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Swiss commerce register¶

The Federal Office of Justice maintains ZEFIX, the swiss commerce register for all legally operating businesses.

The register provides us with company name, type, description, and address.
ZEFIX is also available as Linked Data.

Setup¶

SPARQL endpoints¶

For companies data¶

Swiss commerce register data can be accessed with SPARQL queries.
You can send queries using HTTP requests. The API endpoint is https://lindas.admin.ch/query/.

For geodata¶

To understand companies' location, we will work with Swiss geodata. It can be accessed with GeoSPARQL under https://ld.geo.admin.ch/query.

SPARQL client¶

Let's use SparqlClient from graphly to communicate with both databases. Graphly will allow us to:

send SPARQL queries
automatically add prefixes to all queries
format response to pandas or geopandas

import json
import os.path

import folium
import mapclassify
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import geopandas as gpd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

from graphly.api_client import SparqlClient

%matplotlib inline

# Uncomment to install dependencies in Colab environment
#!pip install mapclassify
#!pip install git+https://github.com/zazuko/graphly.git

sparql = SparqlClient("https://lindas.admin.ch/query")
geosparql = SparqlClient("https://ld.geo.admin.ch/query")

sparql.add_prefixes({
    "schema": "<http://schema.org/>",
    "admin": "<https://schema.ld.admin.ch/>"
})

geosparql.add_prefixes({
    "dct": "<http://purl.org/dc/terms/>",
    "geonames": "<http://www.geonames.org/ontology#>",
    "schema": "<http://schema.org/>",
    "geosparql": "<http://www.opengis.net/ont/geosparql#>",
})

SPARQL queries can become very long. To improve the readibility, we will work wih prefixes.

Using the add_prefixes method, we can define persistent prefixes. Every time you send a query, graphly will now automatically add the prefixes for you.

Companies by company type¶

Swiss law offers many opportnities to business owners. They can choose from several legal entites. What entities can we find in ZEFIX? Which ones are the most popular among enterpreneurs?

query = """
SELECT ?type (COUNT(DISTINCT ?company_iri) AS ?ccount)
WHERE {
    ?company_iri a admin:ZefixOrganisation.
    ?company_iri schema:additionalType/schema:name ?type .
  
  FILTER(LANG(?type) = "de")
}
GROUP BY ?type
ORDER BY DESC(?ccount)
"""

df = sparql.send_query(query)

# Let's rename variables to english

de2en = {'Kommanditgesellschaft': "Limited Partnership",
         'Ausländische Niederlassung im Handelsregister eingetragen': "Foreign Branch",
         'Genossenschaft': "Cooperative",
         'Verein': "Association",
         'Kollektivgesellschaft': "General Partnership",
         'Schweizerische Zweigniederlassung im Handelsregister eingetragen': "Swiss Branch",
         'Stiftung': "Foundation",
         'Einzelunternehmen': "Sole proprietorship",
         'Aktiengesellschaft': "Joint-stock Company",
         'Gesellschaft mit beschränkter Haftung GMBH / SARL': "Limited Liability Company"}

df["type"] = df["type"].apply(lambda x: de2en[x] if x in de2en else x)

fig = px.bar(df[df.ccount > 500], y="type", x="ccount", orientation = "h", labels={"type": "", "ccount": "Company Count"})
fig.update_layout(
    title='Which company type is most popular?', 
    title_x=0.5,
)
fig.update_layout(bargap=0.40)
fig.show()

Central Business Name Index - Zefix