title: Max and Min date in pandas groupby
body: <p>I have a dataframe that looks like:</p>
  
  <pre><code>data = {'index': ['2014-06-22 10:46:00', '2014-06-24 19:52:00', '2014-06-25 17:02:00', '2014-06-25 17:55:00', '2014-07-02 11:36:00', '2014-07-06 12:40:00', '2014-07-05 12:46:00', '2014-07-27 15:12:00'],
      'type': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'C'],
      'sum_col': [1, 2, 3, 1, 1, 3, 2, 1]}
  df = pd.DataFrame(data, columns=['index', 'type', 'sum_col'])
  df['index'] = pd.to_datetime(df['index'])
  df = df.set_index('index')
  df['weekofyear'] = df.index.weekofyear
  df['date'] = df.index.date
  df['date'] = pd.to_datetime(df['date'])
  
  
  
                       type sum_col weekofyear   date
  index               
  2014-06-22 10:46:00    A    1       25      2014-06-22
  2014-06-24 19:52:00    B    2       26      2014-06-24
  2014-06-25 17:02:00    C    3       26      2014-06-25
  2014-06-25 17:55:00    A    1       26      2014-06-25
  2014-07-02 11:36:00    B    1       27      2014-07-02
  2014-07-06 12:40:00    C    3       27      2014-07-06
  2014-07-05 12:46:00    A    2       27      2014-07-05
  2014-07-27 15:12:00    C    1       30      2014-07-27
  </code></pre>
  
  <p>I'm looking to groupby the weekofyear, then sum up the sum_col. In addition, I need to find the earliest, and the latest date for the week. The first part is pretty easy:</p>
  
  <pre><code>gb = df.groupby(['type', 'weekofyear'])
  gb['sum_col'].agg({'sum_col' : np.sum})
  </code></pre>
  
  <p>I've tried to find the min/max date with this, but haven't been successful:</p>
  
  <pre><code>gb = df.groupby(['type', 'weekofyear'])
  gb.agg({'sum_col' : np.sum,
          'date' : np.min,
          'date' : np.max})
  </code></pre>
  
  <p>How would one find the earliest/latest date that appears?</p>