diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index bbefb1b..f17013b 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -26,7 +26,7 @@ jobs:
       - name: Set up pixi
         uses: prefix-dev/setup-pixi@a09b6247153796b190642a2b53fac4241043cf6f # v0.10.0
         with:
-          environments: default lint polars-minimal
+          environments: default default-py310
       - name: Install Rust
         run: rustup show
       - name: Cache Rust dependencies
diff --git a/.lefthook.yaml b/.lefthook.yaml
index bdeaf7b..9a0442c 100644
--- a/.lefthook.yaml
+++ b/.lefthook.yaml
@@ -21,7 +21,7 @@ pre-commit:
             run: pixi {run} ruff format --force-exclude
           - name: mypy
             glob: "*.py"
-            run: pixi {run} mypy {staged_files}
+            run: pixi {run} -e default-py310 mypy {staged_files}
           - name: prettier
             glob: "*.{md,yml,yaml}"
             run: pixi {run} prettier --write --no-error-on-unmatched-pattern --list-different --ignore-unknown {staged_files}
diff --git a/README.md b/README.md
index 89014e6..25cd5a0 100644
--- a/README.md
+++ b/README.md
@@ -45,6 +45,7 @@ pip install dataframely
 import dataframely as dy
 import polars as pl
 
+
 class HouseSchema(dy.Schema):
     zip_code = dy.String(nullable=False, min_length=3)
     num_bedrooms = dy.UInt8(nullable=False)
@@ -64,15 +65,16 @@ class HouseSchema(dy.Schema):
 ### Validating data against schema
 
 ```python
-
 import polars as pl
 
-df = pl.DataFrame({
-    "zip_code": ["01234", "01234", "1", "213", "123", "213"],
-    "num_bedrooms": [2, 2, 1, None, None, 2],
-    "num_bathrooms": [1, 2, 1, 1, 0, 8],
-    "price": [100_000, 110_000, 50_000, 80_000, 60_000, 160_000]
-})
+df = pl.DataFrame(
+    {
+        "zip_code": ["01234", "01234", "1", "213", "123", "213"],
+        "num_bedrooms": [2, 2, 1, None, None, 2],
+        "num_bathrooms": [1, 2, 1, 1, 0, 8],
+        "price": [100_000, 110_000, 50_000, 80_000, 60_000, 160_000],
+    }
+)
 
 # Validate the data and cast columns to expected types
 validated_df: dy.DataFrame[HouseSchema] = HouseSchema.validate(df, cast=True)
diff --git a/docs/guides/coding-agents.md b/docs/guides/coding-agents.md
index 8cead3d..49e5801 100644
--- a/docs/guides/coding-agents.md
+++ b/docs/guides/coding-agents.md
@@ -36,15 +36,15 @@ right time.
 For example:
 
 ```python
-def preprocess(raw: dy.LazyFrame[MyRawSchema]) -> dy.DataFrame[MyPreprocessedSchema]:
-    ...
+def preprocess(
+    raw: dy.LazyFrame[MyRawSchema],
+) -> dy.DataFrame[MyPreprocessedSchema]: ...
 ```
 
 gives a coding agent much more information than the schema-less alternative:
 
 ```python
-def load_data(raw: pl.LazyFrame) -> pl.DataFrame:
-    ...
+def load_data(raw: pl.LazyFrame) -> pl.DataFrame: ...
 ```
 
 This convention also makes your code more readable and maintainable for human developers.
diff --git a/docs/guides/features/column-metadata.md b/docs/guides/features/column-metadata.md
index b8db72b..b850f83 100644
--- a/docs/guides/features/column-metadata.md
+++ b/docs/guides/features/column-metadata.md
@@ -8,10 +8,12 @@ For instance, one may use the `metadata` parameter to mark a column as pseudonym
 class UserSchema(dy.Schema):
     id = dy.String(primary_key=True)
     # Mark last name column as pseudonymized and (non-docstring) comment on it.
-    last_name = dy.String(metadata={
-        "pseudonymized": True,
-        "comment": "Pseudonymized using cryptographic hash function"
-    })
+    last_name = dy.String(
+        metadata={
+            "pseudonymized": True,
+            "comment": "Pseudonymized using cryptographic hash function",
+        }
+    )
     # Add information about database column type.
     address = dy.String(metadata={"database-type": "VARCHAR(MAX)"})
 ```
diff --git a/docs/guides/features/data-generation.md b/docs/guides/features/data-generation.md
index 623fc6d..49a67f5 100644
--- a/docs/guides/features/data-generation.md
+++ b/docs/guides/features/data-generation.md
@@ -17,6 +17,7 @@ class InvoiceSchema(dy.Schema):
     discharge_date = dy.Date(nullable=False)
     amount = dy.Decimal(nullable=False)
 
+
 # Get data frame with correct type hint.
 df: dy.DataFrame[InvoiceSchema] = InvoiceSchema.create_empty()
 ```
@@ -34,6 +35,7 @@ class InvoiceSchema(dy.Schema):
     discharge_date = dy.Date(nullable=False)
     amount = dy.Decimal(nullable=False)
 
+
 df: dy.DataFrame[InvoiceSchema] = InvoiceSchema.sample(num_rows=100)
 ```
 
@@ -54,6 +56,7 @@ class InvoiceSchema(dy.Schema):
     def discharge_after_admission(cls) -> pl.Expr:
         return InvoiceSchema.discharge_date.col >= InvoiceSchema.admission_date.col
 
