diff --git a/.gitignore b/.gitignore
index 0f894dc..c051d58 100644
--- a/.gitignore
+++ b/.gitignore
@@ -132,3 +132,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+.vscode/
+.DS_Store
\ No newline at end of file
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index b863077..12108f6 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -5,7 +5,6 @@ python:
     - method: pip
       path: .
       extra_requirements:
-        - duckdb
         - pandas
         - docs
 
diff --git a/docs/api/patito/Model/index.rst b/docs/api/patito/Model/index.rst
index 3ecd10c..c990ac7 100644
--- a/docs/api/patito/Model/index.rst
+++ b/docs/api/patito/Model/index.rst
@@ -32,6 +32,5 @@ patito.Model
    suffix <suffix>
    unique_columns <unique_columns>
    valid_dtypes <valid_dtypes>
-   valid_sql_types <valid_sql_types>
    validate <validate>
    with_fields <with_fields>
diff --git a/docs/api/patito/duckdb/Database/__contains__.rst b/docs/api/patito/duckdb/Database/__contains__.rst
deleted file mode 100644
index 97d43f8..0000000
--- a/docs/api/patito/duckdb/Database/__contains__.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Database.__contains__
-===================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.__contains__
diff --git a/docs/api/patito/duckdb/Database/create_enum_types.rst b/docs/api/patito/duckdb/Database/create_enum_types.rst
deleted file mode 100644
index cc917b8..0000000
--- a/docs/api/patito/duckdb/Database/create_enum_types.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Database.create_enum_types
-========================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.create_enum_types
diff --git a/docs/api/patito/duckdb/Database/create_table.rst b/docs/api/patito/duckdb/Database/create_table.rst
deleted file mode 100644
index e73d253..0000000
--- a/docs/api/patito/duckdb/Database/create_table.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Database.create_table
-===================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.create_table
diff --git a/docs/api/patito/duckdb/Database/create_view.rst b/docs/api/patito/duckdb/Database/create_view.rst
deleted file mode 100644
index 0a34382..0000000
--- a/docs/api/patito/duckdb/Database/create_view.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Database.create_view
-==================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.create_view
diff --git a/docs/api/patito/duckdb/Database/default.rst b/docs/api/patito/duckdb/Database/default.rst
deleted file mode 100644
index caf90a0..0000000
--- a/docs/api/patito/duckdb/Database/default.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Database.default
-==============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.default
diff --git a/docs/api/patito/duckdb/Database/empty_relation.rst b/docs/api/patito/duckdb/Database/empty_relation.rst
deleted file mode 100644
index bc8b5b6..0000000
--- a/docs/api/patito/duckdb/Database/empty_relation.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Database.empty_relation
-=====================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.empty_relation
diff --git a/docs/api/patito/duckdb/Database/execute.rst b/docs/api/patito/duckdb/Database/execute.rst
deleted file mode 100644
index 10ff1d6..0000000
--- a/docs/api/patito/duckdb/Database/execute.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Database.execute:
-
-patito.duckdb.Database.execute
-==============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.execute
diff --git a/docs/api/patito/duckdb/Database/from_connection.rst b/docs/api/patito/duckdb/Database/from_connection.rst
deleted file mode 100644
index f7f7a20..0000000
--- a/docs/api/patito/duckdb/Database/from_connection.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Database.from_connection
-======================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.from_connection
diff --git a/docs/api/patito/duckdb/Database/index.rst b/docs/api/patito/duckdb/Database/index.rst
deleted file mode 100644
index cb6cee2..0000000
--- a/docs/api/patito/duckdb/Database/index.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-.. _duckdb.Database:
-
-patito.duckdb.Database
-======================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.__init__
-
-.. toctree::
-   :caption: Methods
-   :maxdepth: 1
-
-   create_enum_types <create_enum_types>
-   create_table <create_table>
-   create_view <create_view>
-   default <default>
-   empty_relation <empty_relation>
-   execute <execute>
-   from_connection <from_connection>
-   query <query>
-   table <table>
-   to_relation <to_relation>
-   __contains__ <__contains__>
diff --git a/docs/api/patito/duckdb/Database/query.rst b/docs/api/patito/duckdb/Database/query.rst
deleted file mode 100644
index 36315cd..0000000
--- a/docs/api/patito/duckdb/Database/query.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Database.query:
-
-patito.duckdb.Database.query
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.query
diff --git a/docs/api/patito/duckdb/Database/table.rst b/docs/api/patito/duckdb/Database/table.rst
deleted file mode 100644
index d2456d3..0000000
--- a/docs/api/patito/duckdb/Database/table.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Database.table:
-
-patito.duckdb.Database.table
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.table
diff --git a/docs/api/patito/duckdb/Database/to_relation.rst b/docs/api/patito/duckdb/Database/to_relation.rst
deleted file mode 100644
index 44f4bb3..0000000
--- a/docs/api/patito/duckdb/Database/to_relation.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Database.to_relation
-==================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Database.to_relation
diff --git a/docs/api/patito/duckdb/Relation/__add__.rst b/docs/api/patito/duckdb/Relation/__add__.rst
deleted file mode 100644
index 49920b6..0000000
--- a/docs/api/patito/duckdb/Relation/__add__.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.__add__
-==============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.__add__
diff --git a/docs/api/patito/duckdb/Relation/__getitem__.rst b/docs/api/patito/duckdb/Relation/__getitem__.rst
deleted file mode 100644
index 0dc4e7f..0000000
--- a/docs/api/patito/duckdb/Relation/__getitem__.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.__getitem__
-==================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.__getitem__
diff --git a/docs/api/patito/duckdb/Relation/__iter__.rst b/docs/api/patito/duckdb/Relation/__iter__.rst
deleted file mode 100644
index 6ed4f7d..0000000
--- a/docs/api/patito/duckdb/Relation/__iter__.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.__iter__:
-
-patito.duckdb.Relation.__iter__
-===============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.__iter__
diff --git a/docs/api/patito/duckdb/Relation/__len__.rst b/docs/api/patito/duckdb/Relation/__len__.rst
deleted file mode 100644
index 4332cff..0000000
--- a/docs/api/patito/duckdb/Relation/__len__.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.__len__:
-
-patito.duckdb.Relation.__len__
-==============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.__len__
diff --git a/docs/api/patito/duckdb/Relation/__str__.rst b/docs/api/patito/duckdb/Relation/__str__.rst
deleted file mode 100644
index 3bd0c99..0000000
--- a/docs/api/patito/duckdb/Relation/__str__.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.__str__
-==============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.__str__
diff --git a/docs/api/patito/duckdb/Relation/add_prefix.rst b/docs/api/patito/duckdb/Relation/add_prefix.rst
deleted file mode 100644
index a2e8ea2..0000000
--- a/docs/api/patito/duckdb/Relation/add_prefix.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.add_prefix
-=================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.add_prefix
diff --git a/docs/api/patito/duckdb/Relation/add_suffix.rst b/docs/api/patito/duckdb/Relation/add_suffix.rst
deleted file mode 100644
index 146c442..0000000
--- a/docs/api/patito/duckdb/Relation/add_suffix.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.add_suffix
-=================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.add_suffix
diff --git a/docs/api/patito/duckdb/Relation/aggregate.rst b/docs/api/patito/duckdb/Relation/aggregate.rst
deleted file mode 100644
index c7273ea..0000000
--- a/docs/api/patito/duckdb/Relation/aggregate.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.aggregate
-================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.aggregate
diff --git a/docs/api/patito/duckdb/Relation/alias.rst b/docs/api/patito/duckdb/Relation/alias.rst
deleted file mode 100644
index cfdff91..0000000
--- a/docs/api/patito/duckdb/Relation/alias.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-patito.duckdb.Relation.alias
-============================
-
-.. currentmodule:: patito.duckdb
-
-The alias that can be used to refer to the given relation in queries.
-Can be set with :ref:`Relation.set_alias()<duckdb.Relation.set_alias>`.
diff --git a/docs/api/patito/duckdb/Relation/all.rst b/docs/api/patito/duckdb/Relation/all.rst
deleted file mode 100644
index cd153e4..0000000
--- a/docs/api/patito/duckdb/Relation/all.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.all
-==========================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.all
diff --git a/docs/api/patito/duckdb/Relation/case.rst b/docs/api/patito/duckdb/Relation/case.rst
deleted file mode 100644
index 80fe5bf..0000000
--- a/docs/api/patito/duckdb/Relation/case.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.case
-===========================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.case
diff --git a/docs/api/patito/duckdb/Relation/cast.rst b/docs/api/patito/duckdb/Relation/cast.rst
deleted file mode 100644
index 676ee0d..0000000
--- a/docs/api/patito/duckdb/Relation/cast.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Relation.cast:
-
-patito.duckdb.Relation.cast
-===========================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.cast
diff --git a/docs/api/patito/duckdb/Relation/coalesce.rst b/docs/api/patito/duckdb/Relation/coalesce.rst
deleted file mode 100644
index 125002c..0000000
--- a/docs/api/patito/duckdb/Relation/coalesce.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.coalesce
-===============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.coalesce
diff --git a/docs/api/patito/duckdb/Relation/columns.rst b/docs/api/patito/duckdb/Relation/columns.rst
deleted file mode 100644
index 007b893..0000000
--- a/docs/api/patito/duckdb/Relation/columns.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.columns
-==============================
-
-.. currentmodule:: patito.duckdb
-
-.. autoproperty:: Relation.columns
diff --git a/docs/api/patito/duckdb/Relation/count.rst b/docs/api/patito/duckdb/Relation/count.rst
deleted file mode 100644
index cdcff56..0000000
--- a/docs/api/patito/duckdb/Relation/count.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.count:
-
-patito.duckdb.Relation.count
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.count
diff --git a/docs/api/patito/duckdb/Relation/create_table.rst b/docs/api/patito/duckdb/Relation/create_table.rst
deleted file mode 100644
index cc33905..0000000
--- a/docs/api/patito/duckdb/Relation/create_table.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.create_table:
-
-patito.duckdb.Relation.create_table
-===================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.create_table
diff --git a/docs/api/patito/duckdb/Relation/create_view.rst b/docs/api/patito/duckdb/Relation/create_view.rst
deleted file mode 100644
index 0f0156a..0000000
--- a/docs/api/patito/duckdb/Relation/create_view.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Relation.create_view:
-
-patito.duckdb.Relation.create_view
-==================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.create_view
diff --git a/docs/api/patito/duckdb/Relation/distinct.rst b/docs/api/patito/duckdb/Relation/distinct.rst
deleted file mode 100644
index 89c4cc1..0000000
--- a/docs/api/patito/duckdb/Relation/distinct.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Relation.distinct:
-
-patito.duckdb.Relation.distinct
-===============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.distinct
diff --git a/docs/api/patito/duckdb/Relation/drop.rst b/docs/api/patito/duckdb/Relation/drop.rst
deleted file mode 100644
index 5616ec4..0000000
--- a/docs/api/patito/duckdb/Relation/drop.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.drop
-===========================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.drop
diff --git a/docs/api/patito/duckdb/Relation/except_.rst b/docs/api/patito/duckdb/Relation/except_.rst
deleted file mode 100644
index 723eaa5..0000000
--- a/docs/api/patito/duckdb/Relation/except_.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Relation.except_:
-
-patito.duckdb.Relation.except\_
-===============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.except_
diff --git a/docs/api/patito/duckdb/Relation/execute.rst b/docs/api/patito/duckdb/Relation/execute.rst
deleted file mode 100644
index 25bf1bf..0000000
--- a/docs/api/patito/duckdb/Relation/execute.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.execute
-==============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.execute
diff --git a/docs/api/patito/duckdb/Relation/filter.rst b/docs/api/patito/duckdb/Relation/filter.rst
deleted file mode 100644
index 844cca1..0000000
--- a/docs/api/patito/duckdb/Relation/filter.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.filter:
-
-patito.duckdb.Relation.filter
-=============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.filter
diff --git a/docs/api/patito/duckdb/Relation/get.rst b/docs/api/patito/duckdb/Relation/get.rst
deleted file mode 100644
index 1914a51..0000000
--- a/docs/api/patito/duckdb/Relation/get.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.get:
-
-patito.duckdb.Relation.get
-==========================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.get
diff --git a/docs/api/patito/duckdb/Relation/index.rst b/docs/api/patito/duckdb/Relation/index.rst
deleted file mode 100644
index ff65cbb..0000000
--- a/docs/api/patito/duckdb/Relation/index.rst
+++ /dev/null
@@ -1,57 +0,0 @@
-.. _duckdb.Relation.__init__:
-
-patito.duckdb.Relation
-======================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.__init__
-
-.. toctree::
-   :caption: Methods & Properties
-   :maxdepth: 1
-
-   add_prefix <add_prefix>
-   add_suffix <add_suffix>
-   aggregate <aggregate>
-   alias <alias>
-   all <all>
-   case <case>
-   cast <cast>
-   coalesce <coalesce>
-   columns <columns>
-   count <count>
-   create_table <create_table>
-   create_view <create_view>
-   distinct <distinct>
-   drop <drop>
-   except_ <except_>
-   execute <execute>
-   filter <filter>
-   get <get>
-   inner_join <inner_join>
-   insert_into <insert_into>
-   intersect <intersect>
-   join <join>
-   left_join <left_join>
-   limit <limit>
-   model <model>
-   order <order>
-   rename <rename>
-   select <select>
-   set_alias <set_alias>
-   set_model <set_model>
-   to_df <to_df>
-   to_pandas <to_pandas>
-   to_series <to_series>
-   types <types>
-   union <union>
-   with_columns <with_columns>
-   with_missing_defaultable_columns <with_missing_defaultable_columns>
-   with_missing_nullable_columns <with_missing_nullable_columns>
-
-   __add__ <__add__>
-   __getitem__ <__getitem__>
-   __iter__ <__iter__>
-   __len__ <__len__>
-   __str__ <__str__>
diff --git a/docs/api/patito/duckdb/Relation/inner_join.rst b/docs/api/patito/duckdb/Relation/inner_join.rst
deleted file mode 100644
index fde3d74..0000000
--- a/docs/api/patito/duckdb/Relation/inner_join.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.inner_join:
-
-patito.duckdb.Relation.inner_join
-=================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.inner_join
diff --git a/docs/api/patito/duckdb/Relation/insert_into.rst b/docs/api/patito/duckdb/Relation/insert_into.rst
deleted file mode 100644
index 03de7fc..0000000
--- a/docs/api/patito/duckdb/Relation/insert_into.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.insert_into:
-
-patito.duckdb.Relation.insert_into
-==================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.insert_into
diff --git a/docs/api/patito/duckdb/Relation/intersect.rst b/docs/api/patito/duckdb/Relation/intersect.rst
deleted file mode 100644
index ae5e1c0..0000000
--- a/docs/api/patito/duckdb/Relation/intersect.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Relation.intersect:
-
-patito.duckdb.Relation.intersect
-================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.intersect
diff --git a/docs/api/patito/duckdb/Relation/join.rst b/docs/api/patito/duckdb/Relation/join.rst
deleted file mode 100644
index e69d81c..0000000
--- a/docs/api/patito/duckdb/Relation/join.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.join:
-
-patito.duckdb.Relation.join
-===========================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.join
diff --git a/docs/api/patito/duckdb/Relation/left_join.rst b/docs/api/patito/duckdb/Relation/left_join.rst
deleted file mode 100644
index d0e2c4a..0000000
--- a/docs/api/patito/duckdb/Relation/left_join.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.left_join:
-
-patito.duckdb.Relation.left_join
-================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.left_join
diff --git a/docs/api/patito/duckdb/Relation/limit.rst b/docs/api/patito/duckdb/Relation/limit.rst
deleted file mode 100644
index c7b9fa9..0000000
--- a/docs/api/patito/duckdb/Relation/limit.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Relation.limit:
-
-patito.duckdb.Relation.limit
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.limit
diff --git a/docs/api/patito/duckdb/Relation/model.rst b/docs/api/patito/duckdb/Relation/model.rst
deleted file mode 100644
index 0aff9bc..0000000
--- a/docs/api/patito/duckdb/Relation/model.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-patito.duckdb.Relation.model
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. autoproperty:: Relation.model
-
-The model associated with the relation, if set with
-:ref:`Relation.set_model()<duckdb.Relation.set_model>`.
diff --git a/docs/api/patito/duckdb/Relation/order.rst b/docs/api/patito/duckdb/Relation/order.rst
deleted file mode 100644
index 8446fb6..0000000
--- a/docs/api/patito/duckdb/Relation/order.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Relation.order:
-
-patito.duckdb.Relation.order
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.order
diff --git a/docs/api/patito/duckdb/Relation/rename.rst b/docs/api/patito/duckdb/Relation/rename.rst
deleted file mode 100644
index 34b0f27..0000000
--- a/docs/api/patito/duckdb/Relation/rename.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.rename
-=============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.rename
diff --git a/docs/api/patito/duckdb/Relation/select.rst b/docs/api/patito/duckdb/Relation/select.rst
deleted file mode 100644
index 14f8170..0000000
--- a/docs/api/patito/duckdb/Relation/select.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.select:
-
-patito.duckdb.Relation.select
-=============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.select
diff --git a/docs/api/patito/duckdb/Relation/set_alias.rst b/docs/api/patito/duckdb/Relation/set_alias.rst
deleted file mode 100644
index cfd2fd7..0000000
--- a/docs/api/patito/duckdb/Relation/set_alias.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.set_alias:
-
-patito.duckdb.Relation.set_alias
-================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.set_alias
diff --git a/docs/api/patito/duckdb/Relation/set_model.rst b/docs/api/patito/duckdb/Relation/set_model.rst
deleted file mode 100644
index c917a1b..0000000
--- a/docs/api/patito/duckdb/Relation/set_model.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.set_model:
-
-patito.duckdb.Relation.set_model
-================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.set_model
diff --git a/docs/api/patito/duckdb/Relation/to_df.rst b/docs/api/patito/duckdb/Relation/to_df.rst
deleted file mode 100644
index 715f3df..0000000
--- a/docs/api/patito/duckdb/Relation/to_df.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.to_df
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.to_df
diff --git a/docs/api/patito/duckdb/Relation/to_pandas.rst b/docs/api/patito/duckdb/Relation/to_pandas.rst
deleted file mode 100644
index a190f68..0000000
--- a/docs/api/patito/duckdb/Relation/to_pandas.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.to_pandas
-================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.to_pandas
diff --git a/docs/api/patito/duckdb/Relation/to_series.rst b/docs/api/patito/duckdb/Relation/to_series.rst
deleted file mode 100644
index 7bc105d..0000000
--- a/docs/api/patito/duckdb/Relation/to_series.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.to_series
-================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.to_series
diff --git a/docs/api/patito/duckdb/Relation/types.rst b/docs/api/patito/duckdb/Relation/types.rst
deleted file mode 100644
index 14e4c63..0000000
--- a/docs/api/patito/duckdb/Relation/types.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _Relation.types:
-
-patito.duckdb.Relation.types
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. autoattribute:: Relation.types
diff --git a/docs/api/patito/duckdb/Relation/union.rst b/docs/api/patito/duckdb/Relation/union.rst
deleted file mode 100644
index f14674b..0000000
--- a/docs/api/patito/duckdb/Relation/union.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-.. _duckdb.Relation.union:
-
-patito.duckdb.Relation.union
-============================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.union
diff --git a/docs/api/patito/duckdb/Relation/with_columns.rst b/docs/api/patito/duckdb/Relation/with_columns.rst
deleted file mode 100644
index 61331c1..0000000
--- a/docs/api/patito/duckdb/Relation/with_columns.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.with_columns
-===================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.with_columns
diff --git a/docs/api/patito/duckdb/Relation/with_missing_defaultable_columns.rst b/docs/api/patito/duckdb/Relation/with_missing_defaultable_columns.rst
deleted file mode 100644
index d39892d..0000000
--- a/docs/api/patito/duckdb/Relation/with_missing_defaultable_columns.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.with_missing_defaultable_columns
-=======================================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.with_missing_defaultable_columns
diff --git a/docs/api/patito/duckdb/Relation/with_missing_nullable_columns.rst b/docs/api/patito/duckdb/Relation/with_missing_nullable_columns.rst
deleted file mode 100644
index 896fd82..0000000
--- a/docs/api/patito/duckdb/Relation/with_missing_nullable_columns.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-patito.duckdb.Relation.with_missing_nullable_columns
-====================================================
-
-.. currentmodule:: patito.duckdb
-
-.. automethod:: Relation.with_missing_nullable_columns
diff --git a/docs/api/patito/duckdb/index.rst b/docs/api/patito/duckdb/index.rst
deleted file mode 100644
index 8504912..0000000
--- a/docs/api/patito/duckdb/index.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-patito.duckdb
-=============
-
-.. toctree::
-   :caption: DuckDB Functionality
-   :maxdepth: 1
-
-   Database <Database/index>
-   Relation <Relation/index>
diff --git a/docs/index.rst b/docs/index.rst
index d2400c0..38987ac 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -28,12 +28,10 @@ You can simply install Patito with :code:`pip` like so:
 
    pip install patito
 
-DuckDB Integration
-~~~~~~~~~~~~~~~~~~
+Developer Notes
+---------------
 
-Patito can also integrate with `DuckDB <https://duckdb.org/>`_.
-In order to enable this integration you must explicitly specify it during installation:
-
-.. code-block:: console
-
-   pip install 'patito[duckdb]'
+To run the noxfile with pyenv, first run
+```
+pyenv local 3.8 3.9 3.10 3.11
+```
\ No newline at end of file
diff --git a/noxfile.py b/noxfile.py
index 956a62f..67d9654 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -1,16 +1,18 @@
-"""Nox sessions."""
+"""Nox sessions.
+
+Run with `nox -fb venv`
+"""
 import tempfile
 
 import nox  # type: ignore
 
 nox.options.sessions = "lint", "test", "type_check"
 locations = "src", "tests", "noxfile.py", "docs/conf.py"
-supported_python_versions = "3.8", "3.9", "3.10", "3.11"
+supported_python_versions = "3.9", "3.10", "3.11"
 
 
 def install_with_constraints(session, *args, **kwargs):
-    """
-    Install packages constrained by Poetry's lock file.
+    """Install packages constrained by Poetry's lock file.
 
     This function is a wrapper for nox.sessions.Session.install. It
     invokes pip to install packages inside of the session's virtualenv.
@@ -20,9 +22,11 @@ def install_with_constraints(session, *args, **kwargs):
     packages as Poetry development dependencies.
 
     Args:
+    ----
         session: The Session object.
         *args: Command-line arguments for pip.
         **kwargs: Additional keyword arguments for Session.install.
+
     """
     with tempfile.NamedTemporaryFile() as requirements:
         session.run(
@@ -51,7 +55,7 @@ def test(session):
         "install",
         "--only=main",
         "--extras",
-        "caching duckdb pandas",
+        "caching pandas",
         external=True,
     )
     install_with_constraints(
@@ -81,7 +85,7 @@ def type_check(session):
         "install",
         "--only=main",
         "--extras",
-        "caching duckdb pandas",
+        "caching pandas",
         external=True,
     )
     install_with_constraints(
@@ -100,22 +104,21 @@ def lint(session):
         "flake8",
         "flake8-annotations",
         "flake8-bandit",
-        "flake8-black",
         "flake8-bugbear",
         "flake8-docstrings",
-        "flake8-isort",
         "darglint",
+        "ruff",
     )
     session.run("flake8", *args)
+    session.run("ruff check", *args)
 
 
 @nox.session(python="3.11")
 def format(session):
-    """Run the black formatter on the entire code base."""
+    """Run the ruff formatter on the entire code base."""
     args = session.posargs or locations
-    install_with_constraints(session, "black", "isort")
-    session.run("black", *args)
-    session.run("isort", *args)
+    install_with_constraints(session, "ruff")
+    session.run("ruff format", *args)
 
 
 @nox.session(python="3.9")
@@ -126,7 +129,7 @@ def docs(session) -> None:
         "install",
         "--only=main",
         "--extras",
-        "caching duckdb pandas",
+        "caching pandas",
         external=True,
     )
     install_with_constraints(
@@ -135,6 +138,8 @@ def docs(session) -> None:
         "sphinx-autodoc-typehints",
         "sphinx-rtd-theme",
         "sphinx-autobuild",
+        "sphinx-toolbox",
+        "sphinxcontrib-mermaid",
     )
     if "--serve" in session.posargs:
         session.run("sphinx-autobuild", "docs", "docs/_build/html")
diff --git a/poetry.lock b/poetry.lock
index 3b120d2..9ce96ad 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,25 +1,36 @@
-# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
 
 [[package]]
 name = "alabaster"
-version = "0.7.13"
-description = "A configurable sidebar-enabled Sphinx theme"
+version = "0.7.16"
+description = "A light, configurable Sphinx theme"
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.9"
+files = [
+    {file = "alabaster-0.7.16-py3-none-any.whl", hash = "sha256:b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92"},
+    {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"},
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
 files = [
-    {file = "alabaster-0.7.13-py3-none-any.whl", hash = "sha256:1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3"},
-    {file = "alabaster-0.7.13.tar.gz", hash = "sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2"},
+    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
+    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
 ]
 
 [[package]]
 name = "apeye"
-version = "1.4.0"
+version = "1.4.1"
 description = "Handy tools for working with URLs and APIs."
 optional = true
 python-versions = ">=3.6.1"
 files = [
-    {file = "apeye-1.4.0-py3-none-any.whl", hash = "sha256:32f10f5629c39a0d2a4bc00b16827b43b912c56510395329cb4cc823954ec2be"},
-    {file = "apeye-1.4.0.tar.gz", hash = "sha256:db616f14f1e7c09c5ff76230b6a78ebada6e34bed80596bbb9f1146d94107cdb"},
+    {file = "apeye-1.4.1-py3-none-any.whl", hash = "sha256:44e58a9104ec189bf42e76b3a7fe91e2b2879d96d48e9a77e5e32ff699c9204e"},
+    {file = "apeye-1.4.1.tar.gz", hash = "sha256:14ea542fad689e3bfdbda2189a354a4908e90aee4bf84c15ab75d68453d76a36"},
 ]
 
 [package.dependencies]
@@ -34,46 +45,61 @@ limiter = ["cachecontrol[filecache] (>=0.12.6)", "lockfile (>=0.12.2)"]
 
 [[package]]
 name = "apeye-core"
-version = "1.1.4"
+version = "1.1.5"
 description = "Core (offline) functionality for the apeye library."
 optional = true
 python-versions = ">=3.6.1"
 files = [
-    {file = "apeye_core-1.1.4-py3-none-any.whl", hash = "sha256:084bc696448d3ac428fece41c1f2eb08fa9d9ce1d1b2f4d43187e3def4528a60"},
-    {file = "apeye_core-1.1.4.tar.gz", hash = "sha256:72bb89fed3baa647cb81aa28e1d851787edcbf9573853b5d2b5f87c02f50eaf5"},
+    {file = "apeye_core-1.1.5-py3-none-any.whl", hash = "sha256:dc27a93f8c9e246b3b238c5ea51edf6115ab2618ef029b9f2d9a190ec8228fbf"},
+    {file = "apeye_core-1.1.5.tar.gz", hash = "sha256:5de72ed3d00cc9b20fea55e54b7ab8f5ef8500eb33a5368bc162a5585e238a55"},
 ]
 
 [package.dependencies]
 domdf-python-tools = ">=2.6.0"
 idna = ">=2.5"
 
+[[package]]
+name = "argcomplete"
+version = "3.2.2"
+description = "Bash tab completion for argparse"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "argcomplete-3.2.2-py3-none-any.whl", hash = "sha256:e44f4e7985883ab3e73a103ef0acd27299dbfe2dfed00142c35d4ddd3005901d"},
+    {file = "argcomplete-3.2.2.tar.gz", hash = "sha256:f3e49e8ea59b4026ee29548e24488af46e30c9de57d48638e24f54a1ea1000a2"},
+]
+
+[package.extras]
+test = ["coverage", "mypy", "pexpect", "ruff", "wheel"]
+
 [[package]]
 name = "attrs"
-version = "23.1.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
-    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
 ]
 
 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]", "pre-commit"]
+dev = ["attrs[tests]", "pre-commit"]
 docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "autodocsumm"
-version = "0.2.11"
+version = "0.2.12"
 description = "Extended sphinx autodoc including automatic autosummaries"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "autodocsumm-0.2.11-py3-none-any.whl", hash = "sha256:f1d0a623bf1ad64d979a9e23fd360d1fb1b8f869beaf3197f711552cddc174e2"},
-    {file = "autodocsumm-0.2.11.tar.gz", hash = "sha256:183212bd9e9f3b58a96bb21b7958ee4e06224107aa45b2fd894b61b83581b9a9"},
+    {file = "autodocsumm-0.2.12-py3-none-any.whl", hash = "sha256:b842b53c686c07a4f174721ca4e729b027367703dbf42e2508863a3c6d6c049c"},
+    {file = "autodocsumm-0.2.12.tar.gz", hash = "sha256:848fe8c38df433c6635489499b969cb47cc389ed3d7b6e75c8ccbc94d4b3bf9e"},
 ]
 
 [package.dependencies]
@@ -81,88 +107,91 @@ Sphinx = ">=2.2,<8.0"
 
 [[package]]
 name = "babel"
-version = "2.12.1"
+version = "2.14.0"
 description = "Internationalization utilities"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "Babel-2.12.1-py3-none-any.whl", hash = "sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610"},
-    {file = "Babel-2.12.1.tar.gz", hash = "sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455"},
+    {file = "Babel-2.14.0-py3-none-any.whl", hash = "sha256:efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287"},
+    {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"},
 ]
 
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
+[package.extras]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "bandit"
-version = "1.7.5"
+version = "1.7.7"
 description = "Security oriented static analyser for python code."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "bandit-1.7.5-py3-none-any.whl", hash = "sha256:75665181dc1e0096369112541a056c59d1c5f66f9bb74a8d686c3c362b83f549"},
-    {file = "bandit-1.7.5.tar.gz", hash = "sha256:bdfc739baa03b880c2d15d0431b31c658ffc348e907fe197e54e0389dd59e11e"},
+    {file = "bandit-1.7.7-py3-none-any.whl", hash = "sha256:17e60786a7ea3c9ec84569fd5aee09936d116cb0cb43151023258340dbffb7ed"},
+    {file = "bandit-1.7.7.tar.gz", hash = "sha256:527906bec6088cb499aae31bc962864b4e77569e9d529ee51df3a93b4b8ab28a"},
 ]
 
 [package.dependencies]
 colorama = {version = ">=0.3.9", markers = "platform_system == \"Windows\""}
-GitPython = ">=1.0.1"
 PyYAML = ">=5.3.1"
 rich = "*"
 stevedore = ">=1.20.0"
 
 [package.extras]
-test = ["beautifulsoup4 (>=4.8.0)", "coverage (>=4.5.4)", "fixtures (>=3.0.0)", "flake8 (>=4.0.0)", "pylint (==1.9.4)", "stestr (>=2.5.0)", "testscenarios (>=0.5.0)", "testtools (>=2.3.0)", "tomli (>=1.1.0)"]
+baseline = ["GitPython (>=3.1.30)"]
+test = ["beautifulsoup4 (>=4.8.0)", "coverage (>=4.5.4)", "fixtures (>=3.0.0)", "flake8 (>=4.0.0)", "pylint (==1.9.4)", "stestr (>=2.5.0)", "testscenarios (>=0.5.0)", "testtools (>=2.3.0)"]
 toml = ["tomli (>=1.1.0)"]
 yaml = ["PyYAML"]
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.2"
+version = "4.12.3"
 description = "Screen-scraping library"
 optional = true
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
-    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
+    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
+    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "23.7.0"
+version = "24.2.0"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -172,23 +201,23 @@ packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)"]
+d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "blackdoc"
-version = "0.3.8"
+version = "0.3.9"
 description = "run black on documentation code snippets"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -200,129 +229,144 @@ tomli = "*"
 
 [[package]]
 name = "cachecontrol"
-version = "0.13.1"
+version = "0.14.0"
 description = "httplib2 caching for requests"
 optional = true
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
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-msgpack = ">=0.5.2"
+msgpack = ">=0.5.2,<2.0.0"
 requests = ">=2.16.0"
 
 [package.extras]
-dev = ["CacheControl[filecache,redis]", "black", "build", "cherrypy", "mypy", "pytest", "pytest-cov", "sphinx", "tox", "types-redis", "types-requests"]
+dev = ["CacheControl[filecache,redis]", "black", "build", "cherrypy", "furo", "mypy", "pytest", "pytest-cov", "sphinx", "sphinx-copybutton", "tox", "types-redis", "types-requests"]
 filecache = ["filelock (>=3.8.0)"]
 redis = ["redis (>=2.10.5)"]
 
 [[package]]
 name = "certifi"
-version = "2023.5.7"
+version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
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+
+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+
+[package.extras]
+development = ["black", "flake8", "mypy", "pytest", "types-colorama"]
+
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 [package.dependencies]
@@ -431,58 +484,68 @@ toml = ["tomli"]
 
 [[package]]
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+version = "2.9.0"
 description = "A CSS Cascading Style Sheets library for Python"
 optional = true
-python-versions = ">=3.7"
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 files = [
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 ]
 
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+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
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 [[package]]
 name = "dict2css"
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 description = "A μ-library for constructing cascading style sheets from Python dictionaries."
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 python-versions = ">=3.6"
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 ]
 
 [package.dependencies]
 cssutils = ">=2.2.0"
 domdf-python-tools = ">=2.2.0"
 
+[[package]]
+name = "distlib"
+version = "0.3.8"
+description = "Distribution utilities"
+optional = false
+python-versions = "*"
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+]
+
 [[package]]
 name = "docutils"
-version = "0.18.1"
+version = "0.19"
 description = "Docutils -- Python Documentation Utilities"
 optional = true
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.7"
 files = [
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+    {file = "docutils-0.19-py3-none-any.whl", hash = "sha256:5e1de4d849fee02c63b040a4a3fd567f4ab104defd8a5511fbbc24a8a017efbc"},
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 ]
 
 [[package]]
 name = "domdf-python-tools"
-version = "3.6.1"
+version = "3.8.0.post2"
 description = "Helpful functions for Python 🐍 🛠️"
 optional = true
 python-versions = ">=3.6"
 files = [
-    {file = "domdf_python_tools-3.6.1-py3-none-any.whl", hash = "sha256:e18158460850957f18e740eb94ede56f580ddb0cb162ab9d9834ed8bbb1b6431"},
-    {file = "domdf_python_tools-3.6.1.tar.gz", hash = "sha256:acc04563d23bce4d437dd08af6b9bea788328c412772a044d8ca428a7ad861be"},
+    {file = "domdf_python_tools-3.8.0.post2-py3-none-any.whl", hash = "sha256:ad2c763c8d00850a7fa92ad95e9891a1918281ea25322c4dbb1734fd32f905dd"},
+    {file = "domdf_python_tools-3.8.0.post2.tar.gz", hash = "sha256:a1fd255ea29f767b08de462d2da39d360262304389227d980bc307ee8aa3366a"},
 ]
 
 [package.dependencies]
-importlib-metadata = {version = ">=3.6.0", markers = "python_version < \"3.9\""}
 natsort = ">=7.0.1"
 typing-extensions = ">=3.7.4.1"
 
@@ -490,76 +553,15 @@ typing-extensions = ">=3.7.4.1"
 all = ["pytz (>=2019.1)"]
 dates = ["pytz (>=2019.1)"]
 
-[[package]]
-name = "duckdb"
-version = "0.8.1"
-description = "DuckDB embedded database"
-optional = true
-python-versions = "*"
-files = [
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-    {file = "duckdb-0.8.1.tar.gz", hash = "sha256:a54d37f4abc2afc4f92314aaa56ecf215a411f40af4bffe1e86bd25e62aceee9"},
-]
-
 [[package]]
 name = "exceptiongroup"
-version = "1.1.2"
+version = "1.2.0"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.1.2-py3-none-any.whl", hash = "sha256:e346e69d186172ca7cf029c8c1d16235aa0e04035e5750b4b95039e65204328f"},
-    {file = "exceptiongroup-1.1.2.tar.gz", hash = "sha256:12c3e887d6485d16943a309616de20ae5582633e0a2eda17f4e10fd61c1e8af5"},
+    {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
+    {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
 ]
 
 [package.extras]
@@ -567,18 +569,19 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.12.2"
+version = "3.13.1"
 description = "A platform independent file lock."
-optional = true
-python-versions = ">=3.7"
+optional = false
+python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.12.2-py3-none-any.whl", hash = "sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec"},
-    {file = "filelock-3.12.2.tar.gz", hash = "sha256:002740518d8aa59a26b0c76e10fb8c6e15eae825d34b6fdf670333fd7b938d81"},
+    {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
+    {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
+docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "flake8"
@@ -667,13 +670,13 @@ dev = ["coverage", "hypothesis", "hypothesmith (>=0.2)", "pre-commit", "pytest",
 
 [[package]]
 name = "flake8-isort"
-version = "6.0.0"
-description = "flake8 plugin that integrates isort ."
+version = "6.1.1"
+description = "flake8 plugin that integrates isort"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "flake8-isort-6.0.0.tar.gz", hash = "sha256:537f453a660d7e903f602ecfa36136b140de279df58d02eb1b6a0c84e83c528c"},
-    {file = "flake8_isort-6.0.0-py3-none-any.whl", hash = "sha256:aa0cac02a62c7739e370ce6b9c31743edac904bae4b157274511fc8a19c75bbc"},
+    {file = "flake8_isort-6.1.1-py3-none-any.whl", hash = "sha256:0fec4dc3a15aefbdbe4012e51d5531a2eb5fa8b981cdfbc882296a59b54ede12"},
+    {file = "flake8_isort-6.1.1.tar.gz", hash = "sha256:c1f82f3cf06a80c13e1d09bfae460e9666255d5c780b859f19f8318d420370b3"},
 ]
 
 [package.dependencies]
@@ -697,34 +700,6 @@ files = [
 [package.dependencies]
 flake8 = "*"
 
-[[package]]
-name = "gitdb"
-version = "4.0.10"
-description = "Git Object Database"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "gitdb-4.0.10-py3-none-any.whl", hash = "sha256:c286cf298426064079ed96a9e4a9d39e7f3e9bf15ba60701e95f5492f28415c7"},
-    {file = "gitdb-4.0.10.tar.gz", hash = "sha256:6eb990b69df4e15bad899ea868dc46572c3f75339735663b81de79b06f17eb9a"},
-]
-
-[package.dependencies]
-smmap = ">=3.0.1,<6"
-
-[[package]]
-name = "gitpython"
-version = "3.1.32"
-description = "GitPython is a Python library used to interact with Git repositories"
-optional = false
-python-versions = ">=3.7"
-files = [
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-]
-
-[package.dependencies]
-gitdb = ">=4.0.1,<5"
-
 [[package]]
 name = "html5lib"
 version = "1.1"
@@ -748,13 +723,13 @@ lxml = ["lxml"]
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"},
-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
+    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
 [[package]]
@@ -770,20 +745,20 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "6.8.0"
+version = "7.0.1"
 description = "Read metadata from Python packages"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-6.8.0-py3-none-any.whl", hash = "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb"},
-    {file = "importlib_metadata-6.8.0.tar.gz", hash = "sha256:dbace7892d8c0c4ac1ad096662232f831d4e64f4c4545bd53016a3e9d4654743"},
+    {file = "importlib_metadata-7.0.1-py3-none-any.whl", hash = "sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e"},
+    {file = "importlib_metadata-7.0.1.tar.gz", hash = "sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
@@ -800,30 +775,27 @@ files = [
 
 [[package]]
 name = "isort"
-version = "5.12.0"
+version = "5.13.2"
 description = "A Python utility / library to sort Python imports."
 optional = false
 python-versions = ">=3.8.0"
 files = [
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+    {file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"},
+    {file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"},
 ]
 
 [package.extras]
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-plugins = ["setuptools"]
-requirements-deprecated-finder = ["pip-api", "pipreqs"]
+colors = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.2"
+version = "3.1.3"
 description = "A very fast and expressive template engine."
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -847,17 +819,6 @@ files = [
 six = "*"
 tornado = {version = "*", markers = "python_version > \"2.7\""}
 
-[[package]]
-name = "lockfile"
-version = "0.12.2"
-description = "Platform-independent file locking module"
-optional = true
-python-versions = "*"
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-]
-
 [[package]]
 name = "markdown-it-py"
 version = "3.0.0"
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 python-versions = ">=3.7"
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 install-types = ["pip"]
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 types-pytz = ">=2022.1.1"
 
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 python-versions = ">=2.6"
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 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
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 [[package]]
 name = "pluggy"
-version = "1.2.0"
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 [package.extras]
@@ -1316,30 +1315,36 @@ testing = ["pytest", "pytest-benchmark"]
 
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+version = "0.20.10"
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 python-versions = ">=3.8"
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 [package.extras]
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-connectorx = ["connectorx"]
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 fsspec = ["fsspec"]
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 matplotlib = ["matplotlib"]
 numpy = ["numpy (>=1.16.0)"]
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 pandas = ["pandas", "pyarrow (>=7.0.0)"]
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 pyarrow = ["pyarrow (>=7.0.0)"]
 pydantic = ["pydantic"]
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 sqlalchemy = ["pandas", "sqlalchemy"]
 timezone = ["backports.zoneinfo", "tzdata"]
 xlsx2csv = ["xlsx2csv (>=0.8.0)"]
@@ -1347,40 +1352,51 @@ xlsxwriter = ["xlsxwriter"]
 
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 [[package]]
 name = "pyflakes"
@@ -1458,27 +1532,28 @@ files = [
 
 [[package]]
 name = "pygments"
-version = "2.15.1"
+version = "2.17.2"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
 plugins = ["importlib-metadata"]
+windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyright"
-version = "1.1.317"
+version = "1.1.351"
 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -1490,13 +1565,13 @@ dev = ["twine (>=3.4.1)"]
 
 [[package]]
 name = "pytest"
-version = "7.4.0"
+version = "8.0.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -1504,7 +1579,7 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
+pluggy = ">=1.3.0,<2.0"
 tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
@@ -1530,13 +1605,13 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 
 [[package]]
 name = "pytest-watcher"
-version = "0.3.4"
+version = "0.4.1"
 description = "Automatically rerun your tests on file modifications"
 optional = false
 python-versions = ">=3.7.0,<4.0.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1559,13 +1634,13 @@ six = ">=1.5"
 
 [[package]]
 name = "pytz"
-version = "2023.3"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = true
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -1640,124 +1715,152 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rich"
-version = "13.4.2"
+version = "13.7.0"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
 python-versions = ">=3.7.0"
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 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
 name = "ruamel-yaml"
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+version = "0.18.6"
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-python-versions = ">=3"
+python-versions = ">=3.7"
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 [package.extras]
-docs = ["ryd"]
+docs = ["mercurial (>5.7)", "ryd"]
 jinja2 = ["ruamel.yaml.jinja2 (>=0.2)"]
 
 [[package]]
 name = "ruamel-yaml-clib"
-version = "0.2.7"
+version = "0.2.8"
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+python-versions = ">=3.6"
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+    {file = "ruff-0.2.2.tar.gz", hash = "sha256:e62ed7f36b3068a30ba39193a14274cd706bc486fad521276458022f7bccb31d"},
 ]
 
 [[package]]
 name = "setuptools"
-version = "68.0.0"
+version = "69.1.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "setuptools-68.0.0-py3-none-any.whl", hash = "sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f"},
-    {file = "setuptools-68.0.0.tar.gz", hash = "sha256:baf1fdb41c6da4cd2eae722e135500da913332ab3f2f5c7d33af9b492acb5235"},
+    {file = "setuptools-69.1.1-py3-none-any.whl", hash = "sha256:02fa291a0471b3a18b2b2481ed902af520c69e8ae0919c13da936542754b4c56"},
+    {file = "setuptools-69.1.1.tar.gz", hash = "sha256:5c0806c7d9af348e6dd3777b4f4dbb42c7ad85b190104837488eab9a7c945cf8"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "six"
 version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
     {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
-[[package]]
-name = "smmap"
-version = "5.0.0"
-description = "A pure Python implementation of a sliding window memory map manager"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "smmap-5.0.0-py3-none-any.whl", hash = "sha256:2aba19d6a040e78d8b09de5c57e96207b09ed71d8e55ce0959eeee6c8e190d94"},
-    {file = "smmap-5.0.0.tar.gz", hash = "sha256:c840e62059cd3be204b0c9c9f74be2c09d5648eddd4580d9314c3ecde0b30936"},
-]
-
 [[package]]
 name = "snowballstemmer"
 version = "2.2.0"
@@ -1771,13 +1874,13 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.4.1"
+version = "2.5"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "soupsieve-2.4.1-py3-none-any.whl", hash = "sha256:1c1bfee6819544a3447586c889157365a27e10d88cde3ad3da0cf0ddf646feb8"},
-    {file = "soupsieve-2.4.1.tar.gz", hash = "sha256:89d12b2d5dfcd2c9e8c22326da9d9aa9cb3dfab0a83a024f05704076ee8d35ea"},
+    {file = "soupsieve-2.5-py3-none-any.whl", hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"},
+    {file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"},
 ]
 
 [[package]]
@@ -1817,13 +1920,13 @@ test = ["cython", "filelock", "html5lib", "pytest (>=4.6)"]
 
 [[package]]
 name = "sphinx-autobuild"
-version = "2021.3.14"
+version = "2024.2.4"
 description = "Rebuild Sphinx documentation on changes, with live-reload in the browser."
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
-    {file = "sphinx-autobuild-2021.3.14.tar.gz", hash = "sha256:de1ca3b66e271d2b5b5140c35034c89e47f263f2cd5db302c9217065f7443f05"},
-    {file = "sphinx_autobuild-2021.3.14-py3-none-any.whl", hash = "sha256:8fe8cbfdb75db04475232f05187c776f46f6e9e04cacf1e49ce81bdac649ccac"},
+    {file = "sphinx_autobuild-2024.2.4-py3-none-any.whl", hash = "sha256:63fd87ab7505872a89aef468ce6503f65e794a195f4ae62269db3b85b72d4854"},
+    {file = "sphinx_autobuild-2024.2.4.tar.gz", hash = "sha256:cb9d2121a176d62d45471624872afc5fad7755ad662738abe400ecf4a7954303"},
 ]
 
 [package.dependencies]
@@ -1832,7 +1935,7 @@ livereload = "*"
 sphinx = "*"
 
 [package.extras]
-test = ["pytest", "pytest-cov"]
+test = ["pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "sphinx-autodoc-typehints"
@@ -1855,13 +1958,13 @@ type-comment = ["typed-ast (>=1.5.4)"]
 
 [[package]]
 name = "sphinx-jinja2-compat"
-version = "0.2.0"
+version = "0.2.0.post1"
 description = "Patches Jinja2 v3 to restore compatibility with earlier Sphinx versions."
 optional = true
 python-versions = ">=3.6"
 files = [
-    {file = "sphinx_jinja2_compat-0.2.0-py3-none-any.whl", hash = "sha256:a5f3112d6873991c2cf28e37287163a0485d9c0812863b8aa4df7182722501fb"},
-    {file = "sphinx_jinja2_compat-0.2.0.tar.gz", hash = "sha256:c41346d859653e202b623f4236da8936243ed734abf5984adc3bef59d6f9a946"},
+    {file = "sphinx_jinja2_compat-0.2.0.post1-py3-none-any.whl", hash = "sha256:f9d329174bdde8db19dc12c62528367196eb2f6b46c91754eca604acd0c0f6ad"},
+    {file = "sphinx_jinja2_compat-0.2.0.post1.tar.gz", hash = "sha256:974289a12a9f402108dead621e9c15f7004e945d5cfcaea8d6419e94d3fa95a3"},
 ]
 
 [package.dependencies]
@@ -1884,18 +1987,18 @@ Sphinx = "*"
 
 [[package]]
 name = "sphinx-rtd-theme"
-version = "1.2.2"
+version = "2.0.0"
 description = "Read the Docs theme for Sphinx"
 optional = true
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+python-versions = ">=3.6"
 files = [
-    {file = "sphinx_rtd_theme-1.2.2-py2.py3-none-any.whl", hash = "sha256:6a7e7d8af34eb8fc57d52a09c6b6b9c46ff44aea5951bc831eeb9245378f3689"},
-    {file = "sphinx_rtd_theme-1.2.2.tar.gz", hash = "sha256:01c5c5a72e2d025bd23d1f06c59a4831b06e6ce6c01fdd5ebfe9986c0a880fc7"},
+    {file = "sphinx_rtd_theme-2.0.0-py2.py3-none-any.whl", hash = "sha256:ec93d0856dc280cf3aee9a4c9807c60e027c7f7b461b77aeffed682e68f0e586"},
+    {file = "sphinx_rtd_theme-2.0.0.tar.gz", hash = "sha256:bd5d7b80622406762073a04ef8fadc5f9151261563d47027de09910ce03afe6b"},
 ]
 
 [package.dependencies]
-docutils = "<0.19"
-sphinx = ">=1.6,<7"
+docutils = "<0.21"
+sphinx = ">=5,<8"
 sphinxcontrib-jquery = ">=4,<5"
 
 [package.extras]
@@ -1903,45 +2006,45 @@ dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"]
 
 [[package]]
 name = "sphinx-tabs"
-version = "3.4.1"
+version = "3.4.5"
 description = "Tabbed views for Sphinx"
 optional = true
 python-versions = "~=3.7"
 files = [
-    {file = "sphinx-tabs-3.4.1.tar.gz", hash = "sha256:d2a09f9e8316e400d57503f6df1c78005fdde220e5af589cc79d493159e1b832"},
-    {file = "sphinx_tabs-3.4.1-py3-none-any.whl", hash = "sha256:7cea8942aeccc5d01a995789c01804b787334b55927f29b36ba16ed1e7cb27c6"},
+    {file = "sphinx-tabs-3.4.5.tar.gz", hash = "sha256:ba9d0c1e3e37aaadd4b5678449eb08176770e0fc227e769b6ce747df3ceea531"},
+    {file = "sphinx_tabs-3.4.5-py3-none-any.whl", hash = "sha256:92cc9473e2ecf1828ca3f6617d0efc0aa8acb06b08c56ba29d1413f2f0f6cf09"},
 ]
 
 [package.dependencies]
-docutils = ">=0.18.0,<0.19.0"
+docutils = "*"
 pygments = "*"
 sphinx = "*"
 
 [package.extras]
 code-style = ["pre-commit (==2.13.0)"]
-testing = ["bs4", "coverage", "pygments", "pytest (>=7.1,<8)", "pytest-cov", "pytest-regressions", "rinohtype", "sphinx-testing"]
+testing = ["bs4", "coverage", "pygments", "pytest (>=7.1,<8)", "pytest-cov", "pytest-regressions", "rinohtype"]
 
 [[package]]
 name = "sphinx-toolbox"
-version = "3.4.0"
+version = "3.5.0"
 description = "Box of handy tools for Sphinx 🧰 📔"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "sphinx_toolbox-3.4.0-py3-none-any.whl", hash = "sha256:cdf70facee515a2d9406d568a253fa3e89f930fde23c4e8095ba0c675f7c0a48"},
-    {file = "sphinx_toolbox-3.4.0.tar.gz", hash = "sha256:e1cf2a3dea5ce80e175a6a9cee8b5b2792240ecf6c28993d87a63b6fcf606293"},
+    {file = "sphinx_toolbox-3.5.0-py3-none-any.whl", hash = "sha256:20dfd3566717db6f2da7a400a54dc4b946f064fb31250fa44802d54cfb9b8a03"},
+    {file = "sphinx_toolbox-3.5.0.tar.gz", hash = "sha256:e5b5a7153f1997572d71a06aaf6cec225483492ec2c60097a84f15aad6df18b7"},
 ]
 
 [package.dependencies]
 apeye = ">=0.4.0"
 autodocsumm = ">=0.2.0"
 beautifulsoup4 = ">=4.9.1"
-cachecontrol = {version = ">=0.12.6", extras = ["filecache"]}
+cachecontrol = {version = ">=0.13.0", extras = ["filecache"]}
 dict2css = ">=0.2.3"
-docutils = ">=0.16,<0.19"
+docutils = ">=0.16"
 domdf-python-tools = ">=2.9.0"
+filelock = ">=3.8.0"
 html5lib = ">=1.1"
-lockfile = ">=0.12.2"
 "ruamel.yaml" = ">=0.16.12"
 sphinx = ">=3.2.0"
 sphinx-autodoc-typehints = ">=1.11.1"
@@ -1957,47 +2060,50 @@ testing = ["coincidence (>=0.4.3)", "pygments (>=2.7.4,<=2.13.0)"]
 
 [[package]]
 name = "sphinxcontrib-applehelp"
-version = "1.0.4"
+version = "1.0.8"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib-applehelp-1.0.4.tar.gz", hash = "sha256:828f867945bbe39817c210a1abfd1bc4895c8b73fcaade56d45357a348a07d7e"},
-    {file = "sphinxcontrib_applehelp-1.0.4-py3-none-any.whl", hash = "sha256:29d341f67fb0f6f586b23ad80e072c8e6ad0b48417db2bde114a4c9746feb228"},
+    {file = "sphinxcontrib_applehelp-1.0.8-py3-none-any.whl", hash = "sha256:cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4"},
+    {file = "sphinxcontrib_applehelp-1.0.8.tar.gz", hash = "sha256:c40a4f96f3776c4393d933412053962fac2b84f4c99a7982ba42e09576a70619"},
 ]
 
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-devhelp"
-version = "1.0.2"
-description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp document."
+version = "1.0.6"
+description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp documents"
 optional = true
-python-versions = ">=3.5"
+python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib-devhelp-1.0.2.tar.gz", hash = "sha256:ff7f1afa7b9642e7060379360a67e9c41e8f3121f2ce9164266f61b9f4b338e4"},
-    {file = "sphinxcontrib_devhelp-1.0.2-py2.py3-none-any.whl", hash = "sha256:8165223f9a335cc1af7ffe1ed31d2871f325254c0423bc0c4c7cd1c1e4734a2e"},
+    {file = "sphinxcontrib_devhelp-1.0.6-py3-none-any.whl", hash = "sha256:6485d09629944511c893fa11355bda18b742b83a2b181f9a009f7e500595c90f"},
+    {file = "sphinxcontrib_devhelp-1.0.6.tar.gz", hash = "sha256:9893fd3f90506bc4b97bdb977ceb8fbd823989f4316b28c3841ec128544372d3"},
 ]
 
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-htmlhelp"
-version = "2.0.1"
+version = "2.0.5"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib-htmlhelp-2.0.1.tar.gz", hash = "sha256:0cbdd302815330058422b98a113195c9249825d681e18f11e8b1f78a2f11efff"},
-    {file = "sphinxcontrib_htmlhelp-2.0.1-py3-none-any.whl", hash = "sha256:c38cb46dccf316c79de6e5515e1770414b797162b23cd3d06e67020e1d2a6903"},
+    {file = "sphinxcontrib_htmlhelp-2.0.5-py3-none-any.whl", hash = "sha256:393f04f112b4d2f53d93448d4bce35842f62b307ccdc549ec1585e950bc35e04"},
+    {file = "sphinxcontrib_htmlhelp-2.0.5.tar.gz", hash = "sha256:0dc87637d5de53dd5eec3a6a01753b1ccf99494bd756aafecd74b4fa9e729015"},
 ]
 
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["html5lib", "pytest"]
 
 [[package]]
@@ -2041,43 +2147,45 @@ files = [
 
 [[package]]
 name = "sphinxcontrib-qthelp"
-version = "1.0.3"
-description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp document."
+version = "1.0.7"
+description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp documents"
 optional = true
-python-versions = ">=3.5"
+python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib-qthelp-1.0.3.tar.gz", hash = "sha256:4c33767ee058b70dba89a6fc5c1892c0d57a54be67ddd3e7875a18d14cba5a72"},
-    {file = "sphinxcontrib_qthelp-1.0.3-py2.py3-none-any.whl", hash = "sha256:bd9fc24bcb748a8d51fd4ecaade681350aa63009a347a8c14e637895444dfab6"},
+    {file = "sphinxcontrib_qthelp-1.0.7-py3-none-any.whl", hash = "sha256:e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182"},
+    {file = "sphinxcontrib_qthelp-1.0.7.tar.gz", hash = "sha256:053dedc38823a80a7209a80860b16b722e9e0209e32fea98c90e4e6624588ed6"},
 ]
 
