Support DataframeModel and the python API for declaring schema about kedro-pandera

[Galileo-Galilei]

Hi @noklam, I think there are several sub tasks to this ticket but not all with the same priority.

1. I think (to be verified) that if we create a custom resolver which can parse a DataFrameModel it will be enough for the hook to work "as is" with the exact same syntax. Something like :

my_data: 
    type: ...
    filepath: ...
    metadata: 
        pandera: 
            schema: ${pa.python: my_kedro_package.schemas.my_data.MyDataSchema} # we should "just" create the resovler which will import and instantiate the class

Does the design look ok for you? Do you have time to work on this one?

2. We can add a CLI to infer this schema and generate a my_kedro_package.schemas.my_data.py file (pseudo code below):

# my_kedro_package.schemas.my_data.py

from pandera import DataframeModel
from pandera.io import Field

class MyDataSchema(DataframeModel):
    var1: str = Field()
    var2: <var_typ>e= Field()
...

• This second step is optional and low priority.
• I don't think we should create default test. My goal is to have a helper to generate the file and eventually loop over the variables (it is cumbersome to create dozens of entries if the dataset has many variables).
• This may be a template with a jinja loop over variables?

[noklam]

How does 2. different from the current infer CLI? I'll work on 1.

[Galileo-Galilei]

The current CLI has a flag --python for this but it is not implemented. The little difference is that the infer CLI for yaml use a built-in pandera function which creates basic tests and the file, but there is no such function for python so we should create it on our own. that is why I want to keep it really simple, I want to avoid creating boilerplate code, not really infering advanced tests.

[noklam]

The current CLI has a flag --python for this but it is not implemented
I am not sure what do you mean, I thought this function exist already? isn't it using the schema.to_script() method?

Pandera natively support convert DataFrameModel -> DataFrameSchema, but not the other way round.