title: How to modify strings in a pandas dataframe (regex?)
body: <p>Suppose I have the following rows in a Pandas DataFrame:</p>
  
  <pre><code>970 P-A1-1019-03-C15,15 23987896    1   8
  971 P-A1-1019-06-B15,15 23251711    4   8
  972 P-A1-1019-08-C15,15 12160034    2   8
  973 P-A1-1020-01-D15,15 8760012     1   8
  </code></pre>
  
  <p>I'd like to alter the second column to remove the ",15" from the string.  Desired end state would be like this:</p>
  
  <pre><code>970 P-A1-1019-03-C15    23987896    1   8
  971 P-A1-1019-06-B15    23251711    4   8
  972 P-A1-1019-08-C15    12160034    2   8
  973 P-A1-1020-01-D15    8760012     1   8
  </code></pre>
  
  <p>The thing to remove won't always be ",15", as it could be ",10", ",03", ",4", etc.  Additionally, some rows in the input are differently formatted, and may look like this:</p>
  
  <pre><code>4   RR00-0,2020338  24380076    4   12
  5   RR00-0,2020738  10562767    2   12
  6   ,D              24260808    1   12
  7   ,D              23521158    1   12
  </code></pre>
  
  <p>Initially, I'm only interested in the cases where the string DOES fit the form of "P-A1-1019-03-C15", so it would be nice to be able to drop rows which don't match that specific format.</p>
  
  <p>Is there a built in way to do this kind of processing, or will I need to iterate over every row manually?</p>