From 2697b2d2c68d2a4eae71c4c9742dbd9a7817fc14 Mon Sep 17 00:00:00 2001
From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com>
Date: Tue, 14 May 2024 14:17:43 +0100
Subject: [PATCH 1/9] make pandas dtype selector df-agnostic

---
 sklego/preprocessing/pandastransformers.py    | 123 +++++++++++++-----
 .../test_pandastypeselector.py                |  28 ++--
 2 files changed, 111 insertions(+), 40 deletions(-)

diff --git a/sklego/preprocessing/pandastransformers.py b/sklego/preprocessing/pandastransformers.py
index 18af9b279..da19cb2c0 100644
--- a/sklego/preprocessing/pandastransformers.py
+++ b/sklego/preprocessing/pandastransformers.py
@@ -1,11 +1,61 @@
 import narwhals as nw
-import pandas as pd
+from narwhals.dependencies import get_pandas
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.utils.validation import check_is_fitted
 
 from sklego.common import as_list
 
 
+def _nw_match_dtype(dtype, selection):
+    if selection == "number":
+        return any(
+            dtype == x
+            for x in (
+                nw.Int64,
+                nw.Int32,
+                nw.Int16,
+                nw.Int8,
+                nw.UInt64,
+                nw.UInt32,
+                nw.UInt16,
+                nw.UInt8,
+                nw.Float64,
+                nw.Float32,
+            )
+        )
+    if selection == "bool":
+        return dtype == nw.Boolean
+    if selection == "string":
+        return dtype == nw.String
+    if selection == "category":
+        return dtype == nw.Categorical
+    msg = f"Expected {{'number', 'bool', 'string', 'category'}}, got: {selection}"
+    raise ValueError(msg)
+
+
+def _nw_select_dtypes(df, include: str | list[str], exclude: str | list[str]):
+    feature_names = []
+    if isinstance(include, str):
+        include = [include]
+    if isinstance(exclude, str):
+        exclude = [exclude]
+    for name, dtype in df.schema.items():
+        if include and exclude:
+            if any(_nw_match_dtype(dtype, _include) for _include in include) and not any(
+                _nw_match_dtype(dtype, _exclude) for _exclude in exclude
+            ):
+                feature_names.append(name)
+        elif include:
+            if any(_nw_match_dtype(dtype, _include) for _include in include):
+                feature_names.append(name)
+        elif exclude:
+            if not any(_nw_match_dtype(dtype, _exclude) for _exclude in exclude):
+                feature_names.append(name)
+        else:
+            raise ValueError("Must provide at least one of `include` or `exclude`")
+    return df.select(feature_names)
+
+
 class ColumnDropper(BaseEstimator, TransformerMixin):
     """The `ColumnDropper` transformer allows dropping specific columns from a DataFrame by name.
     Can be useful in a sklearn Pipeline.
@@ -173,12 +223,18 @@ def _check_column_names(self, X):
 
 
 class PandasTypeSelector(BaseEstimator, TransformerMixin):
-    """The `PandasTypeSelector` transformer allows to select columns in a pandas DataFrame based on their type.
+    """The `PandasTypeSelector` transformer allows to select columns in a DataFrame based on their type.
     Can be useful in a sklearn Pipeline.
 
-    It uses
-    [pandas.DataFrame.select_dtypes](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.select_dtypes.html)
-    method.
+    - For pandas, it uses
+      [pandas.DataFrame.select_dtypes](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.select_dtypes.html)
+      method.
+    - For non-pandas dataframes (e.g. Polars), the following  inputs are allowed:
+
+      - 'number'
+      - 'string'
+      - 'bool'
+      - 'category'
 
     Parameters
     ----------
@@ -191,7 +247,7 @@ class PandasTypeSelector(BaseEstimator, TransformerMixin):
     ----------
     feature_names_ : list[str]
         The names of the features to keep during transform.
-    X_dtypes_ : pd.Series
+    X_dtypes_ : Series | dict[str, DType]
         The dtypes of the columns in the input DataFrame.
 
     !!! warning
@@ -235,9 +291,9 @@ def fit(self, X, y=None):
 
         Parameters
         ----------
-        X : pd.DataFrame
+        X : DataFrame
             The data on which we apply the column selection.
-        y : pd.Series, default=None
+        y : Series, default=None
             Ignored, present for compatibility.
 
