我用Python爬虫爬取并分析了C站前100用户最高访问的2000篇文章

最近系统地学习了正则表达式，发现正则表达式最大的应用之一——网络爬虫之前一直没有涉猎，遂借此契机顺带写一个爬虫的demo备用。选择对象是CSDN排行榜前100用户，各自按访问量从高到低排序的前20篇文章，使用一些简单的数据分析手段看看技术热点，方便今后拓宽技术栈。

主要爬取的数据是文章标题和访问量，先总体可视化总体文章的技术关键词；然后按访问量分组，可视化每个访问段的技术热点。

获得服务器API

首先我们要知道通过什么接口可以获得网站数据：首先进入博客总榜，按F12进入控制台，选中Network选项卡监视网络请求，然后刷新网页。从下图可以看到在API"https://blog.csdn.net/phoenix/web/blog/all-rank?page=1&pageSize=20"中我们可以拿到我们想要的用户信息——主要是用户名

现在到用户博客首页，同样地，按F12进入控制台，选中Network选项卡监视网络请求，然后点击按访问量排序，则可以发现另一个关键APIhttps://blog.csdn.net/community/home-api/v1/get-business-list?page=1&size=20&businessType=blog&orderby=ViewCount&noMore=false&username={}，如下图所示。

我们与服务器的交互就依靠这两个API进行。

程序总体设计

思考一下，我们总共有如下的公共变量：

# 请求头
headers = {
            'User-Agent':
            'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'
        }
# 排行榜url
rankUrl = "https://blog.csdn.net/phoenix/web/blog/all-rank?page={}&pageSize=20"
# 按访问量排行的文章列表
mostViewArtical = "https://blog.csdn.net/community/home-api/v1/get-business-list?page=1&size=20&businessType=blog&orderby=ViewCount&noMore=false&username={}"

userNames =[] # 用户名列表
titleList = []	# 文章标题列表
viewCntList = [] # 访问量列表

为便于管理，引入一个类进行爬虫，专门负责与服务器进行数据交互

class GetInfo:
    def __init__(self) -> None:
        # 请求头
        self.headers = {
            'User-Agent':
            'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'
        }
        # 排行榜url
        self.rankUrl = "https://blog.csdn.net/phoenix/web/blog/all-rank?page={}&pageSize=20"
        # 按访问量排行的文章列表
        self.mostViewArtical = "https://blog.csdn.net/community/home-api/v1/get-business-list?page=1&size=20&businessType=blog&orderby=ViewCount&noMore=false&username={}"

        self.userNames = []
        self.titleList, self.viewCntList = [], []

交互完成后，再使用别的库进行数据分析，将两个过程分离开

用户名爬取

定义一个私有的初始化函数

def __initRankUsrName(self):
    usrNameList = []
    for i in range(5):
        response = requests.get(url=self.rankUrl.format(i),
                                headers=self.headers)
        response.encoding = 'utf-8'
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        information = json.loads(str(soup))
        for item in information['data']['allRankListItem']:
            usrNameList.append(item['userName'])
    return usrNameList

这里获取用户名主要是为了动态生成第二个API

再定义一个私有函数，输入参数是用户名列表：

def __initArticalInfo(self, usrList):
    titleList = []
    viewCntList = []
    for name in usrList:
        url = self.mostViewArtical.format(name)
        # print(url)
        response = requests.get(url=url, headers=self.headers)
        response.encoding = 'utf-8'
        response.raise_for_status()
        titleList.extend(re.findall(r"\"title\":\"(.*?)\"", response.text))
        viewCntList.extend(re.findall(r"\"viewCount\":(.*?),", response.text))
    return titleList, viewCntList

这里我使用正则表达式直接处理字符串，并返回文章标题列表、访问量列表。可以随便访问一个API做实验，这里以我的用户名为例，可以看到要获取文章标题就是以\"title\":\"(.*?)\"去匹配，其中\用于转义；要获取访问量就是以\"viewCount\":(.*?),去匹配，访问数字没有加引号。

事实上，用正则匹配不需要将返回的字符串加载为Json字典，可能有更快的处理效率(但不如json灵活)

