title: Pulling column values based on conditions
body: <p>I have the following dataframe</p>
  
  <pre><code>df = pd.DataFrame({
      'Column_1': ['Position', 'Start', 'End', 'Position'], 
      'Original_1': ['Open', 'Barn', 'Grass', 'Bubble'], 
      'Latest_1': ['Shut', 'Horn', 'Date', 'Dinner'], 
      'Column_2': ['Start', 'Position', 'End', 'During'], 
      'Original_2': ['Sky', 'Hold', 'Car', 'House'], 
      'Latest_2': ['Pedal', 'Lap', 'Two', 'Force'], 
      'Column_3': ['Start', 'End', 'Position', 'During'], 
      'Original_3': ['Leave', 'Dog', 'Block', 'Hope'], 
      'Latest_3': ['Sear', 'Crawl', 'Enter', 'Night']
  })
  </code></pre>
  
  <p>For every instance where the word Position is in 'Column_1', 'Column_2', or 'Column_3', I want to capture the associated values in 'Original_1', 'Original_2', 'Original_3' and assign them to the new column named 'Original_Values'.</p>
  
  <p>The following code can accomplish that, but only on a column by column basis.</p>
  
  <pre><code>df['Original_Value1'] = df.loc[df['Column_1'] == 'Position', 'Original_1']
  df['Original_Value2'] = df.loc[df['Column_2'] == 'Position', 'Original_2']
  df['Original_Value3'] = df.loc[df['Column_3'] == 'Position', 'Original_3']
  </code></pre>
  
  <p>Is there a way to recreate the above code so that it iterates over the entire data frame (not by specified columns)? </p>
  
  <p>I'm hoping to create one column ('Original_values') with the following result:</p>
  
  <pre><code>0      Open
  1      Hold
  2     Block
  3    Bubble
  Name: Original_Values, dtype: object
  </code></pre>