From c84e18df10c1c55896043f2246598bd925e2eeed Mon Sep 17 00:00:00 2001
From: Oliver Borchert <oliver.borchert@quantco.com>
Date: Thu, 30 Jul 2026 01:26:51 +0200
Subject: [PATCH 1/5] refactor!: Remove explicit `pyarrow` dependency

---
 dataframely/_compat.py             |    7 -
 dataframely/columns/_base.py       |   18 -
 dataframely/columns/any.py         |    7 -
 dataframely/columns/array.py       |   12 -
 dataframely/columns/binary.py      |    4 -
 dataframely/columns/bool.py        |    4 -
 dataframely/columns/categorical.py |    4 -
 dataframely/columns/datetime.py    |   21 -
 dataframely/columns/decimal.py     |    7 -
 dataframely/columns/enum.py        |   10 -
 dataframely/columns/float.py       |   12 -
 dataframely/columns/integer.py     |   36 -
 dataframely/columns/list.py        |    5 -
 dataframely/columns/object.py      |    4 -
 dataframely/columns/string.py      |    4 -
 dataframely/columns/struct.py      |    4 -
 dataframely/schema.py              |   11 -
 docs/_templates/classes/column.rst |    2 +-
 docs/api/schema/conversion.rst     |    1 -
 docs/guides/quickstart.md          |    2 -
 docs/index.md                      |    2 +-
 pixi.lock                          | 8586 ++++++++++++++++++++--------
 pixi.toml                          |    2 +-
 pyproject.toml                     |    3 +-
 tests/column_types/test_object.py  |    8 -
 tests/columns/test_pyarrow.py      |   35 +-
 26 files changed, 6157 insertions(+), 2654 deletions(-)

diff --git a/dataframely/_compat.py b/dataframely/_compat.py
index 154ecb88..2c4d6ac5 100644
--- a/dataframely/_compat.py
+++ b/dataframely/_compat.py
@@ -52,13 +52,6 @@ class PGDialect_psycopg2:  # type: ignore # noqa: N801
     class Dialect:  # type: ignore # noqa: N801
         pass
 
-# -------------------------------------- PYARROW ------------------------------------- #
-
-try:
-    import pyarrow as pa
-except ImportError:  # pragma: no cover
-    pa = _DummyModule("pyarrow")  # type: ignore
-
 # -------------------------------------- PYDANTIC ------------------------------------ #
 
 try:
diff --git a/dataframely/columns/_base.py b/dataframely/columns/_base.py
index abd92218..dc3492ec 100644
--- a/dataframely/columns/_base.py
+++ b/dataframely/columns/_base.py
@@ -220,24 +220,6 @@ def sqlalchemy_column(self, name: str, dialect: sa.Dialect) -> sa.Column:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         """The :mod:`sqlalchemy` dtype equivalent of this column data type."""
 
-    # ------------------------------------ PYARROW ----------------------------------- #
-
-    def pyarrow_field(self, name: str) -> pa.Field:
-        """Obtain the pyarrow field of this column definition.
-
-        Args:
-            name: The name of the column.
-
-        Returns:
-            The :mod:`pyarrow` field definition.
-        """
-        return pa.field(name, self.pyarrow_dtype, nullable=self.nullable)
-
-    @property
-    @abstractmethod
-    def pyarrow_dtype(self) -> pa.DataType:
-        """The :mod:`pyarrow` dtype equivalent of this column data type."""
-
     # ----------------------------------- PYDANTIC ----------------------------------- #
 
     def pydantic_field(self) -> Any:
diff --git a/dataframely/columns/any.py b/dataframely/columns/any.py
index 740407da..4f01f94b 100644
--- a/dataframely/columns/any.py
+++ b/dataframely/columns/any.py
@@ -79,13 +79,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:  # pragma: no cover
                 raise NotImplementedError("SQL column cannot have 'Any' type.")
 
-    def pyarrow_field(self, name: str) -> pa.Field:
-        return pa.field(name, self.pyarrow_dtype, nullable=self.nullable)
-
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.null()
-
     @property
     def _python_type(self) -> AnyType:
         return AnyType
diff --git a/dataframely/columns/array.py b/dataframely/columns/array.py
index d00f2a4c..3058c824 100644
--- a/dataframely/columns/array.py
+++ b/dataframely/columns/array.py
@@ -123,18 +123,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
                     f"SQL column cannot have 'Array' type for dialect '{dialect}'."
                 )
 
-    def _pyarrow_field_of_shape(self, shape: Sequence[int]) -> pa.Field:
-        if shape:
-            size, *rest = shape
-            inner_type = self._pyarrow_field_of_shape(rest)
-            return pa.field("item", pa.list_(inner_type, size), nullable=True)
-        else:
-            return self.inner.pyarrow_field("item")
-
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return self._pyarrow_field_of_shape(self.shape).type
-
     @property
     def _python_type(self) -> Any:
         inner_type = self.inner.pydantic_field()
diff --git a/dataframely/columns/binary.py b/dataframely/columns/binary.py
index 888d1bd3..d1ddf67f 100644
--- a/dataframely/columns/binary.py
+++ b/dataframely/columns/binary.py
@@ -29,10 +29,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.LargeBinary()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.large_binary()
-
     @property
     def _python_type(self) -> Any:
         return bytes
diff --git a/dataframely/columns/bool.py b/dataframely/columns/bool.py
index a1e48d10..2574af47 100644
--- a/dataframely/columns/bool.py
+++ b/dataframely/columns/bool.py
@@ -25,10 +25,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Boolean()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.bool_()
-
     @property
     def _python_type(self) -> Any:
         return bool
diff --git a/dataframely/columns/categorical.py b/dataframely/columns/categorical.py
index aea7412c..facad4c7 100644
--- a/dataframely/columns/categorical.py
+++ b/dataframely/columns/categorical.py
@@ -79,10 +79,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.String()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.dictionary(pa.uint32(), pa.large_string())
-
     @property
     def _python_type(self) -> Any:
         return str
diff --git a/dataframely/columns/datetime.py b/dataframely/columns/datetime.py
index 705a1058..ca0d2a84 100644
--- a/dataframely/columns/datetime.py
+++ b/dataframely/columns/datetime.py
@@ -141,10 +141,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.Date()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.date32()
-
     @property
     def _python_type(self) -> Any:
         return dt.date
@@ -292,10 +288,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.Time()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.time64("ns")
-
     @property
     def _python_type(self) -> Any:
         return dt.time
@@ -442,15 +434,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.DateTime(timezone=timezone_enabled)
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        time_zone = (
-            self.time_zone.tzname(None)
-            if isinstance(self.time_zone, dt.tzinfo)
-            else self.time_zone
-        )
-        return pa.timestamp(self.time_unit, time_zone)
-
     @property
     def _python_type(self) -> Any:
         return dt.datetime
@@ -612,10 +595,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.Interval()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.duration(self.time_unit)
-
     @property
     def _python_type(self) -> Any:
         return dt.timedelta
diff --git a/dataframely/columns/decimal.py b/dataframely/columns/decimal.py
index a160c17c..2b40371b 100644
--- a/dataframely/columns/decimal.py
+++ b/dataframely/columns/decimal.py
@@ -133,13 +133,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         else:
             return sa.Numeric(self.precision, self.scale)
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        # PyArrow requires an explicit value for precision.
-        # If precision is None, we pass decimal128's maximum precision of 38 to be safe.
-        # We do not use decimal256 since its values cannot be represented in SQL Server.
-        return pa.decimal128(self.precision or 38, self.scale)
-
     @property
     def _python_type(self) -> Any:
         return decimal.Decimal
diff --git a/dataframely/columns/enum.py b/dataframely/columns/enum.py
index e6f7336a..10fdc2cc 100644
--- a/dataframely/columns/enum.py
+++ b/dataframely/columns/enum.py
@@ -130,16 +130,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             return sa.CHAR(category_lengths[0])
         return sa.String(max(category_lengths))
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        if len(self.categories) <= 2**8 - 1:
-            dtype = pa.uint8()
-        elif len(self.categories) <= 2**16 - 1:
-            dtype = pa.uint16()
-        else:
-            dtype = pa.uint32()
-        return pa.dictionary(dtype, pa.large_string(), ordered=True)
-
     @property
     def _python_type(self) -> Any:
         return Literal[tuple(self.categories)]
diff --git a/dataframely/columns/float.py b/dataframely/columns/float.py
index 77c28531..60b3a631 100644
--- a/dataframely/columns/float.py
+++ b/dataframely/columns/float.py
@@ -193,10 +193,6 @@ def validate_dtype(self, dtype: PolarsDataType) -> bool:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Float()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.float64()
-
     @classproperty
     def max_value(self) -> float:
         return float(np.finfo(np.float64).max)
@@ -217,10 +213,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.REAL()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.float32()
-
     @classproperty
     def max_value(self) -> float:
         return float(np.finfo(np.float32).max)
@@ -241,10 +233,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Float()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.float64()
-
     @classproperty
     def max_value(self) -> float:
         return float(np.finfo(np.float64).max)
diff --git a/dataframely/columns/integer.py b/dataframely/columns/integer.py
index f7ceca79..cdd0f7cb 100644
--- a/dataframely/columns/integer.py
+++ b/dataframely/columns/integer.py
@@ -186,10 +186,6 @@ def validate_dtype(self, dtype: PolarsDataType) -> bool:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Integer()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int64()
-
     @classproperty
     def num_bytes(self) -> int:
         return 8
@@ -210,10 +206,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.SmallInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int8()
-
     @classproperty
     def num_bytes(self) -> int:
         return 1
@@ -234,10 +226,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.SmallInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int16()
-
     @classproperty
     def num_bytes(self) -> int:
         return 2
@@ -258,10 +246,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Integer()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int32()
-
     @classproperty
     def num_bytes(self) -> int:
         return 4
@@ -282,10 +266,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.BigInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.int64()
-
     @classproperty
     def num_bytes(self) -> int:
         return 8
@@ -312,10 +292,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 return sa.SmallInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.uint8()
-
     @classproperty
     def num_bytes(self) -> int:
         return 1
@@ -336,10 +312,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.Integer()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.uint16()
-
     @classproperty
     def num_bytes(self) -> int:
         return 2
@@ -360,10 +332,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.BigInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.uint32()
-
     @classproperty
     def num_bytes(self) -> int:
         return 4
@@ -384,10 +352,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         return sa.BigInteger()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.uint64()
-
     @classproperty
     def num_bytes(self) -> int:
         return 8
diff --git a/dataframely/columns/list.py b/dataframely/columns/list.py
index 8b8f4d80..41dae9a8 100644
--- a/dataframely/columns/list.py
+++ b/dataframely/columns/list.py
@@ -144,11 +144,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
                     f"SQL column cannot have 'List' type for dialect '{dialect}'."
