[Python] How to convert a list of Pydantic BaseModels to Pandas Dataframe

I can't seem to find any built-in way of simply converting a list of Pydantic BaseModels to a Pandas Dataframe.

from pydantic import BaseModel
import pandas as pd

class SomeModel(BaseModel):
col1: str
col2: str

data = [SomeModel(**{'col1': 'foo', 'col2': 'bar'})] * 10
pd.DataFrame(data)


Output

>> 0 1
>> 0 (col1, foo) (col2, bar)
>> 1 (col1, foo) (col2, bar)
>> ...


In this way the columns are loaded as data. A workaround is to do the following

pd.DataFrame([model.dict() for model in data])


Output

>> col1 col2
>> 0 foo bar
>> 1 foo bar
>> ...


However this method is a bit slow for larger amounts of data. Is there a faster way?

Continue reading...