Remove "n-grams" first, before stopwords with this handy class that extends the functionality of scikit-learn's CountVectorizer. Substitute the class extension for other types of vectorizers such as TfIDF in the class definition at the top.

custom_vectorizer.py

# defines a custom vectorizer class
class CustomVectorizer(CountVectorizer): 
       
    stop_grams = []    
    
    def __init__(self, stop_grams = [], **opts):
        self.stop_grams = stop_grams
        super().__init__(**opts)
    
    def remove_ngrams(self, doc):
        for stop_gram in self.stop_grams:
            doc = doc.replace(stop_gram, "")
        return doc
    
    # overwrite the build_analyzer method, allowing one to
    # create a custom analyzer for the vectorizer
    def build_analyzer(self):
        
        # load stop words using CountVectorizer's built in method
        stop_words = list(self.get_stop_words())
        
        preprocessor = self.build_preprocessor()
        tokenizer = self.build_tokenizer()
        remove_ngrams = self.remove_ngrams
        
        
        # create the analyzer that will be returned by this method
        def analyser(doc):
                
            # apply the preprocessing and tokenzation steps
            doc_clean = preprocessor(doc.lower())
            
            # remove phrase stopwords
            doc_clean = remove_ngrams(doc)
            
            # tokenize using default tokenizer
            tokens = tokenizer(doc_clean)            
            
            # use CountVectorizer's _word_ngrams built in method
            # to remove stop words and extract n-grams
            return(self._word_ngrams(tokens, stop_words))
        
        return(analyser)

example.py

from sklearn.feature_extraction.text import CountVectorizer, ENGLISH_STOP_WORDS

text_input = [
    "Never put fox news or CNN in your pocket or bookbag.",
    "I put fox news in my mother jones bookbag",
    "Takimag and fox news are both conservative leaning outlets."
]

df = pd.DataFrame(text_input, columns=["text"])

stop_grams = ["fox news", "mother jones"]

vectorizer = CustomVectorizer(stop_words="english", stop_grams = stop_grams)
X = vectorizer.fit_transform(df['text'])

text = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names())
text