Python爬虫 | 爬取微博和哔哩哔哩数据

目录

一、bill_comment.py

二、bili_comment_pic.py

三、bilibili.py

四、bilihot_pic.py

五、bilisearch_pic.py

六、draw_cloud.py

七、weibo.py

八、weibo_comment.py

九、weibo_comment_pic.py

十、weibo_pic.py

十一、weibo_top.py

十二、weibo_top_pic.py

十三、weibo_top_pie.py

十四、pachong.py

十五、代码文件说明

---

一、bill_comment.py

import requests# 发送请求
import pandas as pd#保存csv文件
import os # 判断文件是否存在
import time
from time import sleep# 设置等待，防止反爬
import json
import random# 生成随机数
import os.path
import requests
import csv
import re
import bili_comment_pic

def trans_date(v_timestamp):
    """"10位时间戳转换为时间字符串"""
    timeArray=time.localtime(v_timestamp)
    otherStyleTime = time.strftime("%Y-%m-%d %H: %M:%S", timeArray)
    return otherStyleTime

def getoid(bv):
    resp=requests.get("https://www.bilibili.com/video/"+bv)
    obj=re.compile(f'"aid":(?P.*?),"bvid":"{bv}"')     #在网页源代码里可以找到id，用正则获取到
    oid=obj.search(resp.text).group('id')
    print('oid是'+oid)    #在程序运行时告诉我们已经获取到了参数oid
    return oid



def get_bili_comment(bv_list,max_page):
    for bvid in bv_list:
        #保存文件名
        bili_file='biliComment_{}pages_{}.csv'.format(max_page,bvid)
        #如果csv存在，先删除
        if os.path.exists(bili_file):
            os.remove(bili_file)
            print('存在，已删除：{}'.format(bili_file))
        #
        # # 请求头
        # headers = {
        #         'Authority':'api.bilibili.com',
        #         'Accept':'application/json, text/plain, */*',
        #         'Accept-Encoding':'gzip, deflate, br',
        #         'Accept-Language':'zh-CN,zh;q=0.9',
        #         #需要定期更换cookie
        #         'Cookie':
        #         'buvid3=09193776-D54E-C4E9-D77E-A3CEC61048A052609infoc; b_nut=1666432252; i-wanna-go-back=-1; b_ut=7; _uuid=9837E983-2521-B3D3-E815-AF3877BF973253126infoc; buvid_fp=bca1b3ca8709dc8fafd31a3014e880cb; nostalgia_conf=-1; PVID=1; CURRENT_FNVAL=4048; rpdid=0z9ZwfQgnR|lkoRrAma|2ss|3w1Q0AxQ; sid=73446m9u; buvid4=FFE4C4F3-FFE7-4A1B-F2E9-BA77F904B1B753643-022102217-RoU6Io6eaXN5hT%2FTDpMpDggrSpyQiYXaOp1a506ie3QU%2FFwMxK3Zhw%3D%3D; b_lsid=E6E6D472_1883D6194B0',
        #         'Origin':'https://www.bilibili.com',
        #         'Referer':'https://www.bilibili.com/video/BV1zh4y1H7ZS/?spm_id_from=333.999.0.0&vd_source=7dd889e8bc19f867cf9a8b6d62c711ee',
        #         'Sec-Ch-Ua':'"Google Chrome";v="113", "Chromium";v="113", "Not-A.Brand";v="24"',
        #         'Sec-Ch-Ua-Mobile':'?0',
        #         'Sec-Ch-Ua-Platform':'"macOS"',
        #         'Sec-Fetch-Dest':'empty',
        #         'Sec-Fetch-Mode':'cors',
        #         'Sec-Fetch-Site':'same-site',
        #         'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36'
        #
        # }

        # # 更简单的网页头
        headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36",
            "referer": "https://www.bilibili.com/"
        }

        for page in range(1,max_page + 1):
            #请求参数
            params = {
                'jsonp':'jsonp',
                'mode': '3',#mode=3代表按热门排序，mode=2代表按时间排序
                'oid': getoid(bvid),
                'next':page,
                'type': '1',
            }
            # type:评论类型,这里固定值1
            # oid: 哪个视频
            # pn: 第几页的评论
            # sort: 排序。0: 按照时间排序。2：按照热度排序。默认2

            url = (f"https://api.bilibili.com/x/v2/reply/main")  # 获得网页源码
            response = requests.get(url, headers=headers,params=params,)
            print(response.status_code)

            data_list=response.json()['data']['replies']#解析评论数据
            comment_list=[]#评论内容空列表
            time_list=[]#评论时间空列表
            #location_list=[]#评论IP空列表
            user_list=[]#评论用户名空列表
            like_list=[]#评论点赞数空列表
            replyCount_list=[]#评论回复数空列表
            userid_list=[]#评论用户id空列表
            #循环爬取每一条评论数据
            for a in data_list:
                #评论内容
                comment=a['content']['message']
                comment_list.append(comment)
                #评论时间
                time=a['ctime']
                time_list.append(trans_date(time))
                #time_list.append(trans_date(v_str=i) for i in range(time))
                # #IP属地(评论后一段时间会消失，所以不爬了)
                # location = a['source']
                # location_list.append(location)
                #评论回复数
                replyCount = a['rcount']
                replyCount_list.append(replyCount)
                #点赞数
                like = a['like']
                like_list.append(like)
                # 评论用户名
                user = a['member']['uname']
                user_list.append(user)
                # 评论用户名
                userid = a['member']['mid']
                userid_list.append(userid)

                #把列表拼接为dataFrame数据
                df=pd.DataFrame({
                    #'视频链接':'https://www.bilibili.com/video/'+v_bid,
                    '评论页码':page,
                    '评论时间':time_list,
                    '评论作者':user_list,
                    '评论id': userid_list,
                    #'IP属地':location_list,
                    '点赞数':like_list,
                    '评论回复数':replyCount_list,
                    '评论内容':comment_list,
                })
                # 表头
                if os.path.exists(bili_file):
                    header = None
                else:
                    header = ['评论页码','评论时间', '评论作者', '评论id', '点赞数', '评论回复数', '评论内容']
                column=['评论页码','评论时间', '评论作者', '评论id', '点赞数', '评论回复数', '评论内容']

