import codecs def get_character(): f = codecs.open('D:/Rourou/Study/3.2/ZHSJ/KGQA_HLM-master/KGQA_HLM-master/raw_data/relation.txt','r','utf-8') data = [] for line in f.readlines(): array = line.strip("\n").split(",") arr = [array[0],array[1]] data.extend(arr) return data 这段代码是什么作用？

def get_json(character_arr): data={} for i in set(character_arr): print(i) url=r'https://baike.baidu.com/item/'+i url = quote(url, safe = string.printable) req = request.Request(url, headers=headers) response = request.urlopen(req, timeout=20) try: html = response.read().decode('utf-8') soup = BeautifulSoup(html, 'html.parser', ) res = soup.find(class_="summary-pic") pic_name = str(i) + '.jpg' img_src = res.find('img').get('src') request.urlretrieve(img_src,pic_name) except : print("找不到图片") res_key=soup.find_all(class_ ="basicInfo-item name") res_val=soup.find_all(class_ ="basicInfo-item value") key=[ik.get_text().strip().replace("\n","、") for ik in res_key] value = [iv.get_text().strip().replace("\n", "、") for iv in res_val] item=dict(zip(key,value)) data[str(i)]=item if not os.path.exists("../json"): os.mkdir("../json") f = codecs.open('../json/data.json','w','utf-8') f.write(json.dumps(data, ensure_ascii=False)) if name == "main": character_arr=get_character() os.chdir(os.path.join(os.getcwd(), 'images')) get_json(character_arr)

这段代码看起来是一个从百度百科获取角色信息并保存为JSON文件的函数。它的主要步骤如下： 1. 导入必要的模块和库。 2. 定义一个函数get_json，它接受...此外，还需要定义get_character()函数来获取角色数组的值。

import os import codecs import argparse def load_file(file_path): if not os.path.exists(file_path): return None with codecs.open(file_path, 'r', encoding='utf-8') as fd: for line in fd: yield line

如果文件存在，则使用 codecs.open 函数以 utf-8 编码打开文件，并使用 yield 语句逐行生成文件内容。这段代码还定义了一个 main 函数和一个 parse_arguments 函数，但没有给出具体实现。根据代码片段的内容，main...

请详细解释以下代码并给每行代码添加注释：#导入requests库 import requests #导入beautifulsoup库 from bs4 import BeautifulSoup import codecs #目标url URL = "https://movie.douban.com/top250" #请求头 HEADERS = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Safari/537.36'} def download_page(url): data = requests.get(url, headers=HEADERS).content return data def parse_html(html): soup = BeautifulSoup(html, 'html.parser') # 测试时可以使用print soup.prettify()打印查看获得的页面 # 根据css获取页面信息 movie_list_ol = soup.find('ol', attrs={'class':'grid_view'}) movie_name_list = [] # 遍历页面中有关的信息 for movie_li in movie_list_ol.find_all('li'): # 电影描述 detail = movie_li.find('div', attrs={'class':'hd'}) # 电影名字 movie_name = detail.find('span', attrs={'class':'title'}).getText() movie_name_list.append(movie_name) # 找到下一页 next_page = soup.find('span', attrs={'class':'next'}).find('a') if next_page: # 拼接下一页的url，继续爬取下一页 return movie_name_list, URL + next_page['href'] return movie_name_list, None def main(): url = URL with codecs.open('movies.txt', 'w', encoding='utf-8') as fp: movies_all = [] while url: html = download_page(url) movies, url = parse_html(html) movies_all.extend(movies) for index, movie in enumerate(movies_all): index += 1 # 将获得的信息写入文件 fp.write('{index}.{movie}\n'.format(index=index, movie=movie)) if name == 'main': main() print('爬取成功')

with codecs.open('movies.txt', 'w', encoding='utf-8') as fp: movies_all = [] while url: #下载页面 html = download_page(url) #解析页面 movies, url = parse_html(html) movies_all.extend(movies) ...

