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Merged
merged 20 commits into from
Dec 10, 2016
Merged

CLN/PERF: clean-up of the benchmarks #14099

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89cfc25
CLN/PERF: clean-up indexing benchmarks
jorisvandenbossche Jul 13, 2016
b9e6665
Clean-up parser_vb benchmarks
jorisvandenbossche Jul 25, 2016
3e05108
Clean-up packers benchmarks
jorisvandenbossche Jul 25, 2016
f5ccece
Clean-up sql benchmarks
jorisvandenbossche Jul 25, 2016
a12dbfd
Clean-up join_merge benchmarks
jorisvandenbossche Jul 25, 2016
cc23fc9
Clean-up timeseries/timedelta benchmarks
jorisvandenbossche Jul 25, 2016
d1ecbe8
Clean-up string benchmarks
jorisvandenbossche Jul 25, 2016
7aad492
Clean-up merge asof benchmarks
jorisvandenbossche Aug 28, 2016
731f50c
Clean-up algos/attrs/binary_ops benchmarks
jorisvandenbossche Aug 28, 2016
f174d8d
Clean-up categorical benchmarks
jorisvandenbossche Aug 28, 2016
cce95ee
Clean-up constructor benchmarks
jorisvandenbossche Aug 28, 2016
f0fbaed
Clean-up eval + panel benchmarks
jorisvandenbossche Aug 28, 2016
65d720a
Clean-up frame_methods benchmarks
jorisvandenbossche Sep 12, 2016
16104d2
Clean-up gil bencmarks
jorisvandenbossche Oct 24, 2016
f56e26a
Clean-up indexing benchmarks
jorisvandenbossche Oct 25, 2016
1baf3fc
Clean-up inference/miscellaneous benchmarks
jorisvandenbossche Oct 26, 2016
d54f18c
Clean-up timedelta/period/plotting benchmarks
jorisvandenbossche Oct 26, 2016
3826147
Cleanup indexing benchmarks
jorisvandenbossche Oct 28, 2016
fbe8c5e
Clean-up groupby benchmarks
jorisvandenbossche Nov 18, 2016
0748cb0
Clean-up HDFStore benchmarks
jorisvandenbossche Dec 10, 2016
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21 changes: 14 additions & 7 deletions asv_bench/benchmarks/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
from pandas.util import testing as tm


class algorithm(object):
class Algorithms(object):
goal_time = 0.2

def setup(self):
Expand All @@ -24,21 +24,28 @@ def setup(self):
self.arrneg = np.arange(-1000000, 0)
self.arrmixed = np.array([1, -1]).repeat(500000)

def time_int_factorize(self):
# match
self.uniques = tm.makeStringIndex(1000).values
self.all = self.uniques.repeat(10)

def time_factorize_int(self):
self.int.factorize()

def time_float_factorize(self):
def time_factorize_float(self):
self.int.factorize()

def time_int_unique_duplicated(self):
def time_duplicated_int_unique(self):
self.int_unique.duplicated()

def time_int_duplicated(self):
def time_duplicated_int(self):
self.int.duplicated()

def time_float_duplicated(self):
def time_duplicated_float(self):
self.float.duplicated()

def time_match_strings(self):
pd.match(self.all, self.uniques)

def time_add_overflow_pos_scalar(self):
self.checked_add(self.arr, 1)

Expand All @@ -58,7 +65,7 @@ def time_add_overflow_mixed_arr(self):
self.checked_add(self.arr, self.arrmixed)


class hashing(object):
class Hashing(object):
goal_time = 0.2

def setup(self):
Expand Down
23 changes: 16 additions & 7 deletions asv_bench/benchmarks/attrs_caching.py
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Original file line number Diff line number Diff line change
@@ -1,23 +1,32 @@
from .pandas_vb_common import *
from pandas.util.decorators import cache_readonly


class getattr_dataframe_index(object):
class DataFrameAttributes(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(10, 6))
self.cur_index = self.df.index

def time_getattr_dataframe_index(self):
def time_get_index(self):
self.foo = self.df.index

def time_set_index(self):
self.df.index = self.cur_index


class setattr_dataframe_index(object):
class CacheReadonly(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(10, 6))
self.cur_index = self.df.index

def time_setattr_dataframe_index(self):
self.df.index = self.cur_index
class Foo:

