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- simple groupby_bins 10x slower than numpy · 3 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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| 1176918900 | https://github.com/pydata/xarray/issues/6758#issuecomment-1176918900 | https://api.github.com/repos/pydata/xarray/issues/6758 | IC_kwDOAMm_X85GJlt0 | dcherian 2448579 | 2022-07-07T01:03:17Z | 2022-07-07T01:03:17Z | MEMBER | {
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simple groupby_bins 10x slower than numpy 1295939038 | ||
| 1176730713 | https://github.com/pydata/xarray/issues/6758#issuecomment-1176730713 | https://api.github.com/repos/pydata/xarray/issues/6758 | IC_kwDOAMm_X85GI3xZ | dcherian 2448579 | 2022-07-06T20:54:23Z | 2022-07-06T20:54:23Z | MEMBER | Yes that's right. For this simple problem you could combine
And then wrap this using apply_ufunc. See https://github.com/ml31415/numpy-groupies/blob/412be938dcdfd74c6d673dd29012d18dc25dc94f/numpy_groupies/aggregate_numpy.py#L8-L28 for inspiration. |
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simple groupby_bins 10x slower than numpy 1295939038 | |
| 1176441916 | https://github.com/pydata/xarray/issues/6758#issuecomment-1176441916 | https://api.github.com/repos/pydata/xarray/issues/6758 | IC_kwDOAMm_X85GHxQ8 | dcherian 2448579 | 2022-07-06T16:40:47Z | 2022-07-06T16:40:47Z | MEMBER | On xarray main with flox installed: ``` python import numpy as np import xarray as xr display(xr.version) N = 3728 ds = xr.Dataset() ds["latitude"] = ("x", 0 + 20 * np.random.standard_normal(N)) ds["data"] = ("x", 0 + 100 * np.random.standard_normal(N)) %timeit ds.groupby_bins("latitude", np.arange(-40, 40, 0.1)).sum() ``` 50.3 ms ± 203 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) You could try it on our pre-release (https://docs.xarray.dev/en/latest/whats-new.html#v2022-06-0rc0-9-june-2022) or use xhistogram which should be faster even. |
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simple groupby_bins 10x slower than numpy 1295939038 |
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