Web Scraping & Bots · Lesson

Extracting Data from HTML Tables

Learn to reliably parse tabular HTML data, handle rowspan and colspan, and convert messy tables into clean structured rows.

Extracting Data from HTML Tables is a free Web Scraping & Bots lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Web Scraping & Bots learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Tables Are Tricky

HTML <table> elements look simple but are one of the most error-prone targets in scraping. Rows can merge cells, headers can repeat, and layout tables masquerade as data tables.

Data tables hold real records you want.
Layout tables only control visual structure.

This lesson focuses on extracting clean rows from genuine data tables.

Anatomy of a Table

A table is built from a few key tags:

<thead> / <tbody> group header and body rows.
<tr> is a single row.
<th> is a header cell, <td> is a data cell.

Knowing these landmarks lets you target rows precisely instead of grabbing raw text.

<table>
  <thead><tr><th>Name</th><th>Price</th></tr></thead>
  <tbody>
    <tr><td>Widget</td><td>$9.99</td></tr>
    <tr><td>Gadget</td><td>$14.50</td></tr>
  </tbody>
</table>

Selecting Rows

With a parser like BeautifulSoup you first find the table, then iterate its rows. Always scope your selection to tbody when present so the header row does not contaminate your data.

from bs4 import BeautifulSoup

soup = BeautifulSoup(html, 'html.parser')
table = soup.select_one('table')
rows = table.select('tbody tr')
print(len(rows), 'data rows found')

Reading Cell Values

For each row, collect the cell text. Use get_text(strip=True) to drop surrounding whitespace and nested tag noise.

for row in rows:
    cells = [c.get_text(strip=True) for c in row.select('td')]
    print(cells)

Mapping Headers to Values

Raw lists of cells are fragile. Pair each value with its column header so your output is self-describing and column-order changes do not break downstream code.

headers = [h.get_text(strip=True) for h in table.select('thead th')]
records = []
for row in rows:
    cells = [c.get_text(strip=True) for c in row.select('td')]
    records.append(dict(zip(headers, cells)))
print(records[0])

Handling colspan

A cell with colspan="2" visually spans two columns. If you ignore it, every following cell shifts left and misaligns with its header. Read the attribute and pad accordingly.

span = int(cell.get('colspan', 1))
values.extend([text] * span)

Handling rowspan

rowspan is harder: a cell carries down into rows below it. Track a buffer of pending values keyed by column index and inject them into subsequent rows until the span is exhausted.

pending = {}
for r_idx, row in enumerate(rows):
    col = 0
    for cell in row.select('td'):
        while col in pending and pending[col][1] > 0:
            col += 1
        rs = int(cell.get('rowspan', 1))
        if rs > 1:
            pending[col] = [cell.get_text(strip=True), rs]
        col += 1

Cleaning Extracted Values

Cells often contain currency symbols, thousands separators, or stray unicode. Normalize before storing:

Strip $, , and whitespace.
Cast numeric strings to numbers.
Replace non-breaking spaces.

def clean_price(text):
    text = text.replace('$', '').replace(',', '').strip()
    return float(text) if text else None

print(clean_price('$1,299.00'))

Pandas read_html Shortcut

For well-formed tables, pandas.read_html parses every table on a page into DataFrames in one call. Use it for quick wins, then fall back to manual parsing for tables with merged cells.

import pandas as pd
tables = pd.read_html(html)
df = tables[0]
print(df.head())

Detecting Layout vs Data Tables

Before extracting, confirm the table holds real data. Heuristics:

Has a <thead> or repeated <th> cells.
Multiple rows with consistent column counts.
No nested tables used purely for spacing.

Skip tables that fail these checks.

Putting It Together

A robust table extractor: locate the data table, read headers, walk rows while resolving spans, clean each value, and emit a list of dictionaries. This pipeline survives most real-world markup.

def extract_table(table):
    headers = [h.get_text(strip=True) for h in table.select('thead th')]
    out = []
    for row in table.select('tbody tr'):
        vals = [c.get_text(strip=True) for c in row.select('td')]
        out.append(dict(zip(headers, vals)))
    return out

Quick Check

Test your understanding of table parsing.

Recap

You learned to extract clean records from HTML tables: scope to tbody, map headers to values, resolve colspan and rowspan, clean cell text, and use pandas.read_html for simple cases.

With these skills you can turn even messy tabular markup into reliable structured data.

Frequently asked questions

Is the “Extracting Data from HTML Tables” lesson free?

Yes — the full text of “Extracting Data from HTML Tables” is free to read here on the web, and the Web Scraping & Bots course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Web Scraping & Bots course, upgrade to CoddyKit PRO.

What will I learn in “Extracting Data from HTML Tables”?

Learn to reliably parse tabular HTML data, handle rowspan and colspan, and convert messy tables into clean structured rows. You practise Web Scraping & Bots with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Web Scraping & Bots?

No prior experience is required. Web Scraping & Bots on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Extracting Data from HTML Tables” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Web Scraping & Bots lesson?

Yes. Every Web Scraping & Bots lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

← Back to Web Scraping & Bots