                # 保存到csv文件
                df.to_csv(bili_file, mode='a+', index=False, columns=column,header=header, encoding='utf-8-sig')
                #print('csv保存成功：{}'.format(bili_file))
            print('第{}页爬取完成'.format(page))
            #print(df)
            # 数据清洗、去重
        df = pd.read_csv(bili_file, engine='python', encoding='utf-8-sig')
        os.remove(bili_file)
        # 删除重复数据
        df.drop_duplicates(subset='评论内容', inplace=True, keep='first')
        # 再次保存csv文件
        column=header = ['评论页码', '评论时间', '评论作者', '评论id', '点赞数', '评论回复数', '评论内容']
        df.to_csv(bili_file, mode='a+', index=False, columns=column,header=header, encoding='utf-8-sig')
        print('数据清洗完成')
        bili_comment_pic.main(bili_file)


if __name__=='__main__':
    #视频bv号,循环爬取多个视频评论
    #bv_list=['BV1Ss4y1M7KT','BV1VM411N7qc']
    bv_list = [str(x) for x in input("请输入视频bv号(示例：BV1Ss4y1M7KT,BV1VM411N7qc),以逗号分隔：").split(',')]
    #最大爬取页
    max_page=int(input("请输入搜索的页数"))
    #调用爬取
    get_bili_comment(bv_list=bv_list,max_page=max_page)

二、bili_comment_pic.py

# 允许副本存在，忽略报错
import os
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import font_manager
import numpy as np

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


def view(info,bili_file):
    my_font = font_manager.FontProperties(fname='./STHeiti-TC-Medium.ttf')  # 设置中文字体（图标中能显示中文）
    likes = info['点赞数']  # 点赞
    reply = info['评论回复数']  # 回复
    comment = info['评论内容']  # 内容
    # print(comment)

    # 为了坐标轴上能显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False

    # **********************************************************************综合评分和播放量对比
    # *******点赞数条形图
    fig, ax1 = plt.subplots()
    length = len(comment)
    plt.bar(x=np.arange(length), tick_label=comment, height=likes, color='red')  # 设置柱状图
    plt.title('点赞数和评论数数据分析', fontproperties=my_font)  # 表标题
    ax1.tick_params(labelsize=6)
    plt.xlabel('评论内容')  # 横轴名
    plt.ylabel('点赞数')  # 纵轴名
    plt.xticks(rotation=90, color='green')  # 设置横坐标变量名旋转度数和颜色

    # *******评论数折线图
    ax2 = ax1.twinx()  # 组合图必须加这个
    ax2.plot(reply, color='cyan')  # 设置线粗细，节点样式
    plt.ylabel('评论数')  # y轴

    plt.plot(1, label='点赞数', color="red", linewidth=5.0)  # 图例
    #plt.plot(1, label='评论回复数', color="cyan", linewidth=1.0, linestyle="-")  # 图例
    plt.legend()

    plt.savefig('.\图片\pic-{}.png'.format(bili_file), dpi=1000, bbox_inches='tight')  # 保存至本地

    plt.show()


def main(bili_file):

    info = pd.read_csv(bili_file,engine='python', encoding='utf-8-sig')
    info=info.nlargest(60,'点赞数')
    info=info.reset_index(drop=True)
    view(info,bili_file)


if __name__ == '__main__':
    main('biliComment_15pages_BV1Ss4y1M7KT.csv')

三、bilibili.py

import requests
from urllib.parse import quote
import json
import time
from time import sleep
import pandas as pd
import hashlib
import bilihot_pic
import bilisearch_pic

"""
    bilisearch类的需求功能
    1.初始化需要输入参数
        search:你需要搜索的数据
        page:需要查看的页数
    2.使用方法
        a = blisearch(serch,page)  初始化类
        a.findall()    将爬取的数据存入excel文件中
"""


class bilisearch():
    # 第一个输入的参数是搜索数据，第二个是搜素页数
    def __init__(self, search, page):
        # 对输入进行编码
        self.search = search
        self.searchurl = '&keyword=' + quote(search, 'utf-8')

        # 构造浏览器访问请求头
        # 大概是一定要cookie才能访问的  测试一下cookie过段时间还能不能访问
        self.head = {
            'authority': 'api.bilibili.com',
            'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.44',
            'Referer': "https://search.bilibili.com/all?from_source=webtop_search&spm_id_from=333.1007&search_source=5keyword=",
            'referer': 'https://www.bilibili.com/',
            'cookie': 'buvid3=05746C34-6526-44A7-9132-4C0A7180E63C148796infoc; LIVE_BUVID=AUTO4216287558369376; i-wanna-go-back=-1; CURRENT_BLACKGAP=0; buvid4=CE2658E1-DE0F-1555-42F9-BBE8E7E701B973047-022012116-NXuDwzBl0l7IPmxDzx269g%3D%3D; buvid_fp_plain=undefined; blackside_state=0; is-2022-channel=1; _uuid=136F106D6-AA102-198A-C5DD-7351A72CFDE849203infoc; b_nut=100; rpdid=0zbfvWJdeE|54lJB1MA|2Ln|3w1OVksf; CURRENT_QUALITY=80; hit-new-style-dyn=1; CURRENT_PID=b98a29b0-cd2f-11ed-9194-494fac97dd7c; fingerprint=5050e9471226aa5c2be3ac56100522f8; header_theme_version=CLOSE; nostalgia_conf=-1; hit-dyn-v2=1; home_feed_column=5; CURRENT_FNVAL=4048; bp_video_offset_329341133=781400043392336000; SESSDATA=0948d8e9%2C1696396399%2Cef62d%2A42; bili_jct=cb7a5dbbd0153907fff4b713334d6833; DedeUserID=329341133; DedeUserID__ckMd5=acfa5c750e5b3e7f; PVID=1; b_ut=5; innersign=0; b_lsid=7C37E147_1875B2E5B1D; bsource=search_bing; buvid_fp=5050e9471226aa5c2be3ac56100522f8'
        }

        # 需要爬取的页数
        self.page = page

        # 保存的数据
        # self.data=[]

    def dataProcess(self, data):
        # 存入csv的数据集
        storedata = []

