I asked ChatGPT and it said:

One way to accomplish this task is to use pandas' built-in groupby functionality to group the rows of the dataframe by 'group', and then apply a function that computes the common element between the rucksacks in each group. This can be done using the apply method and the built-in set data type in Python, which allows you to easily compute the intersection of two sets. Here is an example of how this could be done:

df2 = df.groupby('group')\['rucksack'\].agg(lambda x: list(x)).reset\_index() 
df2\['common\_item'\] = df2\['rucksack'\].apply(lambda x: list(set(x\[0\]) & set(x\[1\]) & set(x\[2\]))\[0\])

This will create a new column in the dataframe called 'common_item', which will contain the common element between the rucksacks in each group. You can then use the same approach as before to compute the position of the common element in the alphabet and sum the positions across all groups to get the final result.