diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 9f59958b4e827..d04d0eaee6ec4 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -121,6 +121,8 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
       pandas/io/parsers/ \
       pandas/io/sas/ \
       pandas/io/sql.py \
+      pandas/io/formats/format.py \
+      pandas/io/formats/style.py \
       pandas/tseries/
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
diff --git a/doc/source/_static/style/df_pipe.png b/doc/source/_static/style/df_pipe.png
new file mode 100644
index 0000000000000..071a481ad5acc
Binary files /dev/null and b/doc/source/_static/style/df_pipe.png differ
diff --git a/doc/source/whatsnew/v1.3.2.rst b/doc/source/whatsnew/v1.3.2.rst
index 116bdd6e1d98f..8723b1b766485 100644
--- a/doc/source/whatsnew/v1.3.2.rst
+++ b/doc/source/whatsnew/v1.3.2.rst
@@ -19,6 +19,7 @@ Fixed regressions
 - Regression in :meth:`DataFrame.from_records` with empty records (:issue:`42456`)
 - Fixed regression in :meth:`DataFrame.shift` where TypeError occurred when shifting DataFrame created by concatenation of slices and fills with values (:issue:`42719`)
 - Regression in :meth:`DataFrame.agg` when the ``func`` argument returned lists and ``axis=1`` (:issue:`42727`)
+- Regression in :meth:`DataFrame.drop` does nothing if :class:`MultiIndex` has duplicates and indexer is a tuple or list of tuples (:issue:`42771`)
 - Fixed regression where :meth:`pandas.read_csv` raised a ``ValueError`` when parameters ``names`` and ``prefix`` were both set to None (:issue:`42387`)
 - Fixed regression in comparisons between :class:`Timestamp` object and ``datetime64`` objects outside the implementation bounds for nanosecond ``datetime64`` (:issue:`42794`)
 -
@@ -29,7 +30,7 @@ Fixed regressions
 
 Bug fixes
 ~~~~~~~~~
--
+- 1D slices over extension types turn into N-dimensional slices over ExtensionArrays (:issue:`42430`)
 -
 
 .. ---------------------------------------------------------------------------
diff --git a/doc/source/whatsnew/v1.4.0.rst b/doc/source/whatsnew/v1.4.0.rst
index e42360558d284..ad6a9d994bf7b 100644
--- a/doc/source/whatsnew/v1.4.0.rst
+++ b/doc/source/whatsnew/v1.4.0.rst
@@ -37,6 +37,7 @@ Other enhancements
 - :meth:`Series.ewm`, :meth:`DataFrame.ewm`, now support a ``method`` argument with a ``'table'`` option that performs the windowing operation over an entire :class:`DataFrame`. See :ref:`Window Overview <window.overview>` for performance and functional benefits (:issue:`42273`)
 - Added ``sparse_index`` and ``sparse_columns`` keyword arguments to :meth:`.Styler.to_html` (:issue:`41946`)
 - Added keyword argument ``environment`` to :meth:`.Styler.to_latex` also allowing a specific "longtable" entry with a separate jinja2 template (:issue:`41866`)
+- :meth:`.GroupBy.cummin` and :meth:`.GroupBy.cummax` now support the argument ``skipna`` (:issue:`34047`)
 -
 
 .. ---------------------------------------------------------------------------
@@ -170,6 +171,8 @@ Performance improvements
 - Performance improvement in :meth:`.GroupBy.transform` for user-defined functions (:issue:`41598`)
 - Performance improvement in constructing :class:`DataFrame` objects (:issue:`42631`)
 - Performance improvement in :meth:`GroupBy.shift` when ``fill_value`` argument is provided (:issue:`26615`)
+- Performance improvement in :meth:`DataFrame.corr` for ``method=pearson`` on data without missing values (:issue:`40956`)
+-
 
 .. ---------------------------------------------------------------------------
 
@@ -229,6 +232,8 @@ Indexing
 - Bug in :meth:`Series.loc` when with a :class:`MultiIndex` whose first level contains only ``np.nan`` values (:issue:`42055`)
 - Bug in indexing on a :class:`Series` or :class:`DataFrame` with a :class:`DatetimeIndex` when passing a string, the return type depended on whether the index was monotonic (:issue:`24892`)
 - Bug in indexing on a :class:`MultiIndex` failing to drop scalar levels when the indexer is a tuple containing a datetime-like string (:issue:`42476`)
+- Bug in :meth:`DataFrame.sort_values` and :meth:`Series.sort_values` when passing an ascending value, failed to raise or incorrectly raising ``ValueError`` (:issue:`41634`)
