将列的数值转换为列名columns

import pandas as pd

dataframe = pd.read_csv("merge_yield.csv")

a = dataframe.iloc[:,1:]

a

	ProductID	LotID	Mark LotID	Pass	Fail	Yield	Bin#
0	1460A	AL4578	#2 D7	54	11	0.830769	10
1	1460A	AL4578	#2 D7	54	11	0.830769	10
2	1460A	AL4578	#2 D7	54	11	0.830769	7
3	1460A	AL4578	#2 D7	54	11	0.830769	10
4	1460A	AL4578	#2 D7	54	11	0.830769	10
...	...	...	...	...	...	...	...
121	1460A	AL4578	#2 D2	61	0	1.000000	10
122	1460A	AL4578	#2 D2	61	0	1.000000	10
123	1460A	AL4578	#2 D2	61	0	1.000000	10
124	1460A	AL4578	#2 D2	61	0	1.000000	10
125	1460A	AL4578	#2 D2	61	0	1.000000	10

126 rows × 7 columns

b= a.groupby(['ProductID','LotID','Mark LotID','Pass','Fail','Yield','Bin#'],as_index=False).size()

b

	ProductID	LotID	Mark LotID	Pass	Fail	Yield	Bin#	size
0	1460A	AL4578	#2 D2	61	0	1.000000	10	61
1	1460A	AL4578	#2 D7	54	11	0.830769	7	11
2	1460A	AL4578	#2 D7	54	11	0.830769	10	54

f = pd.pivot(b,index = ['ProductID','LotID','Mark LotID','Pass','Fail','Yield'], columns='Bin#')  # 转换

f

						size
					Bin#	7	10
ProductID	LotID	Mark LotID	Pass	Fail	Yield
1460A	AL4578	#2 D2	61	0	1.000000	NaN	61.0
		#2 D7	54	11	0.830769	11.0	54.0

f.columns = f.columns.droplevel()  # 删除多层索引

f.columns = list(f.columns.values)

f.reset_index().fillna(0)

	ProductID	LotID	Mark LotID	Pass	Fail	Yield	7	10
0	1460A	AL4578	#2 D2	61	0	1.000000	0.0	61.0
1	1460A	AL4578	#2 D7	54	11	0.830769	11.0	54.0

参考文章：
https://blog.csdn.net/weixin_44285988/article/details/111885047
https://www.codenong.com/22233488/