Pyspark find existing set of rows in a dataframe and replace it with values from another dataframe

I have a Pyspark dataframe_Old (dfo) as below:

Id	neighbor_sid	neighbor	division
a1	1100	Naalehu	Hawaii
a2	1101	key-west-fl	Miami
a3	1102	lubbock	Texas
a10	1202	bay-terraces	California

I have a Pyspark dataframe_new (dfn) as below:

Id	neighbor_sid	neighbor	division
a1	1100	Naalehu	Hawaii
a2	1111	key-largo-fl	Miami
a3	1103	grapevine	Texas
a4	1115	meriden-ct	Connecticut
a12	2002	east-louisville	Kentucky

My objective is to find the Ids from dataframe_new in dataframe_old and replace them with the new values from dataframe_new

Final expected Pyspark dataframe updated – dataframe_old

Id	neighbor_sid	neighbor	division
a1	1100	Naalehu	Hawaii
a2	1111	key-largo-fl	Miami
a3	1103	grapevine	Texas
a4	1115	meriden-ct	Connecticut
a10	1202	bay-terraces	California
a12	2002	east-louisville	Kentucky

My wrong attempt at solving it as it is comparing column wise instead of row

dfO.alias('a').join(dfN.alias('b'), on=['id'], how='left')
    .select(
        'id',
        f.when(
            ~f.isnull(f.col('b.id')),
            f.col('b.id')
        ).otherwise(f.col('a.id')).alias('id'),
        'b.col_3'
    )
    .union(dfO)
    .dropDuplicates()
    .sort('id')
    .show()

Please help – would really appreciate any guidance!

Answer

We can do an outer join on the id fields and then use coalesce() to prioritize the fields from dfn.

columns = ['id', 'neighbor_sid', 'neighbor', 'division']

dfo. 
    join(dfn, 'id', 'outer'). 
    select(*['id'] + [func.coalesce(dfn[k], dfo[k]).alias(k) for k in columns if k != 'id']). 
    orderBy('id'). 
    show()

# +---+------------+------------+-----------+
# | id|neighbor_sid|    neighbor|   division|
# +---+------------+------------+-----------+
# | a1|        1100|     Naalehu|     Hawaii|
# |a10|        1202|bay-terraces| California|
# | a2|        1111|key-largo-fl|      Miami|
# | a3|        1103|   grapevine|      Texas|
# | a4|        1115|  meriden-ct|Connecticut|
# +---+------------+------------+-----------+