+- Bug in updating values of :class:`pandas.Series` using boolean index, created by using :meth:`pandas.DataFrame.pop` (:issue:`42530`)
 
 Missing
 ^^^^^^^
diff --git a/pandas/_libs/algos.pyx b/pandas/_libs/algos.pyx
index ff46c699c71e7..6c5388a38c345 100644
--- a/pandas/_libs/algos.pyx
+++ b/pandas/_libs/algos.pyx
@@ -326,8 +326,12 @@ def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
         Py_ssize_t i, j, xi, yi, N, K
         bint minpv
         float64_t[:, ::1] result
+        # Initialize to None since we only use in the no missing value case
+        float64_t[::1] means=None, ssqds=None
         ndarray[uint8_t, ndim=2] mask
+        bint no_nans
         int64_t nobs = 0
+        float64_t mean, ssqd, val
         float64_t vx, vy, dx, dy, meanx, meany, divisor, ssqdmx, ssqdmy, covxy
 
     N, K = (<object>mat).shape
@@ -339,25 +343,57 @@ def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
 
     result = np.empty((K, K), dtype=np.float64)
     mask = np.isfinite(mat).view(np.uint8)
+    no_nans = mask.all()
+
+    # Computing the online means and variances is expensive - so if possible we can
+    # precompute these and avoid repeating the computations each time we handle
+    # an (xi, yi) pair
+    if no_nans:
+        means = np.empty(K, dtype=np.float64)
+        ssqds = np.empty(K, dtype=np.float64)
+
+        with nogil:
+            for j in range(K):
+                ssqd = mean = 0
+                for i in range(N):
+                    val = mat[i, j]
+                    dx = val - mean
+                    mean += 1 / (i + 1) * dx
+                    ssqd += (val - mean) * dx
+
+                means[j] = mean
+                ssqds[j] = ssqd
 
     with nogil:
         for xi in range(K):
             for yi in range(xi + 1):
-                # Welford's method for the variance-calculation
-                # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
-                nobs = ssqdmx = ssqdmy = covxy = meanx = meany = 0
-                for i in range(N):
-                    if mask[i, xi] and mask[i, yi]:
+                covxy = 0
+                if no_nans:
+                    for i in range(N):
                         vx = mat[i, xi]
                         vy = mat[i, yi]
-                        nobs += 1
-                        dx = vx - meanx
-                        dy = vy - meany
-                        meanx += 1 / nobs * dx
-                        meany += 1 / nobs * dy
-                        ssqdmx += (vx - meanx) * dx
-                        ssqdmy += (vy - meany) * dy
-                        covxy += (vx - meanx) * dy
+                        covxy += (vx - means[xi]) * (vy - means[yi])
+
+                    ssqdmx = ssqds[xi]
+                    ssqdmy = ssqds[yi]
+                    nobs = N
+
+                else:
+                    nobs = ssqdmx = ssqdmy = covxy = meanx = meany = 0
+                    for i in range(N):
+                        # Welford's method for the variance-calculation
+                        # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+                        if mask[i, xi] and mask[i, yi]:
+                            vx = mat[i, xi]
+                            vy = mat[i, yi]
+                            nobs += 1
+                            dx = vx - meanx
+                            dy = vy - meany
+                            meanx += 1 / nobs * dx
+                            meany += 1 / nobs * dy
+                            ssqdmx += (vx - meanx) * dx
+                            ssqdmy += (vy - meany) * dy
+                            covxy += (vx - meanx) * dy
 
                 if nobs < minpv:
                     result[xi, yi] = result[yi, xi] = NaN
diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index 354b87e03e6c4..91921ba0e64c2 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -1317,6 +1317,7 @@ cdef group_cummin_max(groupby_t[:, ::1] out,
                       const intp_t[:] labels,
                       int ngroups,
                       bint is_datetimelike,
+                      bint skipna,
                       bint compute_max):
     """
     Cumulative minimum/maximum of columns of `values`, in row groups `labels`.
