Repository: pandas-dev/pandas (python)

Issue:
BUG: df.plot() "Subplots" changes behavior of how values are stacked using the "Stacked" property
### Pandas version checks

- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [x] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.


### Reproducible Example

```python
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame([(30, 10, 10), (20, 20, 20), (10, 30, 30)], columns=list('ABC'))
df.plot(kind="bar", stacked=True)
df.plot(subplots= [('A','B')],kind="bar", stacked=True)
plt.show()
print(df)
```

### Issue Description

Using both the "stacked" and "subplots" option when drawing a bar graph changes how the bar graph is stacked.

Illustrated with image below where instead of numberically stacking the values for A and B it just physically overlays them. My guess is it doesn't properly use the "bottom" attribute when drawing B. 

![Image](https://github.com/user-attachments/assets/30d4655d-1a88-4321-be17-6ea88bbf2646)

### Expected Behavior

The behavior of the subplot version should be inline with when the option is not used. So the total of the values should equal to A+B for each element

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit                : 0691c5cf90477d3503834d983f69350f250a6ff7
python                : 3.11.0
python-bits           : 64
OS                    : Windows
OS-release            : 10
Version               : 10.0.22631
machine               : AMD64
processor             : AMD64 Family 25 Model 33 Stepping 2, AuthenticAMD
byteorder             : little
LC_ALL                : None
LANG                  : en_US.UTF-8
LOCALE                : English_United States.1252

pandas                : 2.2.3
numpy                 : 1.24.2
pytz                  : 2025.1
dateutil              : 2.8.2
pip                   : 25.0
Cython                : None
sphinx                : None
IPython               : 8.12.0
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : 4.13.3
blosc                 : None
bottleneck            : None
dataframe-api-compat  : None
fastparquet           : None
fsspec                : None
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : 3.1.2
lxml.etree            : 5.3.0
matplotlib            : 3.10.0
numba                 : None
numexpr               : None
odfpy                 : None
openpyxl              : None
pandas_gbq            : None
psycopg2              : None
pymysql               : None
pyarrow               : None
pyreadstat            : None
pytest                : None
python-calamine       : None
pyxlsb                : None
s3fs                  : None
scipy                 : None
sqlalchemy            : 2.0.9
tables                : None
tabulate              : None
xarray                : None
xlrd                  : None
xlsxwriter            : None
zstandard             : None
tzdata                : 2025.1
qtpy                  : None
pyqt5                 : None

</details>


Pull request description:
BUG: Fixed issue with bar plots not stacking correctly when 'stacked' and 'subplots' are used together
- [x] closes #61018
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

Added check for when stacked and subplots are used in conjunction for bar plots. And logic dictating offsets for individual subplots to account for plots with non-concurrent columns being graphed. 

Currently, does not take into account column order in subplot entry (eg: (A, B) vs (B, A)) when stacking

Patch:
diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml
index e0d68a3487..f330d0e6cb 100644
--- a/.github/workflows/wheels.yml
+++ b/.github/workflows/wheels.yml
@@ -153,7 +153,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.23.2
+        uses: pypa/cibuildwheel@v2.23.3
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index f76d94036c..ab3316e7fc 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -795,6 +795,7 @@ Period
 Plotting
 ^^^^^^^^
 - Bug in :meth:`.DataFrameGroupBy.boxplot` failed when there were multiple groupings (:issue:`14701`)
+- Bug in :meth:`DataFrame.plot.bar` when ``subplots`` and ``stacked=True`` are used in conjunction which causes incorrect stacking. (:issue:`61018`)
 - Bug in :meth:`DataFrame.plot.bar` with ``stacked=True`` where labels on stacked bars with zero-height segments were incorrectly positioned at the base instead of the label position of the previous segment (:issue:`59429`)
 - Bug in :meth:`DataFrame.plot.line` raising ``ValueError`` when set both color and a ``dict`` style (:issue:`59461`)
 - Bug in :meth:`DataFrame.plot` that causes a shift to the right when the frequency multiplier is greater than one. (:issue:`57587`)
@@ -806,10 +807,12 @@ Groupby/resample/rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 - Bug in :meth:`.DataFrameGroupBy.__len__` and :meth:`.SeriesGroupBy.__len__` would raise when the grouping contained NA values and ``dropna=False`` (:issue:`58644`)
 - Bug in :meth:`.DataFrameGroupBy.any` that returned True for groups where all Timedelta values are NaT. (:issue:`59712`)
+- Bug in :meth:`.DataFrameGroupBy.groups` and :meth:`.SeriesGroupBy.groups` would fail when the groups were :class:`Categorical` with an NA value (:issue:`61356`)
 - Bug in :meth:`.DataFrameGroupBy.groups` and :meth:`.SeriesGroupby.groups` that would not respect groupby argument ``dropna`` (:issue:`55919`)
 - Bug in :meth:`.DataFrameGroupBy.median` where nat values gave an incorrect result. (:issue:`57926`)
 - Bug in :meth:`.DataFrameGroupBy.quantile` when ``interpolation="nearest"`` is inconsistent with :meth:`DataFrame.quantile` (:issue:`47942`)
 - Bug in :meth:`.Resampler.interpolate` on a :class:`DataFrame` with non-uniform sampling and/or indices not aligning with the resulting resampled index would result in wrong interpolation (:issue:`21351`)
+- Bug in :meth:`.Series.rolling` when used with a :class:`.BaseIndexer` subclass and computing min/max (:issue:`46726`)
 - Bug in :meth:`DataFrame.ewm` and :meth:`Series.ewm` when passed ``times`` and aggregation functions other than mean (:issue:`51695`)
 - Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` were not keeping the index name when the index had :class:`ArrowDtype` timestamp dtype (:issue:`61222`)
 - Bug in :meth:`DataFrame.resample` changing index type to :class:`MultiIndex` when the dataframe is empty and using an upsample method (:issue:`55572`)
@@ -835,6 +838,7 @@ Reshaping
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when ``sort=False`` (:issue:`54987`, :issue:`55516`)
 - Bug in :meth:`DataFrame.merge` when merging two :class:`DataFrame` on ``intc`` or ``uintc`` types on Windows (:issue:`60091`, :issue:`58713`)
 - Bug in :meth:`DataFrame.pivot_table` incorrectly subaggregating results when called without an ``index`` argument (:issue:`58722`)
+- Bug in :meth:`DataFrame.pivot_table` incorrectly ignoring the ``values`` argument when also supplied to the ``index`` or ``columns`` parameters (:issue:`57876`, :issue:`61292`)
 - Bug in :meth:`DataFrame.stack` with the new implementation where ``ValueError`` is raised when ``level=[]`` (:issue:`60740`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)
 - Bug in :meth:`concat` where concatenating DataFrame and Series with ``ignore_index = True`` drops the series name (:issue:`60723`, :issue:`56257`)
diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx
index 2baed13cbd..04b3f8ab46 100644
--- a/pandas/_libs/window/aggregations.pyx
+++ b/pandas/_libs/window/aggregations.pyx
@@ -6,6 +6,7 @@ from libc.math cimport (
     sqrt,
 )
 from libcpp.deque cimport deque
+from libcpp.stack cimport stack
 from libcpp.unordered_map cimport unordered_map
 
