Converting objects to Pandas Dataframe

Question

I have the following variable

my_res = RecognizeEntitiesResult(id=0, entities=[
    CategorizedEntity(text=50,000, category=Quantity, subcategory=Number, offset=10, length=6, confidence_score=0.8), 
    CategorizedEntity(text=infectious, category=Skill, subcategory=None, offset=29, length=10, confidence_score=0.8)], 
    warnings=[], statistics=None, is_error=False)

I want to have a Dataframe where each CategorizedEntity is a row, and the column for each row is the text, the category, and the confidence_score.

I tried the below, but with no success.

[x.as_dict() for x in my_res]

I could of course manually go through each CategorizedEntity and create a new row one by one but that doesnt seem very Pandas/Python-ic.

Any better way of doing this?

Alexandra Dudkina · Accepted Answer · 2020-10-07 11:28:29Z

1

I'm afraid the most simple way to map data to dictionary would be:

from azure.ai.textanalytics import RecognizeEntitiesResult
from azure.ai.textanalytics import CategorizedEntity
from collections import defaultdict
import pandas as pd

d = defaultdict(list)

my_res = RecognizeEntitiesResult(id=0, entities=[
    CategorizedEntity(text='50,000', category='Quantity', subcategory='Number', offset=10, length=6, confidence_score=0.8), 
    CategorizedEntity(text='infectious', category='Skill', subcategory=None, offset=29, length=10, confidence_score=0.8)], 
    warnings=[], statistics=None, is_error=False)

for e in my_res.entities:
  for k, v in e.items():
    if (k in ['text', 'category', 'confidence_score']):
      d[k].append(v)

df = pd.DataFrame(d)

df.head()

answered Oct 7, 2020 at 11:28

Alexandra Dudkina

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