今天分享一个用Python来爬取小说的小脚本！（附源码）[Python基础]

Z时代
2024-01-10
分类：综合

python

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以下文章来源于大邓和他的Python ，作者：大邓

Python爬取知乎数据案例讲解视频

https://www.bilibili.com/video/BV1fz4y1Z7zz/

前言

问题链接

https://www.zhihu.com/question/432119474/answer/1597194524

爬虫设计流程

探寻网址规则尝试对某些网页访问解析学术的数据存储到csv整理汇总代码

1.探寻网址规则

按F12键打开开发者工具，

进入网络面板，点击查看全部6217个回答

准备观察开发者工具中的监测到的网址

对每个网址经过下图456操作

点击预览

查看内容与当前页面的回答是否一致

最终发现网址如7中的红色方框，请求方式为GET方法

依旧是7位于的页面，滑动到最下方，可以看到offset和limit

发现的网址（注意最后一行的偏移）

https://www.zhihu.com/api/v4/questions/432119474/answers?include=data%5B*%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Cis_sticky%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cattachment%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Ccreated_time%2Cupdated_time%2Creview_info%2Crelevant_info%2Cquestion%2Cexcerpt%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%2Cis_labeled%2Cis_recognized%2Cpaid_info%2Cpaid_info_content%3Bdata%5B*%5D.mark_infos%5B*%5D.url%3Bdata%5B*%5D.author.follower_count%2Cbadge%5B*%5D.topics%3Bsettings.table_of_content.enabled%3B&offset=3&limit=5&sort_by=default&platform=desktop

中也存在offset，该单词的意思是偏移量。

offset我猜测该值数值页面页面数

limit每个网址能展现多少个回答，至少5个。

网址模板（注意模板内最后一行偏移）

https://www.zhihu.com/api/v4/questions/432119474/answers?include=data%5B*%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Cis_sticky%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cattachment%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Ccreated_time%2Cupdated_time%2Creview_info%2Crelevant_info%2Cquestion%2Cexcerpt%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%2Cis_labeled%2Cis_recognized%2Cpaid_info%2Cpaid_info_content%3Bdata%5B*%5D.mark_infos%5B*%5D.url%3Bdata%5B*%5D.author.follower_count%2Cbadge%5B*%5D.topics%3Bsettings.table_of_content.enabled%3B&offset={offset}&limit=5&sort_by=default&platform=desktop

当前回答一共有6200多个，每页5个，那么offset可以有1240页。

2.尝试对某些网页访问

import requests
template = "https://www.zhihu.com/api/v4/questions/432119474/answers?include=data%5B*%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Cis_sticky%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cattachment%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Ccreated_time%2Cupdated_time%2Creview_info%2Crelevant_info%2Cquestion%2Cexcerpt%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%2Cis_labeled%2Cis_recognized%2Cpaid_info%2Cpaid_info_content%3Bdata%5B*%5D.mark_infos%5B*%5D.url%3Bdata%5B*%5D.author.follower_count%2Cbadge%5B*%5D.topics%3Bsettings.table_of_content.enabled%3B&offset={offset}&limit=5&sort_by=default&platform=desktop"
#for page in range(1, 1240):
url = template.format(offset=1)
headers = {"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.67 Safari/537.36"}
resp = requests.get(url, headers=headers)
resp

