PERF: periodarr_to_dt64arr #35171
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PERF: periodarr_to_dt64arr #35171
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jreback
requested changes
Jul 8, 2020
pandas/_libs/tslibs/period.pyx
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| @@ -946,9 +947,26 @@ def periodarr_to_dt64arr(const int64_t[:] periodarr, int freq): | |||
| int64_t[:] out | |||
| Py_ssize_t i, l | |||
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| l = len(periodarr) | |||
| if freq >= FR_DAY: | |||
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can you add a comment here that you are short-circuiting for perf
pandas/_libs/tslibs/period.pyx
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| out = np.empty(l, dtype='i8') | ||
| l = len(periodarr) |
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can you add a comment on which cases this hits
jreback
added
Performance
jreback
reviewed
Jul 8, 2020
pandas/_libs/tslibs/period.pyx
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| out = np.empty(l, dtype='i8') | ||
| l = len(periodarr) | ||
| out = np.empty(l, dtype="i8") |
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this could be a nogil loop right? (actually couldn't this entire function)?
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this cant be nogil becuase it calls check_dts_bounds, which may raise
As mentioned in the OP, i dont understand why we see slowdowns this big. It should just be one extra |
…rf-periodarr_to_dt64arr
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i think i might be able to fastpath some more cases, will give that a shot |
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OK, i think i got these addressed: We still have some cases that see a 2-3x slowdown, but those are now all length-0 or length-1 cases where the actual time difference is tiny (and makes sense) |
jreback
approved these changes
Jul 9, 2020
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thanks |
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black pandasgit diff upstream/master -u -- "*.py" | flake8 --diffRe-use
ensure_datetime64nsfor the subset of cases where that is applicable. I'm at a loss for why we get such a slowdown for the other cases.