FIX-#6781: Use pandas.api.types.pandas_dtype to convert to valid nu…

…mpy and pandas only dtypes (#6788)

Signed-off-by: JignyasAnand <[email protected]>

modin/core/dataframe/pandas/dataframe/dataframe.py

@@ -1960,7 +1960,8 @@ def _compute_tree_reduce_metadata(self, axis, new_parts, dtypes=None):
             dtypes = self.copy_dtypes_cache()
         elif dtypes is not None:
             dtypes = pandas.Series(
-                [np.dtype(dtypes)] * len(new_axes[1]), index=new_axes[1]
+                [pandas.api.types.pandas_dtype(dtypes)] * len(new_axes[1]),
+                index=new_axes[1],
             )
 
         result = self.__constructor__(

modin/pandas/test/dataframe/test_reduce.py

@@ -18,7 +18,7 @@
 from pandas._testing import assert_series_equal
 
 import modin.pandas as pd
-from modin.config import NPartitions, StorageFormat
+from modin.config import Engine, NPartitions, StorageFormat
 from modin.pandas.test.utils import (
     arg_keys,
     assert_dtypes_equal,
@@ -306,6 +306,14 @@ def test_sum(data, axis, skipna, is_transposed):
     df_equals(modin_result, pandas_result)
 
 
+@pytest.mark.skipif(Engine.get() == "Native", reason="Fails on HDK")
+@pytest.mark.parametrize("dtype", ["int64", "Int64"])
+def test_dtype_consistency(dtype):
+    # test for issue #6781
+    res_dtype = pd.DataFrame([1, 2, 3, 4], dtype=dtype).sum().dtype
+    assert res_dtype == pandas.api.types.pandas_dtype(dtype)
+
+
 @pytest.mark.parametrize("fn", ["prod, sum"])
 @pytest.mark.parametrize(
     "numeric_only", bool_arg_values, ids=arg_keys("numeric_only", bool_arg_keys)