import sys import re import jieba import codecs import gensim import numpy as np import pandas as pd def segment(doc: str): stop_words = pd.read_csv('data/stopwords.txt', index_col=False, quoting=3, names=['stopword'], sep='\n', encoding='utf-8') stop_words = list(stop_words.stopword) reg_html = re.compile(r'<[^>]+>', re.S) # 去掉html标签数字等 doc = reg_html.sub('', doc) doc = re.sub('[０-９]', '', doc) doc = re.sub('\s', '', doc) word_list = list(jieba.cut(doc)) out_str = '' for word in word_list: if word not in stop_words: out_str += word out_str += ' ' segments = out_str.split(sep=' ') return segments def doc2vec(file_name, model): start_alpha = 0.01 infer_epoch = 1000 doc = segment(codecs.open(file_name, 'r', 'utf-8').read()) vector = model.docvecs[doc_id] return model.infer_vector(doc) # 计算两个向量余弦值 def similarity(a_vect, b_vect): dot_val = 0.0 a_norm = 0.0 b_norm = 0.0 cos = None for a, b in zip(a_vect, b_vect): dot_val += a * b a_norm += a 2 b_norm += b 2 if a_norm == 0.0 or b_norm == 0.0: cos = -1 else: cos = dot_val / ((a_norm * b_norm) ** 0.5) return cos def test_model(file1, file2): print('导入模型') model_path = 'tmp/zhwk_news.doc2vec' model = gensim.models.Doc2Vec.load(model_path) vect1 = doc2vec(file1, model) # 转成句子向量 vect2 = doc2vec(file2, model) print(sys.getsizeof(vect1)) # 查看变量占用空间大小 print(sys.getsizeof(vect2)) cos = similarity(vect1, vect2) print('相似度：%0.2f%%' % (cos * 100)) if name == 'main': file1 = 'data/corpus_test/t1.txt' file2 = 'data/corpus_test/t2.txt' test_model(file1, file2) 有什么问题，怎么解决

doc = segment(codecs.open(file_name, 'r', 'utf-8').read()) return model.infer_vector(doc, alpha=start_alpha, steps=infer_epoch) # 计算两个向量余弦值 def similarity(a_vect, b_vect): dot_val = 0.0 ...

def read_text(filename): with codecs.open(filename, "r", encoding="utf-8") as f: text = f.read() words = jieba.cut(text)

具体来说，它打开一个文件，使用 codecs 库以 UTF-8 编码读取其中的文本，然后使用 jieba.cut() 方法对文本进行分词，并将分词后的结果保存在一个名为 words 的变量中。jieba.cut() 方法的作用是将中文文本分成一个...

import codecs,csv headers={ 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.44', } url='https://www.mafengwo.cn/hotel/10183/?sFrom=mdd' res=requests.get(url,headers=headers) html=etree.HTML(res.text) total=html.xpath('//ul[@class="clearfix"]/li') f=open('住宿攻略1.csv','w+',newline='',encoding='utf-8-sig') filename=['name','num'] writer=csv.DictWriter(f,filename) writer.writeheader() for item in total: spot={} spot['name']=item.xpath('.//strong/text()')[0] spot['num']=item.xpath('.//span/text()')[0] print(spot) writer.writerow(spot) 逐行标注这段代码

import codecs,csv # 导入codecs和csv模块 headers={ 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.44', ...

import os import codecs def search_files(rootdir, keyword): for filename in os.listdir(rootdir): filepath = os.path.join(rootdir, filename) if os.path.isdir(filepath): search_files(filepath, keyword) else: try: with codecs.open(filepath, 'r', encoding='utf-8') as f: content = f.read() if keyword in content: print("包含 '{0}' 文字的文件路径： '{1}'".format(keyword, filepath)) except UnicodeDecodeError: #print("Error decoding file '{0}'".format(filepath)) print("",end='') adress = "D:\liziqian20221128\C\qtYuanDaiMa\ToolManageSystem" key = "" print("--"*20+"下面是包含关键词\"" + key + "\"文件路径") search_files(adress,key) 在这段代码中添加查到的文件文字中的行数

with codecs.open(filepath, 'r', encoding='utf-8') as f: content = f.readlines() count = 0 for line in content: # 遍历文件的每一行 if keyword in line: count += 1 if count > 0: # 如果该文件...

