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ENH: gb.is_monotonic_increasing pandas-dev#17015 rebase to master
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@No-Stream
No-Stream committed Dec 15, 2017
2 parents e5a90fa + 0303d0d commit 1a15209
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Showing 54 changed files with 1,438 additions and 1,730 deletions.
42 changes: 29 additions & 13 deletions asv_bench/benchmarks/gil.py
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Expand Up @@ -180,19 +180,35 @@ def setup(self, method):
raise NotImplementedError
win = 100
arr = np.random.rand(100000)
rolling = {'rolling_median': rolling_median,
'rolling_mean': rolling_mean,
'rolling_min': rolling_min,
'rolling_max': rolling_max,
'rolling_var': rolling_var,
'rolling_skew': rolling_skew,
'rolling_kurt': rolling_kurt,
'rolling_std': rolling_std}

@test_parallel(num_threads=2)
def parallel_rolling():
rolling[method](arr, win)
self.parallel_rolling = parallel_rolling
if hasattr(DataFrame, 'rolling'):
rolling = {'rolling_median': 'median',
'rolling_mean': 'mean',
'rolling_min': 'min',
'rolling_max': 'max',
'rolling_var': 'var',
'rolling_skew': 'skew',
'rolling_kurt': 'kurt',
'rolling_std': 'std'}
df = DataFrame(arr).rolling(win)

@test_parallel(num_threads=2)
def parallel_rolling():
getattr(df, rolling[method])()
self.parallel_rolling = parallel_rolling
else:
rolling = {'rolling_median': rolling_median,
'rolling_mean': rolling_mean,
'rolling_min': rolling_min,
'rolling_max': rolling_max,
'rolling_var': rolling_var,
'rolling_skew': rolling_skew,
'rolling_kurt': rolling_kurt,
'rolling_std': rolling_std}

@test_parallel(num_threads=2)
def parallel_rolling():
rolling[method](arr, win)
self.parallel_rolling = parallel_rolling

def time_rolling(self, method):
self.parallel_rolling()
Expand Down
200 changes: 28 additions & 172 deletions asv_bench/benchmarks/rolling.py
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@@ -1,185 +1,41 @@
from .pandas_vb_common import *
import pandas as pd
import numpy as np

from .pandas_vb_common import setup # noqa

class DataframeRolling(object):
goal_time = 0.2

def setup(self):
self.N = 100000
self.Ns = 10000
self.df = pd.DataFrame({'a': np.random.random(self.N)})
self.dfs = pd.DataFrame({'a': np.random.random(self.Ns)})
self.wins = 10
self.winl = 1000
class Methods(object):

def time_rolling_quantile_0(self):
(self.df.rolling(self.wins).quantile(0.0))
sample_time = 0.2
params = (['DataFrame', 'Series'],
[10, 1000],
['int', 'float'],
['median', 'mean', 'max', 'min', 'std', 'count', 'skew', 'kurt',
'sum', 'corr', 'cov'])
param_names = ['contructor', 'window', 'dtype', 'method']

def time_rolling_quantile_1(self):
(self.df.rolling(self.wins).quantile(1.0))
def setup(self, contructor, window, dtype, method):
N = 10**5
arr = np.random.random(N).astype(dtype)
self.roll = getattr(pd, contructor)(arr).rolling(window)

def time_rolling_quantile_median(self):
(self.df.rolling(self.wins).quantile(0.5))
def time_rolling(self, contructor, window, dtype, method):
getattr(self.roll, method)()

def time_rolling_median(self):
(self.df.rolling(self.wins).median())

def time_rolling_mean(self):
(self.df.rolling(self.wins).mean())
class Quantile(object):

def time_rolling_max(self):
(self.df.rolling(self.wins).max())
sample_time = 0.2
params = (['DataFrame', 'Series'],
[10, 1000],
['int', 'float'],
[0, 0.5, 1])
param_names = ['contructor', 'window', 'dtype', 'percentile']

def time_rolling_min(self):
(self.df.rolling(self.wins).min())
def setup(self, contructor, window, dtype, percentile):
N = 10**5
arr = np.random.random(N).astype(dtype)
self.roll = getattr(pd, contructor)(arr).rolling(window)

def time_rolling_std(self):
(self.df.rolling(self.wins).std())

def time_rolling_count(self):
(self.df.rolling(self.wins).count())

def time_rolling_skew(self):
(self.df.rolling(self.wins).skew())

def time_rolling_kurt(self):
(self.df.rolling(self.wins).kurt())

