issue_comments: 553832745

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https://github.com/pydata/xarray/issues/3514#issuecomment-553832745 https://api.github.com/repos/pydata/xarray/issues/3514 553832745 MDEyOklzc3VlQ29tbWVudDU1MzgzMjc0NQ== 6213168 2019-11-14T10:47:43Z 2019-11-14T10:47:43Z MEMBER

I prefer pandas.utils.cache_readonly to the Python 3.8 cachedproperty. Besides not needing a dict, it is also written in cython, which should make a substantial difference.

max-sixty could you post your benchmark where you measure 150us? I tried caching that property with @cache_readonly and I only get a boost of 7us.

```python import xarray ds = xarray.Dataset({'d': ('x', [1, 2]), 'x': [10, 20]}) ds ds.to_netcdf("foo.nc") ds.close() ds = xarray.open_dataset("foo.nc")

%timeit ds.isel(x=[0]) ` With@property: 187us With@cache_readonly``: 180us

 
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