text = text.lower() # 去除字符串中的标点符号 translator = str.maketrans('', '', string.punctuation) text = text.translate(translator) # 使用空格将文本分割成单词列表 words = text.split() # 创建一个空字典来存储单词计数 word_count = {} # 遍历单词列表,统计每个单词的出现次数 for word in ...
Lastly, an important application of strings is thesplitmethod, which returns a list of all the words in the initial string and it automatically splits by any white space. It can optionally take a parameter and split the strings by another character, like a comma or a dot 4. Formatting str...
然后,您将完成两个不同的编程项目:一个存储多个文本字符串的简单剪贴板和一个自动完成格式化文本片段的枯燥工作的程序。 使用字符串 让我们看看 Python 允许你在代码中编写、打印和访问字符串的一些方法。 字符串字面值 用Python 代码键入字符串值相当简单:它们以单引号开始和结束。但是你怎么能在字符串中使用引号呢...
我们可以使用Mermaid语法来绘制一个简单的类图,以帮助理解代码的结构。 TextAnalyzer- text: string- words: list+__init__(text: string)+split_into_words() : list+find_longest_word() : string+print_longest_word() 上面的类图显示了一个名为TextAnalyzer的类,它具有一些属性和方法来处理文本并找到最长的...
split) Help on built-in function split: split(...) S.split([sep [,maxsplit]]) -> list of strings #sep为分隔符,默认为空格 最大分隔次数 Return a list of the words in the string S, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done. If ...
``` # Python script to count words in a text file def count_words(file_path): with open(file_path, 'r') as f: text = f.read() word_count = len(text.split()) return word_count ``` 说明: 此Python脚本读取一个文本文件并计算它包含的单词数。它可用于快速分析文本文档的内容或跟踪写作...
split()的一个常见用法是沿着换行符拆分多行字符串。在交互式 Shell 中输入以下内容: >>>spam ='''Dear Alice, How have you been? I am fine. There is a container in the fridge that is labeled "Milk Experiment." Please do not drink it. ...
>>>evaluate(text, seg1) 63 The final step is to search for the pattern of zeros and ones that maximizes this objective function, shown in Example 3-4. Notice that the best segmentation includes “words” like thekitty, since there’s not enough evidence in the data to split this any ...
Large language models (LLMs) can be used for many tasks, but often have a limited context size that can be smaller than documents you might want to use. To use documents of larger length, you often have to split your text into chunks to fit within this context size. ...
=[text.split()]dictionary = corpora.Dictionary(documents)corpus =[dictionary.doc2bow(document) for document in documents]lda_model = models.LdaModel(corpus, num_topics=1, id2word=dictionary)keywords = lda_model.print_topics(num_words=10)[0][1].split('+')keywords =[keyword.strip().split...