title: Convert unix time to readable date in pandas dataframe
body: <p>I have a dataframe with unix times and prices in it. I want to convert the index column so that it shows in human readable dates. </p>
  
  <p>So for instance I have <code>date</code> as <code>1349633705</code> in the index column but I'd want it to show as <code>10/07/2012</code> (or at least <code>10/07/2012 18:15</code>). </p>
  
  <p>For some context, here is the code I'm working with and what I've tried already:</p>
  
  <pre><code>import json
  import urllib2
  from datetime import datetime
  response = urllib2.urlopen('http://blockchain.info/charts/market-price?&amp;format=json')
  data = json.load(response)   
  df = DataFrame(data['values'])
  df.columns = ["date","price"]
  #convert dates 
  df.date = df.date.apply(lambda d: datetime.strptime(d, "%Y-%m-%d"))
  df.index = df.date   
  </code></pre>
  
  <p>As you can see I'm using
  <code>df.date = df.date.apply(lambda d: datetime.strptime(d, "%Y-%m-%d"))</code> here which doesn't work since I'm working with integers, not strings. I think I need to use <code>datetime.date.fromtimestamp</code> but I'm not quite sure how to apply this to the whole of <code>df.date</code>. </p>
  
  <p>Thanks.</p>