        # 每一页的数据量是30个
        for i in range(30):
            if (data[i]['type'] == 'picture_ad_0'):
                continue

            # 作者
            author = data[i]['author']

            # 标题  替换   
            title = data[i]['title'].replace('', '').replace('', '')

            # 播放量
            play = data[i]['play']

            # 简介
            description = data[i]['description']

            # 封面
            pic = data[i]['pic']

            # 播放地址
            arcurl = data[i]['arcurl']

            # id
            id = data[i]['id']

            # 时间
            pubdate = data[i]['pubdate']
            # 10位时间戳转换为时间字符串
            timeArray = time.localtime(pubdate)
            pubdate = time.strftime("%Y-%m-%d %H: %M:%S", timeArray)

            # 将数据以字典的格式存入data序列中
            # self.data.append({'author':author,'title':title,'play':play,'description':description,'pic':pic,'arcurl':arcurl,'id':id})
            storedata.append([author, title, play, description, pic, arcurl, id, pubdate])
        return storedata

    def reverse(self, page):
        timenow = int(time.time())
        if (page == 1):
            an = f'refresh=true&_extra=&ad_resource=5646&context=&duration=&from_source=&from_spmid=333.337&highlight=1&keyword={self.search}&order=&page=1&page_size=42&platform=pc&qv_id=EfNJjEtrA0N5DxzPVKch7Kz6v33ezlFR&single_column=0&source_tag=3&web_location=1430654&wts={timenow}'
            wt = '55540207d820a7368ab7e104169d409d'
            data = an + wt
            md = hashlib.md5(data.encode('UTF-8'))
            return md.hexdigest(), timenow
        else:
            an = f'refresh=true&_extra=&ad_resource=5654&category_id=&context=&dynamic_offset={str((page - 1) * 30)}&from_source=&from_spmid=333.337&gaia_vtoken=&highlight=1&keyword={self.search}&page={page}&page_size=42&platform=pc&qv_id=hJgZIEUY51fw9Pp7s8pidIVEJ7Z08KaS&search_type=video&single_column=0&source_tag=3&web_location=1430654&wts={timenow}'
            wt = '55540207d820a7368ab7e104169d409d'
            data = an + wt
            md = hashlib.md5(data.encode('UTF-8'))
            return md.hexdigest(), timenow

    # 综合排序
    def findall(self):
        for pnum in range(1, int(self.page) + 1):
            # 拼接关键字，请求数据
            w_rid, timenow = self.reverse(pnum)
            if (pnum == 1):
                target = requests.get(
                    f'https://api.bilibili.com/x/web-interface/wbi/search/all/v2?__refresh__=true&_extra=&context=&page={pnum}&page_size=42&order=&duration=&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword={self.search}&qv_id=noyCOTfEBm8ZzMVGopKgzYbiqLFxoAn1&ad_resource=5646&source_tag=3&web_location=1430654&w_rid={w_rid}&wts={timenow}',
                    headers=self.head)
            else:
                target = requests.get(
                    f'https://api.bilibili.com/x/web-interface/wbi/search/all/v2?refresh=true&_extra=&ad_resource=5654&category_id=&context=&dynamic_offset={(pnum - 1) * 30}&from_source=&from_spmid=333.337&gaia_vtoken=&highlight=1&keyword={self.search}&page={pnum}&page_size=42&platform=pc&qv_id=hJgZIEUY51fw9Pp7s8pidIVEJ7Z08KaS&search_type=video&single_column=0&source_tag=3&web_location=1430654&w_rid={w_rid}&wts={timenow}',
                    headers=self.head)
            # 将数据转换为py对象
            data = json.loads(target.text)

            # 存入csv的数据集
            storedata = self.dataProcess(data['data']['result'][10]['data'])

            print('第', pnum, '页完成')
            # 调用storeCsvdata
            self.storeCsvdata('b站清单_' + str(self.search) + '_第' + str(pnum) + '页.csv', storedata, pnum)
            # 设置等待1s
            sleep(1)

    # 写入文件模块
    def storeCsvdata(self, filename, storedata, pagenum):
        with open(filename, 'a+') as fp:
            # 构造列表头
            name = ['作者', '标题', '播放量', '简介', '封面', '播放地址', 'id', '时间']

            # 写入文件
            writer = pd.DataFrame(storedata, columns=name)
            writer.to_csv(filename, index=False, encoding='utf-8-sig')
            bilisearch_pic.main(filename)
            fp.close()


"""
    bilihot类的功能
    1.初始化需要的参数
        无
    2.使用方法
        a = bilihot()  初始化
        a.findall()    调用搜索
        a.storeCsvdata()   储存数据
        a.data   可以查看数据   
        a.data[i][j]  i为第几个数据集合 j为['作者','标题','播放量','简介','封面','id','播放地址','时间','分区']
"""


class bilihot():
    def __init__(self):
        # 构造浏览器访问请求头
        self.head = {
            'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.44',
            'Referer': "https://search.bilibili.com/all?from_source=webtop_search&spm_id_from=333.1007&search_source=5keyword=",
            'referer': 'https://www.bilibili.com/v/popular/rank/all',
            'authority': 'api.bilibili.com',
        }

        # 保存一份数据
        self.data = []

    def findall(self):
        # 请求数据
        target = requests.get('https://api.bilibili.com/x/web-interface/ranking/v2?rid=0&type=all', headers=self.head)

        # 将数据转换为py对象
        data = json.loads(target.text)

        for i in data['data']['list']:
            # 作者
            author = i['owner']['name']

            # 标题
            title = i['title']

            # 播放量
            play = i['stat']['view']

            # 简介
            desc = i['desc']

            # 封面
            pic = i['pic']

            # id
            id = i['aid']

            # 播放地址
            arcurl = i['short_link_v2']

            # 发布日期
            pubdate = i['pubdate']
            # 10位时间戳转换为时间字符串
            timeArray = time.localtime(pubdate)
            pubdate = time.strftime("%Y-%m-%d %H: %M:%S", timeArray)

