An `asfreq` method without `resample`, and clarify or improve resample().asfreq() behavior for down-sampling

MCVE Code Sample

# Your code here
>>> import numpy as np
>>> import xarray as xr
>>> import pandas as pd

>>> data = np.random.random(300)

# Make a time grid that doesn't start exactly on the hour.
>>> time = pd.date_range('2019-01-01', periods=300, freq='T') + pd.Timedelta('3T')
>>> time
DatetimeIndex(['2019-01-01 00:03:00', '2019-01-01 00:04:00',
               '2019-01-01 00:05:00', '2019-01-01 00:06:00',
               '2019-01-01 00:07:00', '2019-01-01 00:08:00',
               '2019-01-01 00:09:00', '2019-01-01 00:10:00',
               '2019-01-01 00:11:00', '2019-01-01 00:12:00',
               ...
               '2019-01-01 04:53:00', '2019-01-01 04:54:00',
               '2019-01-01 04:55:00', '2019-01-01 04:56:00',
               '2019-01-01 04:57:00', '2019-01-01 04:58:00',
               '2019-01-01 04:59:00', '2019-01-01 05:00:00',
               '2019-01-01 05:01:00', '2019-01-01 05:02:00'],
              dtype='datetime64[ns]', length=300, freq='T')

>>> da = xr.DataArray(data, dims=['time'], coords={'time': time})
>>> resampled = da.resample(time='H').asfreq()
>>> resampled
<xarray.DataArray (time: 6)>
array([0.478601, 0.488425, 0.496322, 0.479256, 0.523395, 0.201718])
Coordinates:
  * time     (time) datetime64[ns] 2019-01-01 ... 2019-01-01T05:00:00

# The value is actually the mean over the time window, eg. the third value is:
>>> da.loc['2019-01-01T02:00:00':'2019-01-01T02:59:00'].mean()
<xarray.DataArray ()>
array(0.496322)

Expected Output

Docs say this:

Return values of original object at the new up-sampling frequency; 
essentially a re-index with new times set to NaN.

I suppose this doc is not technically wrong, since upon careful reading, I realize it does not define a behavior for down-sampling. But it's easy to: (1) assume the same behavior (reindexing) for down-sampling and up-sampling and/or (2) expect behavior similar to df.asfreq() in pandas.

Problem Description

I would argue for an asfreq method without resampling that matches the pandas behavior, which AFAIK, is to reindex starting at the first timestamp, at the specified interval.

>>> df = pd.DataFrame(da, index=time)
>>> df.asfreq('H')
                            0
2019-01-01 00:03:00  0.065304
2019-01-01 01:03:00  0.325814
2019-01-01 02:03:00  0.841201
2019-01-01 03:03:00  0.610266
2019-01-01 04:03:00  0.613906

This can currently easily be achieved, so it's not a blocker.

>>> da.reindex(time=pd.date_range(da.time[0].values, da.time[-1].values, freq='H'))
<xarray.DataArray (time: 5)>
array([0.065304, 0.325814, 0.841201, 0.610266, 0.613906])
Coordinates:
  * time     (time) datetime64[ns] 2019-01-01T00:03:00 ... 2019-01-01T04:03:00

Why I argue for asfreq functionality outside of resampling is that asfreq(freq) in pandas is purely a reindex, compared to eg resample(freq).first() which would give you a different time index.

Output of xr.show_versions()

Still on python27, show_versions actually throws an exception, because some HDF5 library doesn't have a magic property. I don't think this detail is relevant here though.

Start with the resample(time='H').asfreq() behavior shown in the MCVE and compare it with pandas DataFrame.asfreq() and the suggested reindex approach. Define whether a standalone asfreq method, clarified down-sampling semantics, or both are needed; done means the chosen behavior is documented and supported by matching examples or tests.