 from pandas._libs.algos cimport TiebreakEnumType
@@ -988,39 +989,29 @@ def roll_median_c(const float64_t[:] values, ndarray[int64_t] start,
 
 # ----------------------------------------------------------------------
 
-# Moving maximum / minimum code taken from Bottleneck
-# Licence at LICENSES/BOTTLENECK_LICENCE
-
-
-cdef float64_t init_mm(float64_t ai, Py_ssize_t *nobs, bint is_max) noexcept nogil:
-
-    if ai == ai:
-        nobs[0] = nobs[0] + 1
-    elif is_max:
-        ai = MINfloat64
-    else:
-        ai = MAXfloat64
-
-    return ai
-
-
-cdef void remove_mm(float64_t aold, Py_ssize_t *nobs) noexcept nogil:
-    """ remove a value from the mm calc """
-    if aold == aold:
-        nobs[0] = nobs[0] - 1
-
-
-cdef float64_t calc_mm(int64_t minp, Py_ssize_t nobs,
-                       float64_t value) noexcept nogil:
-    cdef:
-        float64_t result
+cdef int64_t bisect_left(
+    deque[int64_t]& a,
+    int64_t x,
+    int64_t lo=0,
+    int64_t hi=-1
+) nogil:
+    """Same as https://docs.python.org/3/library/bisect.html."""
+
+    cdef int64_t mid
+    if hi == -1:
+        hi = a.size()
+    while lo < hi:
+        mid = (lo + hi) // 2
+        if a.at(mid) < x:
+            lo = mid + 1
+        else:
+            hi = mid
+    return lo
 
-    if nobs >= minp:
-        result = value
-    else:
-        result = NaN
+from libc.math cimport isnan
 
-    return result
+# Prior version of moving maximum / minimum code taken from Bottleneck
+# Licence at LICENSES/BOTTLENECK_LICENCE
 
 
 def roll_max(ndarray[float64_t] values, ndarray[int64_t] start,
@@ -1068,69 +1059,110 @@ def roll_min(ndarray[float64_t] values, ndarray[int64_t] start,
     return _roll_min_max(values, start, end, minp, is_max=0)
 
 
-cdef _roll_min_max(ndarray[float64_t] values,
-                   ndarray[int64_t] starti,
-                   ndarray[int64_t] endi,
-                   int64_t minp,
-                   bint is_max):
+def _roll_min_max(
+    ndarray[float64_t] values,
+    ndarray[int64_t] start,
+    ndarray[int64_t] end,
+    int64_t minp,
+    bint is_max
+):
     cdef:
-        float64_t ai
-        int64_t curr_win_size, start
-        Py_ssize_t i, k, nobs = 0, N = len(starti)
-        deque Q[int64_t]  # min/max always the front
-        deque W[int64_t]  # track the whole window for nobs compute
+        Py_ssize_t i, i_next, k, valid_start, last_end, last_start, N = len(start)
+        # Indices of bounded extrema in `values`. `candidates[i]` is always increasing.
+        # `values[candidates[i]]` is decreasing for max and increasing for min.
+        deque candidates[int64_t]
+        # Indices of largest windows that "cover" preceding windows.
+        stack dominators[int64_t]
         ndarray[float64_t, ndim=1] output
 
+        Py_ssize_t this_start, this_end, stash_start
+        int64_t q_idx
+
     output = np.empty(N, dtype=np.float64)
-    Q = deque[int64_t]()
-    W = deque[int64_t]()
+    candidates = deque[int64_t]()
+    dominators = stack[int64_t]()
+
+    # This function was "ported" / translated from sliding_min_max()
+    # in /pandas/core/_numba/kernels/min_max_.py.
+    # (See there for credits and some comments.)
+    # Code translation assumptions/rules:
+    # - min_periods --> minp
+    # - deque[0] --> front()
+    # - deque[-1] --> back()
+    # - stack[-1] --> top()
+    # - bool(stack/deque) --> !empty()
+    # - deque.append()    --> push_back()
+    # - stack.append()    --> push()
+    # - deque.popleft     --> pop_front()
+    # - deque.pop()       --> pop_back()
 
     with nogil:
+        if minp < 1:
+            minp = 1
+
+        if N>2:
+            i_next = N - 1
+            for i in range(N - 2, -1, -1):
+                if start[i_next] < start[i] \
+                    and (
+                           dominators.empty()
+                        or start[dominators.top()] > start[i_next]
+                ):
+                    dominators.push(i_next)
+                i_next = i
+
+        # NaN tracking to guarantee minp
+        valid_start = -minp
+
+        last_end = 0
+        last_start = -1
 
-        # This is using a modified version of the C++ code in this
-        # SO post: https://stackoverflow.com/a/12239580
-        # The original impl didn't deal with variable window sizes
-        # So the code was optimized for that
-
-        # first window's size
-        curr_win_size = endi[0] - starti[0]
-        # GH 32865
-        # Anchor output index to values index to provide custom
-        # BaseIndexer support
         for i in range(N):
+            this_start = start[i]
+            this_end = end[i]
 