@@ -1336,6 +1337,8 @@ cdef group_cummin_max(groupby_t[:, ::1] out,
         Number of groups, larger than all entries of `labels`.
     is_datetimelike : bool
         True if `values` contains datetime-like entries.
+    skipna : bool
+        If True, ignore nans in `values`.
     compute_max : bool
         True if cumulative maximum should be computed, False
         if cumulative minimum should be computed
@@ -1356,9 +1359,9 @@ cdef group_cummin_max(groupby_t[:, ::1] out,
         accum[:] = -np.inf if compute_max else np.inf
 
     if mask is not None:
-        masked_cummin_max(out, values, mask, labels, accum, compute_max)
+        masked_cummin_max(out, values, mask, labels, accum, skipna, compute_max)
     else:
-        cummin_max(out, values, labels, accum, is_datetimelike, compute_max)
+        cummin_max(out, values, labels, accum, skipna, is_datetimelike, compute_max)
 
 
 @cython.boundscheck(False)
@@ -1367,6 +1370,7 @@ cdef cummin_max(groupby_t[:, ::1] out,
                 ndarray[groupby_t, ndim=2] values,
                 const intp_t[:] labels,
                 groupby_t[:, ::1] accum,
+                bint skipna,
                 bint is_datetimelike,
                 bint compute_max):
     """
@@ -1375,8 +1379,24 @@ cdef cummin_max(groupby_t[:, ::1] out,
     """
     cdef:
         Py_ssize_t i, j, N, K
-        groupby_t val, mval
+        groupby_t val, mval, na_val
+        uint8_t[:, ::1] seen_na
         intp_t lab
+        bint na_possible
+
+    if groupby_t is float64_t or groupby_t is float32_t:
+        na_val = NaN
+        na_possible = True
+    elif is_datetimelike:
+        na_val = NPY_NAT
+        na_possible = True
+    # Will never be used, just to avoid uninitialized warning
+    else:
+        na_val = 0
+        na_possible = False
+
+    if na_possible:
+        seen_na = np.zeros((<object>accum).shape, dtype=np.uint8)
 
     N, K = (<object>values).shape
     with nogil:
@@ -1385,18 +1405,22 @@ cdef cummin_max(groupby_t[:, ::1] out,
             if lab < 0:
                 continue
             for j in range(K):
-                val = values[i, j]
-                if not _treat_as_na(val, is_datetimelike):
-                    mval = accum[lab, j]
-                    if compute_max:
-                        if val > mval:
-                            accum[lab, j] = mval = val
-                    else:
-                        if val < mval:
-                            accum[lab, j] = mval = val
-                    out[i, j] = mval
+                if not skipna and na_possible and seen_na[lab, j]:
+                    out[i, j] = na_val
                 else:
-                    out[i, j] = val
+                    val = values[i, j]
+                    if not _treat_as_na(val, is_datetimelike):
+                        mval = accum[lab, j]
+                        if compute_max:
+                            if val > mval:
+                                accum[lab, j] = mval = val
+                        else:
+                            if val < mval:
+                                accum[lab, j] = mval = val
+                        out[i, j] = mval
+                    else:
+                        seen_na[lab, j] = 1
+                        out[i, j] = val
 
 
 @cython.boundscheck(False)
@@ -1406,6 +1430,7 @@ cdef masked_cummin_max(groupby_t[:, ::1] out,
                        uint8_t[:, ::1] mask,
                        const intp_t[:] labels,
                        groupby_t[:, ::1] accum,
+                       bint skipna,
                        bint compute_max):
     """
     Compute the cumulative minimum/maximum of columns of `values`, in row groups
@@ -1414,25 +1439,32 @@ cdef masked_cummin_max(groupby_t[:, ::1] out,
