由于此解析作业需要提取文本和属性 值，它不能完全通过开箱即用"的功能来完成，例如 pd.read_html.其中一些必须手动完成.

Since this parsing job requires the extraction of both text and attribute values, it can not be done entirely "out-of-the-box" by a function such as pd.read_html. Some of it has to be done by hand.

使用 lxml ，您可以使用XPath提取属性值:

Using lxml, you could extract the attribute values with XPath:

import lxml.html as LH
import pandas as pd

content = '''
<table>
<tbody>
<tr>
<td>265</td>
<td> <a href="/j/jones03.shtml">Jones</a>Blue</td>
<td >29</td>
</tr>
<tr >
<td>266</td>
<td> <a href="/s/smith01.shtml">Smith</a></td>
<td>34</td>
</tr>
</tbody>
</table>'''

table = LH.fromstring(content)
for df in pd.read_html(content):
    df['refname'] = table.xpath('//tr/td/a/@href')
    df['refname'] = df['refname'].str.extract(r'([^./]+)[.]')
    print(df)

收益

     0          1   2  refname
0  265  JonesBlue  29  jones03
1  266      Smith  34  smith01

上面的内容可能有用，因为它只需要几个 额外的代码行来添加refname列.

The above may be useful since it requires only a few extra lines of code to add the refname column.

但是LH.fromstring和pd.read_html都解析HTML. 因此，通过删除pd.read_html和 用LH.fromstring解析表一次:

But both LH.fromstring and pd.read_html parse the HTML. So it's efficiency could be improved by removing pd.read_html and parsing the table once with LH.fromstring:

table = LH.fromstring(content)
# extract the text from `<td>` tags
data = [[elt.text_content() for elt in tr.xpath('td')] 
        for tr in table.xpath('//tr')]
df = pd.DataFrame(data, columns=['id', 'name', 'val'])
for col in ('id', 'val'):
    df[col] = df[col].astype(int)
# extract the href attribute values
df['refname'] = table.xpath('//tr/td/a/@href')
df['refname'] = df['refname'].str.extract(r'([^./]+)[.]')
print(df)

收益

    id        name  val  refname
0  265   JonesBlue   29  jones03
1  266       Smith   34  smith01