issue_comments: 327884467
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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/1560#issuecomment-327884467 | https://api.github.com/repos/pydata/xarray/issues/1560 | 327884467 | MDEyOklzc3VlQ29tbWVudDMyNzg4NDQ2Nw== | 1217238 | 2017-09-07T18:27:27Z | 2017-09-07T18:27:27Z | MEMBER | Actually, the timings above were with pandas 0.19. It's still somewhat slow using the dev version of pandas, but it's more like 10x slower rather than 100x slower: ``` In [4]: idx1 = pd.MultiIndex.from_product([np.arange(1000), np.arange(1000)]) ...: idx2 = pd.MultiIndex.from_product([np.arange(1000), np.arange(1000)]) ...: %time idx1.get_indexer(idx2) ...: CPU times: user 215 ms, sys: 81.8 ms, total: 297 ms Wall time: 319 ms Out[4]: array([ 0, 1, 2, ..., 999997, 999998, 999999]) In [5]: idx1 = pd.MultiIndex.from_product([np.arange(1000), np.arange(1000)]) ...: idx2 = pd.MultiIndex.from_product([np.arange(1000), np.arange(1000)]) ...: %time idx1.equals(idx2) ...: CPU times: user 19.8 ms, sys: 9.29 ms, total: 29.1 ms Wall time: 32.1 ms Out[5]: True ``` |
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