+
 # `@dy.rule`s will be respected as well for data generation.
 df: dy.DataFrame[InvoiceSchema] = InvoiceSchema.sample(num_rows=100)
 ```
@@ -79,25 +82,30 @@ The column-wise specification specifies an iterable of values for each specified
 from datetime import date
 
 # Override values for specific columns.
-df: dy.DataFrame[InvoiceSchema] = InvoiceSchema.sample(overrides={
-    # Use either <schema>.<column>.name or just the column name as a string.
-    InvoiceSchema.invoice_id.name: ["1234567890", "2345678901", "3456789012"],
-    # Dataframely will automatically infer the number of rows based on the longest given
-    # sequence of values and broadcast all other columns to that shape.
-    "admission_date": date(2025, 1, 1),
-})
+df: dy.DataFrame[InvoiceSchema] = InvoiceSchema.sample(
+    overrides={
+        # Use either <schema>.<column>.name or just the column name as a string.
+        InvoiceSchema.invoice_id.name: ["1234567890", "2345678901", "3456789012"],
+        # Dataframely will automatically infer the number of rows based on the longest given
+        # sequence of values and broadcast all other columns to that shape.
+        "admission_date": date(2025, 1, 1),
+    }
+)
 ```
 
 The row-wise specification implements an iterable of mappings for the rows that should be sampled. It is particularly helpful if you want to make it easy to understand how values will be combined in specific rows (e.g., when each row represents one object).
 
 ```python
 from datetime import date
+
 # Override values for specific columns.
-df: dy.DataFrame[InvoiceSchema] = InvoiceSchema.sample(overrides=[
-    {"invoice_id": "1234567890", "admission_date": date(2025, 1, 1)},
-    {"invoice_id": "2345678901", "admission_date": date(2025, 1, 1)},
-    {"invoice_id": "3456789012", "admission_date": date(2025, 1, 1)},
-])
+df: dy.DataFrame[InvoiceSchema] = InvoiceSchema.sample(
+    overrides=[
+        {"invoice_id": "1234567890", "admission_date": date(2025, 1, 1)},
+        {"invoice_id": "2345678901", "admission_date": date(2025, 1, 1)},
+        {"invoice_id": "3456789012", "admission_date": date(2025, 1, 1)},
+    ]
+)
 ```
 
 ### Providing custom column overrides
@@ -107,6 +115,8 @@ Complex validation rules (such as dependencies between columns or ordering crite
 ```python
 import polars as pl
 import dataframely as dy
+
+
 class OrderedSchema(dy.Schema):
     """A schema that requires `iter` to be ordered with respect to `a` and `b`."""
 
@@ -116,7 +126,9 @@ class OrderedSchema(dy.Schema):
 
     @dy.rule()
     def iter_order_correct(cls) -> pl.Expr:
-        return pl.col("iter").rank(method="ordinal") == pl.struct(pl.col("a"), pl.col("b")).rank(method="ordinal")
+        return pl.col("iter").rank(method="ordinal") == pl.struct(
+            pl.col("a"), pl.col("b")
+        ).rank(method="ordinal")
 
     @classmethod
     def _sampling_overrides(cls) -> dict[str, pl.Expr]:
@@ -124,6 +136,7 @@ class OrderedSchema(dy.Schema):
             "iter": pl.struct(pl.col("a"), pl.col("b")).rank(method="ordinal"),
         }
 
+
 result = OrderedSchema.sample(100)
 ```
 
@@ -137,10 +150,12 @@ class DiagnosisSchema(dy.Schema):
     invoice_id = dy.String(primary_key=True)
     code = dy.String(nullable=False, regex=r"[A-Z][0-9]{2,4}")
 
+
 class HospitalInvoiceData(dy.Collection):
     invoice: dy.LazyFrame[InvoiceSchema]
     diagnosis: dy.LazyFrame[DiagnosisSchema]
 
+
 invoice_data: HospitalInvoiceData = HospitalInvoiceData.sample(num_rows=10)
 ```
 
@@ -153,6 +168,7 @@ class DiagnosisSchema(dy.Schema):
     invoice_id = dy.String(primary_key=True)
     code = dy.String(primary_key=True, regex=r"[A-Z][0-9]{2,4}")
 
+
 class HospitalInvoiceData(dy.Collection):
     invoice: dy.LazyFrame[InvoiceSchema]
     diagnosis: dy.LazyFrame[DiagnosisSchema]
@@ -190,7 +206,9 @@ class HospitalInvoiceData(dy.Collection):
 
     @classmethod
     @override
-    def _preprocess_sample(cls, sample: dict[str, Any], index: int, generator: Generator):
+    def _preprocess_sample(
+        cls, sample: dict[str, Any], index: int, generator: Generator
+    ):
         # Set common primary key.
         if "invoice_id" not in sample:
             sample["invoice_id"] = str(index)
diff --git a/docs/guides/features/sql-generation.md b/docs/guides/features/sql-generation.md
index d186eb6..563c2d1 100644
--- a/docs/guides/features/sql-generation.md
+++ b/docs/guides/features/sql-generation.md
@@ -56,11 +56,7 @@ Uploading data can then be handled by {meth}`polars.DataFrame.write_database`:
 ```python
 df: dy.DataFrame[MySchema]
 
-df.write_database(
-    connection=engine,
-    table_name=my_table.name,
-    if_table_exists="append"
-)
+df.write_database(connection=engine, table_name=my_table.name, if_table_exists="append")
 ```
 
 ```{note}
diff --git a/docs/guides/migration/v1-v2.md b/docs/guides/migration/v1-v2.md
index 5357cce..aae8202 100644
--- a/docs/guides/migration/v1-v2.md
+++ b/docs/guides/migration/v1-v2.md
@@ -44,8 +44,7 @@ class MySchema(dy.Schema):
     ...
 