     cdef:
         Py_ssize_t i, j, N, K
         groupby_t val, mval
+        uint8_t[:, ::1] seen_na
         intp_t lab
 
     N, K = (<object>values).shape
+    seen_na = np.zeros((<object>accum).shape, dtype=np.uint8)
     with nogil:
         for i in range(N):
             lab = labels[i]
             if lab < 0:
                 continue
             for j in range(K):
-                if not mask[i, j]:
-                    val = values[i, j]
-                    mval = accum[lab, j]
-                    if compute_max:
-                        if val > mval:
-                            accum[lab, j] = mval = val
+                if not skipna and seen_na[lab, j]:
+                    mask[i, j] = 1
+                else:
+                    if not mask[i, j]:
+                        val = values[i, j]
+                        mval = accum[lab, j]
+                        if compute_max:
+                            if val > mval:
+                                accum[lab, j] = mval = val
+                        else:
+                            if val < mval:
+                                accum[lab, j] = mval = val
+                        out[i, j] = mval
                     else:
-                        if val < mval:
-                            accum[lab, j] = mval = val
-                    out[i, j] = mval
+                        seen_na[lab, j] = 1
 
 
 @cython.boundscheck(False)
@@ -1442,7 +1474,8 @@ def group_cummin(groupby_t[:, ::1] out,
                  const intp_t[:] labels,
                  int ngroups,
                  bint is_datetimelike,
-                 uint8_t[:, ::1] mask=None) -> None:
+                 uint8_t[:, ::1] mask=None,
+                 bint skipna=True) -> None:
     """See group_cummin_max.__doc__"""
     group_cummin_max(
         out,
@@ -1451,6 +1484,7 @@ def group_cummin(groupby_t[:, ::1] out,
         labels,
         ngroups,
         is_datetimelike,
+        skipna,
         compute_max=False
     )
 
@@ -1462,7 +1496,8 @@ def group_cummax(groupby_t[:, ::1] out,
                  const intp_t[:] labels,
                  int ngroups,
                  bint is_datetimelike,
-                 uint8_t[:, ::1] mask=None) -> None:
+                 uint8_t[:, ::1] mask=None,
+                 bint skipna=True) -> None:
     """See group_cummin_max.__doc__"""
     group_cummin_max(
         out,
@@ -1471,5 +1506,6 @@ def group_cummax(groupby_t[:, ::1] out,
         labels,
         ngroups,
         is_datetimelike,
+        skipna,
         compute_max=True
     )
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 4d380c6831071..7d51b50f783a5 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -1221,7 +1221,7 @@ def factorize(self, sort: bool = False, na_sentinel: int | None = -1):
         """
 
     @doc(_shared_docs["searchsorted"], klass="Index")
-    def searchsorted(self, value, side="left", sorter=None) -> np.ndarray:
+    def searchsorted(self, value, side="left", sorter=None) -> npt.NDArray[np.intp]:
         return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)
 
     def drop_duplicates(self, keep="first"):
@@ -1232,5 +1232,5 @@ def drop_duplicates(self, keep="first"):
     @final
     def _duplicated(
         self, keep: Literal["first", "last", False] = "first"
-    ) -> np.ndarray:
+    ) -> npt.NDArray[np.bool_]:
         return duplicated(self._values, keep=keep)
diff --git a/pandas/core/construction.py b/pandas/core/construction.py
index 89591f27e9092..f84aaa907f3fc 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -402,7 +402,7 @@ def extract_array(
     >>> extract_array([1, 2, 3])
     [1, 2, 3]
 
-    For an ndarray-backed Series / Index a PandasArray is returned.
