Consolidate and improve reset_index

python/cudf/cudf/core/_base_index.py

@@ -1414,6 +1414,16 @@ def from_pandas(cls, index, nan_as_null=None):
     def _constructor_expanddim(self):
         return cudf.MultiIndex
 
+    def _split_columns_by_levels(self, levels):
+        if isinstance(levels, int) and levels > 0:
+            raise ValueError(f"Out of bound level: {levels}")
+        return (
+            [self._data[self.name]],
+            [],
+            ["index" if self.name is None else self.name],
+            [],
+        )
+
 
 def _get_result_name(left_name, right_name):
     if left_name == right_name:

python/cudf/cudf/core/dataframe.py

@@ -58,6 +58,7 @@
     _FrameIndexer,
     _get_label_range_or_mask,
     _indices_from_labels,
+    doc_reset_index_template,
 )
 from cudf.core.multiindex import MultiIndex
 from cudf.core.resample import DataFrameResampler
@@ -2429,29 +2430,13 @@ def set_index(
         df.index = idx
         return df if not inplace else None
 
-    def reset_index(
-        self, level=None, drop=False, inplace=False, col_level=0, col_fill=""
-    ):
-        """
-        Reset the index.
-
-        Reset the index of the DataFrame, and use the default one instead.
-
-        Parameters
-        ----------
-        drop : bool, default False
-            Do not try to insert index into dataframe columns. This resets
-            the index to the default integer index.
-        inplace : bool, default False
-            Modify the DataFrame in place (do not create a new object).
-
-        Returns
-        -------
-        DataFrame or None
-            DataFrame with the new index or None if ``inplace=True``.
-
-        Examples
-        --------
+    @docutils.doc_apply(
+        doc_reset_index_template.format(
+            klass="DataFrame",
+            argument="",
+            return_type="DataFrame or None",
+            return_doc="",
+            example="""
         >>> df = cudf.DataFrame([('bird', 389.0),
         ...                    ('bird', 24.0),
         ...                    ('mammal', 80.5),
@@ -2476,45 +2461,51 @@ class max_speed
         1    bird      24.0
         2  mammal      80.5
         3  mammal      <NA>
-        """
-        if level is not None:
-            raise NotImplementedError("level parameter is not supported yet.")
-
-        if col_level != 0:
-            raise NotImplementedError(
-                "col_level parameter is not supported yet."
-            )
 
-        if col_fill != "":
-            raise NotImplementedError(
-                "col_fill parameter is not supported yet."
-            )
-
-        result = self if inplace else self.copy()
-
-        if not drop:
-            if isinstance(self.index, MultiIndex):
-                names = tuple(
-                    name if name is not None else f"level_{i}"
-                    for i, name in enumerate(self.index.names)
+        You can also use ``reset_index`` with MultiIndex.
+
+        >>> index = cudf.MultiIndex.from_tuples([('bird', 'falcon'),
+        ...                                     ('bird', 'parrot'),
+        ...                                     ('mammal', 'lion'),
+        ...                                     ('mammal', 'monkey')],
+        ...                                     names=['class', 'name'])
+        >>> df = cudf.DataFrame([(389.0, 'fly'),
+        ...                      ( 24.0, 'fly'),
+        ...                      ( 80.5, 'run'),
+        ...                      (np.nan, 'jump')],
+        ...                      index=index,
+        ...                      columns=('speed', 'type'))
+        >>> df
+                       speed  type
+        class  name
+        bird   falcon  389.0   fly
+               parrot   24.0   fly
+        mammal lion     80.5   run
+               monkey   <NA>  jump
+        >>> df.reset_index(level='class')
+                 class  speed  type
+        name
+        falcon    bird  389.0   fly
+        parrot    bird   24.0   fly
+        lion    mammal   80.5   run
+        monkey  mammal   <NA>  jump
+        """,
+        )
+    )
+    def reset_index(
+        self, level=None, drop=False, inplace=False, col_level=0, col_fill=""
+    ):
+        return self._mimic_inplace(
+            DataFrame._from_data(
+                *self._reset_index(
+                    level=level,
+                    drop=drop,
+                    col_level=col_level,
+                    col_fill=col_fill,
                 )
-            else:
-                if self.index.name is None:
-                    if "index" in self._data.names:
-                        names = ("level_0",)
-                    else:
-                        names = ("index",)
-                else:
-                    names = (self.index.name,)
-
-            index_columns = self.index._data.columns
-            for name, index_column in zip(
-                reversed(names), reversed(index_columns)
-            ):
-                result.insert(0, name, index_column)
-        result.index = RangeIndex(len(self))
-        if not inplace:
-            return result
+            ),
+            inplace=inplace,
+        )
 
     def take(self, indices, axis=0, keep_index=None):
         axis = self._get_axis_from_axis_arg(axis)

python/cudf/cudf/core/frame.py

@@ -157,8 +157,8 @@ def _from_columns(
         n_index_columns = 0
         if index_names is not None:
             n_index_columns = len(index_names)
-            index = cudf.core.index._index_from_data(
-                dict(zip(range(n_index_columns), columns))
+            index = cudf.core.index._index_from_columns(
+                columns[:n_index_columns]
             )
             if isinstance(index, cudf.MultiIndex):
                 index.names = index_names

python/cudf/cudf/core/index.py

@@ -114,6 +114,13 @@ def _index_from_data(data: MutableMapping, name: Any = None):
     return index_class_type._from_data(data, None, name)
 
