From ce0d54720a0e7e24b7de670c3064fe76832519b3 Mon Sep 17 00:00:00 2001
From: hnu202309010114 <1736774990@qq.com>
Date: Mon, 27 May 2024 12:56:22 +0800
Subject: [PATCH] ADD file via upload

---
 2024头歌大作业2.py | 102 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 2024头歌大作业2.py

diff --git a/2024头歌大作业2.py b/2024头歌大作业2.py
new file mode 100644
index 0000000..99e486d
--- /dev/null
+++ b/2024头歌大作业2.py
@@ -0,0 +1,102 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun May 26 19:24:00 2024
+
+@author: Panda
+"""
+
+import requests
+import pandas as pd
+import re
+import matplotlib.pyplot as plt
+from bs4 import BeautifulSoup
+
+url_head = "https://top.chinaz.com/gongsi/index_zhuce"
+Headers = {
+        "User-Agent" : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/104.0.5112.102"
+        }  
+
+#建立一个二重列表存储数据
+global tl
+tl = []
+
+#爬虫核心代码
+def crawl(index):
+    response = requests.get(url_head + index + ".html", headers = Headers)
+    if response.status_code == 200:
+        page_doc = response.text
+    
+    #使用BeautifulSoup4 解析页面Html源码
+    # soup = BeautifulSoup(page_doc, features='lxml') #旧版本lxml与bs4的写法
+    soup = BeautifulSoup(page_doc, features='html.parser') #至少lxml 4.6.3起的写法
+    
+    #获取所有<div>标签
+    div_list = soup.find_all('div')
+    
+    #遍历标签寻找属性class为"CoListTxt"的元素
+    for div in div_list:
+        attrib = div["class"]
+        if attrib == ["CoListTxt"]:
+            string = div.text
+            row = string.strip('\n').replace('\n',' ').split(' ')
+            row = list(filter(None, row))
+            company = row[0]
+            t = row[1]
+            person = row[1][t.find('：')+1:]
+            if len(row) >=8:
+                row[2] = row[2] + row[3]
+                row.remove(row[3])
+            t = row[2]
+            capital = row[2][t.find('：')+1:]
+            #转换为万元单位的数字
+            capnum = [float(t) for t in re.findall(r"-?\d+\.?\d*", capital)][0]
+            if '亿' in capital:
+                capnum = capnum * 10000
+            t = row[3]
+            date = row[3][t.find('：')+1:]
+            if len(row) >=7:
+                t = row[4]
+                stock_type = row[4][t.find('：')+1:]
+                t = row[5]
+                stock_code = row[5][t.find('：')+1:]
+            else:
+                stock_type = '暂无'
+                stock_code = None
+            row = [company, person, capnum, date, stock_type, stock_code]
+            #将爬取的单条数据整合进二重列表
+            tl.append(row)
+
+
+def main():
+    inds = []
+    pages = 17 #需获取的网站页数
+    for i in range(1, pages+1):
+        ch = '_' + str(i)
+        inds.append(ch)
+    inds[0] = ''
+    for ind in inds:
+        crawl(ind)
+    #将二重列表转化为Dataframe        
+    df = pd.DataFrame(tl, columns=['公司', '法人', '注册资本', '注册时间', '证券', '股票代码'])
+    #保存为csv文件
+    df.to_csv('result_2.csv', index=False, encoding='utf_8_sig')
+    
+    #输出注册资金500强信息
+    print(df[['公司', '注册资本']].iloc[:20])
+    
+    #可视化绘制条形图
+    plt.rcParams['font.sans-serif'] = 'SimHei'
+    plt.rcParams['axes.unicode_minus'] = False
+    ndf = df[['公司', '注册资本']].iloc[:20]
+    ndf = ndf.set_index(ndf['公司'])
+    ndf.plot(kind='bar')
+    plt.xlabel('公司名')
+    plt.xticks(rotation=90)
+    plt.ylabel('注册资金(万元)')  
+    plt.title('注册资金前20强公司')  
+
+    plt.show()
+    return None
+
+if __name__=='__main__':
+    main()
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