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:

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.