title: Pandas: Transpose a list in column into rows
body: <p>So I have pandas dataframe df_dates as below. </p>
  
  <pre><code>   PERSON_ID   MIN_DATE   MAX_DATE
  0  000099-48 2016-02-01 2017-03-20
  1     000184 2016-02-05 2017-01-19
  2  000461-48 2016-03-07 2017-03-20
  3  000791-48 2016-02-01 2017-03-07
  4  000986-48 2016-02-01 2017-03-17
  5     001617 2016-02-01 2017-02-20
  6  001768-48 2016-02-01 2017-03-20
  7     001937 2016-02-01 2017-03-17
  8  002223-48 2016-02-04 2017-03-16
  9  002481-48 2016-02-05 2017-03-17
  </code></pre>
  
  <p>I am trying to add all dates between the Min and Max as row each Person_ID. Here is what tried.</p>
  
  <pre><code>df_dates.groupby('PERSON_ID').apply(lambda x: pd.date_range(x['MIN_DATE'].values[0], x['MAX_DATE'].values[0]))
  </code></pre>
  
  <p>But what I get with this is, is there any way to transpose that series into rows for each Person_ID? or any other better way of doing it?</p>
  
  <pre><code>PERSON_ID
  0-L2ID        DatetimeIndex(['2016-08-05', '2016-08-06', '20...
  0-LlID        DatetimeIndex(['2016-02-03', '2016-02-04', '20...
  000099-48     DatetimeIndex(['2016-02-01', '2016-02-02', '20...
  000184        DatetimeIndex(['2016-02-05', '2016-02-06', '20...
  000276        DatetimeIndex(['2016-02-01', '2016-02-02', '20...
  000461-48     DatetimeIndex(['2016-03-07', '2016-03-08', '20...
  000493-48     DatetimeIndex(['2016-02-01', '2016-02-02', '20...
  000615-48     DatetimeIndex(['2016-02-02', '2016-02-03', '20...
  000791-48     DatetimeIndex(['2016-02-01', '2016-02-02', '20...
  000986-48     DatetimeIndex(['2016-02-01', '2016-02-02', '20...
  dtype: object
  </code></pre>
  
  <p>Here is what I am trying achieve:</p>
  
  <pre><code>PERSON_ID   Date
  000099-48   2/1/2016
  000099-48   2/2/2016
  000099-48   2/3/2016
  000099-48   2/4/2016
  :
  :
  000099-48   3/18/2016
  000099-48   3/19/2016
  000099-48   3/20/2016
  000184  2/5/2016
  000184  2/6/2016
  000184  2/7/2016
  :
  :
  000184  1/17/2017
  000184  1/18/2017
  000184  1/19/2017
  </code></pre>