你可以用apply和lambda来实现.我们将每个列名转换为浮点数，可以通过访问这些列名.名称我们使用轴1对柱进行操作.
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)
输出df:
0.5 2
0 0.25 20.0
1 0.10 10.0
2 -0.10 -10.0
3 0.25 20.0
编辑数据透视表解决方案:
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)
输出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的数据编辑:
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)
输出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
输出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