我希望將所有列值乘以資料透視表中顯示的 pandas 資料框中的列標題。有沒有人對此有很好的解決方案？
df = pd.DataFrame({'ID':['one2', 'one3', 'one3', 'one4' ],
'Requested Volume in Million':[5.0, 6.0, 7.0, 2.2],
'BOX':[1,2,3,4],
'Date':['2018-06-14', '2016-12-06', '2017-01-05', '2016-11-22']})
df2=pd.pivot_table(df, columns ='Requested Volume in Million',index =['ID','Date'], values ='BOX', aggfunc ='count', margins=True, fill_value=0)
df2 = df2.apply(lambda x : x * float((x.name)[0]) if type(x.name[0]) != float else x, axis=0)
uj5u.com熱心網友回復：
您可以使用 apply 和 lambda 來做到這一點。我們轉換為浮點數的每個列名都可以使用 .name 訪問。我們使用軸 1 對列進行操作。
df = pd.DataFrame({'0.5':[0.5, 0.2, -0.2, 0.5 ],
'2':[10, 5, -5, 10]})
df = df.apply(lambda x : x * float(x.name), axis=0)
輸出df：
0.5 2
0 0.25 20.0
1 0.10 10.0
2 -0.10 -10.0
3 0.25 20.0
編輯資料透視表解決方案：
import numpy as np
import pandas as pd
df = pd.DataFrame({5: {0: 20130320, 1: 20130320, 2: 20130320, 3: 20130320, 4: 20130320},
10: {0: 8, 1: 12, 2: 16, 3: 20, 4: 24},
20: {0: 1, 1: 3, 2: 5, 3: 7, 4: 9},
30: {0: 20, 1: 30, 2: 10, 3: 40, 4: 30},
40: {0: 400, 1: 500, 2: 500, 3: 200, 4: 300},
50: {0: 1000, 1: 1100, 2: 900, 3: 1300, 4: 800}})
df = pd.pivot_table(df, values=[30, 40, 50], index=[5], columns=[10], aggfunc='mean', margins=True)
df = df.apply(lambda x : x * float((x.name)[1]) if type(x.name[1]) != str else x, axis=0)
輸出df：
30 40 50
10 8 12 16 20 24 All 8 12 16 20 24 All 8 12 16 20 24 All
5
20130320 20 30 10 40 30 26.0 400 500 500 200 300 380.0 1000 1100 900 1300 800 1020.0
All 20 30 10 40 30 26.0 400 500 500 200 300 380.0 1000 1100 900 1300 800 1020.0
使用 OP 的資料進行編輯：
df = pd.DataFrame({'PID':['CIRC', 'CIRC0006', 'CIRC0054', 'CIRC9876' ],
'Requested Volume in Million':[5.0, 6.0, 7.0, 2.2],
'LiN2 Position in Box':[1,2,3,4],
'Freezing Date':['2018-06-14', '2016-12-06', '2017-01-05', '2016-11-22']})
df2=pd.pivot_table(df, columns ='Requested Volume in Million',index =['PID','Freezing Date'], values ='LiN2 Position in Box', aggfunc ='count', margins=True, fill_value=0)
df3 = df2.apply(lambda x : x * float((x.name)) if type(x.name) != str else x, axis=0)
輸出 df2：
Requested Volume in Million 2.2 5.0 6.0 7.0 All
PID Freezing Date
CIRC 2018-06-14 0 1 0 0 1
CIRC0006 2016-12-06 0 0 1 0 1
CIRC0054 2017-01-05 0 0 0 1 1
CIRC9876 2016-11-22 1 0 0 0 1
All 1 1 1 1 4
輸出df3：
Requested Volume in Million 2.2 5.0 6.0 7.0 All
PID Freezing Date
CIRC 2018-06-14 0.0 5.0 0.0 0.0 1
CIRC0006 2016-12-06 0.0 0.0 6.0 0.0 1
CIRC0054 2017-01-05 0.0 0.0 0.0 7.0 1
CIRC9876 2016-11-22 2.2 0.0 0.0 0.0 1
All 2.2 5.0 6.0 7.0 4
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