     @dy.rule()
-    def my_rule() -> pl.Expr:
-        ...
+    def my_rule() -> pl.Expr: ...
 ```
 
 turns into
@@ -55,8 +54,7 @@ class MySchema(dy.Schema):
     ...
 
     @dy.rule()
-    def my_rule(cls) -> pl.Expr:
-        ...
+    def my_rule(cls) -> pl.Expr: ...
 ```
 
 Within the schema rule, `cls` can now be used to access columns or other information from the schema. Specifically,
diff --git a/docs/guides/migration/v2-v3.md b/docs/guides/migration/v2-v3.md
index 4cb47a8..2995823 100644
--- a/docs/guides/migration/v2-v3.md
+++ b/docs/guides/migration/v2-v3.md
@@ -30,6 +30,7 @@ If `categories` is not specified, the behavior is unchanged.
 import polars as pl
 import dataframely as dy
 
+
 class MySchema(dy.Schema):
     # Column-scoped categories with a specific physical backing type
     a = dy.Categorical(pl.UInt16)
@@ -82,6 +83,7 @@ df = MySchema.read_parquet("data.parquet")
 
 # After (v3)
 import polars as pl
+
 df = MySchema.validate(pl.read_parquet("data.parquet"), cast=True)
 ```
 
@@ -168,6 +170,7 @@ class HouseSchema(dy.Schema):
     def minimum_zip_code_count(cls) -> pl.Expr:
         return pl.len() >= 2
 
+
 # After (v3)
 class HouseSchema(dy.Schema):
     zip_code = dy.String(nullable=False, min_length=3)
diff --git a/docs/guides/quickstart.md b/docs/guides/quickstart.md
index 4d7e24b..428ffbf 100644
--- a/docs/guides/quickstart.md
+++ b/docs/guides/quickstart.md
@@ -3,14 +3,15 @@
 For the purpose of this guide, let's assume that we're working with data that we use to predict housing prices.
 To this end, we want to ensure that all the data we're using meets several expectations.
 As a running example, consider the following data set:
-| `zip_code` | `num_bedrooms` | `num_bathrooms` | `price`|
-|-------|--------------|--------------|----------|
-| "01234" | 2 | 1 | 100,000 |
-| "01234" | 2 | 2 | 110,000 |
-| "1" | 1 | 1 | 50,000 |
-| "213" | NULL | 1 | 80,000 |
-| "123" | NULL | 0 | 60,000 |
-| "213" | 2 | 8 | 160,000
+
+| `zip_code` | `num_bedrooms` | `num_bathrooms` | `price` |
+| ---------- | -------------- | --------------- | ------- |
+| "01234"    | 2              | 1               | 100,000 |
+| "01234"    | 2              | 2               | 110,000 |
+| "1"        | 1              | 1               | 50,000  |
+| "213"      | NULL           | 1               | 80,000  |
+| "123"      | NULL           | 0               | 60,000  |
+| "213"      | 2              | 8               | 160,000 |
 
 ## Creating a {class}`~dataframely.Schema` class
 
@@ -85,12 +86,14 @@ the data set above as follows:
 ```python
 import polars as pl
 
-df = pl.DataFrame({
-    "zip_code": ["01234", "01234", "1", "213", "123", "213"],
-    "num_bedrooms": [2, 2, 1, None, None, 2],
-    "num_bathrooms": [1, 2, 1, 1, 0, 8],
-    "price": [100_000, 110_000, 50_000, 80_000, 60_000, 160_000]
-})
+df = pl.DataFrame(
+    {
+        "zip_code": ["01234", "01234", "1", "213", "123", "213"],
+        "num_bedrooms": [2, 2, 1, None, None, 2],
+        "num_bathrooms": [1, 2, 1, 1, 0, 8],
+        "price": [100_000, 110_000, 50_000, 80_000, 60_000, 160_000],
+    }
+)
 
 # Validate the data and cast columns to expected types
 validated_df = HouseSchema.validate(df, cast=True)
@@ -113,8 +116,7 @@ The generic data frame types allow for more readable function signatures to expr
 expectations on the schema of the data frame, e.g.:
 
 ```python
-def train_model(df: dy.DataFrame[HouseSchema]) -> None:
-    ...
+def train_model(df: dy.DataFrame[HouseSchema]) -> None: ...
 ```
 
