chore: fixes for python 3.9 (all tests passing)

chore(patito): cleanup

src/patito/_pydantic/column_info.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import json
 from typing import (
     Any,

src/patito/_pydantic/dtypes/dtypes.py

@@ -23,7 +23,9 @@
 
 
 @cache
-def valid_dtypes_for_model(cls: Type[ModelType]) -> Mapping[str, FrozenSet[DataTypeClass]]:
+def valid_dtypes_for_model(
+    cls: Type[ModelType],
+) -> Mapping[str, FrozenSet[DataTypeClass]]:
     return {
         column: DtypeResolver(cls.model_fields[column].annotation).valid_polars_dtypes()
         if cls.column_infos[column].dtype is None

src/patito/polars.py

@@ -5,26 +5,25 @@
     TYPE_CHECKING,
     Any,
     Collection,
+    Dict,
     Generic,
     Iterable,
     Optional,
+    Sequence,
+    Tuple,
     Type,
     TypeVar,
     Union,
     cast,
-    Dict,
-    Sequence,
-    Mapping,
-    Tuple
 )
 
 import polars as pl
 from polars.type_aliases import IntoExpr
 from pydantic import AliasChoices, AliasPath, create_model
 from typing_extensions import Literal
 
-from patito.exceptions import MultipleRowsReturned, RowDoesNotExist
 from patito._pydantic.column_info import ColumnInfo
+from patito.exceptions import MultipleRowsReturned, RowDoesNotExist
 
 if TYPE_CHECKING:
     import numpy as np
@@ -360,7 +359,9 @@ def unalias(self: DF) -> DF:
         """
         return self.lazy().unalias().collect()
 
-    def cast(self: DF, strict: bool = False, columns: Optional[Sequence[str]] = None) -> DF:
+    def cast(
+        self: DF, strict: bool = False, columns: Optional[Sequence[str]] = None
+    ) -> DF:
         """
         Cast columns to `dtypes` specified by the associated Patito model.
 
@@ -568,7 +569,7 @@ def fill_null(
             provided ``strategy`` parameter.
 
         Example:
-            >>> import bla_aloo as pt
+            >>> import patito as pt
             >>> class Product(pt.Model):
             ...     name: str
             ...     price: int = 19
@@ -712,7 +713,7 @@ def _pydantic_model(self) -> Type[Model]:
                 **pydantic_annotations,  # pyright: ignore
             ),
         )
-    
+
     def as_polars(self) -> pl.DataFrame:
         return pl.DataFrame._from_pydf(self._df)
 

src/patito/pydantic.py

@@ -4,6 +4,8 @@
 import itertools
 from collections.abc import Iterable
 from datetime import date, datetime, time, timedelta
+from functools import partial
+from inspect import getfullargspec
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -23,9 +25,6 @@
     cast,
     get_args,
 )
-from inspect import getfullargspec
-from functools import partial
-
 
 import polars as pl
 from polars.datatypes import DataType, DataTypeClass
@@ -131,7 +130,7 @@ def columns(cls: Type[ModelType]) -> List[str]:  # type: ignore
             List of column names.
 
         Example:
-            >>> import bla_aloo as pt
+            >>> import patito as pt
             >>> class Product(pt.Model):
             ...     name: str
             ...     price: int
@@ -212,7 +211,7 @@ def defaults(  # type: ignore
 
         Example:
             >>> from typing_extensions import Literal
-            >>> import bla_aloo as pt
+            >>> import patito as pt
             >>> class Product(pt.Model):
             ...     name: str
             ...     price: int = 0
@@ -441,7 +440,6 @@ def _from_polars(
         else:
             return cls.model_construct(**dataframe.to_dicts()[0])
 
-
     @classmethod
     def validate(
         cls,
@@ -462,7 +460,7 @@ def validate(
                 the given schema.
 
         Examples:
-            >>> import bla_aloo as pt
+            >>> import patito as pt
             >>> import polars as pl
 
 
@@ -493,7 +491,6 @@ def validate(
         """
         validate(dataframe=dataframe, columns=columns, schema=cls, **kwargs)
 
-
     @classmethod
     def example_value(  # noqa: C901
         cls,
@@ -1237,8 +1234,10 @@ def _derive_field(
         field_new.metadata = field.metadata
         return field_type, field_new
 
+
 FIELD_KWARGS = getfullargspec(fields.Field)
 
+
 def FieldCI(
     column_info: CI, *args: Any, **kwargs: Any
 ) -> Any:  # annotate with Any to make the downstream type annotations happy

tests/test_dummy_data.py

@@ -90,6 +90,7 @@ class UniqueModel(pt.Model):
 
 def test_enum_field_example_values() -> None:
     """It should produce correct example values for enums."""
+
     class DefaultEnumModel(pt.Model):
         row_number: int
         # Here the first value will be used as the example value

tests/test_model.py

@@ -5,7 +5,7 @@
 import enum
 import re
 from datetime import date, datetime, time
-from typing import ClassVar, Optional, Type
+from typing import Optional, Type
 
 import patito as pt
 import polars as pl
@@ -14,7 +14,6 @@
     DATE_DTYPES,
     TIME_DTYPES,
 )
-from patito.pydantic import ColumnInfo, FieldCI, ModelMetaclass
 from polars.datatypes import DataTypeGroup
 from polars.datatypes.constants import (
     DATETIME_DTYPES,
@@ -23,7 +22,6 @@
     INTEGER_DTYPES,
 )
 from pydantic import AliasChoices, AwareDatetime, ValidationError
-from pydantic import Field as PdField
 
 from tests.examples import CompleteModel, ManyTypes, SmallModel
 

tests/test_polars.py

@@ -77,13 +77,13 @@ def test_create_missing_columns_with_defaults() -> None:
 
     class NestedModel(pt.Model):
         foo: int = 2
-        small_model: SmallModel | None = None
+        small_model: Optional[SmallModel] = None
 
     class DefaultModel(pt.Model):
         foo: int = 2
         bar: Optional[str] = "default"
-        small_model: SmallModel | None = None  # works ok on polars==0.20.3
-        nested_model: NestedModel | None = None  # fails to convert on polars==0.20.3
+        small_model: Optional[SmallModel] = None  # works ok on polars==0.20.3
+        nested_model: Optional[NestedModel] = None  # fails to convert on polars==0.20.3
 
     missing_df = pt.DataFrame({"foo": [1, 2]})
     filled_df = missing_df.set_model(DefaultModel).fill_null(strategy="defaults")
@@ -389,6 +389,7 @@ class Model(pt.Model):
     # Or a list of columns
     assert df.drop(["column_1", "column_2"]).columns == []
 
+
 def test_polars_conversion():
     """You should be able to convert a DataFrame to a polars DataFrame."""
 
@@ -404,6 +405,7 @@ class Model(pt.Model):
     assert polars_df.columns == ["a", "b"]
     assert polars_df.dtypes == [pl.Int64, pl.Utf8]
 
+
 def test_validation_alias() -> None:
     class AliasModel(pt.Model):
         my_val_a: int = pt.Field(validation_alias="myValA")