                 )
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        # NOTE: Polars uses `large_list`s by default.
-        return pa.large_list(self.inner.pyarrow_field("item"))
-
     @property
     def _python_type(self) -> Any:
         inner_type = self.inner.pydantic_field()
diff --git a/dataframely/columns/object.py b/dataframely/columns/object.py
index e157e95b..37d50c2b 100644
--- a/dataframely/columns/object.py
+++ b/dataframely/columns/object.py
@@ -67,10 +67,6 @@ def dtype(self) -> pl.DataType:
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         raise NotImplementedError("SQL column cannot have 'Object' type.")
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        raise NotImplementedError("PyArrow column cannot have 'Object' type.")
-
     @property
     def _python_type(self) -> Any:
         return Any
diff --git a/dataframely/columns/string.py b/dataframely/columns/string.py
index cce4523c..be0bff09 100644
--- a/dataframely/columns/string.py
+++ b/dataframely/columns/string.py
@@ -120,10 +120,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
                 return sa.String(max_length)
         return sa.String()
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.large_string()
-
     @property
     def _python_type(self) -> Any:
         return str
diff --git a/dataframely/columns/struct.py b/dataframely/columns/struct.py
index dca92b07..576e35d3 100644
--- a/dataframely/columns/struct.py
+++ b/dataframely/columns/struct.py
@@ -125,10 +125,6 @@ def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
             case _:
                 raise NotImplementedError("SQL column cannot have 'Struct' type.")
 
-    @property
-    def pyarrow_dtype(self) -> pa.DataType:
-        return pa.struct([col.pyarrow_field(name) for name, col in self.inner.items()])
-
     @property
     def _python_type(self) -> Any:
         fields = {name: col.pydantic_field() for name, col in self.inner.items()}
diff --git a/dataframely/schema.py b/dataframely/schema.py
index dab8d151..a7eb8e9a 100644
--- a/dataframely/schema.py
+++ b/dataframely/schema.py
@@ -1411,17 +1411,6 @@ def to_sqlalchemy_columns(cls, dialect: sa.Dialect) -> list[sa.Column]:
             col.sqlalchemy_column(name, dialect) for name, col in cls.columns().items()
         ]
 
-    @classmethod
-    def to_pyarrow_schema(cls) -> pa.Schema:
-        """Obtain the pyarrow schema for this schema.
-
-        Returns:
-            A :mod:`pyarrow` schema that mirrors the schema defined by this class.
-        """
-        return pa.schema(
-            [col.pyarrow_field(name) for name, col in cls.columns().items()]
-        )
-
     @classmethod
     def to_pydantic_model(cls, name: str | None = None) -> type[pydantic.BaseModel]:
         """Convert this schema to a pydantic model.
diff --git a/docs/_templates/classes/column.rst b/docs/_templates/classes/column.rst
index 032988b5..f72c56bb 100644
--- a/docs/_templates/classes/column.rst
+++ b/docs/_templates/classes/column.rst
@@ -8,6 +8,6 @@
 
 .. autoclass:: {{ name }}
     :members:
-    :exclude-members: as_dict, from_dict, matches, pyarrow_field, pyarrow_dtype, sqlalchemy_dtype, sqlalchemy_column, validate_dtype, validation_rules
+    :exclude-members: as_dict, from_dict, matches, sqlalchemy_dtype, sqlalchemy_column, validate_dtype, validation_rules
     :autosummary:
     :autosummary-nosignatures:
diff --git a/docs/api/schema/conversion.rst b/docs/api/schema/conversion.rst
index 70906514..074049c6 100644
--- a/docs/api/schema/conversion.rst
+++ b/docs/api/schema/conversion.rst
@@ -7,5 +7,4 @@ Conversion
     :toctree: _gen/
 
     Schema.to_sqlalchemy_columns
-    Schema.to_pyarrow_schema
     Schema.to_polars_schema
diff --git a/docs/guides/quickstart.md b/docs/guides/quickstart.md
index 51814ce0..41443559 100644
--- a/docs/guides/quickstart.md
+++ b/docs/guides/quickstart.md
@@ -245,8 +245,6 @@ Lastly, `dataframely` schemas can be used to integrate with external tools:
 - `HouseSchema.to_sqlalchemy_columns()` provides a list of [sqlalchemy](https://www.sqlalchemy.org) columns that can be
   used to
   create SQL tables using types and constraints in line with the schema
-- `HouseSchema.to_pyarrow_schema()` provides a [pyarrow](https://arrow.apache.org/docs/python/index.html) schema with
-  appropriate column dtypes and nullability information
 - You can use `dy.DataFrame[HouseSchema]` (or the `LazyFrame` equivalent) as fields in
   [pydantic](https://pydantic.dev) models, including support for validation and serialization. Integration with
   pydantic is unstable.
diff --git a/docs/index.md b/docs/index.md
index 511a4226..78f1b620 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -15,7 +15,7 @@ schema information to data frame type hints.
 - Introspect validation failure information for run-time failures
 - Enhanced type hints for validated data frames allowing users to clearly express expectations about inputs and
   outputs (i.e., contracts) in data pipelines
-- Integrate schemas with external tools (e.g., `sqlalchemy` or `pyarrow`)
+- Integrate schemas with external tools (e.g., `sqlalchemy`, `pydantic` or `pyarrow`)
 - Generate test data that comply with a schema or collection of schemas and its validation rules
 
 ```{toctree}
diff --git a/pixi.lock b/pixi.lock
index 4ae1e70b..3b452e97 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,10 +1,31 @@
 version: 7
 platforms:
 - name: linux-64
+  virtual-packages:
+  - __unix=0=0
+  - __linux=4.18
+  - __glibc=2.28
+  - __archspec=0=x86_64
 - name: linux-aarch64
+  virtual-packages:
+  - __unix=0=0
+  - __linux=4.18
+  - __glibc=2.28
+  - __archspec=0=aarch64
 - name: osx-64
+  virtual-packages:
+  - __unix=0=0
+  - __osx=13.0
+  - __archspec=0=x86_64
 - name: osx-arm64
+  virtual-packages:
+  - __unix=0=0
+  - __osx=13.0
+  - __archspec=0=m1
 - name: win-64
+  virtual-packages:
+  - __win=10.0
+  - __archspec=0=x86_64
 environments:
   build:
     channels:
@@ -456,24 +477,24 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/arro3-core-0.8.0-py314ha5689aa_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.3-h3aafcba_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.14-h8e43964_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.13.1-hb03c661_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.2-h16e98cb_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.7.1-h9be7a74_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.11.0-hcbcd92d_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.26.3-h955231c_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.15.2-h8af55cf_3.conda
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-  - abseil-cpp =20260107.1
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   license_family: Apache
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.2.0-hc919400_1.conda
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+  - libxml2-16 2.15.3 h3cfd58e_0
+  - libzlib >=1.3.2,<2.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: MIT
+  license_family: MIT
+  run_exports:
+    weak:
+    - libxml2
+    - libxml2-16 >=2.15.3
+  size: 43684
+  timestamp: 1776376992865
 - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.3-hbc0d294_0.conda
   sha256: da68af9d9d28d65a6916db1bef68f8a25c64c4fdcf759f32a2d2f2f143220adf
   md5: e3b5acbb857a12f5d59e8d174bc536c0
@@ -29328,6 +32841,9 @@ packages:
   - vc14_runtime >=14.29.30139
   license: BSD-2-Clause
   license_family: BSD
+  run_exports:
+    weak:
+    - lz4-c >=1.10.0,<1.11.0a0
   size: 139891
   timestamp: 1733741168264
 - conda: https://conda.anaconda.org/conda-forge/win-64/markupsafe-3.0.3-py310hdb0e946_1.conda
@@ -29612,6 +33128,7 @@ packages:
   - nlohmann_json-abi ==3.12.0
   license: MIT
   license_family: MIT
+  run_exports: {}
   size: 134870
   timestamp: 1758194302226
 - conda: https://conda.anaconda.org/conda-forge/win-64/nodejs-24.16.0-h80d1838_0.conda
@@ -29730,25 +33247,28 @@ packages:
   license_family: Apache
   size: 9410183
   timestamp: 1775589779763
-- conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.3.0-h8fc0eb6_0.conda
-  sha256: f65b96be3790bdb90195226dfbcac2025b680bdffdbedc7e87d919161a63f8a7
-  md5: 1e03f610c02a16fdd7fee7430ec23115
+- conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.3.1-hf7eca67_0.conda
+  sha256: 6097e6facda0184c7be60eb0daaf05a03d6d4b9725ea5901553a1d01ba91271c
+  md5: d0e00e318dceb35f9a66f52225609ed2
   depends:
   - tzdata
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
-  - snappy >=1.2.2,<1.3.0a0
+  - zstd >=1.5.7,<1.6.0a0
   - lz4-c >=1.10.0,<1.11.0a0
-  - libabseil >=20260107.1,<20260108.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - snappy >=1.2.2,<1.3.0a0
+  - libprotobuf >=7.35.1,<7.35.2.0a0
+  - libabseil >=20260526.0,<20260527.0a0
   - libabseil * cxx17*
-  - libprotobuf >=6.33.5,<6.33.6.0a0
-  - libzlib >=1.3.1,<2.0a0
-  - zstd >=1.5.7,<1.6.0a0
   license: Apache-2.0
   license_family: APACHE
-  size: 1438607
-  timestamp: 1773230254230
+  run_exports:
+    weak:
+    - orc >=2.3.1,<2.3.2.0a0
+  size: 1465743
+  timestamp: 1784301260606
 - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-2.3.3-py310hed136d8_2.conda
   sha256: 9880f0d721e2ce487cfa0ceeb564b5db080fd3da958ea2d2ad1fb24e8d4de005
   md5: fcec00cec231a3217c53341f74846c26
@@ -30102,6 +33622,9 @@ packages:
   - zlib
   license: MIT
   license_family: MIT
+  run_exports:
+    weak:
+    - prometheus-cpp >=1.3.0,<1.4.0a0
   size: 183665
   timestamp: 1730769570131
 - conda: https://conda.anaconda.org/conda-forge/win-64/propcache-0.5.2-py310hdb0e946_0.conda
