I’m working with NLP spacy library and I created a function to return a list of token from a text.

import spacy    
def preprocess_text_spacy(text):
    stop_words = ["a", "the", "is", "are"]
    nlp = spacy.load('en_core_web_sm')
    tokens = set()
    doc = nlp(text)
    for word in doc:
        if word.is_currency:
            tokens.add(word.lower_)
        elif len(word.lower_) == 1:
            if word.is_digit and float(word.text) == 0:
                tokens.add(word.text)
        elif not word.is_punct and not word.is_space and not word.is_quote and not word.is_bracket and not in stop_words:
            tokens.add(word.lower_)
    return list(tokens)

This function is not correct because removing stop words not working.
Everything is ok only if I delete the last condition and not in stop_words.

How to upgrade this function to remove stop words according a defined list in addition to all other condition statement?

>Solution :

Your code looks fine to me, there is a small change

at the end of elif put and str(word) not in stop_words

import spacy    
def preprocess_text_spacy(text):
    stop_words = ["a", "the", "is", "are"]
    nlp = spacy.load('en_core_web_sm')
    tokens = set()
    doc = nlp(text)
    print(doc)
    for word in doc:
        if word.is_currency:
            tokens.add(word.lower_)
        elif len(word.lower_) == 1:
            if word.is_digit and float(word.text) == 0:
                tokens.add(word.text)
        elif not word.is_punct and not word.is_space and not word.is_quote and not word.is_bracket and str(word) not in stop_words:
            tokens.add(word.lower_)
    return list(tokens)