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-serializinghtml"
-version = "1.1.5"
-description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)."
+version = "1.1.10"
+description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)"
 optional = true
-python-versions = ">=3.5"
+python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib-serializinghtml-1.1.5.tar.gz", hash = "sha256:aa5f6de5dfdf809ef505c4895e51ef5c9eac17d0f287933eb49ec495280b6952"},
-    {file = "sphinxcontrib_serializinghtml-1.1.5-py2.py3-none-any.whl", hash = "sha256:352a9a00ae864471d3a7ead8d7d79f5fc0b57e8b3f95e9867eb9eb28999b92fd"},
+    {file = "sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl", hash = "sha256:326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7"},
+    {file = "sphinxcontrib_serializinghtml-1.1.10.tar.gz", hash = "sha256:93f3f5dc458b91b192fe10c397e324f262cf163d79f3282c158e8436a2c4511f"},
 ]
 
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "stevedore"
-version = "5.1.0"
+version = "5.2.0"
 description = "Manage dynamic plugins for Python applications"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "stevedore-5.1.0-py3-none-any.whl", hash = "sha256:8cc040628f3cea5d7128f2e76cf486b2251a4e543c7b938f58d9a377f6694a2d"},
-    {file = "stevedore-5.1.0.tar.gz", hash = "sha256:a54534acf9b89bc7ed264807013b505bf07f74dbe4bcfa37d32bd063870b087c"},
+    {file = "stevedore-5.2.0-py3-none-any.whl", hash = "sha256:1c15d95766ca0569cad14cb6272d4d31dae66b011a929d7c18219c176ea1b5c9"},
+    {file = "stevedore-5.2.0.tar.gz", hash = "sha256:46b93ca40e1114cea93d738a6c1e365396981bb6bb78c27045b7587c9473544d"},
 ]
 
 [package.dependencies]
@@ -2110,119 +2218,141 @@ files = [
 
 [[package]]
 name = "tornado"
-version = "6.3.2"
+version = "6.4"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = true
 python-versions = ">= 3.8"
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+    {file = "watchdog-4.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:6a4db54edea37d1058b08947c789a2354ee02972ed5d1e0dca9b0b820f4c7f92"},
+    {file = "watchdog-4.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:d31481ccf4694a8416b681544c23bd271f5a123162ab603c7d7d2dd7dd901a07"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8fec441f5adcf81dd240a5fe78e3d83767999771630b5ddfc5867827a34fa3d3"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:6a9c71a0b02985b4b0b6d14b875a6c86ddea2fdbebd0c9a720a806a8bbffc69f"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:557ba04c816d23ce98a06e70af6abaa0485f6d94994ec78a42b05d1c03dcbd50"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:d0f9bd1fd919134d459d8abf954f63886745f4660ef66480b9d753a7c9d40927"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:f9b2fdca47dc855516b2d66eef3c39f2672cbf7e7a42e7e67ad2cbfcd6ba107d"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:73c7a935e62033bd5e8f0da33a4dcb763da2361921a69a5a95aaf6c93aa03a87"},
+    {file = "watchdog-4.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:6a80d5cae8c265842c7419c560b9961561556c4361b297b4c431903f8c33b269"},
+    {file = "watchdog-4.0.0-py3-none-win32.whl", hash = "sha256:8f9a542c979df62098ae9c58b19e03ad3df1c9d8c6895d96c0d51da17b243b1c"},
+    {file = "watchdog-4.0.0-py3-none-win_amd64.whl", hash = "sha256:f970663fa4f7e80401a7b0cbeec00fa801bf0287d93d48368fc3e6fa32716245"},
+    {file = "watchdog-4.0.0-py3-none-win_ia64.whl", hash = "sha256:9a03e16e55465177d416699331b0f3564138f1807ecc5f2de9d55d8f188d08c7"},
+    {file = "watchdog-4.0.0.tar.gz", hash = "sha256:e3e7065cbdabe6183ab82199d7a4f6b3ba0a438c5a512a68559846ccb76a78ec"},
 ]
 
 [package.extras]
@@ -2241,53 +2371,48 @@ files = [
 
 [[package]]
 name = "xdoctest"
-version = "1.1.1"
+version = "1.1.3"
 description = "A rewrite of the builtin doctest module"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "xdoctest-1.1.1-py3-none-any.whl", hash = "sha256:d59d4ed91cb92e4430ef0ad1b134a2bef02adff7d2fb9c9f057547bee44081a2"},
-    {file = "xdoctest-1.1.1.tar.gz", hash = "sha256:2eac8131bdcdf2781b4e5a62d6de87f044b730cc8db8af142a51bb29c245e779"},
+    {file = "xdoctest-1.1.3-py3-none-any.whl", hash = "sha256:9360535bd1a971ffc216d9613898cedceb81d0fd024587cc3c03c74d14c00a31"},
+    {file = "xdoctest-1.1.3.tar.gz", hash = "sha256:84e76a42a11a5926ff66d9d84c616bc101821099672550481ad96549cbdd02ae"},
 ]
 
-[package.dependencies]
-six = "*"
-
 [package.extras]
-all = ["IPython", "IPython", "Pygments", "Pygments", "attrs", "codecov", "colorama", "debugpy", "debugpy", "debugpy", "debugpy", "debugpy", "ipykernel", "ipykernel", "ipython-genutils", "jedi", "jinja2", "jupyter-client", "jupyter-client", "jupyter-core", "nbconvert", "pyflakes", "pytest", "pytest", "pytest", "pytest-cov", "six", "tomli", "typing"]
-all-strict = ["IPython (==7.10.0)", "IPython (==7.23.1)", "Pygments (==2.0.0)", "Pygments (==2.4.1)", "attrs (==19.2.0)", "codecov (==2.0.15)", "colorama (==0.4.1)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.3.0)", "debugpy (==1.6.0)", "ipykernel (==5.2.0)", "ipykernel (==6.0.0)", "ipython-genutils (==0.2.0)", "jedi (==0.16)", "jinja2 (==3.0.0)", "jupyter-client (==6.1.5)", "jupyter-client (==7.0.0)", "jupyter-core (==4.7.0)", "nbconvert (==6.0.0)", "pyflakes (==2.2.0)", "pytest (==4.6.0)", "pytest (==4.6.0)", "pytest (==6.2.5)", "pytest-cov (==3.0.0)", "six (==1.11.0)", "tomli (==0.2.0)", "typing (==3.7.4)"]
+all = ["IPython (>=7.10.0)", "IPython (>=7.23.1)", "Pygments (>=2.0.0)", "Pygments (>=2.4.1)", "attrs (>=19.2.0)", "colorama (>=0.4.1)", "debugpy (>=1.0.0)", "debugpy (>=1.0.0)", "debugpy (>=1.0.0)", "debugpy (>=1.3.0)", "debugpy (>=1.6.0)", "ipykernel (>=5.2.0)", "ipykernel (>=6.0.0)", "ipykernel (>=6.11.0)", "ipython-genutils (>=0.2.0)", "jedi (>=0.16)", "jinja2 (>=3.0.0)", "jupyter-client (>=6.1.5)", "jupyter-client (>=7.0.0)", "jupyter-core (>=4.7.0)", "nbconvert (>=6.0.0)", "nbconvert (>=6.1.0)", "pyflakes (>=2.2.0)", "pytest (>=4.6.0)", "pytest (>=4.6.0)", "pytest (>=6.2.5)", "pytest-cov (>=3.0.0)", "tomli (>=0.2.0)", "typing (>=3.7.4)"]
+all-strict = ["IPython (==7.10.0)", "IPython (==7.23.1)", "Pygments (==2.0.0)", "Pygments (==2.4.1)", "attrs (==19.2.0)", "colorama (==0.4.1)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.3.0)", "debugpy (==1.6.0)", "ipykernel (==5.2.0)", "ipykernel (==6.0.0)", "ipykernel (==6.11.0)", "ipython-genutils (==0.2.0)", "jedi (==0.16)", "jinja2 (==3.0.0)", "jupyter-client (==6.1.5)", "jupyter-client (==7.0.0)", "jupyter-core (==4.7.0)", "nbconvert (==6.0.0)", "nbconvert (==6.1.0)", "pyflakes (==2.2.0)", "pytest (==4.6.0)", "pytest (==4.6.0)", "pytest (==6.2.5)", "pytest-cov (==3.0.0)", "tomli (==0.2.0)", "typing (==3.7.4)"]
 colors = ["Pygments", "Pygments", "colorama"]
-jupyter = ["IPython", "IPython", "attrs", "debugpy", "debugpy", "debugpy", "debugpy", "debugpy", "ipykernel", "ipykernel", "ipython-genutils", "jedi", "jinja2", "jupyter-client", "jupyter-client", "jupyter-core", "nbconvert"]
-optional = ["IPython", "IPython", "Pygments", "Pygments", "attrs", "colorama", "debugpy", "debugpy", "debugpy", "debugpy", "debugpy", "ipykernel", "ipykernel", "ipython-genutils", "jedi", "jinja2", "jupyter-client", "jupyter-client", "jupyter-core", "nbconvert", "pyflakes", "tomli"]
-optional-strict = ["IPython (==7.10.0)", "IPython (==7.23.1)", "Pygments (==2.0.0)", "Pygments (==2.4.1)", "attrs (==19.2.0)", "colorama (==0.4.1)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.3.0)", "debugpy (==1.6.0)", "ipykernel (==5.2.0)", "ipykernel (==6.0.0)", "ipython-genutils (==0.2.0)", "jedi (==0.16)", "jinja2 (==3.0.0)", "jupyter-client (==6.1.5)", "jupyter-client (==7.0.0)", "jupyter-core (==4.7.0)", "nbconvert (==6.0.0)", "pyflakes (==2.2.0)", "tomli (==0.2.0)"]
-runtime-strict = ["six (==1.11.0)"]
-tests = ["codecov", "pytest", "pytest", "pytest", "pytest-cov", "typing"]
+jupyter = ["IPython", "IPython", "attrs", "debugpy", "debugpy", "debugpy", "debugpy", "debugpy", "ipykernel", "ipykernel", "ipykernel", "ipython-genutils", "jedi", "jinja2", "jupyter-client", "jupyter-client", "jupyter-core", "nbconvert", "nbconvert"]
+optional = ["IPython (>=7.10.0)", "IPython (>=7.23.1)", "Pygments (>=2.0.0)", "Pygments (>=2.4.1)", "attrs (>=19.2.0)", "colorama (>=0.4.1)", "debugpy (>=1.0.0)", "debugpy (>=1.0.0)", "debugpy (>=1.0.0)", "debugpy (>=1.3.0)", "debugpy (>=1.6.0)", "ipykernel (>=5.2.0)", "ipykernel (>=6.0.0)", "ipykernel (>=6.11.0)", "ipython-genutils (>=0.2.0)", "jedi (>=0.16)", "jinja2 (>=3.0.0)", "jupyter-client (>=6.1.5)", "jupyter-client (>=7.0.0)", "jupyter-core (>=4.7.0)", "nbconvert (>=6.0.0)", "nbconvert (>=6.1.0)", "pyflakes (>=2.2.0)", "tomli (>=0.2.0)"]
+optional-strict = ["IPython (==7.10.0)", "IPython (==7.23.1)", "Pygments (==2.0.0)", "Pygments (==2.4.1)", "attrs (==19.2.0)", "colorama (==0.4.1)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.3.0)", "debugpy (==1.6.0)", "ipykernel (==5.2.0)", "ipykernel (==6.0.0)", "ipykernel (==6.11.0)", "ipython-genutils (==0.2.0)", "jedi (==0.16)", "jinja2 (==3.0.0)", "jupyter-client (==6.1.5)", "jupyter-client (==7.0.0)", "jupyter-core (==4.7.0)", "nbconvert (==6.0.0)", "nbconvert (==6.1.0)", "pyflakes (==2.2.0)", "tomli (==0.2.0)"]
+tests = ["pytest (>=4.6.0)", "pytest (>=4.6.0)", "pytest (>=6.2.5)", "pytest-cov (>=3.0.0)", "typing (>=3.7.4)"]
 tests-binary = ["cmake", "cmake", "ninja", "ninja", "pybind11", "pybind11", "scikit-build", "scikit-build"]
 tests-binary-strict = ["cmake (==3.21.2)", "cmake (==3.25.0)", "ninja (==1.10.2)", "ninja (==1.11.1)", "pybind11 (==2.10.3)", "pybind11 (==2.7.1)", "scikit-build (==0.11.1)", "scikit-build (==0.16.1)"]
-tests-strict = ["codecov (==2.0.15)", "pytest (==4.6.0)", "pytest (==4.6.0)", "pytest (==6.2.5)", "pytest-cov (==3.0.0)", "typing (==3.7.4)"]
+tests-strict = ["pytest (==4.6.0)", "pytest (==4.6.0)", "pytest (==6.2.5)", "pytest-cov (==3.0.0)", "typing (==3.7.4)"]
 
 [[package]]
 name = "zipp"
-version = "3.16.2"
+version = "3.17.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"},
-    {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"},
+    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [extras]
 caching = ["pyarrow"]
 docs = ["Sphinx", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinx-toolbox", "sphinxcontrib-mermaid"]
-duckdb = ["duckdb", "pyarrow"]
 pandas = ["pandas"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.8"
-content-hash = "65cee0051a8479939b886004fcc59ec87c7a7a6d396a4c55890fe63457557eeb"
+python-versions = "^3.9"
+content-hash = "3553242b8a9b425f05e6acf78c52c03c4cb8dbe273c515b9502abc165299d128"
diff --git a/pyproject.toml b/pyproject.toml
index 4eef66b..6d90ba5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "patito"
-version = "0.5.1"
+version = "0.6.0"
 description = "A dataframe modelling library built on top of polars and pydantic."
 authors = ["Jakob Gerhard Martinussen <jakobgm@gmail.com>"]
 license = "MIT"
@@ -11,14 +11,13 @@ documentation = "https://patito.readthedocs.io"
 keywords = ["validation", "dataframe"]
 
 [tool.poetry.dependencies]
-python = "^3.8"
-pydantic = "^1.7.0"
-polars = ">=0.18.7"
+python = "^3.9"
+pydantic = ">=2.0.0"
+polars = ">=0.20.1"
 # Required for typing.Literal in python3.7
 typing-extensions = "*"
-pandas = {version = "*", optional = true, python = "^3.8"}
-duckdb = {version = ">=0.6.0", optional = true, python = "^3.8"}
-pyarrow = {version = ">=5.0.0", optional = true, python = "^3.8"}
+pandas = {version = "*", optional = true, python = "^3.9"}
+pyarrow = {version = ">=5.0.0", optional = true, python = "^3.9"}
 # Optional docs dependencies
 Sphinx = {version = "<7", optional = true}
 sphinx-rtd-theme = {version = "*", optional = true}
@@ -30,7 +29,6 @@ sphinxcontrib-mermaid = {version = "*", optional = true}
 [tool.poetry.extras]
 # The pyarrow.parquet module is required for writing parquet caches to disk
 caching = ["pyarrow"]
-duckdb = ["duckdb", "pyarrow"]
 pandas = ["pandas"]
 docs = [
   "Sphinx",
@@ -42,7 +40,7 @@ docs = [
 ]
 
 [tool.poetry.group.dev.dependencies]
-black = ">=22.3.0"
+ruff = ">=0.2.1"
 coverage = {version = "*", extras = ["toml"]}
 flake8 = "3.9.2"
 flake8-annotations = "*"
@@ -50,7 +48,6 @@ flake8-bandit = "*"
 flake8-black = "*"
 flake8-bugbear = "*"
 flake8-isort = "*"
-isort = "*"
 pyright = ">=1.1.239"
 pytest = ">=7.1.2"
 pytest-cov = ">=3.0.0"
@@ -63,6 +60,10 @@ pandas-stubs = ">=1.2.0"
 codecov = "^2.1.12"
 blackdoc = "*"
 
+
+[tool.poetry.group.docs.dependencies]
+nox = "^2023.4.22"
+
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
@@ -134,3 +135,11 @@ disable_error_code = [
 [[tool.mypy.overrides]]
 module = ["tests.test_validators"]
 warn_unused_ignores = false
+
+[tool.ruff.lint]
+select = ["E4", "E7", "E9", "F", "I", "B", "D"]
+ignore = []
+
+# Allow fix for all enabled rules (when `--fix`) is provided.
+fixable = ["ALL"]
+unfixable = []
diff --git a/src/patito/__init__.py b/src/patito/__init__.py
index e9b4f7f..b0cd0df 100644
--- a/src/patito/__init__.py
+++ b/src/patito/__init__.py
@@ -1,47 +1,27 @@
 """Patito, a data-modelling library built on top of polars and pydantic."""
 from polars import Expr, Series, col
 
-from patito import exceptions, sql
-from patito.exceptions import ValidationError
+from patito import exceptions
+from patito.exceptions import DataFrameValidationError
 from patito.polars import DataFrame, LazyFrame
 from patito.pydantic import Field, Model
 
 _CACHING_AVAILABLE = False
-_DUCKDB_AVAILABLE = False
 field = col("_")
 __all__ = [
     "DataFrame",
+    "DataFrameValidationError",
     "Expr",
     "Field",
     "LazyFrame",
     "Model",
     "Series",
-    "ValidationError",
     "_CACHING_AVAILABLE",
-    "_DUCKDB_AVAILABLE",
     "col",
     "exceptions",
     "field",
-    "sql",
 ]
 
