I think this is what you want to do (both CSVs I use are identical to what you have in your question):

import pandas as pd

df_1 = pd.read_csv('document1.csv')
df_2 = pd.read_csv('document2.csv')

key_cols = ['job_function', 'job_area', 'title']
merged_df = pd.merge(df_1, df_2,  how='left', left_on=key_cols, right_on=key_cols)

Source: How to join two dataframes on multiple columns

You can use first groupby with join:

df2 = df2.groupby('ID')['Tag'].apply(', '.join).reset_index()
print (df2)
        ID                     Tag
0  3763058     item1, item2, item3
1  3763077  item_4, item_5, item_6

Then is possible use merge, especially if df1 has more columns:

df = pd.merge(df1, df2, on='ID', how='left')
print (df)
        ID  Name                     Tag
0  3763058  Andi     item1, item2, item3
1  3763077  Mark  item_4, item_5, item_6

Solution with map, if need add only one column:

df2 = df2.groupby('ID')['Tag'].apply(', '.join).reset_index()
df2['Name'] = df2['ID'].map(df1.set_index('ID')['Name'])
print (df2)
        ID                     Tag  Name
0  3763058     item1, item2, item3  Andi
1  3763077  item_4, item_5, item_6  Mark

If important position of Name column use insert:

df2 = df2.groupby('ID')['Tag'].apply(', '.join).reset_index()
df2.insert(1, 'Name', df2['ID'].map(df1.set_index('ID')['Name']))
print (df2)
        ID  Name                     Tag
0  3763058  Andi     item1, item2, item3
1  3763077  Mark  item_4, item_5, item_6

According to df.merge docs validate was added in version 0.21.0. You are using an older version so you should update the version of pandas you are using.

Zero's answer is basically a reduce operation. If I had more than a handful of dataframes, I'd put them in a list like this (generated via list comprehensions or loops or whatnot):

dfs = [df0, df1, df2, ..., dfN]

Assuming they have a common column, like name in your example, I'd do the following:

import functools as ft
df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='name'), dfs)

That way, your code should work with whatever number of dataframes you want to merge.

You can create new join key (ie, helperkey) that would uniquely identify each row for your joining columns.

joincols = ['date', 'period', 'company']
df1m = df1.assign(helperkey=df1.groupby(joincols).cumcount())
df2m = df2.assign(helperkey=df2.groupby(joincols).cumcount())

df1m.merge(df2m, on=joincols + ['helperkey'], how='left').drop('helperkey', axis=1)

Output:

         date  period company  value
0  2025-03-01       1      aa    4.0
1  2025-03-01       1      aa    NaN
2  2025-03-02       2       b    8.0

Note: Here, a helperkey column is created using groupby cumcount then added that new temporary column to each dataframe join and drop that helper column.