-
-
Notifications
You must be signed in to change notification settings - Fork 18.4k
/
Copy pathrolling.py
185 lines (127 loc) · 4.91 KB
/
rolling.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
from .pandas_vb_common import *
import pandas as pd
import numpy as np
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
def time_rolling_quantile_0(self):
(self.df.rolling(self.wins).quantile(0.0))
def time_rolling_quantile_1(self):
(self.df.rolling(self.wins).quantile(1.0))
def time_rolling_quantile_median(self):
(self.df.rolling(self.wins).quantile(0.5))
def time_rolling_median(self):
(self.df.rolling(self.wins).median())
def time_rolling_median(self):
(self.df.rolling(self.wins).mean())
def time_rolling_max(self):
(self.df.rolling(self.wins).max())
def time_rolling_min(self):
(self.df.rolling(self.wins).min())
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_median_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_median(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_median_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())