Single DataFrame Index Match Equivalent

user_14840988 0
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I have a dataframe that looks like below, what I would like to do is create a single column for when the value in 'Name' matches the column header. I have tried df.lookup, lists, etc. but keep getting errors.

Existing Dataframe

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

Desired Output

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)
dataframe lookup indexing lambda python
1 Answers
df['new_val'] = df.lookup(df.index,df.Name)
user_47183500
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