+    For an ndarray-backed Series / Index the ndarray is returned.
 
     >>> extract_array(pd.Series([1, 2, 3]))
     array([1, 2, 3])
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 27aa2ed939c1a..48b18a33f9c9f 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -85,6 +85,7 @@
     rewrite_axis_style_signature,
 )
 from pandas.util._validators import (
+    validate_ascending,
     validate_axis_style_args,
     validate_bool_kwarg,
     validate_percentile,
@@ -6202,7 +6203,7 @@ def sort_values(  # type: ignore[override]
     ):
         inplace = validate_bool_kwarg(inplace, "inplace")
         axis = self._get_axis_number(axis)
-
+        ascending = validate_ascending(ascending)
         if not isinstance(by, list):
             by = [by]
         if is_sequence(ascending) and len(by) != len(ascending):
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index bf79e58077179..19dd06074bf78 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -99,7 +99,10 @@
     ABCDataFrame,
     ABCSeries,
 )
-from pandas.core.dtypes.inference import is_hashable
+from pandas.core.dtypes.inference import (
+    is_hashable,
+    is_nested_list_like,
+)
 from pandas.core.dtypes.missing import (
     isna,
     notna,
@@ -4182,6 +4185,7 @@ def _drop_axis(
 
         # Case for non-unique axis
         else:
+            is_tuple_labels = is_nested_list_like(labels) or isinstance(labels, tuple)
             labels = ensure_object(com.index_labels_to_array(labels))
             if level is not None:
                 if not isinstance(axis, MultiIndex):
@@ -4191,9 +4195,14 @@ def _drop_axis(
                 # GH 18561 MultiIndex.drop should raise if label is absent
                 if errors == "raise" and indexer.all():
                     raise KeyError(f"{labels} not found in axis")
-            elif isinstance(axis, MultiIndex) and labels.dtype == "object":
+            elif (
+                isinstance(axis, MultiIndex)
+                and labels.dtype == "object"
+                and not is_tuple_labels
+            ):
                 # Set level to zero in case of MultiIndex and label is string,
                 #  because isin can't handle strings for MultiIndexes GH#36293
+                # In case of tuples we get dtype object but have to use isin GH#42771
                 indexer = ~axis.get_level_values(0).isin(labels)
             else:
                 indexer = ~axis.isin(labels)
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 939cff16bf1ae..e57e48cb3ab11 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2784,10 +2784,11 @@ def cummin(self, axis=0, **kwargs):
         -------
         Series or DataFrame
         """
+        skipna = kwargs.get("skipna", True)
         if axis != 0:
             return self.apply(lambda x: np.minimum.accumulate(x, axis))
 
-        return self._cython_transform("cummin", numeric_only=False)
+        return self._cython_transform("cummin", numeric_only=False, skipna=skipna)
 
     @final
     @Substitution(name="groupby")
@@ -2800,10 +2801,11 @@ def cummax(self, axis=0, **kwargs):
         -------
         Series or DataFrame
         """
+        skipna = kwargs.get("skipna", True)
         if axis != 0:
             return self.apply(lambda x: np.maximum.accumulate(x, axis))
 
-        return self._cython_transform("cummax", numeric_only=False)
+        return self._cython_transform("cummax", numeric_only=False, skipna=skipna)
 
     @final
     def _get_cythonized_result(
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index 07c6a84f75302..7dc59bdb1e840 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -238,10 +238,11 @@ def _format_attrs(self):
         """
         attrs = super()._format_attrs()
         for attrib in self._attributes:
+            # iterating over _attributes prevents us from doing this for PeriodIndex
             if attrib == "freq":
                 freq = self.freqstr
                 if freq is not None:
-                    freq = repr(freq)
+                    freq = repr(freq)  # e.g. D -> 'D'