解释下 bt_btif : bta_av_get_peer_a2dp_codecs() i = 5

这是一个函数调用的语句，其中 bt_btif 是命名空间，bta_av_get_peer_a2dp_codecs() 是函数名，i = 5 是该语句的返回值。具体解释如下： 1. 命名空间 bt_btif 是 Android 系统中的一个蓝牙模块，提供了 Bluetooth ...

import os import glob import pandas as pd import codecs def convert_encoding(file_path, from_encoding, to_encoding): try: with codecs.open(file_path, 'r', encoding=from_encoding) as file: content = file.read() with codecs.open(file_path, 'w', encoding=to_encoding) as file: file.write(content) print(f"Converted {file_path} from {from_encoding} to {to_encoding}") except IOError as e: print(f"Error converting {file_path}: {e}") def batch_convert_encoding(directory, from_encoding, to_encoding): for root, dirs, files in os.walk(directory): for file in files: file_path = os.path.join(root, file) convert_encoding(file_path, from_encoding, to_encoding) # 使用示例 source_folder = input("请输入源文件夹路径：") # target_folder = source_folder batch_convert_encoding(source_folder, 'gb2312', 'utf-8') os.chdir(source_folder) extension = 'csv' # 读取csv文件列表，并过滤掉不想要的文件 all_filenames = [] for f in glob.glob('*.{}'.format(extension)): if f.endswith('FFDEV.csv'): continue all_filenames.append(f) # 在列表中合并所有文件 combined_csv = pd.concat([pd.read_csv(f) for f in all_filenames]) # 导出 csv # print(combined_csv) combined_csv.to_csv( "问题汇总.csv", index=False, encoding='utf-8') print(combined_csv)

你的代码看起来已经可以实现批量将编码从 GB2312 转换为 UTF-8，并且将合并后的 CSV 文件以 UTF-8 编码保存了。你可以按照以下步骤来使用这段代码： 1. 运行代码后，会提示你输入源文件夹的路径。...

# coding=utf-8 import codecs from htmlentitydefs import codepoint2name def encode_for_xml(unicode_data, encoding='ascii'): return unicode_data.encode(encoding, 'xmlcharrefreplace') def html_replace(exc): if isinstance(exc, (UnicodeEncodeError, UnicodeTranslateError)): s = [u'&%s;' % codepoint2name[ord(c)] for c in exc.object[exc.start:exc.end]] return ''.join(s), exc.end else: raise TypeError("can't handle %s" % exc.name) codecs.register_error('html_replace', html_replace) def encode_for_html(unicode_data, encoding='ascii'): return unicode_data.encode(encoding, 'html_replace') if name == 'main': unicode_data = u'\N{LATIN SMALL LETTER A WITH DIAERESIS}' print encode_for_xml(unicode_data, encoding='ascii') data = u''' <html> <head> <title>Encoding Test</title> </head> <body> accented characters: <ur> \xe0 (a + grave) \xe7 (c + cedilla) \xe9 (a + acute) symbols: \xa3 (British pound) \u20ac (Euro) \u221e (infinity) </body></html> ''' print encode_for_xml(data) print encode_for_html(data) 逐句讲解以上代码，并且给出html_replace(exc):函数一个参数例子并进行输出

import codecs from htmlentitydefs import codepoint2name 首先，导入了codecs库和codepoint2name模块，后者包含了HTML实体的名称和对应的Unicode码点。 python def encode_for_xml(unicode_data, encoding...

def search_files(rootdir, keyword): for filename in os.listdir(rootdir): filepath = os.path.join(rootdir, filename) if os.path.isdir(filepath): search_files(filepath, keyword) else: try: with codecs.open(filepath, 'r', encoding='utf-8') as f: content = f.readlines() count = 0 for line in content: # 遍历文件的每一行 if keyword in line: count += 1 if count > 0: # 如果该文件包含关键词 print("包含 '{0}' 文字的文件路径： '{1}'，行数为：{2}".format(keyword, filepath, count)) except UnicodeDecodeError: #print("Error decoding file '{0}'".format(filepath)) print("",end='') 这段代码中匹配大小写，并且增加一个可以指定不需要的目录数组

with codecs.open(filepath, 'r', encoding='utf-8') as f: content = f.readlines() count = 0 for line in content: if keyword in line: count += 1 if count > 0: print("包含 '{0}' 文字的文件路径： '...