         Returns
@@ -248,13 +304,17 @@ def fit(self, X, y=None):
         Raises
         ------
         TypeError
-            If `X` is not a `pd.DataFrame` object.
+            If `X` is not a supported DataFrame.
         ValueError
             If provided type(s) results in empty dataframe.
         """
-        self._check_X_for_type(X)
-        self.X_dtypes_ = X.dtypes
-        self.feature_names_ = list(X.select_dtypes(include=self.include, exclude=self.exclude).columns)
+        if (pd := get_pandas()) is not None and isinstance(X, pd.DataFrame):
+            self.X_dtypes_ = X.dtypes
+            self.feature_names_ = list(X.select_dtypes(include=self.include, exclude=self.exclude).columns)
+        else:
+            X = nw.from_native(X)
+            self.X_dtypes_ = X.schema
+            self.feature_names_ = _nw_select_dtypes(X, include=self.include, exclude=self.exclude).columns
 
         if len(self.feature_names_) == 0:
             raise ValueError("Provided type(s) results in empty dataframe")
@@ -266,49 +326,52 @@ def get_feature_names(self, *args, **kwargs):
         return self.feature_names_
 
     def transform(self, X):
-        """Returns a pandas DataFrame with columns (de)selected based on their dtype.
+        """Returns a DataFrame with columns (de)selected based on their dtype.
 
         Parameters
         ----------
-        X : pd.DataFrame
+        X : DataFrame
             The data to select dtype for.
 
         Returns
         -------
-        pd.DataFrame
+        DataFrame
             The data with the specified columns selected.
 
         Raises
         ------
         TypeError
-            If `X` is not a `pd.DataFrame` object.
+            If `X` is not a supported DataFrame.
         ValueError
             If column dtypes were not equal during fit and transform.
         """
         check_is_fitted(self, ["X_dtypes_", "feature_names_"])
 
-        try:
-            if (self.X_dtypes_ != X.dtypes).any():
+        if (pd := get_pandas()) is not None and isinstance(X, pd.DataFrame):
+            try:
+                if (self.X_dtypes_ != X.dtypes).any():
+                    raise ValueError(
+                        f"Column dtypes were not equal during fit and transform. Fit types: \n"
+                        f"{self.X_dtypes_}\n"
+                        f"transform: \n"
+                        f"{X.dtypes}"
+                    )
+            except ValueError as e:
+                raise ValueError("Columns were not equal during fit and transform") from e
+            transformed_df = X.select_dtypes(include=self.include, exclude=self.exclude)
+        else:
+            X = nw.from_native(X)
+            if self.X_dtypes_ != X.schema:
                 raise ValueError(
                     f"Column dtypes were not equal during fit and transform. Fit types: \n"
                     f"{self.X_dtypes_}\n"
                     f"transform: \n"
-                    f"{X.dtypes}"
+                    f"{X.schema}"
                 )
-        except ValueError as e:
-            raise ValueError("Columns were not equal during fit and transform") from e
-
-        self._check_X_for_type(X)
-        transformed_df = X.select_dtypes(include=self.include, exclude=self.exclude)
+            transformed_df = _nw_select_dtypes(X, include=self.include, exclude=self.exclude)
 
         return transformed_df
 
-    @staticmethod
-    def _check_X_for_type(X):
-        """Checks if input of the Selector is of the required dtype"""
-        if not isinstance(X, pd.DataFrame):
-            raise TypeError("Provided variable X is not of type pandas.DataFrame")
-
 
 class ColumnSelector(BaseEstimator, TransformerMixin):
     """The `ColumnSelector` transformer allows selecting specific columns from a DataFrame by name.
diff --git a/tests/test_preprocessing/test_pandastypeselector.py b/tests/test_preprocessing/test_pandastypeselector.py
index 1ec858d50..1c1ce9a2b 100644
--- a/tests/test_preprocessing/test_pandastypeselector.py
+++ b/tests/test_preprocessing/test_pandastypeselector.py
@@ -2,6 +2,7 @@
 
 import numpy as np
 import pandas as pd
+import polars as pl
 import pytest
 
 from sklego.preprocessing import PandasTypeSelector
@@ -9,16 +10,18 @@
 
 
 @pytest.mark.parametrize("transformer", [PandasTypeSelector(include=["number"])], ids=id_func)
-def test_len_regression(transformer, random_xy_dataset_regr):
+@pytest.mark.parametrize("frame_func", [pd.DataFrame, pl.DataFrame])
+def test_len_regression(transformer, random_xy_dataset_regr, frame_func):
     X, y = random_xy_dataset_regr
-    X = pd.DataFrame(X)
+    X = frame_func(X)
     assert transformer.fit(X, y).transform(X).shape[0] == X.shape[0]
 
