avoid check_dtype, reduce test cases for periods

python/cudf/cudf/tests/test_dataframe.py

@@ -3442,29 +3442,37 @@ def test_get_numeric_data():
 
 
 @pytest.mark.parametrize("dtype", NUMERIC_TYPES)
-@pytest.mark.parametrize("period", [-1, -5, -10, -20, 0, 1, 5, 10, 20])
+@pytest.mark.parametrize("period", [-15, -1, 0, 1, 15])
 @pytest.mark.parametrize("data_empty", [False, True])
 def test_shift(dtype, period, data_empty):
-
+    # TODO : this function currently tests for series.shift()
+    # but should instead test for dataframe.shift()
     if data_empty:
         data = None
     else:
         if dtype == np.int8:
             # to keep data in range
-            data = gen_rand(dtype, 100000, low=-2, high=2)
+            data = gen_rand(dtype, 10, low=-2, high=2)
         else:
-            data = gen_rand(dtype, 100000)
+            data = gen_rand(dtype, 10)
 
-    gdf = cudf.DataFrame({"a": cudf.Series(data, dtype=dtype)})
-    pdf = pd.DataFrame({"a": pd.Series(data, dtype=dtype)})
+    gs = cudf.DataFrame({"a": cudf.Series(data, dtype=dtype)})
+    ps = pd.DataFrame({"a": pd.Series(data, dtype=dtype)})
 
-    shifted_outcome = gdf.a.shift(period).fillna(0)
-    expected_outcome = pdf.a.shift(period).fillna(0).astype(dtype)
+    shifted_outcome = gs.a.shift(period)
+    expected_outcome = ps.a.shift(period)
 
+    # pandas uses NaNs to signal missing value and force converts the
+    # results columns to float types
     if data_empty:
-        assert_eq(shifted_outcome, expected_outcome, check_index_type=False)
+        assert_eq(
+            shifted_outcome,
+            expected_outcome,
+            check_index_type=False,
+            check_dtype=False,
+        )
     else:
-        assert_eq(shifted_outcome, expected_outcome)
+        assert_eq(shifted_outcome, expected_outcome, check_dtype=False)
 
 
 @pytest.mark.parametrize("dtype", NUMERIC_TYPES)