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- Is there a more efficient way to convert a subset of variables to a dataframe? · 3 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue | 
|---|---|---|---|---|---|---|---|---|---|---|---|
| 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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| 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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| 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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