-try:
-    from patito import duckdb
-
-    _DUCKDB_AVAILABLE = True
-    __all__ += ["duckdb"]
-except ImportError:  # pragma: no cover
-    pass
-
-try:
-    from patito.database import Database
-
-    _CACHING_AVAILABLE = True
-    __all__ += ["Database"]
-except ImportError:
-    pass
-
-
 try:
     from importlib.metadata import PackageNotFoundError, version
 except ImportError:  # pragma: no cover
diff --git a/src/patito/_pydantic/__init__.py b/src/patito/_pydantic/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/src/patito/_pydantic/__init__.py
@@ -0,0 +1 @@
+
diff --git a/src/patito/_pydantic/column_info.py b/src/patito/_pydantic/column_info.py
new file mode 100644
index 0000000..c31126f
--- /dev/null
+++ b/src/patito/_pydantic/column_info.py
@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+import json
+from typing import (
+    Any,
+    Dict,
+    Optional,
+    Sequence,
+    Type,
+    TypeVar,
+    Union,
+)
+
+import polars as pl
+from polars.datatypes import DataType, DataTypeClass
+from pydantic import BaseModel, field_serializer
+
+from patito._pydantic.dtypes import parse_composite_dtype
+
+
+class ColumnInfo(BaseModel, arbitrary_types_allowed=True):
+    """patito-side model for storing column metadata
+
+    Args:
+    ----
+        constraints (Union[polars.Expression, List[polars.Expression]): A single
+            constraint or list of constraints, expressed as a polars expression objects.
+            All rows must satisfy the given constraint. You can refer to the given column
+            with ``pt.field``, which will automatically be replaced with
+            ``polars.col(<field_name>)`` before evaluation.
+        derived_from (Union[str, polars.Expr]): used to mark fields that are meant to be derived from other fields. Users can specify a polars expression that will be called to derive the column value when `pt.DataFrame.derive` is called.
+        dtype (polars.datatype.DataType): The given dataframe column must have the given
+            polars dtype, for instance ``polars.UInt64`` or ``pl.Float32``.
+        unique (bool): All row values must be unique.
+
+    """
+
+    dtype: Optional[Union[DataTypeClass, DataType]] = None
+    constraints: Optional[Union[pl.Expr, Sequence[pl.Expr]]] = None
+    derived_from: Optional[Union[str, pl.Expr]] = None
+    unique: Optional[bool] = None
+
+    @field_serializer("constraints", "derived_from")
+    def serialize_exprs(self, exprs: str | pl.Expr | Sequence[pl.Expr] | None) -> Any:
+        if exprs is None:
+            return None
+        elif isinstance(exprs, str):
+            return exprs
+        elif isinstance(exprs, pl.Expr):
+            return self._serialize_expr(exprs)
+        elif isinstance(exprs, Sequence):
+            return [self._serialize_expr(c) for c in exprs]
+        else:
+            raise ValueError(f"Invalid type for exprs: {type(exprs)}")
+
+    def _serialize_expr(self, expr: pl.Expr) -> Dict:
+        if isinstance(expr, pl.Expr):
+            return json.loads(
+                expr.meta.write_json(None)
+            )  # can we access the dictionary directly?
+        else:
+            raise ValueError(f"Invalid type for expr: {type(expr)}")
+
+    @field_serializer("dtype")
+    def serialize_dtype(self, dtype: DataTypeClass | DataType | None) -> Any:
+        """References
+        ----------
+            [1] https://stackoverflow.com/questions/76572310/how-to-serialize-deserialize-polars-datatypes
+
+        """
+        if dtype is None:
+            return None
+        elif isinstance(dtype, DataTypeClass) or isinstance(dtype, DataType):
+            return parse_composite_dtype(dtype)
+        else:
+            raise ValueError(f"Invalid type for dtype: {type(dtype)}")
+
+
+CI = TypeVar("CI", bound=Type[ColumnInfo])
diff --git a/src/patito/_pydantic/dtypes/__init__.py b/src/patito/_pydantic/dtypes/__init__.py
new file mode 100644
index 0000000..f4fd865
--- /dev/null
+++ b/src/patito/_pydantic/dtypes/__init__.py
@@ -0,0 +1,25 @@
+from patito._pydantic.dtypes.dtypes import (
+    DtypeResolver,
+    default_dtypes_for_model,
+    valid_dtypes_for_model,
+    validate_annotation,
+    validate_polars_dtype,
+)
+from patito._pydantic.dtypes.utils import (
+    PYTHON_TO_PYDANTIC_TYPES,
+    dtype_from_string,
+    is_optional,
+    parse_composite_dtype,
+)
+
+__all__ = [
+    "DtypeResolver",
+    "validate_annotation",
+    "validate_polars_dtype",
+    "parse_composite_dtype",
+    "dtype_from_string",
+    "valid_dtypes_for_model",
+    "default_dtypes_for_model",
+    "PYTHON_TO_PYDANTIC_TYPES",
+    "is_optional",
+]
diff --git a/src/patito/_pydantic/dtypes/dtypes.py b/src/patito/_pydantic/dtypes/dtypes.py
new file mode 100644
index 0000000..1348fef
--- /dev/null
+++ b/src/patito/_pydantic/dtypes/dtypes.py
@@ -0,0 +1,256 @@
+from __future__ import annotations
+
+from functools import cache, reduce
+from operator import and_
+from typing import TYPE_CHECKING, Any, Dict, FrozenSet, Mapping, Optional, Type
+
+import polars as pl
+from polars.datatypes import DataType, DataTypeClass, DataTypeGroup
+from pydantic import TypeAdapter
+
+from patito._pydantic.dtypes.utils import (
+    PT_BASE_SUPPORTED_DTYPES,
+    PydanticBaseType,
+    _pyd_type_to_default_dtype,
+    _pyd_type_to_valid_dtypes,
+    _without_optional,
+    dtype_from_string,
+)
+from patito._pydantic.repr import display_as_type
+
+if TYPE_CHECKING:
+    from patito.pydantic import ModelType
+
+
+@cache
+def valid_dtypes_for_model(
+    cls: Type[ModelType],
+) -> Mapping[str, FrozenSet[DataTypeClass]]:
+    return {
+        column: DtypeResolver(cls.model_fields[column].annotation).valid_polars_dtypes()
+        if cls.column_infos[column].dtype is None
+        else DataTypeGroup([cls.dtypes[column]], match_base_type=False)
+        for column in cls.columns
+    }
+
+
+@cache
+def default_dtypes_for_model(
+    cls: Type[ModelType],
+) -> dict[str, DataTypeClass | DataType]:
+    default_dtypes = {}
+    for column in cls.columns:
+        dtype = cls.column_infos[column].dtype
+        if dtype is None:
+            default_dtype = DtypeResolver(
+                cls.model_fields[column].annotation
+            ).default_polars_dtype()
+            if default_dtype is None:
+                raise ValueError(
+                    f"Unable to find a default dtype for column `{column}`"
+                )
+            else:
+                default_dtypes[column] = default_dtype
+        else:
+            default_dtypes[column] = (
+                dtype if isinstance(dtype, DataType) else dtype()
+            )  # if dtype is not instantiated, instantiate it
+    return default_dtypes
+
+
+def validate_polars_dtype(
+    annotation: type[Any] | None,
+    dtype: DataType | DataTypeClass | None,
+    column: Optional[str] = None,
+) -> None:
+    """Check that the polars dtype is valid for the given annotation. Raises ValueError if not.
+
+    Args:
+    ----
+        annotation (type[Any] | None): python type annotation
+        dtype (DataType | DataTypeClass | None): polars dtype
+        column (Optional[str], optional): column name. Defaults to None.
+
+    """
+    if (
+        dtype is None or annotation is None
+    ):  # no potential conflict between type annotation and chosen polars type
+        return
+    valid_dtypes = DtypeResolver(annotation).valid_polars_dtypes()
+    if dtype not in valid_dtypes:
+        if column:
+            column_msg = f" for column `{column}`"
+        else:
+            column_msg = ""
+        raise ValueError(
+            f"Invalid dtype {dtype}{column_msg}. Allowable polars dtypes for {display_as_type(annotation)} are: {', '.join([str(x) for x in valid_dtypes])}."
+        )
+    return
+
+
+def validate_annotation(
+    annotation: type[Any] | Any | None, column: Optional[str] = None
+) -> None:
+    """Check that the provided annotation has polars/patito support (we can resolve it to a default dtype). Raises ValueError if not.
+
+    Args:
+    ----
+        annotation (type[Any] | None): python type annotation
+        column (Optional[str], optional): column name. Defaults to None.
+
+    """
+    default_dtype = DtypeResolver(annotation).default_polars_dtype()
+    if default_dtype is None:
+        valid_polars_dtypes = DtypeResolver(annotation).valid_polars_dtypes()
+        if column:
+            column_msg = f" for column `{column}`"
+        else:
+            column_msg = ""
+        if len(valid_polars_dtypes) == 0:
+            raise ValueError(
+                f"Annotation {display_as_type(annotation)}{column_msg} is not compatible with any polars dtypes."
+            )
+        else:
+            raise ValueError(
+                f"Unable to determine default dtype for annotation {display_as_type(annotation)}{column_msg}. Please provide a valid default polars dtype via the `dtype` argument to `Field`. Valid dtypes are: {', '.join([str(x) for x in valid_polars_dtypes])}."
+            )
+    return
+
+
+class DtypeResolver:
+    def __init__(self, annotation: Any | None):
+        self.annotation = annotation
+        self.schema = TypeAdapter(annotation).json_schema()
+        self.defs = self.schema.get("$defs", {})
+
+    def valid_polars_dtypes(self) -> DataTypeGroup:
+        if self.annotation == Any:
+            return PT_BASE_SUPPORTED_DTYPES
+        return self._valid_polars_dtypes_for_schema(self.schema)
+
+    def default_polars_dtype(self) -> DataTypeClass | DataType | None:
+        if self.annotation == Any:
+            return pl.String
+        return self._default_polars_dtype_for_schema(self.schema)
+
+    def _valid_polars_dtypes_for_schema(
+        self,
+        schema: Dict,
+    ) -> DataTypeGroup:
+        valid_type_sets = []
+        if "anyOf" in schema:
+            schema = _without_optional(schema)
+            for sub_props in schema["anyOf"]:
+                valid_type_sets.append(
+                    self._pydantic_subschema_to_valid_polars_types(sub_props)
+                )
+        else:
+            valid_type_sets.append(
+                self._pydantic_subschema_to_valid_polars_types(schema)
+            )
+        return reduce(and_, valid_type_sets) if valid_type_sets else DataTypeGroup([])
+
+    def _pydantic_subschema_to_valid_polars_types(
+        self,
+        props: Dict,
+    ) -> DataTypeGroup:
+        if "type" not in props:
+            if "enum" in props:
+                raise TypeError("Mixed type enums not supported by patito.")
+            elif "const" in props:
+                return DtypeResolver(type(props["const"])).valid_polars_dtypes()
+            elif "$ref" in props:
+                return self._pydantic_subschema_to_valid_polars_types(
+                    self.defs[props["$ref"].split("/")[-1]]
+                )
+            return DataTypeGroup([])
+        pyd_type = props.get("type")
+        if pyd_type == "array":
+            if "items" not in props:
+                return DataTypeGroup([])
+            array_props = props["items"]
+            item_dtypes = self._valid_polars_dtypes_for_schema(array_props)
+            # TODO support pl.Array?
+            return DataTypeGroup(
+                [pl.List(dtype) for dtype in item_dtypes], match_base_type=False
+            )
+        elif pyd_type == "object":
+            if "properties" not in props:
+                return DataTypeGroup([])
+            object_props = props["properties"]
+            return DataTypeGroup(
+                [
+                    pl.Struct(
+                        [
+                            pl.Field(
+                                name, self._default_polars_dtype_for_schema(sub_props)
+                            )
+                            for name, sub_props in object_props.items()
+                        ]
+                    )
+                ],
+                match_base_type=False,
+            )  # for structs, return only the default dtype set to avoid combinatoric issues
+        return _pyd_type_to_valid_dtypes(
+            PydanticBaseType(pyd_type), props.get("format"), props.get("enum")
+        )
+
+    def _default_polars_dtype_for_schema(
+        self, schema: Dict
+    ) -> DataTypeClass | DataType | None:
+        if "anyOf" in schema:
+            if len(schema["anyOf"]) == 2:  # look for optionals first
+                schema = _without_optional(schema)
+            if len(schema["anyOf"]) == 1:
+                if "column_info" in schema:
+                    schema["anyOf"][0]["column_info"] = schema[
+                        "column_info"
+                    ]  # push column info through optional
+                schema = schema["anyOf"][0]
+            else:
+                return None
+        return self._pydantic_subschema_to_default_dtype(schema)
+
+    def _pydantic_subschema_to_default_dtype(
+        self,
+        props: Dict,
+    ) -> DataTypeClass | DataType | None:
+        if "column_info" in props:  # user has specified in patito model
+            if props["column_info"]["dtype"] is not None:
+                return dtype_from_string(props["column_info"]["dtype"])
+        if "type" not in props:
+            if "enum" in props:
+                raise TypeError("Mixed type enums not supported by patito.")
+            elif "const" in props:
+                return DtypeResolver(type(props["const"])).default_polars_dtype()
+            elif "$ref" in props:
+                return self._pydantic_subschema_to_default_dtype(
+                    self.defs[props["$ref"].split("/")[-1]]
+                )
+            return None
+        pyd_type = props.get("type")
+        if pyd_type == "array":
+            if "items" not in props:
+                raise NotImplementedError(
+                    "Unexpected error processing pydantic schema. Please file an issue."
+                )
+            array_props = props["items"]
+            inner_default_type = self._default_polars_dtype_for_schema(array_props)
+            if inner_default_type is None:
+                return None
+            return pl.List(inner_default_type)
+        elif pyd_type == "object":
+            if "properties" not in props:
+                raise NotImplementedError(
+                    "dictionaries not currently supported by patito"
+                )
+            object_props = props["properties"]
+            return pl.Struct(
+                [
+                    pl.Field(name, self._default_polars_dtype_for_schema(sub_props))
+                    for name, sub_props in object_props.items()
+                ]
+            )
+        return _pyd_type_to_default_dtype(
+            PydanticBaseType(pyd_type), props.get("format"), props.get("enum")
+        )
diff --git a/src/patito/_pydantic/dtypes/utils.py b/src/patito/_pydantic/dtypes/utils.py
new file mode 100644
index 0000000..af3fc25
--- /dev/null
+++ b/src/patito/_pydantic/dtypes/utils.py
@@ -0,0 +1,229 @@
+from __future__ import annotations
+
+import sys
+from enum import Enum
+from typing import (
+    Any,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Union,
+    cast,
+    get_args,
+    get_origin,
+)
+
+import polars as pl
+from polars.datatypes import DataType, DataTypeClass, DataTypeGroup, convert
+from polars.datatypes.constants import (
+    DATETIME_DTYPES,
+    DURATION_DTYPES,
+    FLOAT_DTYPES,
+    INTEGER_DTYPES,
+)
+from polars.polars import (
+    dtype_str_repr,  # TODO: this is a rust function, can we implement our own string parser for Time/Duration/Datetime?
+)
+
+PYTHON_TO_PYDANTIC_TYPES = {
+    str: "string",
+    int: "integer",
+    float: "number",
+    bool: "boolean",
+    type(None): "null",
+}
+
+BOOLEAN_DTYPES = DataTypeGroup([pl.Boolean])
+STRING_DTYPES = DataTypeGroup([pl.String])
+DATE_DTYPES = DataTypeGroup([pl.Date])
+TIME_DTYPES = DataTypeGroup([pl.Time])
+
+PT_BASE_SUPPORTED_DTYPES = DataTypeGroup(
+    INTEGER_DTYPES
+    | FLOAT_DTYPES
+    | BOOLEAN_DTYPES
+    | STRING_DTYPES
+    | DATE_DTYPES
+    | DATETIME_DTYPES
+    | DURATION_DTYPES
+    | TIME_DTYPES
+)
+
+if sys.version_info >= (3, 10):  # pragma: no cover
+    from types import UnionType  # pyright: ignore
+
+    UNION_TYPES = (Union, UnionType)
+else:
+    UNION_TYPES = (Union,)  # pragma: no cover
+
+
+class PydanticBaseType(Enum):
+    STRING = "string"
+    INTEGER = "integer"
+    NUMBER = "number"
+    BOOLEAN = "boolean"
+    NULL = "null"
+    OBJECT = "object"
+
+
+class PydanticStringFormat(Enum):
+    DATE = "date"
+    DATE_TIME = "date-time"
+    DURATION = "duration"
+    TIME = "time"
+
+
+def is_optional(type_annotation: type[Any] | Any | None) -> bool:
+    """Return True if the given type annotation is an Optional annotation.
+
+    Args:
+    ----
+        type_annotation: The type annotation to be checked.
+
+    Returns:
+    -------
+        True if the outermost type is Optional.
+
+    """
+    return (get_origin(type_annotation) in UNION_TYPES) and (
+        type(None) in get_args(type_annotation)
+    )
+
+
+def parse_composite_dtype(dtype: DataTypeClass | DataType) -> str:
+    """For serialization, converts polars dtype to string representation"""
+    if dtype in pl.NESTED_DTYPES:
+        if dtype == pl.Struct or isinstance(dtype, pl.Struct):
+            raise NotImplementedError("Structs not yet supported by patito")
+        if not isinstance(dtype, pl.List) or isinstance(dtype, pl.Array):
+            raise NotImplementedError(
+                f"Unsupported nested dtype: {dtype} of type {type(dtype)}"
+            )
+        if dtype.inner is None:
+            return convert.DataTypeMappings.DTYPE_TO_FFINAME[dtype.base_type()]
+        return f"{convert.DataTypeMappings.DTYPE_TO_FFINAME[dtype.base_type()]}[{parse_composite_dtype(dtype.inner)}]"
+    elif dtype in pl.TEMPORAL_DTYPES:
+        return cast(str, dtype_str_repr(dtype))
+    else:
+        return convert.DataTypeMappings.DTYPE_TO_FFINAME[dtype]
+
+
+def dtype_from_string(v: str) -> Optional[Union[DataTypeClass, DataType]]:
+    """For deserialization"""
+    # TODO test all dtypes
+    return convert.dtype_short_repr_to_dtype(v)
+
+
+def _pyd_type_to_valid_dtypes(
+    pyd_type: PydanticBaseType, string_format: Optional[str], enum: List[str] | None
+) -> DataTypeGroup:
+    if enum is not None:
+        _validate_enum_values(pyd_type, enum)
+        return DataTypeGroup([pl.Enum(enum), pl.String], match_base_type=False)
+    if pyd_type.value == "integer":
+        return DataTypeGroup(INTEGER_DTYPES | FLOAT_DTYPES)
+    elif pyd_type.value == "number":
+        return (
+            FLOAT_DTYPES
+            if isinstance(FLOAT_DTYPES, DataTypeGroup)
+            else DataTypeGroup(FLOAT_DTYPES)
+        )
+    elif pyd_type.value == "boolean":
+        return BOOLEAN_DTYPES
+    elif pyd_type.value == "string":
+        _string_format = (
+            PydanticStringFormat(string_format) if string_format is not None else None
+        )
+        return _pyd_string_format_to_valid_dtypes(_string_format)
+    elif pyd_type.value == "null":
+        return DataTypeGroup([pl.Null])
+    else:
+        return DataTypeGroup([])
+
+
+def _pyd_type_to_default_dtype(
+    pyd_type: PydanticBaseType, string_format: Optional[str], enum: List[str] | None
+) -> DataTypeClass | DataType:
+    if enum is not None:
+        _validate_enum_values(pyd_type, enum)
+        return pl.Enum(enum)
+    elif pyd_type.value == "integer":
+        return pl.Int64()
+    elif pyd_type.value == "number":
+        return pl.Float64()
+    elif pyd_type.value == "boolean":
+        return pl.Boolean()
+    elif pyd_type.value == "string":
+        _string_format = (
+            PydanticStringFormat(string_format) if string_format is not None else None
+        )
+        return _pyd_string_format_to_default_dtype(_string_format)
+    elif pyd_type.value == "null":
+        return pl.Null()
+    elif pyd_type.value == "object":
+        raise ValueError("pydantic object types not currently supported by patito")
+    else:
+        raise NotImplementedError
+
+
+def _pyd_string_format_to_valid_dtypes(
+    string_format: PydanticStringFormat | None,
+) -> DataTypeGroup:
+    if string_format is None:
+        return STRING_DTYPES
+    elif string_format.value == "date":
+        return DATE_DTYPES
+    elif string_format.value == "date-time":
+        return (
+            DATETIME_DTYPES
+            if isinstance(DATE_DTYPES, DataTypeGroup)
+            else DataTypeGroup(DATE_DTYPES)
+        )
+    elif string_format.value == "duration":
+        return (
+            DURATION_DTYPES
+            if isinstance(DURATION_DTYPES, DataTypeGroup)
+            else DataTypeGroup(DURATION_DTYPES)
+        )
+    elif string_format.value == "time":
+        return TIME_DTYPES
+    else:
+        raise NotImplementedError
+
+
+def _pyd_string_format_to_default_dtype(
+    string_format: PydanticStringFormat | None,
+) -> DataTypeClass | DataType:
+    if string_format is None:
+        return pl.String()
+    elif string_format.value == "date":
+        return pl.Date()
+    elif string_format.value == "date-time":
+        return pl.Datetime()
+    elif string_format.value == "duration":
+        return pl.Duration()
+    elif string_format.value == "time":
+        return pl.Time()
+    else:
+        raise NotImplementedError
+
+
+def _without_optional(schema: Dict) -> Dict:
+    if "anyOf" in schema:
+        for sub_props in schema["anyOf"]:
+            if "type" in sub_props and sub_props["type"] == "null":
+                schema["anyOf"].remove(sub_props)
+    return schema
+
+
+def _validate_enum_values(pyd_type: PydanticBaseType, enum: Sequence[Any]) -> None:
+    enum_types = set(type(value) for value in enum)
+    if len(enum_types) > 1:
+        raise TypeError(
+            f"All enumerated values of enums used to annotate Patito model fields must have the same type. Encountered types: {sorted(map(lambda t: t.__name__, enum_types))}."
+        )
+    if pyd_type.value != "string":
+        raise TypeError(
+            f"Enums used to annotate Patito model fields must be strings. Encountered type: {enum_types.pop().__name__}."
+        )
diff --git a/src/patito/_pydantic/repr.py b/src/patito/_pydantic/repr.py
new file mode 100644
index 0000000..98c8892
--- /dev/null
+++ b/src/patito/_pydantic/repr.py
@@ -0,0 +1,139 @@
+import sys
+import types
+import typing
+from typing import (
+    Any,
+    Callable,
+    Generator,
+    Iterable,
+    Literal,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+    get_args,
+    get_origin,
+)
+
+if typing.TYPE_CHECKING:
+    Loc = Tuple[Union[int, str], ...]
+    ReprArgs = Sequence[Tuple[Optional[str], Any]]
+    RichReprResult = Iterable[
+        Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]
+    ]
+
+try:
+    from typing import _TypingBase  # type: ignore[attr-defined]
+except ImportError:
+    from typing import _Final as _TypingBase  # type: ignore[attr-defined]
+
+typing_base = _TypingBase
+
+if sys.version_info < (3, 9):
+    # python < 3.9 does not have GenericAlias (list[int], tuple[str, ...] and so on)
+    TypingGenericAlias = ()
+else:
+    from typing import GenericAlias as TypingGenericAlias  # type: ignore
+
+if sys.version_info < (3, 10):
+
+    def origin_is_union(tp: Optional[Type[Any]]) -> bool:
+        return tp is typing.Union
+
+    WithArgsTypes = (TypingGenericAlias,)
+
+else:
+
+    def origin_is_union(tp: type[Any] | None) -> bool:
+        return tp is typing.Union or tp is types.UnionType
+
+    WithArgsTypes = typing._GenericAlias, types.GenericAlias, types.UnionType  # type: ignore[attr-defined]
+
+
+class Representation:
+    """Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
+
+    __pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations
+    of objects.
+    """
+
+    __slots__: Tuple[str, ...] = tuple()
+
+    def __repr_args__(self) -> "ReprArgs":
+        """Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
+
+        Can either return:
+        * name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
+        * or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
+        """
+        attrs = ((s, getattr(self, s)) for s in self.__slots__)
+        return [(a, v) for a, v in attrs if v is not None]
+
+    def __repr_name__(self) -> str:
+        """Name of the instance's class, used in __repr__."""
+        return self.__class__.__name__
+
+    def __repr_str__(self, join_str: str) -> str:
+        return join_str.join(
+            repr(v) if a is None else f"{a}={v!r}" for a, v in self.__repr_args__()
+        )
+
+    def __pretty__(
+        self, fmt: Callable[[Any], Any], **kwargs: Any
+    ) -> Generator[Any, None, None]:
+        """Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects"""
+        yield self.__repr_name__() + "("
+        yield 1
+        for name, value in self.__repr_args__():
+            if name is not None:
+                yield name + "="
+            yield fmt(value)
+            yield ","
+            yield 0
+        yield -1
+        yield ")"
+
+    def __str__(self) -> str:
+        return self.__repr_str__(" ")
+
+    def __repr__(self) -> str:
+        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
+
+    def __rich_repr__(self) -> "RichReprResult":
+        """Get fields for Rich library"""
+        for name, field_repr in self.__repr_args__():
+            if name is None:
+                yield field_repr
+            else:
+                yield name, field_repr
+
+
+def display_as_type(obj: Any) -> str:
+    """Pretty representation of a type, should be as close as possible to the original type definition string.
+
+    Takes some logic from `typing._type_repr`.
+    """
+    if isinstance(obj, types.FunctionType):
+        return obj.__name__
+    elif obj is ...:
+        return "..."
+    elif isinstance(obj, Representation):
+        return repr(obj)
+
+    if not isinstance(obj, (typing_base, WithArgsTypes, type)):
+        obj = obj.__class__
+
+    if origin_is_union(get_origin(obj)):
+        args = ", ".join(map(display_as_type, get_args(obj)))
+        return f"Union[{args}]"
+    elif isinstance(obj, WithArgsTypes):
+        if get_origin(obj) == Literal:
+            args = ", ".join(map(repr, get_args(obj)))
+        else:
+            args = ", ".join(map(display_as_type, get_args(obj)))
+        return f"{obj.__qualname__}[{args}]"
+    elif isinstance(obj, type):
+        return obj.__qualname__
+    else:
+        return repr(obj).replace("typing.", "").replace("typing_extensions.", "")
diff --git a/src/patito/_pydantic/schema.py b/src/patito/_pydantic/schema.py
new file mode 100644
index 0000000..ca02255
--- /dev/null
+++ b/src/patito/_pydantic/schema.py
@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+from functools import cache
+from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Type, cast, get_args
+
+from pydantic.fields import FieldInfo
+
+from patito._pydantic.column_info import ColumnInfo
+from patito._pydantic.dtypes import PYTHON_TO_PYDANTIC_TYPES
+
+if TYPE_CHECKING:
+    from patito.pydantic import ModelType
+
+
+@cache
+def schema_for_model(cls: Type[ModelType]) -> Dict[str, Dict[str, Any]]:
+    """Return schema properties where definition references have been resolved.
+
+    Returns
+    -------
+        Field information as a dictionary where the keys are field names and the
+            values are dictionaries containing metadata information about the field
+            itself.
+
+    Raises
+    ------
+        TypeError: if a field is annotated with an enum where the values are of
+            different types.
+
+    """
+    schema = cls.model_json_schema(by_alias=False, ref_template="{model}")
+    fields = {}
+    # first resolve definitions for nested models TODO checks for one-way references, if models are self-referencing this falls apart with recursion depth error
+    for f in cls.model_fields.values():
+        annotation = f.annotation
+        cls._update_dfn(annotation, schema)
+        for a in get_args(annotation):
+            cls._update_dfn(a, schema)
+    for field_name, field_info in schema["properties"].items():
+        fields[field_name] = _append_field_info_to_props(
+            field_info=field_info,
+            field_name=field_name,
+            required=field_name in schema.get("required", set()),
+            model_schema=schema,
+        )
+    schema["properties"] = fields
+    return schema
+
+
+@cache
+def column_infos_for_model(cls: Type[ModelType]) -> Mapping[str, ColumnInfo]:
+    fields = cls.model_fields
+
+    def get_column_info(field: FieldInfo) -> ColumnInfo:
+        if field.json_schema_extra is None:
+            return cast(ColumnInfo, cls.column_info_class())
+        elif callable(field.json_schema_extra):
+            raise NotImplementedError(
+                "Callable json_schema_extra not supported by patito."
+            )
+        return cast(ColumnInfo, field.json_schema_extra["column_info"])
+
+    return {k: get_column_info(v) for k, v in fields.items()}
+
+
+def _append_field_info_to_props(
+    field_info: Dict[str, Any],
+    field_name: str,
+    model_schema: Dict[str, Any],
+    required: Optional[bool] = None,
+) -> Dict[str, Any]:
+    if "$ref" in field_info:  # TODO onto runtime append
+        definition = model_schema["$defs"][field_info["$ref"]]
+        if "enum" in definition and "type" not in definition:
+            enum_types = set(type(value) for value in definition["enum"])
+            if len(enum_types) > 1:
+                raise TypeError(
+                    "All enumerated values of enums used to annotate "
+                    "Patito model fields must have the same type. "
+                    "Encountered types: "
+                    f"{sorted(map(lambda t: t.__name__, enum_types))}."
+                )
+            enum_type = enum_types.pop()
+            definition["type"] = PYTHON_TO_PYDANTIC_TYPES[enum_type]
+        field = definition
+    else:
+        field = field_info
+    if "items" in field_info:
+        field["items"] = _append_field_info_to_props(
+            field_info=field_info["items"],
+            field_name=field_name,
+            model_schema=model_schema,
+        )
+    if required is not None:
+        field["required"] = required
+    if "const" in field_info and "type" not in field_info:
+        field["type"] = PYTHON_TO_PYDANTIC_TYPES[type(field_info["const"])]
+    return field
diff --git a/src/patito/database.py b/src/patito/database.py
deleted file mode 100644
index 3477d79..0000000
--- a/src/patito/database.py
+++ /dev/null
@@ -1,658 +0,0 @@
-"""Module containing utilities for retrieving data from external databases."""
-import glob
-import hashlib
-import inspect
-import re
-from datetime import datetime, timedelta
-from functools import wraps
-from pathlib import Path
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Dict,
-    Generic,
-    Optional,
-    Type,
-    TypeVar,
-    Union,
-    cast,
-    overload,
-)
-
-import polars as pl
-import pyarrow as pa  # type: ignore[import]
-import pyarrow.parquet as pq  # type: ignore[import]
-from typing_extensions import Literal, ParamSpec, Protocol
-
-from patito import xdg
-
-if TYPE_CHECKING:
-    from patito import Model
-
-
-P = ParamSpec("P")
-DF = TypeVar("DF", bound=Union[pl.DataFrame, pl.LazyFrame], covariant=True)
-
-# Increment this integer whenever you make backwards-incompatible changes to
-# the parquet caching implemented in WrappedQueryFunc, then such caches
-# are ejected the next time the wrapper tries to read from them.
-CACHE_VERSION = 1
-
-
-class QueryConstructor(Protocol[P]):
-    """A function taking arbitrary arguments and returning an SQL query string."""
-
-    __name__: str
-
-    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> str:
-        """
-        Return SQL query constructed from the given parameters.
-
-        Args:
-            *args: Positional arguments used to build SQL query.
-            **kwargs: Keyword arguments used to build SQL query.
-        """
-        ...  # pragma: no cover
-
-
-class DatabaseQuery(Generic[P, DF]):
-    """A class acting as a function that returns a polars.DataFrame when called."""
-
-    _cache: Union[bool, Path]
-
-    def __init__(  # noqa: C901
-        self,
-        query_constructor: QueryConstructor[P],
-        cache_directory: Path,
-        query_handler: Callable[..., pa.Table],
-        ttl: timedelta,
-        lazy: bool = False,
-        cache: Union[str, Path, bool] = False,
-        model: Union[Type["Model"], None] = None,
-        query_handler_kwargs: Optional[Dict[Any, Any]] = None,
-    ) -> None:
-        """
-        Convert SQL string query function to polars.DataFrame function.
-
-        Args:
-            query_constructor: A function that takes arbitrary arguments and returns
-                an SQL query string.
-            cache_directory: Path to directory to store parquet cache files in.
-            query_handler: Function used to execute SQL queries and return arrow
-                tables.
-            ttl: See Database.query for documentation.
-            lazy: See Database.query for documentation.
-            cache: See Database.query for documentation.
-            model: See Database.query for documentation.
-            query_handler_kwargs: Arbitrary keyword arguments forwarded to the provided
-                query handler.
-
-        Raises:
-            ValueError: If the given path does not have a '.parquet' file extension.
-        """
-        if not isinstance(cache, bool) and Path(cache).suffix != ".parquet":
-            raise ValueError("Cache paths must have the '.parquet' file extension!")
-
-        if isinstance(cache, (Path, str)):
-            self._cache = cache_directory.joinpath(cache)
-        else:
-            self._cache = cache
-        self._query_constructor = query_constructor
-        self.cache_directory = cache_directory
-
-        self._query_handler_kwargs = query_handler_kwargs or {}
-        # Unless explicitly specified otherwise by the end-user, we retrieve query
-        # results as arrow tables with column types directly supported by polars.
-        # Otherwise the resulting parquet files that are written to disk can not be
-        # lazily read with polars.scan_parquet.
-        self._query_handler_kwargs.setdefault("cast_to_polars_equivalent_types", True)
-
-        # We construct the new function with the same parameter signature as
-        # wrapped_function, but with polars.DataFrame as the return type.
-        @wraps(query_constructor)
-        def cached_func(*args: P.args, **kwargs: P.kwargs) -> DF:
-            query = query_constructor(*args, **kwargs)
-            cache_path = self.cache_path(*args, **kwargs)
-            if cache_path and cache_path.exists():
-                metadata: Dict[bytes, bytes] = pq.read_schema(cache_path).metadata or {}
-
-                # Check if the cache file was produced by an identical SQL query
-                is_same_query = metadata.get(b"query") == query.encode("utf-8")
-
-                # Check if the cache is too old to be re-used
-                cache_created_time = datetime.fromisoformat(
-                    metadata.get(
-                        b"query_start_time", b"1900-01-01T00:00:00.000000"
-                    ).decode("utf-8")
-                )
-                is_fresh_cache = (datetime.now() - cache_created_time) < ttl
-
-                # Check if the cache was produced by an incompatible version
-                cache_version = int.from_bytes(
-                    metadata.get(
-                        b"cache_version",
-                        (0).to_bytes(length=16, byteorder="little", signed=False),
-                    ),
-                    byteorder="little",
-                    signed=False,
-                )
-                is_compatible_version = cache_version >= CACHE_VERSION
-
-                if is_same_query and is_fresh_cache and is_compatible_version:
-                    if lazy:
-                        return pl.scan_parquet(cache_path)  # type: ignore
-                    else:
-                        return pl.read_parquet(cache_path)  # type: ignore
-
-            arrow_table = query_handler(query, **self._query_handler_kwargs)
-            if cache_path:
-                cache_path.parent.mkdir(parents=True, exist_ok=True)
-                # We write the cache *before* any potential model validation since
-                # we don't want to lose the result of an expensive query just because
-                # the model specification is wrong.
-                # We also use pyarrow.parquet.write_table instead of
-                # polars.write_parquet since we want to write the arrow table's metadata
-                # to the parquet file, such as the executed query, time, etc..
-                # This metadata is not preserved by polars.
-                metadata = arrow_table.schema.metadata
-                metadata[
-                    b"wrapped_function_name"
-                ] = self._query_constructor.__name__.encode("utf-8")
-                # Store the cache version as an 16-bit unsigned little-endian number
-                metadata[b"cache_version"] = CACHE_VERSION.to_bytes(
-                    length=16,
-                    byteorder="little",
-                    signed=False,
-                )
-                pq.write_table(
-                    table=arrow_table.replace_schema_metadata(metadata),
-                    where=cache_path,
-                    # In order to support nanosecond-resolution timestamps, we must
-                    # use parquet version >= 2.6.
-                    version="2.6",
-                )
-
-            polars_df = cast(pl.DataFrame, pl.from_arrow(arrow_table))
-            if model:
-                model.validate(polars_df)
-
-            if lazy:
-                if cache_path:
-                    # Delete in-memory representation of data and read from the new
-                    # parquet file instead. That way we get consistent memory pressure
-                    # the first and subsequent times this function is invoked.
-                    del polars_df, arrow_table
-                    return pl.scan_parquet(source=cache_path)  # type: ignore
-                else:
-                    return polars_df.lazy()  # type: ignore
-            else:
-                return polars_df  # type: ignore
-
-        self._cached_func = cached_func
-
-    def cache_path(self, *args: P.args, **kwargs: P.kwargs) -> Optional[Path]:
-        """
-        Return the path to the parquet cache that would store the result of the query.
-
-        Args:
-            args: The positional arguments passed to the wrapped function.
-            kwargs: The keyword arguments passed to the wrapped function.
-
-        Returns:
-            A deterministic path to a parquet cache. None if caching is disabled.
-        """
-        # We convert args+kwargs to kwargs-only and use it to format the string
-        function_signature = inspect.signature(self._query_constructor)
-        bound_arguments = function_signature.bind(*args, **kwargs)
-
-        if isinstance(self._cache, Path):
-            # Interpret relative paths relative to the main query cache directory
-            return Path(str(self._cache).format(**bound_arguments.arguments))
-        elif self._cache is True:
-            directory: Path = self.cache_directory / self._query_constructor.__name__
-            directory.mkdir(exist_ok=True, parents=True)
-            sql_query = self.query_string(*args, **kwargs)
-            sql_query_hash = hashlib.sha1(  # noqa: S324,S303
-                sql_query.encode("utf-8")
-            ).hexdigest()
-            return directory / f"{sql_query_hash}.parquet"
-        else:
-            return None
-
-    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> DF:  # noqa: D102
-        return self._cached_func(*args, **kwargs)
-
-    def query_string(self, *args: P.args, **kwargs: P.kwargs) -> str:
-        """
-        Return the query to be executed for the given parameters.
-
-        Args:
-            *args: Positional arguments used to construct the query string.
-            *kwargs: Keyword arguments used to construct the query string.
-
-        Returns:
-            The query string produced for the given input parameters.
-        """
-        return self._query_constructor(*args, **kwargs)
-
-    def refresh_cache(self, *args: P.args, **kwargs: P.kwargs) -> DF:
-        """
-        Force query execution by refreshing the cache.
-
-        Args:
-            *args: Positional arguments used to construct the SQL query string.
-            *kwargs: Keyword arguments used to construct the SQL query string.
-
-        Returns:
-            A DataFrame representing the result of the newly executed query.
-        """
-        cache_path = self.cache_path(*args, **kwargs)
-        if cache_path and cache_path.exists():
-            cache_path.unlink()
-        return self._cached_func(*args, **kwargs)
-
-    def clear_caches(self) -> None:
-        """Delete all parquet cache files produced by this query wrapper."""
-        if self._cache is False:
-            # Caching is not enabled, so this is simply a no-op
-            return
-
-        if self._cache is True:
-            glob_pattern = str(
-                self.cache_directory / self._query_constructor.__name__ / "*.parquet"
-            )
-        else:
-            # We replace all formatting specifiers of the form '{variable}' with
-            # recursive globs '**' (in case strings containing '/' are inserted) and
-            # search for all occurrences of such file paths.
-            # For example if cache="{a}/{b}.parquet" is specified, we search for
-            # all files matching the glob pattern '**/**.parquet'.
-            glob_pattern = re.sub(  # noqa: PD005
-                # We specify the reluctant qualifier (?) in order to get narrow matches
-                pattern=r"\{.+?\}",
-                repl="**",
-                string=str(self._cache),
-            )
-
-        for parquet_path in glob.iglob(glob_pattern):
-            try:
-                metadata: Dict[bytes, bytes] = (
-                    pq.read_schema(where=parquet_path).metadata or {}
-                )
-                if metadata.get(
-                    b"wrapped_function_name"
-                ) == self._query_constructor.__name__.encode("utf-8"):
-                    Path(parquet_path).unlink()
-            except Exception:  # noqa: S112
-                # If we can't read the parquet metadata for some reason,
-                # it is probably not a cache anyway.
-                continue
-
-
-class Database:
-    """
-    Construct manager for executing SQL queries and caching the results.
-
-    Args:
-        query_handler: The function that the Database object should use for executing
-            SQL queries. Its first argument should be the SQL query string to execute,
-            and it should return the query result as an arrow table, for instance
-            pyarrow.Table.
-        cache_directory: Path to the directory where caches should be stored as parquet
-            files. If not provided, the `XDG`_ Base Directory Specification will be
-            used to determine the suitable cache directory, by default
-            ``~/.cache/patito`` or ``${XDG_CACHE_HOME}/patito``.
-        default_ttl: The default Time To Live (TTL), or with other words, how long to
-            wait until caches are refreshed due to old age. The given default TTL can be
-            overwritten by specifying the ``ttl`` parameter in
-            :func:`Database.query`. The default is 52 weeks.
-
-    Examples:
-        We start by importing the necessary modules:
-
-        >>> from pathlib import Path
-        ...
-        >>> import patito as pt
-        >>> import pyarrow as pa
-
-        In order to construct a ``Database``, we need to provide the constructor with
-        a function that can *execute* query strings. How to construct this function will
-        depend on what you actually want to run your queries against, for example a
-        local or remote database. For the purposes of demonstration we will use
-        SQLite since it is built into Python's standard library, but you can use
-        anything; for example Snowflake or PostgresQL.
-
-        We will use Python's standard library
-        `documentation <https://docs.python.org/3/library/sqlite3.html>`_
-        to create an in-memory SQLite database.
-        It will contain a single table named ``movies`` containing some dummy data.
-        The details do not really matter here, the only important part is that we
-        construct a database which we can run SQL queries against.
-
-        >>> import sqlite3
-        ...
-        >>> def dummy_database() -> sqlite3.Cursor:
-        ...     connection = sqlite3.connect(":memory:")
-        ...     cursor = connection.cursor()
-        ...     cursor.execute("CREATE TABLE movies(title, year, score)")
-        ...     data = [
-        ...         ("Monty Python Live at the Hollywood Bowl", 1982, 7.9),
-        ...         ("Monty Python's The Meaning of Life", 1983, 7.5),
-        ...         ("Monty Python's Life of Brian", 1979, 8.0),
-        ...     ]
-        ...     cursor.executemany("INSERT INTO movies VALUES(?, ?, ?)", data)
-        ...     connection.commit()
-        ...     return cursor
-
-        Using this dummy database, we are now able to construct a function which accepts
-        SQL queries as its first parameter, executes the query, and returns the query
-        result in the form of an Arrow table.
-
-        >>> def query_handler(query: str) -> pa.Table:
-        ...     cursor = dummy_database()
-        ...     cursor.execute(query)
-        ...     columns = [description[0] for description in cursor.description]
-        ...     data = [dict(zip(columns, row)) for row in cursor.fetchall()]
-        ...     return pa.Table.from_pylist(data)
-
-        We can now construct a ``Database`` object, providing ``query_handler``
-        as the way to execute SQL queries.
-
-        >>> db = pt.Database(query_handler=query_handler)
-
-        The resulting object can now be used to execute SQL queries against the database
-        and return the result in the form of a polars ``DataFrame`` object.
-
-        >>> db.query("select * from movies order by year limit 1")
-        shape: (1, 3)
-        ┌──────────────────────────────┬──────┬───────┐
-        │ title                        ┆ year ┆ score │
-        │ ---                          ┆ ---  ┆ ---   │
-        │ str                          ┆ i64  ┆ f64   │
-        ╞══════════════════════════════╪══════╪═══════╡
-        │ Monty Python's Life of Brian ┆ 1979 ┆ 8.0   │
-        └──────────────────────────────┴──────┴───────┘
-
-        But the main way to use a ``Database`` object is to use the
-        ``@Database.as_query`` decarator to wrap functions which return SQL
-        query *strings*.
-
-        >>> @db.as_query()
-        >>> def movies(newer_than_year: int):
-        ...     return f"select * from movies where year > {newer_than_year}"
-
-        This decorator will convert the function from producing query strings, to
-        actually executing the given query and return the query result in the form of
-        a polars ``DataFrame`` object.
-
-        >>> movies(newer_than_year=1980)
-        shape: (2, 3)
-        ┌───────────────────────────────────┬──────┬───────┐
-        │ title                             ┆ year ┆ score │
-        │ ---                               ┆ ---  ┆ ---   │
-        │ str                               ┆ i64  ┆ f64   │
-        ╞═══════════════════════════════════╪══════╪═══════╡
-        │ Monty Python Live at the Hollywo… ┆ 1982 ┆ 7.9   │
-        │ Monty Python's The Meaning of Li… ┆ 1983 ┆ 7.5   │
-        └───────────────────────────────────┴──────┴───────┘
-
-        Caching is not enabled by default, but it can be enabled by specifying
-        ``cache=True`` to the ``@db.as_query(...)`` decorator. Other arguments are
-        also accepted, such as ``lazy=True`` if you want to retrieve the results in the
-        form of a ``LazyFrame`` instead of a ``DataFrame``, ``ttl`` if you want to
-        specify another TTL, and any additional keyword arguments are forwarded to
-        ``query_executor`` when the SQL query is executed. You can read more about these
-        parameters in the documentation of :func:`Database.query`.
-
-    .. _XDG: https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html
-    """
-
-    Query = DatabaseQuery
-
-    def __init__(  # noqa: D107
-        self,
-        query_handler: Callable[..., pa.Table],
-        cache_directory: Optional[Path] = None,
-        default_ttl: timedelta = timedelta(weeks=52),  # noqa: B008
-    ) -> None:
-        self.query_handler = query_handler
-        self.cache_directory = cache_directory or xdg.cache_home(application="patito")
-        self.default_ttl = default_ttl
-
-        self.cache_directory.mkdir(exist_ok=True, parents=True)
-
-    # With lazy = False a DataFrame-producing wrapper is returned
-    @overload
-    def as_query(
-        self,
-        *,
-        lazy: Literal[False] = False,
-        cache: Union[str, Path, bool] = False,
-        ttl: Optional[timedelta] = None,
-        model: Union[Type["Model"], None] = None,
-        **kwargs: Any,  # noqa: ANN401
-    ) -> Callable[[QueryConstructor[P]], DatabaseQuery[P, pl.DataFrame]]:
-        ...  # pragma: no cover
-
-    # With lazy = True a LazyFrame-producing wrapper is returned
-    @overload
-    def as_query(
-        self,
-        *,
-        lazy: Literal[True],
-        cache: Union[str, Path, bool] = False,
-        ttl: Optional[timedelta] = None,
-        model: Union[Type["Model"], None] = None,
-        **kwargs: Any,  # noqa: ANN401
-    ) -> Callable[[QueryConstructor[P]], DatabaseQuery[P, pl.LazyFrame]]:
-        ...  # pragma: no cover
-
-    def as_query(
-        self,
-        *,
-        lazy: bool = False,
-        cache: Union[str, Path, bool] = False,
-        ttl: Optional[timedelta] = None,
-        model: Union[Type["Model"], None] = None,
-        **kwargs: Any,  # noqa: ANN401
-    ) -> Callable[
-        [QueryConstructor[P]], DatabaseQuery[P, Union[pl.DataFrame, pl.LazyFrame]]
-    ]:
-        """
-        Execute the returned query string and return a polars dataframe.
-
-        Args:
-            lazy: If the result should be returned as a LazyFrame rather than a
-                DataFrame. Allows more efficient reading from parquet caches if caching
-                is enabled.
-            cache: If queries should be cached in order to save time and costs.
-                The cache will only be used if the exact same SQL string has
-                been executed before.
-                If the parameter is specified as ``True``, a parquet file is
-                created for each unique query string, and is located at:
-                artifacts/query_cache/<function_name>/<query_md5_hash>.parquet
-                If the a string or ``pathlib.Path`` object is provided, the given path
-                will be used, but it must have a '.parquet' file extension.
-                Relative paths are interpreted relative to artifacts/query_cache/
-                in the workspace root. The given parquet path will be overwritten
-                if the query string changes, so only the latest query string value
-                will be cached.
-            ttl: The Time to Live (TTL) of the cache specified as a datetime.timedelta
-                object. When the cache becomes older than the specified TTL, the query
-                will be re-executed on the next invocation of the query function
-                and the cache will refreshed.
-            model: An optional Patito model used to validate the content of the
-                dataframe before return.
-            **kwargs: Connection parameters forwarded to sql_to_polars, for example
-                db_params.
-
-        Returns:
-            A new function which returns a polars DataFrame based on the query
-            specified by the original function's return string.
-        """
-
-        def wrapper(query_constructor: QueryConstructor) -> DatabaseQuery:
-            return self.Query(
-                query_constructor=query_constructor,
-                lazy=lazy,
-                cache=cache,
-                ttl=ttl if ttl is not None else self.default_ttl,
-                cache_directory=self.cache_directory,
-                model=model,
-                query_handler=_with_query_metadata(self.query_handler),
-                query_handler_kwargs=kwargs,
-            )
-
-        return wrapper
-
-    # With lazy=False, a DataFrame is returned
-    @overload
-    def query(
-        self,
-        query: str,
-        *,
-        lazy: Literal[False] = False,
-        cache: Union[str, Path, bool] = False,
-        ttl: Optional[timedelta] = None,
-        model: Union[Type["Model"], None] = None,
-        **kwargs: Any,  # noqa: ANN401
-    ) -> pl.DataFrame:
-        ...  # pragma: no cover
-
-    # With lazy=True, a LazyFrame is returned
-    @overload
-    def query(
-        self,
-        query: str,
-        *,
-        lazy: Literal[True],
-        cache: Union[str, Path, bool] = False,
-        ttl: Optional[timedelta] = None,
-        model: Union[Type["Model"], None] = None,
-        **kwargs: Any,  # noqa: ANN401
-    ) -> pl.LazyFrame:
-        ...  # pragma: no cover
-
-    def query(
-        self,
-        query: str,
-        *,
-        lazy: bool = False,
-        cache: Union[str, Path, bool] = False,
-        ttl: Optional[timedelta] = None,
-        model: Union[Type["Model"], None] = None,
-        **kwargs: Any,  # noqa: ANN401
-    ) -> Union[pl.DataFrame, pl.LazyFrame]:
-        """
-        Execute the given query and return the query result as a DataFrame or LazyFrame.
-
-        See :ref:`Database.as_query` for a more powerful way to build and execute
-        queries.
-
-        Args:
-            query: The query string to be executed, for instance an SQL query.
-            lazy: If the query  result should be returned in the form of a LazyFrame
-                instead of a DataFrame.
-            cache: If the query result should be saved and re-used the next time the
-                same query is executed. Can also be provided as a path. See
-                :func:`Database.as_query` for full documentation.
-            ttl: How long to use cached results until the query is re-executed anyway.
-            model: A :ref:`Model` to optionally validate the query result.
-            **kwargs: All additional keyword arguments are forwarded to the query
-                handler which executes the given query.
-
-        Returns:
-            The result of the query in the form of a ``DataFrame`` if ``lazy=False``, or
-            a ``LazyFrame`` otherwise.
-
-        Examples:
-            We will use DuckDB as our example database.
-
-            >>> import duckdb
-            >>> import patito as pt
-
-            We will construct a really simple query source from an in-memory database.
-
-            >>> db = duckdb.connect(":memory:")
-            >>> query_handler = lambda query: db.cursor().query(query).arrow()
-            >>> query_source = pt.Database(query_handler=query_handler)
-
-            We can now use :func:`Database.query` in order to execute queries against
-            the in-memory database.
-
-            >>> query_source.query("select 1 as a, 2 as b, 3 as c")
-            shape: (1, 3)
-            ┌─────┬─────┬─────┐
-            │ a   ┆ b   ┆ c   │
-            │ --- ┆ --- ┆ --- │
-            │ i32 ┆ i32 ┆ i32 │
-            ╞═════╪═════╪═════╡
-            │ 1   ┆ 2   ┆ 3   │
-            └─────┴─────┴─────┘
-        """
-
-        def __direct_query() -> str:
-            """
-            A regular named function in order to store parquet files correctly.
-
-            Returns:
-                The user-provided query string.
-            """
-            return query
-
-        return self.as_query(
-            lazy=lazy,  # type: ignore
-            cache=cache,
-            ttl=ttl,
-            model=model,
-            **kwargs,
-        )(__direct_query)()
-
-
-def _with_query_metadata(query_handler: Callable[P, pa.Table]) -> Callable[P, pa.Table]:
-    """
-    Wrap SQL-query handler with additional logic.
-
-    Args:
-        query_handler: Function accepting an SQL query as its first argument and
-            returning an Arrow table.
-
-    Returns:
-        New function that returns Arrow table with additional metedata. Arrow types
-        which are not supported by polars directly have also been converted to
-        compatible ones where applicable.
-    """
-
-    @wraps(query_handler)
-    def wrapped_query_handler(
-        *args: P.args,
-        **kwargs: P.kwargs,
-    ) -> pa.Table:
-        cast_to_polars_equivalent_types = kwargs.pop(
-            "cast_to_polars_equivalent_types", True
-        )
-        start_time = datetime.now()
-        arrow_table = query_handler(*args, **kwargs)
-        finish_time = datetime.now()
-        metadata: dict = arrow_table.schema.metadata or {}
-        if cast_to_polars_equivalent_types:
-            # We perform a round-trip to polars and back in order to get an arrow table
-            # with column types that are directly supported by polars.
-            arrow_table = pl.from_arrow(arrow_table).to_arrow()
-
-        # Store additional metadata which is useful when the arrow table is written to a
-        # parquet file as a caching mechanism.
-        metadata.update(
-            {
-                "query": args[0],
-                "query_start_time": start_time.isoformat(),
-                "query_end_time": finish_time.isoformat(),
-            }
-        )
-        return arrow_table.replace_schema_metadata(metadata)
-
-    return wrapped_query_handler
-
-
-__all__ = ["Database"]
diff --git a/src/patito/duckdb.py b/src/patito/duckdb.py
deleted file mode 100644
index 524916e..0000000
--- a/src/patito/duckdb.py
+++ /dev/null
@@ -1,2793 +0,0 @@
-"""
-Module which wraps around the duckdb module in an opiniated manner.
-"""
-from __future__ import annotations
-
-import hashlib
-from collections.abc import Collection, Iterable, Iterator
-from pathlib import Path
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    Generic,
-    List,
-    Optional,
-    Set,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-    cast,
-)
-
-import numpy as np
-import polars as pl
-import pyarrow as pa  # type: ignore[import]
-from pydantic import create_model
-from typing_extensions import Literal
-
-from patito import sql
-from patito.exceptions import MultipleRowsReturned, RowDoesNotExist
-from patito.polars import DataFrame
-from patito.pydantic import Model, ModelType
-
-try:
-    import pandas as pd
-
-    _PANDAS_AVAILABLE = True
-except ImportError:
-    _PANDAS_AVAILABLE = False
-
-if TYPE_CHECKING:
-    import duckdb
-
-
-# Types which can be used to instantiate a DuckDB Relation object
-RelationSource = Union[
-    DataFrame,
-    pl.DataFrame,
-    "pd.DataFrame",
-    Path,
-    str,
-    "duckdb.DuckDBPyRelation",
-    "Relation",
-]
-
-# Used to refer to type(self) in Relation methods which preserve the type.
-# Hard-coding Relation or Relation[ModelType] does not work for subclasses
-# that return type(self) since that will refer to the parent class.
-# See relevant SO answer: https://stackoverflow.com/a/63178532
-RelationType = TypeVar("RelationType", bound="Relation")
-
-# The SQL types supported by DuckDB
-# See: https://duckdb.org/docs/sql/data_types/overview
-# fmt: off
-DuckDBSQLType = Literal[
-    "BIGINT", "INT8", "LONG",
-    "BLOB", "BYTEA", "BINARY", "VARBINARY",
-    "BOOLEAN", "BOOL", "LOGICAL",
-    "DATE",
-    "DOUBLE", "FLOAT8", "NUMERIC", "DECIMAL",
-    "HUGEINT",
-    "INTEGER", "INT4", "INT", "SIGNED",
-    "INTERVAL",
-    "REAL", "FLOAT4", "FLOAT",
-    "SMALLINT", "INT2", "SHORT",
-    "TIME",
-    "TIMESTAMP", "DATETIME",
-    "TIMESTAMP WITH TIMEZONE", "TIMESTAMPTZ",
-    "TINYINT", "INT1",
-    "UBIGINT",
-    "UINTEGER",
-    "USMALLINT",
-    "UTINYINT",
-    "UUID",
-    "VARCHAR", "CHAR", "BPCHAR", "TEXT", "STRING",
-]
-# fmt: on
-
-# Used for backward-compatible patches
-POLARS_VERSION: Optional[Tuple[int, int, int]]
-try:
-    POLARS_VERSION = cast(
-        Tuple[int, int, int],
-        tuple(map(int, pl.__version__.split("."))),
-    )
-except ValueError:  # pragma: no cover
-    POLARS_VERSION = None
-
-
-def create_pydantic_model(relation: "duckdb.DuckDBPyRelation") -> Type[Model]:
-    """Create pydantic model deserialization of the given relation."""
-    pydantic_annotations = {column: (Any, ...) for column in relation.columns}
-    return create_model(  # type: ignore
-        relation.alias,
-        __base__=Model,
-        **pydantic_annotations,  # pyright: ignore
-    )
-
-
-def _enum_type_name(field_properties: dict) -> str:
-    """
-    Return enum DuckDB SQL type name based on enum values.
-
-    The same enum values, regardless of ordering, will always be given the same name.
-    """
-    enum_values = ", ".join(repr(value) for value in sorted(field_properties["enum"]))
-    value_hash = hashlib.md5(enum_values.encode("utf-8")).hexdigest()  # noqa: #S303
-    return f"enum__{value_hash}"
-
-
-def _is_missing_enum_type_exception(exception: BaseException) -> bool:
-    """
-    Return True if the given exception might be caused by missing enum type definitions.
-
-    Args:
-        exception: Exception raised by DuckDB.
-
-    Returns:
-        True if the exception might be caused by a missing SQL enum type definition.
-    """
-    description = str(exception)
-    # DuckDB version <= 0.3.4
-    old_exception = description.startswith("Not implemented Error: DataType")
-    # DuckDB version >= 0.4.0
-    new_exception = description.startswith("Catalog Error: Type with name enum_")
-    return old_exception or new_exception
-
-
-class Relation(Generic[ModelType]):
-    # The database connection which the given relation belongs to
-    database: Database
-
-    # The underlying DuckDB relation object which this class wraps around
-    _relation: duckdb.DuckDBPyRelation
-
-    # Can be set by subclasses in order to specify the serialization class for rows.
-    # Must accept column names as keyword arguments.
-    model: Optional[Type[ModelType]] = None
-
-    # The alias that can be used to refer to the relation in queries
-    alias: str
-
-    def __init__(  # noqa: C901
-        self,
-        derived_from: RelationSource,
-        database: Optional[Database] = None,
-        model: Optional[Type[ModelType]] = None,
-    ) -> None:
-        """
-        Create a new relation object containing data to be queried with DuckDB.
-
-        Args:
-            derived_from: Data to be represented as a DuckDB relation object.
-                Can be one of the following types:
-
-                - A pandas or polars DataFrame.
-                - An SQL query represented as a string.
-                - A ``Path`` object pointing to a CSV or a parquet file.
-                  The path must point to an existing file with either a ``.csv``
-                  or ``.parquet`` file extension.
-                - A native DuckDB relation object (``duckdb.DuckDBPyRelation``).
-                - A ``patito.duckdb.Relation`` object.
-
-            database: Which database to load the relation into. If not provided,
-                the default DuckDB database will be used.
-
-            model: Sub-class of ``patito.Model`` which specifies how to deserialize rows
-                when fetched with methods such as
-                :ref:`Relation.get()<duckdb.Relation.get>` and ``__iter__()``.
-
-                Will also be used to create a strict table schema if
-                :ref:`Relation.create_table()<duckdb.Relation.create_table>`.
-                schema should be constructed.
-
-                If not provided, a dynamic model fitting the relation schema will be
-                created when required.
-
-                Can also be set later dynamically by invoking
-                :ref:`Relation.set_model()<duckdb.Relation.set_model>`.
-
-        Raises:
-            ValueError: If any one of the following cases are encountered:
-
-                - If a provided ``Path`` object does not have a ``.csv`` or
-                  ``.parquet`` file extension.
-                - If a database and relation object is provided, but the relation object
-                  does not belong to the database.
-
-            TypeError: If the type of ``derived_from`` is not supported.
-
-        Examples:
-            Instantiated from a dataframe:
-
-            >>> import patito as pt
-            >>> df = pt.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
-            >>> pt.duckdb.Relation(df).filter("a > 2").to_df()
-            shape: (1, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 3   ┆ 6   │
-            └─────┴─────┘
-
-            Instantiated from an SQL query:
-
-            >>> pt.duckdb.Relation("select 1 as a, 2 as b").to_df()
-            shape: (1, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 2   │
-            └─────┴─────┘
-        """
-        import duckdb
-
-        if isinstance(derived_from, Relation):
-            if (
-                database is not None
-                and derived_from.database.connection is not database.connection
-            ):
-                raise ValueError(
-                    "Relations can't be casted between database connections."
-                )
-            self.database = derived_from.database
-            self._relation = derived_from._relation
-            self.model = derived_from.model
-            return
-
-        if database is None:
-            self.database = Database.default()
-        else:
-            self.database = database
-
-        if isinstance(derived_from, duckdb.DuckDBPyRelation):
-            relation = derived_from
-        elif isinstance(derived_from, str):
-            relation = self.database.connection.from_query(derived_from)
-        elif _PANDAS_AVAILABLE and isinstance(derived_from, pd.DataFrame):
-            # We must replace pd.NA with np.nan in order for it to be considered
-            # as null by DuckDB. Otherwise it will casted to the string <NA>
-            # or even segfault.
-            derived_from = derived_from.fillna(np.nan)
-            relation = self.database.connection.from_df(derived_from)
-        elif isinstance(derived_from, pl.DataFrame):
-            relation = self.database.connection.from_arrow(derived_from.to_arrow())
-        elif isinstance(derived_from, Path):
-            if derived_from.suffix.lower() == ".parquet":
-                relation = self.database.connection.from_parquet(str(derived_from))
-            elif derived_from.suffix.lower() == ".csv":
-                relation = self.database.connection.from_csv_auto(str(derived_from))
-            else:
-                raise ValueError(
-                    f"Unsupported file suffix {derived_from.suffix!r} for data import!"
-                )
-        else:
-            raise TypeError  # pragma: no cover
-
-        self._relation = relation
-        if model is not None:
-            self.model = model  # pyright: ignore
-
-    def aggregate(
-        self,
-        *aggregations: str,
-        group_by: Union[str, Iterable[str]],
-        **named_aggregations: str,
-    ) -> Relation:
-        """
-        Return relation formed by ``GROUP BY`` SQL aggregation(s).
-
-        Args:
-            aggregations: Zero or more aggregation expressions such as
-                "sum(column_name)" and "count(distinct column_name)".
-            named_aggregations: Zero or more aggregated expressions where the keyword is
-                used to name the given aggregation. For example,
-                ``my_column="sum(column_name)"`` is inserted as
-                ``"sum(column_name) as my_column"`` in the executed SQL query.
-            group_by: A single column name or iterable collection of column names to
-                group by.
-
-        Examples:
-            >>> import patito as pt
-            >>> df = pt.DataFrame({"a": [1, 2, 3], "b": ["X", "Y", "X"]})
-            >>> relation = pt.duckdb.Relation(df)
-            >>> relation.aggregate(
-            ...     "b",
-            ...     "sum(a)",
-            ...     "greatest(b)",
-            ...     max_a="max(a)",
-            ...     group_by="b",
-            ... ).to_df()
-            shape: (2, 4)
-            ┌─────┬────────┬─────────────┬───────┐
-            │ b   ┆ sum(a) ┆ greatest(b) ┆ max_a │
-            │ --- ┆ ---    ┆ ---         ┆ ---   │
-            │ str ┆ f64    ┆ str         ┆ i64   │
-            ╞═════╪════════╪═════════════╪═══════╡
-            │ X   ┆ 4.0    ┆ X           ┆ 3     │
-            │ Y   ┆ 2.0    ┆ Y           ┆ 2     │
-            └─────┴────────┴─────────────┴───────┘
-        """
-        expression = ", ".join(
-            aggregations
-            + tuple(
-                f"{expression} as {column_name}"
-                for column_name, expression in named_aggregations.items()
-            )
-        )
-        relation = self._relation.aggregate(
-            aggr_expr=expression,
-            group_expr=group_by if isinstance(group_by, str) else ", ".join(group_by),
-        )
-        return self._wrap(relation=relation, schema_change=True)
-
-    def add_suffix(
-        self,
-        suffix: str,
-        include: Optional[Collection[str]] = None,
-        exclude: Optional[Collection[str]] = None,
-    ) -> Relation:
-        """
-        Add a suffix to all the columns of the relation.
-
-        Args:
-            suffix: A string to append to add to all columns names.
-            include: If provided, only the given columns will be renamed.
-            exclude: If provided, the given columns will `not` be renamed.
-
-        Raises:
-            TypeError: If both include `and` exclude are provided at the same time.
-
-        Examples:
-            >>> import patito as pt
-            >>> relation = pt.duckdb.Relation("select 1 as column_1, 2 as column_2")
-            >>> relation.add_suffix("_renamed").to_df()
-            shape: (1, 2)
-            ┌──────────────────┬──────────────────┐
-            │ column_1_renamed ┆ column_2_renamed │
-            │ ---              ┆ ---              │
-            │ i64              ┆ i64              │
-            ╞══════════════════╪══════════════════╡
-            │ 1                ┆ 2                │
-            └──────────────────┴──────────────────┘
-
-            >>> relation.add_suffix("_renamed", include=["column_1"]).to_df()
-            shape: (1, 2)
-            ┌──────────────────┬──────────┐
-            │ column_1_renamed ┆ column_2 │
-            │ ---              ┆ ---      │
-            │ i64              ┆ i64      │
-            ╞══════════════════╪══════════╡
-            │ 1                ┆ 2        │
-            └──────────────────┴──────────┘
-
-            >>> relation.add_suffix("_renamed", exclude=["column_1"]).to_df()
-            shape: (1, 2)
-            ┌──────────┬──────────────────┐
-            │ column_1 ┆ column_2_renamed │
-            │ ---      ┆ ---              │
-            │ i64      ┆ i64              │
-            ╞══════════╪══════════════════╡
-            │ 1        ┆ 2                │
-            └──────────┴──────────────────┘
-        """
-        if include is not None and exclude is not None:
-            raise TypeError("Both include and exclude provided at the same time!")
-        elif include is not None:
-            included = lambda column: column in include
-        elif exclude is not None:
-            included = lambda column: column not in exclude
-        else:
-            included = lambda _: True  # noqa: E731
-
-        return self.select(
-            ", ".join(
-                f"{column} as {column}{suffix}" if included(column) else column
-                for column in self.columns
-            )
-        )
-
-    def add_prefix(
-        self,
-        prefix: str,
-        include: Optional[Iterable[str]] = None,
-        exclude: Optional[Iterable[str]] = None,
-    ) -> Relation:
-        """
-        Add a prefix to all the columns of the relation.
-
-        Args:
-            prefix: A string to prepend to add to all the columns names.
-            include: If provided, only the given columns will be renamed.
-            exclude: If provided, the given columns will `not` be renamed.
-
-        Raises:
-            TypeError: If both include `and` exclude are provided at the same time.
-
-        Examples:
-            >>> import patito as pt
-            >>> relation = pt.duckdb.Relation("select 1 as column_1, 2 as column_2")
-            >>> relation.add_prefix("renamed_").to_df()
-            shape: (1, 2)
-            ┌──────────────────┬──────────────────┐
-            │ renamed_column_1 ┆ renamed_column_2 │
-            │ ---              ┆ ---              │
-            │ i64              ┆ i64              │
-            ╞══════════════════╪══════════════════╡
-            │ 1                ┆ 2                │
-            └──────────────────┴──────────────────┘
-
-            >>> relation.add_prefix("renamed_", include=["column_1"]).to_df()
-            shape: (1, 2)
-            ┌──────────────────┬──────────┐
-            │ renamed_column_1 ┆ column_2 │
-            │ ---              ┆ ---      │
-            │ i64              ┆ i64      │
-            ╞══════════════════╪══════════╡
-            │ 1                ┆ 2        │
-            └──────────────────┴──────────┘
-
-            >>> relation.add_prefix("renamed_", exclude=["column_1"]).to_df()
-            shape: (1, 2)
-            ┌──────────┬──────────────────┐
-            │ column_1 ┆ renamed_column_2 │
-            │ ---      ┆ ---              │
-            │ i64      ┆ i64              │
-            ╞══════════╪══════════════════╡
-            │ 1        ┆ 2                │
-            └──────────┴──────────────────┘
-        """
-        if include is not None and exclude is not None:
-            raise TypeError("Both include and exclude provided at the same time!")
-        elif include is not None:
-            included = lambda column: column in include
-        elif exclude is not None:
-            included = lambda column: column not in exclude
-        else:
-            included = lambda _: True
-
-        return self.select(
-            ", ".join(
-                f"{column} as {prefix}{column}" if included(column) else column
-                for column in self.columns
-            )
-        )
-
-    def all(self, *filters: str, **equalities: Union[int, float, str]) -> bool:
-        """
-        Return ``True`` if the given predicate(s) are true for all rows in the relation.
-
-        See :func:`Relation.filter()` for additional information regarding the
-        parameters.
-
-        Args:
-            filters: SQL predicates to satisfy.
-            equalities: SQL equality predicates to satisfy.
-
-        Examples:
-            >>> import patito as pt
-            >>> df = pt.DataFrame(
-            ...     {
-            ...         "even_number": [2, 4, 6],
-            ...         "odd_number": [1, 3, 5],
-            ...         "zero": [0, 0, 0],
-            ...     }
-            ... )
-            >>> relation = pt.duckdb.Relation(df)
-            >>> relation.all(zero=0)
-            True
-            >>> relation.all(
-            ...     "even_number % 2 = 0",
-            ...     "odd_number % 2 = 1",
-            ...     zero=0,
-            ... )
-            True
-            >>> relation.all(zero=1)
-            False
-            >>> relation.all("odd_number % 2 = 0")
-            False
-        """
-        return self.filter(*filters, **equalities).count() == self.count()
-
-    def case(
-        self,
-        *,
-        from_column: str,
-        to_column: str,
-        mapping: Dict[sql.SQLLiteral, sql.SQLLiteral],
-        default: sql.SQLLiteral,
-    ) -> Relation:
-        """
-        Map values of one column over to a new column.
-
-        Args:
-            from_column: Name of column defining the domain of the mapping.
-            to_column: Name of column to insert the mapped values into.
-            mapping: Dictionary defining the mapping. The dictionary keys represent the
-                input values, while the dictionary values represent the output values.
-                Items are inserted into the SQL case statement by their repr() string
-                value.
-            default: Default output value for inputs which have no provided mapping.
-
-        Examples:
-            The following case statement...
-
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> relation = db.to_relation("select 1 as a union select 2 as a")
-            >>> relation.case(
-            ...     from_column="a",
-            ...     to_column="b",
-            ...     mapping={1: "one", 2: "two"},
-            ...     default="three",
-            ... ).order(by="a").to_df()
-            shape: (2, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ str │
-            ╞═════╪═════╡
-            │ 1   ┆ one │
-            │ 2   ┆ two │
-            └─────┴─────┘
-
-            ... is equivalent with:
-
-            >>> case_statement = pt.sql.Case(
-            ...     on_column="a",
-            ...     mapping={1: "one", 2: "two"},
-            ...     default="three",
-            ...     as_column="b",
-            ... )
-            >>> relation.select(f"*, {case_statement}").order(by="a").to_df()
-            shape: (2, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ str │
-            ╞═════╪═════╡
-            │ 1   ┆ one │
-            │ 2   ┆ two │
-            └─────┴─────┘
-        """
-
-        case_statement = sql.Case(
-            on_column=from_column,
-            mapping=mapping,
-            default=default,
-            as_column=to_column,
-        )
-        new_relation = self._relation.project(f"*, {case_statement}")
-        return self._wrap(relation=new_relation, schema_change=True)
-
-    def cast(
-        self: RelationType,
-        model: Optional[ModelType] = None,
-        strict: bool = False,
-        include: Optional[Collection[str]] = None,
-        exclude: Optional[Collection[str]] = None,
-    ) -> RelationType:
-        """
-        Cast the columns of the relation to types compatible with the associated model.
-
-        The associated model must either be set by invoking
-        :ref:`Relation.set_model() <duckdb.Relation.set_model>` or provided with the
-        ``model`` parameter.
-
-        Any columns of the relation that are not part of the given model schema will be
-        left as-is.
-
-        Args:
-            model: If :ref:`Relation.set_model() <duckdb.Relation.set_model>` has not
-                been invoked or is intended to be overwritten.
-            strict: If set to ``False``, columns which are technically compliant with
-                the specified field type, will not be casted. For example, a column
-                annotated with ``int`` is technically compliant with ``SMALLINT``, even
-                if ``INTEGER`` is the default SQL type associated with ``int``-annotated
-                fields. If ``strict`` is set to ``True``, the resulting dtypes will
-                be forced to the default dtype associated with each python type.
-            include: If provided, only the given columns will be casted.
-            exclude: If provided, the given columns will `not` be casted.
-
-        Returns:
-            New relation where the columns have been casted according to the model
-            schema.
-
-        Examples:
-            >>> import patito as pt
-            >>> class Schema(pt.Model):
-            ...     float_column: float
-            ...
-            >>> relation = pt.duckdb.Relation("select 1 as float_column")
-            >>> relation.types["float_column"]
-            INTEGER
-            >>> relation.cast(model=Schema).types["float_column"]
-            DOUBLE
-
-            >>> relation = pt.duckdb.Relation("select 1::FLOAT as float_column")
-            >>> relation.cast(model=Schema).types["float_column"]
-            FLOAT
-            >>> relation.cast(model=Schema, strict=True).types["float_column"]
-            DOUBLE
-
-            >>> class Schema(pt.Model):
-            ...     column_1: float
-            ...     column_2: float
-            ...
-            >>> relation = pt.duckdb.Relation(
-            ...     "select 1 as column_1, 2 as column_2"
-            ... ).set_model(Schema)
-            >>> relation.types
-            {'column_1': INTEGER, 'column_2': INTEGER}
-            >>> relation.cast(include=["column_1"]).types
-            {'column_1': DOUBLE, 'column_2': INTEGER}
-            >>> relation.cast(exclude=["column_1"]).types
-            {'column_1': INTEGER, 'column_2': DOUBLE}
-        """
-        if model is not None:
-            relation = self.set_model(model)
-            schema = model
-        elif self.model is not None:
-            relation = self
-            schema = cast(ModelType, self.model)
-        else:
-            class_name = self.__class__.__name__
-            raise TypeError(
-                f"{class_name}.cast() invoked without "
-                f"{class_name}.model having been set! "
-                f"You should invoke {class_name}.set_model() first "
-                "or explicitly provide a model to .cast()."
-            )
-
-        if include is not None and exclude is not None:
-            raise ValueError(
-                "Both include and exclude provided to "
-                f"{self.__class__.__name__}.cast()!"
-            )
-        elif include is not None:
-            include = set(include)
-        elif exclude is not None:
-            include = set(relation.columns) - set(exclude)
-        else:
-            include = set(relation.columns)
-
-        new_columns = []
-        for column, current_type in relation.types.items():
-            if column not in schema.columns:
-                new_columns.append(column)
-            elif column in include and (
-                strict or current_type not in schema.valid_sql_types[column]
-            ):
-                new_type = schema.sql_types[column]
-                new_columns.append(f"{column}::{new_type} as {column}")
-            else:
-                new_columns.append(column)
-        return cast(RelationType, self.select(*new_columns))
-
-    def coalesce(
-        self: RelationType,
-        **column_expressions: Union[str, int, float],
-    ) -> RelationType:
-        """
-        Replace null-values in given columns with respective values.
-
-        For example, ``coalesce(column_name=value)`` is compiled to:
-        ``f"coalesce({column_name}, {repr(value)}) as column_name"`` in the resulting
-        SQL.
-
-        Args:
-            column_expressions: Keywords indicate which columns to coalesce, while the
-                string representation of the respective arguments are used as the
-                null-replacement.
-
-        Return:
-            Relation: Relation where values have been filled in for nulls in the given
-            columns.
-
-        Examples:
-            >>> import patito as pt
-            >>> df = pt.DataFrame(
-            ...     {
-            ...         "a": [1, None, 3],
-            ...         "b": ["four", "five", None],
-            ...         "c": [None, 8.0, 9.0],
-            ...     }
-            ... )
-            >>> relation = pt.duckdb.Relation(df)
-            >>> relation.coalesce(a=2, b="six").to_df()
-            shape: (3, 3)
-            ┌─────┬──────┬──────┐
-            │ a   ┆ b    ┆ c    │
-            │ --- ┆ ---  ┆ ---  │
-            │ i64 ┆ str  ┆ f64  │
-            ╞═════╪══════╪══════╡
-            │ 1   ┆ four ┆ null │
-            │ 2   ┆ five ┆ 8.0  │
-            │ 3   ┆ six  ┆ 9.0  │
-            └─────┴──────┴──────┘
-        """
-        projections = []
-        for column in self.columns:
-            if column in column_expressions:
-                expression = column_expressions[column]
-                projections.append(f"coalesce({column}, {expression!r}) as {column}")
-            else:
-                projections.append(column)
-        return cast(RelationType, self.select(*projections))
-
-    @property
-    def columns(self) -> List[str]:
-        """
-        Return the columns of the relation as a list of strings.
-
-        Examples:
-            >>> import patito as pt
-            >>> pt.duckdb.Relation("select 1 as a, 2 as b").columns
-            ['a', 'b']
-        """
-        # Under certain specific circumstances columns are suffixed with
-        # :1, which need to be removed from the column name.
-        return [column.partition(":")[0] for column in self._relation.columns]
-
-    def count(self) -> int:
-        """
-        Return the number of rows in the given relation.
-
-        Returns:
-            Number of rows in the relation as an integer.
-
-        Examples:
-            >>> import patito as pt
-            >>> relation = pt.duckdb.Relation("select 1 as a")
-            >>> relation.count()
-            1
-            >>> (relation + relation).count()
-            2
-
-            The :ref:`Relation.__len__()<duckdb.Relation.__len__>` method invokes
-            ``Relation.count()`` under the hood, and is equivalent:
-
-            >>> len(relation)
-            1
-            >>> len(relation + relation)
-            2
-        """
-        return cast(Tuple[int], self._relation.aggregate("count(*)").fetchone())[0]
-
-    def create_table(self: RelationType, name: str) -> RelationType:
-        """
-        Create new database table based on relation.
-
-        If ``self.model`` is set with
-        :ref:`Relation.set_model()<duckdb.Relation.set_model>`, then the model is used
-        to infer the table schema. Otherwise, a permissive table schema is created based
-        on the relation data.
-
-        Returns:
-            Relation: A relation pointing to the newly created table.
-
-        Examples:
-            >>> from typing import Literal
-            >>> import patito as pt
-
-            >>> df = pt.DataFrame({"enum_column": ["A", "A", "B"]})
-            >>> relation = pt.duckdb.Relation(df)
-            >>> relation.create_table("permissive_table").types
-            {'enum_column': VARCHAR}
-
-            >>> class TableSchema(pt.Model):
-            ...     enum_column: Literal["A", "B", "C"]
-            ...
-            >>> relation.set_model(TableSchema).create_table("strict_table").types
-            {'enum_column': enum__7ba49365cc1b0fd57e61088b3bc9aa25}
-        """
-        if self.model is not None:
-            self.database.create_table(name=name, model=self.model)
-            self.insert_into(table=name)
-        else:
-            self._relation.create(table_name=name)
-        return cast(RelationType, self.database.table(name))
-
-    def create_view(
-        self: RelationType,
-        name: str,
-        replace: bool = False,
-    ) -> RelationType:
-        """
-        Create new database view based on relation.
-
-        Returns:
-            Relation: A relation pointing to the newly created view.
-
-        Examples:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> df = pt.DataFrame({"column": ["A", "A", "B"]})
-            >>> relation = db.to_relation(df)
-            >>> relation.create_view("my_view")
-            >>> db.query("select * from my_view").to_df()
-            shape: (3, 1)
-            ┌────────┐
-            │ column │
-            │ ---    │
-            │ str    │
-            ╞════════╡
-            │ A      │
-            │ A      │
-            │ B      │
-            └────────┘
-        """
-        self._relation.create_view(view_name=name, replace=replace)
-        return cast(RelationType, self.database.view(name))
-
-    def drop(self, *columns: str) -> Relation:
-        """
-        Remove specified column(s) from relation.
-
-        Args:
-            columns (str): Any number of string column names to be dropped.
-
-        Examples:
-            >>> import patito as pt
-            >>> relation = pt.duckdb.Relation("select 1 as a, 2 as b, 3 as c")
-            >>> relation.columns
-            ['a', 'b', 'c']
-            >>> relation.drop("c").columns
-            ['a', 'b']
-            >>> relation.drop("b", "c").columns
-            ['a']
-        """
-        new_columns = self.columns.copy()
-        for column in columns:
-            new_columns.remove(column)
-        return self[new_columns]
-
-    def distinct(self: RelationType) -> RelationType:
-        """
-        Drop all duplicate rows of the relation.
-
-        Example:
-            >>> import patito as pt
-            >>> df = pt.DataFrame(
-            ...     [[1, 2, 3], [1, 2, 3], [3, 2, 1]],
-            ...     schema=["a", "b", "c"],
-            ...     orient="row",
-            ... )
-            >>> relation = pt.duckdb.Relation(df)
-            >>> relation.to_df()
-            shape: (3, 3)
-            ┌─────┬─────┬─────┐
-            │ a   ┆ b   ┆ c   │
-            │ --- ┆ --- ┆ --- │
-            │ i64 ┆ i64 ┆ i64 │
-            ╞═════╪═════╪═════╡
-            │ 1   ┆ 2   ┆ 3   │
-            │ 1   ┆ 2   ┆ 3   │
-            │ 3   ┆ 2   ┆ 1   │
-            └─────┴─────┴─────┘
-            >>> relation.distinct().to_df()
-            shape: (2, 3)
-            ┌─────┬─────┬─────┐
-            │ a   ┆ b   ┆ c   │
-            │ --- ┆ --- ┆ --- │
-            │ i64 ┆ i64 ┆ i64 │
-            ╞═════╪═════╪═════╡
-            │ 1   ┆ 2   ┆ 3   │
-            │ 3   ┆ 2   ┆ 1   │
-            └─────┴─────┴─────┘
-        """
-        return self._wrap(self._relation.distinct(), schema_change=False)
-
-    def except_(self: RelationType, other: RelationSource) -> RelationType:
-        """
-        Remove all rows that can be found in the other other relation.
-
-        Args:
-            other: Another relation or something that can be casted to a relation.
-
-        Returns:
-            New relation without the rows that can be found in the other relation.
-
-        Example:
-            >>> import patito as pt
-            >>> relation_123 = pt.duckdb.Relation(
-            ...     "select 1 union select 2 union select 3"
-            ... )
-            >>> relation_123.order(by="1").to_df()
-            shape: (3, 1)
-            ┌─────┐
-            │ 1   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            │ 2   │
-            │ 3   │
-            └─────┘
-            >>> relation_2 = pt.duckdb.Relation("select 2")
-            >>> relation_2.to_df()
-            shape: (1, 1)
-            ┌─────┐
-            │ 2   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 2   │
-            └─────┘
-            >>> relation_123.except_(relation_2).order(by="1").to_df()
-            shape: (2, 1)
-            ┌─────┐
-            │ 1   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            │ 3   │
-            └─────┘
-        """
-        return self._wrap(
-            self._relation.except_(self.database.to_relation(other)._relation),
-            schema_change=False,
-        )
-
-    def execute(self) -> duckdb.DuckDBPyRelation:
-        """
-        Execute built relation query and return result object.
-
-        Returns:
-            A native ``duckdb.DuckDBPyResult`` object representing the executed query.
-
-        Examples:
-            >>> import patito as pt
-            >>> relation = pt.duckdb.Relation(
-            ...     "select 1 as a, 2 as b union select 3 as a, 4 as b"
-            ... )
-            >>> result = relation.aggregate("sum(a)", group_by="").execute()
-            >>> result.description
-            [('sum(a)', 'NUMBER', None, None, None, None, None)]
-            >>> result.fetchall()
-            [(4,)]
-        """
-        # A star-select is here performed in order to work around certain DuckDB bugs
-        return self._relation.project("*").execute()
-
-    def get(self, *filters: str, **equalities: Union[str, int, float]) -> ModelType:
-        """
-        Fetch the single row that matches the given filter(s).
-
-        If you expect a relation to already return one row, you can use get() without
-        any arguments to return that row.
-
-        Raises:
-            RuntimeError: RuntimeError is thrown if not exactly one single row matches
-                the given filter.
-
-        Args:
-            filters (str): A conjunction of SQL where clauses.
-            equalities (Any): A conjunction of SQL equality clauses. The keyword name
-                is the column and the parameter is the value of the equality.
-
-        Returns:
-            Model: A Patito model representing the given row.
-
-        Examples:
-            >>> import patito as pt
-            >>> import polars as pl
-            >>> df = pt.DataFrame({"product_id": [1, 2, 3], "price": [10, 10, 20]})
-            >>> relation = pt.duckdb.Relation(df).set_alias("my_relation")
-
-            The ``.get()`` method will by default return a dynamically constructed
-            Patito model if no model has been associated with the given relation:
-
-            >>> relation.get(product_id=1)
-            my_relation(product_id=1, price=10)
-
-            If a Patito model has been associated with the relation, by the use of
-            :ref:`Relation.set_model()<duckdb.Relation.set_model>`, then the given model
-            will be used to represent the return type:
-
-            >>> class Product(pt.Model):
-            ...     product_id: int = pt.Field(unique=True)
-            ...     price: float
-            ...
-            >>> relation.set_model(Product).get(product_id=1)
-            Product(product_id=1, price=10.0)
-
-            You can invoke ``.get()`` without any arguments on relations containing
-            exactly one row:
-
-            >>> relation.filter(product_id=1).get()
-            my_relation(product_id=1, price=10)
-
-            If the given predicate matches multiple rows a ``MultipleRowsReturned``
-            exception will be raised:
-
-            >>> try:
-            ...     relation.get(price=10)
-            ... except pt.exceptions.MultipleRowsReturned as e:
-            ...     print(e)
-            ...
-            Relation.get(price=10) returned 2 rows!
-
-            If the given predicate matches zero rows a ``RowDoesNotExist`` exception
-            will be raised:
-
-            >>> try:
-            ...     relation.get(price=0)
-            ... except pt.exceptions.RowDoesNotExist as e:
-            ...     print(e)
-            ...
-            Relation.get(price=0) returned 0 rows!
-        """
-        if filters or equalities:
-            relation = self.filter(*filters, **equalities)
-        else:
-            relation = self
-        result = relation.execute()
-        row = result.fetchone()
-        if row is None or result.fetchone() is not None:
-            args = [repr(f) for f in filters]
-            args.extend(f"{key}={value!r}" for key, value in equalities.items())
-            args_string = ",".join(args)
-
-            num_rows = relation.count()
-            if num_rows == 0:
-                raise RowDoesNotExist(f"Relation.get({args_string}) returned 0 rows!")
-            else:
-                raise MultipleRowsReturned(
-                    f"Relation.get({args_string}) returned {num_rows} rows!"
-                )
-        return self._to_model(row=row)
-
-    def _to_model(self, row: tuple) -> ModelType:
-        """
-        Cast row tuple to proper return type.
-
-        If self.model is set, either by a class variable of a subclass or by the
-        invocation of Relation.set_model(), that type is used to construct the return
-        value. Otherwise, a pydantic model is dynamically created based on the column
-        schema of the relation.
-        """
-        kwargs = {column: value for column, value in zip(self.columns, row)}
-        if self.model:
-            return self.model(**kwargs)
-        else:
-            RowModel = create_pydantic_model(relation=self._relation)
-            return cast(
-                ModelType,
-                RowModel(**kwargs),
-            )
-
-    def filter(
-        self: RelationType,
-        *filters: str,
-        **equalities: Union[str, int, float],
-    ) -> RelationType:
-        """
-        Return subset of rows of relation that satisfy the given predicates.
-
-        The method returns self if no filters are provided.
-
-        Args:
-            filters: A conjunction of SQL ``WHERE`` clauses.
-            equalities: A conjunction of SQL equality clauses. The keyword name
-                is the column and the parameter is the value of the equality.
-
-        Returns:
-            Relation: A new relation where all rows satisfy the given criteria.
-
-        Examples:
-            >>> import patito as pt
-            >>> df = pt.DataFrame(
-            ...     {
-            ...         "number": [1, 2, 3, 4],
-            ...         "string": ["A", "A", "B", "B"],
-            ...     }
-            ... )
-            >>> relation = pt.duckdb.Relation(df)
-            >>> relation.filter("number % 2 = 0").to_df()
-            shape: (2, 2)
-            ┌────────┬────────┐
-            │ number ┆ string │
-            │ ---    ┆ ---    │
-            │ i64    ┆ str    │
-            ╞════════╪════════╡
-            │ 2      ┆ A      │
-            │ 4      ┆ B      │
-            └────────┴────────┘
-
-            >>> relation.filter(number=1, string="A").to_df()
-            shape: (1, 2)
-            ┌────────┬────────┐
-            │ number ┆ string │
-            │ ---    ┆ ---    │
-            │ i64    ┆ str    │
-            ╞════════╪════════╡
-            │ 1      ┆ A      │
-            └────────┴────────┘
-        """
-        if not filters and not equalities:
-            return self
-
-        clauses: List[str] = []
-        if filters:
-            clauses.extend(filters)
-        if equalities:
-            clauses.extend(f"{key}={value!r}" for key, value in equalities.items())
-        filter_string = " and ".join(f"({clause})" for clause in clauses)
-        return self._wrap(self._relation.filter(filter_string), schema_change=False)
-
-    def join(
-        self: RelationType,
-        other: RelationSource,
-        *,
-        on: str,
-        how: Literal["inner", "left"] = "inner",
-    ) -> RelationType:
-        """
-        Join relation with other relation source based on condition.
-
-        See :ref:`duckdb.Relation.inner_join() <duckdb.Relation.inner_join>` and
-        :ref:`Relation.left_join() <duckdb.Relation.left_join>` for alternative method
-        shortcuts instead of using ``how``.
-
-        Args:
-            other: A source which can be casted to a ``Relation`` object, and be used
-                as the right table in the join.
-            on: Join condition following the ``INNER JOIN ... ON`` in the SQL query.
-            how: Either ``"left"`` or ``"inner"`` for what type of SQL join operation to
-                perform.
-
-        Returns:
-            Relation: New relation based on the joined relations.
-
-        Example:
-            >>> import patito as pt
-            >>> products_df = pt.DataFrame(
-            ...     {
-            ...         "product_name": ["apple", "banana", "oranges"],
-            ...         "supplier_id": [2, 1, 3],
-            ...     }
-            ... )
-            >>> products = pt.duckdb.Relation(products_df)
-            >>> supplier_df = pt.DataFrame(
-            ...     {
-            ...         "id": [1, 2],
-            ...         "supplier_name": ["Banana Republic", "Applies Inc."],
-            ...     }
-            ... )
-            >>> suppliers = pt.duckdb.Relation(supplier_df)
-            >>> products.set_alias("p").join(
-            ...     suppliers.set_alias("s"),
-            ...     on="p.supplier_id = s.id",
-            ...     how="inner",
-            ... ).to_df()
-            shape: (2, 4)
-            ┌──────────────┬─────────────┬─────┬─────────────────┐
-            │ product_name ┆ supplier_id ┆ id  ┆ supplier_name   │
-            │ ---          ┆ ---         ┆ --- ┆ ---             │
-            │ str          ┆ i64         ┆ i64 ┆ str             │
-            ╞══════════════╪═════════════╪═════╪═════════════════╡
-            │ apple        ┆ 2           ┆ 2   ┆ Applies Inc.    │
-            │ banana       ┆ 1           ┆ 1   ┆ Banana Republic │
-            └──────────────┴─────────────┴─────┴─────────────────┘
-
-            >>> products.set_alias("p").join(
-            ...     suppliers.set_alias("s"),
-            ...     on="p.supplier_id = s.id",
-            ...     how="left",
-            ... ).to_df()
-            shape: (3, 4)
-            ┌──────────────┬─────────────┬──────┬─────────────────┐
-            │ product_name ┆ supplier_id ┆ id   ┆ supplier_name   │
-            │ ---          ┆ ---         ┆ ---  ┆ ---             │
-            │ str          ┆ i64         ┆ i64  ┆ str             │
-            ╞══════════════╪═════════════╪══════╪═════════════════╡
-            │ apple        ┆ 2           ┆ 2    ┆ Applies Inc.    │
-            │ banana       ┆ 1           ┆ 1    ┆ Banana Republic │
-            │ oranges      ┆ 3           ┆ null ┆ null            │
-            └──────────────┴─────────────┴──────┴─────────────────┘
-        """
-        return self._wrap(
-            self._relation.join(
-                self.database.to_relation(other)._relation, condition=on, how=how
-            ),
-            schema_change=True,
-        )
-
-    def inner_join(self: RelationType, other: RelationSource, on: str) -> RelationType:
-        """
-        Inner join relation with other relation source based on condition.
-
-        Args:
-            other: A source which can be casted to a ``Relation`` object, and be used
-                as the right table in the join.
-            on: Join condition following the ``INNER JOIN ... ON`` in the SQL query.
-
-        Returns:
-            Relation: New relation based on the joined relations.
-
-        Example:
-            >>> import patito as pt
-            >>> products_df = pt.DataFrame(
-            ...     {
-            ...         "product_name": ["apple", "banana", "oranges"],
-            ...         "supplier_id": [2, 1, 3],
-            ...     }
-            ... )
-            >>> products = pt.duckdb.Relation(products_df)
-            >>> supplier_df = pt.DataFrame(
-            ...     {
-            ...         "id": [1, 2],
-            ...         "supplier_name": ["Banana Republic", "Applies Inc."],
-            ...     }
-            ... )
-            >>> suppliers = pt.duckdb.Relation(supplier_df)
-            >>> products.set_alias("p").inner_join(
-            ...     suppliers.set_alias("s"),
-            ...     on="p.supplier_id = s.id",
-            ... ).to_df()
-            shape: (2, 4)
-            ┌──────────────┬─────────────┬─────┬─────────────────┐
-            │ product_name ┆ supplier_id ┆ id  ┆ supplier_name   │
-            │ ---          ┆ ---         ┆ --- ┆ ---             │
-            │ str          ┆ i64         ┆ i64 ┆ str             │
-            ╞══════════════╪═════════════╪═════╪═════════════════╡
-            │ apple        ┆ 2           ┆ 2   ┆ Applies Inc.    │
-            │ banana       ┆ 1           ┆ 1   ┆ Banana Republic │
-            └──────────────┴─────────────┴─────┴─────────────────┘
-        """
-        return self._wrap(
-            self._relation.join(
-                other_rel=self.database.to_relation(other)._relation,
-                condition=on,
-                how="inner",
-            ),
-            schema_change=True,
-        )
-
-    def left_join(self: RelationType, other: RelationSource, on: str) -> RelationType:
-        """
-        Left join relation with other relation source based on condition.
-
-        Args:
-            other: A source which can be casted to a Relation object, and be used as
-                the right table in the join.
-            on: Join condition following the ``LEFT JOIN ... ON`` in the SQL query.
-
-        Returns:
-            Relation: New relation based on the joined tables.
-
-        Example:
-            >>> import patito as pt
-            >>> products_df = pt.DataFrame(
-            ...     {
-            ...         "product_name": ["apple", "banana", "oranges"],
-            ...         "supplier_id": [2, 1, 3],
-            ...     }
-            ... )
-            >>> products = pt.duckdb.Relation(products_df)
-            >>> supplier_df = pt.DataFrame(
-            ...     {
-            ...         "id": [1, 2],
-            ...         "supplier_name": ["Banana Republic", "Applies Inc."],
-            ...     }
-            ... )
-            >>> suppliers = pt.duckdb.Relation(supplier_df)
-            >>> products.set_alias("p").left_join(
-            ...     suppliers.set_alias("s"),
-            ...     on="p.supplier_id = s.id",
-            ... ).to_df()
-            shape: (3, 4)
-            ┌──────────────┬─────────────┬──────┬─────────────────┐
-            │ product_name ┆ supplier_id ┆ id   ┆ supplier_name   │
-            │ ---          ┆ ---         ┆ ---  ┆ ---             │
-            │ str          ┆ i64         ┆ i64  ┆ str             │
-            ╞══════════════╪═════════════╪══════╪═════════════════╡
-            │ apple        ┆ 2           ┆ 2    ┆ Applies Inc.    │
-            │ banana       ┆ 1           ┆ 1    ┆ Banana Republic │
-            │ oranges      ┆ 3           ┆ null ┆ null            │
-            └──────────────┴─────────────┴──────┴─────────────────┘
-        """
-        return self._wrap(
-            self._relation.join(
-                other_rel=self.database.to_relation(other)._relation,
-                condition=on,
-                how="left",
-            ),
-            schema_change=True,
-        )
-
-    def limit(self: RelationType, n: int, *, offset: int = 0) -> RelationType:
-        """
-        Remove all but the first n rows.
-
-        Args:
-            n: The number of rows to keep.
-            offset: Disregard the first ``offset`` rows before starting to count which
-                rows to keep.
-
-        Returns:
-            New relation with only n rows.
-
-        Example:
-            >>> import patito as pt
-            >>> relation = (
-            ...     pt.duckdb.Relation("select 1 as column")
-            ...     + pt.duckdb.Relation("select 2 as column")
-            ...     + pt.duckdb.Relation("select 3 as column")
-            ...     + pt.duckdb.Relation("select 4 as column")
-            ... )
-            >>> relation.limit(2).to_df()
-            shape: (2, 1)
-            ┌────────┐
-            │ column │
-            │ ---    │
-            │ i64    │
-            ╞════════╡
-            │ 1      │
-            │ 2      │
-            └────────┘
-            >>> relation.limit(2, offset=2).to_df()
-            shape: (2, 1)
-            ┌────────┐
-            │ column │
-            │ ---    │
-            │ i64    │
-            ╞════════╡
-            │ 3      │
-            │ 4      │
-            └────────┘
-        """
-        return self._wrap(self._relation.limit(n=n, offset=offset), schema_change=False)
-
-    def order(self: RelationType, by: Union[str, Iterable[str]]) -> RelationType:
-        """
-        Change the order of the rows of the relation.
-
-        Args:
-            by: An ``ORDER BY`` SQL expression such as ``"age DESC"`` or
-                ``("age DESC", "name ASC")``.
-
-        Returns:
-            New relation where the rows have been ordered according to ``by``.
-
-        Example:
-            >>> import patito as pt
-            >>> df = pt.DataFrame(
-            ...     {
-            ...         "name": ["Alice", "Bob", "Charles", "Diana"],
-            ...         "age": [20, 20, 30, 35],
-            ...     }
-            ... )
-            >>> df
-            shape: (4, 2)
-            ┌─────────┬─────┐
-            │ name    ┆ age │
-            │ ---     ┆ --- │
-            │ str     ┆ i64 │
-            ╞═════════╪═════╡
-            │ Alice   ┆ 20  │
-            │ Bob     ┆ 20  │
-            │ Charles ┆ 30  │
-            │ Diana   ┆ 35  │
-            └─────────┴─────┘
-            >>> relation = pt.duckdb.Relation(df)
-            >>> relation.order(by="age desc").to_df()
-            shape: (4, 2)
-            ┌─────────┬─────┐
-            │ name    ┆ age │
-            │ ---     ┆ --- │
-            │ str     ┆ i64 │
-            ╞═════════╪═════╡
-            │ Diana   ┆ 35  │
-            │ Charles ┆ 30  │
-            │ Alice   ┆ 20  │
-            │ Bob     ┆ 20  │
-            └─────────┴─────┘
-            >>> relation.order(by=["age desc", "name desc"]).to_df()
-            shape: (4, 2)
-            ┌─────────┬─────┐
-            │ name    ┆ age │
-            │ ---     ┆ --- │
-            │ str     ┆ i64 │
-            ╞═════════╪═════╡
-            │ Diana   ┆ 35  │
-            │ Charles ┆ 30  │
-            │ Bob     ┆ 20  │
-            │ Alice   ┆ 20  │
-            └─────────┴─────┘
-        """
-        order_expr = by if isinstance(by, str) else ", ".join(by)
-        return self._wrap(
-            self._relation.order(order_expr=order_expr),
-            schema_change=False,
-        )
-
-    def insert_into(
-        self: RelationType,
-        table: str,
-    ) -> RelationType:
-        """
-        Insert all rows of the relation into a given table.
-
-        The relation must contain all the columns present in the target table.
-        Extra columns are ignored and the column order is automatically matched
-        with the target table.
-
-        Args:
-            table: Name of table for which to insert values into.
-
-        Returns:
-            Relation: The original relation, i.e. ``self``.
-
-        Examples:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> db.to_relation("select 1 as a").create_table("my_table")
-            >>> db.table("my_table").to_df()
-            shape: (1, 1)
-            ┌─────┐
-            │ a   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            └─────┘
-            >>> db.to_relation("select 2 as a").insert_into("my_table")
-            >>> db.table("my_table").to_df()
-            shape: (2, 1)
-            ┌─────┐
-            │ a   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            │ 2   │
-            └─────┘
-        """
-        table_relation = self.database.table(table)
-        missing_columns = set(table_relation.columns) - set(self.columns)
-        if missing_columns:
-            raise TypeError(
-                f"Relation is missing column(s) {missing_columns} "
-                f"in order to be inserted into table '{table}'!",
-            )
-
-        reordered_relation = self[table_relation.columns]
-        reordered_relation._relation.insert_into(table_name=table)
-        return self
-
-    def intersect(self: RelationType, other: RelationSource) -> RelationType:
-        """
-        Return a new relation containing the rows that are present in both relations.
-
-        This is a set operation which will remove duplicate rows as well.
-
-        Args:
-            other: Another relation with the same column names.
-
-        Returns:
-            Relation[Model]: A new relation with only those rows that are present in
-            both relations.
-
-        Example:
-            >>> import patito as pt
-            >>> df1 = pt.DataFrame({"a": [1, 1, 2], "b": [1, 1, 2]})
-            >>> df2 = pt.DataFrame({"a": [1, 1, 3], "b": [1, 1, 3]})
-            >>> pt.duckdb.Relation(df1).intersect(pt.duckdb.Relation(df2)).to_df()
-            shape: (1, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 1   │
-            └─────┴─────┘
-        """
-        other = self.database.to_relation(other)
-        return self._wrap(
-            self._relation.intersect(other._relation),
-            schema_change=False,
-        )
-
-    def select(
-        self,
-        *projections: Union[str, int, float],
-        **named_projections: Union[str, int, float],
-    ) -> Relation:
-        """
-        Return relation based on one or more SQL ``SELECT`` projections.
-
-        Keyword arguments are converted into ``{arg} as {keyword}`` in the executed SQL
-        query.
-
-        Args:
-            *projections: One or more strings representing SQL statements to be
-                selected. For example ``"2"`` or ``"another_column"``.
-            **named_projections: One ore more keyword arguments where the keyword
-                specifies the name of the new column and the value is an SQL statement
-                defining the content of the new column. For example
-                ``new_column="2 * another_column"``.
-
-        Examples:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> relation = db.to_relation(pt.DataFrame({"original_column": [1, 2, 3]}))
-            >>> relation.select("*").to_df()
-            shape: (3, 1)
-            ┌─────────────────┐
-            │ original_column │
-            │ ---             │
-            │ i64             │
-            ╞═════════════════╡
-            │ 1               │
-            │ 2               │
-            │ 3               │
-            └─────────────────┘
-            >>> relation.select("*", multiplied_column="2 * original_column").to_df()
-            shape: (3, 2)
-            ┌─────────────────┬───────────────────┐
-            │ original_column ┆ multiplied_column │
-            │ ---             ┆ ---               │
-            │ i64             ┆ i64               │
-            ╞═════════════════╪═══════════════════╡
-            │ 1               ┆ 2                 │
-            │ 2               ┆ 4                 │
-            │ 3               ┆ 6                 │
-            └─────────────────┴───────────────────┘
-        """
-        # We expand '*' to an explicit list of columns in order to support redefining
-        # columns within the star expressed columns.
-        expanded_projections: list = list(projections)
-        try:
-            star_index = projections.index("*")
-            if named_projections:
-                # Allow explicitly named projections to overwrite star-selected columns
-                expanded_projections[star_index : star_index + 1] = [
-                    column for column in self.columns if column not in named_projections
-                ]
-            else:
-                expanded_projections[star_index : star_index + 1] = self.columns
-        except ValueError:
-            pass
-
-        projection = ", ".join(
-            expanded_projections
-            + list(  # pyright: ignore
-                f"{expression} as {column_name}"
-                for column_name, expression in named_projections.items()
-            )
-        )
-        try:
-            relation = self._relation.project(projection)
-        except RuntimeError as exc:  # pragma: no cover
-            # We might get a RunTime error if the enum type has not
-            # been created yet. If so, we create all enum types for
-            # this model.
-            if self.model is not None and _is_missing_enum_type_exception(exc):
-                self.database.create_enum_types(model=self.model)
-                relation = self._relation.project(projection)
-            else:
-                raise exc
-        return self._wrap(relation=relation, schema_change=True)
-
-    def rename(self, **columns: str) -> Relation:
-        """
-        Rename columns as specified.
-
-        Args:
-            **columns: A set of keyword arguments where the keyword is the old column
-                name and the value is the new column name.
-
-        Raises:
-            ValueError: If any of the given keywords do not exist as columns in the
-                relation.
-
-        Examples:
-            >>> import patito as pt
-            >>> relation = pt.duckdb.Relation("select 1 as a, 2 as b")
-            >>> relation.rename(b="c").to_df().select(["a", "c"])
-            shape: (1, 2)
-            ┌─────┬─────┐
-            │ a   ┆ c   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 2   │
-            └─────┴─────┘
-        """
-        existing_columns = set(self.columns)
-        missing = set(columns.keys()) - set(existing_columns)
-        if missing:
-            raise ValueError(
-                f"Column '{missing.pop()}' can not be renamed as it does not exist. "
-                f"The columns of the relation are: {', '.join(existing_columns)}."
-            )
-        # If we rename a column to overwrite another existing one, the column should
-        # be overwritten.
-        existing_columns = set(existing_columns) - set(columns.values())
-        relation = self._relation.project(
-            ", ".join(
-                f"{column} as {columns.get(column, column)}"
-                for column in existing_columns
-            )
-        )
-        return self._wrap(relation=relation, schema_change=True)
-
-    def set_alias(self: RelationType, name: str) -> RelationType:
-        """
-        Set SQL alias for the given relation to be used in further queries.
-
-        Args:
-            name: The new alias for the given relation.
-
-        Returns:
-            Relation: A new relation containing the same query but addressable with the
-            new alias.
-
-        Example:
-            >>> import patito as pt
-            >>> relation_1 = pt.duckdb.Relation("select 1 as a, 2 as b")
-            >>> relation_2 = pt.duckdb.Relation("select 1 as a, 3 as c")
-            >>> relation_1.set_alias("x").inner_join(
-            ...     relation_2.set_alias("y"),
-            ...     on="x.a = y.a",
-            ... ).select("x.a", "y.a", "b", "c").to_df()
-            shape: (1, 4)
-            ┌─────┬─────┬─────┬─────┐
-            │ a   ┆ a:1 ┆ b   ┆ c   │
-            │ --- ┆ --- ┆ --- ┆ --- │
-            │ i64 ┆ i64 ┆ i64 ┆ i64 │
-            ╞═════╪═════╪═════╪═════╡
-            │ 1   ┆ 1   ┆ 2   ┆ 3   │
-            └─────┴─────┴─────┴─────┘
-        """
-        return self._wrap(
-            self._relation.set_alias(name),
-            schema_change=False,
-        )
-
-    def set_model(self, model):  # type: ignore[no-untyped-def] # noqa: ANN
-        """
-        Associate a give Patito model with the relation.
-
-        The returned relation has an associated ``.model`` attribute which can in turn
-        be used by several methods such as :ref:`Relation.get()<duckdb.Relation.get>`,
-        :ref:`Relation.create_table()<duckdb.Relation.create_table>`, and
-        :ref:`Relation.__iter__<duckdb.Relation.__iter__>`.
-
-        Args:
-            model: A Patito Model class specifying the intended schema of the relation.
-
-        Returns:
-            Relation[model]: A new relation with the associated model.
-
-        Example:
-            >>> from typing import Literal
-            >>> import patito as pt
-            >>> class MySchema(pt.Model):
-            ...     float_column: float
-            ...     enum_column: Literal["A", "B", "C"]
-            ...
-            >>> relation = pt.duckdb.Relation(
-            ...     "select 1 as float_column, 'A' as enum_column"
-            ... )
-            >>> relation.get()
-            query_relation(float_column=1, enum_column='A')
-            >>> relation.set_model(MySchema).get()
-            MySchema(float_column=1.0, enum_column='A')
-            >>> relation.create_table("unmodeled_table").types
-            {'float_column': INTEGER, 'enum_column': VARCHAR}
-            >>> relation.set_model(MySchema).create_table("modeled_table").types
-            {'float_column': DOUBLE,
-             'enum_column': enum__7ba49365cc1b0fd57e61088b3bc9aa25}
-        """
-        # We are not able to annotate the generic instance of type(self)[type(model)]
-        # due to the lack of higher-kinded generics in python as of this writing.
-        # See: https://github.com/python/typing/issues/548
-        # This cast() will be wrong for sub-classes of Relation...
-        return cast(
-            Relation[model],
-            type(self)(
-                derived_from=self._relation,
-                database=self.database,
-                model=model,
-            ),
-        )
-
-    @property
-    def types(self):  # type: ignore[no-untyped-def]  # noqa
-        """
-        Return the SQL types of all the columns of the given relation.
-
-        Returns:
-            dict[str, str]: A dictionary where the keys are the column names and the
-            values are SQL types as strings.
-
-        Examples:
-            >>> import patito as pt
-            >>> pt.duckdb.Relation("select 1 as a, 'my_value' as b").types
-            {'a': INTEGER, 'b': VARCHAR}
-        """
-        return dict(zip(self.columns, self._relation.types))
-
-    def to_pandas(self) -> "pd.DataFrame":
-        """
-        Return a pandas DataFrame representation of relation object.
-
-        Returns: A ``pandas.DataFrame`` object containing all the data of the relation.
-
-        Example:
-            >>> import patito as pt
-            >>> pt.duckdb.Relation("select 1 as column union select 2 as column").order(
-            ...     by="1"
-            ... ).to_pandas()
-                  column
-               0       1
-               1       2
-        """
-        return self._relation.to_df()
-
-    def to_df(self) -> DataFrame:
-        """
-        Return a polars DataFrame representation of relation object.
-
-        Returns: A ``patito.DataFrame`` object which inherits from ``polars.DataFrame``.
-
-        Example:
-            >>> import patito as pt
-            >>> pt.duckdb.Relation("select 1 as column union select 2 as column").order(
-            ...     by="1"
-            ... ).to_df()
-            shape: (2, 1)
-            ┌────────┐
-            │ column │
-            │ ---    │
-            │ i64    │
-            ╞════════╡
-            │ 1      │
-            │ 2      │
-            └────────┘
-        """
-        # Here we do a star-select to work around certain weird issues with DuckDB
-        self._relation = self._relation.project("*")
-        arrow_table = cast(pa.lib.Table, self._relation.to_arrow_table())
-        try:
-            # We cast `INTEGER`-typed columns to `pl.Int64` when converting to Polars
-            # because polars is much more eager to store integer Series as 64-bit
-            # integers. Otherwise there must be done a lot of manual casting whenever
-            # you cross the boundary between DuckDB and polars.
-            return DataFrame._from_arrow(arrow_table).with_columns(
-                pl.col(pl.Int32).cast(pl.Int64)
-            )
-        except (pa.ArrowInvalid, pl.ArrowError):  # pragma: no cover
-            # Empty relations with enum columns can sometimes produce errors.
-            # As a last-ditch effort, we convert such columns to VARCHAR.
-            casted_columns = [
-                f"{field.name}::VARCHAR as {field.name}"
-                if isinstance(field.type, pa.DictionaryType)
-                else field.name
-                for field in arrow_table.schema
-            ]
-            non_enum_relation = self._relation.project(", ".join(casted_columns))
-            arrow_table = non_enum_relation.to_arrow_table()
-            return DataFrame._from_arrow(arrow_table).with_columns(
-                pl.col(pl.Int32).cast(pl.Int64)
-            )
-
-    def to_series(self) -> pl.Series:
-        """
-        Convert the given relation to a polars Series.
-
-        Raises:
-            TypeError: If the given relation does not contain exactly one column.
-
-        Returns: A ``polars.Series`` object containing the data of the relation.
-
-        Example:
-            >>> import patito as pt
-            >>> relation = pt.duckdb.Relation("select 1 as a union select 2 as a")
-            >>> relation.order(by="a").to_series()
-            shape: (2,)
-            Series: 'a' [i32]
-            [
-                        1
-                        2
-            ]
-        """
-        if len(self._relation.columns) != 1:
-            raise TypeError(
-                f"{self.__class__.__name__}.to_series() was invoked on a relation with "
-                f"{len(self._relation.columns)} columns, while exactly 1 is required!"
-            )
-        dataframe: DataFrame = DataFrame._from_arrow(self._relation.to_arrow_table())
-        return dataframe.to_series(index=0).alias(name=self.columns[0])
-
-    def union(self: RelationType, other: RelationSource) -> RelationType:
-        """
-        Produce a new relation that contains the rows of both relations.
-
-        The ``+`` operator can also be used to union two relations.
-
-        The two relations must have the same column names, but not necessarily in the
-        same order as reordering of columns is automatically performed, unlike regular
-        SQL.
-
-        Duplicates are `not` dropped.
-
-        Args:
-            other: A ``patito.duckdb.Relation`` object or something that can be
-                *casted* to ``patito.duckdb.Relation``.
-                See :ref:`Relation<duckdb.Relation.__init__>`.
-
-        Returns:
-            New relation containing the rows of both ``self`` and ``other``.
-
-        Raises:
-            TypeError: If the two relations do not contain the same columns.
-
-        Examples:
-            >>> import patito as pt
-            >>> relation_1 = pt.duckdb.Relation("select 1 as a")
-            >>> relation_2 = pt.duckdb.Relation("select 2 as a")
-            >>> relation_1.union(relation_2).to_df()
-            shape: (2, 1)
-            ┌─────┐
-            │ a   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            │ 2   │
-            └─────┘
-
-            >>> (relation_1 + relation_2).to_df()
-            shape: (2, 1)
-            ┌─────┐
-            │ a   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            │ 2   │
-            └─────┘
-        """
-        other_relation = self.database.to_relation(other)
-        if set(self.columns) != set(other_relation.columns):
-            msg = "Union between relations with different column names is not allowed."
-            additional_left = set(self.columns) - set(other_relation.columns)
-            additional_right = set(other_relation.columns) - set(self.columns)
-            if additional_left:
-                msg += f" Additional columns in left relation: {additional_left}."
-            if additional_right:
-                msg += f" Additional columns in right relation: {additional_right}."
-            raise TypeError(msg)
-        if other_relation.columns != self.columns:
-            reordered_relation = other_relation[self.columns]
-        else:
-            reordered_relation = other_relation
-        unioned_relation = self._relation.union(reordered_relation._relation)
-        return self._wrap(relation=unioned_relation, schema_change=False)
-
-    def with_columns(
-        self,
-        **named_projections: Union[str, int, float],
-    ) -> Relation:
-        """
-        Return relations with additional columns.
-
-        If the provided columns expressions already exists as a column on the relation,
-        the given column is overwritten.
-
-        Args:
-            named_projections: A set of column expressions, where the keyword is used
-                as the column name, while the right-hand argument is a valid SQL
-                expression.
-
-        Returns:
-            Relation with the given columns appended, or possibly overwritten.
-
-        Examples:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> relation = db.to_relation("select 1 as a, 2 as b")
-            >>> relation.with_columns(c="a + b").to_df()
-            shape: (1, 3)
-            ┌─────┬─────┬─────┐
-            │ a   ┆ b   ┆ c   │
-            │ --- ┆ --- ┆ --- │
-            │ i64 ┆ i64 ┆ i64 │
-            ╞═════╪═════╪═════╡
-            │ 1   ┆ 2   ┆ 3   │
-            └─────┴─────┴─────┘
-        """
-        return self.select("*", **named_projections)
-
-    def with_missing_defaultable_columns(
-        self: RelationType,
-        include: Optional[Iterable[str]] = None,
-        exclude: Optional[Iterable[str]] = None,
-    ) -> RelationType:
-        """
-        Add missing defaultable columns filled with the default values of correct type.
-
-        Make sure to invoke :ref:`Relation.set_model()<duckdb.Relation.set_model>` with
-        the correct model schema before executing
-        ``Relation.with_missing_default_columns()``.
-
-        Args:
-            include: If provided, only fill in default values for missing columns part
-                of this collection of column names.
-            exclude: If provided, do `not` fill in default values for missing columns
-                part of this collection of column names.
-
-        Returns:
-            Relation: New relation where missing columns with default values according
-            to the schema have been filled in.
-
-        Example:
-            >>> import patito as pt
-            >>> class MyModel(pt.Model):
-            ...     non_default_column: int
-            ...     another_non_default_column: int
-            ...     default_column: int = 42
-            ...     another_default_column: int = 42
-            ...
-            >>> relation = pt.duckdb.Relation(
-            ...     "select 1 as non_default_column, 2 as default_column"
-            ... )
-            >>> relation.to_df()
-            shape: (1, 2)
-            ┌────────────────────┬────────────────┐
-            │ non_default_column ┆ default_column │
-            │ ---                ┆ ---            │
-            │ i64                ┆ i64            │
-            ╞════════════════════╪════════════════╡
-            │ 1                  ┆ 2              │
-            └────────────────────┴────────────────┘
-            >>> relation.set_model(MyModel).with_missing_defaultable_columns().to_df()
-            shape: (1, 3)
-            ┌────────────────────┬────────────────┬────────────────────────┐
-            │ non_default_column ┆ default_column ┆ another_default_column │
-            │ ---                ┆ ---            ┆ ---                    │
-            │ i64                ┆ i64            ┆ i64                    │
-            ╞════════════════════╪════════════════╪════════════════════════╡
-            │ 1                  ┆ 2              ┆ 42                     │
-            └────────────────────┴────────────────┴────────────────────────┘
-        """
-        if self.model is None:
-            class_name = self.__class__.__name__
-            raise TypeError(
-                f"{class_name}.with_missing_default_columns() invoked without "
-                f"{class_name}.model having been set! "
-                f"You should invoke {class_name}.set_model() first!"
-            )
-        elif include is not None and exclude is not None:
-            raise TypeError("Both include and exclude provided at the same time!")
-
-        missing_columns = set(self.model.columns) - set(self.columns)
-        defaultable_columns = self.model.defaults.keys()
-        missing_defaultable_columns = missing_columns & defaultable_columns
-
-        if exclude is not None:
-            missing_defaultable_columns -= set(exclude)
-        elif include is not None:
-            missing_defaultable_columns = missing_defaultable_columns & set(include)
-
-        projection = "*"
-        for column_name in missing_defaultable_columns:
-            sql_type = self.model.sql_types[column_name]
-            default_value = self.model.defaults[column_name]
-            projection += f", {default_value!r}::{sql_type} as {column_name}"
-
-        try:
-            relation = self._relation.project(projection)
-        except Exception as exc:  # pragma: no cover
-            # We might get a RunTime error if the enum type has not
-            # been created yet. If so, we create all enum types for
-            # this model.
-            if _is_missing_enum_type_exception(exc):
-                self.database.create_enum_types(model=self.model)
-                relation = self._relation.project(projection)
-            else:
-                raise exc
-        return self._wrap(relation=relation, schema_change=False)
-
-    def with_missing_nullable_columns(
-        self: RelationType,
-        include: Optional[Iterable[str]] = None,
-        exclude: Optional[Iterable[str]] = None,
-    ) -> RelationType:
-        """
-        Add missing nullable columns filled with correctly typed nulls.
-
-        Make sure to invoke :ref:`Relation.set_model()<duckdb.Relation.set_model>` with
-        the correct model schema before executing
-        ``Relation.with_missing_nullable_columns()``.
-
-        Args:
-            include: If provided, only fill in null values for missing columns part of
-                this collection of column names.
-            exclude: If provided, do `not` fill in null values for missing columns
-                part of this collection of column names.
-
-        Returns:
-            Relation: New relation where missing nullable columns have been filled in
-            with null values.
-
-        Example:
-            >>> from typing import Optional
-            >>> import patito as pt
-            >>> class MyModel(pt.Model):
-            ...     non_nullable_column: int
-            ...     nullable_column: Optional[int]
-            ...     another_nullable_column: Optional[int]
-            ...
-            >>> relation = pt.duckdb.Relation("select 1 as nullable_column")
-            >>> relation.to_df()
-            shape: (1, 1)
-            ┌─────────────────┐
-            │ nullable_column │
-            │ ---             │
-            │ i64             │
-            ╞═════════════════╡
-            │ 1               │
-            └─────────────────┘
-            >>> relation.set_model(MyModel).with_missing_nullable_columns().to_df()
-            shape: (1, 2)
-            ┌─────────────────┬─────────────────────────┐
-            │ nullable_column ┆ another_nullable_column │
-            │ ---             ┆ ---                     │
-            │ i64             ┆ i64                     │
-            ╞═════════════════╪═════════════════════════╡
-            │ 1               ┆ null                    │
-            └─────────────────┴─────────────────────────┘
-        """
-        if self.model is None:
-            class_name = self.__class__.__name__
-            raise TypeError(
-                f"{class_name}.with_missing_nullable_columns() invoked without "
-                f"{class_name}.model having been set! "
-                f"You should invoke {class_name}.set_model() first!"
-            )
-        elif include is not None and exclude is not None:
-            raise TypeError("Both include and exclude provided at the same time!")
-
-        missing_columns = set(self.model.columns) - set(self.columns)
-        missing_nullable_columns = self.model.nullable_columns & missing_columns
-
-        if exclude is not None:
-            missing_nullable_columns -= set(exclude)
-        elif include is not None:
-            missing_nullable_columns = missing_nullable_columns & set(include)
-
-        projection = "*"
-        for missing_nullable_column in missing_nullable_columns:
-            sql_type = self.model.sql_types[missing_nullable_column]
-            projection += f", null::{sql_type} as {missing_nullable_column}"
-
-        try:
-            relation = self._relation.project(projection)
-        except Exception as exc:  # pragma: no cover
-            # We might get a RunTime error if the enum type has not
-            # been created yet. If so, we create all enum types for
-            # this model.
-            if _is_missing_enum_type_exception(exc):
-                self.database.create_enum_types(model=self.model)
-                relation = self._relation.project(projection)
-            else:
-                raise exc
-        return self._wrap(relation=relation, schema_change=False)
-
-    def __add__(self: RelationType, other: RelationSource) -> RelationType:
-        """
-        Execute ``self.union(other)``.
-
-        See :ref:`Relation.union()<duckdb.Relation.union>` for full documentation.
-        """
-        return self.union(other)
-
-    def __eq__(self, other: object) -> bool:
-        """Check if Relation is equal to a Relation-able data source."""
-        other_relation = self.database.to_relation(other)  # type: ignore
-        # Check if the number of rows are equal, and then check if each row is equal.
-        # Use zip(self, other_relation, strict=True) when we upgrade to Python 3.10.
-        return self.count() == other_relation.count() and all(
-            row == other_row for row, other_row in zip(self, other_relation)
-        )
-
-    def __getitem__(self, key: Union[str, Iterable[str]]) -> Relation:
-        """
-        Return Relation with selected columns.
-
-        Uses :ref:`Relation.select()<duckdb.Relation.select>` under-the-hood in order to
-        perform the selection. Can technically be used to rename columns,
-        define derived columns, and so on, but prefer the use of Relation.select() for
-        such use cases.
-
-        Args:
-            key: Columns to select, either a single column represented as a string, or
-                an iterable of strings.
-
-        Returns:
-            New relation only containing the column subset specified.
-
-        Example:
-            >>> import patito as pt
-            >>> relation = pt.duckdb.Relation("select 1 as a, 2 as b, 3 as c")
-            >>> relation.to_df()
-            shape: (1, 3)
-            ┌─────┬─────┬─────┐
-            │ a   ┆ b   ┆ c   │
-            │ --- ┆ --- ┆ --- │
-            │ i64 ┆ i64 ┆ i64 │
-            ╞═════╪═════╪═════╡
-            │ 1   ┆ 2   ┆ 3   │
-            └─────┴─────┴─────┘
-            >>> relation[["a", "b"]].to_df()
-            shape: (1, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 2   │
-            └─────┴─────┘
-            >>> relation["a"].to_df()
-            shape: (1, 1)
-            ┌─────┐
-            │ a   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            └─────┘
-        """
-        projection = key if isinstance(key, str) else ", ".join(key)
-        return self._wrap(
-            relation=self._relation.project(projection),
-            schema_change=True,
-        )
-
-    def __iter__(self) -> Iterator[ModelType]:
-        """
-        Iterate over rows in relation.
-
-        If :ref:`Relation.set_model()<duckdb.Relation.set_model>` has been invoked
-        first, the given model will be used to deserialize each row. Otherwise a Patito
-        model is dynamically constructed which fits the schema of the relation.
-
-        Returns:
-            Iterator[Model]: An iterator of patito Model objects representing each row.
-
-        Example:
-            >>> from typing import Literal
-            >>> import patito as pt
-            >>> df = pt.DataFrame({"float_column": [1, 2], "enum_column": ["A", "B"]})
-            >>> relation = pt.duckdb.Relation(df).set_alias("my_relation")
-            >>> for row in relation:
-            ...     print(row)
-            ...
-            float_column=1 enum_column='A'
-            float_column=2 enum_column='B'
-            >>> list(relation)
-            [my_relation(float_column=1, enum_column='A'),
-             my_relation(float_column=2, enum_column='B')]
-
-            >>> class MySchema(pt.Model):
-            ...     float_column: float
-            ...     enum_column: Literal["A", "B", "C"]
-            ...
-            >>> relation = relation.set_model(MySchema)
-            >>> for row in relation:
-            ...     print(row)
-            ...
-            float_column=1.0 enum_column='A'
-            float_column=2.0 enum_column='B'
-            >>> list(relation)
-            [MySchema(float_column=1.0, enum_column='A'),
-             MySchema(float_column=2.0, enum_column='B')]
-        """
-        result = self._relation.execute()
-        while True:
-            row_tuple = result.fetchone()
-            if not row_tuple:
-                return
-            else:
-                yield self._to_model(row_tuple)
-
-    def __len__(self) -> int:
-        """
-        Return the number of rows in the relation.
-
-        See :ref:`Relation.count()<duckdb.Relation.count>` for full documentation.
-        """
-        return self.count()
-
-    def __str__(self) -> str:
-        """
-        Return string representation of Relation object.
-
-        Includes an expression tree, the result columns, and a result preview.
-
-        Example:
-            >>> import patito as pt
-            >>> products = pt.duckdb.Relation(
-            ...     pt.DataFrame(
-            ...         {
-            ...             "product_name": ["apple", "red_apple", "banana", "oranges"],
-            ...             "supplier_id": [2, 2, 1, 3],
-            ...         }
-            ...     )
-            ... ).set_alias("products")
-            >>> print(str(products))  # xdoctest: +SKIP
-            ---------------------
-            --- Relation Tree ---
-            ---------------------
-            arrow_scan(94609350519648, 140317161740928, 140317161731168, 1000000)\
-
-            ---------------------
-            -- Result Columns  --
-            ---------------------
-            - product_name (VARCHAR)
-            - supplier_id (BIGINT)\
-
-            ---------------------
-            -- Result Preview  --
-            ---------------------
-            product_name    supplier_id
-            VARCHAR BIGINT
-            [ Rows: 4]
-            apple   2
-            red_apple       2
-            banana  1
-            oranges 3
-
-            >>> suppliers = pt.duckdb.Relation(
-            ...     pt.DataFrame(
-            ...         {
-            ...             "id": [1, 2],
-            ...             "supplier_name": ["Banana Republic", "Applies Inc."],
-            ...         }
-            ...     )
-            ... ).set_alias("suppliers")
-            >>> relation = (
-            ...     products.set_alias("p")
-            ...     .inner_join(
-            ...         suppliers.set_alias("s"),
-            ...         on="p.supplier_id = s.id",
-            ...     )
-            ...     .aggregate(
-            ...         "supplier_name",
-            ...         num_products="count(product_name)",
-            ...         group_by=["supplier_id", "supplier_name"],
-            ...     )
-            ... )
-            >>> print(str(relation))  # xdoctest: +SKIP
-            ---------------------
-            --- Relation Tree ---
-            ---------------------
-            Aggregate [supplier_name, count(product_name)]
-              Join INNER p.supplier_id = s.id
-                arrow_scan(94609350519648, 140317161740928, 140317161731168, 1000000)
-                arrow_scan(94609436221024, 140317161740928, 140317161731168, 1000000)\
-
-            ---------------------
-            -- Result Columns  --
-            ---------------------
-            - supplier_name (VARCHAR)
-            - num_products (BIGINT)\
-
-            ---------------------
-            -- Result Preview  --
-            ---------------------
-            supplier_name   num_products
-            VARCHAR BIGINT
-            [ Rows: 2]
-            Applies Inc.    2
-            Banana Republic 1
-
-        """
-        return str(self._relation)
-
-    def _wrap(
-        self: RelationType,
-        relation: "duckdb.DuckDBPyRelation",
-        schema_change: bool = False,
-    ) -> RelationType:
-        """
-        Wrap DuckDB Relation object in same Relation wrapper class as self.
-
-        This will preserve the type of the relation, even for subclasses Relation.
-        It should therefore only be used for relations which can be considered schema-
-        compatible with the original relation. Otherwise set schema_change to True
-        in order to create a Relation base object instead.
-        """
-        return type(self)(
-            derived_from=relation,
-            database=self.database,
-            model=self.model if not schema_change else None,
-        )
-
-
-class Database:
-    # Types created in order to represent enum strings
-    enum_types: Set[str]
-
-    def __init__(
-        self,
-        path: Optional[Path] = None,
-        read_only: bool = False,
-        **kwargs: Any,  # noqa: ANN401
-    ) -> None:
-        """
-        Instantiate a new DuckDB database, either persisted to disk or in-memory.
-
-        Args:
-            path: Optional path to store all the data to. If ``None`` the data is
-                persisted in-memory only.
-            read_only: If the database connection should be a read-only connection.
-            **kwargs: Additional keywords forwarded to ``duckdb.connect()``.
-
-        Examples:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> db.to_relation("select 1 as a, 2 as b").create_table("my_table")
-            >>> db.query("select * from my_table").to_df()
-            shape: (1, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 2   │
-            └─────┴─────┘
-        """
-        import duckdb
-
-        self.path = path
-        self.connection = duckdb.connect(
-            database=str(path) if path else ":memory:",
-            read_only=read_only,
-            **kwargs,
-        )
-        self.enum_types: Set[str] = set()
-
-    @classmethod
-    def default(cls) -> Database:
-        """
-        Return the default DuckDB database.
-
-        Returns:
-            A patito :ref:`Database<duckdb.Database>` object wrapping around the given
-            connection.
-
-        Example:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database.default()
-            >>> db.query("select 1 as a, 2 as b").to_df()
-            shape: (1, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 2   │
-            └─────┴─────┘
-        """
-        import duckdb
-
-        return cls.from_connection(duckdb.default_connection)
-
-    @classmethod
-    def from_connection(cls, connection: "duckdb.DuckDBPyConnection") -> Database:
-        """
-        Create database from native DuckDB connection object.
-
-        Args:
-            connection: A native DuckDB connection object created with
-                ``duckdb.connect()``.
-
-        Returns:
-            A :ref:`Database<duckdb.Database>` object wrapping around the given
-            connection.
-
-        Example:
-            >>> import duckdb
-            >>> import patito as pt
-            >>> connection = duckdb.connect()
-            >>> database = pt.duckdb.Database.from_connection(connection)
-        """
-        obj = cls.__new__(cls)
-        obj.connection = connection
-        obj.enum_types = set()
-        return obj
-
-    def to_relation(
-        self,
-        derived_from: RelationSource,
-    ) -> Relation:
-        """
-        Create a new relation object based on data source.
-
-        The given data will be represented as a relation associated with the database.
-        ``Database(x).to_relation(y)`` is equivalent to
-        ``Relation(y, database=Database(x))``.
-
-        Args:
-            derived_from (RelationSource): One of either a polars or pandas
-                ``DataFrame``, a ``pathlib.Path`` to a parquet or CSV file, a SQL query
-                string, or an existing relation.
-
-        Example:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> db.to_relation("select 1 as a, 2 as b").to_df()
-            shape: (1, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 2   │
-            └─────┴─────┘
-            >>> db.to_relation(pt.DataFrame({"c": [3, 4], "d": ["5", "6"]})).to_df()
-            shape: (2, 2)
-            ┌─────┬─────┐
-            │ c   ┆ d   │
-            │ --- ┆ --- │
-            │ i64 ┆ str │
-            ╞═════╪═════╡
-            │ 3   ┆ 5   │
-            │ 4   ┆ 6   │
-            └─────┴─────┘
-        """
-        return Relation(
-            derived_from=derived_from,
-            database=self,
-        )
-
-    def execute(
-        self,
-        query: str,
-        *parameters: Collection[Union[str, int, float, bool]],
-    ) -> None:
-        """
-        Execute SQL query in DuckDB database.
-
-        Args:
-            query: A SQL statement to execute. Does `not` have to be terminated with
-                a semicolon (``;``).
-            parameters: One or more sets of parameters to insert into prepared
-                statements. The values are replaced in place of the question marks
-                (``?``) in the prepared query.
-
-        Example:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> db.execute("create table my_table (x bigint);")
-            >>> db.execute("insert into my_table values (1), (2), (3)")
-            >>> db.table("my_table").to_df()
-            shape: (3, 1)
-            ┌─────┐
-            │ x   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            │ 2   │
-            │ 3   │
-            └─────┘
-
-            Parameters can be specified when executing prepared queries.
-
-            >>> db.execute("delete from my_table where x = ?", (2,))
-            >>> db.table("my_table").to_df()
-            shape: (2, 1)
-            ┌─────┐
-            │ x   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            │ 3   │
-            └─────┘
-
-            Multiple parameter sets can be specified when executing multiple prepared
-            queries.
-
-            >>> db.execute(
-            ...     "delete from my_table where x = ?",
-            ...     (1,),
-            ...     (3,),
-            ... )
-            >>> db.table("my_table").to_df()
-            shape: (0, 1)
-            ┌─────┐
-            │ x   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            └─────┘
-        """
-        duckdb_parameters: Union[
-            Collection[Union[str, int, float, bool]],
-            Collection[Collection[Union[str, int, float, bool]]],
-            None,
-        ]
-        if parameters is None or len(parameters) == 0:
-            duckdb_parameters = []
-            multiple_parameter_sets = False
-        elif len(parameters) == 1:
-            duckdb_parameters = parameters[0]
-            multiple_parameter_sets = False
-        else:
-            duckdb_parameters = parameters
-            multiple_parameter_sets = True
-
-        self.connection.execute(
-            query=query,
-            parameters=duckdb_parameters,
-            multiple_parameter_sets=multiple_parameter_sets,
-        )
-
-    def query(self, query: str, alias: str = "query_relation") -> Relation:
-        """
-        Execute arbitrary SQL select query and return the relation.
-
-        Args:
-            query: Arbitrary SQL select query.
-            alias: The alias to assign to the resulting relation, to be used in further
-                queries.
-
-        Returns: A relation representing the data produced by the given query.
-
-        Example:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> relation = db.query("select 1 as a, 2 as b, 3 as c")
-            >>> relation.to_df()
-            shape: (1, 3)
-            ┌─────┬─────┬─────┐
-            │ a   ┆ b   ┆ c   │
-            │ --- ┆ --- ┆ --- │
-            │ i64 ┆ i64 ┆ i64 │
-            ╞═════╪═════╪═════╡
-            │ 1   ┆ 2   ┆ 3   │
-            └─────┴─────┴─────┘
-
-            >>> relation = db.query("select 1 as a, 2 as b, 3 as c", alias="my_alias")
-            >>> relation.select("my_alias.a").to_df()
-            shape: (1, 1)
-            ┌─────┐
-            │ a   │
-            │ --- │
-            │ i64 │
-            ╞═════╡
-            │ 1   │
-            └─────┘
-        """
-        return Relation(
-            self.connection.query(query=query, alias=alias),
-            database=self,
-        )
-
-    def empty_relation(self, schema: Type[ModelType]) -> Relation[ModelType]:
-        """
-        Create relation with zero rows, but correct schema that matches the given model.
-
-        Args:
-            schema: A patito model which specifies the column names and types of the
-                given relation.
-
-        Example:
-            >>> import patito as pt
-            >>> class Schema(pt.Model):
-            ...     string_column: str
-            ...     bool_column: bool
-            ...
-            >>> db = pt.duckdb.Database()
-            >>> empty_relation = db.empty_relation(Schema)
-            >>> empty_relation.to_df()
-            shape: (0, 2)
-            ┌───────────────┬─────────────┐
-            │ string_column ┆ bool_column │
-            │ ---           ┆ ---         │
-            │ str           ┆ bool        │
-            ╞═══════════════╪═════════════╡
-            └───────────────┴─────────────┘
-            >>> non_empty_relation = db.query(
-            ...     "select 'dummy' as string_column, true as bool_column"
-            ... )
-            >>> non_empty_relation.union(empty_relation).to_df()
-            shape: (1, 2)
-            ┌───────────────┬─────────────┐
-            │ string_column ┆ bool_column │
-            │ ---           ┆ ---         │
-            │ str           ┆ bool        │
-            ╞═══════════════╪═════════════╡
-            │ dummy         ┆ true        │
-            └───────────────┴─────────────┘
-        """
-        return self.to_relation(schema.examples()).limit(0)
-
-    def table(self, name: str) -> Relation:
-        """
-        Return relation representing all the data in the given table.
-
-        Args:
-            name: The name of the table.
-
-        Example:
-            >>> import patito as pt
-            >>> df = pt.DataFrame({"a": [1, 2], "b": [3, 4]})
-            >>> db = pt.duckdb.Database()
-            >>> relation = db.to_relation(df)
-            >>> relation.create_table(name="my_table")
-            >>> db.table("my_table").to_df()
-            shape: (2, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 3   │
-            │ 2   ┆ 4   │
-            └─────┴─────┘
-        """
-        return Relation(
-            self.connection.table(name),
-            database=self.from_connection(self.connection),
-        )
-
-    def view(self, name: str) -> Relation:
-        """
-        Return relation representing all the data in the given view.
-
-        Args:
-            name: The name of the view.
-
-        Example:
-            >>> import patito as pt
-            >>> df = pt.DataFrame({"a": [1, 2], "b": [3, 4]})
-            >>> db = pt.duckdb.Database()
-            >>> relation = db.to_relation(df)
-            >>> relation.create_view(name="my_view")
-            >>> db.view("my_view").to_df()
-            shape: (2, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ i64 │
-            ╞═════╪═════╡
-            │ 1   ┆ 3   │
-            │ 2   ┆ 4   │
-            └─────┴─────┘
-        """
-        return Relation(
-            self.connection.view(name),
-            database=self.from_connection(self.connection),
-        )
-
-    def create_table(
-        self,
-        name: str,
-        model: Type[ModelType],
-    ) -> Relation[ModelType]:
-        """
-        Create table with schema matching the provided Patito model.
-
-        See :ref:`Relation.insert_into()<duckdb.Relation.insert_into>` for how to insert
-        data into the table after creation.
-        The :ref:`Relation.create_table()<duckdb.Relation.create_table>` method can also
-        be used to create a table from a given relation `and` insert the data at the
-        same time.
-
-        Args:
-            name: Name of new database table.
-            model (Type[Model]): Patito model indicating names and types of table
-                columns.
-        Returns:
-            Relation[ModelType]: Relation pointing to the new table.
-
-        Example:
-            >>> from typing import Optional
-            >>> import patito as pt
-            >>> class MyModel(pt.Model):
-            ...     str_column: str
-            ...     nullable_string_column: Optional[str]
-            ...
-            >>> db = pt.duckdb.Database()
-            >>> db.create_table(name="my_table", model=MyModel)
-            >>> db.table("my_table").types
-            {'str_column': VARCHAR, 'nullable_string_column': VARCHAR}
-        """
-        self.create_enum_types(model=model)
-        schema = model.schema()
-        non_nullable = schema.get("required", [])
-        columns = []
-        for column_name, sql_type in model.sql_types.items():
-            column = f"{column_name} {sql_type}"
-            if column_name in non_nullable:
-                column += " not null"
-            columns.append(column)
-        self.connection.execute(f"create table {name} ({','.join(columns)})")
-        # TODO: Fix typing
-        return self.table(name).set_model(model)  # pyright: ignore
-
-    def create_enum_types(self, model: Type[ModelType]) -> None:
-        """
-        Define SQL enum types in DuckDB database.
-
-        Args:
-            model: Model for which all Literal-annotated or enum-annotated string fields
-                will get respective DuckDB enum types.
-
-        Example:
-            >>> import patito as pt
-            >>> class EnumModel(pt.Model):
-            ...     enum_column: Literal["A", "B", "C"]
-            ...
-            >>> db = pt.duckdb.Database()
-            >>> db.create_enum_types(EnumModel)
-            >>> db.enum_types
-            {'enum__7ba49365cc1b0fd57e61088b3bc9aa25'}
-        """
-        import duckdb
-
-        for props in model._schema_properties().values():
-            if "enum" not in props or props["type"] != "string":
-                # DuckDB enums only support string values
-                continue
-
-            enum_type_name = _enum_type_name(field_properties=props)
-            if enum_type_name in self.enum_types:
-                # This enum type has already been created
-                continue
-
-            enum_values = ", ".join(repr(value) for value in sorted(props["enum"]))
-            try:
-                self.connection.execute(
-                    f"create type {enum_type_name} as enum ({enum_values})"
-                )
-            except duckdb.CatalogException as e:
-                if "already exists" not in str(e):
-                    raise e  # pragma: no cover
-            self.enum_types.add(enum_type_name)
-
-    def create_view(
-        self,
-        name: str,
-        data: RelationSource,
-    ) -> Relation:
-        """Create a view based on the given data source."""
-        return self.to_relation(derived_from=data).create_view(name)
-
-    def __contains__(self, table: str) -> bool:
-        """
-        Return ``True`` if the database contains a table with the given name.
-
-        Args:
-            table: The name of the table to be checked for.
-
-        Examples:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> "my_table" in db
-            False
-            >>> db.to_relation("select 1 as a, 2 as b").create_table(name="my_table")
-            >>> "my_table" in db
-            True
-        """
-        try:
-            self.connection.table(table_name=table)
-            return True
-        except Exception:
-            return False
diff --git a/src/patito/exceptions.py b/src/patito/exceptions.py
index 354db0f..41d7a79 100644
--- a/src/patito/exceptions.py
+++ b/src/patito/exceptions.py
@@ -1,14 +1,168 @@
-"""Module containing all custom exceptions raised by patito."""
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    Generator,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    TypedDict,
+    Union,
+)
 
