PYTHON

remove stopwords

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
 
example_sent = """This is a sample sentence,
                  showing off the stop words filtration."""
 
stop_words = set(stopwords.words('english'))
 
word_tokens = word_tokenize(example_sent)
 
filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words]

How to remove stopwords from a string in python

from gensim.parsing.preprocessing import remove_stopwords

text = "Nick likes to play football, however he is not too fond of tennis."
filtered_sentence = remove_stopwords(text)

print(filtered_sentence)

python remove stop words

from nltk.corpus import stopwords
nltk.download("stopwords")
stop = set(stopwords.words("english"))
filtered_words = [word.lower() for word in text.split() if word.lower() not in stop]

how to remove stop words in python

# You need a set of stopwords. You can build it by yourself if OR use built-in sets in modules like nltk and spacy

# in nltk
import nltk
nltk.download('stopwords') # needed once
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize 
stop_words = set(stopwords.words('english')) 
example_sent = "This is my awesome sentence"
# tokenization at the word level
word_tokens = word_tokenize(example_sent) 
# list of words not in the stopword list
filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words] 

# in spacy
# from terminal
python -m spacy download en_core_web_lg # or some other pretrained model
# in your program
import spacy
nlp = spacy.load("en_core_web_lg") 
stop_words = nlp.Defaults.stop_words
example_sent = "This is my awesome sentence"
doc = nlp(example_sent) 
filtered_sentence = [w.text for w in doc if not w.text.lower() in stop_words] 

function to remove stop words in python

import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')

def remove_stopwords(text):
    '''a function for removing the stopword'''
    # removing the stop words and lowercasing the selected words
    text = [word.lower() for word in text.split() if word.lower() not in stopwords.words("english")]
    # joining the list of words with space separator
    return " ".join(text)

remove stopwords

traindf['title'] = traindf['title'].apply(lambda x: ' '.join([word for word in x.lower().split() if word not in 
                                                            stopwords.words('english') and string.punctuation]))

remove stopwords from a sentence

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

example_sent = "This is a sample sentence, showing off the stop words filtration."

stop_words = set(stopwords.words('english'))

word_tokens = word_tokenize(example_sent)

filtered_sentence = [w for w in word_tokens if not w in stop_words]

filtered_sentence = []

for w in word_tokens:
    if w not in stop_words:
        filtered_sentence.append(w)

print(word_tokens)
print(filtered_sentence)