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PERF: Speed up Period construction #50149

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merged 12 commits into from
Jan 18, 2023
Merged

PERF: Speed up Period construction #50149

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a2c7f88
PERF: Speed up Period construction
lithomas1 Dec 9, 2022
942034f
Merge branch 'pandas-dev:main' into period-speedup
lithomas1 Dec 9, 2022
4c351ad
Try to fix CI?
lithomas1 Dec 10, 2022
a2a31c6
Avoid hackiness
lithomas1 Dec 12, 2022
5df970c
debug CI
lithomas1 Dec 20, 2022
5e1caf0
Merge branch 'main' of https://github.com/pandas-dev/pandas into peri…
lithomas1 Dec 20, 2022
ec64579
Merge branch 'main' into period-speedup
lithomas1 Dec 20, 2022
29c3e80
Modify condition
lithomas1 Dec 21, 2022
dafa549
Merge branch 'main' into period-speedup
lithomas1 Jan 11, 2023
6b9eb3e
Merge branch 'main' into period-speedup
lithomas1 Jan 16, 2023
f8a85a7
revert whitespace
lithomas1 Jan 16, 2023
c18b3b3
fix tests
lithomas1 Jan 17, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.0.0.rst
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Expand Up @@ -680,6 +680,7 @@ Performance improvements
- Performance improvement in :func:`merge` when not merging on the index - the new index will now be :class:`RangeIndex` instead of :class:`Int64Index` (:issue:`49478`)
- Performance improvement in :meth:`DataFrame.to_dict` and :meth:`Series.to_dict` when using any non-object dtypes (:issue:`46470`)
- Performance improvement in :func:`read_html` when there are multiple tables (:issue:`49929`)
- Performance improvement in :class:`Period` constructor when constructing from a string or integer (:issue:`38312`)

.. ---------------------------------------------------------------------------
.. _whatsnew_200.bug_fixes:
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4 changes: 4 additions & 0 deletions pandas/_libs/tslibs/parsing.pyx
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Expand Up @@ -421,6 +421,10 @@ cdef parse_datetime_string_with_reso(
parsed = datetime(
dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us
)
if out_bestunit == NPY_DATETIMEUNIT.NPY_FR_ns:
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How does this impact the period construction? Feels a bit strange to re-assign this variable

# No picoseconds, so no nanoseconds.
# Have to have seen microseconds, in order to have "seen" nanoseconds
out_bestunit = NPY_DATETIMEUNIT.NPY_FR_us
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i think doing this will mess up Timestamp inference in a follow-up to #49737

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@lithomas1 lithomas1 Dec 12, 2022 •
edited
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Yeah, I only added this block because somehow CI was failing tests only on Linux(maybe a locale problem).
(Issue was that datetime object was returned, when reso was nanosecond, and datetimes don't have nanosecond support).

pandas/pandas/_libs/tslibs/parsing.pyx

Line 413 in 7ef6a71

if dts.ps != 0 or out_local:
seems buggy.

IMO, we should be checking out_bestunit == NPY_DATETIMEUNIT.NPY_FR_ns. don't know if that will break anything, though.

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Ok, I've updated this block now to checking the bestunit.

@jbrockmendel @WillAyd Would this work fine?

reso = {
NPY_DATETIMEUNIT.NPY_FR_Y: "year",
NPY_DATETIMEUNIT.NPY_FR_M: "month",
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15 changes: 7 additions & 8 deletions pandas/_libs/tslibs/period.pyx
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Expand Up @@ -2574,16 +2574,15 @@ class Period(_Period):
value = str(value)
value = value.upper()
dt, reso = parse_time_string(value, freq)
try:
ts = Timestamp(value)
except ValueError:
nanosecond = 0
else:
nanosecond = ts.nanosecond
if nanosecond != 0:
reso = "nanosecond"
if reso == "nanosecond":
nanosecond = dt.nanosecond

if dt is NaT:
ordinal = NPY_NAT
# Doesn't matter what this is, we just need to have it
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Instead of assigning a random value here would it make more sense to just not have it fail later on?

# so that we don't error in block below. We get converted
# to NaT later on anyways
reso = "nanosecond"

if freq is None:
try:
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