Extracting the highly-valuable data from unstructured text often results in hard-to-read, brittle, difficult-to-maintain code. The problem is that using regular expressions directly embedded in the program control flow does not provide the best level of abstraction. We propose a query language (based on the tuple relational calculus) that facilitates data extraction. Developers can explicitly express their intent declaratively, making their code much easier to write, read, and maintain.
This package allows programmers to express what they are searching for by using higher-level concepts to express their query as tags, locations, and expressions on location relations.
The location of a string of characters within the document is the interval defining its starting and ending position.
Locations are grouped into sets named by tags. Tags can be used in conjunctions and disjunctions of interval relations to query for tuples of locations.
We invite you to view our YouTube video of our presentation from the Playlist for PyData Global 2022.
We presented this material from our GitHub repo:
- pydataG22.pdf slides of our talk.
- ipynb/pydata.ipynb a
jupyter notebookwith examples. - RELEASE_NOTES.rst describes updates for each release.
Use one of these pip or python commands (rev 3 or above) to install from PyPI:
pip install qante python -m pip install qante
Use python docstrings for API Documentation:
python # rev 3 or above
from quante.tagger import Tagger
from quante.query import Query
from quante import loctuple as lt
from quant.table import get_table
help(Tagger) # annotate text with tags using tagRE('tagname', regexp)
help(Query) # Syntax for querying annotated text
help(lt) # Predicates used by queries
help(get_table) # extract table (as dictionaries) from text
See also: "API Documentation" at the end of our jupyter notebook.
We welcome your questions by electronic mail at: qante{at}asgard.com