Python Pandas聚合函數

窗口函數可以與聚合函數一起使用，聚合函數指的是對一組數據求總和、最大值、最小值以及平均值的操作。

應用聚合函數

首先讓我們創建一個 DataFrame 對象，然后對聚合函數進行應用。

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(20).reshape(5,4),index = pd.date_range('12/14/2020', periods=5),columns = ['A', 'B', 'C', 'D'])
print (df)
#窗口大小為3，min_periods 最小觀測值為1
r = df.rolling(window=3,min_periods=1)
print(r)

輸出結果：

             A   B   C   D
2020-12-14   0   1   2   3
2020-12-15   4   5   6   7
2020-12-16   8   9  10  11
2020-12-17  12  13  14  15
2020-12-18  16  17  18  19
Rolling [window=3,min_periods=1,center=False,axis=0,method=single]

1) 對整體聚合

您可以把一個聚合函數傳遞給 DataFrame，示例如下：

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(20).reshape(5,4),index = pd.date_range('12/14/2020', periods=5),columns = ['A', 'B', 'C', 'D'])
print (df)
#窗口大小為3，min_periods 最小觀測值為1
r = df.rolling(window=3,min_periods=1)
print(r.aggregate(np.sum))
# 以下方式也可以
# print(r.sum())

輸出結果：

             A   B   C   D
2020-12-14   0   1   2   3
2020-12-15   4   5   6   7
2020-12-16   8   9  10  11
2020-12-17  12  13  14  15
2020-12-18  16  17  18  19A     B     C     D
2020-12-14   0.0   1.0   2.0   3.0
2020-12-15   4.0   6.0   8.0  10.0
2020-12-16  12.0  15.0  18.0  21.0
2020-12-17  24.0  27.0  30.0  33.0
2020-12-18  36.0  39.0  42.0  45.0

2) 對任意某一列聚合

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(20).reshape(5,4),index = pd.date_range('12/14/2020', periods=5),columns = ['A', 'B', 'C', 'D'])
print (df)
#窗口大小為3，min_periods 最小觀測值為1
r = df.rolling(window=3,min_periods=1)
print(r['B'].aggregate(np.sum))

輸出結果：

             A   B   C   D
2020-12-14   0   1   2   3
2020-12-15   4   5   6   7
2020-12-16   8   9  10  11
2020-12-17  12  13  14  15
2020-12-18  16  17  18  19
2020-12-14     1.0
2020-12-15     6.0
2020-12-16    15.0
2020-12-17    27.0
2020-12-18    39.0
Freq: D, Name: B, dtype: float64

3) 對多列數據聚合

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(20).reshape(5,4),index = pd.date_range('12/14/2020', periods=5),columns = ['A', 'B', 'C', 'D'])
print (df)
#窗口大小為3，min_periods 最小觀測值為1
r = df.rolling(window=3,min_periods=1)
print(r['B','C'].aggregate(np.sum))

輸出結果：

             A   B   C   D
2020-12-14   0   1   2   3
2020-12-15   4   5   6   7
2020-12-16   8   9  10  11
2020-12-17  12  13  14  15
2020-12-18  16  17  18  19B     C
2020-12-14   1.0   2.0
2020-12-15   6.0   8.0
2020-12-16  15.0  18.0
2020-12-17  27.0  30.0
2020-12-18  39.0  42.0

4) 對單列應用多個函數

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(20).reshape(5,4),index = pd.date_range('12/14/2020', periods=5),columns = ['A', 'B', 'C', 'D'])
print (df)
#窗口大小為3，min_periods 最小觀測值為1
r = df.rolling(window=3,min_periods=1)
print(r['B'].aggregate([np.sum,np.mean]))

輸出結果：

             A   B   C   D
2020-12-14   0   1   2   3
2020-12-15   4   5   6   7
2020-12-16   8   9  10  11
2020-12-17  12  13  14  15
2020-12-18  16  17  18  19sum  mean
2020-12-14   1.0   1.0
2020-12-15   6.0   3.0
2020-12-16  15.0   5.0
2020-12-17  27.0   9.0
2020-12-18  39.0  13.0

5) 對不同列應用多個函數

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(20).reshape(5,4),index = pd.date_range('12/14/2020', periods=5),columns = ['A', 'B', 'C', 'D'])
print (df)
#窗口大小為3，min_periods 最小觀測值為1
r = df.rolling(window=3,min_periods=1)
print(r['B','C'].aggregate([np.sum,np.mean]))

輸出結果：

             A   B   C   D
2020-12-14   0   1   2   3
2020-12-15   4   5   6   7
2020-12-16   8   9  10  11
2020-12-17  12  13  14  15
2020-12-18  16  17  18  19B           C      sum  mean   sum  mean
2020-12-14   1.0   1.0   2.0   2.0
2020-12-15   6.0   3.0   8.0   4.0
2020-12-16  15.0   5.0  18.0   6.0
2020-12-17  27.0   9.0  30.0  10.0
2020-12-18  39.0  13.0  42.0  14.0

6) 對不同列應用不同函數

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(20).reshape(5,4),index = pd.date_range('12/14/2020', periods=5),columns = ['A', 'B', 'C', 'D'])
print (df)
#窗口大小為3，min_periods 最小觀測值為1
r = df.rolling(window=3,min_periods=1)
print(r.aggregate({"B":np.sum,"C":np.mean}))

輸出結果：

             A   B   C   D
2020-12-14   0   1   2   3
2020-12-15   4   5   6   7
2020-12-16   8   9  10  11
2020-12-17  12  13  14  15
2020-12-18  16  17  18  19B     C
2020-12-14   1.0   2.0
2020-12-15   6.0   4.0
2020-12-16  15.0   6.0
2020-12-17  27.0  10.0
2020-12-18  39.0  14.0