title: Pandas: Using Unix epoch timestamp as Datetime index
body: <p>My application involves dealing with data (contained in a CSV) which is of the following form:</p>
  
  <pre><code>Epoch (number of seconds since Jan 1, 1970), Value
  1368431149,20.3
  1368431150,21.4
  ..
  </code></pre>
  
  <p>Currently i read the CSV using numpy loadtxt method (can easily use read_csv from Pandas). Currently for my series i am converting the timestamps field as follows:</p>
  
  <pre><code>timestamp_date=[datetime.datetime.fromtimestamp(timestamp_column[i]) for i in range(len(timestamp_column))]
  </code></pre>
  
  <p>I follow this by setting timestamp_date as the Datetime index for my DataFrame. I tried searching at several places to see if there is a quicker (inbuilt) way of using these Unix epoch timestamps, but could not find any. A lot of applications make use of such timestamp terminology. </p>
  
  <ol>
  <li>Is there an inbuilt method for handling such timestamp formats?</li>
  <li>If not, what is the recommended way of handling these formats?</li>
  </ol>