I've personally seen a lot of what looks like memory reuse in numpy and related libraries. I don't think any of this happens explicitly but have never investigated. I would have some expectation that if memory was not being released as expected, that opening and closing the dataset in a loop would increase memory usage, it didn't on the recent library versions I have.

```python Start: 89.71875 MiB Before opening file: 90.203125 MiB After opening file: 96.6875 MiB Filename: test.py

Line # Mem usage Increment Occurrences Line Contents

 6     90.2 MiB     90.2 MiB           1   @profile
 7                                         def main():
 8     90.2 MiB      0.0 MiB           1       path = 'ECMWF_ERA-40_subset.nc'
 9     90.2 MiB      0.0 MiB           1       print(f"Before opening file: {psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2} MiB")
10     96.7 MiB     -0.1 MiB        1001       for i in range(1000):
11     96.7 MiB      6.4 MiB        1000           with xr.open_dataset(path) as ds:
12     96.7 MiB     -0.1 MiB        1000             ...
13     96.7 MiB      0.0 MiB           1       print(f"After opening file: {psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2} MiB")

End: 96.6875 MiB ```