 
 @pytest.mark.parametrize("transformer", [PandasTypeSelector(include=["number"])], ids=id_func)
-def test_len_classification(transformer, random_xy_dataset_clf):
+@pytest.mark.parametrize("frame_func", [pd.DataFrame, pl.DataFrame])
+def test_len_classification(transformer, random_xy_dataset_clf, frame_func):
     X, y = random_xy_dataset_clf
-    X = pd.DataFrame(X)
+    X = frame_func(X)
     assert transformer.fit(X, y).transform(X).shape[0] == X.shape[0]
 
 
@@ -42,21 +45,26 @@ def test_get_params_np(include, exclude):
     assert transformer.get_params() == {"include": include, "exclude": exclude}
 
 
-def test_value_error_differrent_dtyes():
-    fit_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
-    transform_df = pd.DataFrame({"a": [4, 5, 6], "b": ["4", "5", "6"]})
+@pytest.mark.parametrize("frame_func", [pd.DataFrame, pl.DataFrame])
+def test_value_error_differrent_dtyes(frame_func):
+    fit_df = frame_func({"a": [1, 2, 3], "b": [4, 5, 6]})
+    transform_df = frame_func({"a": [4, 5, 6], "b": ["4", "5", "6"]})
     transformer = PandasTypeSelector(exclude=["category"]).fit(fit_df)
 
     with pytest.raises(ValueError):
         transformer.transform(transform_df)
 
 
-def test_get_feature_names():
-    df = pd.DataFrame({"a": [4, 5, 6], "b": ["4", "5", "6"]})
+@pytest.mark.parametrize("frame_func", [pd.DataFrame, pl.DataFrame])
+def test_get_feature_names(frame_func):
+    df = frame_func({"a": [4, 5, 6], "b": ["4", "5", "6"]})
     transformer_number = PandasTypeSelector(include="number").fit(df)
     assert transformer_number.get_feature_names() == ["a"]
 
-    transformer_number = PandasTypeSelector(include="object").fit(df)
+    if frame_func is pd.DataFrame:
+        transformer_number = PandasTypeSelector(include="object").fit(df)
+    else:
+        transformer_number = PandasTypeSelector(include="string").fit(df)
     assert transformer_number.get_feature_names() == ["b"]
 
 

From d2e703cb0a330ab82309b84ab19b53c0915a40df Mon Sep 17 00:00:00 2001
From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com>
Date: Tue, 14 May 2024 15:37:01 +0100
Subject: [PATCH 2/9] bump version

---
 pyproject.toml                             |  2 +-
 sklego/preprocessing/pandastransformers.py | 27 ++++++++++------------
 2 files changed, 13 insertions(+), 16 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index b3f2354e1..1102f82a4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scikit-lego"
-version = "0.8.2"
+version = "0.8.13"
 description="A collection of lego bricks for scikit-learn pipelines"
 
 license = {file = "LICENSE"}
diff --git a/sklego/preprocessing/pandastransformers.py b/sklego/preprocessing/pandastransformers.py
index da19cb2c0..bfd382857 100644
--- a/sklego/preprocessing/pandastransformers.py
+++ b/sklego/preprocessing/pandastransformers.py
@@ -8,20 +8,17 @@
 
 def _nw_match_dtype(dtype, selection):
     if selection == "number":
-        return any(
-            dtype == x
-            for x in (
-                nw.Int64,
-                nw.Int32,
-                nw.Int16,
-                nw.Int8,
-                nw.UInt64,
-                nw.UInt32,
-                nw.UInt16,
-                nw.UInt8,
-                nw.Float64,
-                nw.Float32,
-            )
+        return dtype in (
+            nw.Int64,
+            nw.Int32,
+            nw.Int16,
+            nw.Int8,
+            nw.UInt64,
+            nw.UInt32,
+            nw.UInt16,
+            nw.UInt8,
+            nw.Float64,
+            nw.Float32,
         )
     if selection == "bool":
         return dtype == nw.Boolean
@@ -29,7 +26,7 @@ def _nw_match_dtype(dtype, selection):
         return dtype == nw.String
     if selection == "category":
         return dtype == nw.Categorical
-    msg = f"Expected {{'number', 'bool', 'string', 'category'}}, got: {selection}"
+    msg = f"Expected {{'number', 'bool', 'string', 'category'}}, got: {selection}, which is not (yet!) supported."
     raise ValueError(msg)
 
