Skip to content

CLN/ASV clean-up frame stat ops benchmarks #17205

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Aug 10, 2017
Merged

CLN/ASV clean-up frame stat ops benchmarks #17205

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
100 changes: 22 additions & 78 deletions asv_bench/benchmarks/stat_ops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,92 +1,36 @@
from .pandas_vb_common import *


class stat_ops_frame_mean_float_axis_0(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(100000, 4))
self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape))

def time_stat_ops_frame_mean_float_axis_0(self):
self.df.mean()


class stat_ops_frame_mean_float_axis_1(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(100000, 4))
self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape))

def time_stat_ops_frame_mean_float_axis_1(self):
self.df.mean(1)


class stat_ops_frame_mean_int_axis_0(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(100000, 4))
self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape))

def time_stat_ops_frame_mean_int_axis_0(self):
self.dfi.mean()


class stat_ops_frame_mean_int_axis_1(object):
goal_time = 0.2
def _set_use_bottleneck_False():
try:
pd.options.compute.use_bottleneck = False
except:
from pandas.core import nanops
nanops._USE_BOTTLENECK = False

def setup(self):
self.df = DataFrame(np.random.randn(100000, 4))
self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape))

def time_stat_ops_frame_mean_int_axis_1(self):
self.dfi.mean(1)


class stat_ops_frame_sum_float_axis_0(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(100000, 4))
self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape))

def time_stat_ops_frame_sum_float_axis_0(self):
self.df.sum()


class stat_ops_frame_sum_float_axis_1(object):
class FrameOps(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(100000, 4))
self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape))
param_names = ['op', 'use_bottleneck', 'dtype', 'axis']
params = [['mean', 'sum', 'median'],
[True, False],
['float', 'int'],
[0, 1]]

def time_stat_ops_frame_sum_float_axis_1(self):
self.df.sum(1)
def setup(self, op, use_bottleneck, dtype, axis):
if dtype == 'float':
self.df = DataFrame(np.random.randn(100000, 4))
elif dtype == 'int':
self.df = DataFrame(np.random.randint(1000, size=(100000, 4)))

if not use_bottleneck:
_set_use_bottleneck_False()

class stat_ops_frame_sum_int_axis_0(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(100000, 4))
self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape))

def time_stat_ops_frame_sum_int_axis_0(self):
self.dfi.sum()


class stat_ops_frame_sum_int_axis_1(object):
goal_time = 0.2

def setup(self):
self.df = DataFrame(np.random.randn(100000, 4))
self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape))
self.func = getattr(self.df, op)

def time_stat_ops_frame_sum_int_axis_1(self):
self.dfi.sum(1)
def time_op(self, op, use_bottleneck, dtype, axis):
self.func(axis=axis)


class stat_ops_level_frame_sum(object):
Expand Down