Make PandasTypeSelector selector dataframe-agnostic

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

docs/contribution.md

@@ -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
 

pyproject.toml

@@ -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'",

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 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

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

sklego/preprocessing/pandastransformers.py

@@ -1,11 +1,60 @@
+from __future__ import annotations
+
+import warnings
+
 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 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
+    if selection == "string":
+        return dtype == nw.String
+    if selection == "category":
+        return dtype == nw.Categorical
+    msg = f"Expected {{'number', 'bool', 'string', 'category'}}, got: {selection}, which is not (yet!) supported."
+    raise ValueError(msg)
+
+
+def _nw_select_dtypes(df, include: str | list[str], exclude: str | list[str]):
+    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]
+
+    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)
+
+
 class ColumnDropper(BaseEstimator, TransformerMixin):
     """The `ColumnDropper` transformer allows dropping specific columns from a DataFrame by name.
     Can be useful in a sklearn Pipeline.
@@ -172,13 +221,21 @@ 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 pandas 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.
 
-    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'
+
+    !!! info "New in version 0.9.0"
 
     Parameters
     ----------
@@ -191,7 +248,7 @@ 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
@@ -202,7 +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"],
@@ -211,14 +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
@@ -235,26 +292,30 @@ 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
         -------
-        self : PandasTypeSelector
+        self : TypeSelector
             The fitted transformer.
 
         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")
@@ -266,48 +327,66 @@ 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("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)
+            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 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):