Ничто из этого мне не нравится.Но иногда вам просто нужно получить доступ к вашим данным.
Попытка # 0
a = dict(zip(df2['Attribute:Value'], df2['Item']))
cols = ['VDM', 'MDM', 'OM']
b = {
'Item Number':
[', '.join([str(a[f'{c}:{t._asdict()[c]}']) for c in cols]) for t in df1.itertuples()]
}
df1[['state']].assign(**b)
state Item Number
0 AP 1, 7, 15
1 GOA 1, 7, 11
2 GU 1, 7, 14
3 KA 1, 10, 11
Попытка # 1
a = dict(zip(df2['Attribute:Value'], df2['Item'].astype(str)))
d1 = df1.set_index('state').astype(str)
r1 = (d1.columns + ':' + d1).replace(a) # Thanks @anky_91
# r1 = (d1.columns + ':' + d1).applymap(a.get)
r1
VDM MDM OM
state
AP 1 7 15
GOA 1 7 11
GU 1 7 14
KA 1 10 11
Тогда
pd.DataFrame({'state': r1.index, 'Item Number': [*map(', '.join, zip(*map(r1.get, r1)))]})
state Item Number
0 AP 1, 7, 15
1 GOA 1, 7, 11
2 GU 1, 7, 14
3 KA 1, 10, 11
Попытка # 2
a = dict(zip(df2['Attribute:Value'], df2['Item'].astype(str)))
cols = ['VDM', 'MDM', 'OM']
b = {
'Item Number':
[*map(', '.join, zip(*[[a[f'{c}:{i}'] for i in df1[c]] for c in cols]))]
}
df1[['state']].assign(**b)
state Item Number
0 AP 1, 7, 15
1 GOA 1, 7, 11
2 GU 1, 7, 14
3 KA 1, 10, 11
Попытка # 3
from itertools import cycle
a = dict(zip(zip(*df2['Attribute:Value'].str.split(':').str), df2['Item'].astype(str)))
d = df1.set_index('state')
b = {
'Item Number':
[*map(', '.join, zip(*[map(a.get, zip(cycle(d), np.ravel(d).astype(str)))] * 3))]
}
df1[['state']].assign(**b)
state Item Number
0 AP 1, 7, 15
1 GOA 1, 7, 11
2 GU 1, 7, 14
3 KA 1, 10, 11
Попытка # 4
a = pd.Series(dict(zip(
zip(*df2['Attribute:Value'].str.split(':').str),
df2.Item.astype(str)
)))
df1.set_index('state').stack().astype(str).groupby(level=0).apply(
lambda s: ', '.join(map(a.get, s.xs(s.name).items()))
).reset_index(name='Item Number')
state Item Number
0 AP 1, 7, 15
1 GOA 1, 7, 11
2 GU 1, 7, 14
3 KA 1, 10, 11