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Merged
merged 3 commits into from
Nov 25, 2017
Merged

CLN: ASV binary ops benchmark #18444

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a8ed36e
CLN: ASV binary ops benchmark
mroeschke Nov 23, 2017
f9c1c71
Add param_names
mroeschke Nov 24, 2017
371138a
timezone -> tz
mroeschke Nov 25, 2017
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91 changes: 44 additions & 47 deletions asv_bench/benchmarks/binary_ops.py
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Original file line number Diff line number Diff line change
@@ -1,17 +1,20 @@
from .pandas_vb_common import *
import numpy as np
from pandas import DataFrame, Series, date_range
try:
import pandas.core.computation.expressions as expr
except ImportError:
import pandas.computation.expressions as expr


class Ops(object):

goal_time = 0.2

params = [[True, False], ['default', 1]]
param_names = ['use_numexpr', 'threads']

def setup(self, use_numexpr, threads):
np.random.seed(1234)
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))

Expand All @@ -20,94 +23,88 @@ def setup(self, use_numexpr, threads):
if not use_numexpr:
expr.set_use_numexpr(False)


def time_frame_add(self, use_numexpr, threads):
(self.df + self.df2)
self.df + self.df2

def time_frame_mult(self, use_numexpr, threads):
(self.df * self.df2)
self.df * self.df2

def time_frame_multi_and(self, use_numexpr, threads):
self.df[((self.df > 0) & (self.df2 > 0))]
self.df[(self.df > 0) & (self.df2 > 0)]

def time_frame_comparison(self, use_numexpr, threads):
(self.df > self.df2)
self.df > self.df2

def teardown(self, use_numexpr, threads):
expr.set_use_numexpr(True)
expr.set_numexpr_threads()


class Ops2(object):

goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(1000, 1000))
self.df2 = DataFrame(np.random.randn(1000, 1000))
N = 10**3
np.random.seed(1234)
self.df = DataFrame(np.random.randn(N, N))
self.df2 = DataFrame(np.random.randn(N, N))

self.df_int = DataFrame(
np.random.random_integers(np.iinfo(np.int16).min,
np.iinfo(np.int16).max,
size=(1000, 1000)))
self.df2_int = DataFrame(
np.random.random_integers(np.iinfo(np.int16).min,
np.iinfo(np.int16).max,
size=(1000, 1000)))
self.df_int = DataFrame(np.random.randint(np.iinfo(np.int16).min,
np.iinfo(np.int16).max,
size=(N, N)))
self.df2_int = DataFrame(np.random.randint(np.iinfo(np.int16).min,
np.iinfo(np.int16).max,
size=(N, N)))

## Division
# Division

def time_frame_float_div(self):
(self.df // self.df2)
self.df // self.df2

def time_frame_float_div_by_zero(self):
(self.df / 0)
self.df / 0

def time_frame_float_floor_by_zero(self):
(self.df // 0)
self.df // 0

def time_frame_int_div_by_zero(self):
(self.df_int / 0)
self.df_int / 0

## Modulo
# Modulo

def time_frame_int_mod(self):
(self.df / self.df2)
self.df_int % self.df2_int

def time_frame_float_mod(self):
(self.df / self.df2)
self.df % self.df2


class Timeseries(object):

goal_time = 0.2

def setup(self):
self.N = 1000000
params = [None, 'US/Eastern']
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http://asv.readthedocs.io/en/latest/writing_benchmarks.html

we can add params_names (here) and elsewhere (just for readibility)

param_names = ['tz']

def setup(self, tz):
self.N = 10**6
self.halfway = ((self.N // 2) - 1)
self.s = Series(date_range('20010101', periods=self.N, freq='T'))
self.s = Series(date_range('20010101', periods=self.N, freq='T',
tz=tz))
self.ts = self.s[self.halfway]

self.s2 = Series(date_range('20010101', periods=self.N, freq='s'))
self.s2 = Series(date_range('20010101', periods=self.N, freq='s',
tz=tz))

def time_series_timestamp_compare(self):
(self.s <= self.ts)
def time_series_timestamp_compare(self, tz):
self.s <= self.ts

def time_timestamp_series_compare(self):
(self.ts >= self.s)
def time_timestamp_series_compare(self, tz):
self.ts >= self.s

def time_timestamp_ops_diff1(self):
def time_timestamp_ops_diff(self, tz):
self.s2.diff()

def time_timestamp_ops_diff2(self):
(self.s - self.s.shift())



class TimeseriesTZ(Timeseries):

def setup(self):
self.N = 1000000
self.halfway = ((self.N // 2) - 1)
self.s = Series(date_range('20010101', periods=self.N, freq='T', tz='US/Eastern'))
self.ts = self.s[self.halfway]

self.s2 = Series(date_range('20010101', periods=self.N, freq='s', tz='US/Eastern'))
def time_timestamp_ops_diff_with_shift(self, tz):
self.s - self.s.shift()