PERF-#6762: Carry dtypes information in lazy indices

modin/core/dataframe/algebra/groupby.py

@@ -15,6 +15,7 @@
 
 import pandas
 
+from modin.core.dataframe.pandas.metadata import ModinIndex
 from modin.error_message import ErrorMessage
 from modin.utils import MODIN_UNNAMED_SERIES_LABEL, hashable
 
@@ -407,8 +408,26 @@ def caller(
         # Otherwise `by` was already bound to the Map function in `build_map_reduce_functions`.
         broadcastable_by = getattr(by, "_modin_frame", None)
         apply_indices = list(map_func.keys()) if isinstance(map_func, dict) else None
+        if (
+            broadcastable_by is not None
+            and groupby_kwargs.get("as_index", True)
+            and broadcastable_by.has_materialized_dtypes
+        ):
+            new_index = ModinIndex(
+                # value can be anything here, as it will be reassigned on a parent update
+                value=query_compiler._modin_frame,
+                axis=0,
+                dtypes=broadcastable_by.dtypes,
+            )
+        else:
+            new_index = None
         new_modin_frame = query_compiler._modin_frame.groupby_reduce(
-            axis, broadcastable_by, map_fn, reduce_fn, apply_indices=apply_indices
+            axis,
+            broadcastable_by,
+            map_fn,
+            reduce_fn,
+            apply_indices=apply_indices,
+            new_index=new_index,
         )
 
         result = query_compiler.__constructor__(new_modin_frame)

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

@@ -3911,6 +3911,12 @@ def join_cols(df, *cols):
                 row_lengths=result._row_lengths_cache,
             )
 
+        if not result.has_materialized_index:
+            by_dtypes = ModinDtypes(self._dtypes).lazy_get(by)
+            if by_dtypes.is_materialized:
+                new_index = ModinIndex(value=result, axis=0, dtypes=by_dtypes)
+                result.set_index_cache(new_index)
+
         if result_schema is not None:
             new_dtypes = pandas.Series(result_schema)
 

modin/core/dataframe/pandas/metadata/index.py

@@ -35,13 +35,16 @@ class ModinIndex:
     axis : int, optional
         Specifies an axis the object represents, serves as an optional hint. This parameter
         must be passed in case value is a ``PandasDataframe``.
+    dtypes : pandas.Series, optional
+        Materialized dtypes of index levels.
     """
 
-    def __init__(self, value, axis=None):
+    def __init__(self, value, axis=None, dtypes=None):
         from modin.core.dataframe.pandas.dataframe.dataframe import PandasDataframe
 
         self._is_default_callable = False
         self._axis = axis
+        self._dtypes = dtypes
 
         if callable(value):
             self._value = value
@@ -58,6 +61,25 @@ def __init__(self, value, axis=None):
         self._index_id = uuid.uuid4()
         self._lengths_id = uuid.uuid4()
 
+    def maybe_get_dtypes(self):
+        """
+        Get index dtypes if available.
+
+        Returns
+        -------
+        pandas.Series or None
+        """
+        if self._dtypes is not None:
+            return self._dtypes
+        if self.is_materialized:
+            self._dtypes = (
+                self._value.dtypes
+                if isinstance(self._value, pandas.MultiIndex)
+                else pandas.Series([self._value.dtype], index=[self._value.name])
+            )
+            return self._dtypes
+        return None
+
     @staticmethod
     def _get_default_callable(dataframe_obj, axis):
         """
@@ -308,7 +330,7 @@ def copy(self, copy_lengths=False) -> "ModinIndex":
         idx_cache = self._value
         if not callable(idx_cache):
             idx_cache = idx_cache.copy()
-        result = ModinIndex(idx_cache, axis=self._axis)
+        result = ModinIndex(idx_cache, axis=self._axis, dtypes=self._dtypes)
         result._index_id = self._index_id
         result._is_default_callable = self._is_default_callable
         if copy_lengths:

modin/core/storage_formats/pandas/query_compiler.py

@@ -61,6 +61,7 @@
 from modin.core.dataframe.pandas.metadata import (
     DtypesDescriptor,
     ModinDtypes,
+    ModinIndex,
     extract_dtype,
 )
 from modin.core.storage_formats.base.query_compiler import BaseQueryCompiler
@@ -589,16 +590,9 @@ def map_func(
                         [right_pandas.dtypes, right_index_dtypes]
                     )[right_renamer.keys()].rename(right_renamer)
 