            # 分区
            tname = i['tname']

            self.data.append([author, title, play, desc, pic, id, arcurl, pubdate, tname])
        print('请求数据成功')

    def storeCsvdata(self):
        with open('b站排行榜.csv', 'a+') as fp:
            # 构造列表头
            name = ['作者', '标题', '播放量', '简介', '封面', 'id', '播放地址', '时间', '分区']

            # 写入文件
            writer = pd.DataFrame(self.data, columns=name)
            writer.to_csv('b站排行榜.csv', index=False, encoding='utf-8-sig')
            print('写入成功')
            bilihot_pic.main('b站排行榜.csv')
            fp.close()


if __name__ == '__main__':
    # search: 你需要搜索的数据
    search = input("请输入搜索的关键词")
    # page: 需要查看的页数
    page = int(input("请输入搜索的页数"))
    # 初始化类
    a = bilisearch(search, page)
    # 将爬取的数据存入excel文件中
    a.findall()
    # 初始化
    b = bilihot()
    # 调用搜索
    b.findall()
    # 储存数据
    b.storeCsvdata()

四、bilihot_pic.py

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import font_manager
import numpy as np

def view(info,bili_file):
    # 设置中文字体（图标中能显示中文）
    my_font = font_manager.FontProperties(fname='./STHeiti-TC-Medium.ttf')
    # 为了坐标轴上能显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False

    title = info['标题']
    views = info['播放量']

    # *******播放量条形图
    fig, ax1 = plt.subplots()
    length = len(title)
    plt.barh(y=np.arange(length), tick_label=title, width=views, color='cyan')  # 设置柱状图
    plt.title('标题和播放量的数据分析', fontproperties=my_font)  # 表标题
    ax1.tick_params(labelsize=6)
    plt.xlabel('播放量')  # 横轴名
    plt.ylabel('标题')  # 纵轴名
    plt.yticks(color='green')  # 设置横坐标变量名旋转度数和颜色

    plt.plot(1, label='播放量', color="cyan", linewidth=5.0)  # 图例
    plt.legend()

    plt.savefig('.\图片\pic-{}.png'.format(bili_file), dpi=1000, bbox_inches='tight')  # 保存至本地

    plt.show()


def main(bili_file):
    info = pd.read_csv(bili_file,engine='python', encoding='utf-8-sig')
    info = info.nlargest(50, '播放量')
    info = info.sort_values('播放量', ascending=True)
    view(info,bili_file)


if __name__ == '__main__':
    main('b站排行榜.csv')

五、bilisearch_pic.py

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import font_manager
import numpy as np

def view(info,bili_file):
    # 设置中文字体（图标中能显示中文）
    my_font = font_manager.FontProperties(fname='./STHeiti-TC-Medium.ttf')
    # 为了坐标轴上能显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False

    title = info['标题']
    views = info['播放量']

    # *******播放量条形图
    fig, ax1 = plt.subplots()
    length = len(title)
    plt.barh(y=np.arange(length), tick_label=title, width=views, color='green')  # 设置柱状图
    plt.title('标题和播放量的数据分析', fontproperties=my_font)  # 表标题
    ax1.tick_params(labelsize=6)
    plt.xlabel('播放量')  # 横轴名
    plt.ylabel('标题')  # 纵轴名
    plt.yticks(color='blue')  # 设置纵坐标变量名颜色

    plt.plot(1, label='播放量', color="green", linewidth=5.0)  # 图例
    plt.legend()

    plt.savefig('.\图片\pic-{}.png'.format(bili_file), dpi=1000, bbox_inches='tight')  # 保存至本地

    plt.show()


def main(bili_file):
    info = pd.read_csv(bili_file,engine='python', encoding='utf-8-sig')
    info = info.sort_values('播放量', ascending=True)
    view(info,bili_file)


if __name__ == '__main__':
    main('b站清单_疫情_第1页.csv')

六、draw_cloud.py

import numpy as np
import pandas as pd
from wordcloud import WordCloud, ImageColorGenerator
from PIL import Image

def draw_cloud(weibo_file):
    image = Image.open('.\\background.jpg')  # 作为背景轮廓图
    graph = np.array(image)
    # 参数分别是指定字体、背景颜色、最大的词的大小、使用给定图作为背景形状
    wc = WordCloud(font_path='msyh.ttc',background_color='white',max_words=100, mask=graph)
    fp = pd.read_csv(weibo_file,engine='python', encoding='utf-8-sig')  # 读取词频文件
    name = list(fp['热搜内容'])  # 词
    value = fp['热搜热度'] # 词的频率
    for i in range(len(name)):
        name[i] = str(name[i])
    dic = dict(zip(name, value))  # 词频以字典形式存储
    print(dic)
    wc.generate_from_frequencies(dic)  # 根据给定词频生成词云
    image_color = ImageColorGenerator(graph)#生成词云的颜色
    wc.to_file('.\图片\draw_cloud-{}.png'.format(weibo_file))  # 图片命名

if __name__ == '__main__':
    draw_cloud('微博top_fun.csv')

七、weibo.py

import os.path
import re
from jsonpath import jsonpath
import requests
import pandas as pd
import datetime
from fake_useragent import UserAgent
import weibo_pic

def trans_time(v_str):
    """转换GMT时间为标准格式"""
    GMT_FORMAT='%a %b %d %H:%M:%S +0800 %Y'
    timearray=datetime.datetime.strptime(v_str,GMT_FORMAT)
    ret_time=timearray.strftime("%Y-%m-%d %H:%M:%S")
    return ret_time

