Export Web Tables to JSON for Pandas — Skip the Scraping

Writing a BeautifulSoup scraper for a simple table export? There's a faster way. Export HTML tables directly to JSON and load them into Pandas quickly.

Why JSON over CSV?

CSV is great, but JSON has advantages for Python workflows:

• Type preservation: Numbers stay numbers, strings stay strings
• Nested data: JSON handles complex structures that CSV can't
• Direct to dict: JSON loads directly as Python dictionaries
• API compatibility: Same format as most web APIs

For quick data analysis, CSV works fine. For building pipelines or working with APIs, JSON is often cleaner.

Exporting HTML tables to JSON

HTML Table Exporter

Loading JSON into Pandas

Once you have your JSON file, loading it into Pandas is trivial:

import pandas as pd

# Load the exported JSON
df = pd.read_json('table-1.json')

# That's it. Your data is ready.
print(df.head())
print(df.info())

The JSON export uses the "records" orientation by default, which is the most compatible with Pandas:

[
  {"Column1": "Value1", "Column2": "Value2"},
  {"Column1": "Value3", "Column2": "Value4"}
]

NDJSON for streaming and big data

For large datasets or streaming pipelines, HTML Table Exporter PRO offers NDJSON (Newline-Delimited JSON) export:

{"Column1": "Value1", "Column2": "Value2"}
{"Column1": "Value3", "Column2": "Value4"}

Each line is a separate JSON object. This format works well for:

• BigQuery imports: Native format for BigQuery loading
• Streaming processing: Process line-by-line without loading entire file
• Log pipelines: Works with tools like jq, Elasticsearch, Splunk
• Large files: Memory-efficient for huge datasets

Loading NDJSON in Pandas:

df = pd.read_json('table-1.ndjson', lines=True)

When to still use web scraping

Browser extensions are great for one-off exports and small batches. Use scraping when you need:

• Automated pipelines: Data that updates daily and needs to be fetched automatically
• Hundreds of pages: Bulk extraction across many URLs
• API-like access: Programmatic control over the extraction process
• CI/CD integration: Data extraction as part of automated workflows

For exploratory analysis, one-time exports, or prototyping your data pipeline, the browser extension is faster than writing scraper code.

If you just need Excel or CSV output, the process is even simpler—see how to export HTML tables to CSV.