title: Keep the most recent values and drop older rows (pandas)
body: <p>I have a dataframe table below which contains new and old values. I would like to drop all the old values while keeping the new values.</p>
  
  <pre><code>ID    Name     Time    Comment
  0     Foo   12:17:37   Rand
  1     Foo   12:17:37   Rand1
  2     Foo   08:20:00   Rand2
  3     Foo   08:20:00   Rand3
  4     Bar   09:01:00   Rand4
  5     Bar   09:01:00   Rand5
  6     Bar   08:50:50   Rand6
  7     Bar   08:50:00   Rand7
  </code></pre>
  
  <p>As such it should look like this:</p>
  
  <pre><code>ID    Name     Time    Comment
  0     Foo   12:17:37   Rand
  1     Foo   12:17:37   Rand1
  4     Bar   09:01:00   Rand4
  5     Bar   09:01:00   Rand5
  </code></pre>
  
  <p>I tried to use the code below but this removes 1 new and 1 old value.</p>
  
  <pre><code>df[~df[['Time', 'Comment']].duplicated(keep='first')]
  </code></pre>
  
  <p>Can anyone provide a correct solution?</p>