# -*- coding: utf-8 -*-
"""
Created on Sat May 24 23:23:07 2025

@author: 15733
"""

import requests
import re
import csv
import pandas as pd
import matplotlib.pyplot as plt

# 1. 爬取数据
def scrape_company_data():
    headers = {
        "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"
    }
    message = []
    
    # 总共16个页面的数据
    for page in range(16):
        # 组装url
        if page == 0:
            url = "https://top.chinaz.com/gongsitop/index_500top.html"
        else:
            url = f"https://top.chinaz.com/gongsitop/index_500top_{page + 1}.html"
        
        try:
            response = requests.get(url, headers=headers)
            response.raise_for_status()  # 检查请求是否成功
            html = response.text
            
            # 使用正则表达式提取数据
            company = re.findall('<a.*?target="_blank">(.+?)</a></h3>', html)
            person = re.findall('法定代表人：</span>(.*?)</p>', html)
            signDate = re.findall('注册时间：</span>(.*?)</p>', html)
            category = re.findall('证券类别：</span>(.*?)</p>', html)
            
            # 确保每页数据长度一致
            min_length = min(len(company), len(person), len(signDate), len(category))
            page_data = list(zip(
                company[:min_length], 
                person[:min_length], 
                signDate[:min_length], 
                category[:min_length]
            ))
            
            message.extend(page_data)
            print(f"第{page+1}页数据爬取完成，共{len(page_data)}条记录")
            
        except Exception as e:
            print(f"第{page+1}页爬取失败: {e}")
            continue
    
    return message

# 2. 保存数据到CSV
def save_to_csv(data, filename="china_top500_companies.csv"):
    with open(filename, "w", newline="", encoding="utf-8-sig") as f:
        writer = csv.writer(f)
        writer.writerow(["公司名", "法定代表人", "注册时间", "证券类别"])  # 写入标题行
        writer.writerows(data)
    print(f"数据已保存到 {filename}")

# 3. 分析证券占比并绘制饼图
def analyze_and_plot(filename="china_top500_companies.csv"):
    # 读取数据
    df = pd.read_csv(filename)
    
    # 统计证券类别占比
    category_counts = df["证券类别"].value_counts()
    
    # 设置中文字体
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    
    # 绘制饼图
    plt.figure(figsize=(10, 8))
    plt.pie(category_counts, 
            labels=category_counts.index, 
            autopct='%1.1f%%',
            startangle=90,
            textprops={'fontsize': 12})
    plt.title("中国500强公司证券类别占比", fontsize=16)
    plt.axis('equal')  # 保证饼图是圆形
    
    # 保存图片
    plt.savefig("证券类别占比.png", dpi=300, bbox_inches='tight')
    print("证券类别占比分析图已保存为 证券类别占比.png")
    plt.show()
    
    return category_counts

# 主程序
if __name__ == "__main__":
    # 1. 爬取数据
    company_data = scrape_company_data()
    
    # 2. 保存到CSV
    save_to_csv(company_data)
    
    # 3. 分析并绘制饼图
    category_distribution = analyze_and_plot()
    print("\n证券类别分布统计:")
    print(category_distribution)