Web Scraping Acceleration

HTML Attribute Extraction

def extract_links(html: str) -> list[str]:
    links = []
    i = 0
    while True:
        i = html.find('href="', i)
        if i == -1:
            break
        i += 6
        end = html.find('"', i)
        links.append(html[i:end])
    return links

Table Parsing

def parse_table_rows(html: str) -> list[list[str]]:
    rows = []
    i = 0
    while True:
        tr_start = html.find('', i)
        if tr_start == -1:
            break
        tr_end = html.find('', tr_start)
        row_html = html[tr_start + 4:tr_end]
        cells = []
        ci = 0
        while True:
            td_start = row_html.find('', ci)
            if td_start == -1:
                break
            td_end = row_html.find('', td_start)
            cells.append(row_html[td_start + 4:td_end])
            ci = td_end + 5
        rows.append(cells)
        i = tr_end + 5
    return rows

When to Use BeautifulSoup

For complex HTML with nested structures, BeautifulSoup's C-accelerated parser is better. Use Pyvorin for simple regex-like extraction on clean markup.