


前言
关于python版本,我一开始看很多资料说python2比较好,因为很多库还不支持3,但是使用到现在为止觉得还是pythin3比较好用,因为编码什么的问题,觉得2还是没有3方便。而且在网上找到的2中的一些资料稍微改一下也还是可以用。
好了,开始说爬百度百科的事。
这里设定的需求是爬取北京地区n个景点的全部信息,n个景点的名称是在文件中给出的。没有用到api,只是单纯的爬网页信息。
1、根据关键字获取url
由于只需要爬取信息,而且不涉及交互,可以使用简单的方法而不需要模拟浏览器。
可以直接
<strong>http://www.php.cn/"guanjianci"</strong>
<strong>for </strong>l <strong>in </strong>view_names: <strong>'''http://baike.baidu.com/search/word?word=''' </strong><em># 得到url的方法 </em><em> </em>name=urllib.parse.quote(l) name.encode(<strong>'utf-8'</strong>) url=<strong>'http://baike.baidu.com/search/word?word='</strong>+name
这里要注意关键词是中午所以要注意编码问题,由于url中不能出现空格,所以需要用quote
函数处理一下。
关于quote():
在 Python2.x 中的用法是:urllib.quote(text)
。Python3.x 中是urllib.parse.quote(text)
。按照标准,URL只允许一部分ASCII 字符(数字字母和部分符号),其他的字符(如汉字)是不符合URL标准的。所以URL中使用其他字符就需要进行URL编码。URL中传参数的部分(query String),格式是:name1=value1&name2=value2&name3=value3
。假如你的name或者value值中的『&』或者『=』等符号,就当然会有问题。所以URL中的参数字符串也需要把『&=』等符号进行编码。URL编码的方式是把需要编码的字符转化为%xx的形式。通常URL编码是基于UTF-8的(当然这和浏览器平台有关)
例子:
比如『我,unicode 为 0x6211,UTF-8编码为0xE60x880x91,URL编码就是 %E6%88%91。
Python的urllib库中提供了quote
和quote_plus
两种方法。这两种方法的编码范围不同。不过不用深究,这里用quote
就够了。
2、下载url
用urllib库轻松实现,见下面的代码中def download(self,url)
3、利用Beautifulsoup获取html
4、数据分析
百科中的内容是并列的段,所以在爬的时候不能自然的按段逻辑存储(因为全都是并列的)。所以必须用正则的方法。
基本的想法就是把整个html文件看做是str,然后用正则的方法截取想要的内容,在重新把这段内容转换成beautifulsoup
对象,然后在进一步处理。
可能要花些时间看一下正则。
代码中还有很多细节,忘了再查吧只能,下次绝对应该边做编写文档,或者做完马上写。。。
贴代码!
# coding:utf-8 ''' function:爬取百度百科所有北京景点, author:yi ''' import urllib.request from urllib.request import urlopen from urllib.error import HTTPError import urllib.parse from bs4 import BeautifulSoup import re import codecs import json class BaikeCraw(object): def __init__(self): self.urls =set() self.view_datas= {} def craw(self,filename): urls = self.getUrls(filename) if urls == None: print("not found") else: for urll in urls: print(urll) try: html_count=self.download(urll) self.passer(urll, html_count) except: print("view do not exist") '''file=self.view_datas["view_name"] self.craw_pic(urll,file,html_count) print(file)''' def getUrls (self, filename): new_urls = set() file_object = codecs.open(filename, encoding='utf-16', ) try: all_text = file_object.read() except: print("文件打开异常!") file_object.close() file_object.close() view_names=all_text.split(" ") for l in view_names: if '?' in l: view_names.remove(l) for l in view_names: '''http://baike.baidu.com/search/word?word=''' # 得到url的方法 name=urllib.parse.quote(l) name.encode('utf-8') url='http://baike.baidu.com/search/word?word='+name new_urls.add(url) print(new_urls) return new_urls def manger(self): pass def passer(self,urll,html_count): soup = BeautifulSoup(html_count, 'html.parser', from_encoding='utf_8') self._get_new_data(urll, soup) return def download(self,url): if url is None: return None response = urllib.request.urlopen(url) if response.getcode() != 200: return None return response.read() def _get_new_data(self, url, soup): ##得到数据 if soup.find('p',class_="main-content").find('h1') is not None: self.view_datas["view_name"]=soup.find('p',class_="main-content").find('h1').get_text()#景点名 print(self.view_datas["view_name"]) else: self.view_datas["view_name"] = soup.find("p", class_="feature_poster").find("h1").get_text() self.view_datas["view_message"] = soup.find('p', class_="lemma-summary").get_text()#简介 self.view_datas["basic_message"]=soup.find('p', class_="basic-info cmn-clearfix").get_text() #基本信息 self.view_datas["basic_message"]=self.view_datas["basic_message"].split("\n") get=[] for line in self.view_datas["basic_message"]: if line != "": get.append(line) self.view_datas["basic_message"]=get i=1 get2=[] tmp="%%" for line in self.view_datas["basic_message"]: if i % 2 == 1: tmp=line else: a=tmp+":"+line get2.append(a) i=i+1 self.view_datas["basic_message"] = get2 self.view_datas["catalog"] = soup.find('p', class_="lemma-catalog").get_text().split("\n")#目录整体 get = [] for line in self.view_datas["catalog"]: if line != "": get.append(line) self.view_datas["catalog"] = get #########################百科内容 view_name=self.view_datas["view_name"] html = urllib.request.urlopen(url) soup2 = BeautifulSoup(html.read(), 'html.parser').decode('utf-8') p = re.compile(r'', re.DOTALL) # 尾 r = p.search(content_data_node) content_data = content_data_node[0:r.span(0)[0]] lists = content_data.split('') i = 1 for list in lists:#每一大块 final_soup = BeautifulSoup(list, "html.parser") name_list = None try: part_name = final_soup.find('h2', class_="title-text").get_text().replace(view_name, '').strip() part_data = final_soup.get_text().replace(view_name, '').replace(part_name, '').replace('编辑', '') # 历史沿革 name_list = final_soup.findAll('h3', class_="title-text") all_name_list = {} na="part_name"+str(i) all_name_list[na] = part_name final_name_list = []########### for nlist in name_list: nlist = nlist.get_text().replace(view_name, '').strip() final_name_list.append(nlist) fin="final_name_list"+str(i) all_name_list[fin] = final_name_list print(all_name_list) i=i+1 #正文 try: p = re.compile(r'', re.DOTALL) final_soup = final_soup.decode('utf-8') r = p.search(final_soup) final_part_data = final_soup[r.span(0)[0]:] part_lists = final_part_data.split('') for part_list in part_lists: final_part_soup = BeautifulSoup(part_list, "html.parser") content_lists = final_part_soup.findAll("p", class_="para") for content_list in content_lists: # 每个最小段 try: pic_word = content_list.find("p", class_="lemma-picture text-pic layout-right").get_text() # 去掉文字中的图片描述 try: pic_word2 = content_list.find("p", class_="description").get_text() # 去掉文字中的图片描述 content_list = content_list.get_text().replace(pic_word, '').replace(pic_word2, '') except: content_list = content_list.get_text().replace(pic_word, '') except: try: pic_word2 = content_list.find("p", class_="description").get_text() # 去掉文字中的图片描述 content_list = content_list.get_text().replace(pic_word2, '') except: content_list = content_list.get_text() r_part = re.compile(r'\[\d.\]|\[\d\]') part_result, number = re.subn(r_part, "", content_list) part_result = "".join(part_result.split()) #print(part_result) except: final_part_soup = BeautifulSoup(list, "html.parser") content_lists = final_part_soup.findAll("p", class_="para") for content_list in content_lists: try: pic_word = content_list.find("p", class_="lemma-picture text-pic layout-right").get_text() # 去掉文字中的图片描述 try: pic_word2 = content_list.find("p", class_="description").get_text() # 去掉文字中的图片描述 content_list = content_list.get_text().replace(pic_word, '').replace(pic_word2, '') except: content_list = content_list.get_text().replace(pic_word, '') except: try: pic_word2 = content_list.find("p", class_="description").get_text() # 去掉文字中的图片描述 content_list = content_list.get_text().replace(pic_word2, '') except: content_list = content_list.get_text() r_part = re.compile(r'\[\d.\]|\[\d\]') part_result, number = re.subn(r_part, "", content_list) part_result = "".join(part_result.split()) #print(part_result) except: print("error") return def output(self,filename): json_data = json.dumps(self.view_datas, ensure_ascii=False, indent=2) fout = codecs.open(filename+'.json', 'a', encoding='utf-16', ) fout.write( json_data) # print(json_data) return def craw_pic(self,url,filename,html_count): soup = BeautifulSoup(html_count, 'html.parser', from_encoding='utf_8') node_pic=soup.find('p',class_='banner').find("a", href=re.compile("/photo/poi/....\.")) if node_pic is None: return None else: part_url_pic=node_pic['href'] full_url_pic=urllib.parse.urljoin(url,part_url_pic) #print(full_url_pic) try: html_pic = urlopen(full_url_pic) except HTTPError as e: return None soup_pic=BeautifulSoup(html_pic.read()) pic_node=soup_pic.find('p',class_="album-list") print(pic_node) return if __name__ =="__main__" : spider=BaikeCraw() filename="D:\PyCharm\\view_spider\\view_points_part.txt" spider.craw(filename)
总结
用python3根据关键词爬取百度百科的内容到这就基本结束了,希望这篇文章能对大家学习python有所帮助。
更多python3根据关键词爬取百度百科的内容相关文章请关注PHP中文网!

