How to efficiently resample a DatetimeIndex
<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>