Trjtdtk

I'm using mlxtends apriori algorithm on transactional data, which works like a charm. My simple script looks like the following:

dataset = #some dataset

## Transform/prep dataset into list data
dataset_tx = dataset.groupby(['ReceiptCode'])['ItemCategoryName'].apply(list).values.tolist()

## Define classifier
te = TransactionEncoder()

## Binary-transform dataset
te_ary = te.fit(dataset_tx).transform(dataset_tx)

## Fit to new dataframe (sparse dataframe)
df = pd.SparseDataFrame(te_ary, columns=te.columns_)
frequent_itemsets = apriori(df, min_support=0.009, use_colnames=True)

and my dataset looks like this:

+----------------------+--+------------------+--+------------------+
| ReceiptCode | | ItemCategoryName | | StoreCountryName |
+----------------------+--+------------------+--+------------------+
| 0000P70322000031467 | | Food | | Denmark |
| 0000P70322000031867 | | Food | | Denmark |
| 0000P70322000051467 | | Interior | | Germany |
| 0000P70322000087468 | | Kitchen | | Switzerland |
| 0000P70322000031469 | | Leisure | | Germany |
| 0000P70322000031439 | | Food | | Switzerland |
+----------------------+--+------------------+--+------------------+

Now I would like to perform the same analysis but distinguished by the various countries (StoreCountryName in my dataframe). I know I can repeat the above code for each country, and name the variables accordingly, i.e. df_Denmark, df_Germany, etc.
However, I would like to automate this using a loop, a function or something else.

The wanted output would be a list of frequent itemsets for each country, or something similar. I just need to be able to distinguish between the outputs, but I have no idea how to achieve this.