我们注意到5种数据可以展开，大概率是json样式的数据。

所以尝试使用resp.json（）来拿到格式化的字典数据。

3.解析研究的数据

由于resp.json（）返回的是字典，解析定位数据会很方便。

咱们就需求简单点，只采集作者，id，摘录三个变量。

其中作者内部含有很多更丰富的信息，研究的可以把作者再整理下，此处对作者不做过多清洗。

for info in resp.json()["data"]:
    author = info["author"]
    Id = info["id"]
    text = info["excerpt"]
    data = {"author": author,
"id": Id,
"text": text}
print(data)
{"author": {"id": "10779c1a8b8de0b7f14bb6c217100680", "url_token": "da-mo-wang-81-18", "name": "紧衣卫", "avatar_url": "https://pic2.zhimg.com/v2-ebdfa810ec26e81720b8bc14fc6c1d76_l.jpg?source=1940ef5c", "avatar_url_template": "https://pic3.zhimg.com/v2-ebdfa810ec26e81720b8bc14fc6c1d76.jpg?source=1940ef5c", "is_org": False, "type": "people", "url": "https://www.zhihu.com/api/v4/people/10779c1a8b8de0b7f14bb6c217100680", "user_type": "people", "headline": "梦想太大  心太执着", "badge": [{"type": "identity", "description": "江苏省宝应县公安局 警察", "topics": []}], "badge_v2": {"title": "江苏省宝应县公安局 警察", "merged_badges": [{"type": "identity", "detail_type": "identity", "title": "认证", "description": "江苏省宝应县公安局 警察", "url": "https://www.zhihu.com/account/verification/intro", "sources": [], "icon": "", "night_icon": ""}], "detail_badges": [{"type": "identity", "detail_type": "identity_people", "title": "已认证的个人", "description": "江苏省宝应县公安局 警察", "url": "https://www.zhihu.com/account/verification/intro", "sources": [], "icon": "https://pic1.zhimg.com/v2-235258cecb8a0f184c4d38483cd6f6b6_l.png", "night_icon": "https://pic3.zhimg.com/v2-45e870b8f0982bcd7537ea4627afbd00_l.png"}], "icon": "https://pic4.zhimg.com/v2-235258cecb8a0f184c4d38483cd6f6b6_l.png", "night_icon": "https://pic3.zhimg.com/v2-45e870b8f0982bcd7537ea4627afbd00_l.png"}, "gender": 1, "is_advertiser": False, "follower_count": 3914, "is_followed": False, "is_privacy": False}, "id": 1597225705, "text": "我也不喜欢过洋节，但我知道最起码的人情世故。俗话说出手不打笑脸人 阿姨是给你送巧克力，又不是给你送鹤顶红 如果内心不愿意接受就婉拒好了 这个有什么好举报的？即使阿姨真的是为了庆祝感恩节 你又想得到一个什么样的举报结果呢？据说举报人是个博士生，我看也别研究别的了，转方向先学习学习最基本的为人处事方法吧。"}
{"author": {"id": "09c8a026f91155c87188ee6e45301f05", "url_token": "cao-ling-er-36", "name": "曹小灵", "avatar_url": "https://pic1.zhimg.com/v2-942365af26ff3abd44f6c75c9899306e_l.jpg?source=1940ef5c", "avatar_url_template": "https://pic2.zhimg.com/v2-942365af26ff3abd44f6c75c9899306e.jpg?source=1940ef5c", "is_org": False, "type": "people", "url": "https://www.zhihu.com/api/v4/people/09c8a026f91155c87188ee6e45301f05", "user_type": "people", "headline": "伪商业分析*抽风式咨询*个人成长", "badge": [{"type": "identity", "description": "Backbase 管理分析咨询师", "topics": []}], "badge_v2": {"title": "Backbase 管理分析咨询师", "merged_badges": [{"type": "identity", "detail_type": "identity", "title": "认证", "description": "Backbase 管理分析咨询师", "url": "https://www.zhihu.com/account/verification/intro", "sources": [], "icon": "", "night_icon": ""}], "detail_badges": [{"type": "identity", "detail_type": "identity_people", "title": "已认证的个人", "description": "Backbase 管理分析咨询师", "url": "https://www.zhihu.com/account/verification/intro", "sources": [], "icon": "https://pic1.zhimg.com/v2-235258cecb8a0f184c4d38483cd6f6b6_l.png", "night_icon": "https://pic1.zhimg.com/v2-45e870b8f0982bcd7537ea4627afbd00_l.png"}], "icon": "https://pic2.zhimg.com/v2-235258cecb8a0f184c4d38483cd6f6b6_l.png", "night_icon": "https://pic2.zhimg.com/v2-45e870b8f0982bcd7537ea4627afbd00_l.png"}, "gender": 1, "is_advertiser": False, "follower_count": 412637, "is_followed": False, "is_privacy": False}, "id": 1597380398, "text": "亲人们，知道为什么很多机关办事人员会僵化么？知道为什么很多机构不愿意创新么？知道为什么很多组织都是照章办事缺乏灵活么？你看到的这个案例，就是原因。很多事情，你不做，就不会错；做了，取得的回报很少，但是承担的风险很高。你永远不知道什么地方会突然飞过来一泡口水，把你的好意扭曲为恶意，把你的行为定性为「不妥」。如果是在影视行业，不断在边缘疯狂的试探，还可以理解，这样可以给观众带来新的刺激，换来一句…"}