请注释下面代码的每一行from os.path import join from codecs import open def build_corpus(split, make_vocab=True, data_dir="./ResumeNER"): """读取数据""" assert split in ['train', 'dev', 'test'] word_lists = [] tag_lists = [] with open(join(data_dir, split + ".char.bmes"), 'r', encoding='utf-8') as f: word_list = [] tag_list = [] for line in f: if line != '\n': word, tag = line.strip('\n').split() word_list.append(word) tag_list.append(tag) else: word_lists.append(word_list) tag_lists.append(tag_list) word_list = [] tag_list = [] # 如果make_vocab为True，还需要返回word2id和tag2id if make_vocab: word2id = build_map(word_lists) tag2id = build_map(tag_lists) return word_lists, tag_lists, word2id, tag2id else: return word_lists, tag_lists def build_map(lists): maps = {} for list_ in lists: for e in list_: if e not in maps: maps[e] = len(maps) return maps

2. from codecs import open：从codecs模块中导入open函数，用于以指定编码打开文件。 3. def build_corpus(split, make_vocab=True, data_dir="./ResumeNER"):：定义了一个名为build_corpus的函数，用于读取...

请为下面代码写详细注释import random import urllib.request from bs4 import BeautifulSoup import codecs from time import sleep def main(url, headers): # 发送请求 page = urllib.request.Request(url, headers=headers) page = urllib.request.urlopen(page) contents = page.read() # 用BeautifulSoup解析网页 soup = BeautifulSoup(contents, "html.parser") infofile.write("") print('爬取豆瓣电影250: \n') for tag in soup.find_all(attrs={"class": "item"}): # 爬取序号 num = tag.find('em').get_text() print(num) infofile.write(num + "\r\n") # 电影名称 name = tag.find_all(attrs={"class": "title"}) zwname = name[0].get_text() print('[中文名称]', zwname) infofile.write("[中文名称]" + zwname + "\r\n") # 网页链接 url_movie = tag.find(attrs={"class": "hd"}).a urls = url_movie.attrs['href'] print('[网页链接]', urls) infofile.write("[网页链接]" + urls + "\r\n") # 爬取评分和评论数 info = tag.find(attrs={"class": "star"}).get_text() info = info.replace('\n', ' ') info = info.lstrip() print('[评分评论]', info) # 获取评语 info = tag.find(attrs={"class": "inq"}) if (info): # 避免没有影评调用get_text()报错 content = info.get_text() print('[影评]', content) infofile.write(u"[影评]" + content + "\r\n") print('') if name == 'main': # 存储文件 infofile = codecs.open("03-movie-bs4.txt", 'a', 'utf-8') # 消息头 headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'} # 翻页 i = 0 while i < 10: print('页码', (i + 1)) num = i * 25 # 每次显示25部 URL序号按25增加 url = 'https://movie.douban.com/top250?start=' + str(num) + '&filter=' main(url, headers) sleep(5 + random.random()) infofile.write("\r\n\r\n") i = i + 1 infofile.close()

infofile = codecs.open("03-movie-bs4.txt", 'a', 'utf-8') # 消息头 headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 ...

解释以下代码：class ReadConfig: def init(self): try: fd = open(configPath, 'r', encoding='utf-8') data = fd.read() fd.close() except FileNotFoundError: print('配置文件缺失，请检查.后续功能会存在问题') data = '' # raise FileNotFoundError('配置文件缺失，请检查') # 清除BOM if data[:3] == codecs.BOM_UTF8: data = data[:3] filec = codecs.open(configPath, 'w') filec.write(data) filec.close() try: self.cf = configparser.RawConfigParser() self.cf.read(configPath, encoding='utf-8') except Exception as e: print(e) print('配置文件编码出错，请确认config.ini保存为UTF-8格式')

这段代码定义了一个名为 ReadConfig 的类，它有一个 __init__ 方法。在 __init__ 方法中，首先会尝试以只读方式打开一个 configPath 文件并读取其中的内容，如果成功则将文件内容保存到 data 变量中。...

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import os import codecs import argparse def load_file(file_path): if not os.path.exists(file_path): return None with codecs.open(file_path, 'r', encoding='utf-8') as fd: for line in fd: yield line

def read_text(filename): with codecs.open(filename, "r", encoding="utf-8") as f: text = f.read() words = jieba.cut(text)

解释下 bt_btif : bta_av_get_peer_a2dp_codecs() i = 5

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