Support numpy types

[Kludex]

The idea here is to support the numpy types mentioned on https://numpy.org/doc/stable/reference/arrays.scalars.html.

[yezz123]

+1

[Kludex]

Actually, numpy has a pretty big artifact. Should we create pydantic-numpy-types? 😅

[yezz123]

Actually, numpy has a pretty big artifact. Should we create pydantic-numpy-types? 😅

Can't deny the same regarding the #24 but I guess it can be good if we have all of the types in one package!

[Kludex]

Maybe the extra all can be created so people can do pip install pydantic-extra-types[all], or the extra numpy. Or... Just don't install numpy, and show a message like "You need to install numpy".

[yezz123]

Maybe the extra all can be created so people can do pip install pydantic-extra-types[all], or the extra numpy. Or... Just don't install numpy, and show a message like "You need to install numpy".

I agree with both options - either displaying a message that prompts the user to install Numpy, or proceeding with the extra requirements since it functions properly in our current scenario, this will allow for the possibility of adding any big package as extra

[kroncatti]

Hey folks,

Just so I understand a little bit better our objective with the numpy type here. I imagine you intend to have something like (correct me if I am talking nonsense):

Numpy(value=np.float64(12))

Do we want to have some conversions as well ? because my first idea was to validate using isinstance(value, np.generic). However, when running isinstance(12, np.generic) the result is False (the same happens with a python float). Would we want to convert those elements to numpy when someone invoke the type ?

The reason why I am asking this is the following: if we want to do those conversions we would probably need to make some decisions (e.g., we have int32, int64, and so on).

If my reasoning is not fair, just let me know 😄

[lig]

@kroncatti Pydantic has two modes of parsing/validation: strict and lax. In lax mode it already coerces types in many cases such as this:

    class Model(BaseModel):
        foo: int
    
    m = Model(foo='12')
    print(m)
    # foo=12
    print(type(m.foo))
    # <class 'int'>

So, it seems natural for Pydantic to coerce values to numpy types on validation.

[kroncatti]

Thanks @lig,

Just to check if I properly understood. If we set:

class Model(BaseModel):
    foo: Numpy

m = Model(foo='12')
print(m)
# foo=12
print(type(m.foo))
# <class 'numpy.int64'>

This should be the outcome ?

Shouldn't the user have to specify what numpy type we are going to coerce ? such as int64, float64, etc.

[Kludex]

I think the idea here is to support the following types: https://numpy.org/doc/stable/user/basics.types.html#array-types-and-conversions-between-types

We should create the following, and all the analogous:

• pydantic_extra_types.NumPyFloatHalf / pydantic_extra_types.NumPyFloat16
• pydantic_extra_types.NumPySingle
• pydantic_extra_types.NumPyDouble

I guess we also want np.array, np.datetime64, and others.

[kroncatti]

That makes sense. so we are basically creating one extra type for each one of those types instead of having a generic type for all of them. Cool!