title: How to round/remove trailing ".0" zeros in pandas column?
body: <p>I'm trying to see if I can remove the trailing zeros from this phone number column.</p>
  
  <p>Example:</p>
  
  <pre><code>0
  1      8.00735e+09
  2      4.35789e+09
  3      6.10644e+09
  </code></pre>
  
  <p>The type in this column is an object, and I tried to round it but I am getting an error.  I checked a couple of them I know they are in this format "8007354384.0", and want to get rid of the trailing zeros with the decimal point.  </p>
  
  <p>Sometimes I received in this format and sometimes I don't, they will be integer numbers.  I would like to check if the phone column has a trailing zero, then remove it.</p>
  
  <p>I have this code but I'm stuck on how to check for trailing zeros for each row.</p>
  
  <pre><code>data.ix[data.phone.str.contains('.0'), 'phone']
  </code></pre>
  
  <p>I get an error => <code>*** ValueError: cannot index with vector containing NA / NaN values</code>.  I believe the issue is because some rows have empty data, which sometime I do receive.  The code above should be able to skip an empty row.</p>
  
  <p>Does anybody have any suggestions? I'm new to pandas but so far it's an useful library.  Your help will be appreciated.</p>
  
  <p><strong>Note</strong>
  The provided example above, the first row has an empty data, which I do sometimes I get.  Just to make sure this is not represented as 0 for phone number.</p>
  
  <p>Also empty data is considered a string, so it's a mix of floats and string, if rows are empty.</p>