-            curr_win_size = endi[i] - starti[i]
-            if i == 0:
-                start = starti[i]
-            else:
-                start = endi[i - 1]
-
-            for k in range(start, endi[i]):
-                ai = init_mm(values[k], &nobs, is_max)
-                # Discard previous entries if we find new min or max
-                if is_max:
-                    while not Q.empty() and ((ai >= values[Q.back()]) or
-                                             values[Q.back()] != values[Q.back()]):
-                        Q.pop_back()
-                else:
-                    while not Q.empty() and ((ai <= values[Q.back()]) or
-                                             values[Q.back()] != values[Q.back()]):
-                        Q.pop_back()
-                Q.push_back(k)
-                W.push_back(k)
-
-            # Discard entries outside and left of current window
-            while not Q.empty() and Q.front() <= starti[i] - 1:
-                Q.pop_front()
-            while not W.empty() and W.front() <= starti[i] - 1:
-                remove_mm(values[W.front()], &nobs)
-                W.pop_front()
-
-            # Save output based on index in input value array
-            if not Q.empty() and curr_win_size > 0:
-                output[i] = calc_mm(minp, nobs, values[Q.front()])
+            if (not dominators.empty() and dominators.top() == i):
+                dominators.pop()
+
+            if not (this_end > last_end
+                    or (this_end == last_end and this_start >= last_start)):
+                raise ValueError(
+                    "Start/End ordering requirement is violated at index {}".format(i))
+
+            if dominators.empty():
+                stash_start = this_start
             else:
+                stash_start = min(this_start, start[dominators.top()])
+
+            while not candidates.empty() and candidates.front() < stash_start:
+                candidates.pop_front()
+
+            for k in range(last_end, this_end):
+                if not isnan(values[k]):
+                    valid_start += 1
+                    while valid_start >= 0 and isnan(values[valid_start]):
+                        valid_start += 1
+
+                    if is_max:
+                        while (not candidates.empty()
+                                and values[k] >= values[candidates.back()]):
+                            candidates.pop_back()
+                    else:
+                        while (not candidates.empty()
+                                and values[k] <= values[candidates.back()]):
+                            candidates.pop_back()
+                    candidates.push_back(k)
+
+            if candidates.empty() or this_start > valid_start:
                 output[i] = NaN
+            elif candidates.front() >= this_start:
+                # ^^ This is here to avoid costly bisection for fixed window sizes.
+                output[i] = values[candidates.front()]
+            else:
+                q_idx = bisect_left(candidates, this_start, lo=1)
+                output[i] = values[candidates[q_idx]]
+            last_end = this_end
+            last_start = this_start
 
     return output
 
diff --git a/pandas/core/_numba/kernels/min_max_.py b/pandas/core/_numba/kernels/min_max_.py
index 68aa1446bb..c03f20c871 100644
--- a/pandas/core/_numba/kernels/min_max_.py
+++ b/pandas/core/_numba/kernels/min_max_.py
@@ -9,7 +9,10 @@ Mirrors pandas/_libs/window/aggregation.pyx
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
 
 import numba
 import numpy as np
@@ -18,6 +21,20 @@ if TYPE_CHECKING:
     from pandas._typing import npt
 
 
+@numba.njit(nogil=True, parallel=False)
+def bisect_left(a: list[Any], x: Any, lo: int = 0, hi: int = -1) -> int:
+    """Same as https://docs.python.org/3/library/bisect.html; not in numba yet!"""
+    if hi == -1:
+        hi = len(a)
+    while lo < hi:
+        mid = (lo + hi) // 2
+        if a[mid] < x:
+            lo = mid + 1
+        else:
+            hi = mid
+    return lo
+
+
 @numba.jit(nopython=True, nogil=True, parallel=False)
 def sliding_min_max(
     values: np.ndarray,
@@ -27,55 +44,87 @@ def sliding_min_max(
     min_periods: int,
     is_max: bool,
 ) -> tuple[np.ndarray, list[int]]:
+    # Basic idea of the algorithm: https://stackoverflow.com/a/12239580
+    # It was generalized to work with an arbitrary list of any window size and position
+    # by adding the Dominators stack.
+
     N = len(start)
-    nobs = 0
-    output = np.empty(N, dtype=result_dtype)
     na_pos = []
-    # Use deque once numba supports it
-    # https://github.com/numba/numba/issues/7417
-    Q: list = []
-    W: list = []
-    for i in range(N):
-        curr_win_size = end[i] - start[i]
-        if i == 0:
-            st = start[i]
-        else:
-            st = end[i - 1]
-
-        for k in range(st, end[i]):
-            ai = values[k]
-            if not np.isnan(ai):
-                nobs += 1
-            elif is_max:
-                ai = -np.inf
-            else:
-                ai = np.inf
-            # Discard previous entries if we find new min or max
-            if is_max:
-                while Q and ((ai >= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
-                    Q.pop()
-            else:
-                while Q and ((ai <= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
-                    Q.pop()
-            Q.append(k)
-            W.append(k)
-
-        # Discard entries outside and left of current window
-        while Q and Q[0] <= start[i] - 1:
-            Q.pop(0)
-        while W and W[0] <= start[i] - 1:
-            if not np.isnan(values[W[0]]):
-                nobs -= 1
-            W.pop(0)
-
-        # Save output based on index in input value array
-        if Q and curr_win_size > 0 and nobs >= min_periods:
-            output[i] = values[Q[0]]
+    output = np.empty(N, dtype=result_dtype)
+
+    def cmp(a: Any, b: Any, is_max: bool) -> bool:
+        if is_max:
+            return a >= b
         else:
+            return a <= b
+
+    # Indices of bounded extrema in `values`. `candidates[i]` is always increasing.
+    # `values[candidates[i]]` is decreasing for max and increasing for min.
+    candidates: list[int] = []  # this is a queue
+    # Indices of largest windows that "cover" preceding windows.
+    dominators: list[int] = []  # this is a stack
+
+    if min_periods < 1:
+        min_periods = 1
+
+    if N > 2:
+        i_next = N - 1  # equivalent to i_next = i+1 inside the loop
+        for i in range(N - 2, -1, -1):
+            next_dominates = start[i_next] < start[i]
+            if next_dominates and (
+                not dominators or start[dominators[-1]] > start[i_next]
+            ):
+                dominators.append(i_next)
+            i_next = i
+
+    # NaN tracking to guarantee min_periods
+    valid_start = -min_periods
+
+    last_end = 0
+    last_start = -1
+
+    for i in range(N):
+        this_start = start[i].item()
+        this_end = end[i].item()
+
+        if dominators and dominators[-1] == i:
+            dominators.pop()
+
+        if not (
+            this_end > last_end or (this_end == last_end and this_start >= last_start)
+        ):
+            raise ValueError(
+                "Start/End ordering requirement is violated at index " + str(i)
+            )
+
+        stash_start = (
+            this_start if not dominators else min(this_start, start[dominators[-1]])
+        )
+        while candidates and candidates[0] < stash_start:
+            candidates.pop(0)
+
+        for k in range(last_end, this_end):
+            if not np.isnan(values[k]):
+                valid_start += 1
+                while valid_start >= 0 and np.isnan(values[valid_start]):
+                    valid_start += 1
+                while candidates and cmp(values[k], values[candidates[-1]], is_max):
+                    candidates.pop()  # Q.pop_back()
+                candidates.append(k)  # Q.push_back(k)
+
+        if not candidates or (this_start > valid_start):
             if values.dtype.kind != "i":
                 output[i] = np.nan
             else:
                 na_pos.append(i)
+        elif candidates[0] >= this_start:
+            # ^^ This is here to avoid costly bisection for fixed window sizes.
+            output[i] = values[candidates[0]]
+        else:
+            q_idx = bisect_left(candidates, this_start, lo=1)
+            output[i] = values[candidates[q_idx]]
+        last_end = this_end
+        last_start = this_start
 
