title: Pandas converting row with unix timestamp (in milliseconds) to datetime
body: <p>I need to process a huge amount of CSV files where the time stamp is always a string representing the unix timestamp in milliseconds. I could not find a method yet to modify these columns efficiently.</p>
  
  <p>This is what I came up with, however this of course duplicates only the column and I have to somehow put it back to the original dataset. I'm sure it can be done when creating the <code>DataFrame</code>?</p>
  
  <pre><code>import sys
  if sys.version_info[0] &lt; 3:
      from StringIO import StringIO
  else:
      from io import StringIO
  import pandas as pd
  
  data = 'RUN,UNIXTIME,VALUE\n1,1447160702320,10\n2,1447160702364,20\n3,1447160722364,42'
  
  df = pd.read_csv(StringIO(data))
  
  convert = lambda x: datetime.datetime.fromtimestamp(x / 1e3)
  converted_df = df['UNIXTIME'].apply(convert)
  </code></pre>
  
  <p>This will pick the column 'UNIXTIME' and change it from</p>
  
  <pre><code>0    1447160702320
  1    1447160702364
  2    1447160722364
  Name: UNIXTIME, dtype: int64
  </code></pre>
  
  <p>into this</p>
  
  <pre><code>0   2015-11-10 14:05:02.320
  1   2015-11-10 14:05:02.364
  2   2015-11-10 14:05:22.364
  Name: UNIXTIME, dtype: datetime64[ns]
  </code></pre>
  
  <p>However, I would like to use something like <code>pd.apply()</code> to get the whole dataset returned with the converted column or as I already wrote, simply create datetimes when generating the DataFrame from CSV.</p>