我正在尝试从两个不同的 HTML 源中抓取 HTML 表格。两者非常相似，每个表都包含相同的数据，但它们的结构可能不同，具有不同的列名称等。对于一个源，所有数据可能包含在一个表中，而另一个源可能将数据分解为两个单独的桌子。作为一个例子，我们可以看看 AAPL 和 MMM 股票的内部持有者。屏幕截图在这里 - https://i.stack.imgur.com/dt6Pa.jpg可以说，最终目标是提取内部人士持有的股份总数——一个单一数字。每个表格的结构可能不同，但应该相似的是“受益”或“股票”等关键词。任何帮助将不胜感激。在上一篇文章中，我能够提取一些数据。但如果结构不同，则不能循环或重复。根据特定列标题提取 HTML 表 - Pythondf = pd.read_html("https://www.sec.gov/Archives/edgar/data/66740/000120677420000907/mmm3661701-def14a.htm", attrs={'style': 'border-collapse: collapse; width: 100%; font: 9pt Arial, Helvetica, Sans-Serif'}, match="Name/address")df = df[0]df = df.dropna(axis = 'columns')还尝试过BSurl = 'https://www.sec.gov/Archives/edgar/data/66740/000120677420000907/mmm3661701-def14a.htm'r = requests.get(url) soup = BeautifulSoup(r.text, 'html.parser')tables = soup.find_all('table')rows = tables.find_all('tr')
1 回答
素胚勾勒不出你
TA贡献1827条经验 获得超9个赞
这确实很复杂，但我们开始吧:)。
import requests
from bs4 import BeautifulSoup
import re
import pandas as pd
urls = ['https://www.sec.gov/Archives/edgar/data/320193/000119312520001450/d799303ddef14a.htm',
'https://www.sec.gov/Archives/edgar/data/66740/000120677420000907/mmm3661701-def14a.htm']
def main(urls):
with requests.Session() as req:
for url in urls:
r = req.get(url)
soup = BeautifulSoup(r.content, 'html.parser')
for item in soup.findAll("a", text=re.compile("^Security")):
item = item.get("href")[1:]
catch = soup.find("a", {'name': item}).find_next("table")
df = pd.read_html(str(catch))
print(df)
df[0].to_csv(f"{item}.csv", index=False, header=None)
main(urls)
输出：
[ 0 ... 8
0 NaN ... NaN
1 NaN ... NaN
2 Name of Beneficial Owner ... NaN
3 NaN ... NaN
4 The Vanguard Group ... %
5 NaN ... NaN
6 BlackRock, Inc. ... %
7 NaN ... NaN
8 Berkshire Hathaway Inc. / Warren E. Buffett ... %
9 NaN ... NaN
10 Kate Adams ... NaN
11 NaN ... NaN
12 Angela Ahrendts ... NaN
13 NaN ... NaN
14 James Bell ... NaN
15 NaN ... NaN
16 Tim Cook ... NaN
17 NaN ... NaN
18 Al Gore ... NaN
19 NaN ... NaN
20 Andrea Jung ... NaN
21 NaN ... NaN
22 Art Levinson ... NaN
23 NaN ... NaN
24 Luca Maestri ... NaN
25 NaN ... NaN
26 Deirdre O’Brien ... NaN
27 NaN ... NaN
28 Ron Sugar ... NaN
29 NaN ... NaN
30 Sue Wagner ... NaN
31 NaN ... NaN
32 Jeff Williams ... NaN
33 NaN ... NaN
34 All current executive officers and directors a... ... NaN
[35 rows x 9 columns]]
[ 0 1 ... 18 19
0 Name and principal position NaN ... Percent of Class NaN
1 Thomas “Tony” K. Brown, Director NaN ... (5) NaN
2 Pamela J. Craig, Director NaN ... (5) NaN
3 David B. Dillon, Director NaN ... (5) NaN
4 Michael L. Eskew, Director NaN ... (5) NaN
5 Herbert L. Henkel, Director NaN ... (5) NaN
6 Amy E. Hood, Director NaN ... (5) NaN
7 Muhtar Kent, Director NaN ... (5) NaN
8 Edward M. Liddy, Director NaN ... (5) NaN
9 Dambisa F. Moyo, Director NaN ... (5) NaN
10 Gregory R. Page, Director NaN ... (5) NaN
11 Patricia A. Woertz, Director NaN ... (5) NaN
12 Michael F. Roman, Chairman of the Board, Presi... NaN ... (5) NaN
13 Inge G. Thulin, Former Executive Chairman of t... NaN ... (5) NaN
14 Nicholas C. Gangestad, Senior Vice President a... NaN ... (5) NaN
15 Ashish K. Khandpur, Executive Vice President, ... NaN ... (5) NaN
16 Julie L. Bushman, Executive Vice President, In... NaN ... (5) NaN
17 Joaquin Delgado, Former Executive Vice Preside... NaN ... (5) NaN
18 Michael G. Vale, Executive Vice President, Saf... NaN ... (5) NaN
19 All Directors and Executive Officers as a Grou... NaN ... (5) NaN
[20 rows x 20 columns]]
[ 0 1 ... 6 7
0 Name/address NaN ... Percent of Class NaN
1 The Vanguard Group(1) 100 Vanguard Blvd. Malve... NaN ... 8.78 NaN
2 State Street Corporation(2) State Street Finan... NaN ... 7.36 NaN
3 BlackRock, Inc.(3) 55 East 52nd Street New Yor... NaN ... 7.30 NaN
[4 rows x 8 columns]]
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