Python - Data Aggregation - Adglob Infosystem Pvt Ltd

Python has several methods are available to perform aggregations on data. It is done using the pandas and numpy libraries. The data must be available or converted to a data frame to apply the aggregation functions.

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

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