Python从 Pandas 数据框中删除停用词

Question

我想从我的“推文”列中删除停用词。如何迭代每一行和每一项？

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"]
test["tweet"] = test["tweet"].str.lower().str.split()

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

Answer 1

我们可以从 nltk.corpus 导入 stopwords，如下所示。有了这个，我们用 Python 的列表理解和 pandas.DataFrame.apply 排除停用词。

# 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

也可以通过pandas.Series.str.replace来排除。

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

如果不能导入停用词，可以如下下载。

import nltk
nltk.download('stopwords')

另一种回答方法是从 sklearn.feature_extraction 导入 text.ENGLISH_STOP_WORDS。

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

请注意，scikit-learn 停用词和 nltk 停用词中的单词数量不同。