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56 lines
1.8 KiB
56 lines
1.8 KiB
import csv
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import requests
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import re
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import pandas as pd
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from matplotlib import pyplot as plt
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# 用黑体显示中文
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plt.rcParams['font.sans-serif'] = ['SimHei']
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# 设置请求头信息
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headers = {
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"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.69 Safari/537.36"
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}
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# 存储内容
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message = []
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# 总共16个页面的数据
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for page in range(16):
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# 组装url
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if page == 0:
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url = "https://top.chinaz.com/gongsitop/index_500top.html"
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else:
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url = "https://top.chinaz.com/gongsitop/index_500top_{}.html".format(page + 1)
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# 使用reqeusts模快发起 GET 请求
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response = requests.get(url, headers=headers)
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html = response.text
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# 使用 findall 函数来获取数据
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# 公司名
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company = re.findall('<a.*?target="_blank">(.+?)</a></h3>', html)
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# 法定代表人
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person = re.findall('法定代表人：</span>(.*?)</p>', html)
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# 注册时间
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signDate = re.findall('注册时间：</span>(.*?)</p>', html)
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# 证券类别
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category = re.findall('证券类别：</span>(.*?)</p>', html)
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pageOne = list(zip(company, person, signDate, category))
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# 合并列表
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message.extend(pageOne)
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with open("content.csv", "w") as f:
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w = csv.writer(f)
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w.writerows(message)
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# 读取数据
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df = pd.read_csv("content.csv", names=["company", "person", "signDate", "category"],encoding='gbk')
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# 根据证券类型进行分组
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df1 = df.groupby("category").count()["company"]
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print(df1)
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# 每个扇形的标签
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labels = df1.index
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# 每个扇形的占比
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sizes = df1.values
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plt.figure(figsize=(80,40),dpi=80)
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fig1, ax1 = plt.subplots()
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# 绘制饼图
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ax1.pie(sizes, labels=labels, autopct='%d%%',radius=1.3,textprops={'fontsize': 20},
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shadow=False, startangle=90)
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ax1.axis()
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plt.show()
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