def get_weibo_list(v_keyword,v_max_page):
    """
    爬取微博内容列表
    :param v_keyword: 搜索关键字
    :param v_max_page: 爬取前几页
    :return: None
    """
    # 保存文件名
    v_weibo_file = '微博清单_{}_前{}页.csv'.format(v_keyword,v_max_page)
    # 如果csv存在，先删除
    if os.path.exists(v_weibo_file):
        os.remove(v_weibo_file)
        print('微博清单存在，已删除：{}'.format(v_weibo_file))
    for page in range(1,v_max_page+1):
        print('===开始爬取第{}页微博==='.format(page))
        # 请求头
        ua = UserAgent()
        headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.42",
            "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
            "accept-encording": "gzip, deflate, br"
        }
        #请求地址
        url='https://m.weibo.cn/api/container/getIndex'
        #请求参数
        params={
            "containerid":"100103type=1&q={}".format(v_keyword),
            "page_type":"searchall",
            "page":page
        }
        #发送请求
        r=requests.get(url,headers=headers,params=params)
        print(r.status_code)
        #解析json数据
        cards=r.json()["data"]["cards"]
        #微博内容
        text_list=jsonpath(cards,'$..mblog.text')
        #微博内容-正则表达式数据清洗
        dr=re.compile(r'<[^>]+>',re.S)
        text2_list=[]
        print('text_list is:')
        print(text_list)
        if not text_list:#如果未获取到微博内容，则进入下一轮循环
            continue
        if type(text_list)==list and len (text_list)>0:
            for text in text_list:
                text2=dr.sub('',text)#正则表达式提取微博内容
                print(text2)
                text2_list.append(text2)
        #微博创建时间
        time_list = jsonpath(cards, '$..mblog.created_at')
        time_list=[trans_time(v_str=i) for i in time_list]
        #微博作者
        author_list = jsonpath(cards, '$..mblog.user.screen_name')
        #微博id
        id_list = jsonpath(cards, '$..mblog.user.id')
        # 微博bid
        bid_list = jsonpath(cards, '$..mblog.bid')
        # 转发数
        reposts_count_list = jsonpath(cards, '$..mblog.reposts_count')
        # 评论数
        comments_count_list = jsonpath(cards, '$..mblog.comments_count')
        # 点赞数
        attitudes_count_list = jsonpath(cards, '$..mblog.attitudes_count')
        df=pd.DataFrame(
            {
                '页码':[page]*len(id_list),
                '微博id':id_list,
                '微博bid': bid_list,
                '微博作者': author_list,
                '发布时间': time_list,
                '微博内容': text2_list,
                '转发数': reposts_count_list,
                '评论数': comments_count_list,
                '点赞数': attitudes_count_list
            }
        )
        #表头
        if os.path.exists(v_weibo_file):
            header=None
        else:
            header=['页码','微博id','微博bid','微博作者','发布时间','微博内容','转发数','评论数','点赞数']
        column=['页码','微博id','微博bid','微博作者','发布时间','微博内容','转发数','评论数','点赞数']
        #保存到csv文件
        df.to_csv(v_weibo_file,mode='a+',index=False,columns=column, header=header,encoding='utf-8-sig')
        print('csv保存成功：{}'.format(v_weibo_file))
    # 数据清洗、去重
    df = pd.read_csv(v_weibo_file, engine='python', encoding='utf-8-sig')
    os.remove(v_weibo_file)
    # 删除重复数据
    df.drop_duplicates(subset='微博bid', inplace=True, keep='first')
    # 再次保存csv文件
    header = ['页码','微博id','微博bid','微博作者','发布时间','微博内容','转发数','评论数','点赞数']
    column=header
    df.to_csv(v_weibo_file, mode='a+', index=False, columns=column, header=header,encoding='utf-8-sig')
    print('数据清洗完成')
    weibo_pic.main(v_weibo_file)


if __name__=='__main__':
    # 爬取关键字
    search_keyword = input("请输入搜索的关键词")
    #爬取页数
    max_search_page=int(input("请输入搜索的页数"))
    #调用爬取微博函数
    get_weibo_list(v_keyword=search_keyword,v_max_page=max_search_page)

八、weibo_comment.py

import requests# 发送请求
import pandas as pd#保存csv文件
import os # 判断文件是否存在
import datetime
import time
from time import sleep# 设置等待，防止反爬
import json
import random# 生成随机数
import os.path
import requests
import csv
import re
import weibo_comment_pic

def trans_time(v_str):
    """转换GMT时间为标准格式"""
    GMT_FORMAT='%a %b %d %H:%M:%S +0800 %Y'
    timearray=datetime.datetime.strptime(v_str,GMT_FORMAT)
    ret_time=timearray.strftime("%Y-%m-%d %H:%M:%S")
    return ret_time

def get_bili_comment(weiboID_list,max_page):
    for weibo_id in weiboID_list:

        #保存文件名
        wbComment_file='weiboComment_{}pages_{}.csv'.format(max_page,weibo_id)
        #如果csv存在，先删除
        if os.path.exists(wbComment_file):
            os.remove(wbComment_file)
            print('存在，已删除：{}'.format(wbComment_file))
        #请求头
        headers = {
            #不加cookie只能爬一页
            'cookie':'__bid_n=1883c7fc76e10d57174207; FPTOKEN=IBsER/uKazbtpMIEgvaOTfAuHsmYQM5g0VL9U1G3ybs72PsWHEBbiKv0w+R59BrOvSwxDKJevIDwL0SSwPV5yWd3lIFsx6KXQ/qYPpPTjTRW5kFr+j74rsScC6MKc1G9142e5tEEf7atvY/zTxl9B6jy/y7MEo0ETLT0VjL6nbpzkWe/SnIw97Tjb+9lqYoGHS6lPqZ5yAhDPKn0KK4htwxqr0qMglAG6ZcT7mn+BUZAygRSrqWZwZ6KSE0r27qsR0bDTAI8dsQFq1gPfYONp5UHfw9FFsBiscLULixqm31wTHYziK8gxi0/R6yIQ8Tq3OQkNmx+Kw7E/8YknGOiVmpjfRn5FNShZs3/t8SNBJEcZ9qaQnw/iF/jwPoFkMXz87Tp22aQUmFgeQu/u0wAYQ==|wC9ITrusKUtoBk6wTqvs+jaY6iwSJyX4pD0y+hSvnOA=|10|acf98643db3def55913fefef5034d5ee; WEIBOCN_FROM=1110106030; loginScene=102003; SUB=_2A25JbkPWDeRhGeNH7FIV-SjKzjyIHXVqkW2erDV6PUJbkdAGLRbkkW1NSoXhCHcUhbni8gGXfjdc5HNqec9qABj_; MLOGIN=1; _T_WM=98495433469; XSRF-TOKEN=a62fb7; mweibo_short_token=9f0e28d6c9; M_WEIBOCN_PARAMS=oid%3D4903111417922777%26luicode%3D20000061%26lfid%3D4903111417922777%26uicode%3D20000061%26fid%3D4903111417922777',
            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36",
            'X-Xsrf-Token':'a62fb7'
        }
        max_id = ''
        for page in range(1,max_page + 1):

