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Light-weight tools for web scraping

Project description

# scrapekit

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](, the library will handles
caching, threading and logging.

See the [full documentation](

## Example

from scrapekit import Scraper

scraper = Scraper('example')

def get_index():
url = ''
doc = scraper.get(url).html()
for row in doc.findall('.//tr'):
yield row

def get_row(row):
columns = row.findall('./td')
print columns

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]( for HTML/XML parsing; much faster and more
flexible than [BeautifulSoup](
* [dataset]( is a sister library of scrapekit
that simplifies storing semi-structured data in SQL databases.

## Existing tools

* [Scrapy]( 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]( is a thin wrapper
around requests that does throttling, retries and caching.
* [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](, [ANCIR]( and

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