 The type checker (typically `mypy`) then ensures that it is actually a
diff --git a/pixi.lock b/pixi.lock
index f30a734..d3841a3 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -35,434 +35,429 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314h4a8dc5f_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cmarkgfm-2024.11.20-py314h5bd0f2a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cryptography-48.0.0-py314h7fe84b3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.1.0-py314h4a8dc5f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/comrak-0.54.0-hb17b654_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cryptography-50.0.0-py314h7fe84b3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h24cb091_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h54a6638_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.46.1-default_hbd61a6d_102.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-8_h4a7cf45_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-8_h0358290_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_19.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.88.1-h0d30a3d_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-16.1.0-ha9f2e26_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-16.1.0-h69a702a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-16.1.0-h79bb938_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.88.3-h0d30a3d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-16.1.0-he0feb66_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-8_h47877c9_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.33-pthreads_h94d23a6_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.1-h5347b49_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/maturin-1.13.3-py310h2b5ca13_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.4-hf4e2dac_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-16.1.0-h934c35e_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.2-h5347b49_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/maturin-1.14.1-py310hf7265a2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/nh3-0.3.5-py310hd8a072f_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.6-py314h2b28147_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.2-h35e630c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/nh3-0.3.6-py310hd8a072f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.5.1-py314h2b28147_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.3-h35e630c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-haa7fec5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/polars-runtime-32-1.41.2-py310h49dadd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.5-habeac84_100_cp314.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/polars-runtime-32-1.43.2-py310h9585f58_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.6-habeac84_101_cp314.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/secretstorage-3.4.1-py314hdafbbf9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/secretstorage-3.5.0-py314hdafbbf9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/sed-4.10-h19d0853_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd70dff1_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
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   license: BSD-3-Clause
   license_family: BSD
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@@ -9491,23 +10957,26 @@ packages:
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+  license: MIT
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.15.15-h6a952e8_1.conda
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   - libgcc >=14
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   - _openmp_mutex >=4.5
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   - numpy >=1.23,<3
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   license_family: BSD
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-  timestamp: 1765801260253
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   license: BSD-3-Clause
   license_family: BSD
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   size: 16417101
   timestamp: 1739791865060
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   license_family: BSD
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   - numpy >=1.23,<3
-  - numpy >=1.25.2
+  - numpy >=2.0.0
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   license_family: BSD
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   - numpy >=1.23,<3
-  - numpy >=1.25.2
+  - numpy >=2.0.0
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   license_family: BSD
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-  - numpy >=1.25.2
+  - numpy >=2.0.0
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   license_family: BSD
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   license: BSD-3-Clause
   license_family: BSD
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-  timestamp: 1763045408414
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   license_family: GPL
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+  license: Apache-2.0
+  license_family: Apache
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   license: MIT OR Apache-2.0
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   - libiconv >=1.18,<2.0a0
   license: LGPL-2.1-only
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   license_family: MIT
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   timestamp: 1753484064210
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h09e67af_11.conda
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   license: MPL-2.0
   license_family: MOZILLA
+  run_exports:
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   timestamp: 1779123989774
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-  - libzlib 1.3.2 h25fd6f3_2
+  - libzlib 1.3.2 h25fd6f3_3
   license: Zlib
   license_family: Other
   run_exports:
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   license_family: BSD
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   license: BSD-3-Clause
   license_family: BSD
+  run_exports:
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+    - _openmp_mutex >=4.5
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   timestamp: 1770939656741
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+  - python >=3.10,<3.11.0a0 *_cpython
+  - python_abi 3.10.* *_cp310
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+  license_family: MIT
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/argon2-cffi-bindings-25.1.0-py314h51f160d_2.conda
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@@ -13210,22 +14483,25 @@ packages:
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   license: MIT
   license_family: MIT
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   size: 37380
   timestamp: 1762509526737
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-auth-0.10.3-h2a131b4_1.conda
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-  license_family: APACHE
-  size: 142797
-  timestamp: 1779877500313
