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


												


																

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