     return output, na_pos
 
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 884107d4bc..486a6a2a02 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -3568,6 +3568,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
         elif formatters is None and float_format is not None:
             formatters_ = partial(_wrap, alt_format_=lambda v: v)
         format_index_ = [index_format_, column_format_]
+        format_index_names_ = [index_format_, column_format_]
 
         # Deal with hiding indexes and relabelling column names
         hide_: list[dict] = []
@@ -3616,6 +3617,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
             relabel_index=relabel_index_,
             format={"formatter": formatters_, **base_format_},
             format_index=format_index_,
+            format_index_names=format_index_names_,
             render_kwargs=render_kwargs_,
         )
 
@@ -3628,6 +3630,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
         relabel_index: dict | list[dict] | None = None,
         format: dict | list[dict] | None = None,
         format_index: dict | list[dict] | None = None,
+        format_index_names: dict | list[dict] | None = None,
         render_kwargs: dict | None = None,
     ):
         """
@@ -3672,7 +3675,13 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
         self = cast("DataFrame", self)
         styler = Styler(self, uuid="")
 
-        for kw_name in ["hide", "relabel_index", "format", "format_index"]:
+        for kw_name in [
+            "hide",
+            "relabel_index",
+            "format",
+            "format_index",
+            "format_index_names",
+        ]:
             kw = vars()[kw_name]
             if isinstance(kw, dict):
                 getattr(styler, kw_name)(**kw)
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 33539e8d29..3daee98371 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3810,19 +3810,26 @@ class GroupBy(BaseGroupBy[NDFrameT]):
         )
 
     @final
-    def expanding(self, *args, **kwargs) -> ExpandingGroupby:
+    def expanding(
+        self,
+        min_periods: int = 1,
+        method: str = "single",
+    ) -> ExpandingGroupby:
         """
         Return an expanding grouper, providing expanding functionality per group.
 
-        Arguments are the same as `:meth:DataFrame.rolling` except that ``step`` cannot
-        be specified.
-
         Parameters
         ----------
-        *args : tuple
-            Positional arguments passed to the expanding window constructor.
-        **kwargs : dict
-            Keyword arguments passed to the expanding window constructor.
+        min_periods : int, default 1
+            Minimum number of observations in window required to have a value;
+            otherwise, result is ``np.nan``.
+
+        method : str {'single', 'table'}, default 'single'
+            Execute the expanding operation per single column or row (``'single'``)
+            or over the entire object (``'table'``).
+
+            This argument is only implemented when specifying ``engine='numba'``
+            in the method call.
 
         Returns
         -------
@@ -3845,7 +3852,7 @@ class GroupBy(BaseGroupBy[NDFrameT]):
         ...     }
         ... )
         >>> df
-          Class  Value
+        Class  Value
         0     A     10
         1     A     20
         2     A     30
@@ -3854,7 +3861,7 @@ class GroupBy(BaseGroupBy[NDFrameT]):
         5     B     60
 
         >>> df.groupby("Class").expanding().mean()
-                 Value
+                Value
         Class
         A     0   10.0
               1   15.0
@@ -3867,9 +3874,9 @@ class GroupBy(BaseGroupBy[NDFrameT]):
 
         return ExpandingGroupby(
             self._selected_obj,
-            *args,
+            min_periods=min_periods,
+            method=method,
             _grouper=self._grouper,
-            **kwargs,
         )
 
     @final
diff --git a/pandas/core/groupby/grouper.py b/pandas/core/groupby/grouper.py
index c9d874fc08..f8e92b7e26 100644
--- a/pandas/core/groupby/grouper.py
+++ b/pandas/core/groupby/grouper.py
@@ -12,11 +12,16 @@ from typing import (
 
 import numpy as np
 
+from pandas._libs import (
+    algos as libalgos,
+)
 from pandas._libs.tslibs import OutOfBoundsDatetime
 from pandas.errors import InvalidIndexError
 from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_platform_int,
     is_list_like,
     is_scalar,
 )
@@ -38,7 +43,10 @@ from pandas.core.indexes.api import (
 )
 from pandas.core.series import Series
 
-from pandas.io.formats.printing import pprint_thing
+from pandas.io.formats.printing import (
+    PrettyDict,
+    pprint_thing,
+)
 
 if TYPE_CHECKING:
     from collections.abc import (
@@ -668,8 +676,14 @@ class Grouping:
     def groups(self) -> dict[Hashable, Index]:
         codes, uniques = self._codes_and_uniques
         uniques = Index._with_infer(uniques, name=self.name)
-        cats = Categorical.from_codes(codes, uniques, validate=False)
-        return self._index.groupby(cats)
+
+        r, counts = libalgos.groupsort_indexer(ensure_platform_int(codes), len(uniques))
+        counts = ensure_int64(counts).cumsum()
+        _result = (r[start:end] for start, end in zip(counts, counts[1:]))
+        # map to the label
+        result = {k: self._index.take(v) for k, v in zip(uniques, _result)}
+
+        return PrettyDict(result)
 
