怎么使用Python爬虫

本篇内容介绍了"怎么使用Python爬虫"的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！

1.导入模块

import re from bs4 import BeautifulSoup import requests import time import json import pandas as pd import numpy as np

2.状态码

r = requests.get('https://github.com/explore') r.status_code

3. 爬取*乎

#浏览器header和cookies headers = {'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.87 Safari/537.36'} cookies = {'cookie':'_zap=3d979dbb-f25b-4014-8770-89045dec48f6; d_c0="APDvML4koQ-PTqFU56egNZNd2wd-eileT3E=|1561292196"; tst=r; _ga=GA1.2.910277933.1582789012; q_c1=9a429b07b08a4ae1afe0a99386626304|1584073146000|1561373910000; _xsrf=bf1c5edf-75bd-4512-8319-02c650b7ad2c; _gid=GA1.2.1983259099.1586575835; l_n_c=1; l_cap_id="NDIxM2M4OWY4N2YwNDRjM2E3ODAxMDdmYmY2NGFiMTQ=|1586663749|ceda775ba80ff485b63943e0baf9968684237435"; r_cap_id="OWY3OGQ1MDJhMjFjNDBiYzk0MDMxMmVlZDIwNzU0NzU=|1586663749|0948d23c731a8fa985614d3ed58edb6405303e99"; cap_id="M2I5NmJkMzRjMjc3NGZjNDhiNzBmNDMyNDQ3NDlmNmE=|1586663749|dacf440ab7ad64214a939974e539f9b86ddb9eac"; n_c=1; Hm_lvt_98beee57fd2ef70ccdd5ca52b9740c49=1586585625,1586587735,1586667228,1586667292; Hm_lpvt_98beee57fd2ef70ccdd5ca52b9740c49=1586667292; SESSIONID=GWBltmMTwz5oFeBTjRm4Akv8pFF6p8Y6qWkgUP4tjp6; JOID=UVkSBEJI6EKgHAipMkwAEWAkvEomDbkAwmJn4mY1kHHPVGfpYMxO3voUDK88UO62JqgwW5Up4hC2kX_KGO9xoKI=; osd=UlEXAU5L4EelEAuhN0kMEmghuUYlBbwFzmFv52M5k3nKUWvqaMlL0vkcCaowU-azI6QzU5As7hO-lHrGG-d0pa4=; capsion_ticket="2|1:0|10:1586667673|14:capsion_ticket|44:YTJkYmIyN2Q4YWI4NDI0Mzk0NjQ1YmIwYmUxZGYyNzY=|b49eb8176314b73e0ade9f19dae4b463fb970c8cbd1e6a07a6a0e535c0ab8ac3"; z_c0="2|1:0|10:1586667694|4:z_c0|92:Mi4xOGc1X0dnQUFBQUFBOE84d3ZpU2hEeVlBQUFCZ0FsVk5ydTVfWHdDazlHMVM1eFU5QjlqamJxWVhvZ2xuWlhTaVJ3|bcd3601ae34951fe72fd3ffa359bcb4acd60462715edcd1e6c4e99776f9543b3"; unlock_ticket="AMCRYboJGhEmAAAAYAJVTbankl4i-Y7Pzkta0e4momKdPG3NRc6GUQ=="; KLBRSID=fb3eda1aa35a9ed9f88f346a7a3ebe83|1586667697|1586660346'}  start_url = 'https://www.zhihu.com/api/v3/feed/topstory/recommend?session_token=c03069ed8f250472b687fd1ee704dd5b&desktop=true&page_number=5&limit=6&action=pull&ad_interval=-1&before_id=23'

4. beautifulsoup解析

s = requests.Session() start_url = 'https://www.zhihu.com/' html = s.get(url = start_url, headers = headers,cookies = cookies,timeout = 5) soup = BeautifulSoup(html.content)  question = [] ## 名称 question_address = [] ## url  temp1 = soup.find_all('div',class_='Card TopstoryItem TopstoryItem-isRecommend') for item in temp1:     temp2 = item.find_all('div',itemprop="zhihu:question") #     print(temp2)     if temp2 != []: #### 存在专栏等情况,暂时跳过         question_address.append(temp2[0].find('meta',itemprop='url').get('content'))         question.append(temp2[0].find('meta',itemprop='name').get('content'))

5. 存储信息

question_focus_number = [] #关注量 question_answer_number = [] # 回答量 for url in question_address:     test = s.get(url = url,headers = headers,cookies = cookies,timeout = 5)     soup = BeautifulSoup(test.content)     info = soup.find_all('div',class_='QuestionPage')[0] #     print(info)     focus_number = info.find('meta',itemprop="answerCount").get('content')     answer_number = info.find('meta',itemprop="zhihu:followerCount").get('content')     question_focus_number.append(focus_number)     question_answer_number.append(answer_number)

6. 整理信息并输出

question_info = pd.DataFrame(list(zip(question,question_focus_number,question_answer_number)),columns = ['问题名称','关注人数','回答人数'] for item in ['关注人数','回答人数']:     question_info[item] = np.array(question_info[item],dtype = 'int') question_info.sort_values(by='关注人数',ascending = False)

输出：

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