Python數據聚合

Python有幾種方法可用於對數據執行聚合。 它使用Pandas和numpy庫完成。 數據必須可用或轉換爲數據框才能應用聚合功能。

在DataFrame上應用聚合

創建一個DataFrame並在其上應用聚合。

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

r = df.rolling(window=3,min_periods=1)
print (r)

執行上面示例代碼,得到以下結果 -

                    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]

可以通過將函數傳遞給整個DataFrame進行聚合,或者通過標準的get item方法選擇一個列。

在整個數據框上應用聚合-

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

r = df.rolling(window=3,min_periods=1)
print (r.aggregate(np.sum))

執行上面示例代碼,得到以下結果 -

                    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]

可以通過將函數傳遞給整個DataFrame進行聚合,或者通過標準的get item方法選擇一個列。

在整個數據幀上應用聚合

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)

r = df.rolling(window=3,min_periods=1)
print (r.aggregate(np.sum))

執行上面示例代碼,得到以下結果 -

                    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

在數據幀的單個列上應用聚合

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)
r = df.rolling(window=3,min_periods=1)
print (r['A'].aggregate(np.sum))

執行上面示例代碼,得到以下結果 -

                 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

在DataFrame的多列上應用聚合

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)
r = df.rolling(window=3,min_periods=1)
print (r[['A','B']].aggregate(np.sum))

執行上面示例代碼,得到以下結果-

                 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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