diff --git a/pandas/_libs/groupby.pyi b/pandas/_libs/groupby.pyi
new file mode 100644
index 0000000000000..8721624e9881c
--- /dev/null
+++ b/pandas/_libs/groupby.pyi
@@ -0,0 +1,168 @@
+from typing import Literal
+
+import numpy as np
+
+def group_median_float64(
+    out: np.ndarray,       # ndarray[float64_t, ndim=2]
+    counts: np.ndarray,    # ndarray[int64_t]
+    values: np.ndarray,    # ndarray[float64_t, ndim=2]
+    labels: np.ndarray,    # ndarray[int64_t]
+    min_count: int = ...,  # Py_ssize_t
+) -> None: ...
+
+def group_cumprod_float64(
+    out: np.ndarray,     # float64_t[:, ::1]
+    values: np.ndarray,  # const float64_t[:, :]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    skipna: bool = ...,
+) -> None: ...
+
+def group_cumsum(
+    out: np.ndarray,     # numeric[:, ::1]
+    values: np.ndarray,  # ndarray[numeric, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    skipna: bool = ...,
+) -> None: ...
+
+
+def group_shift_indexer(
+    out: np.ndarray,     # int64_t[::1]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    periods: int,
+) -> None: ...
+
+
+def group_fillna_indexer(
+    out: np.ndarray,     # ndarray[int64_t]
+    labels: np.ndarray,  # ndarray[int64_t]
+    mask: np.ndarray,    # ndarray[uint8_t]
+    direction: Literal["ffill", "bfill"],
+    limit: int,          # int64_t
+    dropna: bool,
+) -> None: ...
+
+
+def group_any_all(
+    out: np.ndarray,     # uint8_t[::1]
+    values: np.ndarray,  # const uint8_t[::1]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: np.ndarray,    # const uint8_t[::1]
+    val_test: Literal["any", "all"],
+    skipna: bool,
+) -> None: ...
+
+def group_add(
+    out: np.ndarray,     # complexfloating_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[complexfloating_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...
+) -> None: ...
+
+def group_prod(
+    out: np.ndarray,     # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...
+) -> None: ...
+
+def group_var(
+    out: np.ndarray,       # floating[:, ::1]
+    counts: np.ndarray,    # int64_t[::1]
+    values: np.ndarray,    # ndarray[floating, ndim=2]
+    labels: np.ndarray,    # const intp_t[:]
+    min_count: int = ...,  # Py_ssize_t
+    ddof: int = ...,       # int64_t
+) -> None: ...
+
+def group_mean(
+    out: np.ndarray,     # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...
+) -> None: ...
+
+def group_ohlc(
+    out: np.ndarray,     # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...
+) -> None: ...
+
+def group_quantile(
+    out: np.ndarray,     # ndarray[float64_t]
+    values: np.ndarray,  # ndarray[numeric, ndim=1]
+    labels: np.ndarray,  # ndarray[int64_t]
+    mask: np.ndarray,    # ndarray[uint8_t]
+    q: float,            # float64_t
+    interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
+) -> None: ...
+
+def group_last(
+    out: np.ndarray,       # rank_t[:, ::1]
+    counts: np.ndarray,    # int64_t[::1]
+    values: np.ndarray,    # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,    # const int64_t[:]
+    min_count: int = ...,  # Py_ssize_t
+) -> None: ...
+
+def group_nth(
+    out: np.ndarray,        # rank_t[:, ::1]
+    counts: np.ndarray,     # int64_t[::1]
+    values: np.ndarray,     # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,     # const int64_t[:]
+    min_count: int = ...,   # int64_t
+    rank: int = ...,        # int64_t
+) -> None: ...
+
+def group_rank(
+    out: np.ndarray,     # float64_t[:, ::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    ties_method: Literal["aveage", "min", "max", "first", "dense"] = ...,
+    ascending: bool = ...,
+    pct: bool = ...,
+    na_option: Literal["keep", "top", "bottom"] = ...,
+) -> None: ...
+
+def group_max(
+    out: np.ndarray,     # groupby_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,
+) -> None: ...
+
+def group_min(
+    out: np.ndarray,     # groupby_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,
+) -> None: ...
+
+def group_cummin(
+    out: np.ndarray,      # groupby_t[:, ::1]
+    values: np.ndarray,   # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,   # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+) -> None: ...
+
+def group_cummax(
+    out: np.ndarray,      # groupby_t[:, ::1]
+    values: np.ndarray,   # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,   # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+) -> None: ...
diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index ed8911b6cd929..e7cd7cd898d5b 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -1239,12 +1239,12 @@ def group_min(groupby_t[:, ::1] out,
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def group_cummin_max(groupby_t[:, ::1] out,
-                     ndarray[groupby_t, ndim=2] values,
-                     const intp_t[:] labels,
-                     int ngroups,
-                     bint is_datetimelike,
-                     bint compute_max):
+cdef group_cummin_max(groupby_t[:, ::1] out,
+                      ndarray[groupby_t, ndim=2] values,
+                      const intp_t[:] labels,
+                      int ngroups,
+                      bint is_datetimelike,
+                      bint compute_max):
     """
     Cumulative minimum/maximum of columns of `values`, in row groups `labels`.