主要是分割run的,当一个段落有run组成,可以使用split达到分割开的效果。 print(result[4].text.split(':')) print(result[4].text.split(':')[1]) 1. 2. 有如下结果:可见被分割,达到分开的效果 4.增加、修改内容 # 修改 print(result[0].text = '调研分析报告') #增加内容 # 方法1 result[1].a...
python 分离word文本 python文本切割 1.我们可以使用Python中的string.split()的方法将其切分 >>>mySent='This!!! book is the best book on Python or M.L I have ever laid eyes upon'>>>mySent.split()>>>['This!!!','book','is','the','best','book','on','Python','or','M.L.','...
split() if wrd.istitle()])) trainDF['upper_case_word_count'] = trainDF['text'].apply(lambda x: len([wrd for wrd in x.split() if wrd.isupper()])) trainDF['char_count'] = trainDF['text'].apply(len) trainDF['word_count'] = trainDF['text'].apply(lambda x: len(x.split...
在Python中,split方法是在字符串对象上调用的,它的基本语法如下: `python str.split([sep[, maxsplit]]) 其中,str是要分割的字符串,sep是分隔符,默认为所有空字符,包括空格、换行符、制表符等。maxsplit是可选参数,表示最大分割次数,如果指定了该参数,则最多只会分割出maxsplit个子字符串。 下面是一个简单的...
text = "Python is a great programming language. It is easy to learn and use. Python is used for many purposes, such as web development, scientific computing, data analysis, artificial intelligence, machine learning, and more."# 将文本分割成单词words = text.split()# 统计单词出现次数word_coun...
trainDF['title_word_count'] = trainDF['text'].apply(lambda x: len([wrd for wrd in x.split() if wrd.istitle()])) trainDF['upper_case_word_count'] = trainDF['text'].apply(lambda x: len([wrd for wrd in x.split() ...
pythonCopy code sentence = "This is a sample sentence." words = sentence.split() # 使用split...
fromdocximportDocument# 打开一个Word文档doc=Document('example.docx')# 提取文档中的文本text=[para.textforparaindoc.paragraphs]# 对提取的文本进行处理# 例如:统计文档的单词数word_count=sum(len(para.split())forparaintext)print(f'文档中的单词数为:{word_count}') ...
合并多个word文件到一个文件中 :param files:待合并文件的列表 :param output_file_path 新的文件路径 :return: """composer = Composer(Document())forfileinfiles: composer.append(Document(file))# 保存到新的文件中composer.save(output_file_path) ...
(t.translate(str.maketrans('', '', punctuation)).lower().rstrip()) tokenized = [word_tokenize(x) for x in clean_text] all_text = [] for tokens in tokenized: for t in tokens: all_text.append(t) return tokenized, set(all_text) reviews, vocab = split_words_reviews(data) reviews[...