getting the index of the max value in list and returning the values in subsequent lists

I have a df

review                          review_id   word_count
['abc def', 'ghi']              [1, 2]      [2, 1]
['jkl mno pqr', 'stu', 'vwx']   [3, 4, 5]   [3, 1, 1]

For each row in the df, I would like to create new columns for review and review_id whereby there will only consist of the review/review_id with the maximum word count.
Thus, the output should look something like this.

review         review_id
abc def        1      
jkl mno pqr    3

I can only think of getting the index of the max value in word_count. Any sugeestions will be welcomed. Thanks!

>Solution :

Use DataFrame.explode with compare maximal values per groups (from rows) and select rows by boolean indexing if need all maximal values:

df = df.explode(['review','review_id','word_count'])
#for oldier pandas versions
#df = df.apply(lambda x: x.explode())

df = df[df['word_count'].eq(df.groupby(level=0)['word_count'].transform('max'))]
print (df)
        review review_id word_count
0      abc def         1          2
1  jkl mno pqr         3          3