compute n-grams by category column with pandas

Question

I'm trying to find the most used n-grams of a pandas column in python. I managed to gather the following code allowing me to do exactly that.

However I would like to have the results split by "category" column. Instead of having a line with bi-gram|total frequency like

"blue orange"|1

I would like three columns of bi-gram|frequency fruit|frequency|meat like

"blue orange"|1|0

from sklearn.feature_extraction.text import CountVectorizer

data = {'text':['blue orange is tired', 'an apple', 'meat are great for my stomach'],
        'category':['fruit', 'fruit', 'meat']}
df = pd.DataFrame(data)

word_vectorizer = CountVectorizer(ngram_range = (2, 3), analyzer = 'word')
sparse_matrix = word_vectorizer.fit_transform(df['text'])
frequencies = sum(sparse_matrix).toarray()[0]
df_ngrams = pd.DataFrame(frequencies, index = word_vectorizer.get_feature_names_out(), columns = ['frequency'])
df_ngrams.sort_values('frequency', ascending = False).head(50)