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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
874205134 https://github.com/pydata/xarray/issues/5567#issuecomment-874205134 https://api.github.com/repos/pydata/xarray/issues/5567 MDEyOklzc3VlQ29tbWVudDg3NDIwNTEzNA== andreall 25382032 2021-07-05T15:48:50Z 2021-07-05T15:48:50Z NONE

oh I get it now. Thanks. Indeed it works now when chunking lat and lon from the start.

 
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   quantile to_netcdf loading original data 935818279
873123273 https://github.com/pydata/xarray/issues/5567#issuecomment-873123273 https://api.github.com/repos/pydata/xarray/issues/5567 MDEyOklzc3VlQ29tbWVudDg3MzEyMzI3Mw== andreall 25382032 2021-07-02T16:37:03Z 2021-07-02T16:37:03Z NONE

ds.chunk({'time': -1})

I suspect this is making your entire dataset one big chunk. I would chunk along lat and lon in open_mfdataset first.

But if I am doing ds.quantile(quantiles, dim='time') and assigning it again to ds, wouldn't that erase the big dataset from memory? (sorry for the ignorance). Thanks

 
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   quantile to_netcdf loading original data 935818279
661972749 https://github.com/pydata/xarray/issues/1086#issuecomment-661972749 https://api.github.com/repos/pydata/xarray/issues/1086 MDEyOklzc3VlQ29tbWVudDY2MTk3Mjc0OQ== andreall 25382032 2020-07-21T16:41:52Z 2020-07-21T16:41:52Z NONE

Hi @darothen , Thanks a lot..I hadn't thought of processing each file and then merging. Will give it a try, Thanks,

 
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   Is there a more efficient way to convert a subset of variables to a dataframe? 187608079
661940009 https://github.com/pydata/xarray/issues/1086#issuecomment-661940009 https://api.github.com/repos/pydata/xarray/issues/1086 MDEyOklzc3VlQ29tbWVudDY2MTk0MDAwOQ== andreall 25382032 2020-07-21T15:44:54Z 2020-07-21T15:46:06Z NONE

Hi,

``` import xarray as xr from pathlib import Path

dir_input = Path('.') data_ww3 = xr.open_mfdataset(dir_input.glob('*/' + 'WW3_EUR-11_CCCma-CanESM2_r1i1p1_CLMcom-CCLM4-8-17_v1_6hr_.nc'))

data_ww3 = data_ww3.isel(latitude=74, longitude=18) df_ww3 = data_ww3[['hs', 't02', 't0m1', 't01', 'fp', 'dir', 'spr', 'dp']].to_dataframe() ```

You can download one file here: https://nasgdfa.ugr.es:5001/d/f/566168344466602780 (3.5 GB). I did a profiler when opening 2 .nc files an it said the to_dataframe() call was the one taking most of the time.

I'm just wondering if there's a way to reduce computing time. I need to open 95 files and it takes about 1.5 hour.

Thanks, 

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   Is there a more efficient way to convert a subset of variables to a dataframe? 187608079
661775197 https://github.com/pydata/xarray/issues/1086#issuecomment-661775197 https://api.github.com/repos/pydata/xarray/issues/1086 MDEyOklzc3VlQ29tbWVudDY2MTc3NTE5Nw== andreall 25382032 2020-07-21T10:29:48Z 2020-07-21T10:29:48Z NONE

I am running into the same problem, this might be a long shot but @naught101 , do you remember if you managed to convert to dataframe in a more efficient way? Thanks,

 
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   Is there a more efficient way to convert a subset of variables to a dataframe? 187608079

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