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 <http://jplusplus.org/sv>_, and Robin Linderborg.

Installing

.. code:: bash

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 <http://web05.lansstyrelsen.se/transtat_O/transtat.asp>_.

.. code:: python

from statscraper.scrapers import Cranes

scraper = Cranes() scraper.items # List available datasets []

dataset = scraper["Number of cranes"] dataset.dimensions [<Dimension: date (Day of the month)>, , ]

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:

.. code:: python

@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

.. code:: bash

git clone https://github.com/jplusplus/statscraper python setup.py install

This repo includes statscraper-datatypes as a subtree. To update this, do:

.. code:: bash

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:

.. code:: bash

python3 -m pytest tests/test-datatypes.py

Changelog

The changelog has been moved to CHANGELOG.md <CHANGELOG.md>_.

.. _Facebook: https://www.facebook.com/groups/skrejperpark .. _ReadTheDocs: http://statscraper.readthedocs.io