Using grlc from python

Being written in python itself, it is easy to use grlc from python. Here we show how to use grlc to run a SPARQL query which is stored on github.

First we start by importing grlc (and a couple other libraries we use for working with the data).



In [1]:

    
import pandas as pd
from io import StringIO

import grlc
import grlc.utils as utils
import grlc.swagger as swagger









    



INFO:rdflib:RDFLib Version: 4.2.1

We can load the grlc specification for a github repository. For example, my github username is c-martinez and my SPARQL queries are on grlc-queries repo.



In [2]:

    
user = 'c-martinez'
repo = 'grlc-queries'
spec = swagger.build_spec(user, repo)









    



INFO:grlc.gquery:Decorator guessed endpoint: http://dbpedia.org/sparql
INFO:grlc.gquery:Decorator guessed endpoint: http://dbpedia.org/sparql



In [11]:

    
print spec[0].keys()









    



['call_name', 'params', 'description', 'tags', 'item_properties', 'query', 'method', 'summary']



In [10]:

    
print spec[0]['query']









    



PREFIX dbo: <http://dbpedia.org/ontology/>

SELECT ?country_name ?capital_name ?population
WHERE {
  ?country rdf:type dbo:Country .
  ?country dbo:capital ?capital .

  ?capital rdfs:label  ?capital_name .
  ?country rdfs:label  ?country_name .
  ?country dbo:populationTotal ?population .

  FILTER (lang(?capital_name) = 'en')
  FILTER (lang(?country_name) = 'en')
  FILTER NOT EXISTS { ?country dbo:dissolutionYear ?yearEnd }
  FILTER (?population > 500000)
} LIMIT 1000

We can use dispatch_query functions to load data from a specific query (dbpediaCapitals in this case). For this example, we are loading data in text/csv format.

NOTE: dbpediaCapitals query loads data from dbpedia.org -- the endpoint is specified via the endpoint decorator on the query file itself.



In [4]:

    
query_name = 'dbpediaCapitals'
acceptHeader = 'text/csv'

data, code, headers = utils.dispatch_query(user, repo, query_name, acceptHeader=acceptHeader)









    



INFO:grlc.gquery:Decorator guessed endpoint: http://dbpedia.org/sparql

Now we just transform these results to a pandas dataframe.



In [5]:

    
data_grlc = pd.read_csv(StringIO(data))
data_grlc.head(10)









    Out[5]:







  
    
      
      country_name
      capital_name
      population
    
  
  
    
      0
      Albania
      Tirana
      2886026
    
    
      1
      Algeria
      Algiers
      40400000
    
    
      2
      Greece
      Athens
      10955000
    
    
      3
      Azerbaijan
      Baku
      9754830
    
    
      4
      Germany
      Berlin
      82175700
    
    
      5
      Brazil
      Brasília
      206440850
    
    
      6
      European Union
      Brussels
      510056011
    
    
      7
      Syria
      Damascus
      17064854
    
    
      8
      Finland
      Helsinki
      5488543
    
    
      9
      Indonesia
      Jakarta
      255461700

Grlc via http

Another alternative is to load data via a running grlc server (in this case grlc.io).



In [6]:

    
import requests



In [7]:

    
headers = {'accept': 'text/csv'}
resp = requests.get("http://grlc.io/api/c-martinez/grlc-queries/dbpediaCapitals", headers=headers)



In [8]:

    
data_requests = pd.read_csv(StringIO(resp.text))
data_requests.head(10)









    Out[8]:







  
    
      
      country_name
      capital_name
      population
    
  
  
    
      0
      Albania
      Tirana
      2886026
    
    
      1
      Algeria
      Algiers
      40400000
    
    
      2
      Greece
      Athens
      10955000
    
    
      3
      Azerbaijan
      Baku
      9754830
    
    
      4
      Germany
      Berlin
      82175700
    
    
      5
      Brazil
      Brasília
      206440850
    
    
      6
      European Union
      Brussels
      510056011
    
    
      7
      Syria
      Damascus
      17064854
    
    
      8
      Finland
      Helsinki
      5488543
    
    
      9
      Indonesia
      Jakarta
      255461700

	country_name	capital_name	population
0	Albania	Tirana	2886026
1	Algeria	Algiers	40400000
2	Greece	Athens	10955000
3	Azerbaijan	Baku	9754830
4	Germany	Berlin	82175700
5	Brazil	Brasília	206440850
6	European Union	Brussels	510056011
7	Syria	Damascus	17064854
8	Finland	Helsinki	5488543
9	Indonesia	Jakarta	255461700