nltk.download('stopwords') nltk.download('punkt') def get_most_common_words(texts, num_words=10): all_words = [] for text in texts: all_words.extend(nltk.word_tokenize(text.lower())) stop_words = set(nltk.corpus.stopwords.words('english')) words = [word for word in all_words if...
可以使用 Java 中的 Apache Commons Text 和 Apache Commons Collections 库来计算文本中词频统计,例如使用 Commons Text 中的 getWords method 来获取文本中的单词。 JavaScript:JavaScript 是一种前端编程语言,也可以用于后端开发。可以使用 JavaScript 中的 Node.js 和 npm 包管理器来运行文本处理和统计任务,例如...
text = f.read() # 创建词云对象 wordcloud =WordCloud(width=800, height=400, background_color='white', font_path='simhei.ttf', max_words=200, max_font_size=150, min_font_size=10, random_state=42).generate(text) # 显示词云图 plt.figure(figsize=(10,5)) plt.imshow(wordcloud, interpol...
new_data = re.findall('[\u4e00-\u9fa5]+', data, re.S) new_data = " ".join(new_data) # 文本分词 seg_list_exact = jieba.cut(new_data, cut_all=True) result_list = [] with open('stop_words.txt', encoding='utf-8') as f: con = f.readlines() stop_words = set() for ...
str.find() 查找 代码语言:javascript 代码运行次数:0 运行 AI代码解释 In [90]: help(s1.find) Help on built-in function find: find(...) S.find(sub [,start [,end]]) -> int Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]...
html=etree.HTML(res.text)reverse,last_num=False,Nonefori,a_taginenumerate(html.xpath("//dl[@class='cat_box']/dd/a")):data.append([re.sub("\s+"," ",a_tag.text),a_tag.attrib["href"]])nums=re.findall("第(\d+)章",a_tag.text)ifnums:iflast_num andint(nums[0])<last_nu...
《流畅的python》是一本适合python进阶的书, 里面介绍的基本都是高级的python用法. 对于初学python的人来说, 基础大概也就够用了, 但往往由于够用让他们忘了...
"find","here","thing","give","many","well"]forwordinngram:ifwordincommonWords:returnTruereturnFalsedefcleanText(input):input= re.sub('\n+'," ",input).lower()input= re.sub('\[[0-9]*\]',"",input)input= re.sub(' +'," ",input)input= re.sub("u\.s\.","us",input)...
In the end, most of the issues covered in this chapter do not affect programmers who deal only with ASCII text. But even if that is your case, there is no escaping the str versus byte divide. As a bonus, you’ll find that the specialized binary sequence types provide features that the...
Let's see which of these tags are the most common in the news category of the Brown corpus: >>> from nltk.corpus import brown >>> brown_news_tagged = brown.tagged_words(categories='news', simplify_tags=True) >>> tag_fd = nltk.FreqDist(tag for (word, tag) in brown_news_tagged)...