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dti = pd.date_range("2016-01-01", periods=10_000)
dta = dti._data

In [4]: %timeit dta.is_normalized
354 µs ± 3.62 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)   # <- main
122 µs ± 2.71 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)  # <- PR

In [5]: %timeit dta.resolution
493 µs ± 4.86 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)  # <- main
258 µs ± 10.3 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)  # <- PR

@mroeschke mroeschke added Datetime Datetime data dtype Performance Memory or execution speed performance labels Dec 27, 2023
@mroeschke mroeschke added this to the 3.0 milestone Dec 27, 2023
@mroeschke mroeschke merged commit 0643a18 into pandas-dev:main Dec 27, 2023
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Thanks @jbrockmendel

@jbrockmendel jbrockmendel deleted the perf-resolution branch December 27, 2023 23:43
cbpygit pushed a commit to cbpygit/pandas that referenced this pull request Jan 2, 2024
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