@@ -30221,27 +33744,176 @@ packages:
   license_family: BSD
   size: 249950
   timestamp: 1769678167309
-- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-24.0.0-py314h86ab7b2_0.conda
-  sha256: fdf414b7269ed3474c381689344ad71a626541c1354967f9d595398a3d384198
-  md5: 152580a594ef1924366fe6a934dac602
-  depends:
-  - libarrow-acero 24.0.0.*
-  - libarrow-dataset 24.0.0.*
-  - libarrow-substrait 24.0.0.*
-  - libparquet 24.0.0.*
-  - pyarrow-core 24.0.0 *_0_*
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-25.0.0-py310h5588dad_0.conda
+  sha256: 3f1c6d45dc48cd7cd0d6d6fbb2b3c55e7e7d9156afc01020a7cbaac5533ac37d
+  md5: a98c88fd089d6aaf9769e890b51570b8
+  depends:
+  - libarrow-acero 25.0.0.*
+  - libarrow-dataset 25.0.0.*
+  - libarrow-substrait 25.0.0.*
+  - libparquet 25.0.0.*
+  - pyarrow-core 25.0.0 *_0_*
+  - python >=3.10,<3.11.0a0
+  - python_abi 3.10.* *_cp310
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 26576
+  timestamp: 1784258911988
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-25.0.0-py311h1ea47a8_0.conda
+  sha256: 9212c35b287413f8ee682889f3e656de5be4ceda883cf67f310197edd05ca422
+  md5: 2053c863bd645c7e072d83e03b1cfd03
+  depends:
+  - libarrow-acero 25.0.0.*
+  - libarrow-dataset 25.0.0.*
+  - libarrow-substrait 25.0.0.*
+  - libparquet 25.0.0.*
+  - pyarrow-core 25.0.0 *_0_*
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 26452
+  timestamp: 1784258920901
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-25.0.0-py312h2e8e312_0.conda
+  sha256: 6be53f03679e361a052d54e028dc5623795c6730abad6992f763276e27098505
+  md5: e2d238b9b91a6bbf66b6daee1b382661
+  depends:
+  - libarrow-acero 25.0.0.*
+  - libarrow-dataset 25.0.0.*
+  - libarrow-substrait 25.0.0.*
+  - libparquet 25.0.0.*
+  - pyarrow-core 25.0.0 *_0_*
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 26426
+  timestamp: 1784258808135
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-25.0.0-py313hfa70ccb_0.conda
+  sha256: fadb3e471826dea0ef6d6624306fc06c7745ddae3b774368e905228e4b7b2574
+  md5: 89af264563b2bccc30221d4619fdf0b3
+  depends:
+  - libarrow-acero 25.0.0.*
+  - libarrow-dataset 25.0.0.*
+  - libarrow-substrait 25.0.0.*
+  - libparquet 25.0.0.*
+  - pyarrow-core 25.0.0 *_0_*
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 26381
+  timestamp: 1784258855203
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-25.0.0-py314h86ab7b2_0.conda
+  sha256: 050a5a54d49f5877f15566cd4df55233b2c78bb240f97a53c3a5828f68f5c5c9
+  md5: 7c6d60d53f7b0b09e424b58cd9fdfd37
+  depends:
+  - libarrow-acero 25.0.0.*
+  - libarrow-dataset 25.0.0.*
+  - libarrow-substrait 25.0.0.*
+  - libparquet 25.0.0.*
+  - pyarrow-core 25.0.0 *_0_*
   - python >=3.14,<3.15.0a0
   - python_abi 3.14.* *_cp314
   license: Apache-2.0
   license_family: APACHE
-  size: 27124
-  timestamp: 1776928424429
-- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-24.0.0-py314h159fc0c_0_cpu.conda
-  sha256: ce48dc60dc471037d2d97c1104b443cb2e8edb06dbd827804a8409ac28a5b912
-  md5: c4ee1bdf0e766307d105eafbcb720035
-  depends:
-  - libarrow 24.0.0.* *cpu
-  - libarrow-compute 24.0.0.* *cpu
+  run_exports: {}
+  size: 26584
+  timestamp: 1784258906758
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py310hc34f82c_0_cpu.conda
+  sha256: c39d8566f4561ae9454f5e59288072db4e1c36ad892b0f3183a51e04588aeab9
+  md5: 00decf10cd4d6d48001cbf9434761c54
+  depends:
+  - libarrow 25.0.0.* *cpu
+  - libarrow-compute 25.0.0.* *cpu
+  - libzlib >=1.3.2,<2.0a0
+  - python >=3.10,<3.11.0a0
+  - python_abi 3.10.* *_cp310
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - numpy >=1.23,<3
+  - libprotobuf >=6.33.5
+  - apache-arrow-proc * cpu
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 3705819
+  timestamp: 1784258813972
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py311hee28c0d_0_cpu.conda
+  sha256: 2f83a99040888fb6e2e91033b8ca4558c5c9c67f1294acbab3cfc35d7ed67adf
+  md5: 07f0c5be86fabe9df29fad4df7b659d1
+  depends:
+  - libarrow 25.0.0.* *cpu
+  - libarrow-compute 25.0.0.* *cpu
+  - libzlib >=1.3.2,<2.0a0
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - libprotobuf >=6.33.5
+  - numpy >=1.23,<3
+  - apache-arrow-proc * cpu
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 3761042
+  timestamp: 1784258836073
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py312h12c7521_0_cpu.conda
+  sha256: 1fc4c2777ca2363a9dfeb4ed1123706a5a9fcae903584f04f1ad75723df0ace8
+  md5: 53cffbf212097b8333f1baeb1e13ead9
+  depends:
+  - libarrow 25.0.0.* *cpu
+  - libarrow-compute 25.0.0.* *cpu
+  - libzlib >=1.3.2,<2.0a0
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - libprotobuf >=6.33.5
+  - numpy >=1.23,<3
+  - apache-arrow-proc * cpu
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 3649324
+  timestamp: 1784279006824
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py313hc76bb52_0_cpu.conda
+  sha256: cb368ff79a4e7fc1b89f9f2902056fd2b4ccecac787e0dc27fbdffd365ff4d3c
+  md5: ce4c745809c6510984265ab4775866fb
+  depends:
+  - libarrow 25.0.0.* *cpu
+  - libarrow-compute 25.0.0.* *cpu
+  - libzlib >=1.3.2,<2.0a0
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - libprotobuf >=6.33.5
+  - apache-arrow-proc * cpu
+  - numpy >=1.23,<3
+  license: Apache-2.0
+  license_family: APACHE
+  run_exports: {}
+  size: 3680813
+  timestamp: 1784258807433
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py314h159fc0c_0_cpu.conda
+  sha256: 2e0422f3b9cc1aa067638cccb0a8e917809550a91eb781cfbf64d5a7e2b8e7ab
+  md5: 54155aba0ef1fe72c52022617e453d52
+  depends:
+  - libarrow 25.0.0.* *cpu
+  - libarrow-compute 25.0.0.* *cpu
   - libzlib >=1.3.2,<2.0a0
   - python >=3.14,<3.15.0a0
   - python_abi 3.14.* *_cp314
@@ -30254,8 +33926,9 @@ packages:
   - libprotobuf >=6.33.5
   license: Apache-2.0
   license_family: APACHE
-  size: 3670958
-  timestamp: 1776928382916
+  run_exports: {}
+  size: 3689897
+  timestamp: 1784258892556
 - conda: https://conda.anaconda.org/conda-forge/win-64/pydantic-core-2.46.4-py310h034784e_0.conda
   sha256: d6d849410667ba1c48857779ebe18e68ec27f3c23d135c6aaa32ac3917906aff
   md5: 1e83b6a712136b7024fac0c001494a8b
@@ -30703,15 +34376,19 @@ packages:
   license_family: MIT
   size: 1023176
   timestamp: 1775566016879
-- conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-ha104f34_1.conda
-  sha256: 345b1ed8288d81510101f886aaf547e3294370e5dab340c4c3fcb0b25e5d99e0
-  md5: 6807f05dcf3f1736ad6cc9525b8b8725
+- conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-haef9524_2.conda
+  sha256: eb11c0e948c2576378a8e7f22768a967044d54bfbdea9ef358d31074b91c21f0
+  md5: 5d51319ef836277672d665c2fc04bab0
   depends:
-  - libre2-11 2025.11.05 h04e5de1_1
+  - libre2-11 2025.11.05 h45f70d9_2
   license: BSD-3-Clause
   license_family: BSD
-  size: 220305
-  timestamp: 1768190225351
+  run_exports:
+    weak:
+    - libre2-11 >=2025.11.5
+    - re2
+  size: 222198
+  timestamp: 1781312702688
 - conda: https://conda.anaconda.org/conda-forge/win-64/rpds-py-0.30.0-py310h034784e_0.conda
   sha256: a9176da0165e1fdc0582945ec22cbfac03c1bb88120389c7fe0b7406b5fee08f
   md5: f2ae7538b9ab9a7cd375fc23e320c2b0
@@ -31000,6 +34677,9 @@ packages:
   - ucrt >=10.0.20348.0
   license: BSD-3-Clause
   license_family: BSD
+  run_exports:
+    weak:
+    - snappy >=1.2.2,<1.3.0a0
   size: 67417
   timestamp: 1762948090450
 - conda: https://conda.anaconda.org/conda-forge/win-64/sqlalchemy-2.0.50-py314hc5dbbe4_0.conda
@@ -31127,6 +34807,7 @@ packages:
   - vc14_runtime >=14.51.36231
   license: BSD-3-Clause
   license_family: BSD
+  run_exports: {}
   size: 20170
   timestamp: 1780005880423
 - conda: https://conda.anaconda.org/conda-forge/win-64/winpty-0.4.3-4.tar.bz2
@@ -31312,6 +34993,9 @@ packages:
   - vc14_runtime >=14.44.35208
   license: Zlib
   license_family: Other
+  run_exports:
+    weak:
+    - libzlib >=1.3.2,<2.0a0
   size: 850351
   timestamp: 1774072891049
 - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
diff --git a/pixi.toml b/pixi.toml
index 610e5628..94c4acf0 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -47,6 +47,7 @@ moto = "*"
 mypy = ">=1.13"
 pandas = "*"
 pandas-stubs = "*"
+pyarrow = "*"
 pyodbc = "*"
 pytest = "*"
 pytest-benchmark = "*"
@@ -64,7 +65,6 @@ test-no-optional = "pytest -m 'not with_optionals'"
 
 [feature.optionals.dependencies]
 deltalake = "*"
-pyarrow = "*"
 pydantic = ">=2.11.9,<3"
 pydantic-core = ">=2.33.2,<3"
 sqlalchemy = ">=2"
diff --git a/pyproject.toml b/pyproject.toml
index 4279d9c3..0c6fa01b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,7 +31,6 @@ version = "0.0.0"
 
 [project.optional-dependencies]
 deltalake = ["deltalake"]
-pyarrow = ["pyarrow"]
 pydantic = ["pydantic>=2"]
 s3 = ["s3fs>=2025.9"]
 sqlalchemy = ["sqlalchemy"]
@@ -97,7 +96,7 @@ warn_unused_ignores = true
 
 [[tool.mypy.overrides]]
 ignore_missing_imports = true
-module = ["boto3.*", "fsspec.*", "pyarrow.*", "pytest_benchmark.*", "sklearn.*"]
+module = ["boto3.*", "fsspec.*", "pytest_benchmark.*", "sklearn.*"]
 
 [tool.typos.default.extend-identifiers]
 plain_serializer_function_ser_schema = "plain_serializer_function_ser_schema"
diff --git a/tests/column_types/test_object.py b/tests/column_types/test_object.py
index 321e2e89..62451660 100644
--- a/tests/column_types/test_object.py
+++ b/tests/column_types/test_object.py
@@ -43,14 +43,6 @@ def test_validate_dtype(column: Column, dtype: pl.DataType, is_valid: bool) -> N
     assert column.validate_dtype(dtype) == is_valid
 
 
-def test_pyarrow_dtype_raises() -> None:
-    column = dy.Object()
-    with pytest.raises(
-        NotImplementedError, match="PyArrow column cannot have 'Object' type."
