技术总编:王子一 Stata&Python云端课程来啦!特惠腾讯 好雨知时节,课程课堂当春乃发生。上线快手刷网站免费雷神为了谢谢你们长久以来的价格支持和信任,爬虫俱乐部为你们送福利啦!美丽!质量!不变Stata&Python特价课程双双上线腾讯课堂~原价2400元的特惠腾讯Python编程培训课程,现在仅需100元,课程课堂详情请查看推文。上线关于Stata,价格爬虫俱乐部推出了系列课程,美丽内容包括字符串函数、质量正则表达式、不变爬虫专题和文本剖析,特惠腾讯快手刷网站免费雷神可以随心搭配,价格美丽,物超所值,更多信息可查看Stata系列推文、等。变的是价钱,不变的是课程质量和答疑服务。对报考有任何疑惑欢迎在公众号后台和腾讯课堂留言哦! 细雨霏霏柳眼开,云烟缭绕似仙台。 一江春水清悠淌,十里桃花锦绣裁。 李子柒,一个将人生书写成诗,生活在现代世外桃源的男子微博粉丝链接,让沉睡的桃源迷梦落入现实微博粉丝链接,她所诠释的“雪沫乳花浮午盏,蓼茸高笋试春盘”式的人间清欢,充满了烟火气与田园独有的甜蜜。这种惬意的生活仿佛繁华都市里的一股清泉,流入每一位粉丝的心底。 今天,小编将从数据角度出发,和你们一起看一下李子柒微博粉丝的地区分布。Start~ 爬虫思路 微博粉丝用户ID爬取 首先,通过URL步入李子柒的微博粉丝页面: 通过检测查看粉丝抓包信息: 不同的粉丝页面所对应的URL: 比较两个URL可知,粉丝页面是通过URL中since_id这个参数的改变进行翻页的。因此,我们可以通过设置since_id(值域:1-250)来获取至多5000个粉丝的用户ID。
# 粉丝用户ID爬取 ## 导入相关库 import re import time import random import requests from tqdm import tqdm_notebook ### 该库用于进度条的配置
def get_userid(url): header_list = [ "Opera/12.0(Windows NT 5.2;U;en)Presto/22.9.168 Version/12.00", "Opera/12.0(Windows NT 5.1;U;en)Presto/22.9.168 Version/12.00", "Mozilla/5.0 (Windows NT 5.1) Gecko/20100101 Firefox/14.0 Opera/12.0", "Opera/9.80 (Windows NT 6.1; WOW64; U; pt) Presto/2.10.229 Version/11.62", "Opera/9.80 (Windows NT 6.0; U; pl) Presto/2.10.229 Version/11.62", ] header = { 'user-agent': random.choice(header_list) } pat = 'since_id=(.*)' with open('D:/python爬虫/李子柒微博粉丝地区分布/user_id.txt', 'w') as f: for page in tqdm_notebook(range(1, 251), desc='进度条:'): try: print(url) r = requests.get(url, headers=header) all_user = r.json()['data']['cards'][0]['card_group'] since_id = r.json()['data']['cardlistInfo']['since_id'] for user in all_user: f.write(str(user.get('user')['id'])+'\n') url = re.sub(pat, 'since_id='+str(since_id), url) time.sleep(random.randint(1, 2)) except Exception as e: print(e)
if __name__ == '__main__': start_url = "https://m.weibo.cn/api/container/getIndex?containerid=231051_-_fans_-_2970452952&since_id=21" get_userid(start_url)
运行结果如下: 当进度条显示100%时,所有用户ID就早已抓取完毕啦~ 接下来,我们按照前面抓取到的粉丝用户ID来获取粉丝的公开信息。 首先,导入相关库。 # 根据爬取的粉丝用户ID获取粉丝的基本公开信息 import requests from lxml import etree import pandas as pd import numpy as np import re import time import random import os os.chdir("D:\python爬虫\李子柒微博粉丝地区分布")
其次,登录旧版微博网页,进入李子柒的微博页面,获取headers信息。 headers = { "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", "cookie": "输入自己的cookie", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.72 Safari/537.36" }
然后,抓取粉丝公开信息。
new_url = "https://weibo.cn/u/" data = [] count = 0 def get_id(ID): with open(ID, 'r') as f: user_list = f.readlines() user_id = np.char.rstrip(user_list, '\n') return user_id def gethtml(url, header): r = requests.get(url, headers = headers) if r.status_code == 200: return r.text else: print("网络连接异常") for user_id in get_id('user_id.txt'): try: url = new_url + user_id r_text = gethtml(url, headers) tree = etree.HTML(r_text.encode('utf-8')) user_name_xpath = "//tr/td[2]/div/span[1]/text()[1]" user_name = tree.xpath(user_name_xpath) Inf_xpath = "//tr/td[2]/div/span[1]/text()[2]" Inf = tree.xpath(Inf_xpath) focusnumber_xpath = "//div[4]/div/a[1]/text()" focusnumber = tree.xpath(focusnumber_xpath) fansnumber_xpath = "//div[4]/div/a[2]/text()" fansnumber = tree.xpath(fansnumber_xpath) data.append([user_name, Inf, focusnumber, fansnumber]) count += 1 print("第{ }个用户信息录入完毕".format(count)) time.sleep(random.randint(1,2)) except: print("用户信息录入失败")
最后,保存数据。
file = r"D:\python爬虫\李子柒微博粉丝地区分布\粉丝公开信息.xlsx" df = pd.DataFrame(data, columns = ['user_name', 'Inf', 'focusnumber', 'fansnumber']) df.to_excel(file, index = None) print("程序执行完毕")
运行结果如下: 我们所抓取到的粉丝信息不规整,不易于后续绘图所用,因此,我们须要进行数据清洗,清洗后的结果如下: 粉丝信息数据可视化 在获取粉丝数据然后,我们借助Python中的pyecharts模块来看一下李子柒微博粉丝的地区分布图。 ## 导入相关库并读入数据 import pandas as pd import numpy as np from pyecharts.charts import Map from pyecharts import options as opts
df = pd.read_excel("粉丝信息.xlsx") df
地图Map
## 绘制粉丝地区分布图 address=pd.DataFrame(df['Inf'].value_counts()) ### 汇总每个地区的粉丝数量 city=np.char.rstrip(list(address.index)) ### 城市名称 Map1 = ( Map(init_opts=opts.InitOpts(width="1200px",height="800px")) .add("", [list(z) for z in zip(city,address['Inf'])], "china", is_roam = False, is_map_symbol_show = False ) .set_global_opts( title_opts = opts.TitleOpts(title = "李子柒微博粉丝地区分布"), visualmap_opts = opts.VisualMapOpts(max_ = 1500, is_piecewise = True, pieces=[ { "max": 1500, "min": 1000, "label": ">1000", "color": "#2F7F50"}, { "max": 999, "min": 600, "label": "600-999", "color": "#FFFFE0"}, { "max": 599, "min": 200, "label": "200-599", "color": "#7FFFD4"}, { "max": 199, "min": 1, "label": "1-199", "color": "#00FFFF"}, { "max": 0, "min": 0, "label": "0", "color": "#EE82EE"},]) ) ) Map1.render("粉丝分布图.html")
地理座标Geo
from pyecharts import options as opts from pyecharts.charts import Geo from pyecharts.globals import ChartType
g = ( Geo(init_opts=opts.InitOpts(width="1200px",height="800px")) .add_schema( maptype = "china", itemstyle_opts = opts.ItemStyleOpts(color = "#5F9EA0", border_color = "#2F4F4F"), ) .add("", [list(z) for z in zip(city,address['Inf'])], label_opts = opts.LabelOpts(is_show = False), type_ = ChartType.EFFECT_SCATTER ) .set_global_opts( title_opts = opts.TitleOpts(title = "李子柒微博粉丝地区分布"), visualmap_opts = opts.VisualMapOpts(max_ = 1500, is_piecewise = True, pieces=[ { "max": 1500, "min": 1000, "label": ">1000", "color": "#2F7F50"}, { "max": 999, "min": 600, "label": "600-999", "color": "#FFFFE0"}, { "max": 599, "min": 200, "label": "200-599", "color": "#FF4500"}, { "max": 199, "min": 1, "label": "1-199", "color": "#6A5ACD"}, { "max": 0, "min": 0, "label": "0", "color": "FF0000"},]) ) ) g.render("粉丝分布图3.html")
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