scikit-learn
diff --git a/‎sklearn/compose/_column_transformer.py
Lines changed: 2 additions & 3 deletions b/‎sklearn/compose/_column_transformer.py
Lines changed: 2 additions & 3 deletions
diff --git a/‎sklearn/compose/tests/test_column_transformer.py
Lines changed: 18 additions & 7 deletions b/‎sklearn/compose/tests/test_column_transformer.py
Lines changed: 18 additions & 7 deletions
diff --git a/‎sklearn/utils/validation.py
Lines changed: 4 additions & 1 deletion b/‎sklearn/utils/validation.py
Lines changed: 4 additions & 1 deletion
@@ -16,8 +16,7 @@
 from ..base import clone, TransformerMixin
 from ..utils import Parallel, delayed
 from ..externals import six
-from ..pipeline import (
-    _fit_one_transformer, _fit_transform_one, _transform_one, _name_estimators)
+from ..pipeline import _fit_transform_one, _transform_one, _name_estimators
 from ..preprocessing import FunctionTransformer
 from ..utils import Bunch
 from ..utils.metaestimators import _BaseComposition
@@ -517,7 +516,7 @@ def _check_X(X):
     """Use check_array only on lists and other non-array-likes / sparse"""
     if hasattr(X, '__array__') or sparse.issparse(X):
         return X
-    return check_array(X)
+    return check_array(X, force_all_finite='allow-nan', dtype=np.object)
 
 
 def _check_key_type(key, superclass):
 
@@ -279,13 +279,24 @@ def test_column_transformer_sparse_array():
 
 
 def test_column_transformer_list():
-    X_list = [[1, 2, 3]]
-    X_res = np.array([[0, 0, 0]])
-
-    ct = ColumnTransformer([('trans1', StandardScaler(), [0, 1, 2])])
-
-    assert_array_equal(ct.fit_transform(X_list), X_res)
-    assert_array_equal(ct.fit(X_list).transform(X_list), X_res)
+    X_list = [
+        [1, float('nan'), 'a'],
+        [0, 0, 'b']
+    ]
+    expected_result = np.array([
+        [1, float('nan'), 1, 0],
+        [-1, 0, 0, 1],
+    ])
+
+    ct = ColumnTransformer([
+        ('numerical', StandardScaler(), [0, 1]),
+        ('categorical', OneHotEncoder(), [2]),
+    ])
+
+    with pytest.warns(None) as record:
+        assert_array_equal(ct.fit_transform(X_list), expected_result)
+        assert_array_equal(ct.fit(X_list).transform(X_list), expected_result)
+    assert [w.message for w in record.list] == []
 
 
 def test_column_transformer_sparse_stacking():
 
@@ -438,6 +438,7 @@ def check_array(array, accept_sparse=False, accept_large_sparse=True,
     warn_on_dtype : boolean (default=False)
         Raise DataConversionWarning if the dtype of the input data structure
         does not match the requested dtype, causing a memory copy.
+        This warning is not raised if the original data has an object dtype.
 
     estimator : str or estimator instance (default=None)
         If passed, include the name of the estimator in warning messages.
@@ -584,7 +585,9 @@ def check_array(array, accept_sparse=False, accept_large_sparse=True,
                              % (n_features, shape_repr, ensure_min_features,
                                 context))
 
-    if warn_on_dtype and dtype_orig is not None and array.dtype != dtype_orig:
+    if (warn_on_dtype and dtype_orig is not None
+            and dtype_orig.kind != 'O'
+            and array.dtype != dtype_orig):
         msg = ("Data with input dtype %s was converted to %s%s."
                % (dtype_orig, array.dtype, context))
         warnings.warn(msg, DataConversionWarning)