-import pydantic
+from patito._pydantic.repr import Representation
 
+if TYPE_CHECKING:
+    from pydantic import BaseModel
 
-class ValidationError(pydantic.ValidationError):
-    """Exception raised when dataframe does not match schema."""
+    Loc = Tuple[Union[int, str], ...]
 
+    class _ErrorDictRequired(TypedDict):
+        loc: Loc
+        msg: str
+        type: str
 
-class ErrorWrapper(pydantic.error_wrappers.ErrorWrapper):
-    """Wrapper for specific column validation error."""
+    class ErrorDict(_ErrorDictRequired, total=False):
+        ctx: Dict[str, Any]
+
+    from patito._pydantic.repr import ReprArgs
+
+
+__all__ = "ErrorWrapper", "DataFrameValidationError"
+
+
+class ErrorWrapper(Representation):
+    __slots__ = "exc", "_loc"
+
+    def __init__(self, exc: Exception, loc: Union[str, "Loc"]) -> None:
+        self.exc = exc
+        self._loc = loc
+
+    def loc_tuple(self) -> "Loc":
+        if isinstance(self._loc, tuple):
+            return self._loc
+        else:
+            return (self._loc,)
+
+    def __repr_args__(self) -> "ReprArgs":
+        return [("exc", self.exc), ("loc", self.loc_tuple())]
+
+
+# ErrorList is something like Union[List[Union[List[ErrorWrapper], ErrorWrapper]], ErrorWrapper]
+# but recursive, therefore just use:
+ErrorList = Union[Sequence[Any], ErrorWrapper]
+
+
+class DataFrameValidationError(Representation, ValueError):
+    __slots__ = "raw_errors", "model", "_error_cache"
+
+    def __init__(self, errors: Sequence[ErrorList], model: Type["BaseModel"]) -> None:
+        self.raw_errors = errors
+        self.model = model
+        self._error_cache: Optional[List["ErrorDict"]] = None
+
+    def errors(self) -> List["ErrorDict"]:
+        if self._error_cache is None:
+            self._error_cache = list(flatten_errors(self.raw_errors))
+        return self._error_cache
+
+    def __str__(self) -> str:
+        errors = self.errors()
+        no_errors = len(errors)
+        return (
+            f'{no_errors} validation error{"" if no_errors == 1 else "s"} for {self.model.__name__}\n'
+            f"{display_errors(errors)}"
+        )
+
+    def __repr_args__(self) -> "ReprArgs":
+        return [("model", self.model.__name__), ("errors", self.errors())]
+
+
+def display_errors(errors: List["ErrorDict"]) -> str:
+    return "\n".join(
+        f'{_display_error_loc(e)}\n  {e["msg"]} ({_display_error_type_and_ctx(e)})'
+        for e in errors
+    )
+
+
+def _display_error_loc(error: "ErrorDict") -> str:
+    return " -> ".join(str(e) for e in error["loc"])
+
+
+def _display_error_type_and_ctx(error: "ErrorDict") -> str:
+    t = "type=" + error["type"]
+    ctx = error.get("ctx")
+    if ctx:
+        return t + "".join(f"; {k}={v}" for k, v in ctx.items())
+    else:
+        return t
+
+
+def flatten_errors(
+    errors: Sequence[Any], loc: Optional["Loc"] = None
+) -> Generator["ErrorDict", None, None]:
+    for error in errors:
+        if isinstance(error, ErrorWrapper):
+            if loc:
+                error_loc = loc + error.loc_tuple()
+            else:
+                error_loc = error.loc_tuple()
+
+            if isinstance(error.exc, DataFrameValidationError):
+                yield from flatten_errors(error.exc.raw_errors, error_loc)
+            else:
+                yield error_dict(error.exc, error_loc)
+        elif isinstance(error, list):
+            yield from flatten_errors(error, loc=loc)
+        else:
+            raise RuntimeError(f"Unknown error object: {error}")
+
+
+def error_dict(exc: Exception, loc: "Loc") -> "ErrorDict":
+    type_ = get_exc_type(exc.__class__)
+    msg_template = getattr(exc, "msg_template", None)
+    ctx = exc.__dict__
+    if msg_template:
+        msg = msg_template.format(**ctx)
+    else:
+        msg = str(exc)
+
+    d: "ErrorDict" = {"loc": loc, "msg": msg, "type": type_}
+
+    if ctx:
+        d["ctx"] = ctx
+
+    return d
+
+
+_EXC_TYPE_CACHE: Dict[Type[Exception], str] = {}
+
+
+def get_exc_type(cls: Type[Exception]) -> str:
+    # slightly more efficient than using lru_cache since we don't need to worry about the cache filling up
+    try:
+        return _EXC_TYPE_CACHE[cls]
+    except KeyError:
+        r = _get_exc_type(cls)
+        _EXC_TYPE_CACHE[cls] = r
+        return r
+
+
+def _get_exc_type(cls: Type[Exception]) -> str:
+    if issubclass(cls, AssertionError):
+        return "assertion_error"
+
+    base_name = "type_error" if issubclass(cls, TypeError) else "value_error"
+    if cls in (TypeError, ValueError):
+        # just TypeError or ValueError, no extra code
+        return base_name
+
+    # if it's not a TypeError or ValueError, we just take the lowercase of the exception name
+    # no chaining or snake case logic, use "code" for more complex error types.
+    code = getattr(cls, "code", None) or cls.__name__.replace("Error", "").lower()
+    return base_name + "." + code
 
 
 class WrongColumnsError(TypeError):
@@ -19,7 +173,7 @@ class MissingColumnsError(WrongColumnsError):
     """Exception for when a dataframe is missing one or more columns."""
 
 
-class SuperflousColumnsError(WrongColumnsError):
+class SuperfluousColumnsError(WrongColumnsError):
     """Exception for when a dataframe has one ore more non-specified columns."""
 
 
diff --git a/src/patito/polars.py b/src/patito/polars.py
index 7125807..108e6b3 100644
--- a/src/patito/polars.py
+++ b/src/patito/polars.py
@@ -5,9 +5,12 @@
     TYPE_CHECKING,
     Any,
     Collection,
+    Dict,
     Generic,
     Iterable,
     Optional,
+    Sequence,
+    Tuple,
     Type,
     TypeVar,
     Union,
@@ -16,9 +19,10 @@
 
 import polars as pl
 from polars.type_aliases import IntoExpr
-from pydantic import create_model
+from pydantic import AliasChoices, AliasPath, create_model
 from typing_extensions import Literal
 
+from patito._pydantic.column_info import ColumnInfo
 from patito.exceptions import MultipleRowsReturned, RowDoesNotExist
 
 if TYPE_CHECKING:
@@ -42,25 +46,27 @@ class LazyFrame(pl.LazyFrame, Generic[ModelType]):
     def _construct_lazyframe_model_class(
         cls: Type[LDF], model: Optional[Type[ModelType]]
     ) -> Type[LazyFrame[ModelType]]:
-        """
-        Return custom LazyFrame sub-class where LazyFrame.model is set.
+        """Return custom LazyFrame sub-class where LazyFrame.model is set.
 
         Can be used to construct a LazyFrame class where
         DataFrame.set_model(model) is implicitly invoked at collection.
 
         Args:
+        ----
             model: A patito model which should be used to validate the final dataframe.
                 If None is provided, the regular LazyFrame class will be returned.
 
         Returns:
+        -------
             A custom LazyFrame model class where LazyFrame.model has been correctly
                 "hard-coded" to the given model.
+
         """
         if model is None:
             return cls
 
         new_class = type(
-            f"{model.schema()['title']}LazyFrame",
+            f"{model.__name__}LazyFrame",
             (cls,),
             {"model": model},
         )
@@ -68,41 +74,145 @@ def _construct_lazyframe_model_class(
 
     def collect(
         self,
-        type_coercion: bool = True,
-        predicate_pushdown: bool = True,
-        projection_pushdown: bool = True,
-        simplify_expression: bool = True,
-        no_optimization: bool = False,
-        slice_pushdown: bool = True,
-        common_subplan_elimination: bool = True,
-        streaming: bool = False,
+        *args,
+        **kwargs,
     ) -> "DataFrame[ModelType]":  # noqa: DAR101, DAR201
-        """
-        Collect into a DataFrame.
+        """Collect into a DataFrame.
 
