A base class for building web scrapers for statistical data.
Project description
Statscraper is a base library for building web scrapers for statistical data, with a helper ontology for (primarily Swedish) statistical data. A set of ready-to-use scrapers are included.
For users
You can use Statscraper as a foundation for your next scraper, or try out any of the included scrapers. With Statscraper comes a unified interface for scraping, and some useful helper methods for scraper authors.
Full documentation: ReadTheDocs
For updates and discussion: Facebook
By Journalism++ Stockholm, and Robin Linderborg.
Installing
pip install statscraper
Using a scraper
Scrapers acts like “cursors” that move around a hierarchy of datasets and collections of datasets. Collections and datasets are refered to as “items”.
┏━ Collection ━━━ Collection ━┳━ Dataset ROOT ━╋━ Collection ━┳━ Dataset ┣━ Dataset ┗━ Collection ┣━ Dataset ┗━ Dataset ┗━ Dataset ╰─────────────────────────┬───────────────────────╯ items
Here’s a simple example, with a scraper that returns only a single dataset: The number of cranes spotted at Hornborgarsjön each day as scraped from Länsstyrelsen i Västra Götalands län.
>>> from statscraper.scrapers import Cranes
>>> scraper = Cranes()
>>> scraper.items # List available datasets
[<Dataset: Number of cranes>]
>>> dataset = scraper["Number of cranes"]
>>> dataset.dimensions
[<Dimension: date (Day of the month)>, <Dimension: month>, <Dimension: year>]
>>> row = dataset.data[0] # first row in this dataset
>>> row
<Result: 7 (value)>
>>> row.dict
{'value': '7', u'date': u'7', u'month': u'march', u'year': u'2015'}
>>> df = dataset.data.pandas # get this dataset as a Pandas dataframe
Building a scraper
Scrapers are built by extending a base scraper, or a derative of that. You need to provide a method for listing datasets or collections of datasets, and for fetching data.
Statscraper is built for statistical data, meaning that it’s most useful when the data you are scraping/fetching can be organized with a numerical value in each row:
city |
year |
value |
---|---|---|
Voi |
2009 |
45483 |
Kabarnet |
2006 |
10191 |
Taveta |
2009 |
67505 |
A scraper can override these methods:
_fetch_itemslist(item) to yield collections or datasets at the current cursor position
_fetch_data(dataset) to yield rows from the currently selected dataset
_fetch_dimensions(dataset) to yield dimensions available for the currently selected dataset
_fetch_allowed_values(dimension) to yield allowed values for a dimension
A number of hooks are avaiable for more advanced scrapers. These are called by adding the on decorator on a method:
@BaseScraper.on("up")
def my_method(self):
# Do something when the user moves up one level
For developers
These instructions are for developers working on the BaseScraper. See above for instructions for developing a scraper using the BaseScraper.
Downloading
git clone https://github.com/jplusplus/statscraper
python setup.py install
This repo includes statscraper-datatypes as a subtree. To update this, do:
git subtree pull --prefix statscraper/datatypes git@github.com:jplusplus/statscraper-datatypes.git master --squash
Tests
Since 2.0.0 we are using pytest. To run an individual test:
python3 -m pytest tests/test-datatypes.py
Changelog
The changelog has been moved to <CHANGELOG.md>.
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