 

From d96e427a45f452215ed49c0a9b30752e49e6e82c Mon Sep 17 00:00:00 2001
From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com>
Date: Tue, 14 May 2024 16:26:20 +0100
Subject: [PATCH 3/9] 3.8 compat

---
 sklego/preprocessing/pandastransformers.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sklego/preprocessing/pandastransformers.py b/sklego/preprocessing/pandastransformers.py
index bfd382857..6c98d9b93 100644
--- a/sklego/preprocessing/pandastransformers.py
+++ b/sklego/preprocessing/pandastransformers.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import narwhals as nw
 from narwhals.dependencies import get_pandas
 from sklearn.base import BaseEstimator, TransformerMixin

From 4f6b1ea3b99bc533051b3c04d30abb4bbf01fac1 Mon Sep 17 00:00:00 2001
From: Marco Edward Gorelli <marcogorelli@protonmail.com>
Date: Wed, 15 May 2024 11:57:07 +0100
Subject: [PATCH 4/9] Update sklego/preprocessing/pandastransformers.py

Co-authored-by: Francesco Bruzzesi <42817048+FBruzzesi@users.noreply.github.com>
---
 sklego/preprocessing/pandastransformers.py | 28 ++++++++++------------
 1 file changed, 13 insertions(+), 15 deletions(-)

diff --git a/sklego/preprocessing/pandastransformers.py b/sklego/preprocessing/pandastransformers.py
index 6c98d9b93..dd1449c09 100644
--- a/sklego/preprocessing/pandastransformers.py
+++ b/sklego/preprocessing/pandastransformers.py
@@ -33,25 +33,23 @@ def _nw_match_dtype(dtype, selection):
 
 
 def _nw_select_dtypes(df, include: str | list[str], exclude: str | list[str]):
-    feature_names = []
+    if not include and not exclude:
+        raise ValueError("Must provide at least one of `include` or `exclude`")
+
     if isinstance(include, str):
         include = [include]
     if isinstance(exclude, str):
         exclude = [exclude]
-    for name, dtype in df.schema.items():
-        if include and exclude:
-            if any(_nw_match_dtype(dtype, _include) for _include in include) and not any(
-                _nw_match_dtype(dtype, _exclude) for _exclude in exclude
-            ):
-                feature_names.append(name)
-        elif include:
-            if any(_nw_match_dtype(dtype, _include) for _include in include):
-                feature_names.append(name)
-        elif exclude:
-            if not any(_nw_match_dtype(dtype, _exclude) for _exclude in exclude):
-                feature_names.append(name)
-        else:
-            raise ValueError("Must provide at least one of `include` or `exclude`")
+
+    include = include or ["string", "number", "bool", "category"]
+    exclude = exclude or []
+
+    feature_names = [
+        name
+        for name, dtype in df.schema.items()
+        if any(_nw_match_dtype(dtype, _include) for _include in include)
+        and not any(_nw_match_dtype(dtype, _exclude) for _exclude in exclude)
+    ]
     return df.select(feature_names)
 
 

From 243f0a5c4a9530ca29f9bff5338bfdd90b95b33f Mon Sep 17 00:00:00 2001
From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com>
Date: Wed, 15 May 2024 11:58:52 +0100
Subject: [PATCH 5/9] fixup pyproject.toml

---
 pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 1102f82a4..1f1a7a2a5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scikit-lego"
-version = "0.8.13"
+version = "0.8.2"
 description="A collection of lego bricks for scikit-learn pipelines"
 
 license = {file = "LICENSE"}
@@ -20,7 +20,7 @@ maintainers = [
 ]
 
 dependencies = [
-    "narwhals>=0.8.12",
+    "narwhals>=0.8.13",
     "pandas>=1.1.5",
     "scikit-learn>=1.0",
     "importlib-metadata >= 1.0; python_version < '3.8'",