                 # Argument 1 to "append" of "list" has incompatible type
                 # "Tuple[str, Optional[str]]"; expected "Tuple[str, Union[str, int]]"
                 attrs.append(("freq", freq))  # type: ignore[arg-type]
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 3b42d1c4505da..60179b69f56a4 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -642,7 +642,7 @@ def _get_setitem_indexer(self, key):
             self._ensure_listlike_indexer(key)
 
         if self.axis is not None:
-            return self._convert_tuple(key, is_setter=True)
+            return self._convert_tuple(key)
 
         ax = self.obj._get_axis(0)
 
@@ -653,12 +653,12 @@ def _get_setitem_indexer(self, key):
 
         if isinstance(key, tuple):
             with suppress(IndexingError):
-                return self._convert_tuple(key, is_setter=True)
+                return self._convert_tuple(key)
 
         if isinstance(key, range):
             return list(key)
 
-        return self._convert_to_indexer(key, axis=0, is_setter=True)
+        return self._convert_to_indexer(key, axis=0)
 
     def _ensure_listlike_indexer(self, key, axis=None, value=None):
         """
@@ -755,21 +755,19 @@ def _is_nested_tuple_indexer(self, tup: tuple) -> bool:
             return any(is_nested_tuple(tup, ax) for ax in self.obj.axes)
         return False
 
-    def _convert_tuple(self, key, is_setter: bool = False):
+    def _convert_tuple(self, key):
         keyidx = []
         if self.axis is not None:
             axis = self.obj._get_axis_number(self.axis)
             for i in range(self.ndim):
                 if i == axis:
-                    keyidx.append(
-                        self._convert_to_indexer(key, axis=axis, is_setter=is_setter)
-                    )
+                    keyidx.append(self._convert_to_indexer(key, axis=axis))
                 else:
                     keyidx.append(slice(None))
         else:
             self._validate_key_length(key)
             for i, k in enumerate(key):
-                idx = self._convert_to_indexer(k, axis=i, is_setter=is_setter)
+                idx = self._convert_to_indexer(k, axis=i)
                 keyidx.append(idx)
 
         return tuple(keyidx)
@@ -867,8 +865,8 @@ def _getitem_nested_tuple(self, tup: tuple):
         # a tuple passed to a series with a multi-index
         if len(tup) > self.ndim:
             if self.name != "loc":
-                # This should never be reached, but lets be explicit about it
-                raise ValueError("Too many indices")
+                # This should never be reached, but let's be explicit about it
+                raise ValueError("Too many indices")  # pragma: no cover
             if all(is_hashable(x) or com.is_null_slice(x) for x in tup):
                 # GH#10521 Series should reduce MultiIndex dimensions instead of
                 #  DataFrame, IndexingError is not raised when slice(None,None,None)
@@ -911,7 +909,7 @@ def _getitem_nested_tuple(self, tup: tuple):
 
         return obj
 
-    def _convert_to_indexer(self, key, axis: int, is_setter: bool = False):
+    def _convert_to_indexer(self, key, axis: int):
         raise AbstractMethodError(self)
 
     def __getitem__(self, key):
@@ -1176,7 +1174,7 @@ def _get_slice_axis(self, slice_obj: slice, axis: int):
             #  return a DatetimeIndex instead of a slice object.
             return self.obj.take(indexer, axis=axis)
 
-    def _convert_to_indexer(self, key, axis: int, is_setter: bool = False):
+    def _convert_to_indexer(self, key, axis: int):
         """
         Convert indexing key into something we can use to do actual fancy
         indexing on a ndarray.
@@ -1486,7 +1484,7 @@ def _get_slice_axis(self, slice_obj: slice, axis: int):
         labels._validate_positional_slice(slice_obj)
         return self.obj._slice(slice_obj, axis=axis)
 
-    def _convert_to_indexer(self, key, axis: int, is_setter: bool = False):
+    def _convert_to_indexer(self, key, axis: int):
         """
         Much simpler as we only have to deal with our valid types.