-    ):
-        column.pyarrow_dtype
-
-
 def test_sampling_raises() -> None:
     column = dy.Object()
     with pytest.raises(
diff --git a/tests/columns/test_pyarrow.py b/tests/columns/test_pyarrow.py
index d3238567..08bc5ecf 100644
--- a/tests/columns/test_pyarrow.py
+++ b/tests/columns/test_pyarrow.py
@@ -3,6 +3,7 @@
 
 from typing import TypeVar
 
+import pyarrow as pa
 import pytest
 from polars._typing import TimeUnit
 
@@ -32,7 +33,7 @@ def _nullable(column_type: type[T]) -> T:
 @pytest.mark.parametrize("column_type", ALL_COLUMN_TYPES)
 def test_equal_to_polars_schema(column_type: type[Column]) -> None:
     schema = create_schema("test", {"a": _nullable(column_type)})
-    actual = schema.to_pyarrow_schema()
+    actual = pa.schema(schema)
     expected = schema.create_empty().to_arrow().schema
     assert actual == expected
 
@@ -52,7 +53,7 @@ def test_equal_to_polars_schema(column_type: type[Column]) -> None:
 )
 def test_equal_polars_schema_enum(categories: list[str]) -> None:
     schema = create_schema("test", {"a": dy.Enum(categories, nullable=True)})
-    actual = schema.to_pyarrow_schema()
+    actual = pa.schema(schema)
     expected = schema.create_empty().to_arrow().schema
     assert actual == expected
 
@@ -66,7 +67,7 @@ def test_equal_polars_schema_enum(categories: list[str]) -> None:
 )
 def test_equal_polars_schema_list(inner: Column) -> None:
     schema = create_schema("test", {"a": dy.List(inner, nullable=True)})
-    actual = schema.to_pyarrow_schema()
+    actual = pa.schema(schema)
     expected = schema.create_empty().to_arrow().schema
     assert actual == expected
 
@@ -91,7 +92,7 @@ def test_equal_polars_schema_list(inner: Column) -> None:
 )
 def test_equal_polars_schema_array(inner: Column, shape: int | tuple[int, ...]) -> None:
     schema = create_schema("test", {"a": dy.Array(inner, shape, nullable=True)})
-    actual = schema.to_pyarrow_schema()
+    actual = pa.schema(schema)
     expected = schema.create_empty().to_arrow().schema
     assert actual == expected
 
@@ -108,7 +109,7 @@ def test_equal_polars_schema_array(inner: Column, shape: int | tuple[int, ...])
 )
 def test_equal_polars_schema_struct(inner: Column) -> None:
     schema = create_schema("test", {"a": dy.Struct({"a": inner}, nullable=True)})
-    actual = schema.to_pyarrow_schema()
+    actual = pa.schema(schema)
     expected = schema.create_empty().to_arrow().schema
     assert actual == expected
 
@@ -117,13 +118,13 @@ def test_equal_polars_schema_struct(inner: Column) -> None:
 @pytest.mark.parametrize("nullable", [True, False])
 def test_nullability_information(column_type: type[Column], nullable: bool) -> None:
     schema = create_schema("test", {"a": column_type(nullable=nullable)})
-    assert ("not null" in str(schema.to_pyarrow_schema())) != nullable
+    assert ("not null" in str(pa.schema(schema))) != nullable
 
 
 @pytest.mark.parametrize("nullable", [True, False])
 def test_nullability_information_enum(nullable: bool) -> None:
     schema = create_schema("test", {"a": dy.Enum(["a", "b"], nullable=nullable)})
-    assert ("not null" in str(schema.to_pyarrow_schema())) != nullable
+    assert ("not null" in str(pa.schema(schema))) != nullable
 
 
 @pytest.mark.parametrize(
@@ -139,7 +140,7 @@ def test_nullability_information_enum(nullable: bool) -> None:
 @pytest.mark.parametrize("nullable", [True, False])
 def test_nullability_information_list(inner: Column, nullable: bool) -> None:
     schema = create_schema("test", {"a": dy.List(inner, nullable=nullable)})
-    assert ("not null" in str(schema.to_pyarrow_schema())) != nullable
+    assert ("not null" in str(pa.schema(schema))) != nullable
 
 
 @pytest.mark.parametrize(
@@ -155,7 +156,7 @@ def test_nullability_information_list(inner: Column, nullable: bool) -> None:
 @pytest.mark.parametrize("nullable", [True, False])
 def test_nullability_information_struct(inner: Column, nullable: bool) -> None:
     schema = create_schema("test", {"a": dy.Struct({"a": inner}, nullable=nullable)})
-    assert ("not null" in str(schema.to_pyarrow_schema())) != nullable
+    assert ("not null" in str(pa.schema(schema))) != nullable
 
 
 @pytest.mark.parametrize("column_type", COLUMN_TYPES)
@@ -165,7 +166,7 @@ def test_inner_nullability_struct(
 ) -> None:
     inner = column_type(nullable=inner_nullable)
     schema = create_schema("test", {"a": dy.Struct({"a": inner})})
-    pa_schema = schema.to_pyarrow_schema()
+    pa_schema = pa.schema(schema)
     struct_field = pa_schema.field("a")
     inner_field = struct_field.type[0]
     assert inner_field.nullable == inner_nullable
@@ -178,7 +179,7 @@ def test_inner_nullability_list(
 ) -> None:
     inner = column_type(nullable=inner_nullable)
     schema = create_schema("test", {"a": dy.List(inner)})
-    pa_schema = schema.to_pyarrow_schema()
+    pa_schema = pa.schema(schema)
     list_field = pa_schema.field("a")
     inner_field = list_field.type.value_field
     assert inner_field.nullable == inner_nullable
@@ -201,7 +202,7 @@ def test_nested_struct_in_list_preserves_nullability() -> None:
             )
         },
     )
-    pa_schema = schema.to_pyarrow_schema()
+    pa_schema = pa.schema(schema)
     list_field = pa_schema.field("a")
     struct_type = list_field.type.value_field.type
     assert not struct_type[0].nullable
@@ -219,7 +220,7 @@ def test_nested_list_in_struct_preserves_nullability() -> None:
             )
         },
     )
-    pa_schema = schema.to_pyarrow_schema()
+    pa_schema = pa.schema(schema)
     struct_field = pa_schema.field("a")
     list_type = struct_field.type[0].type
     assert not list_type.value_field.nullable
@@ -243,7 +244,7 @@ def test_deeply_nested_nullability() -> None:
             )
         },
     )
-    pa_schema = schema.to_pyarrow_schema()
+    pa_schema = pa.schema(schema)
     outer_struct = pa_schema.field("a").type
     inner_struct = outer_struct[0].type
     assert not inner_struct[0].nullable  # required field
@@ -254,7 +255,7 @@ def test_multiple_columns() -> None:
     schema = create_schema(
         "test", {"a": dy.Int32(nullable=False), "b": dy.Integer(nullable=True)}
     )
-    assert str(schema.to_pyarrow_schema()).split("\n") == [
+    assert str(pa.schema(schema)).split("\n") == [
         "a: int32 not null",
         "b: int64",
     ]
@@ -265,7 +266,7 @@ def test_datetime_time_unit(time_unit: TimeUnit) -> None:
     schema = create_schema(
         "test", {"a": dy.Datetime(time_unit=time_unit, nullable=True)}
     )
-    assert str(schema.to_pyarrow_schema()) == f"a: timestamp[{time_unit}]"
+    assert str(pa.schema(schema)) == f"a: timestamp[{time_unit}]"
 
 
 @pytest.mark.parametrize("time_unit", ["ns", "us", "ms"])
@@ -273,4 +274,4 @@ def test_duration_time_unit(time_unit: TimeUnit) -> None:
     schema = create_schema(
         "test", {"a": dy.Duration(time_unit=time_unit, nullable=True)}
     )
-    assert str(schema.to_pyarrow_schema()) == f"a: duration[{time_unit}]"
+    assert str(pa.schema(schema)) == f"a: duration[{time_unit}]"

From cb13a8c098e6eef39d32b7b8edd35bcdfa2e99fc Mon Sep 17 00:00:00 2001
From: Oliver Borchert <oliver.borchert@quantco.com>
Date: Thu, 30 Jul 2026 01:27:36 +0200
Subject: [PATCH 2/5] refactor!: Remove explicit `pyarrow` dependency

---
 pixi.lock                     | 4595 ++++++++++++++++++++++++++-------
 tests/columns/test_pyarrow.py |    3 -
 2 files changed, 3699 insertions(+), 899 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 3b452e97..cb35ba64 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -477,24 +477,24 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.4-hb7a77c6_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.14-h2aa3ae6_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.14.2-hb03c661_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.2-h720e601_4.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.12.8-h46fcd08_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.13.1-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.2-h16e98cb_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.7.1-h9be7a74_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.11.0-hcbcd92d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.26.3-h955231c_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.15.2-h8af55cf_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.12.3-h00bea6e_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.4-h16e98cb_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.10-h16e98cb_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.38.3-h7b0d4b4_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.747-h5a171d8_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.16.2-h206d751_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.13.3-hed0cdb0_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.17.0-hf824e48_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.13.0-ha7a2c86_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.15.0-h1e5b466_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
@@ -503,8 +503,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py314h42812f9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/deltalake-1.5.0-py310h1c74b4c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.3.1-h54a6638_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hb7133d2_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/greenlet-3.5.1-py314h42812f9_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda
@@ -512,19 +512,19 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-25.0.0-h32b7842_3_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-25.0.0-h635bf11_3_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-compute-25.0.0-h53684a4_3_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-25.0.0-h635bf11_3_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-25.0.0-h66fbfdd_3_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20260107.1-cxx17_h7b12aa8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-24.0.0-h61d77b5_4_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-24.0.0-h635bf11_4_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-compute-24.0.0-h53684a4_4_cpu.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-8_h0358290_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.21.0-hae6b9f4_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.20.0-hcf29cc6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-3.5.0-h8d2ee43_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-1.27.0-h9692893_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.27.0-ha770c72_0.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.33.5-h6eeba95_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h0dc7533_1.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py314h0f05182_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-25.0.0-py314hdafbbf9_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-25.0.0-py314h969be7f_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-24.0.0-py314hdafbbf9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-24.0.0-py314h969be7f_0_cpu.conda
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@@ -586,12 +585,12 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2025.11.05-h94463f1_2.conda
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+  license_family: MIT
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   size: 436462
   timestamp: 1781284116423
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+  - openssl >=3.5.6,<4.0a0
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   size: 128710
   timestamp: 1781268693348
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-files-datalake-cpp-12.15.0-hfea7fb9_0.conda
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+  - azure-storage-common-cpp >=12.13.0,<12.13.1.0a0
+  - libcxx >=19
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-files-datalake-cpp-12.16.0-h16bb3af_1.conda
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@@ -28178,6 +30101,16 @@ packages:
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   timestamp: 1780000580140
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@@ -28205,6 +30138,17 @@ packages:
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/glog-0.7.1-heb240a5_0.conda
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@@ -28323,6 +30267,19 @@ packages:
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   timestamp: 1780061235406
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20260107.1-cxx17_h2062a1b_0.conda
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+  md5: bb65152e0d7c7178c0f1ee25692c9fd1
+  depends:
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+  - libabseil-static =20260107.1=cxx17*
+  license: Apache-2.0
+  license_family: Apache
+  size: 1229639
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20260526.0-cxx17_h2062a1b_1.conda
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-24.0.0-h5b0bd9a_4_cpu.conda
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-25.0.0-h3cd3611_3_cpu.conda
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   timestamp: 1784538876211
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-24.0.0-ha4f4840_4_cpu.conda
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+  - libarrow-compute 24.0.0 h8d10c55_4_cpu
+  - libcxx >=21
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+  - libprotobuf >=6.33.5,<6.33.6.0a0
+  license: Apache-2.0
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-25.0.0-heaff1e6_3_cpu.conda
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   timestamp: 1784539317258
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-compute-24.0.0-h8d10c55_4_cpu.conda
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+  - libcxx >=21
+  - libopentelemetry-cpp >=1.27.0,<1.28.0a0
+  - libprotobuf >=6.33.5,<6.33.6.0a0
+  - libre2-11 >=2025.11.5
+  - libutf8proc >=2.11.3,<2.12.0a0
+  - re2
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-compute-25.0.0-h93a2d87_3_cpu.conda
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   timestamp: 1784539006166
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-24.0.0-ha4f4840_4_cpu.conda
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+  - libarrow-acero 24.0.0 ha4f4840_4_cpu
+  - libarrow-compute 24.0.0 h8d10c55_4_cpu
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+  - libparquet 24.0.0 h840b369_4_cpu
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-25.0.0-heaff1e6_3_cpu.conda
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-25.0.0-h2208e57_3_cpu.conda
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   - libssh2 >=1.11.1,<2.0a0
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   license_family: MIT
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+  - openssl >=3.5.5,<4.0a0
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+  license: Apache-2.0
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.82.1-hf3e839d_0.conda
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   timestamp: 1776995497368
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopentelemetry-cpp-1.27.0-h08d5cc3_0.conda
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+  - libprotobuf >=6.33.5,<6.33.6.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - nlohmann_json
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+  license: Apache-2.0
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@@ -28845,6 +30980,13 @@ packages:
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+  - libthrift >=0.22.0,<0.22.1.0a0
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-25.0.0-h0de02aa_3_cpu.conda
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   timestamp: 1784539263990
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   timestamp: 1775587972483
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   timestamp: 1771850357278
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   timestamp: 1784043506729
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   timestamp: 1784042894276
+- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-compression-0.3.2-h57cc34f_1.conda
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   timestamp: 1784075740626
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+  license_family: APACHE
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   timestamp: 1784075730337
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   timestamp: 1784054860660
+- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-mqtt-0.15.2-h38eb7cb_3.conda
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+  - aws-c-http >=0.11.0,<0.11.1.0a0
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+  license: Apache-2.0
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   timestamp: 1784087564779
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+  license: Apache-2.0
+  license_family: APACHE
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+  - aws-c-http >=0.11.0,<0.11.1.0a0
+  - aws-c-s3 >=0.12.3,<0.12.4.0a0
+  - aws-c-event-stream >=0.7.1,<0.7.2.0a0
+  - aws-c-common >=0.13.1,<0.13.2.0a0
+  license: Apache-2.0
+  license_family: APACHE
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+  - aws-crt-cpp >=0.38.3,<0.38.4.0a0
+  - aws-c-event-stream >=0.7.1,<0.7.2.0a0
+  license: Apache-2.0
+  license_family: APACHE
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   timestamp: 1775238920903
+- conda: https://conda.anaconda.org/conda-forge/win-64/azure-identity-cpp-1.13.3-h5ffce34_1.conda
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   size: 425897
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   timestamp: 1781283965517
+- conda: https://conda.anaconda.org/conda-forge/win-64/azure-storage-common-cpp-12.13.0-h5ffce34_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/win-64/azure-storage-files-datalake-cpp-12.15.0-h85968ff_0.conda
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+  - ucrt >=10.0.20348.0
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+- conda: https://conda.anaconda.org/conda-forge/win-64/c-ares-1.34.6-hfd05255_0.conda
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@@ -32154,6 +34639,20 @@ packages:
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+  - abseil-cpp =20260107.1
+  license: Apache-2.0
+  license_family: Apache
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-24.0.0-hab65375_4_cpu.conda
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+  - zstd >=1.5.7,<1.6.0a0
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+  - parquet-cpp <0.0a0
+  - arrow-cpp <0.0a0
+  license: Apache-2.0
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@@ -32212,6 +34748,20 @@ packages:
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-acero-24.0.0-h7d8d6a5_4_cpu.conda
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+  license: Apache-2.0
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@@ -32229,6 +34779,22 @@ packages:
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   timestamp: 1784543398446
+- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-compute-24.0.0-h081cd8e_4_cpu.conda
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+  - libre2-11 >=2025.11.5
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+  license: Apache-2.0
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 - conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-compute-25.0.0-h081cd8e_3_cpu.conda
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   timestamp: 1784543230425
+- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-dataset-24.0.0-h7d8d6a5_4_cpu.conda
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+  - libparquet 24.0.0 h7051d1f_4_cpu
+  - ucrt >=10.0.20348.0
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-substrait-24.0.0-h524e9bd_4_cpu.conda
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+  - libarrow-dataset 24.0.0 h7d8d6a5_4_cpu
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libcurl-8.20.0-h8206538_0.conda
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+  - vc14_runtime >=14.44.35208
+  constrains:
+  - libgoogle-cloud 3.5.0 *_1
+  license: Apache-2.0
+  license_family: Apache
+  size: 18087
+  timestamp: 1780034913635
 - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-3.7.0-hfe3f7e9_0.conda
   sha256: 7caeceff8cc78b6ccdf416c3950ee7c86390be9c43969e6f0df11ffe46db8d32
   md5: 4957e887b8ff6e7c1108e217b879ebc3
@@ -32453,6 +35086,22 @@ packages:
     - libgoogle-cloud >=3.7.0,<3.8.0a0
   size: 17240
