title: Keeping the latest date when counting uniques in another column
body: <p>I have the following dataframe:</p>
  
  <pre><code>             date                  name
  0      20/06/2014            Allan Watt
  1      20/06/2014            Cindy Mark 
  2      20/06/2014         Luisa Mostert
  3      19/06/2014            Allan Watt
  </code></pre>
  
  <p>I would like to end up with the following dataframe that counts the unique values in 'name' and uses the latest date value. For example:</p>
  
  <pre><code>      latest_date                  name      count
  0      20/06/2014            Allan Watt          2
  1      20/06/2014            Cindy Mark          1 
  2      20/06/2014         Luisa Mostert          1
  </code></pre>
  
  <p>Currently, I am adding the 'count' column by doing:</p>
  
  <pre><code>df = pd.DataFrame({'count': df.groupby(['name']).size()}).reset_index()
  
                     name      count
  0            Allan Watt          2
  1            Cindy Mark          1 
  2         Luisa Mostert          1
  </code></pre>
  
  <p>But this drops the date column off completely. Whereas:</p>
  
  <pre><code>df = pd.DataFrame({'count': df.groupby(['name', 'date']).size()}).reset_index()
  </code></pre>
  
  <p>Obviously, groups by the date as well to leave me with:</p>
  
  <pre><code>      latest_date                  name      count
  0      20/06/2014            Allan Watt          1
  1      20/06/2014            Cindy Mark          1 
  2      20/06/2014         Luisa Mostert          1
  3      19/06/2014            Allan Watt          1
  </code></pre>
  
  <p>What is the most optimal approach to achieve my intended result?</p>