         """
diff --git a/pandas/core/internals/array_manager.py b/pandas/core/internals/array_manager.py
index bd049b80b281c..79c0aad66229c 100644
--- a/pandas/core/internals/array_manager.py
+++ b/pandas/core/internals/array_manager.py
@@ -167,15 +167,6 @@ def set_axis(self, axis: int, new_labels: Index) -> None:
         axis = self._normalize_axis(axis)
         self._axes[axis] = new_labels
 
-    def consolidate(self: T) -> T:
-        return self
-
-    def is_consolidated(self) -> bool:
-        return True
-
-    def _consolidate_inplace(self) -> None:
-        pass
-
     def get_dtypes(self):
         return np.array([arr.dtype for arr in self.arrays], dtype="object")
 
@@ -1262,9 +1253,6 @@ def _can_hold_na(self) -> bool:
     def is_single_block(self) -> bool:
         return True
 
-    def _consolidate_check(self):
-        pass
-
     def fast_xs(self, loc: int) -> ArrayLike:
         raise NotImplementedError("Use series._values[loc] instead")
 
diff --git a/pandas/core/internals/base.py b/pandas/core/internals/base.py
index 0ee22200ed495..e65318dd29c52 100644
--- a/pandas/core/internals/base.py
+++ b/pandas/core/internals/base.py
@@ -33,6 +33,7 @@ class DataManager(PandasObject):
     def items(self) -> Index:
         raise AbstractMethodError(self)
 
+    @final
     def __len__(self) -> int:
         return len(self.items)
 
@@ -105,6 +106,7 @@ def _equal_values(self: T, other: T) -> bool:
         """
         raise AbstractMethodError(self)
 
+    @final
     def equals(self, other: object) -> bool:
         """
         Implementation for DataFrame.equals
@@ -129,13 +131,27 @@ def apply(
     ) -> T:
         raise AbstractMethodError(self)
 
+    @final
     def isna(self: T, func) -> T:
         return self.apply("apply", func=func)
 
+    # --------------------------------------------------------------------
+    # Consolidation: No-ops for all but BlockManager
+
+    def is_consolidated(self) -> bool:
+        return True
+
+    def consolidate(self: T) -> T:
+        return self
+
+    def _consolidate_inplace(self) -> None:
+        return
+
 
 class SingleDataManager(DataManager):
     ndim = 1
 
+    @final
     @property
     def array(self):
         """
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index adfecb946d822..953ccedaa5222 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -1552,12 +1552,10 @@ def _slice(self, slicer):
     def getitem_block_index(self, slicer: slice) -> ExtensionBlock:
         """
         Perform __getitem__-like specialized to slicing along index.
-
-        Assumes self.ndim == 2
         """
-        # error: Invalid index type "Tuple[ellipsis, slice]" for
-        # "Union[ndarray, ExtensionArray]"; expected type "Union[int, slice, ndarray]"
-        new_values = self.values[..., slicer]  # type: ignore[index]
+        # GH#42787 in principle this is equivalent to values[..., slicer], but we don't
+        # require subclasses of ExtensionArray to support that form (for now).
