抖音快手粉丝点赞代刷_微博粉丝链接
作者:百科 来源:综合 浏览: 【大中小】 发布时间:2024-11-14 21:31:46 评论数:
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此次爬虫要实现的是爬取某个微博用户的关注和粉丝的用户公开基本信息，包括用户爱称、快手id、粉丝粉丝快手刷浏览量机领网性别、微博所在地和其粉丝数目，链接然后将爬取出来的抖音点赞代刷数据保存在MongoDB数据库中，最后再生成几个图表来简单剖析一下我们得到的快手数据。
具体步骤：
这里我们选定的粉丝粉丝爬取站点是，此站点是微博快手刷浏览量机领网微博移动端的站点，我们可以直接查看某个用户的链接微博，比如。抖音点赞代刷
然后查看其关注的快手用户微博粉丝链接，打开开发者工具，粉丝粉丝切换到XHR过滤器，微博一直下拉列表，链接就会听到有好多的Ajax恳求。这些恳求的类型是Get类型，返回结果是Json格式，展开以后才能看见有好多用户的信息。
这些恳求有两个参数，containerid和page，通过改变page的数值，我们能够得到更多的恳求了。获取其粉丝的用户信息的步骤是一样的，除了恳求的链接不同之外，参数也不同，修改一下就可以了。
由于这种恳求返回的结果里只有用户的名称和id等信息，并没有包含用户的性别等基本资料，所以我们点进某个人的微博，然后查看其基本资料，比如这个，打开开发者工具，可以找到下边这个恳求：
由于这个人的id是6857214856，因此我们可以发觉当我们得到一个人的id的时侯，就可以构造获取基本资料的链接和参数了，相关代码如下（uid就是用户的id）：
1 uid_str = "230283" + str(uid) 2 url = "https://m.weibo.cn/api/container/getIndex?containerid={ }_-_INFO&title=%E5%9F%BA%E6%9C%AC%E8%B5%84%E6%96%99&luicode=10000011&lfid={ }&featurecode=10000326".format(uid_str, uid_str) 3 data = { 4 "containerid": "{ }_-_INFO".format(uid_str), 5 "title": "基本资料", 6 "luicode": 10000011, 7 "lfid": int(uid_str), 8 "featurecode": 10000326 9 }然后这个返回的结果也是Json格式，提取上去就很方便，因为很多人的基本资料都不怎样全，所以我提取了用户爱称、性别、所在地和其粉丝数目。而且由于一些帐号并非个人帐号，就没有性别信息，对于那些帐号，我选择将其性别设置为女性。不过在爬取的时侯微博粉丝链接，我发觉一个问题，就是当页数超过250的时侯，返回的结果就早已没有内容了，也就是说这个方式最多只能爬250页。对于爬取出来的用户信息，全都保存在MongoDB数据库中，然后在爬取结束以后，读取这种信息并勾画了几个图表，分别勾画了男女比列扇形图、用户所在地分布图和用户的粉丝数目柱状图。
主要代码：
由于第一页返回的结果和其他页返回的结果格式是不同的，所以要分别进行解析，而且由于部份结果的json格式不同，所以可能报错，因此采用了try…except…把出错缘由复印下来。
爬取第一页并解析的代码如下：
1 def get_and_parse1(url): 2 res = requests.get(url) 3 cards = res.json()['data']['cards'] 4 info_list = [] 5 try: 6 for i in cards: 7 if "title" not in i: 8 for j in i['card_group'][1]['users']: 9 user_name = j['screen_name'] # 用户名10 user_id = j['id'] # 用户id11 fans_count = j['followers_count'] # 粉丝数量12 sex, add = get_user_info(user_id)13 info = { 14 "用户名": user_name,15 "性别": sex,16 "所在地": add,17 "粉丝数": fans_count,18 }19 info_list.append(info)20 else:21 for j in i['card_group']:22 user_name = j['user']['screen_name'] # 用户名23 user_id = j['user']['id'] # 用户id24 fans_count = j['user']['followers_count'] # 粉丝数量25 sex, add = get_user_info(user_id)26 info = { 27 "用户名": user_name,28 "性别": sex,29 "所在地": add,30 "粉丝数": fans_count,31 }32 info_list.append(info)33 if "followers" in url:34 print("第1页关注信息爬取完毕...")35 else:36 print("第1页粉丝信息爬取完毕...")37 save_info(info_list)38 except Exception as e:39 print(e)爬取其他页并解析的代码如下：
1 def get_and_parse2(url, data): 2 res = requests.get(url, headers=get_random_ua(), data=data) 3 sleep(3) 4 info_list = [] 5 try: 6 if 'cards' in res.json()['data']: 7 card_group = res.json()['data']['cards'][0]['card_group'] 8 else: 9 card_group = res.json()['data']['cardlistInfo']['cards'][0]['card_group']10 for card in card_group:11 user_name = card['user']['screen_name'] # 用户名12 user_id = card['user']['id'] # 用户id13 fans_count = card['user']['followers_count'] # 粉丝数量14 sex, add = get_user_info(user_id)15 info = { 16 "用户名": user_name,17 "性别": sex,18 "所在地": add,19 "粉丝数": fans_count,20 }21 info_list.append(info)22 if "page" in data:23 print("第{ }页关注信息爬取完毕...".format(data['page']))24 else:25 print("第{ }页粉丝信息爬取完毕...".format(data['since_id']))26 save_info(info_list)27 except Exception as e:28 print(e)运行结果：
在运行的时侯可能会出现各种各样的错误，有的时侯返回结果为空，有的时侯解析出错，不过还是能成功爬取大部分数据的，这里就放一下最后生成的三张图片吧。
完整代码
login.py