   timestamp: 1784213003080
+- conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-storage-3.5.0-he04ea4c_1.conda
+  sha256: 90c9e66fc403ee42d1fb23dafb5873712bc89b103c22d963ebf932bce6cffefc
+  md5: 7249500fac23f02b60b773878e4668b1
+  depends:
+  - libabseil
+  - libcrc32c >=1.1.2,<1.2.0a0
+  - libcurl
+  - libgoogle-cloud 3.5.0 he22669a_1
+  - libzlib >=1.3.2,<2.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: Apache-2.0
+  license_family: Apache
+  size: 18067
+  timestamp: 1780035234126
 - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-storage-3.7.0-he04ea4c_0.conda
   sha256: 43f9ab1e9055f7fa4981a5da7b359901cda8fc5607e1c489b69152ce23adc3c4
   md5: 58728b379859ceb2d41642f49411e440
@@ -32472,6 +35121,27 @@ packages:
     - libgoogle-cloud-storage >=3.7.0,<3.8.0a0
   size: 17200
   timestamp: 1784213272078
+- conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.78.1-h9ff2b3e_0.conda
+  sha256: e5667a557c6211db4e1de0bf3146b880977cd7447dce5e5f5cb7d9e3dc9afa70
+  md5: 26dbb65607f8fe485df5ee98fa6eb79f
+  depends:
+  - c-ares >=1.34.6,<2.0a0
+  - libabseil * cxx17*
+  - libabseil >=20260107.1,<20260108.0a0
+  - libprotobuf >=6.33.5,<6.33.6.0a0
+  - libre2-11 >=2025.11.5
+  - libzlib >=1.3.1,<2.0a0
+  - openssl >=3.5.5,<4.0a0
+  - re2
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - grpc-cpp =1.78.1
+  license: Apache-2.0
+  license_family: APACHE
+  size: 11546515
+  timestamp: 1774013326223
 - conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.82.1-he6cc664_0.conda
   sha256: 56db0effd12badb6075a2b5877ed72c06e61211b8c9a42843711c7b67a3324e1
   md5: 343241db44cbda08b4b41689608a07af
@@ -32579,6 +35249,32 @@ packages:
     - libopentelemetry-cpp >=1.27.0,<1.28.0a0
   size: 3700486
   timestamp: 1783133622370
+- conda: https://conda.anaconda.org/conda-forge/win-64/libopentelemetry-cpp-1.27.0-hc88f397_0.conda
+  sha256: 61779880ca16472beb82806497d8806d8ebfb0d2f76b6dfdf8199b3318e172dd
+  md5: 23ccf8e4734ffa194b2c3b318c0b3e8f
+  depends:
+  - libabseil * cxx17*
+  - libabseil >=20260107.1,<20260108.0a0
+  - libcurl >=8.20.0,<9.0a0
+  - libgrpc >=1.78.1,<1.79.0a0
+  - libopentelemetry-cpp-headers 1.27.0 h57928b3_0
+  - libprotobuf >=6.33.5,<6.33.6.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - nlohmann_json
+  - prometheus-cpp >=1.3.0,<1.4.0a0
+  constrains:
+  - cpp-opentelemetry-sdk =1.27.0
+  license: Apache-2.0
+  license_family: APACHE
+  size: 3563008
+  timestamp: 1778721903212
+- conda: https://conda.anaconda.org/conda-forge/win-64/libopentelemetry-cpp-headers-1.27.0-h57928b3_0.conda
+  sha256: 12b0774d4cf6b45cfd27a8754428ab908cc928da684d24eb6e84b9f314e6c5a6
+  md5: c661e9d8ebc6100d298f79b66fd078e0
+  license: Apache-2.0
+  license_family: APACHE
+  size: 434894
+  timestamp: 1778721812996
 - conda: https://conda.anaconda.org/conda-forge/win-64/libopentelemetry-cpp-headers-1.27.0-h57928b3_1.conda
   sha256: be65204142e24fdaf48701747cf603a9f6c9b1efa6ea518113f2c7b1fa27e94e
   md5: 85e4342d7e798c99a828d59303909d09
@@ -32587,6 +35283,21 @@ packages:
   run_exports: {}
   size: 436532
   timestamp: 1783133530257
+- conda: https://conda.anaconda.org/conda-forge/win-64/libparquet-24.0.0-h7051d1f_4_cpu.conda
+  build_number: 4
+  sha256: 4e1f54c2a1059f544ab91e42250d0f4d029dada01f3856fc4c7da9c41070fd90
+  md5: 0f5fe62fc82e0bfadaedbe8ea431f90f
+  depends:
+  - libarrow 24.0.0 hab65375_4_cpu
+  - libthrift >=0.22.0,<0.22.1.0a0
+  - openssl >=3.5.6,<4.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: Apache-2.0
+  license_family: APACHE
+  size: 966241
+  timestamp: 1780070847552
 - conda: https://conda.anaconda.org/conda-forge/win-64/libparquet-25.0.0-h7051d1f_3_cpu.conda
   build_number: 3
   sha256: 83398d0ef3b752f94fc99e766a8febeab0547993e55803a267fe618d697eba92
@@ -32605,6 +35316,20 @@ packages:
     - libparquet >=25.0.0,<25.1.0a0
   size: 968008
   timestamp: 1784543362314
+- conda: https://conda.anaconda.org/conda-forge/win-64/libprotobuf-6.33.5-h6cf2d3c_1.conda
+  sha256: dce2820ebc4059b4919158814aa6ea2ccd31be699d9e3d74824de8d31ec66864
+  md5: 712686431de276d81eb02d87483f6f10
+  depends:
+  - libabseil * cxx17*
+  - libabseil >=20260107.1,<20260108.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 7162612
+  timestamp: 1780005438640
 - conda: https://conda.anaconda.org/conda-forge/win-64/libprotobuf-7.35.1-h1a71995_2.conda
   sha256: 0901c04727c76556a0afb1bf3f32e4ba9f4f8b50e8a6f96d02085f47875b70e0
   md5: 86341febfb82a8beb84058167f41c3d5
@@ -32637,6 +35362,21 @@ packages:
     - libpsl >=0.22.0,<0.23.0a0
   size: 73209
   timestamp: 1785342721916
+- conda: https://conda.anaconda.org/conda-forge/win-64/libre2-11-2025.11.05-h04e5de1_1.conda
+  sha256: 7e26b7868b10e40bc441e00c558927835eacef7e5a39611c2127558edd660c8f
+  md5: 3d863f1a19f579ca511f6ac02038ab5a
+  depends:
+  - libabseil * cxx17*
+  - libabseil >=20260107.0,<20260108.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - re2 2025.11.05.*
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 266062
+  timestamp: 1768190189553
 - conda: https://conda.anaconda.org/conda-forge/win-64/libre2-11-2025.11.05-h45f70d9_2.conda
   sha256: 255d32d01d72d7d89ab5c60c20b124216e0731428af350e40ed0e001d249bcb5
   md5: 52d096d535c65d26ffb4b84a4bba2aa1
@@ -33247,6 +35987,25 @@ packages:
   license_family: Apache
   size: 9410183
   timestamp: 1775589779763
+- conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.3.0-h8fc0eb6_0.conda
+  sha256: f65b96be3790bdb90195226dfbcac2025b680bdffdbedc7e87d919161a63f8a7
+  md5: 1e03f610c02a16fdd7fee7430ec23115
+  depends:
+  - tzdata
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - snappy >=1.2.2,<1.3.0a0
+  - lz4-c >=1.10.0,<1.11.0a0
+  - libabseil >=20260107.1,<20260108.0a0
+  - libabseil * cxx17*
+  - libprotobuf >=6.33.5,<6.33.6.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - zstd >=1.5.7,<1.6.0a0
+  license: Apache-2.0
+  license_family: APACHE
+  size: 1438607
+  timestamp: 1773230254230
 - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.3.1-hf7eca67_0.conda
   sha256: 6097e6facda0184c7be60eb0daaf05a03d6d4b9725ea5901553a1d01ba91271c
   md5: d0e00e318dceb35f9a66f52225609ed2
@@ -33744,6 +36503,21 @@ packages:
   license_family: BSD
   size: 249950
   timestamp: 1769678167309
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-24.0.0-py314h86ab7b2_0.conda
+  sha256: fdf414b7269ed3474c381689344ad71a626541c1354967f9d595398a3d384198
+  md5: 152580a594ef1924366fe6a934dac602
+  depends:
+  - libarrow-acero 24.0.0.*
+  - libarrow-dataset 24.0.0.*
+  - libarrow-substrait 24.0.0.*
+  - libparquet 24.0.0.*
+  - pyarrow-core 24.0.0 *_0_*
+  - python >=3.14,<3.15.0a0
+  - python_abi 3.14.* *_cp314
+  license: Apache-2.0
+  license_family: APACHE
+  size: 27124
+  timestamp: 1776928424429
 - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-25.0.0-py310h5588dad_0.conda
   sha256: 3f1c6d45dc48cd7cd0d6d6fbb2b3c55e7e7d9156afc01020a7cbaac5533ac37d
   md5: a98c88fd089d6aaf9769e890b51570b8
@@ -33824,6 +36598,26 @@ packages:
   run_exports: {}
   size: 26584
   timestamp: 1784258906758
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-24.0.0-py314h159fc0c_0_cpu.conda
+  sha256: ce48dc60dc471037d2d97c1104b443cb2e8edb06dbd827804a8409ac28a5b912
+  md5: c4ee1bdf0e766307d105eafbcb720035
+  depends:
+  - libarrow 24.0.0.* *cpu
+  - libarrow-compute 24.0.0.* *cpu
+  - libzlib >=1.3.2,<2.0a0
+  - python >=3.14,<3.15.0a0
+  - python_abi 3.14.* *_cp314
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  constrains:
+  - apache-arrow-proc * cpu
+  - numpy >=1.23,<3
+  - libprotobuf >=6.33.5
+  license: Apache-2.0
+  license_family: APACHE
+  size: 3670958
+  timestamp: 1776928382916
 - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-25.0.0-py310hc34f82c_0_cpu.conda
   sha256: c39d8566f4561ae9454f5e59288072db4e1c36ad892b0f3183a51e04588aeab9
   md5: 00decf10cd4d6d48001cbf9434761c54
@@ -34376,6 +37170,15 @@ packages:
   license_family: MIT
   size: 1023176
   timestamp: 1775566016879
+- conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-ha104f34_1.conda
+  sha256: 345b1ed8288d81510101f886aaf547e3294370e5dab340c4c3fcb0b25e5d99e0
+  md5: 6807f05dcf3f1736ad6cc9525b8b8725
+  depends:
+  - libre2-11 2025.11.05 h04e5de1_1
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 220305
+  timestamp: 1768190225351
 - conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-haef9524_2.conda
   sha256: eb11c0e948c2576378a8e7f22768a967044d54bfbdea9ef358d31074b91c21f0
   md5: 5d51319ef836277672d665c2fc04bab0
diff --git a/tests/columns/test_pyarrow.py b/tests/columns/test_pyarrow.py
index 08bc5ecf..6cff28c3 100644
--- a/tests/columns/test_pyarrow.py
+++ b/tests/columns/test_pyarrow.py
@@ -17,9 +17,6 @@
     create_schema,
 )
 
-pytestmark = pytest.mark.with_optionals
-
-
 T = TypeVar("T", bound=dy.Column)
 
 

From af89578bc5c23301fc198488f171814e7af4a9cf Mon Sep 17 00:00:00 2001
From: Oliver Borchert <oliver.borchert@quantco.com>
Date: Thu, 30 Jul 2026 01:53:24 +0200
Subject: [PATCH 3/5] Fix

---
 dataframely/_compat.py             | 1 -
 dataframely/columns/_base.py       | 2 +-
 dataframely/columns/any.py         | 2 +-
 dataframely/columns/array.py       | 3 +--
 dataframely/columns/binary.py      | 2 +-
 dataframely/columns/bool.py        | 2 +-
 dataframely/columns/categorical.py | 2 +-
 dataframely/columns/datetime.py    | 2 +-
 dataframely/columns/decimal.py     | 2 +-
 dataframely/columns/enum.py        | 2 +-
 dataframely/columns/float.py       | 2 +-
 dataframely/columns/integer.py     | 2 +-
 dataframely/columns/list.py        | 2 +-
 dataframely/columns/object.py      | 2 +-
 dataframely/columns/string.py      | 2 +-
 dataframely/columns/struct.py      | 2 +-
 dataframely/schema.py              | 2 +-
 17 files changed, 16 insertions(+), 18 deletions(-)

diff --git a/dataframely/_compat.py b/dataframely/_compat.py
index 2c4d6ac5..cd54a34b 100644
--- a/dataframely/_compat.py
+++ b/dataframely/_compat.py
@@ -90,7 +90,6 @@ class Dialect:  # type: ignore # noqa: N801
     "Dialect",
     "MSDialect_pyodbc",
     "PartitionSchemeOrSinkDirectory",
-    "pa",
     "PGDialect_psycopg2",
     "pydantic_core_schema",
     "pydantic",
diff --git a/dataframely/columns/_base.py b/dataframely/columns/_base.py
