当有了滚动，扩展和ewm对象创建了以后，就有几种方法可以对数据执行聚合。
DataFrame应用聚合
可以通过向整个DataFrame传递一个函数来进行聚合，或者通过标准的获取项目方法来选择一个列。
在整个数据框上应用聚合
import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(10, 4), index = pd.date_range('1/1/2000', periods=10), columns = ['A', 'B', 'C', 'D']) print(df) print('\n') r = df.rolling(window=3,min_periods=1) print(r) print('\n') print(r.aggregate(np.sum))
输出结果：
A B C D
2000-01-01 1.081883 -1.133242 -0.477461 0.669900
2000-01-02 -1.120673 -0.889724 0.232907 0.391879
2000-01-03 -0.050530 -0.213853 0.100309 0.296723
2000-01-04 0.165836 -0.015513 -1.008884 -1.877693
2000-01-05 0.210501 -1.395490 -0.495589 -0.072882
2000-01-06 -0.639261 -2.301506 0.703845 -0.867376
2000-01-07 -0.225980 0.684229 0.985126 0.763059
2000-01-08 -0.748013 1.274504 -0.195817 2.293899
2000-01-09 -1.683620 -1.466185 0.491427 -1.895749
2000-01-10 0.842794 1.598099 0.843714 0.777707
Rolling [window=3,min_periods=1,center=False,axis=0]
A B C D
2000-01-01 1.081883 -1.133242 -0.477461 0.669900
2000-01-02 -0.038790 -2.022966 -0.244553 1.061778
2000-01-03 -0.089320 -2.236820 -0.144245 1.358501
2000-01-04 -1.005367 -1.119090 -0.675668 -1.189091
2000-01-05 0.325807 -1.624856 -1.404165 -1.653851
2000-01-06 -0.262924 -3.712509 -0.800629 -2.817951
2000-01-07 -0.654740 -3.012767 1.193381 -0.177199
2000-01-08 -1.613253 -0.342773 1.493154 2.189581
2000-01-09 -2.657613 0.492548 1.280736 1.161209
2000-01-10 -1.588839 1.406418 1.139325 1.175857
在数据框的单个列上应用聚合
import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(10, 4), index = pd.date_range('1/1/2000', periods=10), columns = ['A', 'B', 'C', 'D'])
print (df) print("\n")
r = df.rolling(window=3,min_periods=1) print (r['A'].aggregate(np.sum))
输出结果：
A B C D
2000-01-01 -1.095530 -0.415257 -0.446871 -1.267795
2000-01-02 -0.405793 -0.002723 0.040241 -0.131678
2000-01-03 -0.136526 0.742393 -0.692582 -0.271176
2000-01-04 0.318300 -0.592146 -0.754830 0.239841
2000-01-05 -0.125770 0.849980 0.685083 0.752720
2000-01-06 1.410294 0.054780 0.297992 -0.034028
2000-01-07 0.463223 -1.239204 -0.056420 0.440893
2000-01-08 -2.244446 -0.516937 -2.039601 -0.680606
2000-01-09 0.991139 0.026987 -2.391856 0.585565
2000-01-10 0.112228 -0.701284 -1.139827 1.484032
2000-01-01 -1.095530
2000-01-02 -1.501323
2000-01-03 -1.637848
2000-01-04 -0.224018
2000-01-05 0.056004
2000-01-06 1.602824
2000-01-07 1.747747
2000-01-08 -0.370928
2000-01-09 -0.790084
2000-01-10 -1.141079
Freq: D, Name: A, dtype: float64
在DataFrame的多列上应用聚合
import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(10, 4), index = pd.date_range('1/1/2018', periods=10), columns = ['A', 'B', 'C', 'D'])
print (df) print ("\n")
r = df.rolling(window=3,min_periods=1) print (r[['A','B']].aggregate(np.sum))
输出结果： A B C D
2018-01-01 0.518897 0.988917 0.435691 -1.005703
2018-01-02 1.793400 0.130314 2.313787 0.870057
2018-01-03 -0.297601 0.504137 -0.951311 -0.146720
2018-01-04 0.282177 0.142360 -0.059013 0.633174
2018-01-05 2.095398 -0.153359 0.431514 -1.185657
2018-01-06 0.134847 0.188138 0.828329 -1.035120
2018-01-07 0.780541 0.138942 -1.001229 0.714896
2018-01-08 0.579742 -0.642858 0.835013 -1.504110
2018-01-09 -1.692986 -0.861327 -1.125359 0.006687
2018-01-10 -0.263689 1.182349 -0.916569 0.617476
A B
