Status: Current best available technique as of 2026. Treat as standard practice, not a workaround.
The Problem
Three naive approaches fail on modern sites:
-
view-source / static fetch — returns server HTML before JavaScript runs. JS-rendered tables show only empty
<tbody>tags. - Screenshot + OCR — slow, pixel-dependent, brittle, compounds errors on numeric data.
- Screenshot + vision model — expensive, context-limited, fails on tables larger than one viewport.
Root cause: The web has shifted to client-side rendering. Data lives in JavaScript runtime state, not HTML.
The Method
Intuition: Programmatic equivalent of: Highlight table → Copy → Paste into Notepad → Import to Excel → Delete irrelevant columns → Sort and count.
Steps:
- Load page in headless browser (Playwright recommended) — JavaScript executes, table renders
- Interact with any dropdowns or filters, wait for
networkidle - Call
inner_text()on the table element - Write extracted text to file (audit trail, enables re-parsing)
- Parse in Python — split on newlines/tabs, cast numerics, filter and count
Why It Works
The accessibility tree is structured, semantic, not pixel-dependent, already parsed by the browser, and fast. No OCR transcription errors on numbers.
Pseudocode
python
from playwright.sync_api import sync_playwright
import re
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
page = browser.new_page()
page.goto(URL, wait_until="networkidle")
page.select_option("select#view-filter", label="All Cities")
page.wait_for_load_state("networkidle")
table_text = page.query_selector("table").inner_text()
browser.close()
with open("table_output.txt", "w") as f:
f.write(table_text)
lines = table_text.strip().split("\n")
rows = [line.split("\t") for line in lines[1:] if line.strip()]
temps = [float(re.sub(r"[^\d.\-]", "", r[2])) for r in rows if r[2].strip()]
print(f"Below 32°F: {sum(t < 32 for t in temps)}")
print(f"Above 100°F: {sum(t > 100 for t in temps)}")
Real Example
Source: timeanddate.com, 472-city weather table, “Somewhat Popular” view.
• Execution time: ~8 seconds
• Cities below 32°F: 47
• Cities above 100°F: 12
• OCR errors: 0
Limitations
• Requires real browser runtime (Playwright/Puppeteer)
• Some sites block headless automation
• Canvas-rendered tables require page.accessibility.snapshot() fallback
• Infinite scroll requires simulating scroll events
• Always prefer an official API if one exists
Full writeup with detailed tips and examples on GitHub: https://github.com/hottbunny/LLM-AI-Perplexity-Skills-and-Updates/blob/hottbunny-tested-works-htmlsearchtablecrawldataretrivalskill/dom_extraction_method.md