+  - _python_abi3_support 1.*
+  - cpython >=3.10
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   license: Python-2.0
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   - tzdata
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   license: Python-2.0
-  size: 14306513
-  timestamp: 1772728906052
+  run_exports:
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.12.13-h91f4b29_0_cpython.conda
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   - python_abi 3.12.* *_cp312
   license: Python-2.0
+  run_exports:
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+    noarch:
+    - python
   size: 13757191
   timestamp: 1772728951853
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.13.13-h11c0449_100_cp313.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.13.14-h6185564_100_cp313.conda
   build_number: 100
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   - libgcc >=14
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   - tk >=8.6.13,<8.7.0a0
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   license: Python-2.0
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   python_site_packages_path: lib/python3.13/site-packages
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   - tk >=8.6.13,<8.7.0a0
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   license: Python-2.0
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+    noarch:
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   python_site_packages_path: lib/python3.14/site-packages
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-librt-0.11.0-py310hef25091_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-librt-0.11.0-py311h51cfe5d_0.conda
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+  - libgcc >=14
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   license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-librt-0.11.0-py313h62ef0ea_0.conda
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   license: MIT
   license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-librt-0.11.0-py314h2e8dab5_0.conda
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   license: MIT
   license_family: MIT
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-  timestamp: 1778511637007
+  run_exports: {}
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyyaml-6.0.3-py310h2d8da20_1.conda
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+  - python >=3.10,<3.11.0a0 *_cpython
+  - python_abi 3.10.* *_cp310
+  - yaml >=0.2.5,<0.3.0a0
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+  license_family: MIT
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyyaml-6.0.3-py314h807365f_1.conda
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   - yaml >=0.2.5,<0.3.0a0
   license: MIT
   license_family: MIT
+  run_exports: {}
   size: 195678
   timestamp: 1770223441816
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyzmq-27.1.0-py310hbea53bb_3.conda
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyzmq-27.1.0-py312hdf0a211_3.conda
   noarch: python
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@@ -16372,8 +17259,24 @@ packages:
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   license: BSD-3-Clause
   license_family: BSD
+  run_exports: {}
   size: 212016
   timestamp: 1779483886884
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/rapidfuzz-3.14.5-py310h57cea71_0.conda
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+  - python >=3.10,<3.11.0a0 *_cpython
+  - python_abi 3.10.* *_cp310
+  license: MIT
+  license_family: MIT
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/rapidfuzz-3.14.5-py314h02b5315_0.conda
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@@ -16386,17 +17289,9 @@ packages:
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   license: MIT
   license_family: MIT
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   size: 989361
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/re2-2025.11.05-he0da282_1.conda
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   license_family: GPL
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/rpds-py-2026.6.3-py314h721bf50_0.conda
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   license: MIT
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ruamel.yaml.clib-0.2.15-py314h2e8dab5_1.conda
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   timestamp: 1766159541094
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ruff-0.15.15-h88be79b_1.conda
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlicommon-1.2.0-h8616949_1.conda
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   - tzdata
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   license: Python-2.0
-  size: 15664115
-  timestamp: 1772730794934
+  run_exports:
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.12.13-ha9537fe_0_cpython.conda
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   license: Python-2.0
+  run_exports:
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+    - python_abi 3.12.* *_cp312
+    noarch:
+    - python
   size: 13672169
   timestamp: 1772730464626
-- conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.13.13-h3d5d122_100_cp313.conda
+- conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.13.14-h3d5d122_100_cp313.conda
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-  - openssl >=3.5.6,<4.0a0
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   - tk >=8.6.13,<8.7.0a0
   - tzdata
   license: Python-2.0
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   python_site_packages_path: lib/python3.13/site-packages
-- conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.14.5-h7c6738f_100_cp314.conda
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   - liblzma >=5.8.3,<6.0a0
   - libmpdec >=4.0.0,<5.0a0
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-  - openssl >=3.5.6,<4.0a0
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   - tk >=8.6.13,<8.7.0a0
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   license: Python-2.0
-  size: 14450441
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   license: MIT
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@@ -22489,8 +23497,23 @@ packages:
   - yaml >=0.2.5,<0.3.0a0
   license: MIT
   license_family: MIT
+  run_exports: {}
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   timestamp: 1770226344240
+- conda: https://conda.anaconda.org/conda-forge/osx-64/pyzmq-27.1.0-py310h30bd05a_3.conda
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+  license_family: BSD
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/pyzmq-27.1.0-py312h2ac7433_3.conda
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@@ -22504,8 +23527,23 @@ packages:
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   license_family: BSD
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@@ -22517,17 +23555,9 @@ packages:
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   license: MIT
   license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/re2-2025.11.05-h77e0585_1.conda
