issue_comments: 525086439
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/3267#issuecomment-525086439 | https://api.github.com/repos/pydata/xarray/issues/3267 | 525086439 | MDEyOklzc3VlQ29tbWVudDUyNTA4NjQzOQ== | 5635139 | 2019-08-27T00:36:29Z | 2019-08-27T00:36:29Z | MEMBER | Thanks for the clear issue @aspitarl I tried on master - it looks like it's back to the old timings. Do you want to confirm? ```python In [1]: ...: ...: import numpy as np ...: import xarray as xr ...: import pandas as pd ...: import time ...: ...: size = 1000000 ...: data = np.random.random(size) ...: times = pd.date_range('2019-01-01', periods=size, freq='ms') ...: da = xr.DataArray(data, dims=['time'], coords={'time': times}) ...: ...: start = time.time() ...: ...: da.resample(time='s').mean() ...: ...: print('Elapsed time: ' + str(time.time() - start)) ...: print('xarray version: ' + str(xr.version)) Elapsed time: 0.19948101043701172 xarray version: 0.12.3+74.ge3b3bed2 ``` |
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