How do I extract tables from web pages and export them as JSON?

You can do this in a few different ways depending on the site and how many tables you need.

Option 1: Python with pandas.read_html() for simple HTML tables

If the table is in the page’s HTML, this is the easiest approach.

Install

pip install pandas lxml

Example

import pandas as pd

url = "https://example.com/page-with-table"
tables = pd.read_html(url)

print(f"Found {len(tables)} tables")

# Export the first table to JSON
tables[0].to_json("table.json", orient="records", indent=2)

Common JSON formats

• orient="records" → list of row objects
• orient="split" → rows/columns structure
• orient="index" → keyed by row index

Example:

tables[0].to_json("table.json", orient="records", indent=2)

Produces:

[
  {"Name":"Alice","Age":30},
  {"Name":"Bob","Age":25}
]

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Option 2: requests + BeautifulSoup + custom parsing

Use this if pandas.read_html() fails or you need more control.

Install

pip install requests beautifulsoup4

Example

import requests
from bs4 import BeautifulSoup
import json

url = "https://example.com/page-with-table"
html = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")

table = soup.find("table")
headers = [th.get_text(strip=True) for th in table.find_all("th")]

rows = []
for tr in table.find_all("tr"):
    cells = [td.get_text(strip=True) for td in tr.find_all("td")]
    if cells:
        rows.append(dict(zip(headers, cells)))

with open("table.json", "w", encoding="utf-8") as f:
    json.dump(rows, f, indent=2, ensure_ascii=False)

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Option 3: Selenium or Playwright for JavaScript-rendered tables

If the table is loaded dynamically by JavaScript, use a browser automation tool.

Example with Selenium

pip install selenium pandas

Then load the page, wait for the table to render, grab page_source, and use pandas.read_html() or parse it with BeautifulSoup.

from selenium import webdriver
import pandas as pd

driver = webdriver.Chrome()
driver.get("https://example.com/dynamic-table")

html = driver.page_source
tables = pd.read_html(html)
tables[0].to_json("table.json", orient="records", indent=2)

driver.quit()

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Option 4: Scrapy for large-scale crawling

If you need to extract tables from many pages, Scrapy is a good framework.

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Important notes

• Check the site’s robots.txt and terms of service.
• Add delays to avoid overwhelming servers.
• Some sites block scraping; use headers or a browser-based approach if needed.
• Tables may have:
  • merged cells (rowspan, colspan)
  • nested tables
  • hidden rows/columns
  • pagination

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If you want a one-liner

For many pages with standard HTML tables:

import pandas as pd
pd.read_html("https://example.com")[0].to_json("table.json", orient="records", indent=2)

If you want, I can also give you:

1. a script that extracts all tables from a page, or
2. a version that handles JavaScript-rendered tables.