Simon Willison’s Weblog

6 items tagged “scraping”

scrapely. Neat twist on a screen scraping library: this one lets you “train it” by feeding it examples of URLs paired with a dictionary of the data you would like to have extracted from that URL, then uses an instance based learning earning algorithm to run against new URLs. Slightly confusing name since it’s maintained by the scrapy team but is a totally independent project from the scrapy web crawling framework. # 10th July 2018, 8:25 pm

sqlitebiter. SImilar to my csvs-to-sqlite tool, but sqlitebiter handles “CSV/Excel/HTML/JSON/LTSV/Markdown/SQLite/SSV/TSV/Google-Sheets”. Most interestingly, it works against HTML pages—run “sqlitebiter -v url ’https://en.wikipedia.org/wiki/Comparison_of_firewalls’” and it will scrape that Wikipedia page and create a SQLite table for each of the HTML tables it finds there. # 17th May 2018, 10:40 pm

kennethreitz/requests-html: HTML Parsing for Humans™ (via) Neat and tiny wrapper around requests, lxml and html2text that provides a Kenneth Reitz grade API design for intuitively fetching and scraping web pages. The inclusion of html2text means you can use a CSS selector to select a specific HTML element and then convert that to the equivalent markdown in a one-liner. # 25th February 2018, 4:49 pm

Using “import refs” to iteratively import data into Django

I’ve been writing a few scripts to backfill my blog with content I originally posted elsewhere. So far I’ve imported answers I posted on Quora (background), answers I posted on Ask MetaFilter and content I recovered from the Internet Archive.

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YQL—converting the web to JSON with mock SQL. YQL just got a whole lot more interesting to me—I had no idea they were exposing an HTML and RSS scraping tool over a JSONP API in addition to all of the Yahoo! web service methods. # 13th December 2008, 9:39 am

Data Scraping Wikipedia with Google Spreadsheets. I hadn’t played with =importHTML in Google spreadsheets, which lets you suck in data from an HTML table or list somewhere on the web. This tutorial takes it further, bringing Wikipedia, Yahoo! Pipes and KML in to the mix. # 16th October 2008, 2:37 pm