Integrations

As trrex builds a regular expression pattern, it can be used by any library that expects a regular expression

Working with pandas

import trrex as tx
import pandas as pd

df = pd.DataFrame(["The quick brown fox", "jumps over", "the lazy dog"], columns=["text"])
pattern = tx.make(["dog", "fox"])
df["text"].str.contains(pattern)

As you can see from the above example it works with any pandas function that receives a regular expression.

Efficient gazetteer for spacy

It can be used in conjunction with spacy EntityRuler to build a gazetteer

import trrex as tx
from spacy.lang.en import English

nlp = English()
ruler = nlp.add_pipe("entity_ruler")
patterns = [ {
"label": "ORG", "pattern": [ {"TEXT": {"REGEX": tx.make(["Amazon", "Apple", "Netflix", "Netlify"])}} ], },
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER":
"francisco"}]}, ]
ruler.add_patterns(patterns)

doc = nlp("Netflix HQ is in Los Gatos.")
[(ent.text, [ent.label]()) for ent in doc.ents]

Fuzzy matching with regex

We can take advantage of the fuzzy matching of the regex module:

import regex
import trrex as tx

pattern = tx.make(["monkey", "monster", "dog", "cat"], prefix="", suffix=r"{1<=e<=2}")
regex.search(pattern, "This is really a master dag", regex.BESTMATCH)