单个 DataFrame 索引匹配等价物

Question

我有一个如下所示的数据框，我想做的是在 'Name' 中的值与列 header 匹配时创建一个列。我尝试了 df.lookup、列表等，但总是出错。

现有数据框

	Name	Proj	Ceil	Floor	sim_id	lj	nc	bn	jl
0	lj	22.72	37	9.8	0	50	55	25	20
1	lj	22.72	37	9.8	1	49	54	24	19
2	lj	22.72	37	9.8	2	33	2	27	18
3	lj	22.72	37	9.8	3	14	60	17	35
4	lj	22.72	37	9.8	4	45	40	48	10
5	lj	22.72	37	9.8	5	10	15	35	30
6	lj	22.72	37	9.8	6	57	75	27	27
7	lj	22.72	37	9.8	7	22	17	18	11
8	lj	22.72	37	9.8	8	3	6	26	36
9	lj	22.72	37	9.8	9	12	32	5	3
10	nc	13.24	30.9	4.4	0	50	55	25	20
11	nc	13.24	30.9	4.4	1	49	54	24	19
12	nc	13.24	30.9	4.4	2	33	2	27	18

期望输出

	Name	Proj	Ceil	Floor	sim_id	lj	nc	bn	jl	new_val
0	lj	22.72	37	9.8	0	50	55	25	20	50
1	lj	22.72	37	9.8	1	49	54	24	19	49
2	lj	22.72	37	9.8	2	33	2	27	18	33
3	lj	22.72	37	9.8	3	14	60	17	35	14
4	lj	22.72	37	9.8	4	45	40	48	10	45
5	lj	22.72	37	9.8	5	10	15	35	30	10
6	lj	22.72	37	9.8	6	57	75	27	27	57
7	lj	22.72	37	9.8	7	22	17	18	11	22
8	lj	22.72	37	9.8	8	3	6	26	36	3
9	lj	22.72	37	9.8	9	12	32	5	3	12
10	nc	13.24	30.9	4.4	0	50	55	25	20	55
11	nc	13.24	30.9	4.4	1	49	54	24	19	54
12	nc	13.24	30.9	4.4	2	33	2	27	18	2

import pandas as pd

data1 = [['lj', 22.72, 37, 9.8], ['nc', 13.24, 30.9, 4.4], ['bm', 13.77, 26.3, 9.3], ['jl', 12, 25.9, 7.2]]
df = pd.DataFrame(data1, columns=['Name', 'Proj', 'Ceil', 'Floor'])


data2 = [['0', 50, 55, 25, 20], ['1', 49, 54, 24, 19], ['2', 33, 2, 27, 18], ['3', 14, 60, 17, 35],
         ['4', 45, 40, 48, 10],
         ['5', 10, 15, 35, 30], ['6', 57, 75, 27, 27], ['7', 22, 17, 18, 11], ['8', 3, 6, 26, 36], ['9', 12, 32, 5, 3]]
df2 = pd.DataFrame(data2, columns=['sim_id', 'lj', 'nc', 'bn', 'jl'])

df3 = df.assign(temp=1).merge(df2.assign(temp=1), on='temp').drop('temp', 1)
df = df3

df['new_val'] = df.lookup(df.index,df.Name)

Answer 1

df['new_val'] = df.lookup(df.index,df.Name)

单个 DataFrame 索引匹配等价物

Single DataFrame Index Match Equivalent

python

lookup

indexing

lambda

dataframe