several and very large <em>dataframes</em> and looking for a pythonic way of doing this.</p>