diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py
index 501573463829..681f04ae711d 100644
--- a/python/mxnet/_numpy_op_doc.py
+++ b/python/mxnet/_numpy_op_doc.py
@@ -89,8 +89,7 @@ def _np_linalg_slogdet(a):
     -------
     sign : (...) ndarray
         A number representing the sign of the determinant. For a real matrix,
-        this is 1, 0, or -1. For a complex matrix, this is a complex number
-        with absolute value 1 (i.e., it is on the unit circle), or else 0.
+        this is 1, 0, or -1.
     logdet : (...) array_like
         The natural log of the absolute value of the determinant.
 
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index 1a342ed0a171..58c6dbb40f76 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -135,8 +135,8 @@ def tensordot_backward(a, b, axes=2):
                 test_tensordot = TestTensordot(axes)
                 if hybridize:
                     test_tensordot.hybridize()
-                a = rand_ndarray(shape = a_shape, dtype = dtype).as_np_ndarray()
-                b = rand_ndarray(shape = b_shape, dtype = dtype).as_np_ndarray()
+                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+                b = rand_ndarray(shape=b_shape, dtype=dtype).as_np_ndarray()
                 a.attach_grad()
                 b.attach_grad()
 
@@ -161,7 +161,7 @@ def tensordot_backward(a, b, axes=2):
                     b_sym = mx.sym.Variable("b").as_np_ndarray()
                     mx_sym = mx.sym.np.tensordot(a_sym, b_sym, axes).as_nd_ndarray()
                     check_numeric_gradient(mx_sym, [a.as_nd_ndarray(), b.as_nd_ndarray()],
-                      rtol=1e-1, atol=1e-1, dtype = dtype)
+                      rtol=1e-1, atol=1e-1, dtype=dtype)
 
 
 @with_seed()
@@ -236,14 +236,14 @@ def hybrid_forward(self, F, a):
                 test_det = TestDet()
                 if hybridize:
                     test_det.hybridize()
-                a = rand_ndarray(shape = a_shape, dtype = dtype).as_np_ndarray()
+                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
                 a.attach_grad()
 
                 np_out = _np.linalg.det(a.asnumpy())
                 with mx.autograd.record():
                     mx_out = test_det(a)
                 assert mx_out.shape == np_out.shape
-                assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-1, atol = 1e-1)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1, atol=1e-1)
                 mx_out.backward()
 
                 # Test imperative once again
@@ -255,7 +255,7 @@ def hybrid_forward(self, F, a):
                 a_sym = mx.sym.Variable("a").as_np_ndarray()
                 mx_sym = mx.sym.np.linalg.det(a_sym).as_nd_ndarray()
                 check_numeric_gradient(mx_sym, [a.as_nd_ndarray()],
-                    rtol=1e-1, atol=1e-1, dtype = dtype)
+                    rtol=1e-1, atol=1e-1, dtype=dtype)
 
 
 @with_seed()
@@ -282,7 +282,7 @@ def hybrid_forward(self, F, a):
                 test_slogdet = TestSlogdet()
                 if hybridize:
                     test_slogdet.hybridize()
-                a = rand_ndarray(shape = a_shape, dtype = dtype).as_np_ndarray()
+                a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
                 a.attach_grad()
 
                 np_out = _np.linalg.slogdet(a.asnumpy())