项目需求

采集csdn 热度排行榜

网页分析

F12 启动开发者模式，分析网络传输，捕获传输请求URL如下

综合
https://blog.csdn.net/phoenix/web/blog/hot-rank?page=0&pageSize=25

悬赏
https://bizapi.csdn.net/mp/ask/v1/questions/getRewardRankList?pageNo=1&pageSize=20

C/C++
ttps://blog.csdn.net/phoenix/web/blog/hot-rank?page=0&pageSize=25&child_channel=c%2Fc%2B%2B

java
https://blog.csdn.net/phoenix/web/blog/hot-rank?page=0&pageSize=25&child_channel=java

javascript
https://blog.csdn.net/phoenix/web/blog/hot-rank?page=0&pageSize=25&child_channel=javascript

python
https://blog.csdn.net/phoenix/web/blog/hot-rank?page=0&pageSize=25&child_channel=python

人工智能
https://blog.csdn.net/phoenix/web/blog/hot-rank?page=0&pageSize=25&child_channel=人工智能

猜测其他领域榜单变的只有child_channel参数，验证证实
随机改变pageSize和page 观察响应 每次最多只响应50条数据，且只有两页page
滚动排行榜，如下图所示，综合榜单只有100，初步证实猜测，
领域排行榜只显示前50，但可以请求前100数据

具体爬虫过程中的处理思路写在后面完整代码的注释里了

完整代码

import requests
import pandas as pd
from fake_useragent import UserAgent
import json
import urllib.parse
import time


def parse_data(response):
    """解析请求返回的数据，整理成dataframe格式"""

    data = response.content.decode('utf-8')

    df = pd.DataFrame(json.loads(data)['data'])

    # 重命名字段名称
    col = {"hotRankScore": "热度", "pcHotRankScore": "pcHotRankScore", "loginUserIsFollow": "loginUserIsFollow",
           "nickName": "作者", "avatarUrl": "作者头像图片", "userName": "作者id", "articleTitle": "文章名称",
           "articleDetailUrl": "文章链接", "commentCount": "评论数", "favorCount": "收藏数", "viewCount": "浏览量",
           "hotComment": "hotComment", "picList": "插图链接"}
    df.rename(columns=col, inplace=True)

    # 保留需要的数据字段
    df = df[['作者', '文章名称', '热度', '文章链接', '评论数', '收藏数', '浏览量']]

    return df


def get_rank_all():
    """获取综合排行榜"""
    result = []
    for page in range(2):
        url = f"https://blog.csdn.net/phoenix/web/blog/hot-rank?page={page}&pageSize=50"

        # 生成随机虚拟请求头
        ua = UserAgent()
        headers = {'User-Agent': ua.random}
        response = requests.get(url, headers=headers)

        # 数据解析
        data = parse_data(response)
        result.append(data)
    # 数据合并保存
    all_result = pd.concat(result).reset_index(drop=True)
    all_result["热榜排名"] = all_result.index + 1
    # 获取当前时间
    now = time.strftime("%Y-%m-%d %H_%M_%S", time.localtime())
    all_result.to_csv(f"{now} C站综合热榜.csv", index=False, encoding="gbk")


def get_rank_child_channel(channel):
    """获取领域热榜"""
    # 对中文做url编码，不做也可以识别，没什么影响
    # channel = channel.parse.quote(channel)
    result = []
    for page in range(2):
        url = f"https://blog.csdn.net/phoenix/web/blog/hot-rank?page=0&pageSize=50&child_channel={channel}"
        ua = UserAgent()
        headers = {'User-Agent': ua.random}
        response = requests.get(url, headers=headers)
        # 数据解析
        data = parse_data(response)
        result.append(data)
    # 数据合并保存
    all_result = pd.concat(result).reset_index(drop=True)
    all_result["热榜排名"] = all_result.index + 1
    # 获取当前时间
    now = time.strftime("%Y-%m-%d %H_%M_%S", time.localtime())
    all_result.to_csv(f"{now} C站{channel}领域热榜.csv", index=False, encoding="utf-8-sig")


def main():
    # 获取全站综合当前热度排行
    get_rank_all()

    # 获取领域当前热度排行——列举了部分，具体参考网页推荐
    channel_list = ["人工智能", "C/C++", "Java", "区块链", "大数据", "移动开发", "嵌入式", ]
    # channel = "人工智能"
    for channel in channel_list:
        get_rank_child_channel(channel)


if __name__ == '__main__':
    main()