{"author": {"id": "a82cf4ad4be905dbc044297e70e3b382", "url_token": "alyssa-song", "name": "貂丝女婶教授", "avatar_url": "https://pic4.zhimg.com/v2-2b1a52a861aecec566a3c2c4f00a5e06_l.jpg?source=1940ef5c", "avatar_url_template": "https://pic2.zhimg.com/v2-2b1a52a861aecec566a3c2c4f00a5e06.jpg?source=1940ef5c", "is_org": False, "type": "people", "url": "https://www.zhihu.com/api/v4/people/a82cf4ad4be905dbc044297e70e3b382", "user_type": "people", "headline": "985副教授，少女型大妈，生化环材", "badge": [], "badge_v2": {"title": "", "merged_badges": [], "detail_badges": [], "icon": "", "night_icon": ""}, "gender": 0, "is_advertiser": False, "follower_count": 10200, "is_followed": False, "is_privacy": False}, "id": 1597237844, "text": "我从前就住在哈工大的17公寓。宿管阿姨们真的非常好，非常温暖。我当时有一段生病天天待在寝室，阿姨们白天巡寝的时候还经常问我好点了没有，用不用帮我从楼下带盒饭上来。对我这种从小就缺爱的人，阿姨真的就像自己的亲人一样，让我从内心深处感觉到了家的温暖。住在17公寓的日子真的超级美好。当年哈工大的后勤管理还有一点很好，就是每一届毕业的学生把不穿也不带走的旧大衣捐给学校，新一届本科生入校时候，贫困生可以每人…"}
{"author": {"id": "e6bbeff00614e94be5225c9fb401f989", "url_token": "zai-yi-qi-61-23", "name": "精分的曹小灵", "avatar_url": "https://pic4.zhimg.com/v2-d3325d2f1d7de8511a5e9a328c75a539_l.jpg?source=1940ef5c", "avatar_url_template": "https://pic2.zhimg.com/v2-d3325d2f1d7de8511a5e9a328c75a539.jpg?source=1940ef5c", "is_org": False, "type": "people", "url": "https://www.zhihu.com/api/v4/people/e6bbeff00614e94be5225c9fb401f989", "user_type": "people", "headline": "头脑中存在两种截然相反的思想，开始精分", "badge": [], "badge_v2": {"title": "", "merged_badges": [], "detail_badges": [], "icon": "", "night_icon": ""}, "gender": 1, "is_advertiser": False, "follower_count": 4997, "is_followed": False, "is_privacy": False}, "id": 1597348175, "text": "啊…又来了。对这种道德高地站的稳的学生，张博洋曾经发出过灵魂质问：[图片] [图片] 宿管阿姨明确的说了：这是西方的节日，自己是借着这个节日的机会，感谢同学们对工作的支持。现在很多女生找各种借口过节，很多男生找各种借口送礼，怎么就不能让宿管阿姨给你一个不想奋斗的机会呢？这种人，不配当个男人，至少不配当一个新时代的以阿姨包养为梦想的好男人。你背叛了你的阶级！手动差评！要上纲上线，别急，咱们一层一层的来。先讲历…"}
{"author": {"id": "0ba346aab1e8761d12e8d1cf28fce8f8", "url_token": "xia-xiao-qi-61", "name": "瞄小七.sunshine", "avatar_url": "https://pic2.zhimg.com/v2-fc018e46c81d7828e65d34613f62425c_l.jpg?source=1940ef5c", "avatar_url_template": "https://pic4.zhimg.com/v2-fc018e46c81d7828e65d34613f62425c.jpg?source=1940ef5c", "is_org": False, "type": "people", "url": "https://www.zhihu.com/api/v4/people/0ba346aab1e8761d12e8d1cf28fce8f8", "user_type": "people", "headline": "三甲医院手术室护师/护理学穿搭博主", "badge": [], "badge_v2": {"title": "", "merged_badges": [], "detail_badges": [], "icon": "", "night_icon": ""}, "gender": 0, "is_advertiser": False, "follower_count": 9792, "is_followed": False, "is_privacy": False}, "id": 1598254838, "text": "[图片] 举报这个事，小题大做。本来是一件在寒冬天里温暖人心的好事，也不知道是触动了一个脑回路清奇之人的哪根敏感神经？这位举报爱好者就这样愤愤地向宿管阿姨发出了最后通牒。我心里还在想呢，这么有爱的宿管阿姨，果然是别人学校的宿管阿姨。没想到，有这样的宿舍阿姨，有些同学非但不知珍惜，还要上纲上线举报她。那位举报阿姨的同学，平时心里应该很苦嘴巴才这么毒吧？是自己生活过的太平淡无为才用这种方式在善良的宿管阿…"}