index dc3492ec..1c259e21 100644
--- a/dataframely/columns/_base.py
+++ b/dataframely/columns/_base.py
@@ -13,7 +13,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, pydantic, sa, sa_TypeEngine
+from dataframely._compat import pydantic, sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
diff --git a/dataframely/columns/any.py b/dataframely/columns/any.py
index 4f01f94b..46030b3c 100644
--- a/dataframely/columns/any.py
+++ b/dataframely/columns/any.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_mssql, sa_TypeEngine
+from dataframely._compat import sa, sa_mssql, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
diff --git a/dataframely/columns/array.py b/dataframely/columns/array.py
index 3058c824..b93dda09 100644
--- a/dataframely/columns/array.py
+++ b/dataframely/columns/array.py
@@ -6,12 +6,11 @@
 import math
 import sys
 import warnings
-from collections.abc import Sequence
 from typing import Any, cast
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Check, Column
diff --git a/dataframely/columns/binary.py b/dataframely/columns/binary.py
index d1ddf67f..1845580c 100644
--- a/dataframely/columns/binary.py
+++ b/dataframely/columns/binary.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Column
diff --git a/dataframely/columns/bool.py b/dataframely/columns/bool.py
index 2574af47..14a30a24 100644
--- a/dataframely/columns/bool.py
+++ b/dataframely/columns/bool.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Column
diff --git a/dataframely/columns/categorical.py b/dataframely/columns/categorical.py
index facad4c7..6d63f388 100644
--- a/dataframely/columns/categorical.py
+++ b/dataframely/columns/categorical.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Check, Column
diff --git a/dataframely/columns/datetime.py b/dataframely/columns/datetime.py
index ca0d2a84..9c6bc169 100644
--- a/dataframely/columns/datetime.py
+++ b/dataframely/columns/datetime.py
@@ -10,7 +10,7 @@
 import polars as pl
 from polars._typing import TimeUnit
 
-from dataframely._compat import pa, sa, sa_mssql, sa_TypeEngine
+from dataframely._compat import sa, sa_mssql, sa_TypeEngine
 from dataframely._polars import (
     EPOCH_DATETIME,
     date_matches_resolution,
diff --git a/dataframely/columns/decimal.py b/dataframely/columns/decimal.py
index 2b40371b..673f9cd7 100644
--- a/dataframely/columns/decimal.py
+++ b/dataframely/columns/decimal.py
@@ -8,7 +8,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
diff --git a/dataframely/columns/enum.py b/dataframely/columns/enum.py
index 10fdc2cc..02743d67 100644
--- a/dataframely/columns/enum.py
+++ b/dataframely/columns/enum.py
@@ -10,7 +10,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
diff --git a/dataframely/columns/float.py b/dataframely/columns/float.py
index 60b3a631..c43f30d7 100644
--- a/dataframely/columns/float.py
+++ b/dataframely/columns/float.py
@@ -13,7 +13,7 @@
 import polars as pl
 from polars.datatypes.group import FLOAT_DTYPES
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
diff --git a/dataframely/columns/integer.py b/dataframely/columns/integer.py
index cdd0f7cb..1d6e3a07 100644
--- a/dataframely/columns/integer.py
+++ b/dataframely/columns/integer.py
@@ -10,7 +10,7 @@
 import polars as pl
 from polars.datatypes.group import INTEGER_DTYPES
 
-from dataframely._compat import pa, sa, sa_mssql, sa_TypeEngine
+from dataframely._compat import sa, sa_mssql, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
diff --git a/dataframely/columns/list.py b/dataframely/columns/list.py
index 41dae9a8..8d98fc01 100644
--- a/dataframely/columns/list.py
+++ b/dataframely/columns/list.py
@@ -11,7 +11,7 @@
 from polars.expr.array import ExprArrayNameSpace
 from polars.expr.list import ExprListNameSpace
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
diff --git a/dataframely/columns/object.py b/dataframely/columns/object.py
index 37d50c2b..2a92d370 100644
--- a/dataframely/columns/object.py
+++ b/dataframely/columns/object.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely.random import Generator
 
 from ._base import Check, Column
diff --git a/dataframely/columns/string.py b/dataframely/columns/string.py
index be0bff09..39558fd2 100644
--- a/dataframely/columns/string.py
+++ b/dataframely/columns/string.py
@@ -7,7 +7,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, sa, sa_TypeEngine
+from dataframely._compat import sa, sa_TypeEngine
 from dataframely._native import regex_matching_string_length
 from dataframely.random import Generator
 
diff --git a/dataframely/columns/struct.py b/dataframely/columns/struct.py
index 576e35d3..6e1b35ab 100644
--- a/dataframely/columns/struct.py
+++ b/dataframely/columns/struct.py
@@ -8,7 +8,7 @@
 
 import polars as pl
 
-from dataframely._compat import pa, pydantic, sa, sa_postgresql, sa_TypeEngine
+from dataframely._compat import pydantic, sa, sa_postgresql, sa_TypeEngine
 from dataframely._polars import PolarsDataType
 from dataframely.random import Generator
 
diff --git a/dataframely/schema.py b/dataframely/schema.py
index a7eb8e9a..15602a3c 100644
--- a/dataframely/schema.py
+++ b/dataframely/schema.py
@@ -17,7 +17,7 @@
 from polars._typing import FileSource
 
 from ._base_schema import ORIGINAL_COLUMN_PREFIX, BaseSchema
-from ._compat import PartitionSchemeOrSinkDirectory, deltalake, pa, pydantic, sa
+from ._compat import PartitionSchemeOrSinkDirectory, deltalake, pydantic, sa
 from ._deprecation import deprecated
 from ._match_to_schema import match_to_schema
 from ._native import format_rule_failures

From 644e733914d11228139e21238a2ad5490807f7cc Mon Sep 17 00:00:00 2001
From: Oliver Borchert <oliver.borchert@quantco.com>
Date: Thu, 30 Jul 2026 19:12:03 +0200
Subject: [PATCH 4/5] Update

---
 tests/columns/test_arrow_pycapsule.py | 158 ++++++++++++++-
 tests/columns/test_pyarrow.py         | 267 --------------------------
 2 files changed, 148 insertions(+), 277 deletions(-)
 delete mode 100644 tests/columns/test_pyarrow.py

diff --git a/tests/columns/test_arrow_pycapsule.py b/tests/columns/test_arrow_pycapsule.py
index 31838b9e..aa6e0136 100644
--- a/tests/columns/test_arrow_pycapsule.py
+++ b/tests/columns/test_arrow_pycapsule.py
@@ -8,6 +8,7 @@
 from dataframely.columns import Column
 from dataframely.testing import (
     COLUMN_TYPES,
+    NO_VALIDATION_COLUMN_TYPES,
     SUPERTYPE_COLUMN_TYPES,
     create_schema,
 )
@@ -58,20 +59,157 @@ def test_nullability_information(column_type: type[Column], nullable: bool) -> N
     assert ("not null" in str(result)) != nullable
 
 
+@pytest.mark.parametrize("column_type", COLUMN_TYPES)
+@pytest.mark.parametrize("inner_nullable", [True, False])
+def test_inner_nullability_struct(
+    column_type: type[Column], inner_nullable: bool
+) -> None:
+    # Arrange
+    inner = column_type(nullable=inner_nullable)
+    schema = create_schema("test", {"a": dy.Struct({"a": inner})})
+
+    # Act
+    pa_schema = pa.schema(schema)
+
+    # Assert
+    struct_field = pa_schema.field("a")
+    inner_field = struct_field.type[0]
+    assert inner_field.nullable == inner_nullable
+
+
+@pytest.mark.parametrize("column_type", COLUMN_TYPES)
+@pytest.mark.parametrize("inner_nullable", [True, False])
+def test_inner_nullability_list(
+    column_type: type[Column], inner_nullable: bool
+) -> None:
+    # Arrange
+    inner = column_type(nullable=inner_nullable)
+    schema = create_schema("test", {"a": dy.List(inner)})
+
+    # Act
+    pa_schema = pa.schema(schema)
+
+    # Assert
+    list_field = pa_schema.field("a")
+    inner_field = list_field.type.value_field
+    assert inner_field.nullable == inner_nullable
+
+
 @pytest.mark.parametrize(
-    "column",
-    [
-        dy.List(dy.Int64(), nullable=True),
-        dy.Array(dy.Int64(), shape=2, nullable=True),
-        dy.Struct({"a": dy.Int64()}, nullable=True),
-    ],
+    "column_type", [c for c in NO_VALIDATION_COLUMN_TYPES if c is not dy.Any]
 )
-def test_nested_nullability_information(column: dy.Column) -> None:
+@pytest.mark.parametrize("inner_nullable", [True, False])
+def test_inner_nullability_array(
+    column_type: type[Column], inner_nullable: bool
+) -> None:
     # Arrange
-    schema = create_schema("test", {"a": column})
+    inner = column_type(nullable=inner_nullable)
+    schema = create_schema("test", {"a": dy.Array(inner, 1)})
 
     # Act
-    result = pa.schema(schema)
+    pa_schema = pa.schema(schema)
+
+    # Assert
+    array_field = pa_schema.field("a")
+    inner_field = array_field.type.value_field
+    assert inner_field.nullable == inner_nullable
+
+
+@pytest.mark.parametrize("inner_nullable", [True, False])
+def test_multidimensional_array_nullability(inner_nullable: bool) -> None:
+    # Arrange
+    # Multi-dimensional arrays become nested fixed-size lists in Arrow. Only the
+    # innermost field carries the inner column's nullability; intermediate dimensions
+    # are always nullable.
+    inner = dy.Int64(nullable=inner_nullable)
+    schema = create_schema("test", {"a": dy.Array(inner, (2, 3))})
+
+    # Act
+    pa_schema = pa.schema(schema)
+
+    # Assert
+    intermediate_field = pa_schema.field("a").type.value_field
+    innermost_field = intermediate_field.type.value_field
+    assert intermediate_field.nullable
+    assert innermost_field.nullable == inner_nullable
+
+
+def test_nested_struct_in_list_preserves_nullability() -> None:
+    """Test that nested struct fields in lists preserve nullability."""
+    # Arrange
+    schema = create_schema(
+        "test",
+        {
+            "a": dy.List(
+                dy.Struct(
+                    {
+                        "required": dy.String(nullable=False),
+                        "optional": dy.String(nullable=True),
+                    },
+                    nullable=True,
+                ),
+                nullable=True,
+            )
+        },
+    )
+
+    # Act
+    pa_schema = pa.schema(schema)
+
+    # Assert
+    list_field = pa_schema.field("a")
+    struct_type = list_field.type.value_field.type
+    assert not struct_type[0].nullable
+    assert struct_type[1].nullable
+
+
+def test_nested_list_in_struct_preserves_nullability() -> None:
+    """Test that nested list fields in structs preserve nullability."""