2018-01-01 0.518897 0.988917
2018-01-02 2.312297 1.119232
2018-01-03 2.014697 1.623369
2018-01-04 1.777976 0.776811
2018-01-05 2.079975 0.493138
2018-01-06 2.512422 0.177140
2018-01-07 3.010786 0.173722
2018-01-08 1.495130 -0.315777
2018-01-09 -0.332703 -1.365242
2018-01-10 -1.376932 -0.321836
在DataFrame的单个列上应用多个函数 (用列表包裹多个函数）
import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(10, 4), index = pd.date_range('2019/01/01', periods=10), columns = ['A', 'B', 'C', 'D']) print (df) print("\n") r = df.rolling(window=3,min_periods=1) print (r['A'].aggregate([np.sum,np.mean]))
输出结果：
A B C D
2019-01-01 1.022641 -1.431910 0.780941 -0.029811
2019-01-02 -0.302858 0.009886 -0.359331 -0.417708
2019-01-03 -1.396564 0.944374 -0.238989 -1.873611
2019-01-04 0.396995 -1.152009 -0.560552 -0.144212
2019-01-05 -2.513289 -1.085277 -1.016419 -1.586994
2019-01-06 -0.513179 0.823411 0.670734 1.196546
2019-01-07 -0.363239 -0.991799 0.587564 -1.100096
2019-01-08 1.474317 1.265496 -0.216486 -0.224218
2019-01-09 2.235798 -1.381457 -0.950745 -0.209564
2019-01-10 -0.061891 -0.025342 0.494245 -0.081681
sum mean
2019-01-01 1.022641 1.022641
2019-01-02 0.719784 0.359892
2019-01-03 -0.676780 -0.225593
2019-01-04 -1.302427 -0.434142
2019-01-05 -3.512859 -1.170953
2019-01-06 -2.629473 -0.876491
2019-01-07 -3.389707 -1.129902
2019-01-08 0.597899 0.199300
2019-01-09 3.346876 1.115625
2019-01-10 3.648224 1.216075
在DataFrame的多列上应用多个函数
import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(10, 4), index = pd.date_range('2020/01/01', periods=10), columns = ['A', 'B', 'C', 'D']) print (df) print("\n")
r = df.rolling(window=3,min_periods=1) print (r[['A','B']].aggregate([np.sum,np.mean]))
输出结果：
A B C D
2020-01-01 1.053702 0.355985 0.746638 -0.233968
2020-01-02 0.578520 -1.171843 -1.764249 -0.709913
2020-01-03 -0.491185 0.975212 0.200139 -3.372621
2020-01-04 -1.331328 0.776316 0.216623 0.202313
2020-01-05 -1.023147 -0.913686 1.457512 0.999232
2020-01-06 0.995328 -0.979826 -1.063695 0.057925
2020-01-07 0.576668 1.065767 -0.270744 -0.513707
2020-01-08 0.520258 0.969043 -0.119177 -0.125620
2020-01-09 -0.316480 0.549085 1.862249 1.091265
2020-01-10 0.461321 -0.368662 -0.988323 0.543011
A B
sum mean sum mean
2020-01-01 1.053702 1.053702 0.355985 0.355985
2020-01-02 1.632221 0.816111 -0.815858 -0.407929
2020-01-03 1.141037 0.380346 0.159354 0.053118
2020-01-04 -1.243993 -0.414664 0.579686 0.193229
2020-01-05 -2.845659 -0.948553 0.837843 0.279281
2020-01-06 -1.359146 -0.453049 -1.117195 -0.372398
2020-01-07 0.548849 0.182950 -0.827744 -0.275915
2020-01-08 2.092254 0.697418 1.054985 0.351662
2020-01-09 0.780445 0.260148 2.583896 0.861299
2020-01-10 0.665099 0.221700 1.149466 0.383155
将不同的函数应用于DataFrame的不同列
import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(3, 4), index = pd.date_range('2020/01/01', periods=3), columns = ['A', 'B', 'C', 'D'])
print (df) print("\n")
r = df.rolling(window=3,min_periods=1) print (r.aggregate({'A' : np.sum,'B' : np.mean}))
输出结果：
A B C D
2020-01-01 -0.246302 -0.057202 0.923807 -1.019698
2020-01-02 0.285287 1.467206 -0.368735 -0.397260
2020-01-03 -0.163219 -0.401368 1.254569 0.580188
A B
2020-01-01 -0.246302 -0.057202
2020-01-02 0.038985 0.705002
2020-01-03 -0.124234 0.336212