这个爬虫类就设计好了，完整代码如下：

class GetInfo:
    def __init__(self) -> None:
        # 请求头
        self.headers = {
            'User-Agent':
            'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'
        }
        # 排行榜url
        self.rankUrl = "https://blog.csdn.net/phoenix/web/blog/all-rank?page={}&pageSize=20"
        # 按访问量排行的文章列表
        self.mostViewArtical = "https://blog.csdn.net/community/home-api/v1/get-business-list?page=1&size=20&businessType=blog&orderby=ViewCount&noMore=false&username={}"

        self.userNames = self.__initRankUsrName()
        self.titleList, self.viewCntList = self.__initArticalInfo(
            self.userNames)

    def __initArticalInfo(self, usrList):
        titleList = []
        viewCntList = []
        for name in usrList:
            url = self.mostViewArtical.format(name)
            # print(url)
            response = requests.get(url=url, headers=self.headers)
            response.encoding = 'utf-8'
            response.raise_for_status()
            titleList.extend(re.findall(r"\"title\":\"(.*?)\"", response.text))
            viewCntList.extend(
                re.findall(r"\"viewCount\":(.*?),", response.text))
        return titleList, viewCntList

    def __initRankUsrName(self):
        usrNameList = []
        for i in range(5):
            response = requests.get(url=self.rankUrl.format(i),
                                    headers=self.headers)
            response.encoding = 'utf-8'
            response.raise_for_status()
            soup = BeautifulSoup(response.text, 'html.parser')
            information = json.loads(str(soup))
            for item in information['data']['allRankListItem']:
                usrNameList.append(item['userName'])
        return usrNameList


info = GetInfo()

使用也很方便，只需要实例化调用其中的列表属性即可。

将文本数据存成csv格式，先设计表头：

if not os.path.exists("articalInfo.csv"):
    #创建存储csv文件存储数据
    with open('articalInfo.csv', "w", encoding="utf-8-sig", newline='') as f:
        csv_head = csv.writer(f)
        csv_head.writerow(['title', 'viewCnt'])

注意编码格式为utf-8-sig，否则会乱码

接下来存数据：

length = len(info.titleList)
for i in range(length):
    if info.titleList[i]:
        with open('articalInfo.csv', 'a+', encoding='utf-8-sig') as f:
            f.write(info.titleList[i] + ',' + info.viewCntList[i] + '\n')

总体数据可视化

新建一个模块专门用于可视化数据，与爬虫分离开，因为爬虫是慢IO过程，会影响调试效率，后面可以试试用协程来处理爬虫。

首先，把爬虫的信息读取到txt文件去

df = pd.read_csv('articalInfoNor.csv', encoding='utf-8-sig',usecols=['title', 'viewCnt'])
titleList = '，'.join(df['title'].values)
with open('text.txt','a+', encoding='utf-8-sig') as f:
    f.writelines(titleList)

如何返回分词结果:

def getKeyWordText():
    # 读取文件信息
    file = open(path.join(path.dirname(__file__), 'text.txt'), encoding='utf-8-sig').read()
    return ' '.join(jieba.cut(file))

借助词云库可视化一下:

bg_pic = imread('2.jpg')

#生成词云
wordcloud = WordCloud(font_path=r'C:\Windows\Fonts\simsun.ttc',mask=bg_pic,background_color='white',scale=1.5).generate(text)
image_colors = ImageColorGenerator(bg_pic)

#显示词云图片
plt.imshow(wordcloud)
plt.axis('off')
plt.show()

#保存图片
wordcloud.to_file('test.jpg')

这个大大的“的”是什么鬼？显然高频关键词里有太多语气助词、连接词，我们最好设置一个停用词列表把这些明显不需要的词屏蔽掉。我这里采用修饰器的方法让代码更简洁，关于修饰器的内容可以参考Python修饰器

def splitText(mode):
    stopWords = ["的","与","和","建议","收藏","使用","了","实现","我","中","你","在","之","年","月","日"]
    def warpper(func):
        def warp():
            textSplit = func()
            if mode:
                temp = [word for word in textSplit if word not in stopWords]
                return ' '.join(temp)
            else:
                return ' '.join(textSplit)
        return warp
    return warpper

当mode=True时启用屏蔽，否则关闭屏蔽，那么之前的函数应该修改为:

# 返回关键词文本
@splitText(False)
def getKeyWordText():
    # 读取文件信息
    file = open(path.join(path.dirname(__file__), 'text.txt'), encoding='utf-8-sig').read()
    return jieba.cut(file)

