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?