         See documentation of polars.DataFrame.collect for full description of
         parameters.
         """
-        df = super().collect(
-            type_coercion=type_coercion,
-            predicate_pushdown=predicate_pushdown,
-            projection_pushdown=projection_pushdown,
-            simplify_expression=simplify_expression,
-            no_optimization=no_optimization,
-            slice_pushdown=slice_pushdown,
-            common_subplan_elimination=common_subplan_elimination,
-            streaming=streaming,
-        )
+        df = super().collect(*args, **kwargs)
         if getattr(self, "model", False):
             cls = DataFrame._construct_dataframe_model_class(model=self.model)
         else:
             cls = DataFrame
         return cls._from_pydf(df._df)
 
+    def derive(self: LDF, columns: list[str] | None = None) -> LDF:
+        derived_columns = []
+        props = self.model._schema_properties()
+        original_columns = set(self.columns)
+        to_derive = self.model.derived_columns if columns is None else columns
+        for column_name in to_derive:
+            if column_name not in derived_columns:
+                self, _derived_columns = self._derive_column(
+                    self, column_name, self.model.column_infos
+                )
+                derived_columns.extend(_derived_columns)
+        out_cols = [
+            x for x in props if x in original_columns.union(to_derive)
+        ]  # ensure that model columns are first and in the correct order
+        out_cols += [
+            x for x in original_columns.union(to_derive) if x not in out_cols
+        ]  # collect columns originally in data frame that are not in the model and append to end of df
+        return self.select(out_cols)
+
+    def _derive_column(
+        self,
+        df: LDF,
+        column_name: str,
+        column_infos: Dict[str, ColumnInfo],
+    ) -> Tuple[LDF, Sequence[str]]:
+        if (
+            column_infos.get(column_name, None) is None
+            or column_infos[column_name].derived_from is None
+        ):
+            return df, []
+        derived_from = column_infos[column_name].derived_from
+        dtype = self.model.dtypes[column_name]
+        derived_columns = []
+        if isinstance(derived_from, str):
+            df = df.with_columns(pl.col(derived_from).cast(dtype).alias(column_name))
+        elif isinstance(derived_from, pl.Expr):
+            root_cols = derived_from.meta.root_names()
+            while root_cols:
+                root_col = root_cols.pop()
+                df, _derived_columns = self._derive_column(df, root_col, column_infos)
+                derived_columns.extend(_derived_columns)
+            df = df.with_columns(derived_from.cast(dtype).alias(column_name))
+        else:
+            raise TypeError(
+                "Can not derive dataframe column from type " f"{type(derived_from)}."
+            )
+        derived_columns.append(column_name)
+        return df, derived_columns
+
+    def unalias(self: LDF) -> LDF:
+        if not any(fi.validation_alias for fi in self.model.model_fields.values()):
+            return self
+        exprs = []
+
+        def to_expr(va: str | AliasPath | AliasChoices) -> Optional[pl.Expr]:
+            if isinstance(va, str):
+                return pl.col(va) if va in self.columns else None
+            elif isinstance(va, AliasPath):
+                if len(va.path) != 2 or not isinstance(va.path[1], int):
+                    raise NotImplementedError(
+                        f"TODO figure out how this AliasPath behaves ({va})"
+                    )
+                return (
+                    pl.col(va.path[0]).list.get(va.path[1])
+                    if va.path[0] in self.columns
+                    else None
+                )
+            elif isinstance(va, AliasChoices):
+                local_expr: Optional[pl.Expr] = None
+                for choice in va.choices:
+                    if (part := to_expr(choice)) is not None:
+                        local_expr = (
+                            local_expr.fill_null(value=part)
+                            if local_expr is not None
+                            else part
+                        )
+                return local_expr
+            else:
+                raise NotImplementedError(
+                    f"unknown validation_alias type {field_info.validation_alias}"
+                )
+
+        for name, field_info in self.model.model_fields.items():
+            if field_info.validation_alias is None:
+                exprs.append(pl.col(name))
+            else:
+                expr = to_expr(field_info.validation_alias)
+                if name in self.columns:
+                    if expr is None:
+                        exprs.append(pl.col(name))
+                    else:
+                        exprs.append(pl.col(name).fill_null(value=expr))
+                elif expr is not None:
+                    exprs.append(expr.alias(name))
+
+        return self.select(exprs)
+
+    def cast(
+        self: LDF, strict: bool = False, columns: Optional[Sequence[str]] = None
+    ) -> LDF:
+        properties = self.model._schema_properties()
+        valid_dtypes = self.model.valid_dtypes
+        default_dtypes = self.model.dtypes
+        columns = columns or self.columns
+        exprs = []
+        for column, current_dtype in zip(self.columns, self.dtypes):
+            if (column not in columns) or (column not in properties):
+                exprs.append(pl.col(column))
+            elif "dtype" in properties[column]:
+                exprs.append(pl.col(column).cast(properties[column]["dtype"]))
+            elif not strict and current_dtype in valid_dtypes[column]:
+                exprs.append(pl.col(column))
+            else:
+                exprs.append(pl.col(column).cast(default_dtypes[column]))
+        return self.with_columns(exprs)
+
+    @classmethod
+    def from_existing(cls: Type[LDF], lf: pl.LazyFrame) -> LDF:
+        """Constructs a patito.DataFrame object from an existing polars.DataFrame object"""
+        return cls.model.LazyFrame._from_pyldf(lf._ldf).cast()
+
 
 class DataFrame(pl.DataFrame, Generic[ModelType]):
-    """
-    A sub-class of polars.DataFrame with additional functionality related to Model.
+    """A sub-class of polars.DataFrame with additional functionality related to Model.
 
     Two different methods are available for constructing model-aware data frames.
     Assume a simple model with two fields:
@@ -136,34 +246,37 @@ class DataFrame(pl.DataFrame, Generic[ModelType]):
     def _construct_dataframe_model_class(
         cls: Type[DF], model: Type[OtherModelType]
     ) -> Type[DataFrame[OtherModelType]]:
-        """
-        Return custom DataFrame sub-class where DataFrame.model is set.
+        """Return custom DataFrame sub-class where DataFrame.model is set.
 
         Can be used to construct a DataFrame class where
         DataFrame.set_model(model) is implicitly invoked at instantiation.
 
         Args:
+        ----
             model: A patito model which should be used to validate the dataframe.
 
         Returns:
+        -------
             A custom DataFrame model class where DataFrame._model has been correctly
                 "hard-coded" to the given model.
+
         """
         new_class = type(
-            f"{model.schema()['title']}DataFrame",
+            f"{model.model_json_schema()['title']}DataFrame",
             (cls,),
             {"model": model},
         )
         return new_class
 
     def lazy(self: DataFrame[ModelType]) -> LazyFrame[ModelType]:
-        """
-        Convert DataFrame into LazyFrame.
+        """Convert DataFrame into LazyFrame.
 
         See documentation of polars.DataFrame.lazy() for full description.
 
-        Returns:
+        Returns
+        -------
             A new LazyFrame object.
+
         """
         lazyframe_class: LazyFrame[
             ModelType
@@ -174,8 +287,7 @@ def lazy(self: DataFrame[ModelType]) -> LazyFrame[ModelType]:
         return ldf
 
     def set_model(self, model):  # type: ignore[no-untyped-def] # noqa: ANN001, ANN201
-        """
-        Associate a given patito ``Model`` with the dataframe.
+        """Associate a given patito ``Model`` with the dataframe.
 
         The model schema is used by methods that depend on a model being associated with
         the given dataframe such as :ref:`DataFrame.validate() <DataFrame.validate>`
@@ -184,14 +296,17 @@ def set_model(self, model):  # type: ignore[no-untyped-def] # noqa: ANN001, ANN2
         ``DataFrame(...).set_model(Model)`` is equivalent with ``Model.DataFrame(...)``.
 
         Args:
+        ----
             model (Model): Sub-class of ``patito.Model`` declaring the schema of the
                 dataframe.
 
         Returns:
+        -------
             DataFrame[Model]: Returns the same dataframe, but with an attached model
             that is required for certain model-specific dataframe methods to work.
 
         Examples:
+        --------
             >>> from typing_extensions import Literal
             >>> import patito as pt
             >>> import polars as pl
@@ -228,6 +343,7 @@ def set_model(self, model):  # type: ignore[no-untyped-def] # noqa: ANN001, ANN2
             │ 2    ┆ B      │
             └──────┴────────┘
             >>> casted_classes.validate()
+
         """
         cls = self._construct_dataframe_model_class(model=model)
         return cast(
@@ -235,11 +351,27 @@ def set_model(self, model):  # type: ignore[no-untyped-def] # noqa: ANN001, ANN2
             cls._from_pydf(self._df),
         )
 
-    def cast(self: DF, strict: bool = False) -> DF:
+    def unalias(self: DF) -> DF:
+        """Un-aliases column names using information from pydantic validation_alias.
+
+        In order of preference - model field name then validation_aliases in order of occurrence
+
+        limitation - AliasChoice validation type only supports selecting a single element of an array
+
+        Returns
+        -------
+            DataFrame[Model]: A dataframe with columns normalized to model names.
+
         """
-        Cast columns to `dtypes` specified by the associated Patito model.
+        return self.lazy().unalias().collect()
+
+    def cast(
+        self: DF, strict: bool = False, columns: Optional[Sequence[str]] = None
+    ) -> DF:
+        """Cast columns to `dtypes` specified by the associated Patito model.
 
         Args:
+        ----
             strict: If set to ``False``, columns which are technically compliant with
                 the specified field type, will not be casted. For example, a column
                 annotated with ``int`` is technically compliant with ``pl.UInt8``, even
@@ -248,9 +380,11 @@ def cast(self: DF, strict: bool = False) -> DF:
                 be forced to the default dtype associated with each python type.
 
         Returns:
+        -------
             DataFrame[Model]: A dataframe with columns casted to the correct dtypes.
 
         Examples:
+        --------
             Create a simple model:
 
             >>> import patito as pt
@@ -271,44 +405,34 @@ def cast(self: DF, strict: bool = False) -> DF:
             ╞═══════╪════════════╡
             │ apple ┆ 8          │
             └───────┴────────────┘
+
         """
-        properties = self.model._schema_properties()
-        valid_dtypes = self.model.valid_dtypes
-        default_dtypes = self.model.dtypes
-        columns = []
-        for column, current_dtype in zip(self.columns, self.dtypes):
-            if column not in properties:
-                columns.append(pl.col(column))
-            elif "dtype" in properties[column]:
-                columns.append(pl.col(column).cast(properties[column]["dtype"]))
-            elif not strict and current_dtype in valid_dtypes[column]:
-                columns.append(pl.col(column))
-            else:
-                columns.append(pl.col(column).cast(default_dtypes[column]))
-        return self.with_columns(columns)
+        return self.lazy().cast(strict=strict, columns=columns).collect()
 
     def drop(
         self: DF,
         columns: Optional[Union[str, Collection[str]]] = None,
         *more_columns: str,
     ) -> DF:
-        """
-        Drop one or more columns from the dataframe.
+        """Drop one or more columns from the dataframe.
 
         If ``name`` is not provided then all columns `not` specified by the associated
         patito model, for instance set with
         :ref:`DataFrame.set_model <DataFrame.set_model>`, are dropped.
 
         Args:
+        ----
             columns: A single column string name, or list of strings, indicating
                 which columns to drop. If not specified, all columns *not*
                 specified by the associated dataframe model will be dropped.
             more_columns: Additional named columns to drop.
 
         Returns:
+        -------
             DataFrame[Model]: New dataframe without the specified columns.
 
         Examples:
+        --------
             >>> import patito as pt
             >>> class Model(pt.Model):
             ...     column_1: int
@@ -330,25 +454,29 @@ def drop(
         else:
             return self.drop(list(set(self.columns) - set(self.model.columns)))
 
-    def validate(self: DF) -> DF:
-        """
-        Validate the schema and content of the dataframe.
+    def validate(
+        self: DF, columns: Optional[Sequence[str]] = None, **kwargs: Any
+    ) -> DF:
+        """Validate the schema and content of the dataframe.
 
         You must invoke ``.set_model()`` before invoking ``.validate()`` in order
         to specify how the dataframe should be validated.
 
-        Returns:
+        Returns
+        -------
             DataFrame[Model]: The original dataframe, if correctly validated.
 
-        Raises:
+        Raises
+        ------
             TypeError: If ``DataFrame.set_model()`` has not been invoked prior to
                 validation. Note that ``patito.Model.DataFrame`` automatically invokes
                 ``DataFrame.set_model()`` for you.
 
-            patito.exceptions.ValidationError: If the dataframe does not match the
+            patito.exceptions.DataFrameValidationError: If the dataframe does not match the
                 specified schema.
 
-        Examples:
+        Examples
+        --------
             >>> import patito as pt
 
 
@@ -366,7 +494,7 @@ def validate(self: DF) -> DF:
             ... ).set_model(Product)
             >>> try:
             ...     df.validate()
-            ... except pt.ValidationError as exc:
+            ... except pt.DataFrameValidationError as exc:
             ...     print(exc)
             ...
             3 validation errors for Product
@@ -376,18 +504,18 @@ def validate(self: DF) -> DF:
               2 rows with duplicated values. (type=value_error.rowvalue)
             temperature_zone
               Rows with invalid values: {'oven'}. (type=value_error.rowvalue)
+
         """
         if not hasattr(self, "model"):
             raise TypeError(
                 f"You must invoke {self.__class__.__name__}.set_model() "
                 f"before invoking {self.__class__.__name__}.validate()."
             )
-        self.model.validate(dataframe=self)
+        self.model.validate(dataframe=self, columns=columns, **kwargs)
         return self
 
-    def derive(self: DF) -> DF:
-        """
-        Populate columns which have ``pt.Field(derived_from=...)`` definitions.
+    def derive(self: DF, columns: list[str] | None = None) -> DF:
+        """Populate columns which have ``pt.Field(derived_from=...)`` definitions.
 
         If a column field on the data frame model has ``patito.Field(derived_from=...)``
         specified, the given value will be used to define the column. If
@@ -395,14 +523,17 @@ def derive(self: DF) -> DF:
         column name. Alternatively, an arbitrary polars expression can be given, the
         result of which will be used to populate the column values.
 
-        Returns:
+        Returns
+        -------
             DataFrame[Model]: A new dataframe where all derivable columns are provided.
 
-        Raises:
+        Raises
+        ------
             TypeError: If the ``derived_from`` parameter of ``patito.Field`` is given
                 as something else than a string or polars expression.
 
-        Examples:
+        Examples
+        --------
             >>> import patito as pt
             >>> import polars as pl
             >>> class Foo(pt.Model):
@@ -411,32 +542,17 @@ def derive(self: DF) -> DF:
             ...
             >>> Foo.DataFrame({"foo": [1, 2]}).derive()
             shape: (2, 3)
-            ┌─────┬─────┬────────────┐
-            │ foo ┆ bar ┆ double_bar │
-            │ --- ┆ --- ┆ ---        │
-            │ i64 ┆ i64 ┆ i64        │
-            ╞═════╪═════╪════════════╡
-            │ 1   ┆ 1   ┆ 2          │
-            │ 2   ┆ 2   ┆ 4          │
-            └─────┴─────┴────────────┘
+            ┌─────┬────────────┬─────┐
+            │ bar ┆ double_bar ┆ foo │
+            │ --- ┆ ---        ┆ --- │
+            │ i64 ┆ i64        ┆ i64 │
+            ╞═════╪════════════╪═════╡
+            │ 1   ┆ 2          ┆ 1   │
+            │ 2   ┆ 4          ┆ 2   │
+            └─────┴────────────┴─────┘
+
         """
-        df = self.lazy()
-        for column_name, props in self.model._schema_properties().items():
-            if "derived_from" in props:
-                derived_from = props["derived_from"]
-                dtype = self.model.dtypes[column_name]
-                if isinstance(derived_from, str):
-                    df = df.with_columns(
-                        pl.col(derived_from).cast(dtype).alias(column_name)
-                    )
-                elif isinstance(derived_from, pl.Expr):
-                    df = df.with_columns(derived_from.cast(dtype).alias(column_name))
-                else:
-                    raise TypeError(
-                        "Can not derive dataframe column from type "
-                        f"{type(derived_from)}."
-                    )
-        return cast(DF, df.collect())
+        return cast(DF, self.lazy().derive(columns=columns).collect())
 
     def fill_null(
         self: DF,
@@ -449,13 +565,13 @@ def fill_null(
         limit: Optional[int] = None,
         matches_supertype: bool = True,
     ) -> DF:
-        """
-        Fill null values using a filling strategy, literal, or ``Expr``.
+        """Fill null values using a filling strategy, literal, or ``Expr``.
 
         If ``"default"`` is provided as the strategy, the model fields with default
         values are used to fill missing values.
 
         Args:
+        ----
             value: Value used to fill null values.
             strategy: Accepts the same arguments as ``polars.DataFrame.fill_null`` in
                 addition to ``"defaults"`` which will use the field's default value if
@@ -466,10 +582,12 @@ def fill_null(
 
 
         Returns:
+        -------
             DataFrame[Model]: A new dataframe with nulls filled in according to the
             provided ``strategy`` parameter.
 
         Example:
+        -------
             >>> import patito as pt
             >>> class Product(pt.Model):
             ...     name: str
@@ -488,6 +606,7 @@ def fill_null(
             │ apple  ┆ 10    │
             │ banana ┆ 19    │
             └────────┴───────┘
+
         """
         if strategy != "defaults":  # pragma: no cover
             return cast(  # pyright: ignore[redundant-cast]
@@ -501,19 +620,26 @@ def fill_null(
             )
         return self.with_columns(
             [
-                pl.col(column).fill_null(pl.lit(default_value))
+                pl.col(column).fill_null(
+                    pl.lit(default_value, self.model.dtypes[column])
+                )
+                if column in self.columns
+                else pl.Series(
+                    column, [default_value], self.model.dtypes[column]
+                )  # NOTE: hack to get around polars bug https://github.com/pola-rs/polars/issues/13602
+                # else pl.lit(default_value, self.model.dtypes[column]).alias(column)
                 for column, default_value in self.model.defaults.items()
             ]
         ).set_model(self.model)
 
     def get(self, predicate: Optional[pl.Expr] = None) -> ModelType:
-        """
-        Fetch the single row that matches the given polars predicate.
+        """Fetch the single row that matches the given polars predicate.
 
         If you expect a data frame to already consist of one single row,
         you can use ``.get()`` without any arguments to return that row.
 
         Raises:
+        ------
             RowDoesNotExist: If zero rows evaluate to true for the given predicate.
             MultipleRowsReturned: If more than one row evaluates to true for the given
                 predicate.
@@ -522,12 +648,15 @@ def get(self, predicate: Optional[pl.Expr] = None) -> ModelType:
                 same class.
 
         Args:
+        ----
             predicate: A polars expression defining the criteria of the filter.
 
         Returns:
+        -------
             Model: A pydantic-derived base model representing the given row.
 
         Example:
+        -------
             >>> import patito as pt
             >>> import polars as pl
             >>> df = pt.DataFrame({"product_id": [1, 2, 3], "price": [10, 10, 20]})
@@ -574,6 +703,7 @@ def get(self, predicate: Optional[pl.Expr] = None) -> ModelType:
             ...     print(e)
             ...
             DataFrame.get() yielded 0 rows.
+
         """
         row = self if predicate is None else self.filter(predicate)
         if row.height == 0:
@@ -589,12 +719,13 @@ def get(self, predicate: Optional[pl.Expr] = None) -> ModelType:
             return self._pydantic_model().from_row(row)  # type: ignore
 
     def _pydantic_model(self) -> Type[Model]:
-        """
-        Dynamically construct patito model compliant with dataframe.
+        """Dynamically construct patito model compliant with dataframe.
 
-        Returns:
+        Returns
+        -------
             A pydantic model class where all the rows have been specified as
                 `typing.Any` fields.
+
         """
         from patito.pydantic import Model
 
@@ -608,26 +739,31 @@ def _pydantic_model(self) -> Type[Model]:
             ),
         )
 
+    def as_polars(self) -> pl.DataFrame:
+        return pl.DataFrame._from_pydf(self._df)
+
     @classmethod
     def read_csv(  # type: ignore[no-untyped-def]
         cls: Type[DF],
         *args,  # noqa: ANN002
         **kwargs,  # noqa: ANN003
     ) -> DF:
-        r"""
-        Read CSV and apply correct column name and types from model.
+        r"""Read CSV and apply correct column name and types from model.
 
         If any fields have ``derived_from`` specified, the given expression will be used
         to populate the given column(s).
 
         Args:
+        ----
             *args: All positional arguments are forwarded to ``polars.read_csv``.
             **kwargs: All keyword arguments are forwarded to ``polars.read_csv``.
 
         Returns:
+        -------
             DataFrame[Model]: A dataframe representing the given CSV file data.
 
         Examples:
+        --------
             The ``DataFrame.read_csv`` method can be used to automatically set the
             correct column names when reading CSV files without headers.
 
@@ -670,6 +806,7 @@ def read_csv(  # type: ignore[no-untyped-def]
             # ╞═════╪═════╡
             # │ 1.0 ┆ 1   │
             # └─────┴─────┘
+
         """
         kwargs.setdefault("dtypes", cls.model.dtypes)
         if not kwargs.get("has_header", True) and "columns" not in kwargs:
@@ -684,7 +821,7 @@ def filter(  # noqa: D102
             pl.Expr, str, pl.Series, list[bool], np.ndarray[Any, Any], bool
         ],
     ) -> DF:
-        return cast(DF, super().filter(predicate=predicate))
+        return cast(DF, super().filter(predicate))
 
     def select(  # noqa: D102
         self: DF,
diff --git a/src/patito/pydantic.py b/src/patito/pydantic.py
index 2a5cfb1..170891a 100644
--- a/src/patito/pydantic.py
+++ b/src/patito/pydantic.py
@@ -3,27 +3,52 @@
 
 import itertools
 from collections.abc import Iterable
-from datetime import date, datetime
+from datetime import date, datetime, time, timedelta
+from functools import partial
+from inspect import getfullargspec
 from typing import (
     TYPE_CHECKING,
     Any,
     ClassVar,
     Dict,
+    FrozenSet,
+    Generic,
     List,
+    Literal,
+    Mapping,
     Optional,
-    Set,
+    Sequence,
+    Tuple,
     Type,
     TypeVar,
     Union,
     cast,
+    get_args,
 )
 
 import polars as pl
-from polars.datatypes import PolarsDataType
-from pydantic import BaseConfig, BaseModel, Field, create_model  # noqa: F401
-from pydantic.main import ModelMetaclass as PydanticModelMetaclass
-from typing_extensions import Literal, get_args
-
+from polars.datatypes import DataType, DataTypeClass
+from pydantic import (  # noqa: F401
+    BaseModel,
+    create_model,
+    field_serializer,
+    fields,
+)
+from pydantic._internal._model_construction import (
+    ModelMetaclass as PydanticModelMetaclass,
+)
+from zoneinfo import ZoneInfo
+
+from patito._pydantic.column_info import CI, ColumnInfo
+from patito._pydantic.dtypes import (
+    default_dtypes_for_model,
+    dtype_from_string,
+    is_optional,
+    valid_dtypes_for_model,
+    validate_annotation,
+    validate_polars_dtype,
+)
+from patito._pydantic.schema import column_infos_for_model, schema_for_model
 from patito.polars import DataFrame, LazyFrame
 from patito.validators import validate
 
@@ -36,46 +61,35 @@
 
 if TYPE_CHECKING:
     import patito.polars
-    from patito.duckdb import DuckDBSQLType
 
 # The generic type of a single row in given Relation.
 # Should be a typed subclass of Model.
 ModelType = TypeVar("ModelType", bound="Model")
 
-# A mapping from pydantic types to the equivalent type used in DuckDB
-PYDANTIC_TO_DUCKDB_TYPES = {
-    "integer": "BIGINT",
-    "string": "VARCHAR",
-    "number": "DOUBLE",
-    "boolean": "BOOLEAN",
-}
 
-# A mapping from pydantic types to equivalent dtypes used in polars
-PYDANTIC_TO_POLARS_TYPES = {
-    "integer": pl.Int64,
-    "string": pl.Utf8,
-    "number": pl.Float64,
-    "boolean": pl.Boolean,
-}
-
-
-class ModelMetaclass(PydanticModelMetaclass):
-    """
-    Metclass used by patito.Model.
+class ModelMetaclass(PydanticModelMetaclass, Generic[CI]):
+    """Metclass used by patito.Model.
 
     Responsible for setting any relevant model-dependent class properties.
     """
 
-    def __init__(cls, name: str, bases: tuple, clsdict: dict) -> None:
-        """
-        Construct new patito model.
+    column_info_class: ClassVar[Type[ColumnInfo]] = ColumnInfo
+
+    if TYPE_CHECKING:
+        model_fields: ClassVar[Dict[str, fields.FieldInfo]]
+
+    def __init__(cls, name: str, bases: tuple, clsdict: dict, **kwargs) -> None:
+        """Construct new patito model.
 
         Args:
+        ----
             name: Name of model class.
             bases: Tuple of superclasses.
             clsdict: Dictionary containing class properties.
+            **kwargs: Additional keyword arguments.
+
         """
-        super().__init__(name, bases, clsdict)
+        super().__init__(name, bases, clsdict, **kwargs)
         # Add a custom subclass of patito.DataFrame to the model class,
         # where .set_model() has been implicitly set.
         cls.DataFrame = DataFrame._construct_dataframe_model_class(
@@ -86,19 +100,41 @@ def __init__(cls, name: str, bases: tuple, clsdict: dict) -> None:
             model=cls,  # type: ignore
         )
 
-    # --- Class properties ---
-    # These properties will only be available on Model *classes*, not instantiated
-    # objects This is backwards compatible to python versions before python 3.9,
-    # unlike a combination of @classmethod and @property.
+    def __hash__(self) -> int:
+        return super().__hash__()
+
     @property
-    def columns(cls: Type[ModelType]) -> List[str]:  # type: ignore
+    def column_infos(cls: Type[ModelType]) -> Mapping[str, ColumnInfo]:
+        return column_infos_for_model(cls)
+
+    @property
+    def model_schema(cls: Type[ModelType]) -> Mapping[str, Mapping[str, Any]]:
+        """Return schema properties where definition references have been resolved.
+
+        Returns
+        -------
+            Field information as a dictionary where the keys are field names and the
+                values are dictionaries containing metadata information about the field
+                itself.
+
+        Raises
+        ------
+            TypeError: if a field is annotated with an enum where the values are of
+                different types.
+
         """
-        Return the name of the dataframe columns specified by the fields of the model.
+        return schema_for_model(cls)
+
+    @property
+    def columns(cls: Type[ModelType]) -> List[str]:  # type: ignore
+        """Return the name of the dataframe columns specified by the fields of the model.
 
         Returns:
+        -------
             List of column names.
 
         Example:
+        -------
             >>> import patito as pt
             >>> class Product(pt.Model):
             ...     name: str
@@ -106,23 +142,25 @@ def columns(cls: Type[ModelType]) -> List[str]:  # type: ignore
             ...
             >>> Product.columns
             ['name', 'price']
+
         """
-        return list(cls.schema()["properties"].keys())
+        return list(cls.model_fields.keys())
 
     @property
     def dtypes(  # type: ignore
         cls: Type[ModelType],  # pyright: ignore
-    ) -> dict[str, Type[pl.DataType]]:
-        """
-        Return the polars dtypes of the dataframe.
+    ) -> dict[str, DataTypeClass | DataType]:
+        """Return the polars dtypes of the dataframe.
 
         Unless Field(dtype=...) is specified, the highest signed column dtype
         is chosen for integer and float columns.
 
         Returns:
+        -------
             A dictionary mapping string column names to polars dtype classes.
 
         Example:
+        -------
             >>> import patito as pt
             >>> class Product(pt.Model):
             ...     name: str
@@ -130,29 +168,30 @@ def dtypes(  # type: ignore
             ...     price: float
             ...
             >>> Product.dtypes
-            {'name': Utf8, 'ideal_temperature': Int64, 'price': Float64}
+            {'name': String, 'ideal_temperature': Int64, 'price': Float64}
+
         """
-        return {
-            column: valid_dtypes[0] for column, valid_dtypes in cls.valid_dtypes.items()
-        }
+        return default_dtypes_for_model(cls)
 
     @property
     def valid_dtypes(  # type: ignore
         cls: Type[ModelType],  # pyright: ignore
-    ) -> dict[str, List[Union[pl.PolarsDataType, pl.List]]]:
-        """
-        Return a list of polars dtypes which Patito considers valid for each field.
+    ) -> Mapping[str, FrozenSet[DataTypeClass | DataType]]:
+        """Return a list of polars dtypes which Patito considers valid for each field.
 
         The first item of each list is the default dtype chosen by Patito.
 
         Returns:
+        -------
             A dictionary mapping each column string name to a list of valid dtypes.
 
         Raises:
+        ------
             NotImplementedError: If one or more model fields are annotated with types
                 not compatible with polars.
 
         Example:
+        -------
             >>> from pprint import pprint
             >>> import patito as pt
 
@@ -163,265 +202,35 @@ def valid_dtypes(  # type: ignore
             ...     float_column: float
             ...
             >>> pprint(MyModel.valid_dtypes)
-            {'bool_column': [Boolean],
-             'float_column': [Float64, Float32],
-             'int_column': [Int64, Int32, Int16, Int8, UInt64, UInt32, UInt16, UInt8],
-             'str_column': [Utf8]}
-        """
-        valid_dtypes = {}
-        for column, props in cls._schema_properties().items():
-            column_dtypes: List[Union[PolarsDataType, pl.List]]
-            if props.get("type") == "array":
-                array_props = props["items"]
-                item_dtypes = cls._valid_dtypes(props=array_props)
-                if item_dtypes is None:
-                    raise NotImplementedError(
-                        f"No valid dtype mapping found for column '{column}'."
-                    )
-                column_dtypes = [pl.List(dtype) for dtype in item_dtypes]
-            else:
-                column_dtypes = cls._valid_dtypes(props=props)  # pyright: ignore
-
-            if column_dtypes is None:
-                raise NotImplementedError(
-                    f"No valid dtype mapping found for column '{column}'."
-                )
-            valid_dtypes[column] = column_dtypes
-
-        return valid_dtypes
-
-    @staticmethod
-    def _valid_dtypes(  # noqa: C901
-        props: Dict,
-    ) -> Optional[List[pl.PolarsDataType]]:
-        """
-        Map schema property to list of valid polars data types.
-
-        Args:
-            props: Dictionary value retrieved from BaseModel._schema_properties().
-
-        Returns:
-            List of valid dtypes. None if no mapping exists.
-        """
-        if "dtype" in props:
-            return [
-                props["dtype"],
-            ]
-        elif "enum" in props and props["type"] == "string":
-            return [pl.Categorical, pl.Utf8]
-        elif "type" not in props:
-            return None
-        elif props["type"] == "integer":
-            return [
-                pl.Int64,
-                pl.Int32,
-                pl.Int16,
-                pl.Int8,
-                pl.UInt64,
-                pl.UInt32,
-                pl.UInt16,
-                pl.UInt8,
-            ]
-        elif props["type"] == "number":
-            if props.get("format") == "time-delta":
-                return [pl.Duration]
-            else:
-                return [pl.Float64, pl.Float32]
-        elif props["type"] == "boolean":
-            return [pl.Boolean]
-        elif props["type"] == "string":
-            string_format = props.get("format")
-            if string_format is None:
-                return [pl.Utf8]
-            elif string_format == "date":
-                return [pl.Date]
-            # TODO: Find out why this branch is not being hit
-            elif string_format == "date-time":  # pragma: no cover
-                return [pl.Datetime]
-            else:
-                return None  # pragma: no cover
-        elif props["type"] == "null":
-            return [pl.Null]
-        else:  # pragma: no cover
-            return None
-
-    @property
-    def valid_sql_types(  # type: ignore  # noqa: C901
-        cls: Type[ModelType],  # pyright: ignore
-    ) -> dict[str, List["DuckDBSQLType"]]:
-        """
-        Return a list of DuckDB SQL types which Patito considers valid for each field.
-
-        The first item of each list is the default dtype chosen by Patito.
-
-        Returns:
-            A dictionary mapping each column string name to a list of DuckDB SQL types
-            represented as strings.
-
-        Raises:
-            NotImplementedError: If one or more model fields are annotated with types
-                not compatible with DuckDB.
-
-        Example:
-            >>> import patito as pt
-            >>> from pprint import pprint
-
-            >>> class MyModel(pt.Model):
-            ...     bool_column: bool
-            ...     str_column: str
-            ...     int_column: int
-            ...     float_column: float
-            ...
-            >>> pprint(MyModel.valid_sql_types)
-            {'bool_column': ['BOOLEAN', 'BOOL', 'LOGICAL'],
-             'float_column': ['DOUBLE',
-                                                'FLOAT8',
-                                                'NUMERIC',
-                                                'DECIMAL',
-                                                'REAL',
-                                                'FLOAT4',
-                                                'FLOAT'],
-              'int_column': ['INTEGER',
-                                             'INT4',
-                                             'INT',
-                                             'SIGNED',
-                                             'BIGINT',
-                                             'INT8',
-                                             'LONG',
-                                             'HUGEINT',
-                                             'SMALLINT',
-                                             'INT2',
-                                             'SHORT',
-                                             'TINYINT',
-                                             'INT1',
-                                             'UBIGINT',
-                                             'UINTEGER',
-                                             'USMALLINT',
-                                             'UTINYINT'],
-              'str_column': ['VARCHAR', 'CHAR', 'BPCHAR', 'TEXT', 'STRING']}
-        """
-        valid_dtypes: Dict[str, List["DuckDBSQLType"]] = {}
-        for column, props in cls._schema_properties().items():
-            if "sql_type" in props:
-                valid_dtypes[column] = [
-                    props["sql_type"],
-                ]
-            elif "enum" in props and props["type"] == "string":
-                from patito.duckdb import _enum_type_name
-
-                # fmt: off
-                valid_dtypes[column] = [  # pyright: ignore
-                    _enum_type_name(field_properties=props),  # type: ignore
-                    "VARCHAR", "CHAR", "BPCHAR", "TEXT", "STRING",
-                ]
-                # fmt: on
-            elif "type" not in props:
-                raise NotImplementedError(
-                    f"No valid sql_type mapping found for column '{column}'."
-                )
-            elif props["type"] == "integer":
-                # fmt: off
-                valid_dtypes[column] = [
-                    "INTEGER", "INT4", "INT", "SIGNED",
-                    "BIGINT", "INT8", "LONG",
-                    "HUGEINT",
-                    "SMALLINT", "INT2", "SHORT",
-                    "TINYINT", "INT1",
-                    "UBIGINT",
-                    "UINTEGER",
-                    "USMALLINT",
-                    "UTINYINT",
-                ]
-                # fmt: on
-            elif props["type"] == "number":
-                if props.get("format") == "time-delta":
-                    valid_dtypes[column] = [
-                        "INTERVAL",
-                    ]
-                else:
-                    # fmt: off
-                    valid_dtypes[column] = [
-                        "DOUBLE", "FLOAT8", "NUMERIC", "DECIMAL",
-                        "REAL", "FLOAT4", "FLOAT",
-                    ]
-                    # fmt: on
-            elif props["type"] == "boolean":
-                # fmt: off
-                valid_dtypes[column] = [
-                    "BOOLEAN", "BOOL", "LOGICAL",
-                ]
-                # fmt: on
-            elif props["type"] == "string":
-                string_format = props.get("format")
-                if string_format is None:
-                    # fmt: off
-                    valid_dtypes[column] = [
-                        "VARCHAR", "CHAR", "BPCHAR", "TEXT", "STRING",
-                    ]
-                    # fmt: on
-                elif string_format == "date":
-                    valid_dtypes[column] = ["DATE"]
-                # TODO: Find out why this branch is not being hit
-                elif string_format == "date-time":  # pragma: no cover
-                    # fmt: off
-                    valid_dtypes[column] = [
-                        "TIMESTAMP", "DATETIME",
-                        "TIMESTAMP WITH TIMEZONE", "TIMESTAMPTZ",
-                    ]
-                    # fmt: on
-            elif props["type"] == "null":
-                valid_dtypes[column] = [
-                    "INTEGER",
-                ]
-            else:  # pragma: no cover
-                raise NotImplementedError(
-                    f"No valid sql_type mapping found for column '{column}'"
-                )
-
-        return valid_dtypes
+            {'bool_column': DataTypeGroup({Boolean}),
+            'float_column': DataTypeGroup({Float32, Float64}),
+            'int_column': DataTypeGroup({Int8,
+                                        Int16,
+                                        Int32,
+                                        Int64,
+                                        UInt8,
+                                        UInt16,
+                                        UInt32,
+                                        UInt64,
+                                        Float32,
+                                        Float64}),
+            'str_column': DataTypeGroup({String})}
 
-    @property
-    def sql_types(  # type: ignore
-        cls: Type[ModelType],  # pyright: ignore
-    ) -> dict[str, str]:
         """
-        Return compatible DuckDB SQL types for all model fields.
-
-        Returns:
-            Dictionary with column name keys and SQL type identifier strings.
-
-        Example:
-            >>> from typing import Literal
-            >>> import patito as pt
-
-            >>> class MyModel(pt.Model):
-            ...     int_column: int
-            ...     str_column: str
-            ...     float_column: float
-            ...     literal_column: Literal["a", "b", "c"]
-            ...
-            >>> MyModel.sql_types
-            {'int_column': 'INTEGER',
-             'str_column': 'VARCHAR',
-             'float_column': 'DOUBLE',
-             'literal_column': 'enum__4a496993dde04060df4e15a340651b45'}
-        """
-        return {
-            column: valid_types[0]
-            for column, valid_types in cls.valid_sql_types.items()
-        }
+        return valid_dtypes_for_model(cls)
 
     @property
     def defaults(  # type: ignore
         cls: Type[ModelType],  # pyright: ignore
     ) -> dict[str, Any]:
-        """
-        Return default field values specified on the model.
+        """Return default field values specified on the model.
 
         Returns:
+        -------
             Dictionary containing fields with their respective default values.
 
         Example:
+        -------
             >>> from typing_extensions import Literal
             >>> import patito as pt
             >>> class Product(pt.Model):
@@ -431,6 +240,7 @@ def defaults(  # type: ignore
             ...
             >>> Product.defaults
             {'price': 0, 'temperature_zone': 'dry'}
+
         """
         return {
             field_name: props["default"]
@@ -442,47 +252,58 @@ def defaults(  # type: ignore
     def non_nullable_columns(  # type: ignore
         cls: Type[ModelType],  # pyright: ignore
     ) -> set[str]:
-        """
-        Return names of those columns that are non-nullable in the schema.
+        """Return names of those columns that are non-nullable in the schema.
 
         Returns:
+        -------
             Set of column name strings.
 
         Example:
+        -------
             >>> from typing import Optional
             >>> import patito as pt
             >>> class MyModel(pt.Model):
             ...     nullable_field: Optional[int]
-            ...     inferred_nullable_field: int = None
+            ...     another_nullable_field: Optional[int] = None
             ...     non_nullable_field: int
             ...     another_non_nullable_field: str
             ...
             >>> sorted(MyModel.non_nullable_columns)
             ['another_non_nullable_field', 'non_nullable_field']
+
         """
-        return set(cls.schema().get("required", {}))
+        return set(
+            k
+            for k in cls.columns
+            if not (
+                is_optional(cls.model_fields[k].annotation)
+                or cls.model_fields[k].annotation == type(None)
+            )
+        )
 
     @property
     def nullable_columns(  # type: ignore
         cls: Type[ModelType],  # pyright: ignore
     ) -> set[str]:
-        """
-        Return names of those columns that are nullable in the schema.
+        """Return names of those columns that are nullable in the schema.
 
         Returns:
+        -------
             Set of column name strings.
 
         Example:
+        -------
             >>> from typing import Optional
             >>> import patito as pt
             >>> class MyModel(pt.Model):
             ...     nullable_field: Optional[int]
-            ...     inferred_nullable_field: int = None
+            ...     another_nullable_field: Optional[int] = None
             ...     non_nullable_field: int
             ...     another_non_nullable_field: str
             ...
             >>> sorted(MyModel.nullable_columns)
-            ['inferred_nullable_field', 'nullable_field']
+            ['another_nullable_field', 'nullable_field']
+
         """
         return set(cls.columns) - cls.non_nullable_columns
 
@@ -490,13 +311,14 @@ def nullable_columns(  # type: ignore
     def unique_columns(  # type: ignore
         cls: Type[ModelType],  # pyright: ignore
     ) -> set[str]:
-        """
-        Return columns with uniqueness constraint.
+        """Return columns with uniqueness constraint.
 
         Returns:
+        -------
             Set of column name strings.
 
         Example:
+        -------
             >>> from typing import Optional
             >>> import patito as pt
 
@@ -507,70 +329,60 @@ def unique_columns(  # type: ignore
             ...
             >>> sorted(Product.unique_columns)
             ['barcode', 'product_id']
+
         """
-        props = cls._schema_properties()
-        return {column for column in cls.columns if props[column].get("unique", False)}
+        infos = cls.column_infos
+        return {column for column in cls.columns if infos[column].unique}
+
+    @property
+    def derived_columns(
+        cls: Type[ModelType],  # type: ignore[misc]
+    ) -> set[str]:
+        infos = cls.column_infos
+        return {
+            column for column in cls.columns if infos[column].derived_from is not None
+        }
 
 
 class Model(BaseModel, metaclass=ModelMetaclass):
     """Custom pydantic class for representing table schema and constructing rows."""
 
-    # -- Class properties set by model metaclass --
-    # This weird combination of a MetaClass + type annotation
-    # in order to make the following work simultaneously:
-    #     1. Make these dynamically constructed properties of the class.
-    #     2. Have the correct type information for type checkers.
-    #     3. Allow sphinx-autodoc to construct correct documentation.
-    #     4. Be compatible with python 3.7.
-    # Once we drop support for python 3.7, we can replace all of this with just a simple
-    # combination of @property and @classmethod.
-    columns: ClassVar[List[str]]
-
-    unique_columns: ClassVar[Set[str]]
-    non_nullable_columns: ClassVar[Set[str]]
-    nullable_columns: ClassVar[Set[str]]
-
-    dtypes: ClassVar[Dict[str, Type[pl.DataType]]]
-    sql_types: ClassVar[Dict[str, str]]
-    valid_dtypes: ClassVar[Dict[str, List[Type[pl.DataType]]]]
-    valid_sql_types: ClassVar[Dict[str, List["DuckDBSQLType"]]]
-
-    defaults: ClassVar[Dict[str, Any]]
-
-    @classmethod  # type: ignore[misc]
-    @property
-    def DataFrame(
-        cls: Type[ModelType],
-    ) -> Type[DataFrame[ModelType]]:  # pyright: ignore  # noqa
-        """Return DataFrame class where DataFrame.set_model() is set to self."""
-
-    @classmethod  # type: ignore[misc]
-    @property
-    def LazyFrame(
-        cls: Type[ModelType],
-    ) -> Type[LazyFrame[ModelType]]:  # pyright: ignore
-        """Return DataFrame class where DataFrame.set_model() is set to self."""
+    @classmethod
+    def validate_schema(cls: Type[ModelType]):
+        """Users should run this after defining or edit a model. We withhold the checks at model definition time to avoid expensive queries of the model schema."""
+        for column in cls.columns:
+            col_info = cls.column_infos[column]
+            field_info = cls.model_fields[column]
+            if col_info.dtype:
+                validate_polars_dtype(
+                    annotation=field_info.annotation, dtype=col_info.dtype
+                )
+            else:
+                validate_annotation(field_info.annotation)
 
     @classmethod
     def from_row(
-        cls: Type[ModelType],
+        cls: Type[ModelType],  # type: ignore[misc]
         row: Union["pd.DataFrame", pl.DataFrame],
         validate: bool = True,
     ) -> ModelType:
-        """
-        Represent a single data frame row as a Patito model.
+        """Represent a single data frame row as a Patito model.
 
         Args:
+        ----
             row: A dataframe, either polars and pandas, consisting of a single row.
             validate: If ``False``, skip pydantic validation of the given row data.
 
         Returns:
+        -------
             Model: A patito model representing the given row data.
 
         Raises:
+        ------
             TypeError: If the given type is neither a pandas or polars DataFrame.
 
         Example:
+        -------
             >>> import patito as pt
             >>> import polars as pl
 
@@ -588,6 +400,7 @@ def from_row(
             Product(product_id=1, name='product name', price=1.22)
             >>> Product.from_row(df, validate=False)
             Product(product_id='1', name='product name', price='1.22')
+
         """
         if isinstance(row, pl.DataFrame):
             dataframe = row
@@ -605,24 +418,27 @@ def _from_polars(
         dataframe: pl.DataFrame,
         validate: bool = True,
     ) -> ModelType:
-        """
-        Construct model from a single polars row.
+        """Construct model from a single polars row.
 
         Args:
+        ----
             dataframe: A polars dataframe consisting of one single row.
             validate: If ``True``, run the pydantic validators. If ``False``, pydantic
                 will not cast any types in the resulting object.
 
         Returns:
+        -------
             Model: A pydantic model object representing the given polars row.
 
         Raises:
+        ------
             TypeError: If the provided ``dataframe`` argument is not of type
                 ``polars.DataFrame``.
             ValueError: If the given ``dataframe`` argument does not consist of exactly
                 one row.
 
         Example:
+        -------
             >>> import patito as pt
             >>> import polars as pl
 
@@ -640,6 +456,7 @@ def _from_polars(
             Product(product_id=1, name='product name', price=1.22)
             >>> Product._from_polars(df, validate=False)
             Product(product_id='1', name='product name', price='1.22')
+
         """
         if not isinstance(dataframe, pl.DataFrame):
             raise TypeError(
@@ -657,24 +474,30 @@ def _from_polars(
         if validate:
             return cls(**dataframe.to_dicts()[0])
         else:
-            return cls.construct(**dataframe.to_dicts()[0])
+            return cls.model_construct(**dataframe.to_dicts()[0])
 
     @classmethod
     def validate(
         cls,
         dataframe: Union["pd.DataFrame", pl.DataFrame],
+        columns: Optional[Sequence[str]] = None,
+        **kwargs,
     ) -> None:
-        """
-        Validate the schema and content of the given dataframe.
+        """Validate the schema and content of the given dataframe.
 
         Args:
+        ----
             dataframe: Polars DataFrame to be validated.
+            columns: Optional list of columns to validate. If not provided, all columns
+                of the dataframe will be validated.
 
         Raises:
-            patito.exceptions.ValidationError: If the given dataframe does not match
+        ------
+            patito.exceptions.DataFrameValidationError: If the given dataframe does not match
                 the given schema.
 
         Examples:
+        --------
             >>> import patito as pt
             >>> import polars as pl
 
@@ -693,7 +516,7 @@ def validate(
             ... )
             >>> try:
             ...     Product.validate(df)
-            ... except pt.ValidationError as exc:
+            ... except pt.DataFrameValidationError as exc:
             ...     print(exc)
             ...
             3 validation errors for Product
@@ -703,27 +526,32 @@ def validate(
               2 rows with duplicated values. (type=value_error.rowvalue)
             temperature_zone
               Rows with invalid values: {'oven'}. (type=value_error.rowvalue)
+
         """
-        validate(dataframe=dataframe, schema=cls)
+        validate(dataframe=dataframe, columns=columns, schema=cls, **kwargs)
 
     @classmethod
     def example_value(  # noqa: C901
         cls,
-        field: str,
-    ) -> Union[date, datetime, float, int, str, None]:
-        """
-        Return a valid example value for the given model field.
+        field: Optional[str] = None,
+        properties: Optional[Dict[str, Any]] = None,
+    ) -> Union[date, datetime, time, timedelta, float, int, str, None, Mapping, List]:
+        """Return a valid example value for the given model field.
 
         Args:
+        ----
             field: Field name identifier.
 
         Returns:
+        -------
             A single value which is consistent with the given field definition.
 
         Raises:
+        ------
             NotImplementedError: If the given field has no example generator.
 
         Example:
+        -------
             >>> from typing import Literal
             >>> import patito as pt
 
@@ -738,10 +566,36 @@ def example_value(  # noqa: C901
             'dummy_string'
             >>> Product.example_value("temperature_zone")
             'dry'
+
         """
-        field_data = cls._schema_properties()
-        properties = field_data[field]
-        field_type = properties["type"]
+        if field is None and properties is None:
+            raise ValueError(
+                "Either 'field' or 'properties' must be provided as argument."
+            )
+        if field is not None and properties is not None:
+            raise ValueError(
+                "Only one of 'field' or 'properties' can be provided as argument."
+            )
+        if field:
+            properties = cls._schema_properties()[field]
+            info = cls.column_infos[field]
+        else:
+            info = cls.column_info_class()
+        properties = properties or {}
+
+        if "type" in properties:
+            field_type = properties["type"]
+        elif "anyOf" in properties:
+            allowable = [x["type"] for x in properties["anyOf"] if "type" in x]
+            if "null" in allowable:
+                field_type = "null"
+            else:
+                field_type = allowable[0]
+        else:
+            raise NotImplementedError(
+                f"Field type for {properties['title']} not found."
+            )
+
         if "const" in properties:
             # The default value is the only valid value, provided as const
             return properties["const"]
@@ -750,7 +604,10 @@ def example_value(  # noqa: C901
             # A default value has been specified in the model field definition
             return properties["default"]
 
-        elif not properties["required"]:
+        elif not properties.get("required", True):
+            return None
+
+        elif field_type == "null":
             return None
 
         elif "enum" in properties:
@@ -758,12 +615,18 @@ def example_value(  # noqa: C901
 
         elif field_type in {"integer", "number"}:
             # For integer and float types we must check if there are imposed bounds
-            lower = properties.get("minimum") or properties.get("exclusiveMinimum")
-            upper = properties.get("maximum") or properties.get("exclusiveMaximum")
+
+            minimum = properties.get("minimum")
+            exclusive_minimum = properties.get("exclusiveMinimum")
+            maximum = properties.get("maximum")
+            exclusive_maximum = properties.get("exclusiveMaximum")
+
+            lower = minimum if minimum is not None else exclusive_minimum
+            upper = maximum if maximum is not None else exclusive_maximum
 
             # If the dtype is an unsigned integer type, we must return a positive value
-            if "dtype" in properties:
-                dtype = properties["dtype"]
+            if info.dtype:
+                dtype = info.dtype
                 if dtype in (pl.UInt8, pl.UInt16, pl.UInt32, pl.UInt64):
                     lower = 0 if lower is None else max(lower, 0)
 
@@ -798,7 +661,19 @@ def example_value(  # noqa: C901
             elif "format" in properties and properties["format"] == "date":
                 return date(year=1970, month=1, day=1)
             elif "format" in properties and properties["format"] == "date-time":
+                if "column_info" in properties:
+                    dtype_str = properties["column_info"]["dtype"]
+                    dtype = dtype_from_string(dtype_str)
+                    if getattr(dtype, "time_zone", None) is not None:
+                        tzinfo = ZoneInfo(dtype.time_zone)
+                    else:
+                        tzinfo = None
+                    return datetime(year=1970, month=1, day=1, tzinfo=tzinfo)
                 return datetime(year=1970, month=1, day=1)
+            elif "format" in properties and properties["format"] == "time":
+                return time(12, 30)
+            elif "format" in properties and properties["format"] == "duration":
+                return timedelta(1)
             elif "minLength" in properties:
                 return "a" * properties["minLength"]
             elif "maxLength" in properties:
@@ -809,6 +684,18 @@ def example_value(  # noqa: C901
         elif field_type == "boolean":
             return False
 
+        elif field_type == "object":
+            try:
+                props_o = cls.model_schema["$defs"][properties["title"]]["properties"]
+                return {f: cls.example_value(properties=props_o[f]) for f in props_o}
+            except AttributeError:
+                raise NotImplementedError(
+                    "Nested example generation only supported for nested pt.Model classes."
+                )
+
+        elif field_type == "array":
+            return [cls.example_value(properties=properties["items"])]
+
         else:  # pragma: no cover
             raise NotImplementedError
 
@@ -817,8 +704,7 @@ def example(
         cls: Type[ModelType],
         **kwargs: Any,  # noqa: ANN401
     ) -> ModelType:
-        """
-        Produce model instance with filled dummy data for all unspecified fields.
+        """Produce model instance with filled dummy data for all unspecified fields.
 
         The type annotation of unspecified field is used to fill in type-correct
         dummy data, e.g. ``-1`` for ``int``, ``"dummy_string"`` for ``str``, and so
@@ -827,18 +713,22 @@ def example(
         The first item of ``typing.Literal`` annotations are used for dummy values.
 
         Args:
+        ----
             **kwargs: Provide explicit values for any fields which should `not` be
                 filled with dummy data.
 
         Returns:
+        -------
             Model: A pydantic model object filled with dummy data for all unspecified
             model fields.
 
         Raises:
+        ------
             TypeError: If one or more of the provided keyword arguments do not match any
                 fields on the model.
 
         Example:
+        -------
             >>> from typing import Literal
             >>> import patito as pt
 
@@ -849,6 +739,7 @@ def example(
             ...
             >>> Product.example(product_id=1)
             Product(product_id=1, name='dummy_string', temperature_zone='dry')
+
         """
         # Non-iterable values besides strings must be repeated
         wrong_columns = set(kwargs.keys()) - set(cls.columns)
@@ -870,14 +761,14 @@ def pandas_examples(
         data: Union[dict, Iterable],
         columns: Optional[Iterable[str]] = None,
     ) -> "pd.DataFrame":
-        """
-        Generate dataframe with dummy data for all unspecified columns.
+        """Generate dataframe with dummy data for all unspecified columns.
 
         Offers the same API as the pandas.DataFrame constructor.
         Non-iterable values, besides strings, are repeated until they become as long as
         the iterable arguments.
 
         Args:
+        ----
             data: Data to populate the dummy dataframe with. If
                 not a dict, column names must also be provided.
             columns: Ignored if data is a dict. If
@@ -885,14 +776,17 @@ def pandas_examples(
                 resulting dataframe. Defaults to None.
 
         Returns:
+        -------
             A pandas DataFrame filled with dummy example data.
 
         Raises:
+        ------
             ImportError: If pandas has not been installed. You should install
                 patito[pandas] in order to integrate patito with pandas.
             TypeError: If column names have not been specified in the input data.
 
         Example:
+        -------
             >>> from typing import Literal
             >>> import patito as pt
 
@@ -903,9 +797,10 @@ def pandas_examples(
             ...
 
             >>> Product.pandas_examples({"name": ["product A", "product B"]})
-               product_id       name temperature_zone
+            product_id       name temperature_zone
             0          -1  product A              dry
             1          -1  product B              dry
+
         """
         if not _PANDAS_AVAILABLE:
             # Re-trigger the import error, but this time don't catch it
@@ -932,7 +827,7 @@ def pandas_examples(
         dummies = []
         for values in zip(*kwargs.values()):
             dummies.append(cls.example(**dict(zip(kwargs.keys(), values))))
-        return pd.DataFrame([dummy.dict() for dummy in dummies])
+        return pd.DataFrame([dummy.model_dump() for dummy in dummies])
 
     @classmethod
     def examples(
@@ -940,12 +835,12 @@ def examples(
         data: Optional[Union[dict, Iterable]] = None,
         columns: Optional[Iterable[str]] = None,
     ) -> "patito.polars.DataFrame":
-        """
-        Generate polars dataframe with dummy data for all unspecified columns.
+        """Generate polars dataframe with dummy data for all unspecified columns.
 
         This constructor accepts the same data format as polars.DataFrame.
 
         Args:
+        ----
             data: Data to populate the dummy dataframe with. If given as an iterable of
                 values, then column names must also be provided. If not provided at all,
                 a dataframe with a single row populated with dummy data is provided.
@@ -954,14 +849,17 @@ def examples(
                 column names in the resulting dataframe. Defaults to None.
 
         Returns:
+        -------
             A polars dataframe where all unspecified columns have been filled with dummy
             data which should pass model validation.
 
         Raises:
+        ------
             TypeError: If one or more of the model fields are not mappable to polars
                 column dtype equivalents.
 
