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PERF: Speed up Period construction #50149

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Merged
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 @@ -768,6 +768,7 @@ 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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3 changes: 2 additions & 1 deletion pandas/_libs/tslibs/parsing.pyx
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Expand Up @@ -386,7 +386,7 @@ cdef parse_datetime_string_with_reso(
&out_tzoffset, False
)
if not string_to_dts_failed:
if dts.ps != 0 or out_local:
if out_bestunit == NPY_DATETIMEUNIT.NPY_FR_ns or out_local:
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if out_bestunit == NPY_DATETIMEUNIT.NPY_FR_ns is handled here, should it be removed from the dict on L399-L409?

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No, both blocks go through the dict to get the string representation of the reso.

This is just to force the returned datetime object to be Timestamp, which has a nanosecond attribute.

# TODO: the not-out_local case we could do without Timestamp;
# avoid circular import
from pandas import Timestamp
Expand All @@ -395,6 +395,7 @@ cdef parse_datetime_string_with_reso(
parsed = datetime(
dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us
)

reso = {
NPY_DATETIMEUNIT.NPY_FR_Y: "year",
NPY_DATETIMEUNIT.NPY_FR_M: "month",
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14 changes: 5 additions & 9 deletions pandas/_libs/tslibs/period.pyx
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Expand Up @@ -2590,18 +2590,14 @@ 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

if freq is None:
if freq is None and ordinal != NPY_NAT:
# Skip NaT, since it doesn't have a resolution
try:
freq = attrname_to_abbrevs[reso]
except KeyError:
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