@cache_readonly
def prop(self):
return 5
self.obj = Foo()

def time_cache_readonly(self):
self.obj.prop
239 changes: 44 additions & 195 deletions asv_bench/benchmarks/binary_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,193 +2,79 @@
import pandas.computation.expressions as expr


class frame_add(object):
class Ops(object):
goal_time = 0.2

def setup(self):
params = [[True, False], ['default', 1]]
param_names = ['use_numexpr', 'threads']

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

def time_frame_add(self):
(self.df + self.df2)
if threads != 'default':
expr.set_numexpr_threads(threads)
if not use_numexpr:
expr.set_use_numexpr(False)


class frame_add_no_ne(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
expr.set_use_numexpr(False)

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

def teardown(self):
expr.set_use_numexpr(True)
def time_frame_mult(self, use_numexpr, threads):
(self.df * self.df2)

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

class frame_add_st(object):
goal_time = 0.2
def time_frame_comparison(self, use_numexpr, threads):
(self.df > self.df2)

def setup(self):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
expr.set_numexpr_threads(1)

def time_frame_add_st(self):
(self.df + self.df2)

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


class frame_float_div(object):
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))

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

## Division

class frame_float_div_by_zero(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(1000, 1000))
def time_frame_float_div(self):
(self.df // self.df2)

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


class frame_float_floor_by_zero(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(1000, 1000))

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


class frame_float_mod(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(1000, 1000))
self.df2 = DataFrame(np.random.randn(1000, 1000))

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


class frame_int_div_by_zero(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000)))

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

(self.df_int / 0)

class frame_int_mod(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000)))
self.df2 = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000)))
## Modulo

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


class frame_mult(object):
goal_time = 0.2

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

def time_frame_mult(self):
(self.df * self.df2)


class frame_mult_no_ne(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
expr.set_use_numexpr(False)

def time_frame_mult_no_ne(self):
(self.df * self.df2)

def teardown(self):
expr.set_use_numexpr(True)


class frame_mult_st(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
expr.set_numexpr_threads(1)

def time_frame_mult_st(self):
(self.df * self.df2)

def teardown(self):
expr.set_numexpr_threads()


class frame_multi_and(object):
goal_time = 0.2

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

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


class frame_multi_and_no_ne(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
expr.set_use_numexpr(False)

def time_frame_multi_and_no_ne(self):
self.df[((self.df > 0) & (self.df2 > 0))]

def teardown(self):
expr.set_use_numexpr(True)


class frame_multi_and_st(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
expr.set_numexpr_threads(1)

def time_frame_multi_and_st(self):
self.df[((self.df > 0) & (self.df2 > 0))]

def teardown(self):
expr.set_numexpr_threads()
def time_frame_float_mod(self):
(self.df / self.df2)


class series_timestamp_compare(object):
class Timeseries(object):
goal_time = 0.2

def setup(self):
Expand All @@ -197,65 +83,28 @@ def setup(self):
self.s = Series(date_range('20010101', periods=self.N, freq='T'))
self.ts = self.s[self.halfway]

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

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


class timestamp_ops_diff1(object):
goal_time = 0.2
N = 1000000

def setup(self):
self.s = self.create()

def create(self):
return Series(date_range('20010101', periods=self.N, freq='s'))
def time_timestamp_series_compare(self):
(self.ts >= self.s)

def time_timestamp_ops_diff1(self):
self.s.diff()

class timestamp_tz_ops_diff1(timestamp_ops_diff1):
N = 10000

def create(self):
return Series(date_range('20010101', periods=self.N, freq='s', tz='US/Eastern'))

class timestamp_ops_diff2(object):
goal_time = 0.2
N = 1000000

def setup(self):
self.s = self.create()

def create(self):
return Series(date_range('20010101', periods=self.N, freq='s'))
self.s2.diff()

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

class timestamp_tz_ops_diff2(timestamp_ops_diff2):
N = 10000

def create(self):
return Series(date_range('20010101', periods=self.N, freq='s', tz='US/Eastern'))

class timestamp_series_compare(object):
goal_time = 0.2
N = 1000000
class TimeseriesTZ(Timeseries):

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

def create(self):
return Series(date_range('20010101', periods=self.N, freq='T'))

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

class timestamp_tz_series_compare(timestamp_series_compare):
N = 10000

def create(self):
return Series(date_range('20010101', periods=self.N, freq='T', tz='US/Eastern'))
self.s2 = Series(date_range('20010101', periods=self.N, freq='s', tz='US/Eastern'))
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