            if page==1:#第一页没有max_id参数
                 url='https://m.weibo.cn/comments/hotflow?id={}&mid={}&max_id_type=0'.format(weibo_id,weibo_id)


            else:

                if max_id == '0':#max_id=0，说明没有下一页了，结束循环
                    print('max_id==0,break now')
                    break
                url='https://m.weibo.cn/comments/hotflow?id={}&mid={}&max_id={}&max_id_type=0'.format(weibo_id,weibo_id,max_id)

            response = requests.get(url, headers=headers)
            #ok = response.json()['ok']
            #print(ok)
            print(response.status_code)
            max_id=response.json()['data']['max_id']

            #print(response.json()['data']['max_id'])
            print(max_id)



            datas= response.json()['data']['data']
            page_list = []
            id_list = []
            text_list=[]
            time_list=[]
            like_count_list=[]
            source_list=[]
            username_list=[]
            user_id_list=[]
            user_gender_list=[]
            follow_count_list=[]
            followers_count_list=[]

            for data in datas:
                page_list.append(page)
                id_list.append(data['id'])
                dr=re.compile(r'<[^>]+>',re.S)#用正则表达式清洗评论数据

                text2 = dr.sub('', data['text'])
                text_list.append(text2)#评论内容
                time_list.append(trans_time(data['created_at']))#评论时间
                like_count_list.append(data['like_count'])#点赞
                source_list.append(data['source'])#属地
                username_list.append(data['user']['screen_name'])#评论者姓名
                user_id_list.append(data['user']['id'])
                user_gender_list.append(data['user']['gender'])# 评论者性别
                follow_count_list.append(data['user']['follow_count'])#评论者关注数
                followers_count=str(data['user']['followers_count'])
                if(followers_count[-1]=='万'):
                    followers_count=int(float(followers_count.strip('万')))*10000
                followers_count_list.append(followers_count)#评论者粉丝数

                #把列表拼接为dataFrame数据
                df=pd.DataFrame({
                    '评论页码':page_list,
                    '微博id':[weibo_id]*len(time_list),
                    '评论id':id_list,
                    '评论内容':text_list,
                    '评论时间':time_list ,
                    '评论点赞数':like_count_list,
                    '评论属地':source_list,
                    '评论者姓名':username_list ,
                    '评论者id':user_id_list ,
                    '评论者性别':user_gender_list,
                    '评论者关注数':follow_count_list,
                    '评论者粉丝数':followers_count_list,
                })
                # 表头
                if os.path.exists(wbComment_file):
                    header = None
                else:
                    header = ['评论页码','微博id', '评论id','评论内容','评论时间','评论点赞数','评论属地', '评论者姓名','评论者id','评论者性别', '评论者关注数','评论者粉丝数']
                column=['评论页码','微博id', '评论id','评论内容','评论时间','评论点赞数','评论属地', '评论者姓名','评论者id','评论者性别', '评论者关注数','评论者粉丝数']

                # 保存到csv文件
                df.to_csv(wbComment_file, mode='a+', index=False, columns=column, header=header, encoding='utf-8-sig')
                #print('csv保存成功：{}'.format(bili_file))
            #print(df)
            print('第{}页爬取完成'.format(page))


        # 数据清洗、去重
        df = pd.read_csv(wbComment_file, engine='python', encoding='utf-8-sig')
        os.remove(wbComment_file)
        # 删除重复数据
        df.drop_duplicates(subset='评论内容', inplace=True, keep='first')
        # 再次保存csv文件
        column=header = ['评论页码', '微博id', '评论id', '评论内容', '评论时间', '评论点赞数', '评论属地', '评论者姓名',
                  '评论者id', '评论者性别', '评论者关注数', '评论者粉丝数']
        df.to_csv(wbComment_file, mode='a+', index=False, columns=column,header=header, encoding='utf-8-sig')
        print('数据清洗完成')
        weibo_comment_pic.main(wbComment_file)


if __name__=='__main__':
    #目标微博https: // m.weibo.cn / detail / 4903111417922777
    #目标微博ID，可循环爬取多个（这里只爬一个）
    weiboID_list=[str(x) for x in input("请输入微博ID(示例：4903111417922777),以逗号分隔：").split(',')]
    #weiboID_list=['4903111417922777']
    #最大爬取页
    max_page=int(input("请输入搜索的页数"))
    #调用爬取
    get_bili_comment(weiboID_list=weiboID_list,max_page=max_page)

九、weibo_comment_pic.py

# 允许副本存在，忽略报错
import os
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import font_manager
import numpy as np

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


def view(info,weibo_file):
    my_font = font_manager.FontProperties(fname='./STHeiti-TC-Medium.ttf')  # 设置中文字体（图标中能显示中文）
    likes = info['评论点赞数']  # 点赞数
    reply = info['评论者粉丝数']  # 粉丝数
    forward = info['评论者关注数']  # 关注数
    author = info['评论者姓名']  # 作者，因为内容太长了
    # print(comment)

    # 为了坐标轴上能显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False

    # **********************************************************************综合评分和播放量对比
    # *******点赞数条形图
    fig, ax1 = plt.subplots()
    length = len(author)
    plt.bar(x=np.arange(length), tick_label=author, height=likes, color='blue')  # 设置柱状图
    plt.title('评论点赞数、粉丝数和关注数的数据分析', fontproperties=my_font)  # 表标题
    ax1.tick_params(labelsize=6)
    plt.xlabel('微博内容')  # 横轴名
    plt.ylabel('评论点赞数')  # 纵轴名
    plt.xticks(rotation=90, color='green')  # 设置横坐标变量名旋转度数和颜色

