从文档集中提取TF-IDF值的降序排位前200的词汇。用python代码如何实现

可以使用Python的sklearn库中的TfidfVectorizer实现。具体代码如下：

from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd

# 文档集合
docs = [
    "This is the first document",
    "This is the second document",
    "And this is the third one",
    "Is this the first document?"
]

# 使用TfidfVectorizer得到文档中词汇的TF-IDF值
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(docs)

# 得到所有单词对应的tf-idf值，并将其存为列表
features = vectorizer.get_feature_names()
df = pd.DataFrame(X.toarray(), columns=features)

# 将各单词的tf-idf值求和排序，得到前200个单词
top_words = df.sum(axis=0).sort_values(ascending=False)[:200].index.tolist()
print("Top 200 words by TF-IDF:")
print(', '.join(top_words))

此代码将得到文档集中提取TF-IDF值的降序排位前200的词汇。