Use cupy.ndarray (without core) (#8114)

`core` is internal to `cupy` and renamed to reflect that in CuPy 9. However that shouldn't be needed to access `ndarray` as it is `import`ed at the top-level. So just use `cupy.ndarray`.

cc @kkraus14 @rjzamora

Authors:
  - https://github.com/jakirkham

Approvers:
  - Keith Kraus (https://github.com/kkraus14)
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: #8114

python/cudf/cudf/core/algorithms.py

@@ -53,7 +53,7 @@ def factorize(values, sort=False, na_sentinel=-1, size_hint=None):
     if size_hint:
         warn("size_hint is not applicable for cudf.factorize")
 
-    return_cupy_array = isinstance(values, cp.core.core.ndarray)
+    return_cupy_array = isinstance(values, cp.ndarray)
 
     values = Series(values)
 

python/cudf/cudf/tests/test_dataframe.py

@@ -6069,7 +6069,7 @@ def test_dataframe_init_1d_list(data, columns):
 def test_dataframe_init_from_arrays_cols(data, cols, index):
 
     gd_data = data
-    if isinstance(data, cupy.core.ndarray):
+    if isinstance(data, cupy.ndarray):
         # pandas can't handle cupy arrays in general
         pd_data = data.get()
 

python/cudf/cudf/tests/test_factorize.py

@@ -20,7 +20,7 @@ def test_factorize_series_obj(ncats, nelem):
 
     uvals, labels = df["cats"].factorize()
     np.testing.assert_array_equal(labels.to_array(), sorted(set(arr)))
-    assert isinstance(uvals, cp.core.core.ndarray)
+    assert isinstance(uvals, cp.ndarray)
     assert isinstance(labels, Index)
 
     encoder = dict((labels[idx], idx) for idx in range(len(labels)))
@@ -39,7 +39,7 @@ def test_factorize_index_obj(ncats, nelem):
 
     uvals, labels = df.index.factorize()
     np.testing.assert_array_equal(labels.values.get(), sorted(set(arr)))
-    assert isinstance(uvals, cp.core.core.ndarray)
+    assert isinstance(uvals, cp.ndarray)
     assert isinstance(labels, Index)
 
     encoder = dict((labels[idx], idx) for idx in range(len(labels)))
@@ -127,15 +127,15 @@ def test_factorize_result_classes():
 
     labels, cats = cudf.factorize(cudf.Series(data))
 
-    assert isinstance(labels, cp.core.core.ndarray)
+    assert isinstance(labels, cp.ndarray)
     assert isinstance(cats, cudf.Index)
 
     labels, cats = cudf.factorize(cudf.Index(data))
 
-    assert isinstance(labels, cp.core.core.ndarray)
+    assert isinstance(labels, cp.ndarray)
     assert isinstance(cats, cudf.Index)
 
     labels, cats = cudf.factorize(cp.array(data))
 
-    assert isinstance(labels, cp.core.core.ndarray)
-    assert isinstance(cats, cp.core.core.ndarray)
+    assert isinstance(labels, cp.ndarray)
+    assert isinstance(cats, cp.ndarray)