Python 3.x 使用TFIDF矢量器的字频

我正在尝试使用TF-IDF计算消息传递数据帧的词频。到目前为止，我有这个

import nltk
from sklearn.feature_extraction.text import TfidfVectorizer

new_group['tokenized_sents'] = new_group.apply(lambda row: nltk.word_tokenize(row['message']),axis=1).astype(str).lower()
vectoriser=TfidfVectorizer()
new_group['tokenized_vector'] = list(vectoriser.fit_transform(new_group['tokenized_sents']).toarray())

然而，通过上面的代码，我得到了一堆零，而不是单词频率。如何解决此问题以获得消息的正确数字频率。这是我的数据帧

user_id     date          message      tokenized_sents      tokenized_vector
X35WQ0U8S   2019-02-17    Need help    ['need','help']      [0.0,0.0]
X36WDMT2J   2019-03-22    Thank you!   ['thank','you','!']  [0.0,0.0,0.0]

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首先，对于计数，您不希望使用TfidfVectorizer，因为它是标准化的。您想使用CountVectorizer。其次，您不需要对单词进行标记，因为sklearn有一个内置的标记器，它同时具有TfidfVectorizer和CountVectorizer

#add whatever settings you want
countVec =CountVectorizer()

#fit transform
cv = countVec.fit_transform(df['message'].str.lower())

#feature names
cv_feature_names = countVec.get_feature_names()

#feature counts
feature_count = cv.toarray().sum(axis = 0)

#feature name to count
dict(zip(cv_feature_names, feature_count))