         Example:
+        -------
             >>> from typing import Literal
             >>> import patito as pt
 
@@ -976,9 +874,9 @@ def examples(
             ┌──────────────┬──────────────────┬────────────┐
             │ name         ┆ temperature_zone ┆ product_id │
             │ ---          ┆ ---              ┆ ---        │
-            │ str          ┆ cat              ┆ i64        │
+            │ str          ┆ enum             ┆ i64        │
             ╞══════════════╪══════════════════╪════════════╡
-            │ dummy_string ┆ dry              ┆ 0          │
+            │ dummy_string ┆ dry              ┆ 1          │
             └──────────────┴──────────────────┴────────────┘
 
             >>> Product.examples({"name": ["product A", "product B"]})
@@ -986,11 +884,12 @@ def examples(
             ┌───────────┬──────────────────┬────────────┐
             │ name      ┆ temperature_zone ┆ product_id │
             │ ---       ┆ ---              ┆ ---        │
-            │ str       ┆ cat              ┆ i64        │
+            │ str       ┆ enum             ┆ i64        │
             ╞═══════════╪══════════════════╪════════════╡
-            │ product A ┆ dry              ┆ 0          │
-            │ product B ┆ dry              ┆ 1          │
+            │ product A ┆ dry              ┆ 1          │
+            │ product B ┆ dry              ┆ 2          │
             └───────────┴──────────────────┴────────────┘
+
         """
         if data is None:
             # We should create an empty dataframe, but with the correct dtypes
@@ -1014,11 +913,13 @@ def examples(
             if column_name not in kwargs:
                 if column_name in cls.unique_columns:
                     unique_series.append(
-                        pl.first().cumcount().cast(dtype).alias(column_name)
+                        pl.first().cum_count().cast(dtype).alias(column_name)
                     )
                 else:
                     example_value = cls.example_value(field=column_name)
-                    series.append(pl.lit(example_value, dtype=dtype).alias(column_name))
+                    series.append(
+                        pl.Series(column_name, values=[example_value], dtype=dtype)
+                    )
                 continue
 
             value = kwargs.get(column_name)
@@ -1030,7 +931,7 @@ def examples(
             else:
                 series.append(pl.lit(value, dtype=dtype).alias(column_name))
 
-        return DataFrame().with_columns(series).with_columns(unique_series)
+        return cls.DataFrame().with_columns(series).with_columns(unique_series)
 
     @classmethod
     def join(
@@ -1038,8 +939,7 @@ def join(
         other: Type["Model"],
         how: Literal["inner", "left", "outer", "asof", "cross", "semi", "anti"],
     ) -> Type["Model"]:
-        """
-        Dynamically create a new model compatible with an SQL Join operation.
+        """Dynamically create a new model compatible with an SQL Join operation.
 
         For instance, ``ModelA.join(ModelB, how="left")`` will create a model containing
         all the fields of ``ModelA`` and ``ModelB``, but where all fields of ``ModelB``
@@ -1047,14 +947,17 @@ def join(
         SQL operation making all the columns of the right table nullable.
 
         Args:
+        ----
             other: Another patito Model class.
             how: The type of SQL Join operation.
 
         Returns:
+        -------
             A new model type compatible with the resulting schema produced by the given
             join operation.
 
         Examples:
+        --------
             >>> class A(Model):
             ...     a: int
             ...
@@ -1078,6 +981,7 @@ def join(
 
             >>> A.join(B, how="anti") is A
             True
+
         """
         if how in {"semi", "anti"}:
             return cls
@@ -1087,18 +991,13 @@ def join(
             (cls, {"outer"}),
             (other, {"left", "outer", "asof"}),
         ):
-            for field_name, field in model.__fields__.items():
-                field_type = field.type_
-                field_default = field.default
-                if how in nullable_methods and type(None) not in get_args(field.type_):
-                    # This originally non-nullable field has become nullable
-                    field_type = Optional[field_type]
-                elif field.required and field_default is None:
-                    # We need to replace Pydantic's None default value with ... in order
-                    # to make it clear that the field is still non-nullable and
-                    # required.
-                    field_default = ...
-                kwargs[field_name] = (field_type, field_default)
+            for field_name, field in model.model_fields.items():
+                make_nullable = how in nullable_methods and type(None) not in get_args(
+                    field.annotation
+                )
+                kwargs[field_name] = cls._derive_field(
+                    field, make_nullable=make_nullable
+                )
 
         return create_model(
             f"{cls.__name__}{how.capitalize()}Join{other.__name__}",
@@ -1110,19 +1009,22 @@ def join(
     def select(
         cls: Type[ModelType], fields: Union[str, Iterable[str]]
     ) -> Type["Model"]:
-        """
-        Create a new model consisting of only a subset of the model fields.
+        """Create a new model consisting of only a subset of the model fields.
 
         Args:
+        ----
             fields: A single field name as a string or a collection of strings.
 
         Returns:
+        -------
             A new model containing only the fields specified by ``fields``.
 
         Raises:
+        ------
             ValueError: If one or more non-existent fields are selected.
 
         Example:
+        -------
             >>> class MyModel(Model):
             ...     a: int
             ...     b: int
@@ -1134,6 +1036,7 @@ def select(
 
             >>> sorted(MyModel.select(["b", "c"]).columns)
             ['b', 'c']
+
         """
         if isinstance(fields, str):
             fields = [fields]
@@ -1152,17 +1055,19 @@ def select(
 
     @classmethod
     def drop(cls: Type[ModelType], name: Union[str, Iterable[str]]) -> Type["Model"]:
-        """
-        Return a new model where one or more fields are excluded.
+        """Return a new model where one or more fields are excluded.
 
         Args:
+        ----
             name: A single string field name, or a list of such field names,
                 which will be dropped.
 
         Returns:
+        -------
             New model class where the given fields have been removed.
 
         Examples:
+        --------
             >>> class MyModel(Model):
             ...     a: int
             ...     b: int
@@ -1177,6 +1082,7 @@ def drop(cls: Type[ModelType], name: Union[str, Iterable[str]]) -> Type["Model"]
 
             >>> MyModel.drop(["b", "c"]).columns
             ['a']
+
         """
         dropped_columns = {name} if isinstance(name, str) else set(name)
         mapping = {
@@ -1191,16 +1097,18 @@ def drop(cls: Type[ModelType], name: Union[str, Iterable[str]]) -> Type["Model"]
 
     @classmethod
     def prefix(cls: Type[ModelType], prefix: str) -> Type["Model"]:
-        """
-        Return a new model where all field names have been prefixed.
+        """Return a new model where all field names have been prefixed.
 
         Args:
+        ----
             prefix: String prefix to add to all field names.
 
         Returns:
+        -------
             New model class with all the same fields only prefixed with the given prefix.
 
         Example:
+        -------
             >>> class MyModel(Model):
             ...     a: int
             ...     b: int
@@ -1208,6 +1116,7 @@ def prefix(cls: Type[ModelType], prefix: str) -> Type["Model"]:
 
             >>> MyModel.prefix("x_").columns
             ['x_a', 'x_b']
+
         """
         mapping = {f"{prefix}{field_name}": field_name for field_name in cls.columns}
         return cls._derive_model(
@@ -1217,17 +1126,19 @@ def prefix(cls: Type[ModelType], prefix: str) -> Type["Model"]:
 
     @classmethod
     def suffix(cls: Type[ModelType], suffix: str) -> Type["Model"]:
-        """
-        Return a new model where all field names have been suffixed.
+        """Return a new model where all field names have been suffixed.
 
         Args:
+        ----
             suffix: String suffix to add to all field names.
 
         Returns:
+        -------
             New model class with all the same fields only suffixed with the given
             suffix.
 
         Example:
+        -------
             >>> class MyModel(Model):
             ...     a: int
             ...     b: int
@@ -1235,6 +1146,7 @@ def suffix(cls: Type[ModelType], suffix: str) -> Type["Model"]:
 
             >>> MyModel.suffix("_x").columns
             ['a_x', 'b_x']
+
         """
         mapping = {f"{field_name}{suffix}": field_name for field_name in cls.columns}
         return cls._derive_model(
@@ -1244,20 +1156,23 @@ def suffix(cls: Type[ModelType], suffix: str) -> Type["Model"]:
 
     @classmethod
     def rename(cls: Type[ModelType], mapping: Dict[str, str]) -> Type["Model"]:
-        """
-        Return a new model class where the specified fields have been renamed.
+        """Return a new model class where the specified fields have been renamed.
 
         Args:
+        ----
             mapping: A dictionary where the keys are the old field names
                 and the values are the new names.
 
         Returns:
+        -------
             A new model class where the given fields have been renamed.
 
         Raises:
+        ------
             ValueError: If non-existent fields are renamed.
 
         Example:
+        -------
             >>> class MyModel(Model):
             ...     a: int
             ...     b: int
@@ -1265,6 +1180,7 @@ def rename(cls: Type[ModelType], mapping: Dict[str, str]) -> Type["Model"]:
 
             >>> MyModel.rename({"a": "A"}).columns
             ['b', 'A']
+
         """
         non_existent_fields = set(mapping.keys()) - set(cls.columns)
         if non_existent_fields:
@@ -1287,10 +1203,10 @@ def with_fields(
         cls: Type[ModelType],
         **field_definitions: Any,  # noqa: ANN401
     ) -> Type["Model"]:
-        """
-        Return a new model class where the given fields have been added.
+        """Return a new model class where the given fields have been added.
 
         Args:
+        ----
             **field_definitions: the keywords are of the form:
                 ``field_name=(field_type, field_default)``.
                 Specify ``...`` if no default value is provided.
@@ -1298,10 +1214,12 @@ def with_fields(
                 integer field named ``"column_name"``.
 
         Returns:
+        -------
             A new model with all the original fields and the additional field
             definitions.
 
         Example:
+        -------
             >>> class MyModel(Model):
             ...     a: int
             ...
@@ -1310,6 +1228,7 @@ def with_fields(
             ...
             >>> MyModel.with_fields(b=(int, ...)).columns == ExpandedModel.columns
             True
+
         """
         fields = {field_name: field_name for field_name in cls.columns}
         fields.update(field_definitions)
@@ -1319,49 +1238,16 @@ def with_fields(
         )
 
     @classmethod
-    def _schema_properties(cls) -> Dict[str, Dict[str, Any]]:
-        """
-        Return schema properties where definition references have been resolved.
-
-        Returns:
-            Field information as a dictionary where the keys are field names and the
-                values are dictionaries containing metadata information about the field
-                itself.
+    def _schema_properties(cls: Type[ModelType]) -> Mapping[str, Any]:
+        return cls.model_schema["properties"]
 
-        Raises:
-            TypeError: if a field is annotated with an enum where the values are of
-                different types.
-        """
-        schema = cls.schema(ref_template="{model}")
-        required = schema.get("required", set())
-        fields = {}
-        for field_name, field_info in schema["properties"].items():
-            if "$ref" in field_info:
-                definition = schema["definitions"][field_info["$ref"]]
-                if "enum" in definition and "type" not in definition:
-                    enum_types = set(type(value) for value in definition["enum"])
-                    if len(enum_types) > 1:
-                        raise TypeError(
-                            "All enumerated values of enums used to annotate "
-                            "Patito model fields must have the same type. "
-                            "Encountered types: "
-                            f"{sorted(map(lambda t: t.__name__, enum_types))}."
-                        )
-                    enum_type = enum_types.pop()
-                    # TODO: Support time-delta, date, and date-time.
-                    definition["type"] = {
-                        str: "string",
-                        int: "integer",
-                        float: "number",
-                        bool: "boolean",
-                        type(None): "null",
-                    }[enum_type]
-                fields[field_name] = definition
-            else:
-                fields[field_name] = field_info
-            fields[field_name]["required"] = field_name in required
-
-        return fields
+    @classmethod
+    def _update_dfn(cls, annotation: Any, schema: Dict[str, Any]) -> None:
+        try:
+            if issubclass(annotation, Model) and annotation.__name__ != cls.__name__:
+                schema["$defs"][annotation.__name__] = annotation.model_schema
+        except TypeError:
+            pass
 
     @classmethod
     def _derive_model(
@@ -1369,10 +1255,10 @@ def _derive_model(
         model_name: str,
         field_mapping: Dict[str, Any],
     ) -> Type["Model"]:
-        """
-        Derive a new model with new field definitions.
+        """Derive a new model with new field definitions.
 
         Args:
+        ----
             model_name: Name of new model class.
             field_mapping: A mapping where the keys represent field names and the values
                 represent field definitions. String field definitions are used as
@@ -1381,36 +1267,89 @@ def _derive_model(
                 pydantic.create_model.
 
         Returns:
+        -------
             A new model class derived from the model type of self.
+
         """
         new_fields = {}
         for new_field_name, field_definition in field_mapping.items():
             if isinstance(field_definition, str):
                 # A single string, interpreted as the name of a field on the existing
                 # model.
-                old_field = cls.__fields__[field_definition]
-                field_type = old_field.type_
-                field_default = old_field.default
-                if old_field.required and field_default is None:
-                    # The default None value needs to be replaced with ... in order to
-                    # make the field required in the new model.
-                    field_default = ...
-                new_fields[new_field_name] = (field_type, field_default)
+                old_field = cls.model_fields[field_definition]
+                new_fields[new_field_name] = cls._derive_field(old_field)
             else:
                 # We have been given a (field_type, field_default) tuple defining the
                 # new field directly.
-                new_fields[new_field_name] = field_definition
+                field_type = field_definition[0]
+                if field_definition[1] is None and type(None) not in get_args(
+                    field_type
+                ):
+                    field_type = Optional[field_type]
+                new_fields[new_field_name] = (field_type, field_definition[1])
         return create_model(  # type: ignore
             __model_name=model_name,
-            __validators__={"__validators__": cls.__validators__},
             __base__=Model,
             **new_fields,
         )
 
+    @staticmethod
+    def _derive_field(
+        field: fields.FieldInfo,
+        make_nullable: bool = False,
+    ) -> Tuple[Type | None, fields.FieldInfo]:
+        field_type = field.annotation
+        default = field.default
+        extra_attrs = {
+            x: getattr(field, x)
+            for x in field._attributes_set
+            if x in field.__slots__ and x not in ["annotation", "default"]
+        }
+        if make_nullable:
+            if field_type is None:
+                raise TypeError(
+                    "Cannot make field nullable if no type annotation is provided!"
+                )
+            else:
+                # This originally non-nullable field has become nullable
+                field_type = Optional[field_type]
+        elif field.is_required() and default is None:
+            # We need to replace Pydantic's None default value with ... in order
+            # to make it clear that the field is still non-nullable and
+            # required.
+            default = ...
+        field_new = fields.Field(default=default, **extra_attrs)
+        field_new.metadata = field.metadata
+        return field_type, field_new
+
+
+FIELD_KWARGS = getfullargspec(fields.Field)
+
+
+def FieldCI(
+    column_info: CI, *args: Any, **kwargs: Any
+) -> Any:  # annotate with Any to make the downstream type annotations happy
+    ci = column_info(**kwargs)
+    for field in ci.model_fields_set:
+        kwargs.pop(field)
+    if kwargs.pop("modern_kwargs_only", True):
+        for kwarg in kwargs:
+            if kwarg not in FIELD_KWARGS.kwonlyargs and kwarg not in FIELD_KWARGS.args:
+                raise ValueError(
+                    f"unexpected kwarg {kwarg}={kwargs[kwarg]}.  Add modern_kwargs_only=False to ignore"
+                )
+    return fields.Field(
+        *args,
+        json_schema_extra={"column_info": ci},
+        **kwargs,
+    )
+
+
+Field = partial(FieldCI, column_info=ColumnInfo)
+
 
 class FieldDoc:
-    """
-    Annotate model field with additional type and validation information.
+    """Annotate model field with additional type and validation information.
 
     This class is built on ``pydantic.Field`` and you can find its full documentation
     `here <https://pydantic-docs.helpmanual.io/usage/schema/#field-customization>`_.
@@ -1418,14 +1357,16 @@ class FieldDoc:
     these are documented here.
 
     Args:
+    ----
         constraints (Union[polars.Expression, List[polars.Expression]): A single
             constraint or list of constraints, expressed as a polars expression objects.
             All rows must satisfy the given constraint. You can refer to the given column
             with ``pt.field``, which will automatically be replaced with
             ``polars.col(<field_name>)`` before evaluation.
-        unique (bool): All row values must be unique.
+        derived_from (Union[str, polars.Expr]): used to mark fields that are meant to be derived from other fields. Users can specify a polars expression that will be called to derive the column value when `pt.DataFrame.derive` is called.
         dtype (polars.datatype.DataType): The given dataframe column must have the given
             polars dtype, for instance ``polars.UInt64`` or ``pl.Float32``.
+        unique (bool): All row values must be unique.
         gt: All values must be greater than ``gt``.
         ge: All values must be greater than or equal to ``ge``.
         lt: All values must be less than ``lt``.
@@ -1438,10 +1379,12 @@ class FieldDoc:
         max_length (int): Maximum length of all string values in a UTF-8 column.
 
     Return:
+    ------
         FieldInfo: Object used to represent additional constraints put upon the given
         field.
 
     Examples:
+    --------
         >>> import patito as pt
         >>> import polars as pl
         >>> class Product(pt.Model):
@@ -1454,28 +1397,22 @@ class FieldDoc:
         ...     # The product name should be from 3 to 128 characters long
         ...     name: str = pt.Field(min_length=3, max_length=128)
         ...
-        ...     # Represent colors in the form of upper cased hex colors
-        ...     brand_color: str = pt.Field(regex=r"^\\#[0-9A-F]{6}$")
         ...
         >>> Product.DataFrame(
         ...     {
         ...         "product_id": [1, 1],
         ...         "price": [400, 600],
-        ...         "brand_color": ["#ab00ff", "AB00FF"],
         ...     }
         ... ).validate()
         Traceback (most recent call last):
-          ...
-        patito.exceptions.ValidationError: 4 validation errors for Product
+        patito.exceptions.DataFrameValidationError: 3 validation errors for Product
         name
-          Missing column (type=type_error.missingcolumns)
+            Missing column (type=type_error.missingcolumns)
         product_id
-          2 rows with duplicated values. (type=value_error.rowvalue)
+            2 rows with duplicated values. (type=value_error.rowvalue)
         price
-          Polars dtype Int64 does not match model field type. \
-          (type=type_error.columndtype)
-        brand_color
-          2 rows with out of bound values. (type=value_error.rowvalue)
+            Polars dtype Int64 does not match model field type. (type=type_error.columndtype)
+
     """
 
 
diff --git a/src/patito/sql.py b/src/patito/sql.py
deleted file mode 100644
index a52001a..0000000
--- a/src/patito/sql.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""Module containing SQL generation utilities."""
-from typing import Dict, Optional, Union
-
-from typing_extensions import TypeAlias
-
-SQLLiteral: TypeAlias = Union[str, float, int, None]
-
-
-def sql_repr(value: SQLLiteral) -> str:
-    """
-    Convert python value to equivalent SQL literal value representation.
-
-    Args:
-        value: Python object which is convertible to an equivalent SQL value type.
-
-    Returns:
-        A SQL literal representation of the given value as a string.
-    """
-    return "null" if value is None else repr(value)
-
-
-class Case:
-    """Class representing an SQL case statement."""
-
-    def __init__(
-        self,
-        on_column: str,
-        mapping: Dict[SQLLiteral, SQLLiteral],
-        default: SQLLiteral,
-        as_column: Optional[str] = None,
-    ) -> None:
-        """
-        Map values of one column over to a new column.
-
-        Args:
-            on_column: Name of column defining the domain of the mapping.
-            mapping: Dictionary defining the mapping. The dictionary keys represent the
-                input values, while the dictionary values represent the output values.
-                Items are inserted into the SQL case statement by their repr() string
-                value. None is converted to SQL NULL.
-            default: Default output value for inputs which have no provided mapping.
-                If set to None, SQL NULL will be inserted as the default value.
-            as_column: Name of column to insert the mapped values into. If not provided
-                the SQL string expression will not end with "AS <as_column>".
-
-        Examples:
-            >>> import patito as pt
-            >>> db = pt.duckdb.Database()
-            >>> relation = db.to_relation("select 1 as a union select 2 as a")
-            >>> case_statement = pt.sql.Case(
-            ...     on_column="a",
-            ...     mapping={1: "one", 2: "two"},
-            ...     default="three",
-            ...     as_column="b",
-            ... )
-            >>> relation.select(f"*, {case_statement}").order(by="a").to_df()
-            shape: (2, 2)
-            ┌─────┬─────┐
-            │ a   ┆ b   │
-            │ --- ┆ --- │
-            │ i64 ┆ str │
-            ╞═════╪═════╡
-            │ 1   ┆ one │
-            │ 2   ┆ two │
-            └─────┴─────┘
-        """
-        self.on_column = on_column
-        self.as_column = as_column
-        self.mapping = {
-            sql_repr(key): sql_repr(value) for key, value in mapping.items()
-        }
-        self.default_value = sql_repr(default)
-        self.sql_string = f"case {self.on_column} " + (
-            " ".join(f"when {key} then {value}" for key, value in self.mapping.items())
-            + f" else {self.default_value} end"
-        )
-        if self.as_column:
-            self.sql_string += f" as {as_column}"
-
-    def __str__(self) -> str:
-        """
-        Return string representation of SQL case statement.
-
-        Returns:
-            String representing the case expression which can be directly inserted into
-            an SQL query.
-        """
-        return self.sql_string
diff --git a/src/patito/validators.py b/src/patito/validators.py
index ff0e2fa..e22e750 100644
--- a/src/patito/validators.py
+++ b/src/patito/validators.py
@@ -1,29 +1,22 @@
 """Module for validating datastructures with respect to model specifications."""
 from __future__ import annotations
 
-import sys
-from typing import TYPE_CHECKING, Type, Union, cast
+from typing import TYPE_CHECKING, Optional, Sequence, Type, Union, cast, Any
 
 import polars as pl
-from typing_extensions import get_args, get_origin
+from typing_extensions import get_args
 
+from patito._pydantic.dtypes import is_optional
 from patito.exceptions import (
     ColumnDTypeError,
+    DataFrameValidationError,
     ErrorWrapper,
     MissingColumnsError,
     MissingValuesError,
     RowValueError,
-    SuperflousColumnsError,
-    ValidationError,
+    SuperfluousColumnsError,
 )
 
-if sys.version_info >= (3, 10):  # pragma: no cover
-    from types import UnionType  # pyright: ignore
-
-    UNION_TYPES = (Union, UnionType)
-else:
-    UNION_TYPES = (Union,)  # pragma: no cover
-
 try:
     import pandas as pd
 
@@ -38,7 +31,7 @@
 VALID_POLARS_TYPES = {
     "enum": {pl.Categorical},
     "boolean": {pl.Boolean},
-    "string": {pl.Utf8, pl.Datetime, pl.Date},
+    "string": {pl.String, pl.Datetime, pl.Date},
     "number": {pl.Float32, pl.Float64},
     "integer": {
         pl.Int8,
@@ -53,32 +46,19 @@
 }
 
 
-def _is_optional(type_annotation: Type) -> bool:
-    """
-    Return True if the given type annotation is an Optional annotation.
-
-    Args:
-        type_annotation: The type annotation to be checked.
-
-    Returns:
-        True if the outermost type is Optional.
-    """
-    return (get_origin(type_annotation) in UNION_TYPES) and (
-        type(None) in get_args(type_annotation)
-    )
-
-
-def _dewrap_optional(type_annotation: Type) -> Type:
-    """
-    Return the inner, wrapped type of an Optional.
+def _dewrap_optional(type_annotation: Type[Any] | Any) -> Type:
+    """Return the inner, wrapped type of an Optional.
 
     Is a no-op for non-Optional types.
 
     Args:
+    ----
         type_annotation: The type annotation to be dewrapped.
 
     Returns:
+    -------
         The input type, but with the outermost Optional removed.
+
     """
     return (
         next(  # pragma: no cover
@@ -86,7 +66,7 @@ def _dewrap_optional(type_annotation: Type) -> Type:
             for valid_type in get_args(type_annotation)
             if valid_type is not type(None)  # noqa: E721
         )
-        if _is_optional(type_annotation)
+        if is_optional(type_annotation)
         else type_annotation
     )
 
@@ -94,46 +74,61 @@ def _dewrap_optional(type_annotation: Type) -> Type:
 def _find_errors(  # noqa: C901
     dataframe: pl.DataFrame,
     schema: Type[Model],
+    columns: Optional[Sequence[str]] = None,
+    allow_missing_columns: bool = False,
+    allow_superfluous_columns: bool = False,
 ) -> list[ErrorWrapper]:
-    """
-    Validate the given dataframe.
+    """Validate the given dataframe.
 
     Args:
+    ----
         dataframe: Polars DataFrame to be validated.
         schema: Patito model which specifies how the dataframe should be structured.
+        columns: If specified, only validate the given columns. Missing columns will
+            check if any specified columns are missing from the inputted dataframe,
+            and superfluous columns will check if any columns not specified in the
+            schema are present in the columns list.
+        allow_missing_columns: If True, missing columns will not be considered an error.
+        allow_superfluous_columns: If True, additional columns will not be considered an error.
 
     Returns:
+    -------
         A list of patito.exception.ErrorWrapper instances. The specific validation
         error can be retrieved from the "exc" attribute on each error wrapper instance.
 
         MissingColumnsError: If there are any missing columns.
-        SuperflousColumnsError: If there are additional, non-specified columns.
+        SuperfluousColumnsError: If there are additional, non-specified columns.
         MissingValuesError: If there are nulls in a non-optional column.
         ColumnDTypeError: If any column has the wrong dtype.
         NotImplementedError: If validation has not been implement for the given
             type.
+
     """
     errors: list[ErrorWrapper] = []
-    # Check if any columns are missing
-    for missig_column in set(schema.columns) - set(dataframe.columns):
-        errors.append(
-            ErrorWrapper(
-                MissingColumnsError("Missing column"),
-                loc=missig_column,
+    schema_subset = columns or schema.columns
+    column_subset = columns or dataframe.columns
+    if not allow_missing_columns:
+        # Check if any columns are missing
+        for missing_column in set(schema_subset) - set(dataframe.columns):
+            errors.append(
+                ErrorWrapper(
+                    MissingColumnsError("Missing column"),
+                    loc=missing_column,
+                )
             )
-        )
 
-    # Check if any additional columns are included
-    for superflous_column in set(dataframe.columns) - set(schema.columns):
-        errors.append(
-            ErrorWrapper(
-                SuperflousColumnsError("Superflous column"),
-                loc=superflous_column,
+    if not allow_superfluous_columns:
+        # Check if any additional columns are included
+        for superfluous_column in set(column_subset) - set(schema.columns):
+            errors.append(
+                ErrorWrapper(
+                    SuperfluousColumnsError("Superfluous column"),
+                    loc=superfluous_column,
+                )
             )
-        )
 
     # Check if any non-optional columns have null values
-    for column in schema.non_nullable_columns.intersection(dataframe.columns):
+    for column in schema.non_nullable_columns.intersection(column_subset):
         num_missing_values = dataframe.get_column(name=column).null_count()
         if num_missing_values:
             errors.append(
@@ -147,10 +142,12 @@ def _find_errors(  # noqa: C901
             )
 
     for column, dtype in schema.dtypes.items():
+        if column not in column_subset:
+            continue
         if not isinstance(dtype, pl.List):
             continue
 
-        annotation = schema.__annotations__[column]  # type: ignore[unreachable]
+        annotation = schema.model_fields[column].annotation  # type: ignore[unreachable]
 
         # Retrieve the annotation of the list itself,
         # dewrapping any potential Optional[...]
@@ -158,7 +155,7 @@ def _find_errors(  # noqa: C901
 
         # Check if the list items themselves should be considered nullable
         item_type = get_args(list_type)[0]
-        if _is_optional(item_type):
+        if is_optional(item_type):
             continue
 
         num_missing_values = (
@@ -189,7 +186,8 @@ def _find_errors(  # noqa: C901
     valid_dtypes = schema.valid_dtypes
     dataframe_datatypes = dict(zip(dataframe.columns, dataframe.dtypes))
     for column_name, column_properties in schema._schema_properties().items():
-        if column_name not in dataframe.columns:
+        column_info = schema.column_infos[column_name]
+        if column_name not in dataframe.columns or column_name not in column_subset:
             continue
 
         polars_type = dataframe_datatypes[column_name]
@@ -220,7 +218,7 @@ def _find_errors(  # noqa: C901
                     )
                 )
 
-        if column_properties.get("unique", False):
+        if column_info.unique:
             # Coalescing to 0 in the case of dataframe of height 0
             num_duplicated = dataframe[column_name].is_duplicated().sum() or 0
             if num_duplicated > 0:
@@ -241,37 +239,49 @@ def _find_errors(  # noqa: C901
             "multipleOf": lambda v: (col == 0) | ((col % v) == 0),
             "const": lambda v: col == v,
             "pattern": lambda v: col.str.contains(v),
-            "minLength": lambda v: col.str.lengths() >= v,
-            "maxLength": lambda v: col.str.lengths() <= v,
+            "minLength": lambda v: col.str.len_chars() >= v,
+            "maxLength": lambda v: col.str.len_chars() <= v,
         }
-        checks = [
+        if "anyOf" in column_properties:
+            checks = [
+                check(x[key])
+                for key, check in filters.items()
+                for x in column_properties["anyOf"]
+                if key in x
+            ]
+        else:
+            checks = []
+        checks += [
             check(column_properties[key])
             for key, check in filters.items()
             if key in column_properties
         ]
         if checks:
-            lazy_df = dataframe.lazy()
+            n_invalid_rows = 0
             for check in checks:
-                lazy_df = lazy_df.filter(check)
-            valid_rows = lazy_df.collect()
-            invalid_rows = dataframe.height - valid_rows.height
-            if invalid_rows > 0:
+                lazy_df = dataframe.lazy()
+                lazy_df = lazy_df.filter(
+                    ~check
+                )  # get failing rows (nulls will evaluate to null on boolean check, we only want failures (false)))
+                invalid_rows = lazy_df.collect()
+                n_invalid_rows += invalid_rows.height
+            if n_invalid_rows > 0:
                 errors.append(
                     ErrorWrapper(
                         RowValueError(
-                            f"{invalid_rows} row{'' if invalid_rows == 1 else 's'} "
+                            f"{n_invalid_rows} row{'' if n_invalid_rows == 1 else 's'} "
                             "with out of bound values."
                         ),
                         loc=column_name,
                     )
                 )
 
-        if "constraints" in column_properties:
-            custom_constraints = column_properties["constraints"]
+        if column_info.constraints is not None:
+            custom_constraints = column_info.constraints
             if isinstance(custom_constraints, pl.Expr):
                 custom_constraints = [custom_constraints]
             constraints = pl.all_horizontal(
-                [constraint.is_not() for constraint in custom_constraints]
+                [constraint.not_() for constraint in custom_constraints]
             )
             if "_" in constraints.meta.root_names():
                 # An underscore is an alias for the current field
@@ -296,23 +306,37 @@ def _find_errors(  # noqa: C901
 
 
 def validate(
-    dataframe: Union["pd.DataFrame", pl.DataFrame], schema: Type[Model]
+    dataframe: Union["pd.DataFrame", pl.DataFrame],
+    schema: Type[Model],
+    columns: Optional[Sequence[str]] = None,
+    allow_missing_columns: bool = False,
+    allow_superfluous_columns: bool = False,
 ) -> None:
-    """
-    Validate the given dataframe.
+    """Validate the given dataframe.
 
     Args:
+    ----
         dataframe: Polars DataFrame to be validated.
         schema: Patito model which specifies how the dataframe should be structured.
+        allow_missing_columns: If True, missing columns will not be considered an error.
+        allow_superfluous_columns: If True, additional columns will not be considered an error.
 
     Raises:
-        ValidationError: If the given dataframe does not match the given schema.
+    ------
+        DataFrameValidationError: If the given dataframe does not match the given schema.
+
     """
     if _PANDAS_AVAILABLE and isinstance(dataframe, pd.DataFrame):
         polars_dataframe = pl.from_pandas(dataframe)
     else:
         polars_dataframe = cast(pl.DataFrame, dataframe)
 
-    errors = _find_errors(dataframe=polars_dataframe, schema=schema)
+    errors = _find_errors(
+        dataframe=polars_dataframe,
+        schema=schema,
+        columns=columns,
+        allow_missing_columns=allow_missing_columns,
+        allow_superfluous_columns=allow_superfluous_columns,
+    )
     if errors:
-        raise ValidationError(errors=errors, model=schema)
+        raise DataFrameValidationError(errors=errors, model=schema)
diff --git a/src/patito/xdg.py b/src/patito/xdg.py
index 0148dc8..8b60187 100644
--- a/src/patito/xdg.py
+++ b/src/patito/xdg.py
@@ -5,15 +5,17 @@
 
 
 def cache_home(application: Optional[str] = None) -> Path:
-    """
-    Return path to directory containing user-specific non-essential data files.
+    """Return path to directory containing user-specific non-essential data files.
 
     Args:
+    ----
         application: An optional name of an application for which to return an
             application-specific cache directory for.
 
