Inconsistent support of type hints with mypy

[miguel-mi-silva]

Inconsistent support of type hints with mypy

[cosmicBboy]

Hi @miguel-mi-silva if you're using DataFrameModels you should use pa.check_types

@check_types
def process(df: TypeDataFrame[InputSchema]) -> TypeDataFrame[OutputSchema]:
    return df.assign(c="c")

check_input and check_output are for DataFrameSchemas

[miguel-mi-silva]

Hi @miguel-mi-silva if you're using DataFrameModels you should use pa.check_types

@check_types
def process(df: TypeDataFrame[InputSchema]) -> TypeDataFrame[OutputSchema]:
    return df.assign(c="c")

check_input and check_output are for DataFrameSchemas

Thanks for that. However, even with those changes, the error on the return type Incompatible return value type (got "pandas.core.frame.DataFrame", expected "pandera.typing.pandas.DataFrame[OutputSchema]") [return-value] still exists. Is that a known issue or am I doing something wrong?

Here's the new code to facilitate:

from pandera import SchemaModel, check_types
from pandas import DataFrame
from pandera.typing import DataFrame as TypeDataFrame


class InputSchema(SchemaModel):
    a: int
    b: bool

class OutputSchema(SchemaModel):
    a: int
    b: bool
    c: str

df = DataFrame({"a": [1], "b": [True]})

@check_types
def process(df: TypeDataFrame[InputSchema]) -> TypeDataFrame[OutputSchema]:
    return df.assign(c="c")

[cosmicBboy]

What does your mypy config look like?

See the docs for a discussion on mypy support. If you care about type lints there's some guidance there on how to make you mypy linting pass, e.g.

@check_types
def process(df: TypeDataFrame[InputSchema]) -> TypeDataFrame[OutputSchema]:
    return df.assign(c="c").pipe(TypeDataFrame[OutputSchema])

or

from typing import cast

@check_types
def process(df: TypeDataFrame[InputSchema]) -> TypeDataFrame[OutputSchema]:
    return cast(TypeDataFrame[OutputSchema], df.assign(c="c"))