Pandas Python - Agrégations
Une fois le roulement, l'expansion et ewm des objets sont créés, plusieurs méthodes sont disponibles pour effectuer des agrégations sur les données.
Application d'agrégations sur DataFrame
Créons un DataFrame et appliquons-y des agrégations.
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
r = df.rolling(window=3,min_periods=1)
print rSes output est comme suit -
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 0.790670 -0.387854 -0.668132 0.267283
2000-01-03 -0.575523 -0.965025 0.060427 -2.179780
2000-01-04 1.669653 1.211759 -0.254695 1.429166
2000-01-05 0.100568 -0.236184 0.491646 -0.466081
2000-01-06 0.155172 0.992975 -1.205134 0.320958
2000-01-07 0.309468 -0.724053 -1.412446 0.627919
2000-01-08 0.099489 -1.028040 0.163206 -1.274331
2000-01-09 1.639500 -0.068443 0.714008 -0.565969
2000-01-10 0.326761 1.479841 0.664282 -1.361169
Rolling [window=3,min_periods=1,center=False,axis=0]Nous pouvons agréger en passant une fonction à l'ensemble du DataFrame, ou sélectionner une colonne via le standard get item méthode.
Appliquer l'agrégation sur une trame de données entière
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
r = df.rolling(window=3,min_periods=1)
print r.aggregate(np.sum)Ses output est comme suit -
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469Appliquer l'agrégation sur une seule colonne d'un Dataframe
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
r = df.rolling(window=3,min_periods=1)
print r['A'].aggregate(np.sum)Ses output est comme suit -
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
2000-01-01 1.088512
2000-01-02 1.879182
2000-01-03 1.303660
2000-01-04 1.884801
2000-01-05 1.194699
2000-01-06 1.925393
2000-01-07 0.565208
2000-01-08 0.564129
2000-01-09 2.048458
2000-01-10 2.065750
Freq: D, Name: A, dtype: float64Appliquer l'agrégation sur plusieurs colonnes d'un DataFrame
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
r = df.rolling(window=3,min_periods=1)
print r[['A','B']].aggregate(np.sum)Ses output est comme suit -
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
A B
2000-01-01 1.088512 -0.650942
2000-01-02 1.879182 -1.038796
2000-01-03 1.303660 -2.003821
2000-01-04 1.884801 -0.141119
2000-01-05 1.194699 0.010551
2000-01-06 1.925393 1.968551
2000-01-07 0.565208 0.032738
2000-01-08 0.564129 -0.759118
2000-01-09 2.048458 -1.820537
2000-01-10 2.065750 0.383357Appliquer plusieurs fonctions sur une seule colonne d'un DataFrame
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
r = df.rolling(window=3,min_periods=1)
print r['A'].aggregate([np.sum,np.mean])Ses output est comme suit -
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
sum mean
2000-01-01 1.088512 1.088512
2000-01-02 1.879182 0.939591
2000-01-03 1.303660 0.434553
2000-01-04 1.884801 0.628267
2000-01-05 1.194699 0.398233
2000-01-06 1.925393 0.641798
2000-01-07 0.565208 0.188403
2000-01-08 0.564129 0.188043
2000-01-09 2.048458 0.682819
2000-01-10 2.065750 0.688583Appliquer plusieurs fonctions sur plusieurs colonnes d'un DataFrame
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
r = df.rolling(window=3,min_periods=1)
print r[['A','B']].aggregate([np.sum,np.mean])Ses output est comme suit -
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
A B
sum mean sum mean
2000-01-01 1.088512 1.088512 -0.650942 -0.650942
2000-01-02 1.879182 0.939591 -1.038796 -0.519398
2000-01-03 1.303660 0.434553 -2.003821 -0.667940
2000-01-04 1.884801 0.628267 -0.141119 -0.047040
2000-01-05 1.194699 0.398233 0.010551 0.003517
2000-01-06 1.925393 0.641798 1.968551 0.656184
2000-01-07 0.565208 0.188403 0.032738 0.010913
2000-01-08 0.564129 0.188043 -0.759118 -0.253039
2000-01-09 2.048458 0.682819 -1.820537 -0.606846
2000-01-10 2.065750 0.688583 0.383357 0.127786Appliquer différentes fonctions à différentes colonnes d'un Dataframe
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(3, 4),
index = pd.date_range('1/1/2000', periods=3),
columns = ['A', 'B', 'C', 'D'])
print df
r = df.rolling(window=3,min_periods=1)
print r.aggregate({'A' : np.sum,'B' : np.mean})Ses output est comme suit -
A B C D
2000-01-01 -1.575749 -1.018105 0.317797 0.545081
2000-01-02 -0.164917 -1.361068 0.258240 1.113091
2000-01-03 1.258111 1.037941 -0.047487 0.867371
A B
2000-01-01 -1.575749 -1.018105
2000-01-02 -1.740666 -1.189587
2000-01-03 -0.482555 -0.447078