     @property
     def observed_grouping(self) -> Grouping:
diff --git a/pandas/core/reshape/encoding.py b/pandas/core/reshape/encoding.py
index 54704b274b..ad4a5db441 100644
--- a/pandas/core/reshape/encoding.py
+++ b/pandas/core/reshape/encoding.py
@@ -60,13 +60,15 @@ def get_dummies(
     data : array-like, Series, or DataFrame
         Data of which to get dummy indicators.
     prefix : str, list of str, or dict of str, default None
-        String to append DataFrame column names.
+        A string to be prepended to DataFrame column names.
         Pass a list with length equal to the number of columns
         when calling get_dummies on a DataFrame. Alternatively, `prefix`
         can be a dictionary mapping column names to prefixes.
-    prefix_sep : str, default '_'
-        If appending prefix, separator/delimiter to use. Or pass a
-        list or dictionary as with `prefix`.
+    prefix_sep : str, list of str, or dict of str, default '_'
+        Should you choose to prepend DataFrame column names with a prefix, this
+        is the separator/delimiter to use between the two. Alternatively,
+        `prefix_sep` can be a list with length equal to the number of columns,
+        or a dictionary mapping column names to separators.
     dummy_na : bool, default False
         If True, a NaN indicator column will be added even if no NaN values are present.
         If False, NA values are encoded as all zero.
diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py
index 0a8ade581d..4e77f0a6bf 100644
--- a/pandas/core/reshape/pivot.py
+++ b/pandas/core/reshape/pivot.py
@@ -336,6 +336,11 @@ def __internal_pivot_table(
         values = list(values)
 
     grouped = data.groupby(keys, observed=observed, sort=sort, dropna=dropna)
+    if values_passed:
+        # GH#57876 and GH#61292
+        # mypy is not aware `grouped[values]` will always be a DataFrameGroupBy
+        grouped = grouped[values]  # type: ignore[assignment]
+
     agged = grouped.agg(aggfunc, **kwargs)
 
     if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):
diff --git a/pandas/core/sample.py b/pandas/core/sample.py
index 5b1c4b6a33..4f12563e3c 100644
--- a/pandas/core/sample.py
+++ b/pandas/core/sample.py
@@ -123,7 +123,7 @@ def sample(
     random_state: np.random.RandomState | np.random.Generator,
 ) -> np.ndarray:
     """
-    Randomly sample `size` indices in `np.arange(obj_len)`
+    Randomly sample `size` indices in `np.arange(obj_len)`.
 
     Parameters
     ----------
diff --git a/pandas/io/parsers/c_parser_wrapper.py b/pandas/io/parsers/c_parser_wrapper.py
index 818c9f5ff6..aa9f3556c8 100644
--- a/pandas/io/parsers/c_parser_wrapper.py
+++ b/pandas/io/parsers/c_parser_wrapper.py
@@ -258,8 +258,9 @@ class CParserWrapper(ParserBase):
                 )
 
                 columns = _filter_usecols(self.usecols, columns)
+                columns_set = set(columns)
 
-                col_dict = {k: v for k, v in col_dict.items() if k in columns}
+                col_dict = {k: v for k, v in col_dict.items() if k in columns_set}
 
                 return index, columns, col_dict
 
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index a217ee8a86..0e06cb10d2 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -1928,6 +1928,20 @@ class BarPlot(MPLPlot):
         K = self.nseries
 
         data = self.data.fillna(0)
+
+        _stacked_subplots_ind: dict[int, int] = {}
+        _stacked_subplots_offsets = []
+
+        self.subplots: list[Any]
+
+        if bool(self.subplots) and self.stacked:
+            for i, sub_plot in enumerate(self.subplots):
+                if len(sub_plot) <= 1:
+                    continue
+                for plot in sub_plot:
+                    _stacked_subplots_ind[int(plot)] = i
+                _stacked_subplots_offsets.append([0, 0])
+
         for i, (label, y) in enumerate(self._iter_data(data=data)):
             ax = self._get_ax(i)
             kwds = self.kwds.copy()
@@ -1953,7 +1967,28 @@ class BarPlot(MPLPlot):
             start = start + self._start_base
 
             kwds["align"] = self._align
-            if self.subplots:
+
+            if i in _stacked_subplots_ind:
+                offset_index = _stacked_subplots_ind[i]
+                pos_prior, neg_prior = _stacked_subplots_offsets[offset_index]  # type:ignore[assignment]
+                mask = y >= 0
+                start = np.where(mask, pos_prior, neg_prior) + self._start_base
+                w = self.bar_width / 2
+                rect = self._plot(
+                    ax,
+                    self.ax_pos + w,
+                    y,
+                    self.bar_width,
+                    start=start,
+                    label=label,
+                    log=self.log,
+                    **kwds,
+                )
+                pos_new = pos_prior + np.where(mask, y, 0)
+                neg_new = neg_prior + np.where(mask, 0, y)
+                _stacked_subplots_offsets[offset_index] = [pos_new, neg_new]
+
+            elif self.subplots:
                 w = self.bar_width / 2
                 rect = self._plot(
                     ax,
diff --git a/scripts/cibw_before_build_windows.sh b/scripts/cibw_before_build_windows.sh
index dbf1d95d91..8f001db566 100644
--- a/scripts/cibw_before_build_windows.sh
+++ b/scripts/cibw_before_build_windows.sh
@@ -8,8 +8,6 @@ done
 FREE_THREADED_BUILD="$(python -c"import sysconfig; print(bool(sysconfig.get_config_var('Py_GIL_DISABLED')))")"
 if [[ $FREE_THREADED_BUILD == "True" ]]; then
     python -m pip install -U pip
-    # python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple cython
-    # TODO: Remove below and uncomment above once https://github.com/cython/cython/pull/6717 no longer breaks tests
-    python -m pip install git+https://github.com/cython/cython.git@3276b588720a053c78488e5de788605950f4b136
+    python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple cython
     python -m pip install ninja meson-python versioneer[toml] numpy
 fi
diff --git a/web/pandas/community/ecosystem.md b/web/pandas/community/ecosystem.md
index 3555d67c70..1ebd4f3d3f 100644
--- a/web/pandas/community/ecosystem.md
+++ b/web/pandas/community/ecosystem.md
@@ -124,7 +124,7 @@ sns.set_theme()
 Hadley Wickham's [ggplot2](https://ggplot2.tidyverse.org/) is a
 foundational exploratory visualization package for the R language. Based
 on ["The Grammar of
-Graphics"](https://www.cs.uic.edu/~wilkinson/TheGrammarOfGraphics/GOG.html)
+Graphics"](https://doi.org/10.1007/0-387-28695-0)
 it provides a powerful, declarative and extremely general way to
 generate bespoke plots of any kind of data.
 Various implementations to other languages are available.