From a5334cc9a9b934392e4c3a4c2d77e01d87745acd Mon Sep 17 00:00:00 2001
From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com>
Date: Wed, 15 May 2024 12:03:07 +0100
Subject: [PATCH 6/9] unify (and test!) error message

---
 sklego/preprocessing/pandastransformers.py          | 2 +-
 tests/test_preprocessing/test_pandastypeselector.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/sklego/preprocessing/pandastransformers.py b/sklego/preprocessing/pandastransformers.py
index dd1449c09..5fd240c33 100644
--- a/sklego/preprocessing/pandastransformers.py
+++ b/sklego/preprocessing/pandastransformers.py
@@ -354,7 +354,7 @@ def transform(self, X):
                         f"{X.dtypes}"
                     )
             except ValueError as e:
-                raise ValueError("Columns were not equal during fit and transform") from e
+                raise ValueError("Column dtypes were not equal during fit and transform") from e
             transformed_df = X.select_dtypes(include=self.include, exclude=self.exclude)
         else:
             X = nw.from_native(X)
diff --git a/tests/test_preprocessing/test_pandastypeselector.py b/tests/test_preprocessing/test_pandastypeselector.py
index 1c1ce9a2b..fa314d21c 100644
--- a/tests/test_preprocessing/test_pandastypeselector.py
+++ b/tests/test_preprocessing/test_pandastypeselector.py
@@ -82,7 +82,7 @@ def test_value_error_inequal(random_xy_dataset_regr):
     X, y = random_xy_dataset_regr
     X = pd.DataFrame(X)
     if X.shape[0] > 0:
-        with pytest.raises(ValueError):
+        with pytest.raises(ValueError, match="Column dtypes were not equal during fit and transform"):
             transformer.fit(X)
             # Remove column to create error
             transformer.transform(X.iloc[:, :-1])

From 3ad9a106fcac8682c64b36761cf4baa41addc51d Mon Sep 17 00:00:00 2001
From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com>
Date: Wed, 15 May 2024 13:18:18 +0100
Subject: [PATCH 7/9] deprecate

---
 docs/api/preprocessing.md                     |  5 +++
 docs/contribution.md                          |  4 +--
 readme.md                                     |  2 +-
 sklego/preprocessing/__init__.py              |  3 +-
 sklego/preprocessing/pandastransformers.py    | 33 +++++++++++++++----
 .../test_pandastypeselector.py                | 30 ++++++++++-------
 6 files changed, 55 insertions(+), 22 deletions(-)

diff --git a/docs/api/preprocessing.md b/docs/api/preprocessing.md
index 66c510160..b27c971cb 100644
--- a/docs/api/preprocessing.md
+++ b/docs/api/preprocessing.md
@@ -64,3 +64,8 @@
     options:
         show_root_full_path: true
         show_root_heading: true
+
+:::sklego.preprocessing.pandastransformers.TypeSelector
+    options:
+        show_root_full_path: true
+        show_root_heading: true
diff --git a/docs/contribution.md b/docs/contribution.md
index 8ef139619..3dcb1d856 100644
--- a/docs/contribution.md
+++ b/docs/contribution.md
@@ -27,7 +27,7 @@ from sklearn.base import BaseEstimator, TransformerMixin, MetaEstimatorMixin
 from sklearn.utils import check_array, check_X_y
 from sklearn.utils.validation import FLOAT_DTYPES, check_random_state, check_is_fitted
 
-class PandasTypeSelector(BaseEstimator, TransformerMixin):
+class TypeSelector(BaseEstimator, TransformerMixin):
     TYPES = {'number', 'category', 'float', 'int', 'object', 'datetime', 'timedelta'}
     """
     Select columns in a pandas dataframe based on their dtype
@@ -174,7 +174,7 @@ When a new feature is introduced, it should be documented, and typically there a
 - [x] A user guide in the `docs/user-guide/` folder.
 - [x] A python script in the `docs/_scripts/` folder to generate plots and code snippets (see [next section](#working-with-pymdown-snippets-extension))
 - [x] Relevant static files, such as images, plots, tables and html's, should be saved in the `docs/_static/` folder.
-- [x] Edit the `mkdocs.yaml` file to include the new pages in the navigation. 
+- [x] Edit the `mkdocs.yaml` file to include the new pages in the navigation.
 
