title: Remove unwanted parts from strings in Dataframe
body: <p>I am looking for an efficient way to remove unwanted parts from strings in a DataFrame column.</p>
  <p>My dataframe:</p>
  <pre><code>     Passengers
  
  1    Sally Muller, President, Mark Smith, Vicepresident, John Doe, Chief of Staff
  2    Sally Muller, President, Mark Smith, Vicepresident
  3    Sally Muller, President, Mark Smith, Vicepresident, John Doe, Chief of Staff
  4    Mark Smith, Vicepresident, John Doe, Chief of Staff, Peter Parker, Special Effects
  5    Sally Muller, President, John Doe, Chief of Staff, Peter Parker, Special Effects, Lydia Johnson, Vice Chief of Staff
  ...
  </code></pre>
  <p>desired form of df:</p>
  <pre><code>     Passengers
  
  1    Sally Muller, Mark Smith, John Doe
  2    Sally Muller, Mark Smith
  3    Sally Muller, Mark Smith, John Doe
  4    Mark Smith, John Doe, Peter Parker
  5    Sally Muller, John Doe, Peter Parker, Lydia Johnson
  ...
  </code></pre>
  <p>Up to now I did it with endless handmade copy/paste regex list:</p>
  <pre><code>df = df.replace(r'President,','', regex=True) 
  df = df.replace(r'Vicepresident,','', regex=True) 
  df = df.replace(r'Chief of Staff,','', regex=True) 
  df = df.replace(r'Special Effects,','', regex=True) 
  df = df.replace(r'Vice Chief of Staff,','', regex=True) 
  ...
  </code></pre>
  <p>Is there a more comfortable way to do this?</p>
  <p><strong>Edit</strong></p>
  <p>More accurate example of original df:</p>
  <pre><code>     Passengers
  
  1    Sally Muller, President, EL Mark Smith, John Doe, Chief of Staff,  Peter Gordon, Director of Central Command
  2    Sally Muller, President, EL Mark Smith, Vicepresident
  3    Sally Muller, President, EL Mark Smith, Vicepresident, John Doe, Chief of Staff, Peter Gordon, Dir CC 
  4    Mark Smith, Vicepresident, John Doe, Chief of Staff, Peter Parker, Special Effects
  5    President Sally Muller, John Doe Chief of Staff, Peter Parker, Special Effects, Lydia Johnson , Vice Chief of Staff
  ...
  </code></pre>
  <p>desired form of df:</p>
  <pre><code>     Passengers
  
  1    Sally Muller, Mark Smith, John Doe, Peter Gordon
  2    Sally Muller, Mark Smith
  3    Sally Muller, Mark Smith, John Doe, Peter Gordon
  4    Mark Smith, John Doe, Peter Parker
  5    Sally Muller, John Doe, Peter Parker, Lydia Johnson
  ...
  </code></pre>
  <p>Up to now I did it with endless handmade copy/paste regex list:</p>
  <pre><code>df = df.replace(r'President','', regex=True) 
  df = df.replace(r'Director of Central Command,','', regex=True) 
  df = df.replace(r'Dir CC','', regex=True) 
  df = df.replace(r'Vicepresident','', regex=True) 
  df = df.replace(r'Chief of Staff','', regex=True) 
  df = df.replace(r'Special Effects','', regex=True) 
  df = df.replace(r'Vice Chief of Staff','', regex=True) 
  ...
  </code></pre>
  <p>messy output is like:</p>
  <pre><code>     Passengers
  
  1    Sally Muller, , Mark Smith, John Doe, , Peter Gordon,
  2    Sally Muller, Mark Smith,
  3    Sally Muller, Mark Smith,, John Doe, Peter Gordon
  4    Mark Smith, John Doe, Peter Parker
  5    Sally Muller,, John Doe, Peter Parker , Lydia Johnson,
  ...
  </code></pre>