title: How to efficiently resample a DatetimeIndex
body: <p>Pandas has a <code>resample</code> method on a series/dataframe but there seems no way to resample a <code>DatetimeIndex</code> on its own?</p>
  
  <p>Concretely, I have a daily <code>Datetimeindex</code> with possibly missing dates and I want to resample it at an hourly freq but only including days which are in the original daily index.</p>
  
  <p>Is there a better way than my attempt below?</p>
  
  <pre class="lang-python prettyprint-override"><code>In [56]: daily_index = pd.period_range('01-Jan-2017', '31-Jan-2017', freq='B').asfreq('D')
  
  In [57]: daily_index
  Out[57]: 
  PeriodIndex(['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05',
               '2017-01-06', '2017-01-09', '2017-01-10', '2017-01-11',
               '2017-01-12', '2017-01-13', '2017-01-16', '2017-01-17',
               '2017-01-18', '2017-01-19', '2017-01-20', '2017-01-23',
               '2017-01-24', '2017-01-25', '2017-01-26', '2017-01-27',
               '2017-01-30', '2017-01-31'],
              dtype='int64', freq='D')
  
  In [58]: daily_index.shape
  Out[58]: (22,)
  
  In [59]: hourly_index = pd.DatetimeIndex([]).union_many(
      ...:     pd.date_range(day.to_timestamp('H','S'), day.to_timestamp('H','E'), freq='H')
      ...:     for day in daily_index
      ...: )
  
  In [60]: hourly_index
  Out[60]: 
  DatetimeIndex(['2017-01-02 00:00:00', '2017-01-02 01:00:00',
                 '2017-01-02 02:00:00', '2017-01-02 03:00:00',
                 '2017-01-02 04:00:00', '2017-01-02 05:00:00',
                 '2017-01-02 06:00:00', '2017-01-02 07:00:00',
                 '2017-01-02 08:00:00', '2017-01-02 09:00:00',
                 ...
                 '2017-01-31 14:00:00', '2017-01-31 15:00:00',
                 '2017-01-31 16:00:00', '2017-01-31 17:00:00',
                 '2017-01-31 18:00:00', '2017-01-31 19:00:00',
                 '2017-01-31 20:00:00', '2017-01-31 21:00:00',
                 '2017-01-31 22:00:00', '2017-01-31 23:00:00'],
                dtype='datetime64[ns]', length=528, freq=None)
  
  In [61]: 22*24
  Out[61]: 528
  
  In [62]: %%timeit
      ...: hourly_index = pd.DatetimeIndex([]).union_many(
      ...:     pd.date_range(day.to_timestamp('H','S'), day.to_timestamp('H','E'), freq='H')
      ...:     for day in daily_index
      ...: )
  100 loops, best of 3: 13.7 ms per loop
  </code></pre>
  
  <p><strong>UPDATE:</strong></p>
  
  <p>I went with a slight variation of @NTAWolf's answer which has similar performance but won't reorder the input dates in case they're not sorted</p>
  
  <pre class="lang-python prettyprint-override"><code>def resample_index(index, freq):
      """Resamples each day in the daily `index` to the specified `freq`.
  
      Parameters
      ----------
      index : pd.DatetimeIndex
          The daily-frequency index to resample
      freq : str
          A pandas frequency string which should be higher than daily
  
      Returns
      -------
      pd.DatetimeIndex
          The resampled index
  
      """
      assert isinstance(index, pd.DatetimeIndex)
      start_date = index.min()
      end_date = index.max() + pd.DateOffset(days=1)
      resampled_index = pd.date_range(start_date, end_date, freq=freq)[:-1]
      series = pd.Series(resampled_index, resampled_index.floor('D'))
      return pd.DatetimeIndex(series.loc[index].values)
  </code></pre>
  
  <pre class="lang-python prettyprint-override"><code>In [184]: %%timeit
       ...: hourly_index3 = pd.date_range(daily_index.start_time.min(), 
       ...:                               daily_index.end_time.max() + 1, 
       ...:                               normalize=True, freq='H')
       ...: hourly_index3 = hourly_index3[hourly_index3.floor('D').isin(daily_index.start_time)]
  100 loops, best of 3: 2.97 ms per loop
  
  In [185]: %timeit resample_index(daily_index.to_timestamp('D','S'), freq='H')
  100 loops, best of 3: 2.93 ms per loop
  </code></pre>