 
+def _index_from_columns(
+    columns: List[cudf.core.column.ColumnBase], name: Any = None
+):
+    """Construct an index from ``columns``, with levels named 0, 1, 2..."""
+    return _index_from_data(dict(zip(range(len(columns)), columns)), name=name)
+
+
 class RangeIndex(BaseIndex):
     """
     Immutable Index implementing a monotonic integer range.

python/cudf/cudf/core/indexed_frame.py

@@ -5,7 +5,7 @@
 
 import operator
 import warnings
-from collections import abc
+from collections import Counter, abc
 from typing import Callable, Type, TypeVar
 from uuid import uuid4
 
@@ -24,11 +24,37 @@
     is_list_like,
 )
 from cudf.core.column import arange
+from cudf.core.column_accessor import ColumnAccessor
 from cudf.core.frame import Frame
-from cudf.core.index import Index
+from cudf.core.index import Index, RangeIndex, _index_from_columns
 from cudf.core.multiindex import MultiIndex
 from cudf.utils.utils import _gather_map_is_valid, cached_property
 
+doc_reset_index_template = """
+        Reset the index of the {klass}, or a level of it.
+
+        Parameters
+        ----------
+        level : int, str, tuple, or list, default None
+            Only remove the given levels from the index. Removes all levels by
+            default.
+        drop : bool, default False
+            Do not try to insert index into dataframe columns. This resets
+            the index to the default integer index.
+{argument}
+        inplace : bool, default False
+            Modify the DataFrame in place (do not create a new object).
+
+        Returns
+        -------
+        {return_type}
+            {klass} with the new index or None if ``inplace=True``.{return_doc}
+
+        Examples
+        --------
+        {example}
+"""
+
 
 def _indices_from_labels(obj, labels):
     from cudf.core.column import column
@@ -1107,6 +1133,53 @@ def resample(
             else cudf.core.resample.DataFrameResampler(self, by=by)
         )
 
+    def _reset_index(self, level, drop, col_level=0, col_fill=""):
+        """Shared path for DataFrame.reset_index and Series.reset_index."""
+        if level is not None and not isinstance(level, (tuple, list)):
+            level = (level,)
+        _check_duplicate_level_names(level, self._index.names)
+
+        # Split the columns in the index into data and index columns
+        (
+            data_columns,
+            index_columns,
+            data_names,
+            index_names,
+        ) = self._index._split_columns_by_levels(level)
+        if index_columns:
+            index = _index_from_columns(index_columns, name=self._index.name,)
+            if isinstance(index, MultiIndex):
+                index.names = index_names
+            else:
+                index.name = index_names[0]
+        else:
+            index = RangeIndex(len(self))
+
+        if drop:
+            return self._data, index
+
+        new_column_data = {}
+        for name, col in zip(data_names, data_columns):
+            if name == "index" and "index" in self._data:
+                name = "level_0"
+            name = (
+                tuple(
+                    name if i == col_level else col_fill
+                    for i in range(self._data.nlevels)
+                )
+                if self._data.multiindex
+                else name
+            )
+            new_column_data[name] = col
+        # This is to match pandas where the new data columns are always
+        # inserted to the left of existing data columns.
+        return (
+            ColumnAccessor(
+                {**new_column_data, **self._data}, self._data.multiindex
+            ),
+            index,
+        )
+
     def _first_or_last(
         self, offset, idx: int, op: Callable, side: str, slice_func: Callable
     ) -> "IndexedFrame":
@@ -1228,3 +1301,20 @@ def last(self, offset):
             side="right",
             slice_func=lambda i: self.iloc[i:],
         )
+
+
+def _check_duplicate_level_names(specified, level_names):
+    """Raise if any of `specified` has duplicates in `level_names`."""
+    if specified is None:
+        return
+    if len(set(level_names)) == len(level_names):
+        return
+    duplicates = {key for key, val in Counter(level_names).items() if val > 1}
+
+    duplicates_specified = [spec for spec in specified if spec in duplicates]
+    if not len(duplicates_specified) == 0:
+        # Note: pandas raises first encountered duplicates, cuDF raises all.
+        raise ValueError(
+            f"The names {duplicates_specified} occurs multiple times, use a"
+            " level number"
+        )

python/cudf/cudf/core/multiindex.py

@@ -1743,3 +1743,39 @@ def _intersection(self, other, sort=None):
         if sort is None and len(other):
             return midx.sort_values()
         return midx
+
+    def _split_columns_by_levels(self, levels):
+        # This function assumes that for levels with duplicate names, they are
+        # specified by indices, not name by ``levels``. E.g. [None, None] can
+        # only be specified by 0, 1, not "None".
+
+        if levels is None:
+            return (
+                list(self._data.columns),
+                [],
+                [
+                    f"level_{i}" if name is None else name
+                    for i, name in enumerate(self.names)
+                ],
+                [],
+            )
+
+        # Normalize named levels into indices
+        level_names = list(self.names)
+        level_indices = {
+            lv if isinstance(lv, int) else level_names.index(lv)
+            for lv in levels
+        }
+
+        # Split the columns
+        data_columns, index_columns = [], []
+        data_names, index_names = [], []
+        for i, (name, col) in enumerate(zip(self.names, self._data.columns)):
+            if i in level_indices:
+                name = f"level_{i}" if name is None else name
+                data_columns.append(col)
+                data_names.append(name)
+            else:
+                index_columns.append(col)
+                index_names.append(name)
+        return data_columns, index_columns, data_names, index_names