preserve dtypes for empty dataframes

Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com>

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

@@ -3321,8 +3321,11 @@ def _extract_partitions(self):
         if self._partitions.size > 0:
             return self._partitions
         else:
+            dtypes = None
+            if self.has_materialized_dtypes:
+                dtypes = self.dtypes
             return self._partition_mgr_cls.create_partition_from_metadata(
-                index=self.index, columns=self.columns
+                index=self.index, columns=self.columns, dtypes=dtypes
             )
 
     @lazy_metadata_decorator(apply_axis="both")

modin/core/dataframe/pandas/partitioning/partition_manager.py

@@ -21,7 +21,7 @@
 import warnings
 from abc import ABC
 from functools import wraps
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
 
 import numpy as np
 import pandas
@@ -183,12 +183,16 @@ def preprocess_func(cls, map_func):
     # END Abstract Methods
 
     @classmethod
-    def create_partition_from_metadata(cls, **metadata):
+    def create_partition_from_metadata(
+        cls, dtypes: Optional[pandas.Series] = None, **metadata
+    ):
         """
         Create NumPy array of partitions that holds an empty dataframe with given metadata.
 
         Parameters
         ----------
+        dtypes : pandas.Series, optional
+            Dtypes that will be used when calling `astype`.
         **metadata : dict
             Metadata that has to be wrapped in a partition.
 
@@ -197,7 +201,7 @@ def create_partition_from_metadata(cls, **metadata):
         np.ndarray
             A NumPy 2D array of a single partition which contains the data.
         """
-        metadata_dataframe = pandas.DataFrame(**metadata)
+        metadata_dataframe = pandas.DataFrame(**metadata).astype(dtypes)
         return np.array([[cls._partition_class.put(metadata_dataframe)]])
 
     @classmethod

modin/tests/pandas/dataframe/test_join_sort.py

@@ -186,7 +186,7 @@ def test_join_empty(how):
     data = np.random.randint(0, 100, size=(64, 64))
     eval_general(
         *create_test_dfs(data),
-        lambda df: df.join(df.iloc[:0], how=how, lsuffix="_caller"),
+        lambda df: df.join(df.iloc[:0], on=1, how=how, lsuffix="_caller"),
     )