     Returns:
+    -------
         A path object pointing to a directory to store cache files.
+
     """
     path = Path(os.environ.get("XDG_CACHE_HOME", "~/.cache")).resolve()
     if application:
diff --git a/tests/examples.py b/tests/examples.py
new file mode 100644
index 0000000..dd3c549
--- /dev/null
+++ b/tests/examples.py
@@ -0,0 +1,55 @@
+from datetime import date, datetime, time, timedelta
+from typing import List, Literal, Optional
+
+import patito as pt
+import polars as pl
+from pydantic import AwareDatetime
+
+
+class SmallModel(pt.Model):
+    a: int
+    b: str
+    c: AwareDatetime = pt.Field(
+        dtype=pl.Datetime(time_zone="UTC")
+    )  # check that dtype resolver will use patito-specified dtype if passed
+    d: Optional[AwareDatetime] = pt.Field(
+        default=None, dtype=pl.Datetime(time_zone="UTC")
+    )
+    e: int = pt.Field(dtype=pl.Int8)
+
+
+class ManyTypes(pt.Model):
+    int_value: int
+    float_value: float
+    str_value: str
+    bool_value: bool
+    literal_value: Literal["a", "b"]
+    default_value: str = "my_default"
+    optional_value: Optional[int]
+    bounded_value: int = pt.Field(ge=10, le=20)
+    date_value: date
+    datetime_value: datetime
+    pt_model_value: SmallModel
+
+
+class CompleteModel(pt.Model):
+    str_column: str
+    int_column: int
+    float_column: float
+    bool_column: bool
+
+    date_column: date
+    datetime_column: datetime
+    datetime_column2: datetime = pt.Field(dtype=pl.Datetime)
+    aware_datetime_column: AwareDatetime = pt.Field(dtype=pl.Datetime(time_zone="UTC"))
+    duration_column: timedelta
+    time_column: time
+
+    categorical_column: Literal["a", "b", "c"]
+    null_column: None = None
+
+    pt_model_column: SmallModel
+
+    list_int_column: List[int]
+    list_str_column: List[str]
+    list_opt_column: List[Optional[int]]
diff --git a/tests/test_database.py b/tests/test_database.py
deleted file mode 100644
index bd06679..0000000
--- a/tests/test_database.py
+++ /dev/null
@@ -1,569 +0,0 @@
-import os
-import sqlite3
-from datetime import datetime, timedelta
-from pathlib import Path
-from tempfile import TemporaryDirectory
-from typing import TYPE_CHECKING, List, Optional
-
-import polars as pl
-import pytest
-
-import patito as pt
-
-if TYPE_CHECKING:
-    import pyarrow as pa  # type: ignore
-else:
-    # Python 3.7 does not support pyarrow
-    pa = pytest.importorskip("pyarrow")
-
-
-class LoggingQuerySource(pt.Database):
-    """A dummy query source with an associated query execution log."""
-
-    executed_queries: List[str]
-
-
-@pytest.fixture()
-def query_cache(tmp_path) -> LoggingQuerySource:
-    """
-    Return dummy query cache with query execution logger.
-
-    Args:
-        tmp_path: Test-specific temporary directory provided by pytest.
-
-    Returns:
-        A cacher which also keeps track of the executed queries.
-    """
-    # Keep track of the executed queries in a mutable list
-    executed_queries = []
-
-    # Unless other is specified, some dummy data is always returned
-    def query_handler(query, mock_data: Optional[dict] = None) -> pa.Table:
-        executed_queries.append(query)
-        data = {"column": [1, 2, 3]} if mock_data is None else mock_data
-        return pa.Table.from_pydict(data)
-
-    query_cache = LoggingQuerySource(
-        query_handler=query_handler,
-        cache_directory=tmp_path,
-        default_ttl=timedelta(weeks=52),
-    )
-
-    # Attach the query execution log as an attribute of the query source
-    query_cache.executed_queries = executed_queries
-    return query_cache
-
-
-@pytest.fixture
-def query_source(tmpdir) -> LoggingQuerySource:
-    """
-    A QuerySource connected to an in-memory SQLite3 database with dummy data.
-
-    Args:
-        tmpdir: Test-specific temporary directory provided by pytest.
-
-    Returns:
-        A query source which also keeps track of the executed queries.
-    """
-    # Keep track of the executed queries in a mutable list
-    executed_queries = []
-
-    def dummy_database() -> sqlite3.Cursor:
-        connection = sqlite3.connect(":memory:")
-        cursor = connection.cursor()
-        cursor.execute("CREATE TABLE movies(title, year, score)")
-        data = [
-            ("Monty Python Live at the Hollywood Bowl", 1982, 7.9),
-            ("Monty Python's The Meaning of Life", 1983, 7.5),
-            ("Monty Python's Life of Brian", 1979, 8.0),
-        ]
-        cursor.executemany("INSERT INTO movies VALUES(?, ?, ?)", data)
-        connection.commit()
-        return cursor
-
-    def query_handler(query: str) -> pa.Table:
-        cursor = dummy_database()
-        cursor.execute(query)
-        executed_queries.append(query)
-        columns = [description[0] for description in cursor.description]
-        data = [dict(zip(columns, row)) for row in cursor.fetchall()]
-        return pa.Table.from_pylist(data)
-
-    # Attach the query execution log as an attribute of the query source
-    tmp_dir = Path(tmpdir)
-    query_cache = LoggingQuerySource(
-        query_handler=query_handler,
-        cache_directory=tmp_dir,
-    )
-    query_cache.executed_queries = executed_queries
-    return query_cache
-
-
-def test_uncached_query(query_cache: LoggingQuerySource):
-    """It should not cache queries by default."""
-
-    @query_cache.as_query()
-    def products():
-        return "query"
-
-    # First time it is called we should execute the query
-    products()
-    assert query_cache.executed_queries == ["query"]
-    # And no cache file is created
-    assert not any(query_cache.cache_directory.iterdir())
-
-    # The next time the query is executed again
-    products()
-    assert query_cache.executed_queries == ["query", "query"]
-    # And still no cache file
-    assert not any(query_cache.cache_directory.iterdir())
-
-
-def test_cached_query(query_cache: LoggingQuerySource):
-    """It should cache queries if so parametrized."""
-
-    # We enable cache for the given query
-    @query_cache.as_query(cache=True)
-    def products(version: int):
-        return f"query {version}"
-
-    # The cache is stored in the "products" sub-folder
-    cache_dir = query_cache.cache_directory / "products"
-
-    # First time the query is executed
-    products(version=1)
-    assert query_cache.executed_queries == ["query 1"]
-    # And the result is stored in a cache file
-    assert len(list(cache_dir.iterdir())) == 1
-
-    # The next time the query is *not* executed
-    products(version=1)
-    assert query_cache.executed_queries == ["query 1"]
-    # And the cache file persists
-    assert len(list(cache_dir.iterdir())) == 1
-
-    # But if we change the query itself, it is executed
-    products(version=2)
-    assert query_cache.executed_queries == ["query 1", "query 2"]
-    # And it is cached in a separate file
-    assert len(list(cache_dir.iterdir())) == 2
-
-    # If we delete the cache file, the query is re-executed
-    for cache_file in cache_dir.iterdir():
-        cache_file.unlink()
-    products(version=1)
-    assert query_cache.executed_queries == ["query 1", "query 2", "query 1"]
-    # And the cache file is rewritten
-    assert len(list(cache_dir.iterdir())) == 1
-
-    # We clear the cache with .clear_cache()
-    products.refresh_cache(version=1)
-    assert query_cache.executed_queries == ["query 1", "query 2", "query 1", "query 1"]
-    # We can also clear caches that have never existed
-    products.refresh_cache(version=3)
-    assert query_cache.executed_queries[-1] == "query 3"
-
-
-def test_cached_query_with_explicit_path(
-    query_cache: LoggingQuerySource,
-    tmpdir: Path,
-) -> None:
-    """It should cache queries in the provided path."""
-    cache_path = Path(tmpdir / "name.parquet")
-
-    # This time we specify an explicit path
-    @query_cache.as_query(cache=cache_path)
-    def products(version):
-        return f"query {version}"
-
-    # At first the path does not exist
-    assert not cache_path.exists()
-
-    # We then execute and cache the query
-    products(version=1)
-    assert cache_path.exists()
-    assert query_cache.executed_queries == ["query 1"]
-
-    # And the next time it is reused
-    products(version=1)
-    assert query_cache.executed_queries == ["query 1"]
-    assert cache_path.exists()
-
-    # If the query changes, it is re-executed
-    products(version=2)
-    assert query_cache.executed_queries == ["query 1", "query 2"]
-
-    # If a non-parquet file is specified, it will raise
-    with pytest.raises(
-        ValueError,
-        match=r"Cache paths must have the '\.parquet' file extension\!",
-    ):
-
-        @query_cache.as_query(cache=tmpdir / "name.csv")
-        def products(version):
-            return f"query {version}"
-
-
-def test_cached_query_with_relative_path(query_cache: LoggingQuerySource) -> None:
-    """Relative paths should be interpreted relative to the cache directory."""
-    relative_path = Path("foo/bar.parquet")
-
-    @query_cache.as_query(cache=relative_path)
-    def products():
-        return "query"
-
-    products()
-    assert (query_cache.cache_directory / "foo" / "bar.parquet").exists()
-
-
-def test_cached_query_with_format_string(query_cache: LoggingQuerySource) -> None:
-    """Strings with placeholders should be interpolated."""
-
-    @query_cache.as_query(cache="version-{version}.parquet")
-    def products(version: int):
-        return f"query {version}"
-
-    # It should work for both positional arguments...
-    products(1)
-    assert (query_cache.cache_directory / "version-1.parquet").exists()
-    # ... and keywords
-    products(version=2)
-    assert (query_cache.cache_directory / "version-2.parquet").exists()
-
-
-def test_cached_query_with_format_path(query_cache: LoggingQuerySource) -> None:
-    """Paths with placeholders should be interpolated."""
-
-    @query_cache.as_query(
-        cache=query_cache.cache_directory / "version-{version}.parquet"
-    )
-    def products(version: int):
-        return f"query {version}"
-
-    # It should work for both positional arguments...
-    products(1)
-    assert (query_cache.cache_directory / "version-1.parquet").exists()
-    # ... and keywords
-    products(version=2)
-    assert (query_cache.cache_directory / "version-2.parquet").exists()
-
-
-def test_cache_ttl(query_cache: LoggingQuerySource, monkeypatch):
-    """It should automatically refresh the cache according to the TTL."""
-
-    # We freeze the time during the execution of this test
-    class FrozenDatetime:
-        def __init__(self, year: int, month: int, day: int) -> None:
-            self.frozen_time = datetime(year=year, month=month, day=day)
-            monkeypatch.setattr(pt.database, "datetime", self)  # pyright: ignore
-
-        def now(self):
-            return self.frozen_time
-
-        @staticmethod
-        def fromisoformat(*args, **kwargs):
-            return datetime.fromisoformat(*args, **kwargs)
-
-    # The cache should be cleared every week
-    @query_cache.as_query(cache=True, ttl=timedelta(weeks=1))
-    def users():
-        return "query"
-
-    # The first time the query should be executed
-    FrozenDatetime(year=2000, month=1, day=1)
-    users()
-    assert query_cache.executed_queries == ["query"]
-
-    # The next time it should not be executed
-    users()
-    assert query_cache.executed_queries == ["query"]
-
-    # Even if we advance the time by one day,
-    # the cache should still be used.
-    FrozenDatetime(year=2000, month=1, day=2)
-    users()
-    assert query_cache.executed_queries == ["query"]
-
-    # Then we let one week pass, and the cache should be cleared
-    FrozenDatetime(year=2000, month=1, day=8)
-    users()
-    assert query_cache.executed_queries == ["query", "query"]
-
-    # But then it will be reused for another week
-    users()
-    assert query_cache.executed_queries == ["query", "query"]
-
-
-@pytest.mark.parametrize("cache", [True, False])
-def test_lazy_query(query_cache: LoggingQuerySource, cache: bool):
-    """It should return a LazyFrame when specified with lazy=True."""
-
-    @query_cache.as_query(lazy=True, cache=cache)
-    def lazy():
-        return "query"
-
-    @query_cache.as_query(lazy=False, cache=cache)
-    def eager():
-        return "query"
-
-    # We invoke it twice, first not hitting the cache, and then hitting it
-    assert lazy().collect().frame_equal(eager())
-    assert lazy().collect().frame_equal(eager())
-
-
-def test_model_query_model_validation(query_cache: LoggingQuerySource):
-    """It should validate the data model."""
-
-    class CorrectModel(pt.Model):
-        column: int
-
-    @query_cache.as_query(model=CorrectModel)
-    def correct_data():
-        return ""
-
-    assert isinstance(correct_data(), pl.DataFrame)
-
-    class IncorrectModel(pt.Model):
-        column: str
-
-    @query_cache.as_query(model=IncorrectModel)
-    def incorrect_data():
-        return ""
-
-    with pytest.raises(pt.exceptions.ValidationError):
-        incorrect_data()
-
-
-def test_custom_forwarding_of_parameters_to_query_function(
-    query_cache: LoggingQuerySource,
-):
-    """It should forward all additional parameters to the sql_to_arrow function."""
-
-    # The dummy cacher accepts a "data" parameter, specifying the data to be returned
-    data = {"actual_data": [10, 20, 30]}
-
-    @query_cache.as_query(mock_data=data)
-    def custom_data():
-        return "select 1, 2, 3 as dummy_column"
-
-    assert custom_data().frame_equal(pl.DataFrame(data))
-
-    # It should also work without type normalization
-    @query_cache.as_query(mock_data=data, cast_to_polars_equivalent_types=False)
-    def non_normalized_custom_data():
-        return "select 1, 2, 3 as dummy_column"
-
-    assert non_normalized_custom_data().frame_equal(pl.DataFrame(data))
-
-
-def test_clear_caches(query_cache: LoggingQuerySource):
-    """It should clear all cache files with .clear_all_caches()."""
-
-    @query_cache.as_query(cache=True)
-    def products(version: int):
-        return f"query {version}"
-
-    # The cache is stored in the "products" sub-directory
-    products_cache_dir = query_cache.cache_directory / "products"
-
-    # We produce two cache files
-    products(version=1)
-    products(version=2)
-    assert query_cache.executed_queries == ["query 1", "query 2"]
-    assert len(list(products_cache_dir.iterdir())) == 2
-
-    # We also insert another parquet file that should *not* be deleted
-    dummy_parquet_path = products_cache_dir / "dummy.parquet"
-    pl.DataFrame().write_parquet(dummy_parquet_path)
-
-    # And an invalid parquet file
-    invalid_parquet_path = products_cache_dir / "invalid.parquet"
-    invalid_parquet_path.write_bytes(b"invalid content")
-
-    # We delete all caches, but not the dummy parquet file
-    products.clear_caches()
-    assert len(list(products_cache_dir.iterdir())) == 2
-    assert dummy_parquet_path.exists()
-    assert invalid_parquet_path.exists()
-
-    # The next time both queries need to be re-executed
-    products(version=1)
-    products(version=2)
-    assert query_cache.executed_queries == ["query 1", "query 2"] * 2
-    assert len(list(products_cache_dir.iterdir())) == 4
-
-    # If caching is not enabled, clear_caches should be a NO-OP
-    @query_cache.as_query(cache=False)
-    def uncached_products(version: int):
-        return f"query {version}"
-
-    uncached_products.clear_caches()
-
-
-def test_clear_caches_with_formatted_paths(query_cache: LoggingQuerySource):
-    """Formatted paths should also be properly cleared."""
-    # We specify another temporary cache directory to see if caches can be cleared
-    # irregardless of the cache directory's location.
-    tmp_dir = TemporaryDirectory()
-    cache_dir = Path(tmp_dir.name)
-
-    @query_cache.as_query(cache=cache_dir / "{a}" / "{b}.parquet")
-    def users(a: int, b: int):
-        return f"query {a}.{b}"
-
-    users(1, 1)
-    users(1, 2)
-    users(2, 1)
-
-    assert query_cache.executed_queries == ["query 1.1", "query 1.2", "query 2.1"]
-
-    assert {str(path.relative_to(cache_dir)) for path in cache_dir.rglob("*")} == {
-        # Both directories have been created
-        "1",
-        "2",
-        # Two cache files for a=1
-        "1/1.parquet",
-        "1/2.parquet",
-        # One cache file for a=2
-        "2/1.parquet",
-    }
-
-    # We insert another parquet file that should *not* be cleared
-    pl.DataFrame().write_parquet(cache_dir / "1" / "3.parquet")
-
-    # Only directories and non-cached files should be kept
-    users.clear_caches()
-    assert {str(path.relative_to(cache_dir)) for path in cache_dir.rglob("*")} == {
-        "1",
-        "2",
-        "1/3.parquet",
-    }
-    tmp_dir.cleanup()
-
-
-def test_ejection_of_incompatible_caches(query_cache: LoggingQuerySource):
-    """It should clear old, incompatible caches."""
-
-    cache_path = query_cache.cache_directory / "my_cache.parquet"
-
-    @query_cache.as_query(cache=cache_path)
-    def my_query():
-        return "my query"
-
-    # Write a parquet file without any metadata
-    pl.DataFrame().write_parquet(cache_path)
-
-    # The existing parquet file without metadata should be overwritten
-    df = my_query()
-    assert not df.is_empty()
-    assert query_cache.executed_queries == ["my query"]
-
-    # Now we decrement the version number of the cache in order to overwrite it
-    arrow_table = pa.parquet.read_table(cache_path)  # noqa
-    metadata = arrow_table.schema.metadata
-    assert (
-        int.from_bytes(metadata[b"cache_version"], "little")
-        == pt.database.CACHE_VERSION  # pyright: ignore
-    )
-    metadata[b"cache_version"] = (
-        pt.database.CACHE_VERSION - 1  # pyright: ignore
-    ).to_bytes(
-        length=16,
-        byteorder="little",
-        signed=False,
-    )
-    pa.parquet.write_table(
-        arrow_table.replace_schema_metadata(metadata),
-        where=cache_path,
-    )
-
-    # The query should now be re-executed
-    my_query()
-    assert query_cache.executed_queries == ["my query"] * 2
-
-    # Deleting the cache_version alltogether should also retrigger the query
-    del metadata[b"cache_version"]
-    pa.parquet.write_table(
-        arrow_table.replace_schema_metadata(metadata),
-        where=cache_path,
-    )
-    my_query()
-    assert query_cache.executed_queries == ["my query"] * 3
-
-
-def test_adherence_to_xdg_directory_standard(monkeypatch, tmpdir):
-    """It should use XDG Cache Home when no cache directory is specified."""
-    xdg_cache_home = tmpdir / ".cache"
-    os.environ["XDG_CACHE_HOME"] = str(xdg_cache_home)
-    query_source = pt.Database(query_handler=lambda query: pa.Table())
-    assert query_source.cache_directory == xdg_cache_home / "patito"
-
-    del os.environ["XDG_CACHE_HOME"]
-    query_source = pt.Database(query_handler=lambda query: pa.Table())
-    assert query_source.cache_directory == Path("~/.cache/patito").resolve()
-
-
-def test_invoking_query_source_directly_with_query_string(
-    query_source: LoggingQuerySource,
-):
-    """It should accept SQL queries directly, not ony query constructors."""
-    sql = "select * from movies"
-    movies = query_source.query(sql)
-    assert query_source.executed_queries == [sql]
-    assert len(list(query_source.cache_directory.iterdir())) == 0
-    assert movies.height == 3
-
-    for _ in range(2):
-        query_source.query(sql, cache=True)
-        assert query_source.executed_queries == [sql] * 2
-        assert (
-            len(list((query_source.cache_directory / "__direct_query").iterdir())) == 1
-        )
-
-    assert query_source.query(sql, lazy=True).collect().frame_equal(movies)
-
-
-@pytest.mark.skip(reason="TODO: Future feature to implement")
-def test_custom_kwarg_hashing(tmp_path):
-    """You should be able to hash the keyword arguments passed to the query handler."""
-
-    executed_queries = []
-
-    def query_handler(query: str, prod=False) -> pa.Table:
-        executed_queries.append(query)
-        return pa.Table.from_pydict({"column": [1, 2, 3]})
-
-    def query_handler_hasher(query: str, prod: bool) -> bytes:
-        return bytes(prod)
-
-    dummy_source = pt.Database(
-        query_handler=query_handler,
-        query_handler_hasher=query_handler_hasher,  # pyright: ignore
-        cache_directory=tmp_path,
-    )
-
-    # The first time the query should be executed
-    sql_query = "select * from my_table"
-    dummy_source.query(sql_query, cache=True)
-    assert executed_queries == [sql_query]
-    assert len(list(dummy_source.cache_directory.rglob("*.parquet"))) == 1
-
-    # The second time the dev query has been cached
-    dummy_source.query(sql_query, cache=True)
-    assert executed_queries == [sql_query]
-    assert len(list(dummy_source.cache_directory.rglob("*.parquet"))) == 1
-
-    # The production query has never executed, so a new query is executed
-    dummy_source.query(sql_query, cache=True, prod=True)
-    assert executed_queries == [sql_query] * 2
-    assert len(list(dummy_source.cache_directory.rglob("*.parquet"))) == 2
-
-    # Then the production query cache is used
-    dummy_source.query(sql_query, cache=True, prod=True)
-    assert executed_queries == [sql_query] * 2
-    assert len(list(dummy_source.cache_directory.rglob("*.parquet"))) == 2
-
-    # And the dev query cache still remains
-    dummy_source.query(sql_query, cache=True)
-    assert executed_queries == [sql_query] * 2
-    assert len(list(dummy_source.cache_directory.rglob("*.parquet"))) == 2
diff --git a/tests/test_dtypes.py b/tests/test_dtypes.py
new file mode 100644
index 0000000..b557673
--- /dev/null
+++ b/tests/test_dtypes.py
@@ -0,0 +1,195 @@
+from __future__ import annotations
+
+import sys
+from datetime import date, datetime, time, timedelta
+from typing import Dict, List, Literal, Optional, Sequence, Tuple, Union
+
+import polars as pl
+import pytest
+from patito._pydantic.dtypes.dtypes import (
+    DtypeResolver,
+    validate_annotation,
+    validate_polars_dtype,
+)
+from patito._pydantic.dtypes.utils import (
+    BOOLEAN_DTYPES,
+    DATE_DTYPES,
+    STRING_DTYPES,
+    TIME_DTYPES,
+)
+from polars.datatypes import DataTypeGroup
+from polars.datatypes.constants import (
+    DATETIME_DTYPES,
+    DURATION_DTYPES,
+    FLOAT_DTYPES,
+    INTEGER_DTYPES,
+)
+from pydantic import AwareDatetime
+
+from tests.examples import ManyTypes
+
+
+def test_valids_basic_annotations() -> None:
+    # base types
+    assert DtypeResolver(str).valid_polars_dtypes() == STRING_DTYPES
+    assert DtypeResolver(int).valid_polars_dtypes() == DataTypeGroup(
+        INTEGER_DTYPES | FLOAT_DTYPES
+    )
+    assert DtypeResolver(float).valid_polars_dtypes() == FLOAT_DTYPES
+    assert DtypeResolver(bool).valid_polars_dtypes() == BOOLEAN_DTYPES
+
+    # temporals
+    assert DtypeResolver(datetime).valid_polars_dtypes() == DATETIME_DTYPES
+    assert DtypeResolver(date).valid_polars_dtypes() == DATE_DTYPES
+    assert DtypeResolver(time).valid_polars_dtypes() == TIME_DTYPES
+    assert DtypeResolver(timedelta).valid_polars_dtypes() == DURATION_DTYPES
+
+    # other
+    with pytest.raises(TypeError, match="must be strings"):
+        DtypeResolver(Literal[1, 2, 3]).valid_polars_dtypes()  # pyright: ignore
+    with pytest.raises(TypeError, match="Mixed type enums not supported"):
+        DtypeResolver(Literal[1, 2, "3"]).valid_polars_dtypes()  # pyright: ignore
+
+    assert DtypeResolver(Literal["a", "b", "c"]).valid_polars_dtypes() == {  # pyright: ignore
+        pl.Enum(["a", "b", "c"]),
+        pl.String,
+    }
+
+    # combos
+    assert DtypeResolver(Optional[str]).valid_polars_dtypes() == STRING_DTYPES
+    if sys.version_info[1] >= 10:
+        assert (
+            DtypeResolver(str | None | None).valid_polars_dtypes() == STRING_DTYPES
+        )  # superfluous None is ok
+    assert DtypeResolver(Union[int, float]).valid_polars_dtypes() == FLOAT_DTYPES
+    assert (
+        DtypeResolver(Union[str, int]).valid_polars_dtypes() == frozenset()
+    )  # incompatible
+
+    # invalids
+    assert DtypeResolver(object).valid_polars_dtypes() == frozenset()
+
+
+def test_valids_nested_annotations() -> None:
+    assert len(DtypeResolver(List).valid_polars_dtypes()) == 0  # needs inner annotation
+    assert (
+        DtypeResolver(Tuple).valid_polars_dtypes()
+        == DtypeResolver(List).valid_polars_dtypes()
+        == DtypeResolver(Sequence).valid_polars_dtypes()
+    )  # for now, these are the same
+
+    assert DtypeResolver(List[str]).valid_polars_dtypes() == {pl.List(pl.String)}
+    assert DtypeResolver(Optional[List[str]]).valid_polars_dtypes() == {
+        pl.List(pl.String)
+    }
+    assert len(DtypeResolver(List[int]).valid_polars_dtypes()) == len(
+        DataTypeGroup(INTEGER_DTYPES | FLOAT_DTYPES)
+    )
+    assert len(DtypeResolver(List[Union[int, float]]).valid_polars_dtypes()) == len(
+        FLOAT_DTYPES
+    )
+    assert len(DtypeResolver(List[Optional[int]]).valid_polars_dtypes()) == len(
+        DataTypeGroup(INTEGER_DTYPES | FLOAT_DTYPES)
+    )
+    assert DtypeResolver(List[List[str]]).valid_polars_dtypes() == {
+        pl.List(pl.List(pl.String))
+    }  # recursion works as expected
+
+    assert (
+        DtypeResolver(Dict).valid_polars_dtypes() == frozenset()
+    )  # not currently supported
+
+    # support for nested models via struct
+    assert (
+        len(DtypeResolver(ManyTypes).valid_polars_dtypes()) == 1
+    )  # only defaults are valid
+    assert (
+        DtypeResolver(ManyTypes).valid_polars_dtypes()
+        == DtypeResolver(Optional[ManyTypes]).valid_polars_dtypes()
+    )
+
+
+def test_dtype_validation() -> None:
+    validate_polars_dtype(int, pl.Int16)  # no issue
+    validate_polars_dtype(int, pl.Float64)  # no issue
+    with pytest.raises(ValueError, match="Invalid dtype"):
+        validate_polars_dtype(int, pl.String)
+
+    with pytest.raises(ValueError, match="Invalid dtype"):
+        validate_polars_dtype(List[str], pl.List(pl.Float64))
+
+    # some potential corner cases
+    validate_polars_dtype(AwareDatetime, dtype=pl.Datetime(time_zone="UTC"))
+
+
+def test_defaults_basic_annotations() -> None:
+    # base types
+    assert DtypeResolver(str).default_polars_dtype() == pl.String
+    assert DtypeResolver(int).default_polars_dtype() == pl.Int64
+    assert DtypeResolver(float).default_polars_dtype() == pl.Float64
+    assert DtypeResolver(bool).default_polars_dtype() == pl.Boolean
+
+    # temporals
+    assert DtypeResolver(datetime).default_polars_dtype() == pl.Datetime
+    assert DtypeResolver(date).default_polars_dtype() == pl.Date
+    assert DtypeResolver(time).default_polars_dtype() == pl.Time
+    assert DtypeResolver(timedelta).default_polars_dtype() == pl.Duration
+
+    # combos
+    assert DtypeResolver(Optional[str]).default_polars_dtype() == pl.String
+    assert DtypeResolver(Union[int, float]).default_polars_dtype() is None
+    assert DtypeResolver(Union[str, int]).default_polars_dtype() is None
+
+    # other
+    literal = DtypeResolver(Literal["a", "b", "c"]).default_polars_dtype()
+    assert literal == pl.Enum(["a", "b", "c"])
+    assert set(literal.categories) == {"a", "b", "c"}
+
+    # invalids
+    assert DtypeResolver(object).default_polars_dtype() is None
+
+
+def test_defaults_nested_annotations() -> None:
+    assert DtypeResolver(List).default_polars_dtype() is None  # needs inner annotation
+
+    assert DtypeResolver(List[str]).default_polars_dtype() == pl.List(pl.String)
+    assert DtypeResolver(Optional[List[str]]).default_polars_dtype() == pl.List(pl.String)
+    assert DtypeResolver(List[int]).default_polars_dtype() == pl.List(pl.Int64)
+    assert DtypeResolver(List[Optional[int]]).default_polars_dtype() == pl.List(
+        pl.Int64
+    )
+    assert DtypeResolver(List[Union[int, float]]).default_polars_dtype() is None
+    assert DtypeResolver(List[Union[str, int]]).default_polars_dtype() is None
+    assert DtypeResolver(List[List[str]]).default_polars_dtype() == pl.List(
+        pl.List(pl.String)
+    )  # recursion works as expected
+    assert DtypeResolver(List[List[Optional[str]]]).default_polars_dtype() == pl.List(
+        pl.List(pl.String)
+    )
+
+    with pytest.raises(
+        NotImplementedError, match="dictionaries not currently supported"
+    ):
+        DtypeResolver(Dict).default_polars_dtype()
+
+    # support for nested models via struct
+    many_types = DtypeResolver(ManyTypes).default_polars_dtype()
+    assert many_types == pl.Struct
+    assert len(many_types.fields) == len(ManyTypes.columns)
+    assert DtypeResolver(Optional[ManyTypes]).default_polars_dtype() == many_types
+
+
+def test_annotation_validation() -> None:
+    validate_annotation(int)  # no issue
+    validate_annotation(Optional[int])
+
+    with pytest.raises(ValueError, match="Valid dtypes are:"):
+        validate_annotation(Union[int, float])
+    with pytest.raises(ValueError, match="not compatible with any polars dtypes"):
+        validate_annotation(Union[str, int])
+
+    validate_annotation(List[Optional[int]])
+    with pytest.raises(ValueError, match="not compatible with any polars dtypes"):
+        validate_annotation(List[Union[str, int]])
+    with pytest.raises(ValueError, match="Valid dtypes are:"):
+        validate_annotation(List[Union[int, float]])
diff --git a/tests/test_duckdb/__init__.py b/tests/test_duckdb/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/test_duckdb/test_database.py b/tests/test_duckdb/test_database.py
deleted file mode 100644
index 955f620..0000000
--- a/tests/test_duckdb/test_database.py
+++ /dev/null
@@ -1,277 +0,0 @@
-"""Tests for patito.Database."""
-import enum
-from typing import Optional
-
-import polars as pl
-import pytest
-from typing_extensions import Literal
-
-import patito as pt
-
-# Skip test module if DuckDB is not installed
-if not pt._DUCKDB_AVAILABLE:
-    pytest.skip("DuckDB not installed", allow_module_level=True)
-
-
-def test_database(tmp_path):
-    """Test functionality of Database class."""
-    # Create a new in-memory database
-    db = pt.duckdb.Database()
-
-    # Insert a simple dataframe as a new table
-    table_df = pl.DataFrame(
-        {
-            "column_1": [1, 2, 3],
-            "column_2": ["a", "b", "c"],
-        }
-    )
-    db.to_relation(table_df).create_table(name="table_name_1")
-
-    # Check that a round-trip to and from the database preserves the data
-    db_table = db.table("table_name_1").to_df()
-    assert db_table is not table_df
-    assert table_df.frame_equal(db_table)
-
-    # Check that new database objects are isolated from previous ones
-    another_db = pt.duckdb.Database()
-    with pytest.raises(
-        Exception,
-        match=r"Catalog Error\: Table 'table_name_1' does not exist!",
-    ):
-        db_table = another_db.table("table_name_1")
-
-    # Check the parquet reading functionality
-    parquet_path = tmp_path / "tmp.parquet"
-    table_df.write_parquet(str(parquet_path), compression="snappy")
-    new_relation = another_db.to_relation(parquet_path)
-    new_relation.create_table(name="parquet_table")
-    assert another_db.table("parquet_table").count() == 3
-
-
-def test_file_database(tmp_path):
-    """Check if the Database can be persisted to a file."""
-    # Insert some data into a file-backed database
-    db_path = tmp_path / "tmp.db"
-    file_db = pt.duckdb.Database(path=db_path)
-    file_db.to_relation("select 1 as a, 2 as b").create_table(name="table")
-    before_df = file_db.table("table").to_df()
-
-    # Delete the database
-    del file_db
-
-    # And restore tha data from the file
-    restored_db = pt.duckdb.Database(path=db_path)
-    after_df = restored_db.table("table").to_df()
-
-    # The data should still be the same
-    assert before_df.frame_equal(after_df)
-
-
-def test_database_create_table():
-    """Tests for patito.Database.create_table()."""
-
-    # A pydantic basemodel is used to specify the table schema
-    # We inherit here in order to make sure that inheritance works as intended
-    class BaseModel(pt.Model):
-        int_column: int
-        optional_int_column: Optional[int]
-        str_column: str
-
-    class Model(BaseModel):
-        optional_str_column: Optional[str]
-        bool_column: bool
-        optional_bool_column: Optional[bool]
-        enum_column: Literal["a", "b", "c"]
-
-    # We crate the table schema
-    db = pt.duckdb.Database()
-    table = db.create_table(name="test_table", model=Model)
-
-    # We insert some dummy data into the new table
-    dummy_relation = db.to_relation(Model.examples({"optional_int_column": [1, None]}))
-    dummy_relation.insert_into(table="test_table")
-
-    # But we should not be able to insert null data in non-optional columns
-    null_relation = dummy_relation.drop("int_column").select("null as int_column, *")
-    with pytest.raises(
-        Exception,
-        match=("Constraint Error: NOT NULL constraint failed: test_table.int_column"),
-    ):
-        null_relation.insert_into(table="test_table")
-
-    # Check if the correct columns and types have been set
-    assert table.columns == [
-        "int_column",
-        "optional_int_column",
-        "str_column",
-        "optional_str_column",
-        "bool_column",
-        "optional_bool_column",
-        "enum_column",
-    ]
-    assert list(table.types.values()) == [
-        "INTEGER",
-        "INTEGER",
-        "VARCHAR",
-        "VARCHAR",
-        "BOOLEAN",
-        "BOOLEAN",
-        pt.duckdb._enum_type_name(  # pyright: ignore
-            field_properties=Model.schema()["properties"]["enum_column"]
-        ),
-    ]
-
-
-def test_create_view():
-    """It should be able to create a view from a relation source."""
-    db = pt.duckdb.Database()
-    df = pt.DataFrame({"a": [1, 2], "b": [3.0, 4.0]})
-    db.create_view(name="my_view", data=df)
-    assert db.view("my_view").to_df().frame_equal(df)
-
-
-def test_validate_non_nullable_enum_columns():
-    """Enum columns should be null-validated."""
-
-    class EnumModel(pt.Model):
-        non_nullable_enum_column: Literal["a", "b", "c"]
-        nullable_enum_column: Optional[Literal["a", "b", "c"]]
-
-    db = pt.duckdb.Database()
-    db.create_table(name="enum_table", model=EnumModel)
-
-    # We allow null values in nullable_enum_column
-    valid_relation = db.to_relation(
-        "select 'a' as non_nullable_enum_column, null as nullable_enum_column"
-    )
-    valid_relation.insert_into("enum_table")
-
-    # But we do not allow it in non_nullable_enum_column
-    invalid_relation = db.to_relation(
-        "select null as non_nullable_enum_column, 'a' as nullable_enum_column"
-    )
-    with pytest.raises(
-        Exception,
-        match=(
-            "Constraint Error: "
-            "NOT NULL constraint failed: "
-            "enum_table.non_nullable_enum_column"
-        ),
-    ):
-        invalid_relation.insert_into(table="enum_table")
-
-    # The non-nullable enum column should do enum value validation
-    invalid_relation = db.to_relation(
-        "select 'd' as non_nullable_enum_column, 'a' as nullable_enum_column"
-    )
-    with pytest.raises(
-        Exception,
-        match="Conversion Error: Could not convert string 'd' to UINT8",
-    ):
-        invalid_relation.insert_into(table="enum_table")
-
-    # And the nullable enum column should do enum value validation
-    invalid_relation = db.to_relation(
-        "select 'a' as non_nullable_enum_column, 'd' as nullable_enum_column"
-    )
-    with pytest.raises(
-        Exception,
-        match="Conversion Error: Could not convert string 'd' to UINT8",
-    ):
-        invalid_relation.insert_into(table="enum_table")
-
-
-def test_table_existence_check():
-    """You should be able to check for the existence of a table."""
-
-    class Model(pt.Model):
-        column_1: str
-        column_2: int
-
-    # At first there is no table named "test_table"
-    db = pt.duckdb.Database()
-    assert "test_table" not in db
-
-    # We create the table
-    db.create_table(name="test_table", model=Model)
-
-    # And now the table should exist
-    assert "test_table" in db
-
-
-def test_creating_enums_several_tiems():
-    """Enums should be able to be defined several times."""
-
-    class EnumModel(pt.Model):
-        enum_column: Literal["a", "b", "c"]
-
-    db = pt.duckdb.Database()
-    db.create_enum_types(EnumModel)
-    db.enum_types = set()
-    db.create_enum_types(EnumModel)
-
-
-def test_use_of_same_enum_types_from_literal_annotation():
-    """Identical literals should get the same DuckDB SQL enum type."""
-
-    class Table1(pt.Model):
-        column_1: Literal["a", "b"]
-
-    class Table2(pt.Model):
-        column_2: Optional[Literal["b", "a"]]
-
-    db = pt.duckdb.Database()
-    db.create_table(name="table_1", model=Table1)
-    db.create_table(name="table_2", model=Table2)
-
-    assert (
-        db.table("table_1").types["column_1"] == db.table("table_2").types["column_2"]
-    )
-
-
-def test_use_of_same_enum_types_from_enum_annotation():
-    """Identical enums should get the same DuckDB SQL enum type."""
-
-    class ABEnum(enum.Enum):
-        ONE = "a"
-        TWO = "b"
-
-    class BAEnum(enum.Enum):
-        TWO = "b"
-        ONE = "a"
-
-    class Table1(pt.Model):
-        column_1: ABEnum
-
-    class Table2(pt.Model):
-        column_2: Optional[BAEnum]
-
-    db = pt.duckdb.Database()
-    db.create_table(name="table_1", model=Table1)
-    db.create_table(name="table_2", model=Table2)
-
-    assert (
-        db.table("table_1").types["column_1"] == db.table("table_2").types["column_2"]
-    )
-
-
-def test_execute():
-    """It should be able to execute prepared statements."""
-    db = pt.duckdb.Database()
-    db.execute("create table my_table (a int, b int, c int)")
-    db.execute("insert into my_table select ? as a, ? as b, ? as c", [2, 3, 4])
-    assert (
-        db.table("my_table")
-        .to_df()
-        .frame_equal(pt.DataFrame({"a": [2], "b": [3], "c": [4]}))
-    )
-    db.execute(
-        "insert into my_table select ? as a, ? as b, ? as c",
-        [5, 6, 7],
-        [8, 9, 10],
-    )
-    assert (
-        db.table("my_table")
-        .to_df()
-        .frame_equal(pt.DataFrame({"a": [2, 5, 8], "b": [3, 6, 9], "c": [4, 7, 10]}))
-    )
diff --git a/tests/test_duckdb/test_relation.py b/tests/test_duckdb/test_relation.py
deleted file mode 100644
index 5191ed9..0000000
--- a/tests/test_duckdb/test_relation.py
+++ /dev/null
@@ -1,1064 +0,0 @@
-import re
-from datetime import date, timedelta
-from pathlib import Path
-from typing import Optional
-from unittest.mock import MagicMock
-
-import polars as pl
-import pytest
-from typing_extensions import Literal
-
-import patito as pt
-
-# Skip test module if DuckDB is not installed
-if not pt._DUCKDB_AVAILABLE:
-    pytest.skip("DuckDB not installed", allow_module_level=True)
-
-
-def test_relation():
-    """Test functionality of Relation class."""
-    # Create a new in-memory database with dummy data
-    db = pt.duckdb.Database()
-    table_df = pl.DataFrame(
-        {
-            "column_1": [1, 2, 3],
-            "column_2": ["a", "b", "c"],
-        }
-    )
-    db.to_relation(table_df).create_table(name="table_name")
-    table_relation = db.table("table_name")
-
-    # A projection can be done in several different ways
-    assert table_relation.select("column_1", "column_2") == table_relation.select(
-        "column_1, column_2"
-    )
-    assert (
-        table_relation.select("column_1, column_2")
-        == table_relation[["column_1, column_2"]]
-    )
-    assert table_relation[["column_1, column_2"]] == table_relation
-    assert table_relation.select("column_1") != table_relation.select("column_2")
-
-    # We can also use kewyrod arguments to rename columns
-    assert tuple(table_relation.select(column_3="column_1::varchar || column_2")) == (
-        {"column_3": "1a"},
-        {"column_3": "2b"},
-        {"column_3": "3c"},
-    )
-
-    # The .get() method should only work if the filter matches a single row
-    assert table_relation.get(column_1=1).column_2 == "a"
-
-    # But raise if not exactly one matching row is found
-    with pytest.raises(RuntimeError, match="Relation.get(.*) returned 0 rows!"):
-        assert table_relation.get("column_1 = 4")
-    with pytest.raises(RuntimeError, match="Relation.get(.*) returned 2 rows!"):
-        assert table_relation.get("column_1 > 1")
-
-    # The .get() should also accept a positional string
-    assert table_relation.get("column_1 < 2").column_2 == "a"
-
-    # And several positional strings
-    assert table_relation.get("column_1 > 1", "column_1 < 3").column_2 == "b"
-
-    # And a mix of positional and keyword arguments
-    assert table_relation.get("column_1 < 2", column_2="a").column_2 == "a"
-
-    # Order by statements shoud be respected when iterating over the relation
-    assert tuple(table_relation.order("column_1 desc")) == (
-        {"column_1": 3, "column_2": "c"},
-        {"column_1": 2, "column_2": "b"},
-        {"column_1": 1, "column_2": "a"},
-    )
-
-    # The plus operator acts as a union all
-    assert (
-        db.to_relation(table_df[:1])
-        + db.to_relation(table_df[1:2])
-        + db.to_relation(table_df[2:])
-    ) == db.to_relation(table_df)
-
-    # The union all must *not* remove duplicates
-    assert db.to_relation(table_df) + db.to_relation(table_df) != db.to_relation(
-        table_df
-    )
-    assert db.to_relation(table_df) + db.to_relation(table_df) == db.to_relation(
-        pl.concat([table_df, table_df])
-    )
-
-    # You should be able to subscript columns
-    assert table_relation["column_1"] == table_relation.select("column_1")
-    assert table_relation[["column_1", "column_2"]] == table_relation
-
-    # The relation's columns can be retrieved
-    assert table_relation.columns == ["column_1", "column_2"]
-
-    # You should be able to prefix and suffix all columns of a relation
-    assert table_relation.add_prefix("prefix_").columns == [
-        "prefix_column_1",
-        "prefix_column_2",
-    ]
-    assert table_relation.add_suffix("_suffix").columns == [
-        "column_1_suffix",
-        "column_2_suffix",
-    ]
-
-    # You can drop one or more columns
-    assert table_relation.drop("column_1").columns == ["column_2"]
-    assert table_relation.select("*, 1 as column_3").drop(
-        "column_1", "column_2"
-    ).columns == ["column_3"]
-
-    # You can rename columns
-    assert set(table_relation.rename(column_1="new_name").columns) == {
-        "new_name",
-        "column_2",
-    }
-
-    # A value error must be raised if the source column does not exist
-    with pytest.raises(
-        ValueError,
-        match=(
-            "Column 'a' can not be renamed as it does not exist. "
-            "The columns of the relation are: column_[12], column_[12]"
-        ),
-    ):
-        table_relation.rename(a="new_name")
-
-    # Null values should be correctly handled
-    none_df = pl.DataFrame({"column_1": [1, None]})
-    none_relation = db.to_relation(none_df)
-    assert none_relation.filter("column_1 is null") == none_df.filter(
-        pl.col("column_1").is_null()
-    )
-
-    # The .inner_join() method should work as INNER JOIN, not LEFT or OUTER JOIN
-    left_relation = db.to_relation(
-        pl.DataFrame(
-            {
-                "left_primary_key": [1, 2],
-                "left_foreign_key": [10, 20],
-            }
-        )
-    )
-    right_relation = db.to_relation(
-        pl.DataFrame(
-            {
-                "right_primary_key": [10],
-            }
-        )
-    )
-    joined_table = pl.DataFrame(
-        {
-            "left_primary_key": [1],
-            "left_foreign_key": [10],
-            "right_primary_key": [10],
-        }
-    )
-    assert (
-        left_relation.set_alias("l").inner_join(
-            right_relation.set_alias("r"),
-            on="l.left_foreign_key = r.right_primary_key",
-        )
-        == joined_table
-    )
-
-    # But the .left_join() method performs a LEFT JOIN
-    left_joined_table = pl.DataFrame(
-        {
-            "left_primary_key": [1, 2],
-            "left_foreign_key": [10, 20],
-            "right_primary_key": [10, None],
-        }
-    )
-    assert (
-        left_relation.set_alias("l").left_join(
-            right_relation.set_alias("r"),
-            on="l.left_foreign_key = r.right_primary_key",
-        )
-        == left_joined_table
-    )
-
-
-def test_star_select():
-    """It should select all columns with star."""
-    df = pt.DataFrame({"a": [1, 2], "b": [3, 4]})
-    relation = pt.duckdb.Relation(df)
-    assert relation.select("*") == relation
-
-
-def test_casting_relations_between_database_connections():
-    """It should raise when you try to mix databases."""
-    db_1 = pt.duckdb.Database()
-    relation_1 = db_1.query("select 1 as a")
-    db_2 = pt.duckdb.Database()
-    relation_2 = db_2.query("select 1 as a")
-    with pytest.raises(
-        ValueError,
-        match="Relations can't be casted between database connections.",
-    ):
-        relation_1 + relation_2  # pyright: ignore
-
-
-def test_creating_relation_from_pandas_df():
-    """It should be able to create a relation from a pandas dataframe."""
-    pd = pytest.importorskip("pandas")
-    pandas_df = pd.DataFrame({"a": [1, 2]})
-    relation = pt.duckdb.Relation(pandas_df)
-    pd.testing.assert_frame_equal(relation.to_pandas(), pandas_df)
-
-
-def test_creating_relation_from_a_csv_file(tmp_path):
-    """It should be able to create a relation from a CSV path."""
-    df = pl.DataFrame({"a": [1, 2]})
-    csv_path = tmp_path / "test.csv"
-    df.write_csv(csv_path)
-    relation = pt.duckdb.Relation(csv_path)
-    assert relation.to_df().frame_equal(df)
-
-
-def test_creating_relation_from_a_parquet_file(tmp_path):
-    """It should be able to create a relation from a parquet path."""
-    df = pl.DataFrame({"a": [1, 2]})
-    parquet_path = tmp_path / "test.parquet"
-    df.write_parquet(parquet_path, compression="uncompressed")
-    relation = pt.duckdb.Relation(parquet_path)
-    assert relation.to_df().frame_equal(df)
-
-
-def test_creating_relation_from_a_unknown_file_format(tmp_path):
-    """It should raise when you try to create relation from unknown path."""
-    with pytest.raises(
-        ValueError,
-        match="Unsupported file suffix '.unknown' for data import!",
-    ):
-        pt.duckdb.Relation(Path("test.unknown"))
-
-    with pytest.raises(
-        ValueError,
-        match="Unsupported file suffix '' for data import!",
-    ):
-        pt.duckdb.Relation(Path("test"))
-
-
-def test_relation_with_default_database():
-    """It should be constructable with the default DuckDB cursor."""
-    import duckdb
-
-    relation_a = pt.duckdb.Relation("select 1 as a")
-    assert relation_a.database.connection is duckdb.default_connection
-
-    relation_a.create_view("table_a")
-    del relation_a
-
-    relation_b = pt.duckdb.Relation("select 1 as b")
-    relation_b.create_view("table_b")
-    del relation_b
-
-    default_database = pt.duckdb.Database.default()
-    joined_relation = default_database.query(
-        """
-        select *
-        from table_a
-        inner join table_b
-        on a = b
-        """
-    )
-    assert joined_relation.to_df().frame_equal(pl.DataFrame({"a": [1], "b": [1]}))
-
-
-def test_with_columns():
-    """It should be able to crate new additional columns."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as a, 2 as b")
-
-    # We can define a new column
-    extended_relation = relation.with_columns(c="a + b")
-    correct_extended = pl.DataFrame({"a": [1], "b": [2], "c": [3]})
-    assert extended_relation.to_df().frame_equal(correct_extended)
-
-    # Or even overwrite an existing column
-    overwritten_relation = relation.with_columns(a="a + b")
-    correct_overwritten = db.to_relation("select 2 as b, 3 as a").to_df()
-    assert overwritten_relation.to_df().frame_equal(correct_overwritten)
-
-
-def test_rename_to_existing_column():
-    """Renaming a column to overwrite another should work."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as a, 2 as b")
-    renamed_relation = relation.rename(b="a")
-    assert renamed_relation.columns == ["a"]
-    assert renamed_relation.get().a == 2
-
-
-def test_add_suffix():
-    """It should be able to add suffixes to all column names."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as a, 2 as b")
-    assert relation.add_suffix("x").columns == ["ax", "bx"]
-    assert relation.add_suffix("x", exclude=["a"]).columns == ["a", "bx"]
-    assert relation.add_suffix("x", include=["a"]).columns == ["ax", "b"]
-
-    with pytest.raises(
-        TypeError,
-        match="Both include and exclude provided at the same time!",
-    ):
-        relation.add_suffix("x", exclude=["a"], include=["b"])
-
-
-def test_add_prefix():
-    """It should be able to add prefixes to all column names."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as a, 2 as b")
-    assert relation.add_prefix("x").columns == ["xa", "xb"]
-    assert relation.add_prefix("x", exclude=["a"]).columns == ["a", "xb"]
-    assert relation.add_prefix("x", include=["a"]).columns == ["xa", "b"]
-
-    with pytest.raises(
-        TypeError,
-        match="Both include and exclude provided at the same time!",
-    ):
-        relation.add_prefix("x", exclude=["a"], include=["b"])
-
-
-def test_relation_aggregate_method():
-    """Test for Relation.aggregate()."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation(
-        pl.DataFrame(
-            {
-                "a": [1, 1, 2],
-                "b": [10, 100, 1000],
-                "c": [1, 2, 1],
-            }
-        )
-    )
-    aggregated_relation = relation.aggregate(
-        "a",
-        b_sum="sum(b)",
-        group_by="a",
-    )
-    assert tuple(aggregated_relation) == (
-        {"a": 1, "b_sum": 110},
-        {"a": 2, "b_sum": 1000},
-    )
-
-    aggregated_relation_with_multiple_group_by = relation.aggregate(
-        "a",
-        "c",
-        b_sum="sum(b)",
-        group_by=["a", "c"],
-    )
-    assert tuple(aggregated_relation_with_multiple_group_by) == (
-        {"a": 1, "c": 1, "b_sum": 10},
-        {"a": 1, "c": 2, "b_sum": 100},
-        {"a": 2, "c": 1, "b_sum": 1000},
-    )
-
-
-def test_relation_all_method():
-    """Test for Relation.all()."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation(
-        pl.DataFrame(
-            {
-                "a": [1, 2, 3],
-                "b": [100, 100, 100],
-            }
-        )
-    )
-
-    assert not relation.all(a=100)
-    assert relation.all(b=100)
-    assert relation.all("a < 4", b=100)
-
-
-def test_relation_case_method():
-    db = pt.duckdb.Database()
-
-    df = pl.DataFrame(
-        {
-            "shelf_classification": ["A", "B", "A", "C", "D"],
-            "weight": [1, 2, 3, 4, 5],
-        }
-    )
-
-    correct_df = df.with_columns(
-        pl.Series([10, 20, 10, 0, None], dtype=pl.Int32).alias("max_weight")
-    )
-    correct_mapped_actions = db.to_relation(correct_df)
-
-    mapped_actions = db.to_relation(df).case(
-        from_column="shelf_classification",
-        to_column="max_weight",
-        mapping={"A": 10, "B": 20, "D": None},
-        default=0,
-    )
-    assert mapped_actions == correct_mapped_actions
-
-    # We can also use the Case class
-    case_statement = pt.sql.Case(
-        on_column="shelf_classification",
-        mapping={"A": 10, "B": 20, "D": None},
-        default=0,
-    )
-    alt_mapped_actions = db.to_relation(df).select(f"*, {case_statement} as max_weight")
-    assert alt_mapped_actions == correct_mapped_actions
-
-
-def test_relation_coalesce_method():
-    """Test for Relation.coalesce()."""
-    db = pt.duckdb.Database()
-    df = pl.DataFrame(
-        {"column_1": [1.0, None], "column_2": [None, "2"], "column_3": [3.0, None]}
-    )
-    relation = db.to_relation(df)
-    coalesce_result = relation.coalesce(column_1=10, column_2="20").to_df()
-    correct_coalesce_result = pl.DataFrame(
-        {
-            "column_1": [1.0, 10.0],
-            "column_2": ["20", "2"],
-            "column_3": [3.0, None],
-        }
-    )
-    assert coalesce_result.frame_equal(correct_coalesce_result)
-
-
-def test_relation_union_method():
-    """Test for Relation.union and Relation.__add__."""
-    db = pt.duckdb.Database()
-    left = db.to_relation("select 1 as a, 2 as b")
-    right = db.to_relation("select 200 as b, 100 as a")
-    correct_union = pl.DataFrame(
-        {
-            "a": [1, 100],
-            "b": [2, 200],
-        }
-    )
-    assert left + right == correct_union
-    assert right + left == correct_union[["b", "a"]][::-1]
-
-    assert left.union(right) == correct_union
-    assert right.union(left) == correct_union[["b", "a"]][::-1]
-
-    incompatible = db.to_relation("select 1 as a")
-    with pytest.raises(
-        TypeError,
-        match="Union between relations with different column names is not allowed.",
-    ):
-        incompatible + right  # pyright: ignore
-    with pytest.raises(
-        TypeError,
-        match="Union between relations with different column names is not allowed.",
-    ):
-        left + incompatible  # pyright: ignore
-
-
-def test_relation_model_functionality():
-    """The end-user should be able to specify the constructor for row values."""
-    db = pt.duckdb.Database()
-
-    # We have two rows in our relation
-    first_row_relation = db.to_relation("select 1 as a, 2 as b")
-    second_row_relation = db.to_relation("select 3 as a, 4 as b")
-    relation = first_row_relation + second_row_relation
-
-    # Iterating over the relation should yield the same as .get()
-    iterator_value = tuple(relation)[0]
-    get_value = relation.get("a = 1")
-    assert iterator_value == get_value
-    assert iterator_value.a == 1
-    assert get_value.a == 1
-    assert iterator_value.b == 2
-    assert get_value.b == 2
-
-    # The end-user should be able to specify a custom row constructor
-    model_mock = MagicMock(return_value="mock_return")
-    new_relation = relation.set_model(model_mock)
-    assert new_relation.get("a = 1") == "mock_return"
-    model_mock.assert_called_with(a=1, b=2)
-
-    # We create a custom model
-    class MyModel(pt.Model):
-        a: int
-        b: str
-
-    # Some dummy data
-    dummy_df = MyModel.examples({"a": [1, 2], "b": ["one", "two"]})
-    dummy_relation = db.to_relation(dummy_df)
-
-    # Initially the relation has no custom model and it is dynamically constructed
-    assert dummy_relation.model is None
-    assert not isinstance(
-        dummy_relation.limit(1).get(),
-        MyModel,
-    )
-
-    # MyRow can be specified as the deserialization class with Relation.set_model()
-    assert isinstance(
-        dummy_relation.set_model(MyModel).limit(1).get(),
-        MyModel,
-    )
-
-    # A custom relation class which specifies this as the default model
-    class MyRelation(pt.duckdb.Relation):
-        model = MyModel
-
-    assert isinstance(
-        MyRelation(dummy_relation._relation, database=db).limit(1).get(),
-        MyModel,
-    )
-
-    # But the model is "lost" when we use schema-changing methods
-    assert not isinstance(
-        dummy_relation.set_model(MyModel).limit(1).select("a").get(),
-        MyModel,
-    )
-
-
-def test_row_sql_type_functionality():
-    """Tests for mapping pydantic types to DuckDB SQL types."""
-
-    # Two nullable and two non-nullable columns
-    class OptionalRow(pt.Model):
-        a: str
-        b: float
-        c: Optional[str]
-        d: Optional[bool]
-
-    assert OptionalRow.non_nullable_columns == {"a", "b"}
-    assert OptionalRow.nullable_columns == {"c", "d"}
-
-    # All different types of SQL types
-    class TypeModel(pt.Model):
-        a: str
-        b: int
-        c: float
-        d: Optional[bool]
-
-    assert TypeModel.sql_types == {
-        "a": "VARCHAR",
-        "b": "INTEGER",
-        "c": "DOUBLE",
-        "d": "BOOLEAN",
-    }
-
-
-def test_fill_missing_columns():
-    """Tests for Relation.with_missing_{nullable,defaultable}_columns."""
-
-    class MyRow(pt.Model):
-        # This can't be filled
-        a: str
-        # This can be filled with default value
-        b: Optional[str] = "default_value"
-        # This can be filled with null
-        c: Optional[str]
-        # This can be filled with null, but will be set
-        d: Optional[float]
-        # This can befilled with null, but with a different type
-        e: Optional[bool]
-
-    # We check if defaults are easily retrievable from the model
-    assert MyRow.defaults == {"b": "default_value"}
-
-    db = pt.duckdb.Database()
-    df = pl.DataFrame({"a": ["mandatory"], "d": [10.5]})
-    relation = db.to_relation(df).set_model(MyRow)
-
-    # Missing nullable columns b, c, and e are filled in with nulls
-    filled_nullables = relation.with_missing_nullable_columns()
-    assert filled_nullables.set_model(None).get() == {
-        "a": "mandatory",
-        "b": None,
-        "c": None,
-        "d": 10.5,
-        "e": None,
-    }
-    # And these nulls are properly typed
-    assert filled_nullables.types == {
-        "a": "VARCHAR",
-        "b": "VARCHAR",
-        "c": "VARCHAR",
-        "d": "DOUBLE",
-        "e": "BOOLEAN",
-    }
-
-    # Now we fill in the b column with "default_value"
-    filled_defaults = relation.with_missing_defaultable_columns()
-    assert filled_defaults.set_model(None).get().dict() == {
-        "a": "mandatory",
-        "b": "default_value",
-        "d": 10.5,
-    }
-    assert filled_defaults.types == {
-        "a": "VARCHAR",
-        "b": "VARCHAR",
-        "d": "DOUBLE",
-    }
-
-    # We now exclude the b column from being filled with default values
-    excluded_default = relation.with_missing_defaultable_columns(exclude=["b"])
-    assert excluded_default.set_model(None).get().dict() == {
-        "a": "mandatory",
-        "d": 10.5,
-    }
-
-    # We can also specify that we only want to fill a subset
-    included_defualts = relation.with_missing_defaultable_columns(include=["b"])
-    assert included_defualts.set_model(None).get().dict() == {
-        "a": "mandatory",
-        "b": "default_value",
-        "d": 10.5,
-    }
-
-    # We now exclude column b and c from being filled with null values
-    excluded_nulls = relation.with_missing_nullable_columns(exclude=["b", "c"])
-    assert excluded_nulls.set_model(None).get().dict() == {
-        "a": "mandatory",
-        "d": 10.5,
-        "e": None,
-    }
-
-    # Only specify that we want to fill column e with nulls
-    included_nulls = relation.with_missing_nullable_columns(include=["e"])
-    assert included_nulls.set_model(None).get().dict() == {
-        "a": "mandatory",
-        "d": 10.5,
-        "e": None,
-    }
-
-    # We should raise if both include and exclude is specified
-    with pytest.raises(
-        TypeError, match="Both include and exclude provided at the same time!"
-    ):
-        relation.with_missing_nullable_columns(include={"x"}, exclude={"y"})
-
-    with pytest.raises(
-        TypeError, match="Both include and exclude provided at the same time!"
-    ):
-        relation.with_missing_defaultable_columns(include={"x"}, exclude={"y"})
-
-
-def test_with_missing_nullable_enum_columns():
-    """It should produce enums with null values correctly."""
-
-    class EnumModel(pt.Model):
-        enum_column: Optional[Literal["a", "b", "c"]]
-        other_column: int
-
-    db = pt.duckdb.Database()
-
-    # We insert data into a properly typed table in order to get the correct enum type
-    db.create_table(name="enum_table", model=EnumModel)
-    db.to_relation("select 'a' as enum_column, 1 as other_column").insert_into(
-        table="enum_table"
-    )
-    table_relation = db.table("enum_table")
-    assert str(table_relation.types["enum_column"]).startswith("enum__")
-
-    # We generate another dynamic relation where we expect the correct enum type
-    null_relation = (
-        db.to_relation("select 2 as other_column")
-        .set_model(EnumModel)
-        .with_missing_nullable_columns()
-    )
-    assert null_relation.types["enum_column"] == table_relation.types["enum_column"]
-
-    # These two relations should now be unionable
-    union_relation = (null_relation + table_relation).order("other_column asc")
-    assert union_relation.types["enum_column"] == table_relation.types["enum_column"]
-
-    with pl.StringCache():
-        correct_union_df = pl.DataFrame(
-            {
-                "other_column": [1, 2],
-                "enum_column": pl.Series(["a", None]).cast(pl.Categorical),
-            }
-        )
-        assert union_relation.to_df().frame_equal(correct_union_df)
-
-
-def test_with_missing_nullable_enum_columns_without_table():
-    """It should produce enums with null values correctly without a table."""
-
-    class EnumModel(pt.Model):
-        enum_column_1: Optional[Literal["a", "b", "c"]]
-        enum_column_2: Optional[Literal["a", "b", "c"]]
-        other_column: int
-
-    # We should be able to create the correct type without a table
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as other_column")
-    with pytest.raises(
-        TypeError, match=r".*You should invoke Relation.set_model\(\) first!"
-    ):
-        relation.with_missing_nullable_columns()
-
-    model_relation = relation.set_model(EnumModel).with_missing_nullable_columns()
-    assert str(model_relation.types["enum_column_1"]).startswith("enum__")
-    assert (
-        model_relation.types["enum_column_2"] == model_relation.types["enum_column_1"]
-    )
-
-    # And now we should be able to insert it into a new table
-    model_relation.create_table(name="enum_table")
-    table_relation = db.table("enum_table")
-    assert (
-        table_relation.types["enum_column_1"] == model_relation.types["enum_column_1"]
-    )
-    assert (
-        table_relation.types["enum_column_2"] == model_relation.types["enum_column_1"]
-    )
-
-
-def test_with_missing_defualtable_enum_columns():
-    """It should produce enums with default values correctly typed."""
-
-    class EnumModel(pt.Model):
-        enum_column: Optional[Literal["a", "b", "c"]] = "a"
-        other_column: int
-
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as other_column")
-    with pytest.raises(
-        TypeError,
-        match=r".*You should invoke Relation.set_model\(\) first!",
-    ):
-        relation.with_missing_defaultable_columns()
-
-    model_relation = relation.set_model(EnumModel).with_missing_defaultable_columns()
-    assert str(model_relation.types["enum_column"]).startswith("enum__")
-
-
-def test_relation_insert_into():
-    """Relation.insert_into() should automatically order columnns correctly."""
-    db = pt.duckdb.Database()
-    db.execute(
-        """
-        create table foo (
-            a integer,
-            b integer
-        )
-    """
-    )
-    db.to_relation("select 2 as b, 1 as a").insert_into(table="foo")
-    row = db.table("foo").get()
-    assert row.a == 1
-    assert row.b == 2
-
-    with pytest.raises(
-        TypeError,
-        match=re.escape(
-            "Relation is missing column(s) {'a'} "
-            "in order to be inserted into table 'foo'!"
-        ),
-    ):
-        db.to_relation("select 2 as b, 1 as c").insert_into(table="foo")
-
-
-def test_polars_support():
-    # Test converting a polars DataFrame to patito relation
-    df = pl.DataFrame(data={"column_1": ["a", "b", None], "column_2": [1, 2, None]})
-    correct_dtypes = [pl.Utf8, pl.Int64]
-    assert df.dtypes == correct_dtypes
-    db = pt.duckdb.Database()
-    relation = db.to_relation(df)
-    assert relation.get(column_1="a").column_2 == 1
-
-    # Test converting back again the other way
-    roundtrip_df = relation.to_df()
-    assert roundtrip_df.frame_equal(df)
-    assert roundtrip_df.dtypes == correct_dtypes
-
-    # Assert that .to_df() always returns a DataFrame.
-    assert isinstance(relation["column_1"].to_df(), pl.DataFrame)
-
-    # Assert that .to_df() returns an empty DataFrame when the table has no rows
-    empty_dataframe = relation.filter(column_1="missing-column").to_df()
-    # assert empty_dataframe == pl.DataFrame(columns=["column_1", "column_2"])
-    # assert empty_dataframe.frame_equal(pl.DataFrame(columns=["column_1", "column_2"]))
-
-    # The datatype should be preserved
-    assert empty_dataframe.dtypes == correct_dtypes
-
-    # A model should be able to be instantiated with a polars row
-    class MyModel(pt.Model):
-        a: int
-        b: str
-
-    my_model_df = pl.DataFrame({"a": [1, 2], "b": ["x", "y"]})
-    with pytest.raises(
-        ValueError,
-        match=r"MyModel._from_polars\(\) can only be invoked with exactly 1 row.*",
-    ):
-        MyModel.from_row(my_model_df)
-
-    my_model = MyModel.from_row(my_model_df.head(1))
-    assert my_model.a == 1
-    assert my_model.b == "x"
-
-    # Anything besides a polars dataframe should raise TypeError
-    with pytest.raises(TypeError):
-        MyModel.from_row(None)  # pyright: ignore
-
-    # But we can also skip validation if we want
-    unvalidated_model = MyModel.from_row(
-        pl.DataFrame().with_columns(
-            [
-                pl.lit("string").alias("a"),
-                pl.lit(2).alias("b"),
-            ]
-        ),
-        validate=False,
-    )
-    assert unvalidated_model.a == "string"
-    assert unvalidated_model.b == 2
-
-
-def test_series_vs_dataframe_behavior():
-    """Test Relation.to_series()."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as column_1, 2 as column_2")
-
-    # Selecting multiple columns should yield a DataFrame
-    assert isinstance(relation[["column_1", "column_2"]].to_df(), pl.DataFrame)
-
-    # Selecting a single column, but as an item in a list, should yield a DataFrame
-    assert isinstance(relation[["column_1"]].to_df(), pl.DataFrame)
-
-    # Selecting a single column as a string should also yield a DataFrame
-    assert isinstance(relation["column_1"].to_df(), pl.DataFrame)
-
-    # But .to_series() should yield a series
-    series = relation["column_1"].to_series()
-    assert isinstance(series, pl.Series)
-
-    # The name should also be set correctly
-    assert series.name == "column_1"
-
-    # And the content should be correct
-    correct_series = pl.Series([1], dtype=pl.Int32).alias("column_1")
-    assert series.series_equal(correct_series)
-
-    # To series will raise a type error if invoked with anything other than 1 column
-    with pytest.raises(TypeError, match=r".*2 columns, while exactly 1 is required.*"):
-        relation.to_series()
-
-
-def test_converting_enum_column_to_polars():
-    """Enum types should be convertible to polars categoricals."""
-
-    class EnumModel(pt.Model):
-        enum_column: Literal["a", "b", "c"]
-
-    db = pt.duckdb.Database()
-    db.create_table(name="enum_table", model=EnumModel)
-    db.execute(
-        """
-        insert into enum_table
-            (enum_column)
-        values
-            ('a'),
-            ('a'),
-            ('b');
-        """
-    )
-    enum_df = db.table("enum_table").to_df()
-    assert enum_df.frame_equal(pl.DataFrame({"enum_column": ["a", "a", "b"]}))
-    assert enum_df.dtypes == [pl.Categorical]
-
-
-def test_non_string_enum():
-    """It should handle other types than just string enums."""
-
-    class EnumModel(pt.Model):
-        enum_column: Literal[10, 11, 12]
-
-    db = pt.duckdb.Database()
-    db.create_table(name="enum_table", model=EnumModel)
-
-    db.execute(
-        """
-        insert into enum_table
-            (enum_column)
-        values
-            (10),
-            (11),
-            (12);
-        """
-    )
-    enum_df = db.table("enum_table").to_df()
-    assert enum_df.frame_equal(pl.DataFrame({"enum_column": [10, 11, 12]}))
-    assert enum_df.dtypes == [pl.Int64]
-
-
-def test_multiple_filters():
-    """The filter method should AND multiple filters properly."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as a, 2 as b")
-    # The logical or should not make the filter valid for our row
-    assert relation.filter("(1 = 2) or b = 2", a=0).count() == 0
-    assert relation.filter("a=0", "(1 = 2) or b = 2").count() == 0
-
-
-def test_no_filter():
-    """No filters should return all rows."""
-    db = pt.duckdb.Database()
-    relation = db.to_relation("select 1 as a, 2 as b")
-    # The logical or should not make the filter valid for our row
-    assert relation.filter().count()
-
-
-def test_string_representation_of_relation():
-    """It should have a string representation."""
-    relation = pt.duckdb.Relation("select 1 as my_column")
-    relation_str = str(relation)
-    assert "my_column" in relation_str
-
-
-def test_cast():
-    """It should be able to cast to the correct SQL types based on model."""
-
-    class Schema(pt.Model):
-        float_column: float
-
-    relation = pt.duckdb.Relation("select 1 as float_column, 2 as other_column")
-    with pytest.raises(
-        TypeError,
-        match=(
-            r"Relation\.cast\(\) invoked without Relation.model having been set\! "
-            r"You should invoke Relation\.set_model\(\) first or explicitly provide "
-            r"a model to \.cast\(\)."
-        ),
-    ):
-        relation.cast()
-
-    # Originally the type of both columns are integers
-    modeled_relation = relation.set_model(Schema)
-    assert modeled_relation.types["float_column"] == "INTEGER"
-    assert modeled_relation.types["other_column"] == "INTEGER"
-
-    # The casted variant has converted the float column to double
-    casted_relation = relation.set_model(Schema).cast()
-    assert casted_relation.types["float_column"] == "DOUBLE"
-    # But kept the other as-is
-    assert casted_relation.types["other_column"] == "INTEGER"
-
-    # You can either set the model with .set_model() or provide it to cast
-    assert (
-        relation.set_model(Schema)
-        .cast()
-        .to_df()
-        .frame_equal(relation.cast(Schema).to_df())
-    )
-
-    # Other types that should be considered compatible should be kept as-is
-    compatible_relation = pt.duckdb.Relation("select 1::FLOAT as float_column")
-    assert compatible_relation.cast(Schema).types["float_column"] == "FLOAT"
-
-    # Unless the strict parameter is specified
-    assert (
-        compatible_relation.cast(Schema, strict=True).types["float_column"] == "DOUBLE"
-    )
-
-    # We can also specify a specific SQL type
-    class SpecificSQLTypeSchema(pt.Model):
-        float_column: float = pt.Field(sql_type="BIGINT")
-
-    specific_cast_relation = relation.set_model(SpecificSQLTypeSchema).cast()
-    assert specific_cast_relation.types["float_column"] == "BIGINT"
-
-    # Unknown types raise
-    class ObjectModel(pt.Model):
-        object_column: object
-
-    with pytest.raises(
-        NotImplementedError,
-        match=r"No valid sql_type mapping found for column 'object_column'\.",
-    ):
-        pt.duckdb.Relation("select 1 as object_column").set_model(ObjectModel).cast()
-
-    # Check for more specific type annotations
-    class TotalModel(pt.Model):
-        timedelta_column: timedelta
-        date_column: date
-        null_column: None
-
-    df = pt.DataFrame(
-        {
-            "date_column": [date(2022, 9, 4)],
-            "null_column": [None],
-        }
-    )
-    casted_relation = pt.duckdb.Relation(df, model=TotalModel).cast()
-    assert casted_relation.types == {
-        "date_column": "DATE",
-        "null_column": "INTEGER",
-    }
-    assert casted_relation.to_df().frame_equal(df)
-
-    # It is possible to only cast a subset
-    class MyModel(pt.Model):
-        column_1: float
-        column_2: float
-
-    relation = pt.duckdb.Relation("select 1 as column_1, 2 as column_2").set_model(
-        MyModel
-    )
-    assert relation.cast(include=[]).types == {
-        "column_1": "INTEGER",
-        "column_2": "INTEGER",
-    }
-    assert relation.cast(include=["column_1"]).types == {
-        "column_1": "DOUBLE",
-        "column_2": "INTEGER",
-    }
-    assert relation.cast(include=["column_1", "column_2"]).types == {
-        "column_1": "DOUBLE",
-        "column_2": "DOUBLE",
-    }
-
-    assert relation.cast(exclude=[]).types == {
-        "column_1": "DOUBLE",
-        "column_2": "DOUBLE",
-    }
-    assert relation.cast(exclude=["column_1"]).types == {
-        "column_1": "INTEGER",
-        "column_2": "DOUBLE",
-    }
-    assert relation.cast(exclude=["column_1", "column_2"]).types == {
-        "column_1": "INTEGER",
-        "column_2": "INTEGER",
-    }
-
-    # Providing both include and exclude should raise a value error
-    with pytest.raises(
-        ValueError,
-        match=r"Both include and exclude provided to Relation.cast\(\)\!",
-    ):
-        relation.cast(include=["column_1"], exclude=["column_2"])
-
-
-@pytest.mark.xfail(strict=True)
-def test_casting_timedelta_column_back_and_forth():
-    class TotalModel(pt.Model):
-        timedelta_column: timedelta
-        date_column: date
-        null_column: None
-
-    df = pt.DataFrame(
-        {
-            "timedelta_column": [timedelta(seconds=90)],
-            "date_column": [date(2022, 9, 4)],
-            "null_column": [None],
-        }
-    )
-    casted_relation = pt.duckdb.Relation(df, model=TotalModel).cast()
-    assert casted_relation.types == {
-        "timedelta_column": "INTERVAL",
-        "date_column": "DATE",
-        "null_column": "INTEGER",
-    }
-    assert casted_relation.to_df().frame_equal(df)
diff --git a/tests/test_dummy_data.py b/tests/test_dummy_data.py
index ace5765..1564fdc 100644
--- a/tests/test_dummy_data.py
+++ b/tests/test_dummy_data.py
@@ -1,15 +1,13 @@
 """Test of functionality related to the generation of dummy data."""
 from datetime import date, datetime
-from typing import Optional
+from typing import List, Literal, Optional, Sequence
 
+import patito as pt
 import polars as pl
 import pytest
-from typing_extensions import Literal
-
-import patito as pt
 
 
-def test_model_example_df():
+def test_model_example_df() -> None:
     """Test for patito.Model.example()."""
 