import requestsimport timeimport jsonimport base64import rsaimport binasciiclass WeiBoLogin: def __init__(self, username, password): self.username = username self.password = password self.session = requests.session() self.cookie_file = "Cookie.json" self.nonce, self.pubkey, self.rsakv = "", "", "" self.headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'} def save_cookie(self, cookie): """ 保存Cookie :param cookie: Cookie值 :return: """ with open(self.cookie_file, 'w') as f: json.dump(requests.utils.dict_from_cookiejar(cookie), f) def load_cookie(self): """ 导出Cookie :return: Cookie """ with open(self.cookie_file, 'r') as f: cookie = requests.utils.cookiejar_from_dict(json.load(f)) return cookie def pre_login(self): """ 预登录，获取nonce, pubkey, rsakv字段的值 :return: """ url = 'https://login.sina.com.cn/sso/prelogin.php?entry=weibo&su=&rsakt=mod&client=ssologin.js(v1.4.19)&_={ }'.format(int(time.time() * 1000)) res = requests.get(url) js = json.loads(res.text.replace("sinaSSOController.preloginCallBack(", "").rstrip(")")) self.nonce, self.pubkey, self.rsakv = js["nonce"], js['pubkey'], js["rsakv"] def sso_login(self, sp, su): """ 发送加密后的用户名和密码 :param sp: 加密后的用户名 :param su: 加密后的密码 :return: """ data = { 'encoding': 'UTF-8', 'entry': 'weibo', 'from': '', 'gateway': '1', 'nonce': self.nonce, 'pagerefer': 'https://login.sina.com.cn/crossdomain2.php?action=logout&r=https%3A%2F%2Fweibo.com%2Flogout.php%3Fbackurl%3D%252F', 'prelt': '22', 'pwencode': 'rsa2', 'qrcode_flag': 'false', 'returntype': 'META', 'rsakv': self.rsakv, 'savestate': '7', 'servertime': int(time.time()), 'service': 'miniblog', 'sp': sp, 'sr': '1920*1080', 'su': su, 'url': 'https://weibo.com/ajaxlogin.php?framelogin=1&callback=parent.sinaSSOController.feedBackUrlCallBack', 'useticket': '1', 'vsnf': '1'} url = 'https://login.sina.com.cn/sso/login.php?client=ssologin.js(v1.4.19)&_={ }'.format(int(time.time() * 1000)) self.session.post(url, headers=self.headers, data=data) def login(self): """ 模拟登录主函数 :return: """ # Base64加密用户名 def encode_username(usr): return base64.b64encode(usr.encode('utf-8'))[:-1] # RSA加密密码 def encode_password(code_str): pub_key = rsa.PublicKey(int(self.pubkey, 16), 65537) crypto = rsa.encrypt(code_str.encode('utf8'), pub_key) return binascii.b2a_hex(crypto) # 转换成16进制 # 获取nonce, pubkey, rsakv self.pre_login() # 加密用户名 su = encode_username(self.username) # 加密密码 text = str(int(time.time())) + '\t' + str(self.nonce) + '\n' + str(self.password) sp = encode_password(text) # 发送参数，保存cookie self.sso_login(sp, su) self.save_cookie(self.session.cookies) self.session.close() def cookie_test(self): """ 测试Cookie是否有效，这里url要替换成个人主页的url :return: """ session = requests.session() session.cookies = self.load_cookie() url = '' res = session.get(url, headers=self.headers) print(res.text)if __name__ == '__main__': user_name = '' pass_word = '' wb = WeiBoLogin(user_name, pass_word) wb.login() wb.cookie_test()test.py
import randomimport pymongoimport requestsfrom time import sleepimport matplotlib.pyplot as pltfrom multiprocessing import Pool# 返回随机的User-Agentdef get_random_ua(): user_agent_list = [ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1" "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/" "536.11", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6", "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1", "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5", "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 " "Safari/536.3", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3", "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24", "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24" ] return { "User-Agent": random.choice(user_agent_list) }# 返回关注数和粉丝数def get(): res = requests.get("https://m.weibo.cn/profile/info?uid=5720474518") return