fix

Signed-off-by: Anatoly Myachev <[email protected]>

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

@@ -3418,7 +3418,7 @@ def broadcast_apply_select_indices(
  new_partitions, index=new_index, columns=new_columns
  )
 
- def construct_dtype(dtype: str, backend: Optional[str]):
+ def construct_dtype(self, dtype: str, backend: Optional[str]):
  if backend is None:
  return pandas.api.types.pandas_dtype(dtype)
  elif backend == "pyarrow":

modin/core/storage_formats/base/query_compiler.py

@@ -21,7 +21,7 @@
 
 import abc
 import warnings
-from typing import Callable, Hashable, List, Optional
+from typing import TYPE_CHECKING, Callable, Hashable, List, Optional
 
 import numpy as np
 import pandas
@@ -52,6 +52,10 @@
 
 from . import doc_utils
 
+if TYPE_CHECKING:
+ # TODO: should be ModinDataframe
+ from modin.core.dataframe.pandas.dataframe.dataframe import PandasDataframe
+
 
 def _get_axis(axis):
  """
@@ -126,6 +130,8 @@ class BaseQueryCompiler(
  for a list of requirements for subclassing this object.
  """
 
+ _modin_frame: PandasDataframe
+
  def __wrap_in_qc(self, obj):
  """
  Wrap `obj` in query compiler.
@@ -6747,6 +6753,12 @@ def case_when(self, caselist): # noqa: PR01, RT01, D200
  ]
  return SeriesDefault.register(pandas.Series.case_when)(self, caselist=caselist)
 
+ def construct_dtype(self, dtype: str, backend: Optional[str]):
+ return self._modin_frame.construct_dtype(dtype, backend)
+
+ def get_backend(self) -> str:
+ return self._modin_frame._pandas_backend
+
  def repartition(self, axis=None):
  """
  Repartitioning QueryCompiler objects to get ideal partitions inside.

modin/pandas/dataframe.py

@@ -1622,6 +1622,8 @@ def prod(
  skipna is not False
  and numeric_only is False
  and min_count > len(axis_to_apply)
+ # Type inference is not so simple for pyarrow
+ and self._query_compiler.get_backend() == "default"
  ):
  new_index = self.columns if not axis else self.index
  # >>> pd.DataFrame([1,2,3,4], dtype="int64[pyarrow]").prod(min_count=10)
@@ -1630,7 +1632,6 @@ def prod(
  return Series(
  [np.nan] * len(new_index),
  index=new_index,
- # TODO: pyarrow backend?
  dtype=pandas.api.types.pandas_dtype("float64"),
  )
 
@@ -2151,12 +2152,13 @@ def sum(
  skipna is not False
  and numeric_only is False
  and min_count > len(axis_to_apply)
+ # Type inference is not so simple for pyarrow
+ and self._query_compiler.get_backend() == "default"
  ):
  new_index = self.columns if not axis else self.index
  return Series(
  [np.nan] * len(new_index),
  index=new_index,
- # TODO: pyarrow backend?
  dtype=pandas.api.types.pandas_dtype("float64"),
  )