-                    left_index_dtypes = None
-                    if self._modin_frame.has_materialized_index:
-                        left_index_dtypes = (
-                            self.index.dtypes
-                            if isinstance(self.index, pandas.MultiIndex)
-                            else pandas.Series(
-                                [self.index.dtype], index=[self.index.name]
-                            )
-                        )
-
+                    left_index_dtypes = (
+                        self._modin_frame._index_cache.maybe_get_dtypes()
+                    )
                     left_dtypes = (
                         ModinDtypes.concat(
                             [self._modin_frame._dtypes, left_index_dtypes]
@@ -755,12 +749,36 @@ def _reset(df, *axis_lengths, partition_idx):  # pragma: no cover
                         copy_lengths=True
                     )
             else:
-                # concat index dtypes (None, since they're unknown) with column dtypes
+                # concat index dtypes with column dtypes
+                index_dtypes = self._modin_frame._index_cache.maybe_get_dtypes()
                 try:
-                    dtypes = ModinDtypes.concat([None, self._modin_frame._dtypes])
+                    dtypes = ModinDtypes.concat(
+                        [
+                            index_dtypes,
+                            self._modin_frame._dtypes,
+                        ]
+                    )
                 except NotImplementedError:
                     # may raise on duplicated names in materialized 'self.dtypes'
                     dtypes = None
+                if (
+                    # can precompute new columns if we know columns and index names
+                    self._modin_frame.has_materialized_columns
+                    and index_dtypes is not None
+                ):
+                    empty_index = (
+                        pandas.Index([0], name=index_dtypes.index[0])
+                        if len(index_dtypes) == 1
+                        else pandas.MultiIndex.from_arrays(
+                            [[i] for i in range(len(index_dtypes))],
+                            names=index_dtypes.index,
+                        )
+                    )
+                    new_columns = (
+                        pandas.DataFrame(columns=self.columns, index=empty_index)
+                        .reset_index(**kwargs)
+                        .columns
+                    )
 
             return self.__constructor__(
                 self._modin_frame.apply_full_axis(
@@ -4124,12 +4142,29 @@ def compute_groupby(df, drop=False, partition_idx=0):
         else:
             apply_indices = None
 
+        if (
+            # For now handling only simple cases, where 'by' columns are described by a single query compiler
+            agg_kwargs.get("as_index", True)
+            and len(not_broadcastable_by) == 0
+            and len(broadcastable_by) == 1
+            and broadcastable_by[0].has_materialized_dtypes
+        ):
+            new_index = ModinIndex(
+                # value can be anything here, as it will be reassigned on a parent update
+                value=self._modin_frame,
+                axis=0,
+                dtypes=broadcastable_by[0].dtypes,
+            )
+        else:
+            new_index = None
+
         new_modin_frame = self._modin_frame.broadcast_apply_full_axis(
             axis=axis,
             func=lambda df, by=None, partition_idx=None: groupby_agg_builder(
                 df, by, drop, partition_idx
             ),
             other=broadcastable_by,
+            new_index=new_index,
             apply_indices=apply_indices,
             enumerate_partitions=True,
         )

modin/test/storage_formats/pandas/test_internals.py

@@ -2319,3 +2319,50 @@ def test_preserve_dtypes_reset_index(self, drop, has_materialized_index):
                     )
 
         patch.assert_not_called()
+
+    def test_groupby_index_dtype(self):
+        with mock.patch.object(PandasDataframe, "_compute_dtypes") as patch:
+            # case 1: MapReduce impl, Series as an output of groupby
+            df = pd.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]})
+            res = df.groupby("a").size().reset_index(name="new_name")
+            res_dtypes = res._query_compiler._modin_frame._dtypes._value
+            assert "a" in res_dtypes._known_dtypes
+            assert res_dtypes._known_dtypes["a"] == np.dtype(int)
+
+            # case 2: ExperimentalImpl impl, Series as an output of groupby
+            ExperimentalGroupbyImpl.put(True)
+            try:
+                df = pd.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]})
+                res = df.groupby("a").size().reset_index(name="new_name")
+                res_dtypes = res._query_compiler._modin_frame._dtypes._value
+                assert "a" in res_dtypes._known_dtypes
+                assert res_dtypes._known_dtypes["a"] == np.dtype(int)
+            finally:
+                ExperimentalGroupbyImpl.put(False)
+
+            # case 3: MapReduce impl, DataFrame as an output of groupby
+            df = pd.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]})
+            res = df.groupby("a").sum().reset_index()
+            res_dtypes = res._query_compiler._modin_frame._dtypes._value
+            assert "a" in res_dtypes._known_dtypes
+            assert res_dtypes._known_dtypes["a"] == np.dtype(int)
+
+            # case 4: ExperimentalImpl impl, DataFrame as an output of groupby
+            ExperimentalGroupbyImpl.put(True)
+            try:
+                df = pd.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]})
+                res = df.groupby("a").sum().reset_index()
+                res_dtypes = res._query_compiler._modin_frame._dtypes._value
+                assert "a" in res_dtypes._known_dtypes
+                assert res_dtypes._known_dtypes["a"] == np.dtype(int)
+            finally:
+                ExperimentalGroupbyImpl.put(False)
+
+            # case 5: FullAxis impl, DataFrame as an output of groupby
+            df = pd.DataFrame({"a": [1, 2, 2], "b": [3, 4, 5]})
+            res = df.groupby("a").quantile().reset_index()
+            res_dtypes = res._query_compiler._modin_frame._dtypes._value
+            assert "a" in res_dtypes._known_dtypes
+            assert res_dtypes._known_dtypes["a"] == np.dtype(int)
+
+        patch.assert_not_called()