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Modify metaclass to allow DataFrame[Foo] type propagation #99

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Oct 22, 2024
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Modify metaclass to allow DataFrame[Foo] type propagation #99

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fed230b
Modify metaclass to generate DataFrame directly
thomasaarholt Aug 18, 2024
4cb4bd8
Modify frame code to support new metaclass
thomasaarholt Aug 18, 2024
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135 changes: 68 additions & 67 deletions src/patito/polars.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,33 +54,63 @@ class LazyFrame(pl.LazyFrame, Generic[ModelType]):

model: type[ModelType]

@classmethod
def _construct_lazyframe_model_class(
cls: type[LDF], model: type[ModelType] | None
) -> type[LazyFrame[ModelType]]:
"""Return custom LazyFrame sub-class where LazyFrame.model is set.
def set_model(self, model: type[OtherModelType]) -> LazyFrame[OtherModelType]:
"""Associate a given patito ``Model`` with the dataframe.

Can be used to construct a LazyFrame class where
DataFrame.set_model(model) is implicitly invoked at collection.
The model schema is used by methods that depend on a model being associated with
the given dataframe such as :ref:`DataFrame.validate() <DataFrame.validate>`
and :ref:`DataFrame.get() <DataFrame.get>`.

``DataFrame(...).set_model(Model)`` is equivalent with ``Model.DataFrame(...)``.

Args:
model: A patito model which should be used to validate the final dataframe.
If None is provided, the regular LazyFrame class will be returned.
model (Model): Sub-class of ``patito.Model`` declaring the schema of the
dataframe.

Returns:
A custom LazyFrame model class where LazyFrame.model has been correctly
"hard-coded" to the given model.
DataFrame[Model]: Returns the same dataframe, but with an attached model
that is required for certain model-specific dataframe methods to work.

"""
if model is None:
return cls
Examples:
>>> from typing_extensions import Literal
>>> import patito as pt
>>> import polars as pl
>>> class SchoolClass(pt.Model):
... year: int = pt.Field(dtype=pl.UInt16)
... letter: Literal["A", "B"] = pt.Field(dtype=pl.Categorical)
...
>>> classes = pt.DataFrame(
... {"year": [1, 1, 2, 2], "letter": list("ABAB")}
... ).set_model(SchoolClass)
>>> classes
shape: (4, 2)
┌──────┬────────┐
│ year ┆ letter │
│ --- ┆ --- │
│ i64 ┆ str │
╞══════╪════════╡
│ 1 ┆ A │
│ 1 ┆ B │
│ 2 ┆ A │
│ 2 ┆ B │
└──────┴────────┘
>>> casted_classes = classes.cast()
>>> casted_classes
shape: (4, 2)
┌──────┬────────┐
│ year ┆ letter │
│ --- ┆ --- │
│ u16 ┆ cat │
╞══════╪════════╡
│ 1 ┆ A │
│ 1 ┆ B │
│ 2 ┆ A │
│ 2 ┆ B │
└──────┴────────┘
>>> casted_classes.validate()

new_class = type(
f"{model.__name__}LazyFrame",
(cls,),
{"model": model},
)
return new_class
"""
return model.LazyFrame._from_pyldf(self._ldf) # type: ignore

def collect(
self,
Expand All @@ -93,12 +123,11 @@ def collect(
parameters.
"""
background = kwargs.pop("background", False)
df = super().collect(*args, background=background, **kwargs)
df: pl.DataFrame = super().collect(*args, background=background, **kwargs)
df = DataFrame(df)
if getattr(self, "model", False):
cls = DataFrame._construct_dataframe_model_class(model=self.model)
else:
cls = DataFrame
return cls._from_pydf(df._df)
df = df.set_model(self.model)
return df

def derive(self: LDF, columns: list[str] | None = None) -> LDF:
"""Populate columns which have ``pt.Field(derived_from=...)`` definitions.
Expand Down Expand Up @@ -307,7 +336,10 @@ def cast(
@classmethod
def from_existing(cls: type[LDF], lf: pl.LazyFrame) -> LDF:
"""Construct a patito.DataFrame object from an existing polars.DataFrame object."""
return cls.model.LazyFrame._from_pyldf(lf._ldf).cast()
if getattr(cls, "model", False):
return cls.model.LazyFrame._from_pyldf(super().lazy()._ldf) # type: ignore

return LazyFrame._from_pyldf(lf._ldf) # type: ignore


class DataFrame(pl.DataFrame, Generic[ModelType]):
Expand Down Expand Up @@ -341,30 +373,6 @@ class DataFrame(pl.DataFrame, Generic[ModelType]):

model: type[ModelType]

@classmethod
def _construct_dataframe_model_class(
cls: type[DF], model: type[OtherModelType]
) -> type[DataFrame[OtherModelType]]:
"""Return custom DataFrame sub-class where DataFrame.model is set.

