BUG: Fix wrong reading sparse matrix

pandas/core/arrays/sparse/array.py

@@ -414,6 +414,10 @@ def from_spmatrix(cls, data):
         if ncol != 1:
             raise ValueError(f"'data' must have a single column, not '{ncol}'")
 
+        # when sparse data has explicit zeros, eliminate them.
+        if data.nnz != data.count_nonzero():
+            data.eliminate_zeros()
+
         # our sparse index classes require that the positions be strictly
         # increasing. So we need to sort loc, and arr accordingly.
         arr = data.data

pandas/tests/arrays/sparse/test_array.py

@@ -192,6 +192,19 @@ def test_from_spmatrix(self, size, format):
         expected = mat.toarray().ravel()
         tm.assert_numpy_array_equal(result, expected)
 
+    @pytest.mark.parametrize("format", ["coo", "csc", "csr"])
+    @td.skip_if_no_scipy
+    def test_from_spmatrix_including_explicit_zero(self, format):
+        import scipy.sparse
+
+        mat = scipy.sparse.random(10, 1, density=0.5, format=format)
+        mat.data[0] = 0
+        result = SparseArray.from_spmatrix(mat)
+
+        result = np.asarray(result)
+        expected = mat.toarray().ravel()
+        tm.assert_numpy_array_equal(result, expected)
+
     @td.skip_if_no_scipy
     def test_from_spmatrix_raises(self):
         import scipy.sparse