再来一次：

现在就正常多了。可以看到Python和Java是绝对的领先，之后是各位总结的方法论等等，算法的词频反而不高？

我把数据进一步分层为

1、访问量>10W
2、访问量5W~10W
3、访问量1W~5W
4、访问量5K~1W
5、访问量5K以下

先来看看数据分布情况：

我猜如果分段分得再细一点可能趋于正态分布~

分组可视化看看：

>10W的词云

5~10W的词云

1~5W的词云

5k~1W的词云

感觉从这里开始更百花齐放一些，似乎也更关注具体问题的解决

5k以下的词云

不得不感叹python在每个阶段都是牌面

import requests
from bs4 import BeautifulSoup
import os, json, re, csv

class GetInfo:
    def __init__(self) -> None:
        # 请求头
        self.headers = {
            'User-Agent':
            'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'
        }
        # 排行榜url
        self.rankUrl = "https://blog.csdn.net/phoenix/web/blog/all-rank?page={}&pageSize=20"
        # 按访问量排行的文章列表
        self.mostViewArtical = "https://blog.csdn.net/community/home-api/v1/get-business-list?page=1&size=20&businessType=blog&orderby=ViewCount&noMore=false&username={}"

        self.userNames = self.__initRankUsrName()
        self.titleList, self.viewCntList = self.__initArticalInfo(
            self.userNames)

    def __initArticalInfo(self, usrList):
        titleList = []
        viewCntList = []
        for name in usrList:
            url = self.mostViewArtical.format(name)
            # print(url)
            response = requests.get(url=url, headers=self.headers)
            response.encoding = 'utf-8'
            response.raise_for_status()
            titleList.extend(re.findall(r"\"title\":\"(.*?)\"", response.text))
            viewCntList.extend(
                re.findall(r"\"viewCount\":(.*?),", response.text))
        return titleList, viewCntList

    def __initRankUsrName(self):
        usrNameList = []
        for i in range(5):
            response = requests.get(url=self.rankUrl.format(i),
                                    headers=self.headers)
            response.encoding = 'utf-8'
            response.raise_for_status()
            soup = BeautifulSoup(response.text, 'html.parser')
            information = json.loads(str(soup))
            for item in information['data']['allRankListItem']:
                usrNameList.append(item['userName'])
        return usrNameList
        
info = GetInfo()

if not os.path.exists("articalInfo.csv"):
    #创建存储csv文件存储数据
    with open('articalInfo.csv', "w", encoding="utf-8-sig", newline='') as f:
        csv_head = csv.writer(f)
        csv_head.writerow(['title', 'viewCnt'])

length = len(info.titleList)
for i in range(length):
    if info.titleList[i]:
        with open('articalInfo.csv', 'a+', encoding='utf-8-sig') as f:
            f.write(info.titleList[i] + ',' + info.viewCntList[i] + '\n')

from wordcloud import WordCloud,ImageColorGenerator
import matplotlib.pyplot as plt
from imageio import imread
import jieba
import pandas as pd
from os import path

df = pd.read_csv('articalInfoCom.csv', encoding='utf-8-sig',usecols=['title', 'viewCnt'])
titleList = '，'.join(df['title'].values)
with open('text.txt','a+', encoding='utf-8-sig') as f:
    f.writelines(titleList)

def splitText(mode):
    stopWords = ["的","与","和","建议","收藏","使用","了","实现","我","中","你","在","之","年","月","日"]
    def warpper(func):
        def warp():
            textSplit = func()
            if mode:
                temp = [word for word in textSplit if word not in stopWords]
                return ' '.join(temp)
            else:
                return ' '.join(textSplit)
        return warp
    return warpper

# 返回关键词文本
@splitText(True)
def getKeyWordText():
    # 读取文件信息
    file = open(path.join(path.dirname(__file__), 'text.txt'), encoding='utf-8-sig').read()
    return jieba.cut(file)


text = getKeyWordText()
#读取txt文件、背景图片
bg_pic = imread('2.jpg')

#生成词云
wordcloud = WordCloud(font_path=r'C:\Windows\Fonts\simsun.ttc',mask=bg_pic,background_color='white',scale=1.5).generate(text)
image_colors = ImageColorGenerator(bg_pic)

#显示词云图片
plt.imshow(wordcloud)
plt.axis('off')
plt.show()

#保存图片
wordcloud.to_file('test.jpg')