<jupyter_start><jupyter_text>BnF AnalysisThis notebook contains an analysis of data from BnF.<jupyter_code>import sys
sys.path.insert(0, '../../utils/')

from SPARQLWrapper import SPARQLWrapper, TURTLE, JSON
import sparql_utils

endpoint = SPARQLWrapper('http://wikibase-test-srv01.kbr.be/sparql/namespace/bnf-authors-persons/sparql')<jupyter_output><empty_output><jupyter_text>Data Source Overview<jupyter_code>sparql_utils.getClassOverview(endpoint)
sparql_utils.getPropertyOverview(endpoint)
#todo: adapt query for BnF data model
#sparql_utils.getNationalityOverview(endpoint)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10673234811165845, 'fraction_numerical': 0.0049261083743842365, 'mean_word_length': 8.838709677419354, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '260422', 'license': 'MIT', 'n_tokens_mistral': 223, 'n_tokens_neox': 217, 'n_words': 34, 'path': 'data-sources/bnf/bnf-analysis.ipynb', 'repo_name': 'kbrbe/beltrans-data-integration', 'size': '609'}