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issue 1

  • pd.Grouper support? · 13 ✖

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  • NONE · 13 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
751259037 https://github.com/pydata/xarray/issues/364#issuecomment-751259037 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc1MTI1OTAzNw== stale[bot] 26384082 2020-12-25T14:49:52Z 2020-12-25T14:49:52Z NONE

In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the stale label; otherwise it will be marked as closed automatically

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  pd.Grouper support? 60303760
347735817 https://github.com/pydata/xarray/issues/364#issuecomment-347735817 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDM0NzczNTgxNw== lexual 410907 2017-11-29T02:47:44Z 2017-11-29T02:47:44Z NONE

pd.TimeGrouper is deprecated in latest pandas release, so I imagine this bug should be closed.

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231021167 https://github.com/pydata/xarray/issues/364#issuecomment-231021167 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDIzMTAyMTE2Nw== saulomeirelles 7504461 2016-07-07T08:54:46Z 2016-07-07T08:59:15Z NONE

Thanks, @shoyer !

Here is an example of how I circumvented the problem:

data = np.random.rand(24*5) times = pd.date_range('2000-01-01', periods=24*5, freq='H') foo = xray.DataArray(data, coords=[times], dims=['time']) foo = foo.to_dataset(dim=foo.dims,name='foo')

T = time.mktime( dt.datetime(1970,1,1,12+1,25,12).timetuple() ) # 12.42 hours Tint = [ int( time.mktime( t.timetuple() ) / T ) for t in foo.time.values.astype('datetime64[s]').tolist()] foo2 = xray.DataArray( Tint, coords=foo.time.coords, dims=foo.time.dims) foo.merge(foo2.to_dataset(name='Tint'), inplace=True)

foo_grp = foo.groupby('Tint')

foo_grp.group.plot.line()

In my case, the dataset is quite large then it costed a lot of computational time to merge the new variable Tint.

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  pd.Grouper support? 60303760
228723336 https://github.com/pydata/xarray/issues/364#issuecomment-228723336 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDIyODcyMzMzNg== saulomeirelles 7504461 2016-06-27T11:45:09Z 2016-06-27T11:45:09Z NONE

This is a very useful functionality. I am wondering if I can specify the time window, for example, like ds.groupby(time=pd.TimeGrouper('12.42H')). Is there a way to do that in xarray?

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78239807 https://github.com/pydata/xarray/issues/364#issuecomment-78239807 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc4MjM5ODA3 naught101 167164 2015-03-11T10:38:05Z 2015-03-11T10:38:05Z NONE

Ah, yep, making the dimension using data.coords['timeofday'] = ('time', [np.timedelta64(60 * int(h) + int(m), 'm') for h,m in zip(data['time.hour'], data['time.minute'])]) works. Thanks for all the help :)

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78211171 https://github.com/pydata/xarray/issues/364#issuecomment-78211171 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc4MjExMTcx naught101 167164 2015-03-11T06:17:10Z 2015-03-11T06:17:10Z NONE

