Did you know the entire web was made of data? You probably did.
Scrapekit helps you get that data with simple Python scripts. Based on
[requests](http://docs.python-requests.org/) the library will handles
caching, threading and logging.
See the [full documentation](http://scrapekit.readthedocs.org/)
from scrapekit import Scraper
scraper = Scraper('example')
url = 'http://databin.pudo.org/t/b2d9cf'
doc = scraper.get(url).html()
for row in doc.findall('.//tr'):
columns = row.findall('./td')
pipeline = get_index | get_row
if __name__ == '__main__':
## Works well with
Scrapekit doesn't aim to provide all functionality necessary for
scraping. Specifically, it doesn't address HTML parsing, data storage
and data validation. For these needs, check the following libraries:
* [lxml](http://lxml.de/) for HTML/XML parsing; much faster and more
flexible than [BeautifulSoup](http://www.crummy.com/software/BeautifulSoup/)
* [dataset](http://dataset.rtfd.org) is a sister library of scrapekit
that simplifies storing semi-structured data in SQL databases.
## Existing tools
* [Scrapy](http://scrapy.org/) is a much more mature and comprehensive
framework for developing scrapers. On the other hand, it requires you to
develop scrapers within its class system. This can be too heavyweight
for a simple script to grab data off a web site.
* [scrapelib](http://scrapelib.readthedocs.org/) is a thin wrapper
around requests that does throttling, retries and caching.
* [MechanicalSoup](https://github.com/hickford/MechanicalSoup) binds
BeautifulSoup and requests into an imperative, stateful API.
## Credits and license
Scrapekit is licensed under the terms of the MIT license, which is also
included in [LICENSE](LICENSE). It was developed through projects of
[ICFJ](http://icfj.org), [ANCIR](http://investigativecenters.org) and
TODO: Brief introduction on what you do with files - including link to relevant help section.