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

Python provides a variety of ways to download files from the Internet, which can be downloaded over HTTP using the urllib package or the requests library. This tutorial will explain how to use these libraries to download files from URLs from Python. requests library requests is one of the most popular libraries in Python. It allows sending HTTP/1.1 requests without manually adding query strings to URLs or form encoding of POST data. The requests library can perform many functions, including: Add form data Add multi-part file Access Python response data Make a request head

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

PDF files are popular for their cross-platform compatibility, with content and layout consistent across operating systems, reading devices and software. However, unlike Python processing plain text files, PDF files are binary files with more complex structures and contain elements such as fonts, colors, and images. Fortunately, it is not difficult to process PDF files with Python's external modules. This article will use the PyPDF2 module to demonstrate how to open a PDF file, print a page, and extract text. For the creation and editing of PDF files, please refer to another tutorial from me. Preparation The core lies in using external module PyPDF2. First, install it using pip: pip is P

This tutorial demonstrates how to leverage Redis caching to boost the performance of Python applications, specifically within a Django framework. We'll cover Redis installation, Django configuration, and performance comparisons to highlight the bene

Natural language processing (NLP) is the automatic or semi-automatic processing of human language. NLP is closely related to linguistics and has links to research in cognitive science, psychology, physiology, and mathematics. In the computer science

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

SublimeText3 Chinese version
Chinese version, very easy to use

SublimeText3 Mac version
God-level code editing software (SublimeText3)

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

Dreamweaver CS6
Visual web development tools

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software