 ### Working with pymdown snippets extension
 
diff --git a/readme.md b/readme.md
index fbd570456..a10095cfa 100644
--- a/readme.md
+++ b/readme.md
@@ -120,7 +120,7 @@ Here's a list of features that this library currently offers:
 - `sklego.preprocessing.InformationFilter` transformer that can de-correlate features
 - `sklego.preprocessing.IdentityTransformer` returns the same data, allows for concatenating pipelines
 - `sklego.preprocessing.OrthogonalTransformer` makes all features linearly independent
-- `sklego.preprocessing.PandasTypeSelector` selects columns based on pandas type
+- `sklego.preprocessing.TypeSelector` selects columns based on pandas type
 - `sklego.preprocessing.RandomAdder` adds randomness in training
 - `sklego.preprocessing.RepeatingBasisFunction` repeating feature engineering, useful for timeseries
 - `sklego.preprocessing.DictMapper` assign numeric values on categorical columns
diff --git a/sklego/preprocessing/__init__.py b/sklego/preprocessing/__init__.py
index 644ada48d..bd068a397 100644
--- a/sklego/preprocessing/__init__.py
+++ b/sklego/preprocessing/__init__.py
@@ -10,6 +10,7 @@
     "OrthogonalTransformer",
     "OutlierRemover",
     "PandasTypeSelector",
+    "TypeSelector",
     "RandomAdder",
     "RepeatingBasisFunction",
 ]
@@ -20,7 +21,7 @@
 from sklego.preprocessing.identitytransformer import IdentityTransformer
 from sklego.preprocessing.intervalencoder import IntervalEncoder
 from sklego.preprocessing.outlier_remover import OutlierRemover
-from sklego.preprocessing.pandastransformers import ColumnDropper, ColumnSelector, PandasTypeSelector
+from sklego.preprocessing.pandastransformers import ColumnDropper, ColumnSelector, PandasTypeSelector, TypeSelector
 from sklego.preprocessing.projections import InformationFilter, OrthogonalTransformer
 from sklego.preprocessing.randomadder import RandomAdder
 from sklego.preprocessing.repeatingbasis import RepeatingBasisFunction
diff --git a/sklego/preprocessing/pandastransformers.py b/sklego/preprocessing/pandastransformers.py
index 5fd240c33..cc965dc5f 100644
--- a/sklego/preprocessing/pandastransformers.py
+++ b/sklego/preprocessing/pandastransformers.py
@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import warnings
+
 import narwhals as nw
 from narwhals.dependencies import get_pandas
 from sklearn.base import BaseEstimator, TransformerMixin
@@ -219,20 +221,22 @@ def _check_column_names(self, X):
             raise KeyError(f"{list(non_existent_columns)} column(s) not in DataFrame")
 
 
-class PandasTypeSelector(BaseEstimator, TransformerMixin):
-    """The `PandasTypeSelector` transformer allows to select columns in a DataFrame based on their type.
+class TypeSelector(BaseEstimator, TransformerMixin):
+    """The `TypeSelector` transformer allows to select columns in a DataFrame based on their type.
     Can be useful in a sklearn Pipeline.
 
     - For pandas, it uses
       [pandas.DataFrame.select_dtypes](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.select_dtypes.html)
       method.
-    - For non-pandas dataframes (e.g. Polars), the following  inputs are allowed:
+    - For non-pandas dataframes (e.g. Polars), the following inputs are allowed:
 
       - 'number'
       - 'string'
       - 'bool'
       - 'category'
 
+    !!! info "New in version 0.9.0"
+
     Parameters
     ----------
     include : scalar or list-like
@@ -255,7 +259,7 @@ class PandasTypeSelector(BaseEstimator, TransformerMixin):
     --------
     ```py
     import pandas as pd
-    from sklego.preprocessing import PandasTypeSelector
+    from sklego.preprocessing import TypeSelector
 
     df = pd.DataFrame({
         "name": ["Swen", "Victor", "Alex"],
@@ -264,14 +268,14 @@ class PandasTypeSelector(BaseEstimator, TransformerMixin):
     })
 
     #Excluding single column
-    PandasTypeSelector(exclude="int64").fit_transform(df)
+    TypeSelector(exclude="int64").fit_transform(df)
     #	name	length
     #0	Swen	1.82
     #1	Victor	1.85
     #2	Alex	1.80
 
     #Including multiple columns
-    PandasTypeSelector(include=["int64", "object"]).fit_transform(df)
+    TypeSelector(include=["int64", "object"]).fit_transform(df)
     #	name	shoesize
     #0	Swen	42
     #1	Victor	44
@@ -295,7 +299,7 @@ def fit(self, X, y=None):
 
         Returns
         -------
-        self : PandasTypeSelector
+        self : TypeSelector
             The fitted transformer.
 
