Once the rolling, expanding and ewm objects are created, several methods are available to perform aggregations on data.
Applying Aggregations on DataFrame
Let us create a DataFrame and apply aggregations on it.
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
Its output is as follows −
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]
We can aggregate by passing a function to the entire DataFrame, or select a column via the standard get item method.
Apply Aggregation on a Whole 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.aggregate(np.sum)
Its output is as follows −
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 -3.201469
Apply Aggregation on a Single Column of a 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)
Its output is as follows −
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: float64
Apply Aggregation on Multiple Columns of a 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)
Its output is as follows −
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 0.383357
Apply Multiple Functions on a Single Column of a 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])
Its output is as follows −
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 0.688583
Apply Multiple Functions on Multiple Columns of a 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])
Its output is as follows −
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 0.127786
Apply Different Functions to Different Columns of a 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})
Its output is as follows −
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