This is a scraper for statistical data from http://www.vantetider.se built on top of the Statscraper package <https://github.com/jplusplus/statscraper>.

Install

pip install -r requirements.txt

The scraper has to do a lot of requests and uses requests-cache <https://pypi.python.org/pypi/requests-cache> to store queries.

Example usage

from vantetider import VantetiderScraper

scraper = VantetiderScraper()
scraper.items  # List _implemeted_ datasets
# [<VantetiderDataset: VantatKortareAn60Dagar (Väntat kortare än 60 dagar )>, <VantetiderDataset: Overbelaggning (Överbeläggningar)>, <VantetiderDataset: PrimarvardTelefon (Telefontillgänglighet)>, <VantetiderDataset: PrimarvardBesok (Läkarbesök)>, <VantetiderDataset: SpecialiseradBesok (Förstabesök)>, <VantetiderDataset: SpecialiseradOperation (Operation/åtgärd)>]

dataset = scraper.get("Overbelaggning")  # Get a specific dataset

# List all available dimensions
print dataset.dimensions

print datatset.regions  # List available region
print datatset.years  # List available years

# Make a query, you have to explicitly define all dimension values you want
# to query. By default the scraper will fetch default values.
res = dataset.fetch({
  "region": "Blekinge",
  "year": "2016",
  "period": "Februari",
  # Currenty we can only query by id of dimension value
  "type_of_overbelaggning": ["0", "1"], # "Somatik" and "Psykiatri"
  })

# Do something with the result
df = res.pandas

Practical application, using dataset.py for storege.

from vantetider import VantetiderScraper
from vantetider.allowed_values import TYPE_OF_OVERBELAGGNING, PERIODS
import dataset

db = dataset.connect('sqlite:///vantetider.db')

TOPIC = "Overbelaggning"

# Set up local db
table = db.create_table(TOPIC)
scraper = VantetiderScraper()

dataset = scraper.get(TOPIC)

# Get all available regions and years for query
years = [x.value for x in dataset.years]
regions = [x.value for x in dataset.regions]

# Query in chunks to be able to store to database on the run
for region in regions:
    for year in years:
        res = dataset.fetch({
            "year": year,
            "type_of_overbelaggning": [x[0] for x in TYPE_OF_OVERBELAGGNING],
            "period": PERIODS,
            "region": region,
            })
        df = res.pandas
        data = res.list_of_dicts
        table.insert_many(data)

TODO

• Implement scraping of "Aterbesok", "Undersokningar", "BUPdetalj", "BUP".
• Enable querying on label names on all dimensions
• Add more allowed values to vantetider/allowed_values.py
• Make requests-cache optional.

Devlop

Run tests:

make tests