How to convert an HTML table to an array in python

Question

I have an html document, and I want to pull the tables out of this document and return them as arrays. I'm picturing 2 functions, one that finds all the html tables in a document, and a second one that turns html tables into 2-dimensional arrays.

Something like this:

htmltables = get_tables(htmldocument)
for table in htmltables:
    array=make_array(table)

There's 2 catches: 1. The number tables varies day to day 2. The tables have all kinds of weird extra formatting, like bold and blink tags, randomly thrown in.

Thanks!

Answer 1

Use BeautifulSoup (I recommend 3.0.8). Finding all tables is trivial:

import BeautifulSoup

def get_tables(htmldoc):
    soup = BeautifulSoup.BeautifulSoup(htmldoc)
    return soup.findAll('table')

However, in Python, an array is 1-dimensional and constrained to pretty elementary types as items (integers, floats, that elementary). So there's no way to squeeze an HTML table in a Python array.

Maybe you mean a Python list instead? That's also 1-dimensional, but anything can be an item, so you could have a list of lists (one sublist per tr tag, I imagine, containing one item per td tag).

That would give:

def makelist(table):
  result = []
  allrows = table.findAll('tr')
  for row in allrows:
    result.append([])
    allcols = row.findAll('td')
    for col in allcols:
      thestrings = [unicode(s) for s in col.findAll(text=True)]
      thetext = ''.join(thestrings)
      result[-1].append(thetext)
  return result

This may not yet be quite what you want (doesn't skip HTML comments, the items of the sublists are unicode strings and not byte strings, etc) but it should be easy to adjust.

Answer 2

Pandas can extract all of the tables in your html to a list of dataframes right out of the box, saving you from having to parse the page yourself (reinventing the wheel). A DataFrame is a powerful type of 2-dimensional array.

I recommend continuing to work with the data via Pandas since it's a great tool, but you can also convert to other formats if you prefer (list, dictionary, csv file, etc.).

Example

"""Extract all tables from an html file, printing and saving each to csv file."""

import pandas as pd

df_list = pd.read_html('my_file.html')

for i, df in enumerate(df_list):
    print df
    df.to_csv('table {}.csv'.format(i))

Getting the html content directly from the web instead of from a file would only require a slight modification:

import requests

html = requests.get('my_url').content
df_list = pd.read_html(html)