scikit-learn
diff --git a/‎sklearn/preprocessing/data.py
Lines changed: 3 additions & 3 deletions b/‎sklearn/preprocessing/data.py
Lines changed: 3 additions & 3 deletions
diff --git a/‎sklearn/preprocessing/tests/test_data.py
Lines changed: 14 additions & 0 deletions b/‎sklearn/preprocessing/tests/test_data.py
Lines changed: 14 additions & 0 deletions
@@ -49,12 +49,12 @@
 ]
 
 
-def _as_row_vector(X, copy=False):
+def _as_row_vector(X):
     """In case X is an array with shape[1]=1, interpret it as a row vector.
     It assumed that X is already 2d.
     """
     if X.shape[1] == 1:
-        return np.transpose(np.array(X, copy=copy))
+        return X.transpose()
         return X
 
@@ -498,7 +498,7 @@ def partial_fit(self, X, y=None):
         # Treat 1D arrays as row of a matrix (instead of 1 column)
         X = check_array(X, accept_sparse='csr', copy=self.copy,
                         ensure_2d=True)
-        X = _as_row_vector(X, copy=self.copy)
+        X = _as_row_vector(X)
 
         # First partial_fit
         if not hasattr(self, 'n_samples_seen_'):
 
@@ -28,6 +28,7 @@
 from sklearn.preprocessing.data import _transform_selected
 from sklearn.preprocessing.data import _mean_and_std
 from sklearn.preprocessing.data import _incremental_mean_and_var
+from sklearn.preprocessing.data import _as_row_vector
 from sklearn.preprocessing.data import Binarizer
 from sklearn.preprocessing.data import KernelCenterer
 from sklearn.preprocessing.data import Normalizer
@@ -382,6 +383,19 @@ def test_standard_scaler_partial_fit_numerical_stability():
     # assert) while mean is OK.
 
 
+def test_partial_fit_sparse_input():
+
+    # Check that sparsity is not destroyed
+    X = np.array([[1], [1], [2], [5]])
+    X_csr = sparse.csr_matrix(X)
+    X_csr_T = _as_row_vector(X_csr)
+
+    assert_array_almost_equal(X_csr.todense().T,
+                              X_csr_T.todense())  # same result
+    assert_true(sparse.issparse(X_csr_T))  # still sparse
+    assert_true(sparse.isspmatrix_csc(X_csr_T))  # it changes the sparsity type
+
+
 def test_trasform_with_partial_fit():
     # Check some postconditions after applying partial_fit and transform
     rng = np.random.RandomState(0)