python xarray resample variables only on specific dates

Question

I have a Xarray dataset with irregular values for daily data. Some times there are two values for one day sometimes there is a gap for several days.

[Timestamp('2015-04-01 00:00:00'),
 Timestamp('2015-04-01 00:00:00'),
 Timestamp('2015-04-03 00:00:00'),
 Timestamp('2015-04-03 00:00:00'),
 Timestamp('2015-04-05 00:00:00'),
 Timestamp('2015-04-06 00:00:00'),
 Timestamp('2015-04-06 00:00:00')]

If I apply resample()

model.resample(time='1D').mean()

I end up with

[Timestamp('2015-04-01 00:00:00'),
 Timestamp('2015-04-02 00:00:00'),
 Timestamp('2015-04-03 00:00:00'),
 Timestamp('2015-04-04 00:00:00'),
 Timestamp('2015-04-05 00:00:00'),
 Timestamp('2015-04-06 00:00:00'),
 Timestamp('2015-04-07 00:00:00')]

But I am looking for resample the data like this

[Timestamp('2015-04-01 00:00:00'),
 Timestamp('2015-04-03 00:00:00'),
 Timestamp('2015-04-05 00:00:00'),
 Timestamp('2015-04-06 00:00:00')]

What options do I have to get the .mean() of values on equal days without adding new times to the model? I try to reproduce the problem in a small sample:

value_1 = np.arange(0,7,1)
times = np.array(['2015-04-01', '2015-04-01', '2018-01-03', '2018-01-03', '2018-01-05', '2018-01-05', '2018-01-06'], dtype='datetime64')

time_ = xr.Dataset(
        data_vars={'value':    (('time'), value_1)},
        coords={'time': times})

time_resample = time_.resample(time='1D').mean().sel(time=slice('2015-04-01', '2015-04-06'))

print(time_.time, time_resample.time)


<xarray.DataArray 'time' (time: 7)>
array(['2015-04-01T00:00:00.000000000', '2015-04-01T00:00:00.000000000',
       '2018-01-03T00:00:00.000000000', '2018-01-03T00:00:00.000000000',
       '2018-01-05T00:00:00.000000000', '2018-01-05T00:00:00.000000000',
       '2018-01-06T00:00:00.000000000'], dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 2015-04-01 2015-04-01 ... 2018-01-06 <xarray.DataArray 'time' (time: 6)>
array(['2015-04-01T00:00:00.000000000', '2015-04-02T00:00:00.000000000',
       '2015-04-03T00:00:00.000000000', '2015-04-04T00:00:00.000000000',
       '2015-04-05T00:00:00.000000000', '2015-04-06T00:00:00.000000000'],
      dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 2015-04-01 2015-04-02 ... 2015-04-06

If you solved your issue you can answer your question and accept it (or any other answer). It is a better way than editing the solution inside the question — Tomerikoo
– Tomerikoo, Commented Jan 5, 2020 at 22:40

dl.meteo · Accepted Answer · 2020-01-05 17:26:46Z

1

You have to group by time and apply the function mean.

time_groupby = time_.value.groupby('time').mean()

xarray is quite similar to pandas on that point.

answered Jan 5, 2020 at 17:26

dl.meteo

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