4.存储到csv

设置三个细分，存储到csv。

注意现在只是对某些页面的数据进行的存储。

import csv
csvf = open("zhihu.csv", "a+", encoding="utf-8", newline="")
fieldnames = ["author", "id", "text"]
writer = csv.DictWriter(csvf, fieldnames=fieldnames)
writer.writeheader()
for info in resp.json()["data"]:
    author = info["author"]
    Id = info["id"]
    text = info["excerpt"]
    data = {"author": author,
"id": Id,
"text": text}
    writer.writerow(data)
csvf.close()

5.整理汇总代码

将1234代码合并整理，就是一个对知乎问题回答的批量采集爬虫。

注意复制标签代码，要注意层次关系，上下变量名要对应衔接接。

import requests
import csv
import time
#新建csv，存储数据
csvf = open("zhihu.csv", "a+", encoding="utf-8", newline="")
fieldnames = ["author", "id", "text"]
writer = csv.DictWriter(csvf, fieldnames=fieldnames)
writer.writeheader()
#伪装头，用于反爬
headers = {"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.67 Safari/537.36"}
#网址模板
template = "https://www.zhihu.com/api/v4/questions/432119474/answers?include=data%5B*%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Cis_sticky%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cattachment%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Ccreated_time%2Cupdated_time%2Creview_info%2Crelevant_info%2Cquestion%2Cexcerpt%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%2Cis_labeled%2Cis_recognized%2Cpaid_info%2Cpaid_info_content%3Bdata%5B*%5D.mark_infos%5B*%5D.url%3Bdata%5B*%5D.author.follower_count%2Cbadge%5B*%5D.topics%3Bsettings.table_of_content.enabled%3B&offset={offset}&limit=5&sort_by=default&platform=desktop"
#批量采集，
#目前可爬1240+页数据
#这里仅抓取前100页
for page in range(1, 100):
#对第page页进行访问
    url = template.format(offset=page)
    resp = requests.get(url, headers=headers)
#解析定位第page页的数据
for info in resp.json()["data"]:
        author = info["author"]
        Id = info["id"]
        text = info["excerpt"]
        data = {"author": author,
"id": Id,
"text": text}
#存入csv
        writer.writerow(data)
#降低爬虫对知乎的访问速度
    time.
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