         Raises
@@ -370,6 +374,21 @@ def transform(self, X):
         return transformed_df
 
 
+class PandasTypeSelector(TypeSelector):
+    """
+    !!! warning "Deprecated since version 0.9.0, please use TypeSelector instead"
+    """
+
+    def __init__(self, include=None, exclude=None):
+        warnings.warn(
+            "PandasTypeSelector is deprecated and will be removed in a future version. "
+            "Please use `from sklego.preprocessing import TypeSelector` instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        super().__init__(include=include, exclude=exclude)
+
+
 class ColumnSelector(BaseEstimator, TransformerMixin):
     """The `ColumnSelector` transformer allows selecting specific columns from a DataFrame by name.
     Can be useful in a sklearn Pipeline.
diff --git a/tests/test_preprocessing/test_pandastypeselector.py b/tests/test_preprocessing/test_pandastypeselector.py
index fa314d21c..71644dc24 100644
--- a/tests/test_preprocessing/test_pandastypeselector.py
+++ b/tests/test_preprocessing/test_pandastypeselector.py
@@ -5,11 +5,11 @@
 import polars as pl
 import pytest
 
-from sklego.preprocessing import PandasTypeSelector
+from sklego.preprocessing import PandasTypeSelector, TypeSelector
 from tests.conftest import id_func
 
 
-@pytest.mark.parametrize("transformer", [PandasTypeSelector(include=["number"])], ids=id_func)
+@pytest.mark.parametrize("transformer", [TypeSelector(include=["number"])], ids=id_func)
 @pytest.mark.parametrize("frame_func", [pd.DataFrame, pl.DataFrame])
 def test_len_regression(transformer, random_xy_dataset_regr, frame_func):
     X, y = random_xy_dataset_regr
@@ -17,7 +17,7 @@ def test_len_regression(transformer, random_xy_dataset_regr, frame_func):
     assert transformer.fit(X, y).transform(X).shape[0] == X.shape[0]
 
 
-@pytest.mark.parametrize("transformer", [PandasTypeSelector(include=["number"])], ids=id_func)
+@pytest.mark.parametrize("transformer", [TypeSelector(include=["number"])], ids=id_func)
 @pytest.mark.parametrize("frame_func", [pd.DataFrame, pl.DataFrame])
 def test_len_classification(transformer, random_xy_dataset_clf, frame_func):
     X, y = random_xy_dataset_clf
@@ -30,7 +30,7 @@ def test_len_classification(transformer, random_xy_dataset_clf, frame_func):
     [_ for _ in it.combinations(["number", "datetime", "timedelta", "category", "datetimetz", None], 2)],
 )
 def test_get_params_str(include, exclude):
-    transformer = PandasTypeSelector(include=include, exclude=exclude)
+    transformer = TypeSelector(include=include, exclude=exclude)
 
     assert transformer.get_params() == {"include": include, "exclude": exclude}
 
@@ -40,7 +40,7 @@ def test_get_params_str(include, exclude):
     [_ for _ in it.combinations([np.int64, np.float64, np.datetime64, np.timedelta64], 2)],
 )
 def test_get_params_np(include, exclude):
-    transformer = PandasTypeSelector(include=include, exclude=exclude)
+    transformer = TypeSelector(include=include, exclude=exclude)
 
     assert transformer.get_params() == {"include": include, "exclude": exclude}
 
@@ -49,7 +49,7 @@ def test_get_params_np(include, exclude):
 def test_value_error_differrent_dtyes(frame_func):
     fit_df = frame_func({"a": [1, 2, 3], "b": [4, 5, 6]})
     transform_df = frame_func({"a": [4, 5, 6], "b": ["4", "5", "6"]})
-    transformer = PandasTypeSelector(exclude=["category"]).fit(fit_df)
+    transformer = TypeSelector(exclude=["category"]).fit(fit_df)
 
