scikit-learn
diff --git a/‎doc/whats_new/v1.3.rst
Lines changed: 5 additions & 0 deletions b/‎doc/whats_new/v1.3.rst
Lines changed: 5 additions & 0 deletions
diff --git a/‎sklearn/preprocessing/_data.py
Lines changed: 23 additions & 10 deletions b/‎sklearn/preprocessing/_data.py
Lines changed: 23 additions & 10 deletions
diff --git a/‎sklearn/preprocessing/tests/test_data.py
Lines changed: 19 additions & 0 deletions b/‎sklearn/preprocessing/tests/test_data.py
Lines changed: 19 additions & 0 deletions
@@ -640,6 +640,11 @@ Changelog
   using `method="box-cox"` on data with a constant `np.nan` column.
   :pr:`26400` by :user:`Yao Xiao <Charlie-XIAO>`.
 
+- |Fix| :class:`preprocessing.PowerTransformer` with `method="yeo-johnson"` now leaves
+  constant features unchanged instead of transforming with an arbitrary value for
+  the `lambdas_` fitted parameter.
+  :pr:`26566` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
+
 :mod:`sklearn.svm`
 ..................
 
 
@@ -3150,21 +3150,34 @@ def _fit(self, X, y=None, force_transform=False):
         if not self.copy and not force_transform:  # if call from fit()
             X = X.copy()  # force copy so that fit does not change X inplace
 
+        n_samples = X.shape[0]
+        mean = np.mean(X, axis=0, dtype=np.float64)
+        var = np.var(X, axis=0, dtype=np.float64)
+
         optim_function = {
             "box-cox": self._box_cox_optimize,
             "yeo-johnson": self._yeo_johnson_optimize,
         }[self.method]
+
+        transform_function = {
+            "box-cox": boxcox,
+            "yeo-johnson": self._yeo_johnson_transform,
+        }[self.method]
+
         with np.errstate(invalid="ignore"):  # hide NaN warnings
-            self.lambdas_ = np.array([optim_function(col) for col in X.T])
-
-        if self.standardize or force_transform:
-            transform_function = {
-                "box-cox": boxcox,
-                "yeo-johnson": self._yeo_johnson_transform,
-            }[self.method]
-            for i, lmbda in enumerate(self.lambdas_):
-                with np.errstate(invalid="ignore"):  # hide NaN warnings
-                    X[:, i] = transform_function(X[:, i], lmbda)
+            self.lambdas_ = np.empty(X.shape[1], dtype=X.dtype)
+            for i, col in enumerate(X.T):
+                # For yeo-johnson, leave constant features unchanged
+                # lambda=1 corresponds to the identity transformation
+                is_constant_feature = _is_constant_feature(var[i], mean[i], n_samples)
+                if self.method == "yeo-johnson" and is_constant_feature:
+                    self.lambdas_[i] = 1.0
+                    continue
+
+                self.lambdas_[i] = optim_function(col)
+
+                if self.standardize or force_transform:
+                    X[:, i] = transform_function(X[:, i], self.lambdas_[i])
 
         if self.standardize:
             self._scaler = StandardScaler(copy=False)
 
@@ -2669,3 +2669,22 @@ def test_kernel_centerer_feature_names_out():
     names_out = centerer.get_feature_names_out()
     samples_out2 = X_pairwise.shape[1]
     assert_array_equal(names_out, [f"kernelcenterer{i}" for i in range(samples_out2)])
+
+
+@pytest.mark.parametrize("standardize", [True, False])
+def test_power_transformer_constant_feature(standardize):
+    """Check that PowerTransfomer leaves constant features unchanged."""
+    X = [[-2, 0, 2], [-2, 0, 2], [-2, 0, 2]]
+
+    pt = PowerTransformer(method="yeo-johnson", standardize=standardize).fit(X)
+
+    assert_allclose(pt.lambdas_, [1, 1, 1])
+
+    Xft = pt.fit_transform(X)
+    Xt = pt.transform(X)
+
+    for Xt_ in [Xft, Xt]:
+        if standardize:
+            assert_allclose(Xt_, np.zeros_like(X))
+        else:
+            assert_allclose(Xt_, X)