Parse HTML table to Python list?

I'd like to take an HTML table and parse through it to get a list of dictionaries. Each list element would be a dictionary corresponding to a row in the table.

If, for example, I had an HTML table with three columns (ma开发者_StackOverflow社区rked by header tags), "Event", "Start Date", and "End Date" and that table had 5 entries, I would like to parse through that table to get back a list of length 5 where each element is a dictionary with keys "Event", "Start Date", and "End Date".

Thanks for the help!

You should use some HTML parsing library like lxml:

from lxml import etree
s = """<table>
  <tr><th>Event</th><th>Start Date</th><th>End Date</th></tr>
  <tr><td>a</td><td>b</td><td>c</td></tr>
  <tr><td>d</td><td>e</td><td>f</td></tr>
  <tr><td>g</td><td>h</td><td>i</td></tr>
</table>
"""
table = etree.HTML(s).find("body/table")
rows = iter(table)
headers = [col.text for col in next(rows)]
for row in rows:
    values = [col.text for col in row]
    print dict(zip(headers, values))

prints

{'End Date': 'c', 'Start Date': 'b', 'Event': 'a'}
{'End Date': 'f', 'Start Date': 'e', 'Event': 'd'}
{'End Date': 'i', 'Start Date': 'h', 'Event': 'g'}

Hands down the easiest way to parse a HTML table is to use pandas.read_html() - it accepts both URLs and HTML.

import pandas as pd
url = r'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
tables = pd.read_html(url) # Returns list of all tables on page
sp500_table = tables[0] # Select table of interest

Only downside is that read_html() doesn't preserve hyperlinks.

Sven Marnach excellent solution is directly translatable into ElementTree which is part of recent Python distributions:

from xml.etree import ElementTree as ET

s = """<table>
  <tr><th>Event</th><th>Start Date</th><th>End Date</th></tr>
  <tr><td>a</td><td>b</td><td>c</td></tr>
  <tr><td>d</td><td>e</td><td>f</td></tr>
  <tr><td>g</td><td>h</td><td>i</td></tr>
</table>
"""

table = ET.XML(s)
rows = iter(table)
headers = [col.text for col in next(rows)]
for row in rows:
    values = [col.text for col in row]
    print(dict(zip(headers, values)))

same output as Sven Marnach's answer...

If the HTML is not XML you can't do it with etree. But even then, you don't have to use an external library for parsing a HTML table. In python 3 you can reach your goal with HTMLParser from html.parser. I've the code of the simple derived HTMLParser class here in a github repo.

You can use that class (here named HTMLTableParser) the following way:

import urllib.request
from html_table_parser import HTMLTableParser

target = 'http://www.twitter.com'

# get website content
req = urllib.request.Request(url=target)
f = urllib.request.urlopen(req)
xhtml = f.read().decode('utf-8')

# instantiate the parser and feed it
p = HTMLTableParser()
p.feed(xhtml)
print(p.tables)

The output of this is a list of 2D-lists representing tables. It looks maybe like this:

[[['   ', ' Anmelden ']],
 [['Land', 'Code', 'Für Kunden von'],
  ['Vereinigte Staaten', '40404', '(beliebig)'],
  ['Kanada', '21212', '(beliebig)'],
  ...
  ['3424486444', 'Vodafone'],
  ['  Zeige SMS-Kurzwahlen für andere Länder ']]]