Creating a pandas pivot table to count number of times items appear in a list together

I am trying to count the number of times users look at pages in the same session.

I am starting with a data frame listing user_ids and the page slugs they have visited:

user_id page_view_page_slug
1       slug1
1       slug2
1       slug3
1       slug4
2       slug5
2       slug3
2       slug2
2       slug1

What I am looking to get is a pivot table counting user_ids of the cross section of slugs

.	slug1	slug2	slug3	slug4	slug5
slug1	2	2	2	1	1
slug2	2	2	2	1	1
slug3	2	2	2	1	1
slug4	1	1	1	1	0
slug5	1	1	1	0	1

I realize this will be the same data reflected when we see slug1 and slug2 vs slug2 and slug1 but I can't think of a better way. So far I have done a listagg

def listagg(df, grouping_idx):
    return df.groupby(grouping_idx).agg(list)
new_df = listagg(df,'user_id')

Returning:

          page_view_page_slug
user_id                                                   
1        [slug1, slug2, slug3, slug4]
2        [slug5, slug3, slug2, slug2]
7        [slug6, slug4, slug7]
9        [slug3, slug5, slug1]

But I am struggling to think of loop to count when items appear in a list together (despite the order) and how to store it. Then I also do not know how I would get this in a pivotable format.

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