Test patch:
diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py
index e49be8c00b..cae3013642 100644
--- a/pandas/tests/groupby/test_categorical.py
+++ b/pandas/tests/groupby/test_categorical.py
@@ -506,6 +506,23 @@ def test_observed_groups(observed):
     tm.assert_dict_equal(result, expected)
 
 
+def test_groups_na_category(dropna, observed):
+    # https://github.com/pandas-dev/pandas/issues/61356
+    df = DataFrame(
+        {"cat": Categorical(["a", np.nan, "a"], categories=list("adb"))},
+        index=list("xyz"),
+    )
+    g = df.groupby("cat", observed=observed, dropna=dropna)
+
+    result = g.groups
+    expected = {"a": Index(["x", "z"])}
+    if not dropna:
+        expected |= {np.nan: Index(["y"])}
+    if not observed:
+        expected |= {"b": Index([]), "d": Index([])}
+    tm.assert_dict_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     "keys, expected_values, expected_index_levels",
     [
diff --git a/pandas/tests/io/formats/test_to_latex.py b/pandas/tests/io/formats/test_to_latex.py
index 8d46442611..ebc6ff5be1 100644
--- a/pandas/tests/io/formats/test_to_latex.py
+++ b/pandas/tests/io/formats/test_to_latex.py
@@ -824,6 +824,46 @@ class TestToLatexEscape:
         )
         assert result == expected
 
+    def test_to_latex_escape_special_chars_in_index_names(self):
+        # https://github.com/pandas-dev/pandas/issues/61309
+        # https://github.com/pandas-dev/pandas/issues/57362
+        index = "&%$#_{}}~^\\"
+        df = DataFrame({index: [1, 2, 3]}).set_index(index)
+        result = df.to_latex(escape=True)
+        expected = _dedent(
+            r"""
+            \begin{tabular}{l}
+            \toprule
+            \&\%\$\#\_\{\}\}\textasciitilde \textasciicircum \textbackslash  \\
+            \midrule
+            1 \\
+            2 \\
+            3 \\
+            \bottomrule
+            \end{tabular}
+            """
+        )
+        assert result == expected
+
+    def test_to_latex_escape_special_chars_in_column_name(self):
+        df = DataFrame({"A": [1, 2, 3], "B": ["a", "b", "c"]})
+        df.columns.name = "_^~"
+        result = df.to_latex(escape=True)
+        expected = _dedent(
+            r"""
+            \begin{tabular}{lrl}
+            \toprule
+            \_\textasciicircum \textasciitilde  & A & B \\
+            \midrule
+            0 & 1 & a \\
+            1 & 2 & b \\
+            2 & 3 & c \\
+            \bottomrule
+            \end{tabular}
+            """
+        )
+        assert result == expected
+
     def test_to_latex_specified_header_special_chars_without_escape(self):
         # GH 7124
         df = DataFrame({"a": [1, 2], "b": ["b1", "b2"]})
diff --git a/pandas/tests/plotting/test_misc.py b/pandas/tests/plotting/test_misc.py
index 43e1255404..f4a0f1e792 100644
--- a/pandas/tests/plotting/test_misc.py
+++ b/pandas/tests/plotting/test_misc.py
@@ -681,3 +681,138 @@ class TestDataFramePlots:
             (a.get_text() == b.get_text())
             for a, b in zip(s.plot.bar().get_xticklabels(), expected)
         )
+
+
+@pytest.fixture
+def df_bar_data():
+    return np.random.default_rng(3).integers(0, 100, 5)
+
+
+@pytest.fixture
+def df_bar_df(df_bar_data) -> DataFrame:
+    df_bar_df = DataFrame(
+        {
+            "A": df_bar_data,
+            "B": df_bar_data[::-1],
+            "C": df_bar_data[0],
+            "D": df_bar_data[-1],
+        }
+    )
+    return df_bar_df
+
+
+def _df_bar_xyheight_from_ax_helper(df_bar_data, ax, subplot_division):
+    subplot_data_df_list = []
+
+    # get xy and height of squares representing data, separated by subplots
+    for i in range(len(subplot_division)):
+        subplot_data = np.array(
+            [
+                (x.get_x(), x.get_y(), x.get_height())
+                for x in ax[i].findobj(plt.Rectangle)
+                if x.get_height() in df_bar_data
+            ]
+        )
+        subplot_data_df_list.append(
+            DataFrame(data=subplot_data, columns=["x_coord", "y_coord", "height"])
+        )
+
+    return subplot_data_df_list
+
+
+def _df_bar_subplot_checker(df_bar_data, df_bar_df, subplot_data_df, subplot_columns):
+    subplot_sliced_by_source = [
+        subplot_data_df.iloc[
+            len(df_bar_data) * i : len(df_bar_data) * (i + 1)
+        ].reset_index()
+        for i in range(len(subplot_columns))
+    ]
+    expected_total_height = df_bar_df.loc[:, subplot_columns].sum(axis=1)
+
+    for i in range(len(subplot_columns)):
+        sliced_df = subplot_sliced_by_source[i]
+        if i == 0:
+            # Checks that the bar chart starts y=0
+            assert (sliced_df["y_coord"] == 0).all()
+            height_iter = sliced_df["y_coord"].add(sliced_df["height"])
+        else:
+            height_iter = height_iter + sliced_df["height"]
+
+        if i + 1 == len(subplot_columns):
+            # Checks final height matches what is expected
+            tm.assert_series_equal(
+                height_iter, expected_total_height, check_names=False, check_dtype=False
+            )
+
+        else:
+            # Checks each preceding bar ends where the next one starts
+            next_start_coord = subplot_sliced_by_source[i + 1]["y_coord"]
+            tm.assert_series_equal(
+                height_iter, next_start_coord, check_names=False, check_dtype=False
+            )
+
+
+# GH Issue 61018
+@pytest.mark.parametrize("columns_used", [["A", "B"], ["C", "D"], ["D", "A"]])
+def test_bar_1_subplot_1_double_stacked(df_bar_data, df_bar_df, columns_used):
+    df_bar_df_trimmed = df_bar_df[columns_used]
+    subplot_division = [columns_used]
+    ax = df_bar_df_trimmed.plot(subplots=subplot_division, kind="bar", stacked=True)
+    subplot_data_df_list = _df_bar_xyheight_from_ax_helper(
+        df_bar_data, ax, subplot_division
+    )
+    for i in range(len(subplot_data_df_list)):
+        _df_bar_subplot_checker(
+            df_bar_data, df_bar_df_trimmed, subplot_data_df_list[i], subplot_division[i]
+        )
+
+
+@pytest.mark.parametrize(
+    "columns_used", [["A", "B", "C"], ["A", "C", "B"], ["D", "A", "C"]]
+)
+def test_bar_2_subplot_1_double_stacked(df_bar_data, df_bar_df, columns_used):
+    df_bar_df_trimmed = df_bar_df[columns_used]
+    subplot_division = [(columns_used[0], columns_used[1]), (columns_used[2],)]
+    ax = df_bar_df_trimmed.plot(subplots=subplot_division, kind="bar", stacked=True)
+    subplot_data_df_list = _df_bar_xyheight_from_ax_helper(
+        df_bar_data, ax, subplot_division
+    )
+    for i in range(len(subplot_data_df_list)):
+        _df_bar_subplot_checker(
+            df_bar_data, df_bar_df_trimmed, subplot_data_df_list[i], subplot_division[i]
+        )
+
+
+@pytest.mark.parametrize(
+    "subplot_division",
+    [
+        [("A", "B"), ("C", "D")],
+        [("A", "D"), ("C", "B")],
+        [("B", "C"), ("D", "A")],
+        [("B", "D"), ("C", "A")],
+    ],
+)
+def test_bar_2_subplot_2_double_stacked(df_bar_data, df_bar_df, subplot_division):
+    ax = df_bar_df.plot(subplots=subplot_division, kind="bar", stacked=True)
+    subplot_data_df_list = _df_bar_xyheight_from_ax_helper(
+        df_bar_data, ax, subplot_division
+    )
+    for i in range(len(subplot_data_df_list)):
+        _df_bar_subplot_checker(
+            df_bar_data, df_bar_df, subplot_data_df_list[i], subplot_division[i]
+        )
+
+
+@pytest.mark.parametrize(
+    "subplot_division",
+    [[("A", "B", "C")], [("A", "D", "B")], [("C", "A", "D")], [("D", "C", "A")]],
+)
+def test_bar_2_subplots_1_triple_stacked(df_bar_data, df_bar_df, subplot_division):
+    ax = df_bar_df.plot(subplots=subplot_division, kind="bar", stacked=True)
+    subplot_data_df_list = _df_bar_xyheight_from_ax_helper(
+        df_bar_data, ax, subplot_division
+    )
+    for i in range(len(subplot_data_df_list)):
+        _df_bar_subplot_checker(
+            df_bar_data, df_bar_df, subplot_data_df_list[i], subplot_division[i]
+        )
diff --git a/pandas/tests/reshape/test_pivot.py b/pandas/tests/reshape/test_pivot.py
index 614200ae5b..2a58815c1c 100644
--- a/pandas/tests/reshape/test_pivot.py
+++ b/pandas/tests/reshape/test_pivot.py
@@ -2554,6 +2554,46 @@ class TestPivotTable:
 
