Add type literals to engine argument in groupby aggregations

pandas/core/groupby/groupby.py

@@ -351,6 +351,46 @@ class providing the base-class of operations.
 {example}
 """
 
+_groupby_agg_method_engine_template = """
+Compute {fname} of group values.
+
+Parameters
+----------
+numeric_only : bool, default {no}
+ Include only float, int, boolean columns.
+
+ .. versionchanged:: 2.0.0
+
+ numeric_only no longer accepts ``None``.
+
+min_count : int, default {mc}
+ The required number of valid values to perform the operation. If fewer
+ than ``min_count`` non-NA values are present the result will be NA.
+
+engine : str, default None {e}
+ * ``'cython'`` : Runs rolling apply through C-extensions from cython.
+ * ``'numba'`` : Runs rolling apply through JIT compiled code from numba.
+ Only available when ``raw`` is set to ``True``.
+ * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+engine_kwargs : dict, default None {ek}
+ * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+ * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+ and ``parallel`` dictionary keys. The values must either be ``True`` or
+ ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+ ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+ applied to both the ``func`` and the ``apply`` groupby aggregation.
+
+Returns
+-------
+Series or DataFrame
+ Computed {fname} of values within each group.
+
+Examples
+--------
+{example}
+"""
+
 _pipe_template = """
 Apply a ``func`` with arguments to this %(klass)s object and return its result.
 
@@ -2231,7 +2271,7 @@ def hfunc(bvalues: ArrayLike) -> ArrayLike:
  def mean(
  self,
  numeric_only: bool = False,
- engine: str = "cython",
+ engine: Literal["cython", "numba"] | None = None,
  engine_kwargs: dict[str, bool] | None = None,
  ):
  """
@@ -2402,7 +2442,7 @@ def median(self, numeric_only: bool = False):
  def std(
  self,
  ddof: int = 1,
- engine: str | None = None,
+ engine: Literal["cython", "numba"] | None = None,
  engine_kwargs: dict[str, bool] | None = None,
  numeric_only: bool = False,
  ):
@@ -2511,7 +2551,7 @@ def std(
  def var(
  self,
  ddof: int = 1,
- engine: str | None = None,
+ engine: Literal["cython", "numba"] | None = None,
  engine_kwargs: dict[str, bool] | None = None,
  numeric_only: bool = False,
  ):
@@ -2909,10 +2949,12 @@ def size(self) -> DataFrame | Series:
 
  @final
  @doc(
- _groupby_agg_method_template,
+ _groupby_agg_method_engine_template,
  fname="sum",
  no=False,
  mc=0,
+ e=None,
+ ek=None,
  example=dedent(
  """\
  For SeriesGroupBy:
@@ -2952,7 +2994,7 @@ def sum(
  self,
  numeric_only: bool = False,
  min_count: int = 0,
- engine: str | None = None,
+ engine: Literal["cython", "numba"] | None = None,
  engine_kwargs: dict[str, bool] | None = None,
  ):
  if maybe_use_numba(engine):
@@ -3026,10 +3068,12 @@ def prod(self, numeric_only: bool = False, min_count: int = 0):
 
  @final
  @doc(
- _groupby_agg_method_template,
+ _groupby_agg_method_engine_template,
  fname="min",
  no=False,
  mc=-1,
+ e=None,
+ ek=None,
  example=dedent(
  """\
  For SeriesGroupBy:
@@ -3069,7 +3113,7 @@ def min(
  self,
  numeric_only: bool = False,
  min_count: int = -1,
- engine: str | None = None,
+ engine: Literal["cython", "numba"] | None = None,
  engine_kwargs: dict[str, bool] | None = None,
  ):
  if maybe_use_numba(engine):
@@ -3092,10 +3136,12 @@ def min(
 
  @final
  @doc(
- _groupby_agg_method_template,
+ _groupby_agg_method_engine_template,
  fname="max",
  no=False,
  mc=-1,
+ e=None,
+ ek=None,
  example=dedent(
  """\
  For SeriesGroupBy:
@@ -3135,7 +3181,7 @@ def max(
  self,
  numeric_only: bool = False,
  min_count: int = -1,
- engine: str | None = None,
+ engine: Literal["cython", "numba"] | None = None,
  engine_kwargs: dict[str, bool] | None = None,
  ):
  if maybe_use_numba(engine):