Python remove stop words from pandas dataframe

If you would like something simple but not get back a list of words:

test["tweet"].apply(lambda words: ' '.join(word.lower() for word in words.split() if word not in stop))

Where stop is defined as OP did.

from nltk.corpus import stopwords
stop = stopwords.words('english')

We can import stopwords from nltk.corpus as below. With that, We exclude stopwords with Python's list comprehension and pandas.DataFrame.apply.

# Import stopwords with nltk.
from nltk.corpus import stopwords
stop = stopwords.words('english')

pos_tweets = [('I love this car', 'positive'),
    ('This view is amazing', 'positive'),
    ('I feel great this morning', 'positive'),
    ('I am so excited about the concert', 'positive'),
    ('He is my best friend', 'positive')]

test = pd.DataFrame(pos_tweets)
test.columns = ["tweet","class"]

# Exclude stopwords with Python's list comprehension and pandas.DataFrame.apply.
test['tweet_without_stopwords'] = test['tweet'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))
print(test)
# Out[40]:
#                                tweet     class tweet_without_stopwords
# 0                    I love this car  positive              I love car
# 1               This view is amazing  positive       This view amazing
# 2          I feel great this morning  positive    I feel great morning
# 3  I am so excited about the concert  positive       I excited concert
# 4               He is my best friend  positive          He best friend

It can also be excluded by using pandas.Series.str.replace.

pat = r'\b(?:{})\b'.format('|'.join(stop))
test['tweet_without_stopwords'] = test['tweet'].str.replace(pat, '')
test['tweet_without_stopwords'] = test['tweet_without_stopwords'].str.replace(r'\s+', ' ')
# Same results.
# 0              I love car
# 1       This view amazing
# 2    I feel great morning
# 3       I excited concert
# 4          He best friend

If you can not import stopwords, you can download as follows.

import nltk
nltk.download('stopwords')

Another way to answer is to import text.ENGLISH_STOP_WORDS from sklearn.feature_extraction.

# Import stopwords with scikit-learn
from sklearn.feature_extraction import text
stop = text.ENGLISH_STOP_WORDS

Notice that the number of words in the scikit-learn stopwords and nltk stopwords are different.

Using List Comprehension

test['tweet'].apply(lambda x: [item for item in x if item not in stop])

Returns:

0               [love, car]
1           [view, amazing]
2    [feel, great, morning]
3        [excited, concert]
4            [best, friend]

Check out pd.DataFrame.replace(), it might work for you:

In [42]: test.replace(to_replace='I', value="",regex=True)
Out[42]:
                              tweet     class
0                     love this car  positive
1              This view is amazing  positive
2           feel great this morning  positive
3   am so excited about the concert  positive
4              He is my best friend  positive

Edit : replace() would search for string(and even substrings). For e.g. it would replace rk from work if rk is a stopword which sometimes is not expected.

Hence the use of regex here :

for i in stop :
    test = test.replace(to_replace=r'\b%s\b'%i, value="",regex=True)