title: Rows of categorical values into columns in pandas
body: <p>So I have this data set consisting of 34 movies with correspondig dates and values. In the end I want to find a correlation between the time trend and
  the values for each movie. The dataset looks something like this:</p>
  
  <pre><code>movie;date;value
  "Movie1";2012-11-23 11:15:00;25.860000
  "Movie1";2012-11-23 11:20:00;25.980000
      .
      .
      .
  "Movie34";2012-11-23 11:25:00;25.950000
  "Sensor34";2012-11-23 11:30:00;26.010000
  "Sensor34";2012-11-23 11:35:00;25.980000
  "Sensor34";2012-11-23 11:40:00;26.010000
  </code></pre>
  
  <p>Now, what I want to do is to change this dataset into something like this:</p>
  
  <pre><code> date;Movie1; ... ;Movie34
   2012-11-23 11:15:00;25.860000;26.010000
       .
       .
       .
   2012-11-21 12:15:00;25.860000;24.010000
  </code></pre>
  
  <p>That is, I want that each movie to have a value for each timestamp. Converting categorical values of movie column into 34 columns. And if a timestamp does not exist perhaps fill it with NA. Is this possible with pandas?</p>