title: SFrame Kmeans - Covert to Int, Float, Dict
body: <p>I'm preparting data to run KMEAMS from Graphlab, and am running into the following error:</p>
  
  <pre><code> tmp = data.select_columns(['a.item_id'])
   tmp['sku'] = tmp['a.item_id'].apply(lambda x: x.split(','))
   tmp = tmp.unpack('sku')
  
   kmeans_model = gl.kmeans.create(tmp, num_clusters=K)
  
   Feature 'sku.0' excluded because of its type. Kmeans features must be int, float, dict, or array.array type.
   Feature 'sku.1' excluded because of its type. Kmeans features must be int, float, dict, or array.array type.
  </code></pre>
  
  <p>Here are the current datatypes of each column:</p>
  
  <pre><code>a.item_id   str
  sku.0   str
  sku.1   str
  </code></pre>
  
  <p>If I can get the datatype from str to int I think it should work.  However, using SFrames is a more tricky than standard python libraries.  Any help getting there is appreciated. </p>