res.json()['data']['user']['follow_count'], res.json()['data']['user']['followers_count']# 获取内容并解析def get_and_parse1(url): res = requests.get(url) cards = res.json()['data']['cards'] info_list = [] try: for i in cards: if "title" not in i: for j in i['card_group'][1]['users']: user_name = j['screen_name'] # 用户名 user_id = j['id'] # 用户id fans_count = j['followers_count'] # 粉丝数量 sex, add = get_user_info(user_id) info = { "用户名": user_name, "性别": sex, "所在地": add, "粉丝数": fans_count, } info_list.append(info) else: for j in i['card_group']: user_name = j['user']['screen_name'] # 用户名 user_id = j['user']['id'] # 用户id fans_count = j['user']['followers_count'] # 粉丝数量 sex, add = get_user_info(user_id) info = { "用户名": user_name, "性别": sex, "所在地": add, "粉丝数": fans_count, } info_list.append(info) if "followers" in url: print("第1页关注信息爬取完毕...") else: print("第1页粉丝信息爬取完毕...") save_info(info_list) except Exception as e: print(e)# 爬取第一页的关注和粉丝信息def get_first_page(): url1 = "https://m.weibo.cn/api/container/getIndex?containerid=231051_-_followers_-_5720474518" # 关注 url2 = "https://m.weibo.cn/api/container/getIndex?containerid=231051_-_fans_-_5720474518" # 粉丝 get_and_parse1(url1) get_and_parse1(url2)# 获取内容并解析def get_and_parse2(url, data): res = requests.get(url, headers=get_random_ua(), data=data) sleep(3) info_list = [] try: if 'cards' in res.json()['data']: card_group = res.json()['data']['cards'][0]['card_group'] else: card_group = res.json()['data']['cardlistInfo']['cards'][0]['card_group'] for card in card_group: user_name = card['user']['screen_name'] # 用户名 user_id = card['user']['id'] # 用户id fans_count = card['user']['followers_count'] # 粉丝数量 sex, add = get_user_info(user_id) info = { "用户名": user_name, "性别": sex, "所在地": add, "粉丝数": fans_count, } info_list.append(info) if "page" in data: print("第{ }页关注信息爬取完毕...".format(data['page'])) else: print("第{ }页粉丝信息爬取完毕...".format(data['since_id'])) save_info(info_list) except Exception as e: print(e)# 爬取关注的用户信息def get_follow(num): url1 = "https://m.weibo.cn/api/container/getIndex?containerid=231051_-_followers_-_5720474518&page={ }".format(num) data1 = { "containerid": "231051_ - _followers_ - _5720474518", "page": num } get_and_parse2(url1, data1)# 爬取粉丝的用户信息def get_followers(num): url2 = "https://m.weibo.cn/api/container/getIndex?containerid=231051_-_fans_-_5720474518&since_id={ }".format(num) data2 = { "containerid": "231051_-_fans_-_5720474518", "since_id": num } get_and_parse2(url2, data2)# 爬取用户的基本资料（性别和所在地）def get_user_info(uid): uid_str = "230283" + str(uid) url2 = "https://m.weibo.cn/api/container/getIndex?containerid={ }_-_INFO&title=%E5%9F%BA%E6%9C%AC%E8%B5%84%E6%96%99&luicode=10000011&lfid={ }&featurecode=10000326".format( uid_str, uid_str) data2 = { "containerid": "{ }_-_INFO".format(uid_str), "title": "基本资料", "luicode": 10000011, "lfid": int(uid_str), "featurecode": 10000326 } res2 = requests.get(url2, headers=get_random_ua(), data=data2) data = res2.json()['data']['cards'][1] if data['card_group'][0]['desc'] == '个人信息': sex = data['card_group'][1]['item_content'] add = data['card_group'][2]['item_content'] else: # 对于企业信息，返回性别为男 sex = "男" add = data['card_group'][1]['item_content'] # 对于所在地有省市的情况，把省份取出来 if ' ' in add: add = add.split(' ')[0] return sex, add# 把数据保存到MongoDB数据库中def save_info(data): conn = pymongo.MongoClient(host="127.0.0.1", port=27017) db = conn["Spider"] db.WeiBoUsers.insert(data)# 绘制男女比例扇形图def plot_sex(): conn = pymongo.MongoClient(host="127.0.0.1", port=27017) col = conn['Spider'].WeiBoUsers sex_data = [] for i in col.find({ }, { "性别": 1}): sex_data.append(i['性别']) labels = '男', '女' sizes = [sex_data.count('男'), sex_data.count('女')] # 设置分离的距离，0表示不分离 explode = (0, 0) plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90) # 保证画出的是圆形 plt.axis('equal') # 保证能够显示中文 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.savefig("sex.jpg") print("已保存为sex.jpg！")