    # *******评论者粉丝数折线图
    ax2 = ax1.twinx()  # 组合图必须加这个
    ax2.plot(reply, color='red')  # 设置线粗细，节点样式
    # *******评论者关注数折线图
    ax2.plot(forward, color='yellow')  # 设置线粗细，节点样式
    plt.ylabel('粉丝/关注数')  # y轴

    plt.plot(1, label='评论者点赞数', color="blue", linewidth=5.0)  # 图例
    #plt.plot(1, label='评论者粉丝数', color="red", linewidth=1.0, linestyle="-")  # 图例
    #plt.plot(1, label='评论者关注数', color="yellow", linewidth=1.0, linestyle="-")  # 图例
    plt.legend()

    plt.savefig('.\图片\pic-{}.png'.format(weibo_file), dpi=1000, bbox_inches='tight')  # 保存至本地

    plt.show()


def main(weibo_file):
    info = pd.read_csv(weibo_file,engine='python', encoding='utf-8-sig')
    info = info.nlargest(100, '评论点赞数')
    info = info.reset_index(drop=True)
    view(info,weibo_file)


if __name__ == '__main__':
    main('weiboComment_15pages_4903111417922777.csv')

十、weibo_pic.py

# 允许副本存在，忽略报错
import os
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import font_manager
import numpy as np

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


def view(info,weibo_file):
    my_font = font_manager.FontProperties(fname='./STHeiti-TC-Medium.ttf')  # 设置中文字体（图标中能显示中文）
    likes = info['点赞数']  # 点赞数
    reply = info['评论数']  # 评论数
    forward = info['转发数']  # 转发数
    author = info['微博作者']  # 作者，因为内容太长了
    # print(comment)

    # 为了坐标轴上能显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False

    # **********************************************************************综合评分和播放量对比
    # *******点赞数条形图
    fig, ax1 = plt.subplots()
    length = len(author)
    plt.bar(x=np.arange(length), tick_label=author, height=likes, color='blue')  # 设置柱状图
    plt.title('点赞数、评论数和转发数的数据分析', fontproperties=my_font)  # 表标题
    ax1.tick_params(labelsize=6)
    plt.xlabel('微博内容')  # 横轴名
    plt.ylabel('点赞数')  # 纵轴名
    plt.xticks(rotation=90, color='green')  # 设置横坐标变量名旋转度数和颜色

    # *******评论数折线图
    ax2 = ax1.twinx()  # 组合图必须加这个
    ax2.plot(reply, color='red')  # 设置线粗细，节点样式
    # *******转发数折线图
    ax2.plot(forward, color='yellow')  # 设置线粗细，节点样式
    plt.ylabel('评论/转发数')  # y轴

    plt.plot(1, label='点赞数', color="blue", linewidth=5.0)  # 图例
    #plt.plot(1, label='评论数', color="red", linewidth=1.0, linestyle="-")  # 图例
    #plt.plot(1, label='转发数', color="yellow", linewidth=1.0, linestyle="-")  # 图例
    plt.legend()

    plt.savefig('.\图片\pic-{}.png'.format(weibo_file), dpi=1000, bbox_inches='tight')  # 保存至本地

    plt.show()


def main(weibo_file):
    info = pd.read_csv(weibo_file,engine='python', encoding='utf-8-sig')
    info = info.nlargest(100, '点赞数')
    info = info.reset_index(drop=True)
    view(info,weibo_file)


if __name__ == '__main__':
    main('微博清单_疫情_前10页.csv')

十一、weibo_top.py

import os.path
import re
from jsonpath import jsonpath
import requests
import pandas as pd
from fake_useragent import UserAgent
import weibo_top_pic
import weibo_top_pie
import draw_cloud

def get_weibo_top():
    keyword=list(['realtimehot','gym','game','fun'])
    for search_keyword in keyword:
        # 保存文件名
        v_weibo_file = '微博top_{}.csv'.format(search_keyword)
        # 如果csv存在，先删除
        if os.path.exists(v_weibo_file):
            os.remove(v_weibo_file)
            print('微博榜单存在，已删除：{}'.format(v_weibo_file))
        print('===开始爬取{}微博榜单==='.format(search_keyword))
        # 请求头
        ua = UserAgent()
        headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.42",
            "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
            "accept-encording": "gzip, deflate, br"
        }
        #请求地址
        url='https://m.weibo.cn/api/container/getIndex'
        #请求参数
        params={
            "containerid":"106003type=25&t=3&disable_hot=1&filter_type={}".format(search_keyword),
            "title": "微博热搜",
            "show_cache_when_error": 1,
            "extparam": "seat=1&dgr=0&filter_type=realtimehot®ion_relas_conf=0&pos=0_0&c_type=30&lcate=1001&mi_cid=100103&cate=10103&display_time=1684642048&pre_seqid=144917672",
            "luicode": 10000011,
            "lfid": 231583,
        }
        #发送请求
        r=requests.get(url,headers=headers,params=params)
        print(r.status_code)
        #解析json数据
        cards=r.json()["data"]["cards"][0]["card_group"]
        #热搜内容
        text_list=jsonpath(cards,'$..desc')
        print('text_list is:')
        print(text_list)
        #热搜连接地址
        href_list = jsonpath(cards, '$..scheme')
        # 热搜排名
        order_list = jsonpath(cards, '$..pic')
        # 热搜热度
        view_count_list = jsonpath(cards, '$..desc_extr')
        j=1
        for i in range(0, len(order_list)):
            if order_list[i] == 'https://simg.s.weibo.com/20210408_search_point_orange.png':
                order_list[i] = '无'
                view_count_list[i]=0
                continue
            if order_list[i] == "https://simg.s.weibo.com/20180205110043_img_search_stick%403x.png":
                view_count_list.insert(0, 0)
                order_list[i] = '无'
                continue
            view_count_list[i]=str(view_count_list[i])
            view_count_list[i]=int(re.sub("\D", "", view_count_list[i]))
            order_list[i] = j
            j = j + 1
        print(len(order_list),len(text_list),len(view_count_list),len(href_list))
        df=pd.DataFrame(
            {
                '热搜排名':order_list,
                '热搜内容': text_list,
                '热搜热度': view_count_list,
                '热搜连接地址': href_list,
            }
        )
        #表头
        if os.path.exists(v_weibo_file):
            header=None
        else:
            header=['热搜排名','热搜内容','热搜热度','热搜连接地址']
        column = ['热搜排名','热搜内容','热搜热度','热搜连接地址']
        #保存到csv文件
        df.to_csv(v_weibo_file,mode='a+',index=False,columns=column, header=header, encoding='utf-8-sig')
        print('csv保存成功：{}'.format(v_weibo_file))
        weibo_top_pic.main(v_weibo_file)
        weibo_top_pie.pie(v_weibo_file)
        #draw_cloud.draw_cloud(v_weibo_file)


if __name__=='__main__':
    #调用爬取微博函数
    get_weibo_top()