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   license_family: GPL
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/rpds-py-2026.6.3-py314h1d56075_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/ruamel.yaml.clib-0.2.15-py310h8bcfd8d_1.conda
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   license: MIT
   license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/ruff-0.15.15-h1ddadc8_1.conda
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/scikit-learn-1.8.0-np2py311heeb2b4f_1.conda
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/scikit-learn-1.8.0-np2py312h47bbdc5_1.conda
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   license: BSD-3-Clause
   license_family: BSD
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   timestamp: 1739792089317
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.17.1-py311h9a58382_1.conda
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   license_family: BSD
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   license_family: BSD
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-  - numpy >=1.25.2
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   license_family: BSD
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+  - python_abi 3.10.* *_cp310
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+  - python >=3.10,<3.11.0a0 *_cpython
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+  license: Apache-2.0
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-- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-auth-0.10.3-h4e4e17b_1.conda
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+  - _python_abi3_support 1.*
+  - cpython >=3.10
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   - libffi >=3.4,<4.0a0
-  - liblzma >=5.8.2,<6.0a0
-  - libsqlite >=3.51.2,<4.0a0
-  - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.5,<4.0a0
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+  - libsqlite >=3.53.2,<4.0a0
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+  - openssl >=3.5.7,<4.0a0
   - tk >=8.6.13,<8.7.0a0
   - tzdata
   - ucrt >=10.0.20348.0
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   constrains:
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   license: Python-2.0
-  size: 16028082
-  timestamp: 1772728853200
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.11.15-h0159041_0_cpython.conda
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-  md5: d09dbf470b41bca48cbe6a78ba1e009b
+  run_exports:
+    weak:
+    - python_abi 3.10.* *_cp310
+    noarch:
+    - python
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+  timestamp: 1781148776322
+- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.11.15-h0159041_1_cpython.conda
+  build_number: 1
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   - libffi >=3.5.2,<3.6.0a0
-  - liblzma >=5.8.2,<6.0a0
-  - libsqlite >=3.51.2,<4.0a0
-  - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.5,<4.0a0
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+  - openssl >=3.5.7,<4.0a0
   - tk >=8.6.13,<8.7.0a0
   - tzdata
   - ucrt >=10.0.20348.0
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   constrains:
   - python_abi 3.11.* *_cp311
   license: Python-2.0
-  size: 18416208
-  timestamp: 1772728847666
+  run_exports:
+    weak:
+    - python_abi 3.11.* *_cp311
+    noarch:
+    - python
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+  timestamp: 1781148714198
 - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.12.13-h0159041_0_cpython.conda
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@@ -29273,21 +29884,26 @@ packages:
   constrains:
   - python_abi 3.12.* *_cp312
   license: Python-2.0
+  run_exports:
+    weak:
+    - python_abi 3.12.* *_cp312
+    noarch:
+    - python
   size: 15840187
   timestamp: 1772728877265
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.13-h09917c8_100_cp313.conda
+- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.14-h09917c8_100_cp313.conda
   build_number: 100
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-  md5: 7065f7067762c4c2bda1912f18d16239
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   - libffi >=3.5.2,<3.6.0a0
-  - liblzma >=5.8.2,<6.0a0
+  - liblzma >=5.8.3,<6.0a0
   - libmpdec >=4.0.0,<5.0a0
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+  - openssl >=3.5.7,<4.0a0
   - python_abi 3.13.* *_cp313
   - tk >=8.6.13,<8.7.0a0
   - tzdata
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   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   license: Python-2.0
-  size: 16618694
-  timestamp: 1775613654892
+  run_exports:
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+    - python_abi 3.13.* *_cp313
+    noarch:
+    - python
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+  timestamp: 1781257712940
   python_site_packages_path: Lib/site-packages
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.5-h4b44e0e_100_cp314.conda
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-  md5: 3f76bc298eebc1ec1497852f4d7f09d9
+- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.6-h4b44e0e_101_cp314.conda
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   - libffi >=3.5.2,<3.6.0a0
   - liblzma >=5.8.3,<6.0a0
   - libmpdec >=4.0.0,<5.0a0
-  - libsqlite >=3.53.1,<4.0a0
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   - libzlib >=1.3.2,<2.0a0
-  - openssl >=3.5.6,<4.0a0
+  - openssl >=3.5.7,<4.0a0
   - python_abi 3.14.* *_cp314
   - tk >=8.6.13,<8.7.0a0
   - tzdata
@@ -29319,12 +29940,17 @@ packages:
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   - zstd >=1.5.7,<1.6.0a0
   license: Python-2.0
-  size: 18375338
-  timestamp: 1779237800732
+  run_exports:
+    weak:
+    - python_abi 3.14.* *_cp314
+    noarch:
+    - python
+  size: 18338767
+  timestamp: 1784911044838
   python_site_packages_path: Lib/site-packages
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.11.0-py310h1637853_0.conda
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-  md5: 3e67e87123d8053c7da2d79c936d41a2
+- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.13.0-py310h1637853_0.conda
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+  md5: 97eb07756f0aa79ad0820487d67d07c7
   depends:
   - python
   - vc >=14.3,<15
@@ -29333,11 +29959,12 @@ packages:
   - python_abi 3.10.* *_cp310
   license: MIT
   license_family: MIT
-  size: 107292
-  timestamp: 1778511739651
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.11.0-py311hf893f09_0.conda
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-  md5: 722f22fd9f85cdb3e57bc236e2ff8ae9
+  run_exports: {}
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+- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.13.0-py311hf893f09_0.conda
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+  md5: 5c09cdd65e33d535cc724ef56f18550a
   depends:
   - python
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   - python_abi 3.11.* *_cp311
   license: MIT
   license_family: MIT
-  size: 107666
-  timestamp: 1778511738391
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.11.0-py312he5662c2_0.conda
