issue_comments: 1410253782

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https://github.com/pydata/xarray/pull/7494#issuecomment-1410253782 https://api.github.com/repos/pydata/xarray/issues/7494 1410253782 IC_kwDOAMm_X85UDsPW 5179430 2023-01-31T12:22:02Z 2023-01-31T12:26:37Z CONTRIBUTOR

Thanks for the PR. However, does that actually make a difference? To me it looks like _contains_cftime_datetimes also only considers one element of the array.

https://github.com/pydata/xarray/blob/b4515582ffc8b7f63632bfccd109d19889d00384/xarray/core/common.py#L1779-L1780

This isn't actually the line of code that's causing the performance bottleneck, it's the access to var.data in the function call that is actually problematic as I explained in the issue thread. You can verify this yourself running this simple example before and after applying the changes in this PR:

```python import numpy as np import xarray as xr

str_array = np.arange(100000000).astype(str) ds = xr.DataArray(dims=('x',), data=str_array).to_dataset(name='str_array') ds = ds.chunk(x=10000) ds['str_array'] = ds.str_array.astype('O') # Needs to actually be object dtype to show the problem ds.to_zarr('str_array.zarr')

%time xr.open_zarr('str_array.zarr') ```

 
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