     with pytest.raises(ValueError):
         transformer.transform(transform_df)
@@ -58,18 +58,26 @@ def test_value_error_differrent_dtyes(frame_func):
 @pytest.mark.parametrize("frame_func", [pd.DataFrame, pl.DataFrame])
 def test_get_feature_names(frame_func):
     df = frame_func({"a": [4, 5, 6], "b": ["4", "5", "6"]})
-    transformer_number = PandasTypeSelector(include="number").fit(df)
+    transformer_number = TypeSelector(include="number").fit(df)
     assert transformer_number.get_feature_names() == ["a"]
 
     if frame_func is pd.DataFrame:
-        transformer_number = PandasTypeSelector(include="object").fit(df)
+        transformer_number = TypeSelector(include="object").fit(df)
     else:
-        transformer_number = PandasTypeSelector(include="string").fit(df)
+        transformer_number = TypeSelector(include="string").fit(df)
     assert transformer_number.get_feature_names() == ["b"]
 
 
+@pytest.mark.parametrize("frame_func", [pd.DataFrame, pl.DataFrame])
+def test_get_feature_names_deprecated(frame_func):
+    df = frame_func({"a": [4, 5, 6], "b": ["4", "5", "6"]})
+    with pytest.deprecated_call(match="Please use `from sklego.preprocessing import TypeSelector`"):
+        transformer_number = PandasTypeSelector(include="number").fit(df)
+    assert transformer_number.get_feature_names() == ["a"]
+
+
 def test_value_error_empty(random_xy_dataset_regr):
-    transformer = PandasTypeSelector(exclude=["number"])
+    transformer = TypeSelector(exclude=["number"])
     X, y = random_xy_dataset_regr
     X = pd.DataFrame(X)
 
@@ -78,7 +86,7 @@ def test_value_error_empty(random_xy_dataset_regr):
 
 
 def test_value_error_inequal(random_xy_dataset_regr):
-    transformer = PandasTypeSelector(include=["number"])
+    transformer = TypeSelector(include=["number"])
     X, y = random_xy_dataset_regr
     X = pd.DataFrame(X)
     if X.shape[0] > 0:

From 070e2fecb8510014d1e943cf4a4f55a574459654 Mon Sep 17 00:00:00 2001
From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com>
Date: Wed, 15 May 2024 13:31:35 +0100
Subject: [PATCH 8/9] update readme

---
 readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/readme.md b/readme.md
index a10095cfa..21bfe1eed 100644
--- a/readme.md
+++ b/readme.md
@@ -120,7 +120,7 @@ Here's a list of features that this library currently offers:
 - `sklego.preprocessing.InformationFilter` transformer that can de-correlate features
 - `sklego.preprocessing.IdentityTransformer` returns the same data, allows for concatenating pipelines
 - `sklego.preprocessing.OrthogonalTransformer` makes all features linearly independent
-- `sklego.preprocessing.TypeSelector` selects columns based on pandas type
+- `sklego.preprocessing.TypeSelector` selects columns based on type
 - `sklego.preprocessing.RandomAdder` adds randomness in training
 - `sklego.preprocessing.RepeatingBasisFunction` repeating feature engineering, useful for timeseries
 - `sklego.preprocessing.DictMapper` assign numeric values on categorical columns

From d5f0413abbc4fa19de29e61007b2e9e5ae0f364d Mon Sep 17 00:00:00 2001
From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com>
Date: Wed, 15 May 2024 13:32:40 +0100
Subject: [PATCH 9/9] undo contribution.md change

---
 docs/contribution.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/contribution.md b/docs/contribution.md
index 3dcb1d856..5ead256da 100644
--- a/docs/contribution.md
+++ b/docs/contribution.md
@@ -27,7 +27,7 @@ from sklearn.base import BaseEstimator, TransformerMixin, MetaEstimatorMixin
 from sklearn.utils import check_array, check_X_y
 from sklearn.utils.validation import FLOAT_DTYPES, check_random_state, check_is_fitted
 
-class TypeSelector(BaseEstimator, TransformerMixin):
+class PandasTypeSelector(BaseEstimator, TransformerMixin):
     TYPES = {'number', 'category', 'float', 'int', 'object', 'datetime', 'timedelta'}
     """
     Select columns in a pandas dataframe based on their dtype