how can I pass table or dataframe instead of text with entity recognition using spacy

Question

The following link shows how to add multiple EntityRuler with spaCy. The code to do that is below:

import spacy
import pandas as pd

from spacy.pipeline import EntityRuler
nlp = spacy.load('en_core_web_sm', disable = ['ner'])
ruler = nlp.add_pipe("entity_ruler")


flowers = ["rose", "tulip", "african daisy"]
for f in flowers:
    ruler.add_patterns([{"label": "flower", "pattern": f}])
animals = ["cat", "dog", "artic fox"]
for a in animals:
    ruler.add_patterns([{"label": "animal", "pattern": a}])



result={}
doc = nlp("cat and artic fox, plant african daisy")
for ent in doc.ents:
        result[ent.label_]=ent.text
df = pd.DataFrame([result])
print(df)

The output:

      animal         flower
0  artic fox  african daisy

The problem is: How can i pass dataframe or table instead of the text:"cat and artic fox, plant african daisy"

Answer 1

Imagine that your dataframe is

df = pd.DataFrame({'Text':["cat and artic fox, plant african daisy"]})

You may define a custom method to extract the entities and then use it with Series.apply:

def get_entities(x):
    result = {}
    doc = nlp(x)
    for ent in doc.ents:
        result[ent.label_]=ent.text
    return result

and then

df['Matches'] = df['Text'].apply(get_entities)
>>> df['Matches']
0    {'animal': 'artic fox', 'flower': 'african daisy'}
Name: Matches, dtype: object