Must have equal len keys and value when setting with an iterable when executing in vs code-CodePudding

I have two dataframes like below. Now, I want to compare two column from both df's and upon matching value it has to append the value of another column to the other df with a new column, but when trying to execute a for loop like below throws the below error. The same code works fine in jupyter notebook but throws error when executed in vs code. I am not understanding the issue, what is the best way to achieve it?

df1
    id            ids_list
0   1           [126, 238]
1   2           [126, 355]
2   3       [1265, 152, 238]
3   4       [1265, 1529,2384,17235]
df2
  from_id   to_id
0   1         2 
1   3         1
2   2         1
3   4         2
4   2         3
 
for y,z in zip(df1['id'],df1['ids_list']):
        df2.loc[df2.from_id == y, 'from_ids'] = z
        df2.loc[df2.to_id == y, 'to_ids'] = z

When executed in jupyter notebook the excepted output is resulted:

  from_id   to_id   from_ids                     to_ids
0   1         2     [126, 238]                  [126, 355]
1   3         1     [1265, 152, 238]            [126, 238]
2   2         1     [126, 355]                  [126, 238]
3   4         2     [1265, 1529,2384,17235]     [126, 355]
4   2         3     [126, 355]                  [1265, 152, 238]

But when running same code in vs code getting below error:

Must have equal len keys and value when setting with an iterable

CodePudding user response：

Use mapping with DataFrame.applymap and dict.get for possible empty list if no match:

d = dict(zip(df1['id'],df1['ids_list']))

f = lambda x: d.get(x, [])
df2[['from_ids','to_ids']] = df2[['from_id','to_id']].applymap(f)
print (df2)
   from_id  to_id                   from_ids            to_ids
0        1      2                 [126, 238]        [126, 355]
1        3      1           [1265, 152, 238]        [126, 238]
2        2      1                 [126, 355]        [126, 238]
3        4      2  [1265, 1529, 2384, 17235]        [126, 355]
4        2      3                 [126, 355]  [1265, 152, 238]

If no match is possible missing values use:

df2[['from_ids','to_ids']] = df2[['from_id','to_id']].apply(lambda x: x.map(d))
print (df2)
   from_id  to_id                   from_ids            to_ids
0        1      2                 [126, 238]        [126, 355]
1        3      1           [1265, 152, 238]        [126, 238]
2        2      1                 [126, 355]        [126, 238]
3        4      2  [1265, 1529, 2384, 17235]        [126, 355]
4        2      3                 [126, 355]  [1265, 152, 238]