         tm.assert_frame_equal(left=result, right=expected)
 
+    @pytest.mark.parametrize(
+        "index, columns, e_data, e_index, e_cols",
+        [
+            (
+                "Category",
+                "Value",
+                [
+                    [1.0, np.nan, 1.0, np.nan],
+                    [np.nan, 1.0, np.nan, 1.0],
+                ],
+                Index(data=["A", "B"], name="Category"),
+                Index(data=[10, 20, 40, 50], name="Value"),
+            ),
+            (
+                "Value",
+                "Category",
+                [
+                    [1.0, np.nan],
+                    [np.nan, 1.0],
+                    [1.0, np.nan],
+                    [np.nan, 1.0],
+                ],
+                Index(data=[10, 20, 40, 50], name="Value"),
+                Index(data=["A", "B"], name="Category"),
+            ),
+        ],
+        ids=["values-and-columns", "values-and-index"],
+    )
+    def test_pivot_table_values_as_two_params(
+        self, index, columns, e_data, e_index, e_cols
+    ):
+        # GH#57876
+        data = {"Category": ["A", "B", "A", "B"], "Value": [10, 20, 40, 50]}
+        df = DataFrame(data)
+        result = df.pivot_table(
+            index=index, columns=columns, values="Value", aggfunc="count"
+        )
+        expected = DataFrame(data=e_data, index=e_index, columns=e_cols)
+        tm.assert_frame_equal(result, expected)
+
 
 class TestPivot:
     def test_pivot(self):
diff --git a/pandas/tests/reshape/test_pivot_multilevel.py b/pandas/tests/reshape/test_pivot_multilevel.py
index 2c9d54c3db..af70210b37 100644
--- a/pandas/tests/reshape/test_pivot_multilevel.py
+++ b/pandas/tests/reshape/test_pivot_multilevel.py
@@ -250,3 +250,52 @@ def test_pivot_df_multiindex_index_none():
         columns=Index(["label1", "label2"], name="label"),
     )
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "index, columns, e_data, e_index, e_cols",
+    [
+        (
+            "index",
+            ["col", "value"],
+            [
+                [50.0, np.nan, 100.0, np.nan],
+                [np.nan, 100.0, np.nan, 200.0],
+            ],
+            Index(data=["A", "B"], name="index"),
+            MultiIndex.from_arrays(
+                arrays=[[1, 1, 2, 2], [50, 100, 100, 200]], names=["col", "value"]
+            ),
+        ),
+        (
+            ["index", "value"],
+            "col",
+            [
+                [50.0, np.nan],
+                [np.nan, 100.0],
+                [100.0, np.nan],
+                [np.nan, 200.0],
+            ],
+            MultiIndex.from_arrays(
+                arrays=[["A", "A", "B", "B"], [50, 100, 100, 200]],
+                names=["index", "value"],
+            ),
+            Index(data=[1, 2], name="col"),
+        ),
+    ],
+    ids=["values-and-columns", "values-and-index"],
+)
+def test_pivot_table_multiindex_values_as_two_params(
+    index, columns, e_data, e_index, e_cols
+):
+    # GH#61292
+    data = [
+        ["A", 1, 50, -1],
+        ["B", 1, 100, -2],
+        ["A", 2, 100, -2],
+        ["B", 2, 200, -4],
+    ]
+    df = pd.DataFrame(data=data, columns=["index", "col", "value", "extra"])
+    result = df.pivot_table(values="value", index=index, columns=columns)
+    expected = pd.DataFrame(data=e_data, index=e_index, columns=e_cols)
+    tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/window/test_numba.py b/pandas/tests/window/test_numba.py
index 887aeca659..ff6a616bc5 100644
--- a/pandas/tests/window/test_numba.py
+++ b/pandas/tests/window/test_numba.py
@@ -12,6 +12,7 @@ from pandas import (
     to_datetime,
 )
 import pandas._testing as tm
+from pandas.api.indexers import BaseIndexer
 from pandas.util.version import Version
 
