Create a dataset from two different dataset - python-3.x

Question

I have two different data set:
1. state VDM MDM OM
AP 1 2 5
GOA 1 2 1
GU 1 2 4
KA 1 5 1
2. Attribute:Value Support Item
VDM:1 4 1
VDM:2 0 2
VDM:3 0 3
VDM:4 0 4
VDM:5 0 5
MDM:1 0 6
MDM:2 3 7
MDM:3 0 8
MDM:4 0 9
MDM:5 1 10
OM:1 2 11
OM:2 0 12
OM:3 0 13
OM:4 1 14
OM:5 1 15
The first dataset only contains 1-5 values.
The second dataset holds the Attribute:Value pair and it's occurrences and a sequence number (Item).
I want a Dataset which looks like below:
state Item Number
AP 1, 7, 15
GOA 1, 7, 11
GU 1, 7, 14
KA 1, 10, 11

Answer 1

None of these are really appealing to me. But sometimes you just have to thrash about to get your data munged.
Attempt #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
Attempt #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
Then
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
Attempt #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
Attempt #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
Attempt #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

Answer 2

Here is another approach using stack, map and unstack:
s = df.set_index('state').stack()
s_map = df2.set_index(['Attribute:Value'])['Item']
s.loc[:] = (s.index.get_level_values(1) + ':' + s.astype(str)).map(s_map)
s.unstack().astype(str).apply(', '.join, axis=1).reset_index(name='Item Number')
[out]
state Item Number
0 AP 1, 7, 15
1 GOA 1, 7, 11
2 GU 1, 7, 14
3 KA 1, 10, 11

Answer 3

I feel like this is merge and pivot problem
s=df2['Attribute:Value'].str.split(':',expand=True).assign(Item=df2.Item)
s[1]=s[1].astype(int)
s1=df1.melt('state')
s1.merge(s,right_on=[0,1],left_on=['variable','value']).pivot('state','variable','Item')
Out[113]:
variable MDM OM VDM
state
AP 7 15 1
GOA 7 11 1
GU 7 14 1
KA 10 11 1