title: How to feature-ize timeseries data in Pandas?
body: <p>I have data that are structured as below:</p>
  
  <pre><code>Group, ID, Time, Feat1, Feat2, Feat3
  A, 1, 0, 1.52, 2.94, 3.1
  A, 1, 2, 1.67, 2.99, 3.3
  A, 1, 4, 1.9, 3.34, 5.6
  </code></pre>
  
  <p>In this data, there are individuals who have been measured repeatedly.</p>
  
  <p>I'd like to restructure the data such that each feature-time combination is a unique column, as below:</p>
  
  <pre><code>Group, ID, Feat1_Time0, Feat1_Time2, Feat1_Time4, Feat2_Time0, Feat2_Time2, Feat2_Time4, Feat3_Time0, Feat3_Time2, Feat3_Time4
  A, 1, 1.52, 2.94, 3.1, 1.67, 2.99, 3.3, 1.9, 3.34, 5.6
  </code></pre>
  
  <p>Is there a simple way to handle this, without using a for-loop? I've tried accomplishing what I need with the for-loop method, but it is inelegant and clunky, and given real data of 10<sup>4</sup> columns, it would take a while as well.</p>