def time_rolling_sum(self):
(self.df.rolling(self.wins).sum())

def time_rolling_corr(self):
(self.dfs.rolling(self.wins).corr())

def time_rolling_cov(self):
(self.dfs.rolling(self.wins).cov())

def time_rolling_quantile_0_l(self):
(self.df.rolling(self.winl).quantile(0.0))

def time_rolling_quantile_1_l(self):
(self.df.rolling(self.winl).quantile(1.0))

def time_rolling_quantile_median_l(self):
(self.df.rolling(self.winl).quantile(0.5))

def time_rolling_median_l(self):
(self.df.rolling(self.winl).median())

def time_rolling_mean_l(self):
(self.df.rolling(self.winl).mean())

def time_rolling_max_l(self):
(self.df.rolling(self.winl).max())

def time_rolling_min_l(self):
(self.df.rolling(self.winl).min())

def time_rolling_std_l(self):
(self.df.rolling(self.wins).std())

def time_rolling_count_l(self):
(self.df.rolling(self.wins).count())

def time_rolling_skew_l(self):
(self.df.rolling(self.wins).skew())

def time_rolling_kurt_l(self):
(self.df.rolling(self.wins).kurt())

def time_rolling_sum_l(self):
(self.df.rolling(self.wins).sum())


class SeriesRolling(object):
goal_time = 0.2

def setup(self):
self.N = 100000
self.Ns = 10000
self.df = pd.DataFrame({'a': np.random.random(self.N)})
self.dfs = pd.DataFrame({'a': np.random.random(self.Ns)})
self.sr = self.df.a
self.srs = self.dfs.a
self.wins = 10
self.winl = 1000

def time_rolling_quantile_0(self):
(self.sr.rolling(self.wins).quantile(0.0))

def time_rolling_quantile_1(self):
(self.sr.rolling(self.wins).quantile(1.0))

def time_rolling_quantile_median(self):
(self.sr.rolling(self.wins).quantile(0.5))

def time_rolling_median(self):
(self.sr.rolling(self.wins).median())

def time_rolling_mean(self):
(self.sr.rolling(self.wins).mean())

def time_rolling_max(self):
(self.sr.rolling(self.wins).max())

def time_rolling_min(self):
(self.sr.rolling(self.wins).min())

def time_rolling_std(self):
(self.sr.rolling(self.wins).std())

def time_rolling_count(self):
(self.sr.rolling(self.wins).count())

def time_rolling_skew(self):
(self.sr.rolling(self.wins).skew())

def time_rolling_kurt(self):
(self.sr.rolling(self.wins).kurt())

def time_rolling_sum(self):
(self.sr.rolling(self.wins).sum())

def time_rolling_corr(self):
(self.srs.rolling(self.wins).corr())

def time_rolling_cov(self):
(self.srs.rolling(self.wins).cov())

def time_rolling_quantile_0_l(self):
(self.sr.rolling(self.winl).quantile(0.0))

def time_rolling_quantile_1_l(self):
(self.sr.rolling(self.winl).quantile(1.0))

def time_rolling_quantile_median_l(self):
(self.sr.rolling(self.winl).quantile(0.5))

def time_rolling_median_l(self):
(self.sr.rolling(self.winl).median())

def time_rolling_mean_l(self):
(self.sr.rolling(self.winl).mean())

def time_rolling_max_l(self):
(self.sr.rolling(self.winl).max())

def time_rolling_min_l(self):
(self.sr.rolling(self.winl).min())

def time_rolling_std_l(self):
(self.sr.rolling(self.wins).std())

def time_rolling_count_l(self):
(self.sr.rolling(self.wins).count())

def time_rolling_skew_l(self):
(self.sr.rolling(self.wins).skew())

def time_rolling_kurt_l(self):
(self.sr.rolling(self.wins).kurt())

def time_rolling_sum_l(self):
(self.sr.rolling(self.wins).sum())
def time_quantile(self, contructor, window, dtype, percentile):
self.roll.quantile(percentile)
30 changes: 0 additions & 30 deletions doc/plots/stats/moment_plots.py
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15 changes: 0 additions & 15 deletions doc/plots/stats/moments_ewma.py
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23 changes: 0 additions & 23 deletions doc/plots/stats/moments_ewmvol.py
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35 changes: 0 additions & 35 deletions doc/plots/stats/moments_expw.py
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24 changes: 0 additions & 24 deletions doc/plots/stats/moments_rolling.py
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