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-  md5: 4d7ec395c6b8688c59fb380f6efea95c
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+- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.13.0-py312he5662c2_0.conda
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+  md5: 3f84f9e1bbc5eda0d7da955cba05c5ca
   depends:
   - python
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@@ -29359,11 +29987,12 @@ packages:
   - python_abi 3.12.* *_cp312
   license: MIT
   license_family: MIT
-  size: 108434
-  timestamp: 1778511745460
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.11.0-py313h5fd188c_0.conda
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-  md5: 68c80ed04e086794346542e38d880da4
+  run_exports: {}
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+- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.13.0-py313h5fd188c_0.conda
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+  md5: 68338cc928ef7fe217130e5dda24043d
   depends:
   - python
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   - python_abi 3.13.* *_cp313
   license: MIT
   license_family: MIT
-  size: 108334
-  timestamp: 1778511739044
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.11.0-py314hc5dbbe4_0.conda
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-  md5: 7cdfc5fda782933981f50c7a9a746039
+  run_exports: {}
+  size: 113930
+  timestamp: 1783529494207
+- conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.13.0-py314hc5dbbe4_0.conda
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+  md5: 51a96b820b9162cebc70f8dc0dc7b896
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   - python
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   license: MIT
   license_family: MIT
-  size: 104479
-  timestamp: 1778511743226
-- conda: https://conda.anaconda.org/conda-forge/win-64/pywin32-311-py314hcaaf0b2_2.conda
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-  md5: 7e2c2a44161e600982eb1b7437125396
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+- conda: https://conda.anaconda.org/conda-forge/win-64/pywin32-312-py310h282bd7d_0.conda
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+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - python_abi 3.10.* *_cp310
+  license: PSF-2.0
+  license_family: PSF
+  run_exports: {}
+  size: 4003035
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+- conda: https://conda.anaconda.org/conda-forge/win-64/pywin32-312-py314hcaaf0b2_0.conda
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+  md5: 4d49ac91c23c4b1878daf5ea9e7c767a
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   - python
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@@ -29398,8 +30043,9 @@ packages:
   - python_abi 3.14.* *_cp314
   license: PSF-2.0
   license_family: PSF
-  size: 6713587
-  timestamp: 1779222949650
+  run_exports: {}
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 - conda: https://conda.anaconda.org/conda-forge/win-64/pywin32-ctypes-0.2.3-py314h86ab7b2_3.conda
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   md5: 2507b24a127696b044f441df16c5571c
@@ -29408,22 +30054,52 @@ packages:
   - python_abi 3.14.* *_cp314
   license: BSD-3-Clause
   license_family: BSD
+  run_exports: {}
   size: 58083
   timestamp: 1762489935449
-- conda: https://conda.anaconda.org/conda-forge/win-64/pywinpty-2.0.15-py314h51f0985_1.conda
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-  md5: dd84e8748bd3c85a5c751b0576488080
+- conda: https://conda.anaconda.org/conda-forge/win-64/pywinpty-3.0.5-py310h9e98ed7_0.conda
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+  - python_abi 3.10.* *_cp310
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  - winpty
+  license: MIT
+  run_exports: {}
+  size: 741607
+  timestamp: 1785742568345
+- conda: https://conda.anaconda.org/conda-forge/win-64/pywinpty-3.0.5-py314h51f0985_0.conda
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+  - python >=3.14,<3.15.0a0
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   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   - winpty
   license: MIT
+  run_exports: {}
+  size: 731340
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+- conda: https://conda.anaconda.org/conda-forge/win-64/pyyaml-6.0.3-py310hdb0e946_1.conda
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+  - python >=3.10,<3.11.0a0
+  - python_abi 3.10.* *_cp310
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - yaml >=0.2.5,<0.3.0a0
+  license: MIT
   license_family: MIT
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-  timestamp: 1759557436167
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 - conda: https://conda.anaconda.org/conda-forge/win-64/pyyaml-6.0.3-py314h2359020_1.conda
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@@ -29436,8 +30112,24 @@ packages:
   - yaml >=0.2.5,<0.3.0a0
   license: MIT
   license_family: MIT
+  run_exports: {}
   size: 181257
   timestamp: 1770223460931
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyzmq-27.1.0-py310h824d4bc_3.conda
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+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - zeromq >=4.3.5,<4.3.6.0a0
+  - python_abi 3.10.* *_cp310
+  license: BSD-3-Clause
+  license_family: BSD
+  run_exports: {}
+  size: 305356
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 - conda: https://conda.anaconda.org/conda-forge/win-64/pyzmq-27.1.0-py312h343a6d4_3.conda
   noarch: python
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@@ -29452,8 +30144,24 @@ packages:
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   license: BSD-3-Clause
   license_family: BSD
+  run_exports: {}
   size: 182831
   timestamp: 1779483925948
+- conda: https://conda.anaconda.org/conda-forge/win-64/rapidfuzz-3.14.5-py310h73ae2b4_0.conda
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+  - python >=3.10,<3.11.0a0
+  - python_abi 3.10.* *_cp310
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: MIT
+  license_family: MIT
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 - conda: https://conda.anaconda.org/conda-forge/win-64/rapidfuzz-3.14.5-py314h13fbf68_0.conda
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@@ -29466,17 +30174,9 @@ packages:
   - vc14_runtime >=14.44.35208
   license: MIT
   license_family: MIT
+  run_exports: {}
   size: 1023176
   timestamp: 1775566016879
-- conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-ha104f34_1.conda
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 - conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-haef9524_2.conda
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     - re2
   size: 222198
   timestamp: 1781312702688
-- conda: https://conda.anaconda.org/conda-forge/win-64/rpds-py-2026.5.1-py314hc980628_0.conda
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-  md5: f56d0b6c5af9a03320a5884efa3c5764
+- conda: https://conda.anaconda.org/conda-forge/win-64/rpds-py-0.30.0-py310h034784e_0.conda
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+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - python_abi 3.10.* *_cp310