     # When inheriting from Model you get a .examples() method for generating dataframes
@@ -36,8 +34,8 @@ class MyRow(pt.Model):
         }
     )
 
-    assert df_1[correct_df.columns].frame_equal(correct_df)
-    assert df_2[correct_df.columns].frame_equal(correct_df)
+    assert df_1[correct_df.columns].equals(correct_df)
+    assert df_2[correct_df.columns].equals(correct_df)
 
     # A TypeError should be raised when you provide wrong keywords
     with pytest.raises(
@@ -47,16 +45,28 @@ class MyRow(pt.Model):
         MyRow.examples({"a": [0], "f": [1], "g": [2]})
 
 
-def test_examples():
+def test_examples() -> None:
     class MyModel(pt.Model):
         a: int
         b: Optional[str]
         c: Optional[int]
+        d: Optional[List[str]] = pt.Field(dtype=pl.List(pl.String))
+        e: List[int]
+        f: int = pt.Field(ge=0)
 
     df = MyModel.examples({"a": [1, 2]})
     assert isinstance(df, pl.DataFrame)
-    assert df.dtypes == [pl.Int64, pl.Utf8, pl.Int64]
-    assert df.columns == ["a", "b", "c"]
+    assert df.dtypes == [
+        pl.Int64,
+        pl.String,
+        pl.Int64,
+        pl.List(pl.String),
+        pl.List(pl.Int64),
+        pl.Int64,
+    ]
+    assert df.columns == ["a", "b", "c", "d", "e", "f"]
+    assert (df["f"] >= 0).all()
+    MyModel.validate(df)
 
     # A TypeError should be raised when you provide no column names
     with pytest.raises(
@@ -66,22 +76,7 @@ class MyModel(pt.Model):
         MyModel.examples([[1, 2]])
 
 
-@pytest.mark.skipif("Database" not in dir(pt), reason="Requires DuckDB")
-def test_creation_of_empty_relation():
-    """You should be able to create a zero-row relation with correct types."""
-
-    class MyModel(pt.Model):
-        a: int
-        b: Optional[str]
-
-    db = pt.duckdb.Database()
-    empty_relation = db.empty_relation(schema=MyModel)
-    assert empty_relation.columns == ["a", "b"]
-    assert empty_relation.types == {"a": "BIGINT", "b": "VARCHAR"}
-    assert empty_relation.count() == 0
-
-
-def test_generation_of_unique_data():
+def test_generation_of_unique_data() -> None:
     """Example data generators should be able to generate unique data."""
 
     class UniqueModel(pt.Model):
@@ -97,7 +92,7 @@ class UniqueModel(pt.Model):
         assert example_df[column].is_duplicated().sum() == 0
 
 
-def test_enum_field_example_values():
+def test_enum_field_example_values() -> None:
     """It should produce correct example values for enums."""
 
     class DefaultEnumModel(pt.Model):
@@ -112,23 +107,29 @@ class DefaultEnumModel(pt.Model):
 
     # Workaround for pola-rs/polars#4253
     example_df = DefaultEnumModel.examples({"row_number": [1]}).with_columns(
-        pl.col("none_default_optional_enum_field").cast(pl.Categorical)
+        pl.col("none_default_optional_enum_field").cast(pl.Enum(["a", "b", "c"]))
     )
 
     correct_example_df = pl.DataFrame(
         [
             pl.Series("row_number", [1], dtype=pl.Int64),
-            pl.Series("enum_field", ["a"], dtype=pl.Categorical),
-            pl.Series("default_enum_field", ["b"], dtype=pl.Categorical),
-            pl.Series("default_optional_enum_field", ["c"], dtype=pl.Categorical),
-            pl.Series("none_default_optional_enum_field", [None], dtype=pl.Categorical),
+            pl.Series("enum_field", ["a"], dtype=pl.Enum(["a", "b", "c"])),
+            pl.Series("default_enum_field", ["b"], dtype=pl.Enum(["a", "b", "c"])),
+            pl.Series(
+                "default_optional_enum_field", ["c"], dtype=pl.Enum(["a", "b", "c"])
+            ),
+            pl.Series(
+                "none_default_optional_enum_field",
+                [None],
+                dtype=pl.Enum(["a", "b", "c"]),
+            ),
         ]
     )
 
     # Workaround for pl.StringCache() not working here for some reason
     assert correct_example_df.dtypes == example_df.dtypes
-    assert example_df.select(pl.all().cast(pl.Utf8)).frame_equal(
-        correct_example_df.select(pl.all().cast(pl.Utf8))
+    assert example_df.select(pl.all().cast(pl.String)).equals(
+        correct_example_df.select(pl.all().cast(pl.String))
     )
 
     example_model = DefaultEnumModel.example()
@@ -136,3 +137,43 @@ class DefaultEnumModel(pt.Model):
     assert example_model.default_enum_field == "b"
     assert example_model.default_optional_enum_field == "c"
     assert example_model.none_default_optional_enum_field is None
+
+
+def test_nested_models() -> None:
+    """It should be possible to create nested models."""
+
+    class NestedModel(pt.Model):
+        nested_field: int
+
+    class ParentModel1(pt.Model):
+        parent_field: int
+        nested_model: NestedModel
+
+    example_model = ParentModel1.example()
+    example_df = ParentModel1.examples()
+    assert isinstance(example_model.nested_model, NestedModel)
+    assert example_model.nested_model.nested_field is not None
+
+    # inheritance also works
+    class ParentModel2(NestedModel):
+        parent_field: int
+
+    example_model = ParentModel2.example()
+    assert example_model.nested_field is not None
+    assert example_model.parent_field is not None
+
+    # and optional nested models are ok
+    class ParentModel3(pt.Model):
+        parent_field: int
+        nested_model: Optional[NestedModel] = None
+
+    example_model = ParentModel3.example()
+    assert example_model.nested_model is None
+
+    # sequences of nested models also work
+    class ParentModel(pt.Model):
+        parent_field: int
+        nested_models: Sequence[NestedModel]
+
+    example_model = ParentModel.example()
+    example_df = ParentModel.examples()
diff --git a/tests/test_model.py b/tests/test_model.py
index 1ee8334..e01e811 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -1,36 +1,38 @@
 """Tests for patito.Model."""
+from __future__ import annotations
+
 # pyright: reportPrivateImportUsage=false
 import enum
 import re
-from datetime import date, datetime, timedelta
-from typing import List, Optional, Type
+from datetime import date, datetime, time
+from typing import Optional, Type
 
+import patito as pt
 import polars as pl
 import pytest
-from pydantic import ValidationError
-from typing_extensions import Literal
-
-import patito as pt
-
-
-def test_model_example():
+from patito._pydantic.dtypes.utils import (
+    DATE_DTYPES,
+    TIME_DTYPES,
+)
+from polars.datatypes import DataTypeGroup
+from polars.datatypes.constants import (
+    DATETIME_DTYPES,
+    DURATION_DTYPES,
+    FLOAT_DTYPES,
+    INTEGER_DTYPES,
+)
+from pydantic import AliasChoices, AwareDatetime, ValidationError
+
+from tests.examples import CompleteModel, ManyTypes, SmallModel
+
+
+def test_model_example() -> None:
     """Test for Model.example()."""
-
     # When inheriting from Model you get a .dummy() method for generating rows with
     # default values according to the type annotation.
-    class MyModel(pt.Model):
-        int_value: int
-        float_value: float
-        str_value: str
-        bool_value: bool
-        literal_value: Literal["a", "b"]
-        default_value: str = "my_default"
-        optional_value: Optional[int]
-        bounded_value: int = pt.Field(ge=10, le=20)
-        date_value: date
-        datetime_value: datetime
-
-    assert MyModel.example().dict() == {
+    SmallModel.example().model_dump()
+
+    assert ManyTypes.example().model_dump() == {
         "int_value": -1,
         "float_value": -0.5,
         "str_value": "dummy_string",
@@ -41,12 +43,13 @@ class MyModel(pt.Model):
         "bounded_value": 15,
         "date_value": date(year=1970, month=1, day=1),
         "datetime_value": datetime(year=1970, month=1, day=1),
+        "pt_model_value": SmallModel.example().model_dump(),
     }
-    assert MyModel.example(
+    assert ManyTypes.example(
         bool_value=True,
         default_value="override",
         optional_value=1,
-    ).dict() == {
+    ).model_dump() == {
         "int_value": -1,
         "float_value": -0.5,
         "str_value": "dummy_string",
@@ -57,11 +60,14 @@ class MyModel(pt.Model):
         "bounded_value": 15,
         "date_value": date(year=1970, month=1, day=1),
         "datetime_value": datetime(year=1970, month=1, day=1),
+        "pt_model_value": SmallModel.example().model_dump(),
     }
 
+    ManyTypes.validate(ManyTypes.examples({"int_value": range(200)}))
+
     # For now, valid regex data is not implemented
     class RegexModel(pt.Model):
-        regex_column: str = pt.Field(regex=r"[0-9a-f]")
+        regex_column: str = pt.Field(pattern=r"[0-9a-f]")
 
     with pytest.raises(
         NotImplementedError,
@@ -70,7 +76,7 @@ class RegexModel(pt.Model):
         RegexModel.example()
 
 
-def test_model_pandas_examples():
+def test_model_pandas_examples() -> None:
     """Test for Row.dummy_pandas()."""
     pd = pytest.importorskip("pandas")
 
@@ -116,7 +122,7 @@ class MyRow(pt.Model):
         MyRow.pandas_examples([[1, 2, 3, 4]])
 
 
-def test_instantiating_model_from_row():
+def test_instantiating_model_from_row() -> None:
     """You should be able to instantiate models from rows."""
 
     class Model(pt.Model):
@@ -138,7 +144,7 @@ class Model(pt.Model):
         Model._from_polars(None)  # pyright: ignore
 
 
-def test_insstantiation_from_pandas_row():
+def test_insstantiation_from_pandas_row() -> None:
     """You should be able to instantiate models from pandas rows."""
     pytest.importorskip("pandas")
 
@@ -153,7 +159,7 @@ class Model(pt.Model):
     assert Model.from_row(pandas_dataframe.loc[0]).a == 1  # type: ignore
 
 
-def test_model_dataframe_class_creation():
+def test_model_dataframe_class_creation() -> None:
     """Each model should get a custom DataFrame class."""
 
     class CustomModel(pt.Model):
@@ -170,126 +176,91 @@ class CustomModel(pt.Model):
     assert CustomModel.LazyFrame.model is CustomModel
 
 
-def test_mapping_to_polars_dtypes():
+def test_mapping_to_polars_dtypes() -> None:
     """Model fields should be mappable to polars dtypes."""
-
-    class CompleteModel(pt.Model):
-        str_column: str
-        int_column: int
-        float_column: float
-        bool_column: bool
-
-        date_column: date
-        datetime_column: datetime
-        duration_column: timedelta
-
-        categorical_column: Literal["a", "b", "c"]
-        null_column: None
-
     assert CompleteModel.dtypes == {
-        "str_column": pl.Utf8,
-        "int_column": pl.Int64,
-        "float_column": pl.Float64,
-        "bool_column": pl.Boolean,
-        "date_column": pl.Date,
-        "datetime_column": pl.Datetime,
-        "duration_column": pl.Duration,
-        "categorical_column": pl.Categorical,
-        "null_column": pl.Null,
+        "str_column": pl.String(),
+        "int_column": pl.Int64(),
+        "float_column": pl.Float64(),
+        "bool_column": pl.Boolean(),
+        "date_column": pl.Date(),
+        "datetime_column": pl.Datetime(),
+        "datetime_column2": pl.Datetime(),
+        "aware_datetime_column": pl.Datetime(time_zone="UTC"),
+        "duration_column": pl.Duration(),
+        "time_column": pl.Time(),
+        "categorical_column": pl.Enum(["a", "b", "c"]),
+        "null_column": pl.Null(),
+        "pt_model_column": pl.Struct(
+            [
+                pl.Field("a", pl.Int64),
+                pl.Field("b", pl.String),
+                pl.Field("c", pl.Datetime(time_zone="UTC")),
+                pl.Field("d", pl.Datetime(time_zone="UTC")),
+                pl.Field("e", pl.Int8),
+            ]
+        ),
+        "list_int_column": pl.List(pl.Int64),
+        "list_str_column": pl.List(pl.String),
+        "list_opt_column": pl.List(pl.Int64),
     }
 
     assert CompleteModel.valid_dtypes == {
-        "str_column": [pl.Utf8],
-        "int_column": [
-            pl.Int64,
-            pl.Int32,
-            pl.Int16,
-            pl.Int8,
-            pl.UInt64,
-            pl.UInt32,
-            pl.UInt16,
-            pl.UInt8,
-        ],
-        "float_column": [pl.Float64, pl.Float32],
-        "bool_column": [pl.Boolean],
-        "date_column": [pl.Date],
-        "datetime_column": [pl.Datetime],
-        "duration_column": [pl.Duration],
-        "categorical_column": [pl.Categorical, pl.Utf8],
-        "null_column": [pl.Null],
-    }
-
-
-def test_mapping_to_polars_dtypes_with_lists():
-    """Model list fields should be mappable to polars dtypes."""
-
-    class CompleteModel(pt.Model):
-        str_column: List[str]
-        int_column: List[int]
-        float_column: List[float]
-        bool_column: List[bool]
-
-        date_column: List[date]
-        datetime_column: List[datetime]
-        duration_column: List[timedelta]
-
-        categorical_column: List[Literal["a", "b", "c"]]
-        null_column: List[None]
-
-    assert CompleteModel.dtypes == {
-        "str_column": pl.List(pl.Utf8),
-        "int_column": pl.List(pl.Int64),
-        "float_column": pl.List(pl.Float64),
-        "bool_column": pl.List(pl.Boolean),
-        "date_column": pl.List(pl.Date),
-        "datetime_column": pl.List(pl.Datetime),
-        "duration_column": pl.List(pl.Duration),
-        "categorical_column": pl.List(pl.Categorical),
-        "null_column": pl.List(pl.Null),
+        "str_column": {pl.String},
+        "int_column": DataTypeGroup(INTEGER_DTYPES | FLOAT_DTYPES),
+        "float_column": FLOAT_DTYPES,
+        "bool_column": {pl.Boolean},
+        "date_column": DATE_DTYPES,
+        "datetime_column": DATETIME_DTYPES,
+        "datetime_column2": {pl.Datetime()},
+        "aware_datetime_column": {pl.Datetime(time_zone="UTC")},
+        "duration_column": DURATION_DTYPES,
+        "time_column": TIME_DTYPES,
+        "categorical_column": {pl.Enum(["a", "b", "c"]), pl.String},
+        "null_column": {pl.Null},
+        "pt_model_column": DataTypeGroup(
+            [
+                pl.Struct(
+                    [
+                        pl.Field("a", pl.Int64),
+                        pl.Field("b", pl.String),
+                        pl.Field("c", pl.Datetime(time_zone="UTC")),
+                        pl.Field("d", pl.Datetime(time_zone="UTC")),
+                        pl.Field("e", pl.Int8),
+                    ]
+                )
+            ]
+        ),
+        "list_int_column": DataTypeGroup(
+            [pl.List(x) for x in DataTypeGroup(INTEGER_DTYPES | FLOAT_DTYPES)]
+        ),
+        "list_str_column": DataTypeGroup([pl.List(pl.String)]),
+        "list_opt_column": DataTypeGroup(
+            [pl.List(x) for x in DataTypeGroup(INTEGER_DTYPES | FLOAT_DTYPES)]
+        ),
     }
 
-    assert CompleteModel.valid_dtypes == {
-        "str_column": [pl.List(pl.Utf8)],
-        "int_column": [
-            pl.List(pl.Int64),
-            pl.List(pl.Int32),
-            pl.List(pl.Int16),
-            pl.List(pl.Int8),
-            pl.List(pl.UInt64),
-            pl.List(pl.UInt32),
-            pl.List(pl.UInt16),
-            pl.List(pl.UInt8),
-        ],
-        "float_column": [pl.List(pl.Float64), pl.List(pl.Float32)],
-        "bool_column": [pl.List(pl.Boolean)],
-        "date_column": [pl.List(pl.Date)],
-        "datetime_column": [pl.List(pl.Datetime)],
-        "duration_column": [pl.List(pl.Duration)],
-        "categorical_column": [pl.List(pl.Categorical), pl.List(pl.Utf8)],
-        "null_column": [pl.List(pl.Null)],
-    }
+    CompleteModel.example(int_column=2)
+    CompleteModel.validate(CompleteModel.examples({"int_column": [1, 2, 3]}))
 
 
-def test_model_joins():
+def test_model_joins() -> None:
     """It should produce models compatible with join statements."""
 
     class Left(pt.Model):
         left: int = pt.Field(gt=20)
-        opt_left: Optional[int]
+        opt_left: Optional[int] = None
 
     class Right(pt.Model):
         right: int = pt.Field(gt=20)
-        opt_right: Optional[int]
+        opt_right: Optional[int] = None
 
     def test_model_validator(model: Type[pt.Model]) -> None:
         """Test if all field validators have been included correctly."""
-        with pytest.raises(
-            ValidationError,
-            match=re.compile(
-                r".*limit_value=20.*\n.*\n.*limit_value=20.*", re.MULTILINE
-            ),
-        ):
+        with pytest.raises(ValidationError) as e:
             model(left=1, opt_left=1, right=1, opt_right=1)
+        pattern = re.compile(r"Input should be greater than 20")
+        assert len(pattern.findall(str(e.value))) == 2
 
     # An inner join should keep nullability information
     InnerJoinModel = Left.join(Right, how="inner")
@@ -317,7 +288,7 @@ def test_model_validator(model: Type[pt.Model]) -> None:
     assert Left.join(Right, how="anti") is Left
 
 
-def test_model_selects():
+def test_model_selects() -> None:
     """It should produce models compatible with select statements."""
 
     class MyModel(pt.Model):
@@ -327,13 +298,13 @@ class MyModel(pt.Model):
     MySubModel = MyModel.select("b")
     assert MySubModel.columns == ["b"]
     MySubModel(b=11)
-    with pytest.raises(ValidationError, match="limit_value=10"):
+    with pytest.raises(ValidationError, match="Input should be greater than 10"):
         MySubModel(b=1)
 
     MyTotalModel = MyModel.select(["a", "b"])
     assert sorted(MyTotalModel.columns) == ["a", "b"]
     MyTotalModel(a=1, b=11)
-    with pytest.raises(ValidationError, match="limit_value=10"):
+    with pytest.raises(ValidationError, match="Input should be greater than 10"):
         MyTotalModel(a=1, b=1)
     assert MyTotalModel.nullable_columns == {"a"}
 
@@ -343,7 +314,7 @@ class MyModel(pt.Model):
         MyModel.select("c")
 
 
-def test_model_prefix_and_suffix():
+def test_model_prefix_and_suffix() -> None:
     """It should produce models where all fields have been prefixed/suffixed."""
 
     class MyModel(pt.Model):
@@ -355,7 +326,7 @@ class MyModel(pt.Model):
     assert NewModel.nullable_columns == {"pre_a_post"}
 
 
-def test_model_field_renaming():
+def test_model_field_renaming() -> None:
     """It should be able to change its field names."""
 
     class MyModel(pt.Model):
@@ -372,7 +343,7 @@ class MyModel(pt.Model):
         MyModel.rename({"c": "C"})
 
 
-def test_model_field_dropping():
+def test_model_field_dropping() -> None:
     """It should be able to drop a subset of its fields"""
 
     class MyModel(pt.Model):
@@ -384,7 +355,7 @@ class MyModel(pt.Model):
     assert MyModel.drop(["b", "c"]).columns == ["a"]
 
 
-def test_with_fields():
+def test_with_fields() -> None:
     """It should allow whe user to add additional fields."""
 
     class MyModel(pt.Model):
@@ -400,7 +371,7 @@ class MyModel(pt.Model):
     assert ExpandedModel.nullable_columns == set("ce")
 
 
-def test_enum_annotated_field():
+def test_enum_annotated_field() -> None:
     """It should use values of enums to infer types."""
 
     class ABCEnum(enum.Enum):
@@ -411,7 +382,7 @@ class ABCEnum(enum.Enum):
     class EnumModel(pt.Model):
         column: ABCEnum
 
-    assert EnumModel.dtypes["column"] == pl.Categorical
+    assert EnumModel.dtypes["column"] == pl.Enum(["a", "b", "c"])
     assert EnumModel.example_value(field="column") == "a"
     assert EnumModel.example() == EnumModel(column="a")
 
@@ -421,10 +392,141 @@ class MultiTypedEnum(enum.Enum):
         ONE = 1
         TWO = "2"
 
-    class InvalidEnumModel(pt.Model):
-        column: MultiTypedEnum
+    with pytest.raises(TypeError, match="Mixed type enums not supported"):
+
+        class InvalidEnumModel(pt.Model):
+            column: MultiTypedEnum
+
+        InvalidEnumModel.validate_schema()
+
+
+def test_model_schema() -> None:
+    class Model(pt.Model):
+        a: int = pt.Field(ge=0, unique=True)
+
+    schema = Model.model_schema
+
+    def validate_model_schema(schema) -> None:
+        assert set(schema) == {"properties", "required", "type", "title"}
+        assert schema["title"] == "Model"
+        assert schema["type"] == "object"
+        assert "a" in schema["properties"]
+        assert schema["properties"]["a"]["type"] == "integer"
+        assert schema["properties"]["a"]["minimum"] == 0
+
+    validate_model_schema(schema)
+
+    # nested models
+    class ParentModel(pt.Model):
+        a: int
+        b: Model
+        c: Optional[float] = None
+
+    schema = ParentModel.model_schema
+    validate_model_schema(
+        schema["$defs"]["Model"]
+    )  # ensure that nested model schema is recorded in definitions
+    validate_model_schema(
+        schema["properties"]["b"]
+    )  # and all info is copied into field properties
+    assert set(schema["properties"]) == {"a", "b", "c"}
+    assert schema["properties"]["a"]["required"]
+    assert schema["properties"]["b"]["required"]
+    assert schema["properties"]["a"]["type"] == "integer"
+    assert not schema["properties"]["c"]["required"]
+
+
+def test_nullable_columns() -> None:
+    class Test1(pt.Model):
+        foo: Optional[str] = pt.Field(dtype=pl.String)
+
+    assert Test1.nullable_columns == {"foo"}
+    assert set(Test1.valid_dtypes["foo"]) == {pl.String}
+
+    class Test2(pt.Model):
+        foo: Optional[int] = pt.Field(dtype=pl.UInt32)
+
+    assert Test2.nullable_columns == {"foo"}
+    assert set(Test2.valid_dtypes["foo"]) == {pl.UInt32}
+
+
+def test_conflicting_type_dtype() -> None:
+    string_dtype_alias = "String" if pl.__version__ < "0.20.3" else "String"
+    with pytest.raises(ValueError, match=f"Invalid dtype {string_dtype_alias}") as e:
+
+        class Test1(pt.Model):
+            foo: int = pt.Field(dtype=pl.String)
+
+        Test1.validate_schema()
+
+    with pytest.raises(ValueError, match="Invalid dtype Float32") as e:
+
+        class Test2(pt.Model):
+            foo: str = pt.Field(dtype=pl.Float32)
+
+        Test2.validate_schema()
+
+    with pytest.raises(ValueError, match="Invalid dtype UInt32") as e:
+
+        class Test3(pt.Model):
+            foo: Optional[str] = pt.Field(dtype=pl.UInt32)
+
+        Test3.validate_schema()
+
+
+def test_polars_python_type_harmonization() -> None:
+    class Test(pt.Model):
+        date: datetime = pt.Field(dtype=pl.Datetime(time_unit="us"))
+        time: time
+
+    assert Test.valid_dtypes["date"] == {pl.Datetime(time_unit="us")}
+    assert Test.valid_dtypes["time"] == TIME_DTYPES
+
+
+def test_column_infos() -> None:
+    class Model(pt.Model):
+        a: int
+        b: int = pt.Field(constraints=[(pl.col("b") < 10)])
+        c: int = pt.Field(derived_from=pl.col("a") + pl.col("b"))
+        d: int = pt.Field(dtype=pl.UInt8)
+        e: int = pt.Field(unique=True)
+
+    schema = Model.model_json_schema()  # no serialization issues
+    props = schema[
+        "properties"
+    ]  # extra fields are stored in modified schema_properties
+    for col in ["b", "c", "d", "e"]:
+        assert "column_info" in props[col]
+    assert props["b"]["column_info"]["constraints"] is not None
+    assert props["c"]["column_info"]["derived_from"] is not None
+    assert props["d"]["column_info"]["dtype"] is not None
+    assert props["e"]["column_info"]["unique"] is not None
+    infos = Model.column_infos
+    assert infos["b"].constraints is not None
+    assert infos["c"].derived_from is not None
+    assert infos["d"].dtype is not None
+    assert infos["e"].unique is not None
+
+
+def test_missing_date_struct():
+    class SubModel(pt.Model):
+        a: int
+        b: AwareDatetime
+
+    class Test(pt.Model):
+        a: int
+        b: int
+        c: Optional[SubModel]
+
+    df = Test.examples({"a": range(5), "c": None})
+    Test.validate(df.cast())
+
+
+def test_validation_alias():
+    class AliasModel(pt.Model):
+        my_val_a: int = pt.Field(validation_alias="myValA")
+        my_val_b: int = pt.Field(validation_alias=AliasChoices("my_val_b", "myValB"))
 
-    if pt._DUCKDB_AVAILABLE:  # pragma: no cover
-        assert EnumModel.sql_types["column"].startswith("enum__")
-        with pytest.raises(TypeError, match=r".*Encountered types: \['int', 'str'\]\."):
-            InvalidEnumModel.sql_types
+    # code from validators _find_errors showing that we need model_json_schema without aliases
+    for column_name, column_properties in AliasModel._schema_properties().items():
+        assert AliasModel.column_infos[column_name] is not None
diff --git a/tests/test_polars.py b/tests/test_polars.py
index c288c0e..cf907b6 100644
--- a/tests/test_polars.py
+++ b/tests/test_polars.py
@@ -1,14 +1,17 @@
 """Tests related to polars functionality."""
 import re
 from datetime import date, datetime
+from typing import Optional
 
+import patito as pt
 import polars as pl
 import pytest
+from pydantic import AliasChoices, AliasPath, ValidationError
 
-import patito as pt
+from tests.examples import SmallModel
 
 
-def test_dataframe_get_method():
+def test_dataframe_get_method() -> None:
     """You should be able to retrieve a single row and cast to model."""
 
     class Product(pt.Model):
@@ -45,7 +48,7 @@ class Product(pt.Model):
     df.filter(pl.col("product_id") == 1).get()
 
 
-def test_dataframe_set_model_method():
+def test_dataframe_set_model_method() -> None:
     """You should be able to set the associated model of a dataframe."""
 
     class MyModel(pt.Model):
@@ -56,7 +59,7 @@ class MyModel(pt.Model):
     assert MyModel.DataFrame.model is MyModel
 
 
-def test_fill_nan_with_defaults():
+def test_fill_nan_with_defaults() -> None:
     """You should be able to fill missing values with declared defaults."""
 
     class DefaultModel(pt.Model):
@@ -66,10 +69,48 @@ class DefaultModel(pt.Model):
     missing_df = pt.DataFrame({"foo": [1, None], "bar": [None, "provided"]})
     filled_df = missing_df.set_model(DefaultModel).fill_null(strategy="defaults")
     correct_filled_df = pt.DataFrame({"foo": [1, 2], "bar": ["default", "provided"]})
-    assert filled_df.frame_equal(correct_filled_df)
+    assert filled_df.equals(correct_filled_df)
+
+
+def test_create_missing_columns_with_defaults() -> None:
+    """Columns that have default values should be created if they are missing."""
+
+    class NestedModel(pt.Model):
+        foo: int = 2
+        small_model: Optional[SmallModel] = None
+
+    class DefaultModel(pt.Model):
+        foo: int = 2
+        bar: Optional[str] = "default"
+        small_model: Optional[SmallModel] = None  # works ok on polars==0.20.3
+        nested_model: Optional[NestedModel] = None  # fails to convert on polars==0.20.3
+
+    missing_df = pt.DataFrame({"foo": [1, 2]})
+    filled_df = missing_df.set_model(DefaultModel).fill_null(strategy="defaults")
+    correct_filled_df = pl.DataFrame(
+        {
+            "foo": [1, 2],
+            "bar": ["default", "default"],
+            "small_model": [None, None],
+            "nested_model": [None, None],
+        },
+        schema=DefaultModel.dtypes,
+    )
+    assert filled_df.equals(correct_filled_df)
 
 
-def test_preservation_of_model():
+def test_create_missing_columns_with_dtype() -> None:
+    class DefaultModel(pt.Model):
+        foo: int
+        bar: Optional[int] = None
+
+    missing_df = pt.DataFrame({"foo": [1, 2]})
+    filled_df = missing_df.set_model(DefaultModel).fill_null(strategy="defaults")
+    assert "bar" in filled_df.columns
+    assert filled_df.dtypes[1] == pl.Int64
+
+
+def test_preservation_of_model() -> None:
     """The model should be preserved on data frames after method invocations."""
 
     class DummyModel(pt.Model):
@@ -106,25 +147,20 @@ class AnotherDummyModel(pt.Model):
     assert type(pt.DataFrame().lazy().collect()) is pt.DataFrame
 
 
-def test_dataframe_model_dtype_casting():
+def test_dataframe_model_dtype_casting() -> None:
     """You should be able to cast columns according to model type annotations."""
 
     class DTypeModel(pt.Model):
-        implicit_int_1: int
-        implicit_int_2: int
+        implicit_int: int
         explicit_uint: int = pt.Field(dtype=pl.UInt64)
         implicit_date: date
         implicit_datetime: datetime
 
     original_df = DTypeModel.DataFrame().with_columns(
         [
-            # This float will be casted to an integer, and since no specific integer
-            # dtype is specified, the default pl.Int64 will be used.
-            pl.lit(1.0).cast(pl.Float64).alias("implicit_int_1"),
             # UInt32 is compatible with the "int" annotation, and since no explicit
             # dtype is specified, it will not be casted to the default pl.Int64
-            pl.lit(1).cast(pl.UInt32).alias("implicit_int_2"),
-            pl.lit(1.0).cast(pl.Float64).alias("explicit_uint"),
+            pl.lit(1).cast(pl.UInt32).alias("implicit_int"),
             # The integer will be casted to datetime 1970-01-01 00:00:00
             pl.lit(0).cast(pl.Int64).alias("implicit_date"),
             # The integer will be casted to date 1970-01-01
@@ -135,9 +171,7 @@ class DTypeModel(pt.Model):
     )
     casted_df = original_df.cast()
     assert casted_df.dtypes == [
-        pl.Int64,
         pl.UInt32,
-        pl.UInt64,
         pl.Date,
         pl.Datetime,
         pl.Boolean,
@@ -146,13 +180,21 @@ class DTypeModel(pt.Model):
     strictly_casted_df = original_df.cast(strict=True)
     assert strictly_casted_df.dtypes == [
         pl.Int64,
-        pl.Int64,
-        pl.UInt64,
         pl.Date,
         pl.Datetime,
         pl.Boolean,
     ]
 
+    some_columns_df = original_df.cast(
+        strict=True, columns=["implicit_int", "implicit_date"]
+    )
+    assert some_columns_df.dtypes == [
+        pl.Int64,
+        pl.Date,
+        pl.Int64,  # not casted
+        pl.Boolean,
+    ]
+
 
 @pytest.mark.xfail(strict=True)
 def test_correct_columns_and_dtype_on_read(tmp_path):
@@ -171,11 +213,11 @@ class Foo(pt.Model):
 
     model_df = Foo.DataFrame.read_csv(csv_path, has_header=False)
     assert model_df.columns == ["a", "b"]
-    assert model_df.dtypes == [pl.Utf8, pl.Int64]
+    assert model_df.dtypes == [pl.String, pl.Int64]
 
     csv_path.write_text("b,a\n1,2")
     colum_specified_df = Foo.DataFrame.read_csv(csv_path, has_header=True)
-    assert colum_specified_df.schema == {"b": pl.Int64, "a": pl.Utf8}
+    assert colum_specified_df.schema == {"b": pl.Int64, "a": pl.String}
 
     dtype_specified_df = Foo.DataFrame.read_csv(
         csv_path, has_header=True, dtypes=[pl.Float64, pl.Float64]
@@ -186,19 +228,19 @@ class Foo(pt.Model):
     csv_path.write_text("1,2,3.1")
     unspecified_column_df = Foo.DataFrame.read_csv(csv_path, has_header=False)
     assert unspecified_column_df.columns == ["a", "b", "column_3"]
-    assert unspecified_column_df.dtypes == [pl.Utf8, pl.Int64, pl.Float64]
+    assert unspecified_column_df.dtypes == [pl.String, pl.Int64, pl.Float64]
 
     class DerivedModel(pt.Model):
         cents: int = pt.Field(derived_from=100 * pl.col("dollars"))
 
     csv_path.write_text("month,dollars\n1,2.99")
     derived_df = DerivedModel.DataFrame.read_csv(csv_path)
-    assert derived_df.frame_equal(
+    assert derived_df.equals(
         DerivedModel.DataFrame({"month": [1], "dollars": [2.99], "cents": [299]})
     )
 
 
-def test_derive_functionality():
+def test_derive_functionality() -> None:
     """Test of Field(derived_from=...) and DataFrame.derive()."""
 
     class DerivedModel(pt.Model):
@@ -208,6 +250,13 @@ class DerivedModel(pt.Model):
         expr_derived: int = pt.Field(derived_from=2 * pl.col("underived"))
         second_order_derived: int = pt.Field(derived_from=2 * pl.col("expr_derived"))
 
+    assert DerivedModel.derived_columns == {
+        "const_derived",
+        "column_derived",
+        "expr_derived",
+        "second_order_derived",
+    }
+
     df = DerivedModel.DataFrame({"underived": [1, 2]})
     assert df.columns == ["underived"]
     derived_df = df.derive()
@@ -220,20 +269,106 @@ class DerivedModel(pt.Model):
             "second_order_derived": [4, 8],
         }
     )
-    assert derived_df.frame_equal(correct_derived_df)
+    assert derived_df.equals(correct_derived_df)
 
     # Non-compatible derive_from arguments should raise TypeError
-    class InvalidModel(pt.Model):
-        incompatible: int = pt.Field(derived_from=object)
+    with pytest.raises(ValidationError):
 
-    with pytest.raises(
-        TypeError,
-        match=r"Can not derive dataframe column from type \<class 'type'\>\.",
-    ):
-        InvalidModel.DataFrame().derive()
+        class InvalidModel(pt.Model):
+            incompatible: int = pt.Field(derived_from=object)
+
+
+def test_recursive_derive() -> None:
+    """Data.Frame.derive() infers proper derivation order and executes it, then returns columns in the order given by the model."""
+
+    class DerivedModel(pt.Model):
+        underived: int
+        const_derived: int = pt.Field(derived_from=pl.lit(3))
+        second_order_derived: int = pt.Field(
+            derived_from=2 * pl.col("expr_derived")
+        )  # requires expr_derived to be derived first
+        column_derived: int = pt.Field(derived_from="underived")
+        expr_derived: int = pt.Field(derived_from=2 * pl.col("underived"))
 
+    df = DerivedModel.DataFrame({"underived": [1, 2]})
+    assert df.columns == ["underived"]
+    derived_df = df.derive()
+
+    correct_derived_df = DerivedModel.DataFrame(
+        {
+            "underived": [1, 2],
+            "const_derived": [3, 3],
+            "second_order_derived": [4, 8],
+            "column_derived": [1, 2],
+            "expr_derived": [
+                2,
+                4,
+            ],  # derived before second_order_derived, but remains in last position in output df according to the model
+        }
+    )
+    assert derived_df.equals(correct_derived_df)
 
-def test_drop_method():
+
+def test_derive_subset() -> None:
+    class DerivedModel(pt.Model):
+        underived: int
+        derived: Optional[int] = pt.Field(default=None, derived_from="underived")
+        expr_derived: int = pt.Field(
+            derived_from=2 * pl.col("derived")
+        )  # depends on derived
+
+    df = DerivedModel.DataFrame({"underived": [1, 2]})
+    correct_derived_df = DerivedModel.DataFrame(
+        {
+            "underived": [1, 2],
+            "expr_derived": [2, 4],
+        }
+    )
+    assert df.derive(
+        columns=["expr_derived"]
+    ).equals(
+        correct_derived_df
+    )  # only include "expr_derived" in output, but ensure that "derived" was derived recursively
+
+
+def test_derive_on_defaults() -> None:
+    class DerivedModel(pt.Model):
+        underived: int
+        derived: Optional[int] = pt.Field(default=None, derived_from="underived")
+
+    df = DerivedModel.DataFrame([DerivedModel(underived=1), DerivedModel(underived=2)])
+    derived_df = df.derive()
+
+    correct_derived_df = DerivedModel.DataFrame(
+        {
+            "underived": [1, 2],
+            "derived": [1, 2],
+        }
+    )
+    assert derived_df.equals(correct_derived_df)
+
+
+def test_lazy_derive() -> None:
+    class DerivedModel(pt.Model):
+        underived: int
+        derived: Optional[int] = pt.Field(default=None, derived_from="underived")
+
+    ldf = DerivedModel.DataFrame({"underived": [1, 2]}).lazy()
+    assert ldf.columns == ["underived"]
+    derived_ldf = ldf.derive()
+    assert derived_ldf.columns == ["underived", "derived"]
+    df = derived_ldf.collect()
+
+    correct_derived_df = DerivedModel.DataFrame(
+        {
+            "underived": [1, 2],
+            "derived": [1, 2],
+        }
+    )
+    assert df.equals(correct_derived_df)
+
+
+def test_drop_method() -> None:
     """We should be able to drop columns not specified by the data frame model."""
 
     class Model(pt.Model):
@@ -253,3 +388,75 @@ class Model(pt.Model):
 
     # Or a list of columns
     assert df.drop(["column_1", "column_2"]).columns == []
+
+
+def test_polars_conversion():
+    """You should be able to convert a DataFrame to a polars DataFrame."""
+
+    class Model(pt.Model):
+        a: int
+        b: str
+
+    df = Model.DataFrame({"a": [1, 2], "b": ["foo", "bar"]})
+    polars_df = df.as_polars()
+    assert isinstance(polars_df, pl.DataFrame)
+    assert not isinstance(polars_df, pt.DataFrame)
+    assert polars_df.shape == (2, 2)
+    assert polars_df.columns == ["a", "b"]
+    assert polars_df.dtypes == [pl.Int64, pl.String]
+
+
+def test_validation_alias() -> None:
+    class AliasModel(pt.Model):
+        my_val_a: int = pt.Field(validation_alias="myValA")
+        my_val_b: int = pt.Field(
+            validation_alias=AliasChoices("my_val_b", "myValB", "myValB2")
+        )
+        my_val_c: int
+        first_name: str = pt.Field(validation_alias=AliasPath("names", 0))
+        last_name: str = pt.Field(
+            validation_alias=AliasChoices("lastName", AliasPath("names", 1))
+        )
+
+    examples = [
+        {"myValA": 1, "myValB": 1, "my_val_c": 1, "names": ["fname1", "lname1"]},
+        {"myValA": 2, "myValB": 2, "my_val_c": 2, "names": ["fname2", "lname2"]},
+        {
+            "my_val_a": 3,
+            "myValB2": 3,
+            "my_val_c": 3,
+            "names": ["fname3"],
+            "last_name": "lname3",
+        },
+        {
+            "my_val_a": 4,
+            "my_val_b": 4,
+            "my_val_c": 4,
+            "first_name": "fname4",
+            "last_name": "lname4",
+        },
+    ]
+
+    # check record with all aliases
+    df = (
+        AliasModel.LazyFrame([examples[0]])
+        .unalias()
+        .cast(strict=True)
+        .collect()
+        .validate()
+    )
+    assert df.columns == AliasModel.columns
+
+    # check record with no aliases
+    df = (
+        AliasModel.LazyFrame([examples[3]])
+        .unalias()
+        .cast(strict=True)
+        .collect()
+        .validate()
+    )
+    assert df.columns == AliasModel.columns
+
+    # check records with mixed aliases
+    df = AliasModel.LazyFrame(examples).unalias().cast(strict=True).collect().validate()
+    assert df.columns == AliasModel.columns
diff --git a/tests/test_validators.py b/tests/test_validators.py
index 793f02a..66ed1ce 100644
--- a/tests/test_validators.py
+++ b/tests/test_validators.py
@@ -1,35 +1,37 @@
 """Tests for the patito.validators module."""
+from __future__ import annotations
+
 import enum
+import re
 import sys
 from datetime import date, datetime
-from typing import List, Optional, Union
+from typing import List, Literal, Optional, Union
 
+import patito as pt
 import polars as pl
 import pytest
-from typing_extensions import Literal
+from patito import DataFrameValidationError
+from patito._pydantic.dtypes import is_optional
+from patito.validators import _dewrap_optional, validate
 
-import patito as pt
-from patito.exceptions import ValidationError
-from patito.validators import _dewrap_optional, _is_optional, validate
 
-
-def test_is_optional():
+def test_is_optional() -> None:
     """It should return True for optional types."""
-    assert _is_optional(Optional[int])
-    assert _is_optional(Union[int, None])
-    assert not _is_optional(int)
+    assert is_optional(Optional[int])
+    assert is_optional(Union[int, None])
+    assert not is_optional(int)
 
 
 @pytest.mark.skipif(
     sys.version_info <= (3, 10),
     reason="Using | as a type union operator is only supported from python 3.10.",
 )
-def test_is_optional_with_pipe_operator():
+def test_is_optional_with_pipe_operator() -> None:
     """It should return True for optional types."""
-    assert _is_optional(int | None)  # typing: ignore  # pragma: noqa  # pyright: ignore
+    assert is_optional(int | None)  # typing: ignore  # pragma: noqa  # pyright: ignore
 
 
-def test_dewrap_optional():
+def test_dewrap_optional() -> None:
     """It should return the inner type of Optional types."""
     assert _dewrap_optional(Optional[int]) is int
     assert _dewrap_optional(Union[int, None]) is int
@@ -40,14 +42,14 @@ def test_dewrap_optional():
     sys.version_info <= (3, 10),
     reason="Using | as a type union operator is only supported from python 3.10.",
 )
-def test_dewrap_optional_with_pipe_operator():
+def test_dewrap_optional_with_pipe_operator() -> None:
     """It should return the inner type of Optional types."""
     assert (  # typing: ignore  # pragma: noqa  # pyright: ignore
         _dewrap_optional(int | None) is int
     )
 
 
-def test_missing_column_validation():
+def test_missing_column_validation() -> None:
     """Validation should catch missing columns."""
 
     class SingleColumnModel(pt.Model):
@@ -55,7 +57,7 @@ class SingleColumnModel(pt.Model):
         column_2: str
 
     # First we raise an error because we are missing column_1
-    with pytest.raises(ValidationError) as e_info:
+    with pytest.raises(DataFrameValidationError) as e_info:
         validate(dataframe=pl.DataFrame(), schema=SingleColumnModel)
 
     errors = e_info.value.errors()
@@ -73,22 +75,35 @@ class SingleColumnModel(pt.Model):
         },
     ]
 
+    df_missing_column_2 = pl.DataFrame({"column_1": [1, 2, 3]})
+    with pytest.raises(DataFrameValidationError) as e_info:
+        validate(dataframe=df_missing_column_2, schema=SingleColumnModel)
+    validate(
+        dataframe=df_missing_column_2,
+        schema=SingleColumnModel,
+        allow_missing_columns=True,
+    )  # does not raise when allow_missing_columns=True
+    SingleColumnModel.validate(
+        df_missing_column_2, allow_missing_columns=True
+    )  # kwargs are passed via model-centric validation API
 
-def test_superflous_column_validation():
-    """Validation should catch superflous columns."""
+
+def test_superfluous_column_validation() -> None:
+    """Validation should catch superfluous columns."""
 
     class SingleColumnModel(pt.Model):
         column_1: int
 
     # We raise an error because we have added column_2
-    with pytest.raises(ValidationError) as e_info:
+    test_df = pl.DataFrame().with_columns(
+        [
+            pl.lit(1).alias("column_1"),
+            pl.lit(2).alias("column_2"),
+        ]
+    )
+    with pytest.raises(DataFrameValidationError) as e_info:
         validate(
-            dataframe=pl.DataFrame().with_columns(
-                [
-                    pl.lit(1).alias("column_1"),
-                    pl.lit(2).alias("column_2"),
-                ]
-            ),
+            dataframe=test_df,
             schema=SingleColumnModel,
         )
 
@@ -96,17 +111,24 @@ class SingleColumnModel(pt.Model):
     assert len(e_info.value.errors()) == 1
     assert errors[0] == {
         "loc": ("column_2",),
-        "msg": "Superflous column",
-        "type": "type_error.superflouscolumns",
+        "msg": "Superfluous column",
+        "type": "type_error.superfluouscolumns",
     }
 
+    validate(
+        test_df, SingleColumnModel, allow_superfluous_columns=True
+    )  # does not raise
+    SingleColumnModel.validate(
+        test_df, allow_superfluous_columns=True
+    )  # model-centric API also works
+
 
-def test_validate_non_nullable_columns():
+def test_validate_non_nullable_columns() -> None:
     """Test for validation logic related to missing values."""
 
     class SmallModel(pt.Model):
         column_1: int
-        column_2: Optional[int]
+        column_2: Optional[int] = None
 
     # We insert nulls into a non-optional column, causing an exception
     wrong_nulls_df = pl.DataFrame().with_columns(
@@ -115,7 +137,7 @@ class SmallModel(pt.Model):
             pl.lit(None).cast(pl.Int64).alias("column_2"),
         ]
     )
-    with pytest.raises(ValidationError) as e_info:
+    with pytest.raises(DataFrameValidationError) as e_info:
         validate(
             dataframe=wrong_nulls_df,
             schema=SmallModel,
@@ -130,7 +152,7 @@ class SmallModel(pt.Model):
     }
 
 
-def test_validate_dtype_checks():
+def test_validate_dtype_checks() -> None:
     """Test dtype-checking logic."""
 
     # An integer field may be validated against several different integer dtypes
@@ -142,11 +164,11 @@ class IntModel(pt.Model):
         dataframe = pl.DataFrame([series])
         validate(dataframe=dataframe, schema=IntModel)
 
-    # But other types, including floating point types, must be considered invalid
-    for dtype in (pl.Utf8, pl.Date, pl.Float32, pl.Float64):
+    # But other types must be considered invalid
+    for dtype in (pl.String, pl.Date):
         series = pl.Series([], dtype=dtype).alias("column")
         dataframe = pl.DataFrame([series])
-        with pytest.raises(ValidationError) as e_info:
+        with pytest.raises(DataFrameValidationError) as e_info:
             validate(dataframe=dataframe, schema=IntModel)
 
         errors = e_info.value.errors()
@@ -170,7 +192,7 @@ class CompleteModel(pt.Model):
     valid_df = pl.DataFrame().with_columns(
         [
             pl.lit(1, dtype=pl.Int16).alias("int_column"),
-            pl.lit("a", dtype=pl.Utf8).alias("string_column"),
+            pl.lit("a", dtype=pl.String).alias("string_column"),
             pl.lit(1.0, dtype=pl.Float32).alias("float_column"),
             pl.lit(datetime.now(), dtype=pl.Datetime).alias("datetime_column"),
             pl.lit(date.today(), dtype=pl.Date).alias("date_column"),
@@ -182,11 +204,11 @@ class CompleteModel(pt.Model):
     # We try to hit each column dtype check
     for column in CompleteModel.columns:
         if column == "int_column":
-            dtype = pl.Float64
+            dtype = pl.String
         else:
             dtype = pl.Int64
 
-        with pytest.raises(ValidationError) as e_info:
+        with pytest.raises(DataFrameValidationError) as e_info:
             validate(
                 dataframe=valid_df.with_columns(pl.lit(1, dtype=dtype).alias(column)),
                 schema=CompleteModel,
@@ -202,24 +224,20 @@ class CompleteModel(pt.Model):
         }
 
     # Anything non-compatible with polars should raise NotImplementedError
-    class NonCompatibleModel(pt.Model):
-        my_field: object
+    with pytest.raises(ValueError, match="not compatible with any polars dtypes"):
 
-    with pytest.raises(
-        NotImplementedError,
-        match="No valid dtype mapping found for column 'my_field'.",
-    ):
-        NonCompatibleModel.valid_dtypes
+        class NonCompatibleModel(pt.Model):
+            my_field: object
+
+        NonCompatibleModel.validate_schema()
 
     # The same goes for list-annotated fields
-    class NonCompatibleListModel(pt.Model):
-        my_field: List[object]
+    with pytest.raises(ValueError, match="not compatible with any polars dtypes"):
 
-    with pytest.raises(
-        NotImplementedError,
-        match="No valid dtype mapping found for column 'my_field'.",
-    ):
-        NonCompatibleListModel.valid_dtypes
+        class NonCompatibleListModel(pt.Model):
+            my_field: List[object]
+
+        NonCompatibleListModel.validate_schema()
 
     # It should also work with pandas data frames
     class PandasCompatibleModel(CompleteModel):
@@ -232,7 +250,7 @@ class PandasCompatibleModel(CompleteModel):
     )
 
 
-def test_uniqueness_validation():
+def test_uniqueness_validation() -> None:
     """It should be able to validate uniqueness."""
 
     class MyModel(pt.Model):
@@ -245,20 +263,18 @@ class MyModel(pt.Model):
     MyModel.validate(empty_df)
 
     duplicated_df = pt.DataFrame({"column": [1, 1, 2]})
-    with pytest.raises(pt.exceptions.ValidationError):
+    with pytest.raises(pt.exceptions.DataFrameValidationError):
         MyModel.validate(duplicated_df)
 
 
-def test_datetime_validation():
-    """
-    Test for date(time) validation.
+def test_datetime_validation() -> None:
+    """Test for date(time) validation.
 
     Both strings, dates, and datetimes are assigned type "string" in the OpenAPI JSON
     schema spec, so this needs to be specifically tested for since the implementation
     needs to check the "format" property on the field schema.
     """
-
-    string_df = pl.DataFrame().with_columns(pl.lit("string", dtype=pl.Utf8).alias("c"))
+    string_df = pl.DataFrame().with_columns(pl.lit("string", dtype=pl.String).alias("c"))
     date_df = pl.DataFrame().with_columns(
         pl.lit(date.today(), dtype=pl.Date).alias("c")
     )
@@ -270,31 +286,31 @@ class StringModel(pt.Model):
         c: str
 
     validate(dataframe=string_df, schema=StringModel)
-    with pytest.raises(ValidationError):
+    with pytest.raises(DataFrameValidationError):
         validate(dataframe=date_df, schema=StringModel)
-    with pytest.raises(ValidationError):
+    with pytest.raises(DataFrameValidationError):
         validate(dataframe=datetime_df, schema=StringModel)
 
     class DateModel(pt.Model):
         c: date
 
     validate(dataframe=date_df, schema=DateModel)
-    with pytest.raises(ValidationError):
+    with pytest.raises(DataFrameValidationError):
         validate(dataframe=string_df, schema=DateModel)
-    with pytest.raises(ValidationError):
+    with pytest.raises(DataFrameValidationError):
         validate(dataframe=datetime_df, schema=DateModel)
 
     class DateTimeModel(pt.Model):
         c: datetime
 
     validate(dataframe=datetime_df, schema=DateTimeModel)
-    with pytest.raises(ValidationError):
+    with pytest.raises(DataFrameValidationError):
         validate(dataframe=string_df, schema=DateTimeModel)
-    with pytest.raises(ValidationError):
+    with pytest.raises(DataFrameValidationError):
         validate(dataframe=date_df, schema=DateTimeModel)
 
 
-def test_enum_validation():
+def test_enum_validation() -> None:
     """Test validation of enum.Enum-typed fields."""
 
     class ABCEnum(enum.Enum):
@@ -309,7 +325,7 @@ class EnumModel(pt.Model):
     validate(dataframe=valid_df, schema=EnumModel)
 
     invalid_df = pl.DataFrame({"column": ["d"]})
-    with pytest.raises(ValidationError) as e_info:
+    with pytest.raises(DataFrameValidationError) as e_info:
         validate(dataframe=invalid_df, schema=EnumModel)
 
     errors = e_info.value.errors()
@@ -321,7 +337,7 @@ class EnumModel(pt.Model):
     }
 
 
-def test_literal_enum_validation():
+def test_literal_enum_validation() -> None:
     """Test validation of typing.Literal-typed fields."""
 
     class EnumModel(pt.Model):
@@ -331,7 +347,7 @@ class EnumModel(pt.Model):
     validate(dataframe=valid_df, schema=EnumModel)
 
     invalid_df = pl.DataFrame({"column": ["d"]})
-    with pytest.raises(ValidationError) as e_info:
+    with pytest.raises(DataFrameValidationError) as e_info:
         validate(dataframe=invalid_df, schema=EnumModel)
 
     errors = e_info.value.errors()
@@ -343,7 +359,7 @@ class EnumModel(pt.Model):
     }
 
 
-def test_uniqueness_constraint_validation():
+def test_uniqueness_constraint_validation() -> None:
     """Uniqueness constraints should be validated."""
 
     class UniqueModel(pt.Model):
@@ -351,7 +367,7 @@ class UniqueModel(pt.Model):
 
     validate(dataframe=pl.DataFrame({"product_id": [1, 2]}), schema=UniqueModel)
 
-    with pytest.raises(ValidationError) as e_info:
+    with pytest.raises(DataFrameValidationError) as e_info:
         validate(dataframe=pl.DataFrame({"product_id": [1, 1]}), schema=UniqueModel)
 
     errors = e_info.value.errors()
@@ -363,7 +379,7 @@ class UniqueModel(pt.Model):
     }
 
 
-def test_validation_of_bounds_checks():
+def test_validation_of_bounds_checks() -> None:
     """Check if value bounds are correctly validated."""
 
     class BoundModel(pt.Model):
@@ -373,8 +389,10 @@ class BoundModel(pt.Model):
         gt_column: float = pt.Field(gt=42.5)
         combined_column: float = pt.Field(gt=42.5, le=43)
         multiple_column: float = pt.Field(multiple_of=0.5)
-        const_column: float = pt.Field(default=3.1415, const=True)
-        regex_column: str = pt.Field(regex=r"value [A-Z]")
+        # const fields should now use Literal instead, but pyright
+        # complains about Literal of float values
+        const_column: Literal[3.1415] = pt.Field(default=3.1415)  # type: ignore
+        regex_column: str = pt.Field(pattern=r"value [A-Z]")
         min_length_column: str = pt.Field(min_length=2)
         max_length_column: str = pt.Field(max_length=2)
 
@@ -396,7 +414,7 @@ class BoundModel(pt.Model):
             + valid[column_index + 1 :]
         )
         invalid_df = pl.DataFrame(data=[data], schema=BoundModel.columns)
-        with pytest.raises(ValidationError) as e_info:
+        with pytest.raises(DataFrameValidationError) as e_info:
             BoundModel.validate(invalid_df)
         errors = e_info.value.errors()
         assert len(errors) == 1
@@ -407,7 +425,7 @@ class BoundModel(pt.Model):
         }
 
 
-def test_validation_of_dtype_specifiers():
+def test_validation_of_dtype_specifiers() -> None:
     """Fields with specific dtype annotations should be validated."""
 
     class DTypeModel(pt.Model):
@@ -439,7 +457,7 @@ class DTypeModel(pt.Model):
     DTypeModel.validate(valid_df)
 
     invalid = [
-        pl.Series([2.5]).cast(pl.Float64),
+        pl.Series(["a"]).cast(pl.String),
         pl.Series([2.5]).cast(pl.Float64),
         pl.Series([2**32]).cast(pl.Int64),
         pl.Series([-2]).cast(pl.Int64),
@@ -448,7 +466,7 @@ class DTypeModel(pt.Model):
     for column_index, (column_name, dtype) in enumerate(
         zip(
             DTypeModel.columns,
-            [pl.Float64, pl.Float64, pl.Int64, pl.Int64, pl.Int64],
+            [pl.String, pl.Float64, pl.Int64, pl.Int64, pl.Int64],
         )
     ):
         data = (
@@ -457,7 +475,7 @@ class DTypeModel(pt.Model):
             + valid[column_index + 1 :]
         )
         invalid_df = pl.DataFrame(data=data, schema=DTypeModel.columns)
-        with pytest.raises(ValidationError) as e_info:
+        with pytest.raises(DataFrameValidationError) as e_info:
             DTypeModel.validate(invalid_df)
         errors = e_info.value.errors()
         assert len(errors) == 1
@@ -468,7 +486,7 @@ class DTypeModel(pt.Model):
         }
 
 
-def test_custom_constraint_validation():
+def test_custom_constraint_validation() -> None:
     """Users should be able to specify custom constraints."""
 
     class CustomConstraintModel(pt.Model):
@@ -478,7 +496,7 @@ class CustomConstraintModel(pt.Model):
         odd_int: int = pt.Field(constraints=pl.col("odd_int") % 2 == 1)
 
     df = CustomConstraintModel.DataFrame({"even_int": [2, 3], "odd_int": [1, 2]})
-    with pytest.raises(ValidationError) as e_info:
+    with pytest.raises(DataFrameValidationError) as e_info:
         df.validate()
     errors = e_info.value.errors()
     assert len(errors) == 2
@@ -502,11 +520,11 @@ class PizzaSlice(pt.Model):
     PizzaSlice.validate(whole_pizza)
 
     part_pizza = pt.DataFrame({"fraction": [0.25, 0.25]})
-    with pytest.raises(ValidationError):
+    with pytest.raises(DataFrameValidationError):
         PizzaSlice.validate(part_pizza)
 
 
-def test_anonymous_column_constraints():
+def test_anonymous_column_constraints() -> None:
     """You should be able to refer to the field column with an anonymous column."""
 
     class Pair(pt.Model):
@@ -517,7 +535,7 @@ class Pair(pt.Model):
 
     pairs = pt.DataFrame({"odd_number": [1, 3, 5], "even_number": [2, 4, 6]})
     Pair.validate(pairs)
-    with pytest.raises(ValidationError):
+    with pytest.raises(DataFrameValidationError):
         Pair.validate(
             pairs.select(
                 [
@@ -528,7 +546,7 @@ class Pair(pt.Model):
         )
 
 
-def test_optional_enum():
+def test_optional_enum() -> None:
     """It should handle optional enums correctly."""
 
     class OptionalEnumModel(pt.Model):
@@ -543,7 +561,7 @@ class OptionalEnumModel(pt.Model):
     sys.version_info <= (3, 10),
     reason="Using | as a type union operator is only supported from python 3.10.",
 )
-def test_optional_pipe_operator():
+def test_optional_pipe_operator() -> None:
     class OptionalEnumModel(pt.Model):
         # Old type annotation syntax
         optional_enum_1: Optional[Literal["A", "B"]]
@@ -565,7 +583,7 @@ class OptionalEnumModel(pt.Model):
     raises=TypeError,
     strict=True,
 )
-def test_validation_of_list_dtypes():
+def test_validation_of_list_dtypes() -> None:
     """It should be able to validate dtypes organized in lists."""
 
     class ListModel(pt.Model):
@@ -597,5 +615,48 @@ class ListModel(pt.Model):
         ("nullable_int_or_null_list", "nullable_int_list"),
     ]:
         # print(old, new)
-        with pytest.raises(ValidationError):
+        with pytest.raises(DataFrameValidationError):
             ListModel.validate(valid_df.with_columns(pl.col(old).alias(new)))
+
+
+def test_nested_field_attrs() -> None:
+    """Ensure that constraints are respected even when embedded inside 'anyOf'"""
+
+    class Test(pt.Model):
+        foo: Optional[int] = pt.Field(
+            dtype=pl.Int64, ge=0, le=100, constraints=pt.field.sum() == 100
+        )
+
+    test_df = Test.DataFrame(
+        {"foo": [110, -10]}
+    )  # meets constraint, but violates bounds (embedded in 'anyOf' in properties)
+    with pytest.raises(DataFrameValidationError) as e:
+        Test.validate(test_df)
+    pattern = re.compile(r"2 rows with out of bound values")
+    assert len(pattern.findall(str(e.value))) == 1
+
+    null_test_df = Test.DataFrame({"foo": [100, None, None]})
+    Test.validate(null_test_df)  # should not raise
+
+
+def test_validation_column_subset() -> None:
+    """Ensure that columns are only validated if they are in the subset"""
+
+    class Test(pt.Model):
+        a: int
+        b: int = pt.Field(dtype=pl.Int64, ge=0, le=100)
+
+    Test.validate(pl.DataFrame({"a": [1, 2, 3], "b": [1, 2, 3]}))  # should pass
+    with pytest.raises(DataFrameValidationError):
+        Test.validate(pl.DataFrame({"a": [1, 2, 3], "b": [101, 102, 103]}))
+
+    # should pass without validating b
+    Test.validate(pl.DataFrame({"a": [1, 2, 3], "b": [101, 102, 103]}), columns=["a"])
+
+    with pytest.raises(DataFrameValidationError):
+        Test.validate(
+            pl.DataFrame({"a": [1, 2, 3], "b": [101, 102, 103]}), columns=["b"]
+        )
+    # test asking for superfluous column
+    with pytest.raises(DataFrameValidationError):
+        Test.validate(pl.DataFrame({"a": [1, 2, 3], "b": [1, 2, 3]}), columns=["c"])