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"""
Period benchmarks that rely only on tslibs. See benchmarks.period for
Period benchmarks that rely on other parts of pandas.
"""
import numpy as np
from pandas._libs.tslibs.period import (
Period,
periodarr_to_dt64arr,
)
from pandas.tseries.frequencies import to_offset
from .tslib import (
_sizes,
_tzs,
tzlocal_obj,
)
try:
from pandas._libs.tslibs.vectorized import dt64arr_to_periodarr
except ImportError:
from pandas._libs.tslibs.period import dt64arr_to_periodarr
class PeriodProperties:
params = (
["M", "min"],
[
"year",
"month",
"day",
"hour",
"minute",
"second",
"is_leap_year",
"quarter",
"qyear",
"week",
"daysinmonth",
"dayofweek",
"dayofyear",
"start_time",
"end_time",
],
)
param_names = ["freq", "attr"]
def setup(self, freq, attr):
self.per = Period("2012-06-01", freq=freq)
def time_property(self, freq, attr):
getattr(self.per, attr)
class PeriodUnaryMethods:
params = ["M", "min"]
param_names = ["freq"]
def setup(self, freq):
self.per = Period("2012-06-01", freq=freq)
if freq == "M":
self.default_fmt = "%Y-%m"
elif freq == "min":
self.default_fmt = "%Y-%m-%d %H:%M"
def time_to_timestamp(self, freq):
self.per.to_timestamp()
def time_now(self, freq):
self.per.now(freq)
def time_asfreq(self, freq):
self.per.asfreq("Y")
def time_str(self, freq):
str(self.per)
def time_repr(self, freq):
repr(self.per)
def time_strftime_default(self, freq):
self.per.strftime(None)
def time_strftime_default_explicit(self, freq):
self.per.strftime(self.default_fmt)
def time_strftime_custom(self, freq):
self.per.strftime("%b. %d, %Y was a %A")
class PeriodConstructor:
params = [["D"], [True, False]]
param_names = ["freq", "is_offset"]
def setup(self, freq, is_offset):
if is_offset:
self.freq = to_offset(freq)
else:
self.freq = freq
def time_period_constructor(self, freq, is_offset):
Period("2012-06-01", freq=freq)
_freq_ints = [
1000,
1011, # Annual - November End
2000,
2011, # Quarterly - November End
3000,
4000,
4006, # Weekly - Saturday End
5000,
6000,
7000,
8000,
9000,
10000,
11000,
12000,
]
class TimePeriodArrToDT64Arr:
params = [
_sizes,
_freq_ints,
]
param_names = ["size", "freq"]
def setup(self, size, freq):
arr = np.arange(10, dtype="i8").repeat(size // 10)
self.i8values = arr
def time_periodarray_to_dt64arr(self, size, freq):
periodarr_to_dt64arr(self.i8values, freq)
class TimeDT64ArrToPeriodArr:
params = [
_sizes,
_freq_ints,
_tzs,
]
param_names = ["size", "freq", "tz"]
def setup(self, size, freq, tz):
if size == 10**6 and tz is tzlocal_obj:
# tzlocal is cumbersomely slow, so skip to keep runtime in check
raise NotImplementedError
# we pick 2**55 because smaller values end up returning
# -1 from npy_datetimestruct_to_datetime with NPY_FR_Y frequency
# this artificially slows down functions since -1 is also the
# error sentinel
arr = np.arange(2**55, 2**55 + 10, dtype="i8").repeat(size // 10)
self.i8values = arr
def time_dt64arr_to_periodarr(self, size, freq, tz):
dt64arr_to_periodarr(self.i8values, freq, tz)
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