issues: 331668890

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
331668890 MDU6SXNzdWUzMzE2Njg4OTA= 2227 Slow performance of isel 4180033 open 0     28 2018-06-12T16:46:14Z 2023-03-14T18:54:16Z   NONE      

Hi,

I get a very slow performance of Dataset.isel or DataArray.isel in comparison with the native numpy approach. Do you know where this comes from?

python ds = xr.Dataset( { "a": ("time", np.arange(55_000_000)) }, coords={ "time": np.arange(55_000_000) } ) time_filter = ds.time > 50_000

Select some values with DataArray.isel: python %timeit ds.a.isel(time=time_filter) 2.22 s ± 375 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Use the native numpy approach: python %timeit ds.a.values[time_filter] 163 ms ± 12.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

INSTALLED VERSIONS ------------------ commit: None python: 3.6.5.final.0 python-bits: 64 OS: Linux OS-release: 3.16.0-4-amd64 machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.utf8 LOCALE: en_US.UTF-8 xarray: 0.10.4 pandas: 0.23.0 numpy: 1.14.2 scipy: 1.1.0 netCDF4: 1.4.0 h5netcdf: 0.5.1 h5py: 2.8.0 Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: None dask: 0.17.5 distributed: 1.21.8 matplotlib: 2.2.2 cartopy: 0.16.0 seaborn: 0.8.1 setuptools: 39.1.0 pip: 9.0.3 conda: None pytest: 3.5.1 IPython: 6.4.0 sphinx: 1.7.4 
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     13221727 issue

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