+    # Arrange
+    schema = create_schema(
+        "test",
+        {
+            "a": dy.Struct(
+                {"list_field": dy.List(dy.String(nullable=False), nullable=True)},
+                nullable=True,
+            )
+        },
+    )
+
+    # Act
+    pa_schema = pa.schema(schema)
+
+    # Assert
+    struct_field = pa_schema.field("a")
+    list_type = struct_field.type[0].type
+    assert not list_type.value_field.nullable
+
+
+def test_deeply_nested_nullability() -> None:
+    # Arrange
+    schema = create_schema(
+        "test",
+        {
+            "a": dy.Struct(
+                {
+                    "nested": dy.Struct(
+                        {
+                            "required": dy.String(nullable=False),
+                            "optional": dy.String(nullable=True),
+                        },
+                        nullable=True,
+                    ),
+                },
+                nullable=True,
+            )
+        },
+    )
+
+    # Act
+    pa_schema = pa.schema(schema)
 
     # Assert
-    assert "not null" in str(result)
+    outer_struct = pa_schema.field("a").type
+    inner_struct = outer_struct[0].type
+    assert not inner_struct[0].nullable  # required field
+    assert inner_struct[1].nullable  # optional field
diff --git a/tests/columns/test_pyarrow.py b/tests/columns/test_pyarrow.py
deleted file mode 100644
index 45429f53..00000000
--- a/tests/columns/test_pyarrow.py
+++ /dev/null
@@ -1,267 +0,0 @@
-# Copyright (c) QuantCo 2025-2026
-# SPDX-License-Identifier: BSD-3-Clause
-
-from typing import TypeVar
-
-import pyarrow as pa
-import pytest
-from polars._typing import TimeUnit
-
-import dataframely as dy
-from dataframely.columns import Column
-from dataframely.testing import (
-    ALL_COLUMN_TYPES,
-    COLUMN_TYPES,
-    NO_VALIDATION_COLUMN_TYPES,
-    SUPERTYPE_COLUMN_TYPES,
-    create_schema,
-)
-
-T = TypeVar("T", bound=dy.Column)
-
-
-def _nullable(column_type: type[T]) -> T:
-    # dy.Any doesn't have the `nullable` parameter.
-    if column_type == dy.Any:
-        return column_type()
-    return column_type(nullable=True)
-
-
-@pytest.mark.parametrize("column_type", ALL_COLUMN_TYPES)
-def test_equal_to_polars_schema(column_type: type[Column]) -> None:
-    schema = create_schema("test", {"a": _nullable(column_type)})
-    actual = pa.schema(schema)
-    expected = schema.create_empty().schema.to_arrow()
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "categories",
-    [
-        ("a", "b"),
-        tuple(str(i) for i in range(2**8 - 2)),
-        tuple(str(i) for i in range(2**8 - 1)),
-        tuple(str(i) for i in range(2**8)),
-        tuple(str(i) for i in range(2**16 - 2)),
-        tuple(str(i) for i in range(2**16 - 1)),
-        tuple(str(i) for i in range(2**16)),
-        tuple(str(i) for i in range(2**17)),
-    ],
-)
-def test_equal_polars_schema_enum(categories: list[str]) -> None:
-    schema = create_schema("test", {"a": dy.Enum(categories, nullable=True)})
-    actual = pa.schema(schema)
-    expected = schema.create_empty().schema.to_arrow()
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in ALL_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in ALL_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Struct({"a": _nullable(t)}, nullable=True) for t in ALL_COLUMN_TYPES],
-)
-def test_equal_polars_schema_list(inner: Column) -> None:
-    schema = create_schema("test", {"a": dy.List(inner, nullable=True)})
-    actual = pa.schema(schema)
-    expected = schema.create_empty().schema.to_arrow()
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [
-        dy.Struct({"a": _nullable(t)}, nullable=True)
-        for t in NO_VALIDATION_COLUMN_TYPES
-    ],
-)
-@pytest.mark.parametrize("shape", [1, 0, (0, 0)])
-def test_equal_polars_schema_array(inner: Column, shape: int | tuple[int, ...]) -> None:
-    schema = create_schema("test", {"a": dy.Array(inner, shape, nullable=True)})
-    actual = pa.schema(schema)
-    expected = schema.create_empty().schema.to_arrow()
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [
-        dy.Struct({"a": _nullable(t)}, nullable=True)
-        for t in NO_VALIDATION_COLUMN_TYPES
-    ],
-)
-def test_equal_polars_schema_struct(inner: Column) -> None:
-    schema = create_schema("test", {"a": dy.Struct({"a": inner}, nullable=True)})
-    actual = pa.schema(schema)
-    expected = schema.create_empty().schema.to_arrow()
-    assert actual == expected
-
-
-@pytest.mark.parametrize("column_type", COLUMN_TYPES + SUPERTYPE_COLUMN_TYPES)
-@pytest.mark.parametrize("nullable", [True, False])
-def test_nullability_information(column_type: type[Column], nullable: bool) -> None:
-    schema = create_schema("test", {"a": column_type(nullable=nullable)})
-    assert ("not null" in str(pa.schema(schema))) != nullable
-
-
-@pytest.mark.parametrize("nullable", [True, False])
-def test_nullability_information_enum(nullable: bool) -> None:
-    schema = create_schema("test", {"a": dy.Enum(["a", "b"], nullable=nullable)})
-    assert ("not null" in str(pa.schema(schema))) != nullable
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [
-        dy.Struct({"a": _nullable(t)}, nullable=True)
-        for t in NO_VALIDATION_COLUMN_TYPES
-    ],
-)
-@pytest.mark.parametrize("nullable", [True, False])
-def test_nullability_information_list(inner: Column, nullable: bool) -> None:
-    schema = create_schema("test", {"a": dy.List(inner, nullable=nullable)})
-    assert ("not null" in str(pa.schema(schema))) != nullable
-
-
-@pytest.mark.parametrize(
-    "inner",
-    [_nullable(c) for c in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.List(_nullable(t), nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [dy.Array(_nullable(t), 1, nullable=True) for t in NO_VALIDATION_COLUMN_TYPES]
-    + [
-        dy.Struct({"a": _nullable(t)}, nullable=True)
-        for t in NO_VALIDATION_COLUMN_TYPES
-    ],
-)
-@pytest.mark.parametrize("nullable", [True, False])
-def test_nullability_information_struct(inner: Column, nullable: bool) -> None:
-    schema = create_schema("test", {"a": dy.Struct({"a": inner}, nullable=nullable)})
-    assert ("not null" in str(pa.schema(schema))) != nullable
-
-
-@pytest.mark.parametrize("column_type", COLUMN_TYPES)
-@pytest.mark.parametrize("inner_nullable", [True, False])
-def test_inner_nullability_struct(
-    column_type: type[Column], inner_nullable: bool
-) -> None:
-    inner = column_type(nullable=inner_nullable)
-    schema = create_schema("test", {"a": dy.Struct({"a": inner})})
-    pa_schema = pa.schema(schema)
-    struct_field = pa_schema.field("a")
-    inner_field = struct_field.type[0]
-    assert inner_field.nullable == inner_nullable
-
-
-@pytest.mark.parametrize("column_type", COLUMN_TYPES)
-@pytest.mark.parametrize("inner_nullable", [True, False])
-def test_inner_nullability_list(
-    column_type: type[Column], inner_nullable: bool
-) -> None:
-    inner = column_type(nullable=inner_nullable)
-    schema = create_schema("test", {"a": dy.List(inner)})
-    pa_schema = pa.schema(schema)
-    list_field = pa_schema.field("a")
-    inner_field = list_field.type.value_field
-    assert inner_field.nullable == inner_nullable
-
-
-def test_nested_struct_in_list_preserves_nullability() -> None:
-    """Test that nested struct fields in lists preserve nullability."""
-    schema = create_schema(
-        "test",
-        {
-            "a": dy.List(
-                dy.Struct(
-                    {
-                        "required": dy.String(nullable=False),
-                        "optional": dy.String(nullable=True),
-                    },
-                    nullable=True,
-                ),
-                nullable=True,
-            )
-        },
-    )
-    pa_schema = pa.schema(schema)
-    list_field = pa_schema.field("a")
-    struct_type = list_field.type.value_field.type
-    assert not struct_type[0].nullable
-    assert struct_type[1].nullable
-
-
-def test_nested_list_in_struct_preserves_nullability() -> None:
-    """Test that nested list fields in structs preserve nullability."""
-    schema = create_schema(
-        "test",
-        {
-            "a": dy.Struct(
-                {"list_field": dy.List(dy.String(nullable=False), nullable=True)},
-                nullable=True,
-            )
-        },
-    )
-    pa_schema = pa.schema(schema)
-    struct_field = pa_schema.field("a")
-    list_type = struct_field.type[0].type
-    assert not list_type.value_field.nullable
-
-
-def test_deeply_nested_nullability() -> None:
-    schema = create_schema(
-        "test",
-        {
-            "a": dy.Struct(
-                {
-                    "nested": dy.Struct(
-                        {
-                            "required": dy.String(nullable=False),
-                            "optional": dy.String(nullable=True),
-                        },
-                        nullable=True,
-                    ),
-                },
-                nullable=True,
-            )
-        },
-    )
-    pa_schema = pa.schema(schema)
-    outer_struct = pa_schema.field("a").type
-    inner_struct = outer_struct[0].type
-    assert not inner_struct[0].nullable  # required field
-    assert inner_struct[1].nullable  # optional field
-
-
-def test_multiple_columns() -> None:
-    schema = create_schema(
-        "test", {"a": dy.Int32(nullable=False), "b": dy.Integer(nullable=True)}
-    )
-    assert str(pa.schema(schema)).split("\n") == [
-        "a: int32 not null",
-        "b: int64",
-    ]
-
-
-@pytest.mark.parametrize("time_unit", ["ns", "us", "ms"])
-def test_datetime_time_unit(time_unit: TimeUnit) -> None:
-    schema = create_schema(
-        "test", {"a": dy.Datetime(time_unit=time_unit, nullable=True)}
-    )
-    assert str(pa.schema(schema)) == f"a: timestamp[{time_unit}]"
-
-
-@pytest.mark.parametrize("time_unit", ["ns", "us", "ms"])
-def test_duration_time_unit(time_unit: TimeUnit) -> None:
-    schema = create_schema(
-        "test", {"a": dy.Duration(time_unit=time_unit, nullable=True)}
-    )
-    assert str(pa.schema(schema)) == f"a: duration[{time_unit}]"

From 841c771a1a0efa1b2a7a0d4e3d8bfc87367a800a Mon Sep 17 00:00:00 2001
From: Oliver Borchert <oliver.borchert@quantco.com>
Date: Thu, 30 Jul 2026 19:15:20 +0200
Subject: [PATCH 5/5] Update

---
 tests/columns/test_arrow_pycapsule.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/tests/columns/test_arrow_pycapsule.py b/tests/columns/test_arrow_pycapsule.py
index ae71a3c1..aa6e0136 100644
--- a/tests/columns/test_arrow_pycapsule.py
+++ b/tests/columns/test_arrow_pycapsule.py
@@ -1,6 +1,7 @@
 # Copyright (c) QuantCo 2025-2026
 # SPDX-License-Identifier: BSD-3-Clause
 
+import pyarrow as pa
 import pytest
 
 import dataframely as dy
@@ -12,10 +13,6 @@
     create_schema,
 )
 
-pa = pytest.importorskip("pyarrow")
-
-pytestmark = pytest.mark.with_optionals
-
 
 @pytest.mark.parametrize(
     "column", [dy.Categorical(nullable=True), dy.Enum(["a", "b"], nullable=True)]