十二、weibo_top_pic.py

# 允许副本存在，忽略报错
import os
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import font_manager
import numpy as np

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


def view(info,weibo_file):
    my_font = font_manager.FontProperties(fname='./STHeiti-TC-Medium.ttf')  # 设置中文字体（图标中能显示中文）
    heat = info['热搜热度']
    content = info['热搜内容']

    # 为了坐标轴上能显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False

    # **********************************************************************综合评分和播放量对比
    # *******点赞数条形图
    fig, ax1 = plt.subplots()
    length=len(content)
    plt.bar(x = np.arange(length),tick_label=content, height=heat, color='blue')  # 设置柱状图
    plt.title('热搜内容和热搜热度的数据分析', fontproperties=my_font)  # 表标题
    ax1.tick_params(labelsize=6)
    plt.xlabel('热搜内容')  # 横轴名
    plt.ylabel('热搜热度')  # 纵轴名
    plt.xticks(rotation=90, color='green')  # 设置横坐标变量名旋转度数和颜色

    plt.plot(1, label='热搜热度', color="blue", linewidth=5.0)  # 图例
    plt.legend()

    plt.savefig('.\图片\pic-{}.png'.format(weibo_file), dpi=1000, bbox_inches='tight')  # 保存至本地

    plt.show()

十三、weibo_top_pie.py

import pandas as pd
import numpy as np
from pyecharts import options as opts
from pyecharts.charts import Pie
import matplotlib.pyplot as plt

def pie(weibo_file):
    plt.rcParams['font.family']=['SimHei']
    plt.rcParams['axes.unicode_minus']=False
    data=pd.read_csv(weibo_file,engine='python', encoding='utf-8-sig')
    df1=data['热搜内容']
    df2=data['热搜热度']

    X=df1
    Y=[]
    s=sum(df2)
    for i in df2:
        a=i/s
        a=round(a,2)
        Y.append(a)

    plt.figure(figsize=(12, 12))

    plt.pie(x=Y,
           labels=X,
           wedgeprops={'width': 0.4},
           startangle=90,
            autopct='%.2f%%',
            pctdistance=0.9
          )
    plt.title('热搜对应的热度占比',fontsize=20)
    plt.savefig('.\图片\pie-{}.png'.format(weibo_file), dpi=1000, bbox_inches='tight')  # 保存至本地
    plt.show()

if __name__ == '__main__':
    pie('微博top_realtimehot.csv')

十四、pachong.py

import weibo
import weibo_top
import weibo_comment
import bilibili
import bili_comment
net=int(input("请选择爬取的网站：1.微博 2.b站 3.停止爬取"))
while(net!=3):
    if (net==1):
        choice1=int(input("请选择爬取的方向：1.排行榜 2.关键词 3.评论"))
        if(choice1==1):
            # 调用爬取微博函数
            weibo_top.get_weibo_top()
        if (choice1 == 2):
            # 爬取关键字
            search_keyword = input("请输入搜索的关键词")
            # 爬取页数
            max_search_page = int(input("请输入搜索的页数"))
            # 调用爬取微博函数
            weibo.get_weibo_list(v_keyword=search_keyword, v_max_page=max_search_page)
        if (choice1 == 3):
            # 目标微博ID，可循环爬取多个（这里只爬一个）
            weiboID_list = [str(x) for x in input("请输入微博ID(示例：4903111417922777),以逗号分隔：").split(',')]
            # 最大爬取页
            max_page = int(input("请输入搜索的页数"))
            # 调用爬取
            weibo_comment.get_bili_comment(weiboID_list=weiboID_list, max_page=max_page)
    if (net==2):
        choice2=int(input("请选择爬取的方向：1.排行榜 2.关键词 3.评论"))
        if(choice2==1):
            # 初始化
            b = bilibili.bilihot()
            # 调用搜索
            b.findall()
            # 储存数据
            b.storeCsvdata()
        if (choice2 == 2):
            # search: 你需要搜索的数据
            search = input("请输入搜索的关键词")
            # page: 需要查看的页数
            page = int(input("请输入搜索的页数"))
            # 初始化类
            a = bilibili.bilisearch(search, page)
            # 将爬取的数据存入excel文件中
            a.findall()
        if (choice2 == 3):
            # 视频bv号,循环爬取多个视频评论
            bv_list = [str(x) for x in input("请输入视频bv号(示例：BV1Ss4y1M7KT,BV1VM411N7qc),以逗号分隔：").split(',')]
            # 最大爬取页
            max_page = int(input("请输入搜索的页数"))
            # 调用爬取
            bili_comment.get_bili_comment(bv_list=bv_list, max_page=max_page)

    net = int(input("请选择爬取的网站：1.微博 2.b站 3.停止爬取"))

十五、代码文件说明

pachong： b站、微博爬虫与数据可视化总程序

b站：
bilibili 爬取b站热搜榜和关键词搜索
bili_comment 爬取b站评论
bilihot_pic b站热搜榜数据可视化(柱形图、折线图)
bilisearch_pic b站关键词搜索数据可视化(柱形图、折线图)
bili_comment_pic b站评论数据可视化(柱形图、折线图)

微博：
weibo_top 爬取微博热搜榜
weibo 爬取微博关键词搜索
weibo_comment 爬取微博评论
weibo_top_pic 微博热搜榜数据可视化(柱形图、折线图)
weibo_top_pie 微博热搜榜数据可视化(环形图)
weibo_pic 微博关键词搜索数据可视化(柱形图、折线图)
weibo_comment_pic 微博评论数据可视化(柱形图、折线图)

draw_cloud 微博热搜榜数据可视化(词图云)