new approach

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

modin/core/dataframe/algebra/binary.py

@@ -85,7 +85,6 @@ def maybe_compute_dtypes_common_cast(
         # belong to the intersection, these will be NaN columns in the result
         mismatch_columns = columns_first ^ columns_second
     elif isinstance(second, dict):
-        # TODO: pyarrow backend
         dtypes_second = {
             key: pandas.api.types.pandas_dtype(type(value))
             for key, value in second.items()
@@ -98,7 +97,6 @@ def maybe_compute_dtypes_common_cast(
         mismatch_columns = columns_first.difference(columns_second)
     else:
         if isinstance(second, (list, tuple)):
-            # TODO: pyarrow backend
             second_dtypes_list = (
                 [pandas.api.types.pandas_dtype(type(value)) for value in second]
                 if axis == 1
@@ -107,7 +105,6 @@ def maybe_compute_dtypes_common_cast(
                 else [np.array(second).dtype] * len(dtypes_first)
             )
         elif is_scalar(second) or isinstance(second, np.ndarray):
-            # TODO: pyarrow backend
             try:
                 dtype = getattr(second, "dtype", None) or pandas.api.types.pandas_dtype(
                     type(second)
@@ -133,7 +130,6 @@ def maybe_compute_dtypes_common_cast(
         mismatch_columns = []
 
     # If at least one column doesn't match, the result of the non matching column would be nan.
-    # TODO: pyarrow backend
     nan_dtype = pandas.api.types.pandas_dtype(type(np.nan))
     dtypes = None
     if func is not None:
@@ -249,7 +245,7 @@ def try_compute_new_dtypes(
     infer_dtypes : {"common_cast", "try_sample", "bool", None}, default: None
         How dtypes should be infered (see ``Binary.register`` doc for more info).
     result_dtype : np.dtype, optional
-        NumPy dtype of the result. If not specified it will be inferred from the `infer_dtypes` parameter. Only NumPy?
+        NumPy dtype of the result. If not specified it will be inferred from the `infer_dtypes` parameter.
     axis : int, default: 0
         Axis to perform the binary operation along.
     func : callable(pandas.DataFrame, pandas.DataFrame) -> pandas.DataFrame, optional
@@ -264,19 +260,8 @@ def try_compute_new_dtypes(
 
     try:
         if infer_dtypes == "bool" or is_bool_dtype(result_dtype):
-            # dataframe can contain types of different backends at the same time, for example:
-            # (Pdb) (pandas.DataFrame([[1,2,3], [4,5,6]]).astype({0: "int64[pyarrow]"}) > 4).dtypes
-            # 0    bool[pyarrow]
-            # 1             bool
-            # 2             bool
-            # dtype: object
-            backend = ""
-            if any("pyarrow" in str(x) for x in first.dtypes) or any(
-                "pyarrow" in str(x) for x in second.dtypes
-            ):
-                backend = "[pyarrow]"
             dtypes = maybe_build_dtypes_series(
-                first, second, dtype=pandas.api.types.pandas_dtype(f"bool{backend}")
+                first, second, dtype=pandas.api.types.pandas_dtype(bool)
             )
         elif infer_dtypes == "common_cast":
             dtypes = maybe_compute_dtypes_common_cast(

modin/core/dataframe/algebra/fold.py

@@ -13,21 +13,14 @@
 
 """Module houses builder class for Fold operator."""
 
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Callable
-
 from .operator import Operator
 
-if TYPE_CHECKING:
-    from modin.core.storage_formats.pandas.query_compiler import PandasQueryCompiler
-
 
 class Fold(Operator):
     """Builder class for Fold functions."""
 
     @classmethod
-    def register(cls, fold_function) -> Callable[..., PandasQueryCompiler]:
+    def register(cls, fold_function):
         """
         Build Fold operator that will be performed across rows/columns.
 
@@ -42,9 +35,7 @@ def register(cls, fold_function) -> Callable[..., PandasQueryCompiler]:
             Function that takes query compiler and executes Fold function.
         """
 
-        def caller(
-            query_compiler: PandasQueryCompiler, fold_axis=None, *args, **kwargs
-        ) -> PandasQueryCompiler:
+        def caller(query_compiler, fold_axis=None, *args, **kwargs):
             """
             Execute Fold function against passed query compiler.
 

modin/core/dataframe/algebra/map.py

@@ -13,15 +13,8 @@
 
 """Module houses builder class for Map operator."""
 
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
 from .operator import Operator
 
-if TYPE_CHECKING:
-    from modin.core.storage_formats.pandas.query_compiler import PandasQueryCompiler
-
 
 class Map(Operator):
     """Builder class for Map operator."""
@@ -48,7 +41,7 @@ def register(cls, function, *call_args, **call_kwds):
             Function that takes query compiler and executes map function.
         """
 
-        def caller(query_compiler: PandasQueryCompiler, *args, **kwargs):
+        def caller(query_compiler, *args, **kwargs):
             """Execute Map function against passed query compiler."""
             shape_hint = call_kwds.pop("shape_hint", None) or query_compiler._shape_hint
             return query_compiler.__constructor__(

modin/core/dataframe/algebra/tree_reduce.py

@@ -35,7 +35,7 @@ def register(
         axis : int, optional
             Specifies axis to apply function along.
         compute_dtypes : callable(pandas.Series, *func_args, **func_kwargs) -> np.dtype, optional
-            Callable for computing dtypes. Only NumPy?
+            Callable for computing dtypes.
 
         Returns
         -------