# 绘制用户所在地条形图def plot_province(): conn = pymongo.MongoClient(host="127.0.0.1", port=27017) col = conn['Spider'].WeiBoUsers province_list = ['北京', '天津', '河北', '山西', '内蒙古', '辽宁', '吉林', '黑龙江', '上海', '江苏', '浙江', '安徽', '福建', '江西', '山东', '河南', '湖北', '湖南', '广东', '广西', '海南', '重庆', '四川', '贵州', '云南', '陕西', '甘肃', '青海', '宁夏', '新疆', '西藏', '台湾', '香港', '澳门', '其他', '海外'] people_data = [0 for _ in range(36)] for i in col.find({ }, { "所在地": 1}): people_data[province_list.index(i['所在地'])] += 1 # 清洗掉人数为0的数据 index_list = [i for i in range(len(people_data)) if people_data[i] == 0] j = 0 for i in range(len(index_list)): province_list.remove(province_list[index_list[i] - j]) people_data.remove(people_data[index_list[i] - j]) j += 1 # 排序 for i in range(len(people_data)): for j in range(len(people_data) - i - 1): if people_data[j] >people_data[j + 1]: people_data[j], people_data[j + 1] = people_data[j + 1], people_data[j] province_list[j], province_list[j + 1] = province_list[j + 1], province_list[j] province_list = province_list[:-1] people_data = people_data[:-1] # 图像绘制 fig, ax = plt.subplots() b = ax.barh(range(len(province_list)), people_data, color='blue', height=0.8) # 添加数据标签 for rect in b: w = rect.get_width() ax.text(w, rect.get_y() + rect.get_height() / 2, '%d' % int(w), ha='left', va='center') # 设置Y轴刻度线标签 ax.set_yticks(range(len(province_list))) ax.set_yticklabels(province_list) plt.xlabel("单位/人") plt.ylabel("所在地") plt.rcParams['font.sans-serif'] = ['SimHei'] plt.savefig("province.jpg") print("已保存为province.jpg！")# 绘制用户粉丝数量柱状图def plot_fans(): conn = pymongo.MongoClient(host="127.0.0.1", port=27017) col = conn['Spider'].WeiBoUsers fans_list = ["1-10", "11-50", "51-100", "101-500", "501-1000", "1000以上"] fans_data = [0 for _ in range(6)] for i in col.find({ }, { "粉丝数": 1}): fans_data[0] += 1 if 1 <= i["粉丝数"] <= 10 else 0 fans_data[1] += 1 if 11 <= i["粉丝数"] <= 50 else 0 fans_data[2] += 1 if 51 <= i["粉丝数"] <= 100 else 0 fans_data[3] += 1 if 101 <= i["粉丝数"] <= 500 else 0 fans_data[4] += 1 if 501 <= i["粉丝数"] <= 1000 else 0 fans_data[5] += 1 if 1001 <= i["粉丝数"] else 0 # print(fans_data) # 绘制柱状图 plt.bar(x=fans_list, height=fans_data, color="green", width=0.5) # 显示柱状图形的值 for x, y in zip(fans_list, fans_data): plt.text(x, y + sum(fans_data) // 50, "%d" % y, ha="center", va="top") plt.xlabel("粉丝数") plt.ylabel("单位/人") plt.rcParams['font.sans-serif'] = ['SimHei'] plt.savefig("fans.jpg") print("已保存为fans.jpg！")if __name__ == '__main__': follow_count, followers_count = get() get_first_page() # 由于当page或者since_id大于250时就已经无法得到内容了，所以要设置最大页数为250 max_page1 = follow_count // 20 + 1 if follow_count < 5000 else 250 max_page2 = followers_count // 20 + 1 if followers_count < 5000 else 250 # 使用进程池加快爬虫的效率 pool = Pool(processes=4) # 爬取关注的用户信息 start1, end1 = 2, 12 for i in range(25): pool.map(get_follow, range(start1, end1)) # 超过max_page则跳出循环 if end1 < max_page1: start1 = end1 end1 = start1 + 10 sleep(5) else: break # 爬取粉丝的用户信息 start2, end2 = 2, 50 for i in range(5): pool.map(get_followers, range(start2, end2)) # 超过max_page则跳出循环 if end2 < max_page2: start2 = end2 end2 = start2 + 50 sleep(10) else: break # 可视化成图表 plot_sex() plot_province() plot_fans()