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好雨知时节，赞赞当春乃发生。下载usmile营销为了谢谢你们长久以来的微博支持和信任，爬虫俱乐部为你们送福利啦！关注！链接！抖音Stata&Python特价课程双双上线腾讯课堂～原价2400元的赞赞Python编程培训课程，如今仅需100元，下载详情请查看推文。微博关于Stata，关注爬虫俱乐部推出了系列课程，链接内容包括字符串函数、抖音正则表达式、赞赞爬虫专题和文本剖析，下载usmile营销可以随心搭配，价钱美丽，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！

烟雨霏霏柳眼开，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，让沉睡的桃源迷梦落入现实，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，饱含了烟火气与田园独有的甜蜜。这些悠闲的生活就像繁华都市里的一股清泉，流入每一位关注的心底。

明天微博粉丝链接，小编将从数据角度出发，和你们一起看一下李子柒微博关注的地分辨布。Start~

爬虫思路

微博关注用户ID爬取

首先，通过URL步入李子柒的微博关注页面：

通过检测查看关注抓包信息：

不同的关注页面所对应的URL：

比较两个URL可知微博关注链接，关注页面是通过URL中since_id这个参数的改变进行翻页的。为此，我们可以通过设置since_id（值域：1-250）来获取至多5000个关注的用户ID。

# 关注用户ID爬取## 导入相关库import reimport time import randomimport requestsfrom 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/李子柒微博关注地区分布/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~

接出来，我们按照前面抓取到的关注用户ID来获取关注的公开信息。

首先，导出相关库。

# 根据爬取的关注用户ID获取关注的基本公开信息import requestsfrom lxml import etreeimport pandas as pdimport numpy as npimport reimport timeimport randomimport osos.chdir("D:\python爬虫\李子柒微博关注地区分布")

其次，登陆旧版微博网页，步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"}

之后，抓取关注公开信息。

new_url = "https://weibo.cn/u/"data = []count = 0def get_id(ID):with open(ID, 'r') as f:user_list = f.readlines()user_id = np.char.rstrip(user_list, '\n')return user_iddef gethtml(url, header):r = requests.get(url, headers = headers)if r.status_code == 200:return r.textelse:print("网络连接异常")for user_id in get_id('user_id.txt'):try:url = new_url + user_idr_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 += 1print("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\李子柒微博关注地区分布\关注公开信息.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：

我们所抓取到的关注信息不规整，不易于后续绘图所用，因而，我们须要进行数据清洗，清洗后的结果如下：

关注信息数据可视化

在获取关注数据然后，我们借助Python中的pyecharts模块来看一下李子柒微博关注的地分辨布图。

## 导入相关库并读入数据import pandas as pdimport numpy as npfrom pyecharts.charts import Mapfrom pyecharts import options as opts
df = pd.read_excel("关注信息.xlsx")df

地图Map

## 绘制关注地区分布图address=pd.DataFrame(df['Inf'].value_counts())  ### 汇总每个地区的关注数量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 = "李子柒微博关注地区分布"),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("关注分布图.html")

地理座标Geo

from pyecharts import options as optsfrom pyecharts.charts import Geofrom 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 = "李子柒微博关注地区分布"),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("关注分布图3.html")

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