+  license: MIT
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 - conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.26100.0-h57928b3_0.conda
   sha256: 3005729dce6f3d3f5ec91dfc49fc75a0095f9cd23bab49efb899657297ac91a5
   md5: 71b24316859acd00bdb8b38f5e2ce328
@@ -29813,57 +30602,64 @@ packages:
   - vc14_runtime >=14.29.30037
   - vs2015_runtime >=14.29.30037
   license: LicenseRef-MicrosoftWindowsSDK10
+  run_exports: {}
   size: 694692
   timestamp: 1756385147981
-- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.5-h1b7c187_38.conda
-  sha256: 61b68e5a4fc71a17f8d64b12e013a2f971ad980bd08e9c389d5e68efe1a67de0
-  md5: 774568633f3b26d7a4a6dd4f9ea6d3e1
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.5-ha367084_41.conda
+  sha256: 35444c55a92e2f7f7ba26bc70f81e56e52344f7d064c0fd4b40a46a58517b79c
+  md5: aa805b5522c2a98fa286e551a1f48546
   depends:
-  - vc14_runtime >=14.51.36231
+  - vc14_runtime >=14.51.36247
   track_features:
   - vc14
   license: BSD-3-Clause
   license_family: BSD
-  size: 20187
-  timestamp: 1780005880049
-- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.51.36231-h1b9f54f_38.conda
-  sha256: 957c7c65583c7107a5e76f39756c6361fcb7b0dc101ac7c0aea86e7ca09fe49c
-  md5: 2cdcd8ea1010920911bb2eacb4c61227
+  run_exports: {}
+  size: 21383
+  timestamp: 1785359368566
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.51.36247-habf1de7_41.conda
+  sha256: 4e4cb599cdc41bf2109d1464c127b5bcbddf548ce3e322e612afb691338b48f8
+  md5: ac5333bb3d429361f23adf704cc49a78
   depends:
   - ucrt >=10.0.20348.0
-  - vcomp14 14.51.36231 h1b9f54f_38
+  - vcomp14 14.51.36247 habf1de7_41
   constrains:
-  - vs2015_runtime 14.51.36231.* *_38
+  - vs2015_runtime 14.51.36247.* *_41
   license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime
   license_family: Proprietary
-  size: 740997
-  timestamp: 1780005875753
-- conda: https://conda.anaconda.org/conda-forge/win-64/vcomp14-14.51.36231-h1b9f54f_38.conda
-  sha256: c645fdc1f0f47718431d973386e946754a10200e7ba2c32032560913a970cacd
-  md5: 63ee70d69d7540e821940dac5d4d9ba2
+  run_exports: {}
+  size: 767955
+  timestamp: 1785359364369
+- conda: https://conda.anaconda.org/conda-forge/win-64/vcomp14-14.51.36247-habf1de7_41.conda
+  sha256: 731e043390c9457299484d39e427221fc868a9249540a498a5a4f6456c7744d1
+  md5: 350bb67a5c8e5f1c53347ac544ab6600
   depends:
   - ucrt >=10.0.20348.0
   constrains:
-  - vs2015_runtime 14.51.36231.* *_38
+  - vs2015_runtime 14.51.36247.* *_41
   license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime
   license_family: Proprietary
-  size: 123561
-  timestamp: 1780005858779
-- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.51.36231-h84cd919_38.conda
-  sha256: c4f38268563dc6b8322b0191481e5d20002fc6e37b076c15e0b955a553c8b4a0
-  md5: 6033851d921b6c33f1c3018205fcba6a
+  run_exports:
+    strong:
+    - vcomp14 >=14.51.36247
+  size: 155910
+  timestamp: 1785359349999
+- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.51.36247-h633cb9f_41.conda
+  sha256: ee0ae67c96b80b9fdb50c3f617c6329dbcdf1562d0267604f6c0e24f494431d6
+  md5: 21504569fa34b5d3a67760219e3ff1cc
   depends:
-  - vc14_runtime >=14.51.36231
+  - vc14_runtime >=14.51.36247
   license: BSD-3-Clause
   license_family: BSD
   run_exports: {}
-  size: 20170
-  timestamp: 1780005880423
+  size: 21386
+  timestamp: 1785359368990
 - conda: https://conda.anaconda.org/conda-forge/win-64/winpty-0.4.3-4.tar.bz2
   sha256: 9df10c5b607dd30e05ba08cbd940009305c75db242476f4e845ea06008b0a283
   md5: 1cee351bf20b830d991dbe0bc8cd7dfe
   license: MIT
   license_family: MIT
+  run_exports: {}
   size: 1176306
 - conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h6a83c73_3.conda
   sha256: 80ee68c1e7683a35295232ea79bcc87279d31ffeda04a1665efdb43cbd50a309
@@ -29877,6 +30673,9 @@ packages:
   - ucrt >=10.0.20348.0
   license: MIT
   license_family: MIT
+  run_exports:
+    weak:
+    - yaml >=0.2.5,<0.3.0a0
   size: 63944
   timestamp: 1753484092156
 - conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h3a581c9_11.conda
@@ -29890,24 +30689,27 @@ packages:
   - krb5 >=1.22.2,<1.23.0a0
   license: MPL-2.0
   license_family: MOZILLA
+  run_exports:
+    weak:
+    - zeromq >=4.3.5,<4.3.6.0a0
   size: 265717
   timestamp: 1779124031378
-- conda: https://conda.anaconda.org/conda-forge/win-64/zizmor-1.25.2-h18a1a76_0.conda
-  sha256: 61a793d1666b9687756874b18018496680135740ed70e968dcf3d8afe92090b4
-  md5: a372a2f8ae0198e10d6f8b9ea7bd1427
+- conda: https://conda.anaconda.org/conda-forge/win-64/zizmor-1.29.0-h18a1a76_0.conda
+  sha256: 0ef5635612c25e1be879420c35fafe78370bd23fb6bcea984fa916b62aff0876
+  md5: 2983d1a01bf141341d0789c21b6a1df7
   depends:
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
   license: MIT
-  license_family: MIT
-  size: 7232434
-  timestamp: 1778922195362
-- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.2-hfd05255_2.conda
-  sha256: ef408f85f664a4b9c9dac3cb2e36154d9baa15a88984ea800e11060e0f2394a1
-  md5: 5187ecf958be3c39110fe691cbd6873e
+  run_exports: {}
+  size: 7380733
+  timestamp: 1785627207412
+- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.2-hfd05255_3.conda
+  sha256: 5a0b55df66ff07b4342e04967cef69f8bf348f6a0fb1cc1eca741c7b015dfe77
+  md5: 945092a9bc1d0f250f7d5ecf51ecd471
   depends:
-  - libzlib 1.3.2 hfd05255_2
+  - libzlib 1.3.2 hfd05255_3
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
@@ -29916,8 +30718,8 @@ packages:
   run_exports:
     weak:
     - libzlib >=1.3.2,<2.0a0
-  size: 850351
-  timestamp: 1774072891049
+  size: 851288
+  timestamp: 1785276674755
 - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
   sha256: 368d8628424966fd8f9c8018326a9c779e06913dd39e646cf331226acc90e5b2
   md5: 053b84beec00b71ea8ff7a4f84b55207
diff --git a/pixi.toml b/pixi.toml
index 65d5995..3e43a23 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -122,6 +122,7 @@ install-polars-nightly = "pip install --pre --no-deps --upgrade --only-binary :a
 [environments]
 build = ["build"]
 default = ["dev", "lint", "optionals", "polars-latest", "py314", "test"]
+default-py310 = ["dev", "lint", "optionals", "polars-latest", "py310", "test"]
 docs = ["docs"]
 lint = { features = ["lint"], no-default-feature = true }
 nightly = ["nightly", "optionals", "test"]
diff --git a/skills/SKILL.md b/skills/SKILL.md
index fd181fe..03e2d91 100644
--- a/skills/SKILL.md
+++ b/skills/SKILL.md
@@ -186,15 +186,19 @@ from polars.testing import assert_frame_equal
 
 
 def test_grouped_sum():
-    df = pl.DataFrame({
-        "col1": [1, 2, 3],
-        "col2": ["a", "a", "b"],
-    }).pipe(MyInputSchema.validate, cast=True)
-
-    expected = pl.DataFrame({
-        "col1": ["a", "b"],
-        "col2": [3, 3],
-    })
+    df = pl.DataFrame(
+        {
+            "col1": [1, 2, 3],
+            "col2": ["a", "a", "b"],
+        }
+    ).pipe(MyInputSchema.validate, cast=True)
+
+    expected = pl.DataFrame(
+        {
+            "col1": ["a", "b"],
+            "col2": [3, 3],
+        }
+    )
 
     result = my_code(df)