是的，你可以使用Python和Pandas解析HTML表格。首先，使用pandas.read_html()函數(shù)提取表格，該函數(shù)可將網(wǎng)頁或字符串中的HTML

元素解析為DataFrame列表；接著，若表格無明確列標(biāo)題，可通過指定header參數(shù)或手動設(shè)置.columns屬性修復(fù)；對於復(fù)雜頁面，可結(jié)合requests庫獲取HTML內(nèi)容或使用BeautifulSoup定位特定表格；注意JavaScript渲染、編碼問題及多表識別等常見陷阱。

Yes, you can parse an HTML table with Python and Pandas — and it's actually pretty straightforward. If you've ever looked at a webpage with tabular data and wished you could get that into a DataFrame quickly, Pandas has a built-in function for that.

Use pandas.read_html() to extract tables

Pandas provides read_html() which scans a webpage or string for HTML <table> elements and tries to parse them into DataFrames.<p> You just need to give it a URL or the raw HTML content: </p> <img src="/static/imghw/default1.png" data-src="https://img.php.cn/upload/article/000/000/000/175212598696473.jpeg" class="lazy" alt="How to parse an HTML table with Python and Pandas"><pre class='brush:php;toolbar:false;'> import pandas as pd url = &amp;#39;https://example.com/table-page&amp;#39; tables = pd.read_html(url)</pre><p> This returns a list of DataFrames — one for each table on the page. You can then pick the one you want by index, like <code>tables[0] .

Sometimes pages have multiple tables and not all are useful. You might need to inspect the output to find which index contains your desired data.

Handle missing headers or messy formatting

Not every HTML table includes clear column headers. If the table doesn't have <th> tags or if they're incomplete, read_html() will assign default column names like 0, 1, 2...

To fix this:

• Look at the page and see if headers are part of the first row ( <tr> ) instead of in <thead> .
• You can manually set column names using .columns = [...] after reading the table.
• Sometimes adding header=0 or header=[0,1] (for multi-indexed headers) helps.

Example:

 df = pd.read_html(url, header=0)[0]

Also be aware that some tables may include merged cells or nested tables, which can confuse the parser. In those cases, the resulting DataFrame might look misaligned.

Deal with complex pages using requests or filtering

If the page needs authentication or JavaScript rendering, read_html() alone won't help. But for static pages, combining it with requests gives more control.

Here's how you can fetch HTML first:

 import requests
import pandas as pd

response = requests.get(url)
tables = pd.read_html(response.text)

If there are many tables and you want to filter by attributes like class name or ID, you'll need to use a parser like BeautifulSoup first to isolate the specific table, then pass that HTML snippet to read_html() .

For example:

 from bs4 import BeautifulSoup

soup = BeautifulSoup(response.text, 'html.parser')
target_table = soup.find('table', {'class': 'data'})
df = pd.read_html(str(target_table))[0]

This is especially helpful when a page has clutter or multiple similar tables.

Watch out for common gotchas

• JavaScript-rendered tables : read_html() only works on static HTML. If the table is loaded dynamically (like with AJAX), you'll need tools like Selenium or Playwright to render the page first.
• Encoding issues : If characters look weird, try setting the correct encoding with response.encoding = 'utf-8' or similar.
• Too many tables? Loop through the list and print shapes or first few rows to identify the right one.

Like:

 for i, df in enumerate(tables):
    print(f"Table {i} shape: {df.shape}")
    print(df.head())

That way, you can visually scan what each parsed table looks like before deciding which one to work with.

基本上就這些。 Parsing HTML tables with Pandas is fast and effective for most basic use cases — just keep an eye out for edge cases like dynamic content or missing headers.

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