Ok, weird. That example works for me, but even if I take a really short slice of my data set, the same thing won't work:

``` In [61]: d = data.sel(time=slice('2002-01-01','2002-01-03')) d

Out[61]:

<xray.Dataset> Dimensions: (time: 143, timeofday: 70128, x: 1, y: 1, z: 1) Coordinates: * x (x) >f8 1.0 * y (y) >f8 1.0 * z (z) >f8 1.0 * time (time) datetime64[ns] 2002-01-01T00:30:00 ... * timeofday (timeofday) timedelta64[ns] 1800000000000 nanoseconds ... Data variables: SWdown (time, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14.58 ... Rainf_qc (time, y, x) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... SWdown_qc (time, y, x) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... Tair (time, z, y, x) float64 282.9 282.9 282.7 282.6 282.4 281.7 281.0 ... Tair_qc (time, y, x) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... LWdown (time, y, x) float64 296.7 297.3 297.3 297.3 297.2 295.9 294.5 ... PSurf_qc (time, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... latitude (y, x) float64 -35.66 Wind (time, z, y, x) float64 2.2 2.188 1.9 2.2 2.5 2.5 2.5 2.25 2.0 2.35 ... LWdown_qc (time, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... Rainf (time, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... Qair_qc (time, y, x) float64 1.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... longitude (y, x) float64 148.2 PSurf (time, y, x) float64 8.783e+04 8.783e+04 8.782e+04 8.781e+04 ... reference_height (y, x) float64 70.0 elevation (y, x) float64 1.2e+03 Qair (time, z, y, x) float64 0.00448 0.004608 0.004692 0.004781 ... Wind_qc (time, y, x) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... Attributes: Production_time: 2012-09-27 12:44:42 Production_source: PALS automated netcdf conversion Contact: palshelp@gmail.com PALS_fluxtower_template_version: 1.0.2 PALS_dataset_name: TumbaFluxnet PALS_dataset_version: 1.4

In [62]: d.groupby('timeofday').mean('time') ```

That last command will not complete - it will run for minutes. Not really sure how to debug that behaviour.

Perhaps it's to do with the long/lat/height variables that really should be coordinates (I'm just using the data as it came, but I can clean that, if necessary)

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  pd.Grouper support? 60303760
78191526 https://github.com/pydata/xarray/issues/364#issuecomment-78191526 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc4MTkxNTI2 naught101 167164 2015-03-11T03:00:03Z 2015-03-11T03:00:03Z NONE

same problem with numpy.timedelta64 too.

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  pd.Grouper support? 60303760
78036587 https://github.com/pydata/xarray/issues/364#issuecomment-78036587 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc4MDM2NTg3 naught101 167164 2015-03-10T11:30:10Z 2015-03-10T11:30:10Z NONE

Dunno if this is related to the ds['time.time'] problem, but I tried creating the daily_cycle using a pandas.Timedelta as the index (timeofday), but it also appeared to just hang indefinitely when doing the data.groupby('timeofday').mean('time') call..

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  pd.Grouper support? 60303760
78008962 https://github.com/pydata/xarray/issues/364#issuecomment-78008962 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc4MDA4OTYy naught101 167164 2015-03-10T07:51:45Z 2015-03-10T07:51:45Z NONE

Nice.

Ok, I have hit a stumbling block, and this is much more of a support request, so feel free to direct me else where, but since we're on the topic, I want to do something like:

start = 2002 n_years = 4 new_data = [] for year in range(start, start + n_years): days = 365 if year%4 else 365 for d in range(days): day_data = mean + annual_cycle.isel(dayofyear=d) + daily_cycle day_data.coords['time'] = datetime.datetime(year,1,1) + datetime.timedelta(day=d, hour=day_data.timeofday.hour, minute=day_data.timeofday.minute) new_data.append(day_data) xray.concat(new_data)

where mean, annual_cycle, and daily_cycle are overall mean, annual cycle at daily resolution, and daily cycle at 30 minute resolution (the latter two bias corrected by subtracting the mean). I'm trying to make a synthetic dataset 4 years long that only includes the mean, seasonal, and daily cycles, but no other variability.

The assignment of day_data['time'] fails because the day_data.timeofday.hour (and .minute) don't work. These are datetime.times, is there an efficient way of converting these to datetime.timedeltas, without first manually taking them out of the DataArray?

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  pd.Grouper support? 60303760
77978458 https://github.com/pydata/xarray/issues/364#issuecomment-77978458 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc3OTc4NDU4 naught101 167164 2015-03-10T01:16:25Z 2015-03-10T01:16:25Z NONE

Ah, cool, thanks for that link, I missed that in the docs.

One thing that would be nice (in both pandas and xray) is a time.timeofday. I can't figure out how to do it with time.hour and time.minute - I need half-hourly resolution averaging. time.time does something in xray, but it seems to never complete, and it doesn't work at all in pandas.

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  pd.Grouper support? 60303760
77824657 https://github.com/pydata/xarray/issues/364#issuecomment-77824657 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc3ODI0NjU3 naught101 167164 2015-03-09T09:46:15Z 2015-03-09T09:46:15Z NONE

Heh, I meant the pandas docs - they don't specify the rule argument format either

time.month and time.hour do exactly what I need. They aren't mentioned in the docs at http://xray.readthedocs.org/en/stable/groupby.html, and I'm not sure how I'd guess that they exist, so perhaps they should be added to that page? It doesn't appear to be something that exists in pandas..

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  pd.Grouper support? 60303760
77810787 https://github.com/pydata/xarray/issues/364#issuecomment-77810787 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc3ODEwNzg3 naught101 167164 2015-03-09T07:34:49Z 2015-03-09T07:34:49Z NONE

Unfortunately I'm not familiar enough with pd.resample and pd.TeimGrouper to know the difference in what they can do. resample looks like it would cover my use-cases, although the docs are pretty limited, and don't actually specify the format of the rule format...

One thing that I would like to be able to do that is not covered by resample, and might be covered by TimeGrouper is to group over month only (not month and year), in order to create a plot of mean seasonal cycle (at monthly resolution), or similarly, a daily cycle at hourly resolution. I haven't figured out if I can do that with TimeGrouper yet though.

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  pd.Grouper support? 60303760
77807590 https://github.com/pydata/xarray/issues/364#issuecomment-77807590 https://api.github.com/repos/pydata/xarray/issues/364 MDEyOklzc3VlQ29tbWVudDc3ODA3NTkw naught101 167164 2015-03-09T06:49:55Z 2015-03-09T06:49:55Z NONE

Looks good to me. I don't know enough to be able to comment on the API question.

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  pd.Grouper support? 60303760

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