title: pandas.Series.interpolate() does nothing. Why?
body: <p>I have a dataframe with DatetimeIndex. This is one of columns:</p>
  
  <pre><code>&gt;&gt;&gt; y.out_brd
  2013-01-01 11:25:00     0.04464286
  2013-01-01 11:30:00            NaN
  2013-01-01 11:35:00            NaN
  2013-01-01 11:40:00    0.005952381
  2013-01-01 11:45:00     0.01785714
  2013-01-01 11:50:00    0.008928571
  Freq: 5T, Name: out_brd, dtype: object
  </code></pre>
  
  <p>When I'm trying to use <code>interpolate()</code> on  function I get absolutly nothing changes:</p>
  
  <pre><code>&gt;&gt;&gt; y.out_brd.interpolate(method='time')
  2013-01-01 11:25:00     0.04464286
  2013-01-01 11:30:00            NaN
  2013-01-01 11:35:00            NaN
  2013-01-01 11:40:00    0.005952381
  2013-01-01 11:45:00     0.01785714
  2013-01-01 11:50:00    0.008928571
  Freq: 5T, Name: out_brd, dtype: object
  </code></pre>
  
  <p>How to make it work?</p>
  
  <p>Update:
  the code for generating such a dataframe.</p>
  
  <pre><code>time_index = pd.date_range(start=datetime(2013, 1, 1, 3),
                         end=datetime(2013, 1, 2, 2, 59),
                         freq='5T')
  grid_columns = [u'in_brd', u'in_alt', u'out_brd', u'out_alt']                           
  
  df = pd.DataFrame(index=time_index, columns=grid_columns)
  </code></pre>
  
  <p>After that I fill cells with some data.</p>
  
  <p>I have dataframe <code>field_data</code> with survey data about boarding and alighting on railroad, and <code>station</code> variable.
  I also have <code>interval_end</code> function defined like this:</p>
  
  <pre><code>interval_end = lambda index, prec_lvl: index.to_datetime() \
                          + timedelta(minutes=prec_lvl - 1,
                                      seconds=59)
  </code></pre>
  
  <p>The code:</p>
  
  <pre><code>for index, row in df.iterrows():
      recs = field_data[(field_data.station_name == station)
                      &amp; (field_data.arrive_time &gt;= index.time())
                      &amp; (field_data.arrive_time &lt;= interval_end(
                                          index, prec_lvl).time())]
      in_recs_num = recs[recs.orientation == u'in'][u'train_number'].count()
      out_recs_num = recs[recs.orientation == u'out'][u'train_number'].count()
  
      if in_recs_num:
          df.loc[index, u'in_brd'] = recs[
                  recs.orientation == u'in'][u'boarding'].sum()    / \
                  (in_recs_num * CAR_CAPACITY)
          df.loc[index, u'in_alt'] = recs[
                  recs.orientation == u'in'][u'alighting'].sum()   / \
                  (in_recs_num * CAR_CAPACITY)
      if out_recs_num:
          df.loc[index, u'out_brd'] = recs[
                  recs.orientation == u'out'][u'boarding'].sum()  / \
                  (out_recs_num * CAR_CAPACITY)
          df.loc[index, u'out_alt'] = recs[
                  recs.orientation == u'out'][u'alighting'].sum() / \
                  (out_recs_num * CAR_CAPACITY)
  </code></pre>