Read tables into rows and records

Turn an HTML <table> into Python data with rows(), records(), and tables(), with rowspan and colspan resolved so the result is rectangular – no pandas dependency.

rows() reads a <table> into a list of rows, each a list[str], with rowspan and colspan resolved by filling every spanned cell, so the result is rectangular and you never resolve spans by hand:

import turbohtml

table = turbohtml.parse(
    "<table>"
    "<tr><th>Region</th><th>Q1</th><th>Q2</th></tr>"
    "<tr><td rowspan=2>West</td><td>10</td><td>12</td></tr>"
    "<tr><td>8</td><td>9</td></tr>"
    "</table>"
).find("table")
for row in table.rows():
    print(row)

['Region', 'Q1', 'Q2']
['West', '10', '12']
['West', '8', '9']

records() keys the first row (the header, typically the thead row) over each later row as a list[dict] – the shape a pandas.read_html user feeds straight to pandas.DataFrame, with no pandas dependency:

for record in table.records():
    print(record)

{'Region': 'West', 'Q1': '10', 'Q2': '12'}
{'Region': 'West', 'Q1': '8', 'Q2': '9'}

tables() returns every table on the page, each as rows(), so you can scan a document without locating each <table> first:

document = turbohtml.parse("<table><tr><td>a</td></tr></table><table><tr><td>b</td><td>c</td></tr></table>")
print(document.tables())

[[['a']], [['b', 'c']]]