Can be used to construct a DataFrame class where
DataFrame.set_model(model) is implicitly invoked at instantiation.

Args:
model: A patito model which should be used to validate the dataframe.

Returns:
A custom DataFrame model class where DataFrame._model has been correctly
"hard-coded" to the given model.

"""
new_class = type(
f"{model.model_json_schema()['title']}DataFrame",
(cls,),
{"model": model},
)
return new_class

def lazy(self: DataFrame[ModelType]) -> LazyFrame[ModelType]:
"""Convert DataFrame into LazyFrame.

Expand All @@ -374,15 +382,12 @@ def lazy(self: DataFrame[ModelType]) -> LazyFrame[ModelType]:
A new LazyFrame object.

"""
lazyframe_class: LazyFrame[ModelType] = (
LazyFrame._construct_lazyframe_model_class(
model=getattr(self, "model", None)
)
) # type: ignore
ldf = lazyframe_class._from_pyldf(super().lazy()._ldf)
return ldf
if getattr(self, "model", False):
return self.model.LazyFrame._from_pyldf(super().lazy()._ldf) # type: ignore

return LazyFrame._from_pyldf(super().lazy()._ldf) # type: ignore

def set_model(self, model): # type: ignore[no-untyped-def] # noqa: ANN001, ANN201
def set_model(self, model: type[OtherModelType]) -> DataFrame[OtherModelType]:
"""Associate a given patito ``Model`` with the dataframe.

The model schema is used by methods that depend on a model being associated with
Expand Down Expand Up @@ -438,11 +443,7 @@ def set_model(self, model): # type: ignore[no-untyped-def] # noqa: ANN001, ANN2
>>> casted_classes.validate()

"""
cls = self._construct_dataframe_model_class(model=model)
return cast(
DataFrame[model],
cls._from_pydf(self._df),
)
return model.DataFrame(self._df)

def unalias(self: DF) -> DF:
"""Un-aliases column names using information from pydantic validation_alias.
Expand Down Expand Up @@ -503,7 +504,6 @@ def cast(
def drop(
self: DF,
columns: str | Collection[str] | None = None,
*more_columns: str,
) -> DF:
"""Drop one or more columns from the dataframe.

Expand All @@ -515,7 +515,6 @@ def drop(
columns: A single column string name, or list of strings, indicating
which columns to drop. If not specified, all columns *not*
specified by the associated dataframe model will be dropped.
more_columns: Additional named columns to drop.

Returns:
DataFrame[Model]: New dataframe without the specified columns.
Expand All @@ -538,7 +537,9 @@ def drop(

"""
if columns is not None:
return self._from_pydf(super().drop(columns)._df)
# I get a single null row if I try to use super() here, so go via
# pl.DataFrame instead.
return self._from_pydf(pl.DataFrame(self._df).drop(columns)._df)
else:
return self.drop(list(set(self.columns) - set(self.model.columns)))

Expand Down Expand Up @@ -706,7 +707,7 @@ def fill_null(
# else pl.lit(default_value, self.model.dtypes[column]).alias(column)
for column, default_value in self.model.defaults.items()
]
).set_model(self.model)
).set_model(self.model) # type: ignore

def get(self, predicate: pl.Expr | None = None) -> ModelType:
"""Fetch the single row that matches the given polars predicate.
Expand Down
18 changes: 11 additions & 7 deletions src/patito/pydantic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,15 +76,19 @@ def __init__(cls, name: str, bases: tuple, clsdict: dict, **kwargs) -> None:

"""
super().__init__(name, bases, clsdict, **kwargs)
# Add a custom subclass of patito.DataFrame to the model class,
# where .set_model() has been implicitly set.
cls.DataFrame = DataFrame._construct_dataframe_model_class(
model=cls, # type: ignore
NewDataFrame = type(
f"{cls.__name__}DataFrame",
(DataFrame,),
{"model": cls},
)
# Similarly for LazyFrame
cls.LazyFrame = LazyFrame._construct_lazyframe_model_class(
model=cls, # type: ignore
cls.DataFrame: type[DataFrame[cls]] = NewDataFrame # type: ignore

NewLazyFrame = type(
f"{cls.__name__}LazyFrame",
(LazyFrame,),
{"model": cls},
)
cls.LazyFrame: type[LazyFrame[cls]] = NewLazyFrame # type: ignore

def __hash__(self) -> int:
"""Return hash of the model class."""
Expand Down
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