How do I use sum and count functions together on different columns in my data frame function?

My data frame is the following:

Advertiser      Product             Price
Company1        A                   10
Company1        A                   10
Company1        B                   8
Company2        C                   5
Company3        D                   3

My current function is:

top_5_products = df.groupby(['Advertiser'])['Product'].value_counts(ascending = False).head(5)

It outputs the following:

Advertiser      Product
Company 1       A   2
                B   1
Company 2       C   1
Company 3       D   1

What is the best way to modify my function to give me the sum of the price as well? Example:

Advertiser      Product     Total Price
Company 1       A   2       20
                B   1       8
Company 2       C   1       5
Company 3       D   1       3

I have looked at the .agg method but I’m lacking examples that use different columns. (I’m also not sure if that’s the best way to go about it) Thanks!

Edit***

df.groupby(['Advertiser', 'Product']).agg({'Product': 'count', 'Price': 'sum'}).head(5)

doesn’t work as it is no longer sorted…

Answer

Someone commented with the answer, but it disappeared shortly after. Thankfully I saw it and it works. Thank you!

The following code works:

df.groupby(['Advertiser','Product']).agg({'Product': 'count', 'Price': 'sum'}).head(5)

EDIT*** This does not work as it no longer sorts.