 pytestmark = [pytest.mark.single_cpu]
@@ -581,3 +582,67 @@ def test_npfunc_no_warnings():
     df = DataFrame({"col1": [1, 2, 3, 4, 5]})
     with tm.assert_produces_warning(False):
         df.col1.rolling(2).apply(np.prod, raw=True, engine="numba")
+
+
+class PrescribedWindowIndexer(BaseIndexer):
+    def __init__(self, start, end):
+        self._start = start
+        self._end = end
+        super().__init__()
+
+    def get_window_bounds(
+        self, num_values=None, min_periods=None, center=None, closed=None, step=None
+    ):
+        if num_values is None:
+            num_values = len(self._start)
+        start = np.clip(self._start, 0, num_values)
+        end = np.clip(self._end, 0, num_values)
+        return start, end
+
+
+@td.skip_if_no("numba")
+class TestMinMaxNumba:
+    @pytest.mark.parametrize(
+        "is_max, has_nan, exp_list",
+        [
+            (True, False, [3.0, 5.0, 2.0, 5.0, 1.0, 5.0, 6.0, 7.0, 8.0, 9.0]),
+            (True, True, [3.0, 4.0, 2.0, 4.0, 1.0, 4.0, 6.0, 7.0, 7.0, 9.0]),
+            (False, False, [3.0, 2.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 7.0, 0.0]),
+            (False, True, [3.0, 2.0, 2.0, 1.0, 1.0, 1.0, 6.0, 6.0, 7.0, 1.0]),
+        ],
+    )
+    def test_minmax(self, is_max, has_nan, exp_list):
+        nan_idx = [0, 5, 8]
+        df = DataFrame(
+            {
+                "data": [5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 6.0, 7.0, 8.0, 9.0],
+                "start": [2, 0, 3, 0, 4, 0, 5, 5, 7, 3],
+                "end": [3, 4, 4, 5, 5, 6, 7, 8, 9, 10],
+            }
+        )
+        if has_nan:
+            df.loc[nan_idx, "data"] = np.nan
+        expected = Series(exp_list, name="data")
+        r = df.data.rolling(
+            PrescribedWindowIndexer(df.start.to_numpy(), df.end.to_numpy())
+        )
+        if is_max:
+            result = r.max(engine="numba")
+        else:
+            result = r.min(engine="numba")
+
+        tm.assert_series_equal(result, expected)
+
+    def test_wrong_order(self):
+        start = np.array(range(5), dtype=np.int64)
+        end = start + 1
+        end[3] = end[2]
+        start[3] = start[2] - 1
+
+        df = DataFrame({"data": start * 1.0, "start": start, "end": end})
+
+        r = df.data.rolling(PrescribedWindowIndexer(start, end))
+        with pytest.raises(
+            ValueError, match="Start/End ordering requirement is violated at index 3"
+        ):
+            r.max(engine="numba")
diff --git a/pandas/tests/window/test_rolling.py b/pandas/tests/window/test_rolling.py
index 8c57781c14..6e8f075d35 100644
--- a/pandas/tests/window/test_rolling.py
+++ b/pandas/tests/window/test_rolling.py
@@ -1946,3 +1946,66 @@ def test_rolling_timedelta_window_non_nanoseconds(unit, tz):
     df.index = df.index.as_unit("ns")
 
     tm.assert_frame_equal(ref_df, df)
+
+
+class PrescribedWindowIndexer(BaseIndexer):
+    def __init__(self, start, end):
+        self._start = start
+        self._end = end
+        super().__init__()
+
+    def get_window_bounds(
+        self, num_values=None, min_periods=None, center=None, closed=None, step=None
+    ):
+        if num_values is None:
+            num_values = len(self._start)
+        start = np.clip(self._start, 0, num_values)
+        end = np.clip(self._end, 0, num_values)
+        return start, end
+
+
+class TestMinMax:
+    @pytest.mark.parametrize(
+        "is_max, has_nan, exp_list",
+        [
+            (True, False, [3.0, 5.0, 2.0, 5.0, 1.0, 5.0, 6.0, 7.0, 8.0, 9.0]),
+            (True, True, [3.0, 4.0, 2.0, 4.0, 1.0, 4.0, 6.0, 7.0, 7.0, 9.0]),
+            (False, False, [3.0, 2.0, 2.0, 1.0, 1.0, 0.0, 0.0, 0.0, 7.0, 0.0]),
+            (False, True, [3.0, 2.0, 2.0, 1.0, 1.0, 1.0, 6.0, 6.0, 7.0, 1.0]),
+        ],
+    )
+    def test_minmax(self, is_max, has_nan, exp_list):
+        nan_idx = [0, 5, 8]
+        df = DataFrame(
+            {
+                "data": [5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 6.0, 7.0, 8.0, 9.0],
+                "start": [2, 0, 3, 0, 4, 0, 5, 5, 7, 3],
+                "end": [3, 4, 4, 5, 5, 6, 7, 8, 9, 10],
+            }
+        )
+        if has_nan:
+            df.loc[nan_idx, "data"] = np.nan
+        expected = Series(exp_list, name="data")
+        r = df.data.rolling(
+            PrescribedWindowIndexer(df.start.to_numpy(), df.end.to_numpy())
+        )
+        if is_max:
+            result = r.max()
+        else:
+            result = r.min()
+
+        tm.assert_series_equal(result, expected)
+
+    def test_wrong_order(self):
+        start = np.array(range(5), dtype=np.int64)
+        end = start + 1
+        end[3] = end[2]
+        start[3] = start[2] - 1
+
+        df = DataFrame({"data": start * 1.0, "start": start, "end": end})
+
+        r = df.data.rolling(PrescribedWindowIndexer(start, end))
+        with pytest.raises(
+            ValueError, match="Start/End ordering requirement is violated at index 3"
+        ):
+            r.max()