FEAT-modin-project#4909: Properly implement map operator

Signed-off-by: Jonathan Shi <[email protected]>

modin/core/dataframe/algebra/binary.py

@@ -44,7 +44,13 @@ def register(cls, func, join_type="outer", labels="replace"):
         """
 
         def caller(
-            query_compiler, other, broadcast=False, *args, dtypes=None, **kwargs
+            query_compiler,
+            other,
+            broadcast=False,
+            *args,
+            dtypes=None,
+            copy_dtypes=False,
+            **kwargs
         ):
             """
             Apply binary `func` to passed operands.
@@ -61,8 +67,14 @@ def caller(
                 at the query compiler level, so this parameter is a hint that passed from a high level API.
             *args : args,
                 Arguments that will be passed to `func`.
-            dtypes : "copy" or None, default: None
-                Whether to keep old dtypes or infer new dtypes from data.
+            dtypes : pandas.Series or scalar type, optional
+                The data types for the result. This is an optimization
+                because there are functions that always result in a particular data
+                type, and this allows us to avoid (re)computing it.
+                If the argument is a scalar type, then that type is assigned to each result column.
+            copy_dtypes : bool, default False
+                If True, the dtypes of the resulting dataframe are copied from the original,
+                and the ``dtypes`` argument is ignored.
             **kwargs : kwargs,
                 Arguments that will be passed to `func`.
 
@@ -76,21 +88,22 @@ def caller(
                 if broadcast:
                     assert (
                         len(other.columns) == 1
-                    ), "Invalid broadcast argument for `broadcast_apply`, too many columns: {}".format(
+                    ), "Invalid broadcast argument for `map` with broadcast, too many columns: {}".format(
                         len(other.columns)
                     )
                     # Transpose on `axis=1` because we always represent an individual
                     # column or row as a single-column Modin DataFrame
                     if axis == 1:
                         other = other.transpose()
                     return query_compiler.__constructor__(
-                        query_compiler._modin_frame.broadcast_apply(
-                            axis,
+                        query_compiler._modin_frame.map(
                             lambda l, r: func(l, r.squeeze(), *args, **kwargs),
-                            other._modin_frame,
+                            axis=axis,
+                            other=other._modin_frame,
                             join_type=join_type,
                             labels=labels,
                             dtypes=dtypes,
+                            copy_dtypes=copy_dtypes,
                         )
                     )
                 else:
@@ -105,17 +118,19 @@ def caller(
                 # TODO: it's possible to chunk the `other` and broadcast them to partitions
                 # accordingly, in that way we will be able to use more efficient `._modin_frame.map()`
                 if isinstance(other, (dict, list, np.ndarray, pandas.Series)):
-                    new_modin_frame = query_compiler._modin_frame.apply_full_axis(
-                        axis,
+                    new_modin_frame = query_compiler._modin_frame.map_full_axis(
                         lambda df: func(df, other, *args, **kwargs),
+                        axis=axis,
                         new_index=query_compiler.index,
                         new_columns=query_compiler.columns,
                         dtypes=dtypes,
+                        copy_dtypes=copy_dtypes,
                     )
                 else:
                     new_modin_frame = query_compiler._modin_frame.map(
                         lambda df: func(df, other, *args, **kwargs),
                         dtypes=dtypes,
+                        copy_dtypes=copy_dtypes,
                     )
                 return query_compiler.__constructor__(new_modin_frame)
 

modin/core/dataframe/base/dataframe/dataframe.py

@@ -19,6 +19,9 @@
 
 from abc import ABC, abstractmethod
 from typing import List, Hashable, Optional, Callable, Union, Dict
+
+import pandas
+
 from modin.core.dataframe.base.dataframe.utils import Axis, JoinType
 
 
@@ -91,8 +94,10 @@ def filter_by_types(self, types: List[Hashable]) -> "ModinDataframe":
     def map(
         self,
         function: Callable,
+        *,
         axis: Optional[Union[int, Axis]] = None,
-        dtypes: Optional[str] = None,
+        dtypes: Optional[Union[pandas.Series, type]] = None,
+        copy_dtypes: bool = False,
     ) -> "ModinDataframe":
         """
         Apply a user-defined function row-wise if `axis`=0, column-wise if `axis`=1, and cell-wise if `axis` is None.
@@ -102,11 +107,14 @@ def map(
         function : callable(row|col|cell) -> row|col|cell
             The function to map across the dataframe.
         axis : int or modin.core.dataframe.base.utils.Axis, optional
-            The axis to map over.
-        dtypes : str, optional
+            The axis to map over. If None, the map will be performed element-wise.
+        dtypes : pandas.Series or scalar type, optional
             The data types for the result. This is an optimization
             because there are functions that always result in a particular data
             type, and this allows us to avoid (re)computing it.
+        copy_dtypes : bool, default: False
+            If True, the dtypes of the resulting dataframe are copied from the original,
+            and the ``dtypes`` argument is ignored.
 
         Returns
         -------
@@ -258,7 +266,8 @@ def reduce(
         self,
         axis: Union[int, Axis],
         function: Callable,
-        dtypes: Optional[str] = None,
+        *,
+        dtypes: Optional[pandas.Series] = None,
     ) -> "ModinDataframe":
         """
         Perform a user-defined aggregation on the specified axis, where the axis reduces down to a singleton.
@@ -269,7 +278,7 @@ def reduce(
             The axis to perform the reduce over.
         function : callable(row|col) -> single value
             The reduce function to apply to each column.
-        dtypes : str, optional
+        dtypes : pandas.Series, optional
             The data types for the result. This is an optimization
             because there are functions that always result in a particular data
             type, and this allows us to avoid (re)computing it.
@@ -291,7 +300,8 @@ def tree_reduce(
         axis: Union[int, Axis],
         map_func: Callable,
         reduce_func: Optional[Callable] = None,
-        dtypes: Optional[str] = None,
+        *,
+        dtypes: Optional[pandas.Series] = None,
     ) -> "ModinDataframe":
         """
         Perform a user-defined aggregation on the specified axis, where the axis reduces down to a singleton using a tree-reduce computation pattern.

modin/core/dataframe/base/dataframe/utils.py

@@ -19,6 +19,12 @@
 """
 
 from enum import Enum
+import sys
+
+if sys.version_info.minor < 8:
+    from typing_extensions import Literal
+else:
+    from typing import Literal
 
 
 class Axis(Enum):  # noqa: PR01
@@ -36,6 +42,10 @@ class Axis(Enum):  # noqa: PR01
     CELL_WISE = None
 
 
+AxisInt = Literal[0, 1]
+"""Type for the two possible integer values of an axis argument (0 or 1)."""
+
+
 class JoinType(Enum):  # noqa: PR01
     """
     An enum that represents the `join_type` argument provided to the algebra operators.