+        new_values = self.values[slicer]
         return type(self)(new_values, self._mgr_locs, ndim=self.ndim)
 
     def fillna(
diff --git a/pandas/core/internals/concat.py b/pandas/core/internals/concat.py
index 34d0137c26fda..9bc2404cefcfa 100644
--- a/pandas/core/internals/concat.py
+++ b/pandas/core/internals/concat.py
@@ -672,6 +672,7 @@ def _combine_concat_plans(plans, concat_axis: int):
                 offset += last_plc.as_slice.stop
 
     else:
+        # singleton list so we can modify it as a side-effect within _next_or_none
         num_ended = [0]
 
         def _next_or_none(seq):
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 8937c2c107c62..9d35e1e8d4929 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -465,19 +465,6 @@ def to_native_types(self: T, **kwargs) -> T:
         """
         return self.apply("to_native_types", **kwargs)
 
-    def is_consolidated(self) -> bool:
-        """
-        Return True if more than one block with the same dtype
-        """
-        if not self._known_consolidated:
-            self._consolidate_check()
-        return self._is_consolidated
-
-    def _consolidate_check(self) -> None:
-        dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate]
-        self._is_consolidated = len(dtypes) == len(set(dtypes))
-        self._known_consolidated = True
-
     @property
     def is_numeric_mixed_type(self) -> bool:
         return all(block.is_numeric for block in self.blocks)
@@ -623,13 +610,6 @@ def consolidate(self: T) -> T:
         bm._consolidate_inplace()
         return bm
 
-    def _consolidate_inplace(self) -> None:
-        if not self.is_consolidated():
-            self.blocks = tuple(_consolidate(self.blocks))
-            self._is_consolidated = True
-            self._known_consolidated = True
-            self._rebuild_blknos_and_blklocs()
-
     def reindex_indexer(
         self: T,
         new_axis: Index,
@@ -1551,6 +1531,29 @@ def _interleave(
 
         return result
 
+    # ----------------------------------------------------------------
+    # Consolidation
+
+    def is_consolidated(self) -> bool:
+        """
+        Return True if more than one block with the same dtype
+        """
+        if not self._known_consolidated:
+            self._consolidate_check()
+        return self._is_consolidated
+
+    def _consolidate_check(self) -> None:
+        dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate]
+        self._is_consolidated = len(dtypes) == len(set(dtypes))
+        self._known_consolidated = True
+
+    def _consolidate_inplace(self) -> None:
+        if not self.is_consolidated():
+            self.blocks = tuple(_consolidate(self.blocks))
+            self._is_consolidated = True
+            self._known_consolidated = True
+            self._rebuild_blknos_and_blklocs()
+
 
 class SingleBlockManager(BaseBlockManager, SingleDataManager):
     """manage a single block with"""
@@ -1710,15 +1713,6 @@ def array_values(self):
     def _can_hold_na(self) -> bool:
         return self._block._can_hold_na
 
-    def is_consolidated(self) -> bool:
-        return True
-
-    def _consolidate_check(self):
-        pass
-
-    def _consolidate_inplace(self):
-        pass
-
     def idelete(self, indexer) -> SingleBlockManager:
         """
         Delete single location from SingleBlockManager.
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 32b56462788e5..ce986f2dd8038 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -57,6 +57,7 @@
     doc,
 )
 from pandas.util._validators import (
+    validate_ascending,
     validate_bool_kwarg,
     validate_percentile,
 )
@@ -69,7 +70,6 @@
 )
 from pandas.core.dtypes.common import (
     ensure_platform_int,
-    is_bool,
     is_dict_like,
     is_integer,
     is_iterator,
@@ -3438,8 +3438,7 @@ def sort_values(
                 )
             ascending = ascending[0]
 
-        if not is_bool(ascending):
-            raise ValueError("ascending must be boolean")
+        ascending = validate_ascending(ascending)
 
         if na_position not in ["first", "last"]:
             raise ValueError(f"invalid na_position: {na_position}")
@@ -4386,7 +4385,7 @@ def _reduce(
                 return op(delegate, skipna=skipna, **kwds)
 
     def _reindex_indexer(
-        self, new_index: Index | None, indexer: np.ndarray | None, copy: bool
+        self, new_index: Index | None, indexer: npt.NDArray[np.intp] | None, copy: bool
     ) -> Series:
         # Note: new_index is None iff indexer is None
         # if not None, indexer is np.intp
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 83e0086958b9a..3fd3d84f90161 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -1956,16 +1956,14 @@ def __call__(self, num: int | float) -> str:
         """
         Formats a number in engineering notation, appending a letter
         representing the power of 1000 of the original number. Some examples:
-
-        >>> format_eng(0)       # for self.accuracy = 0
+        >>> format_eng = EngFormatter(accuracy=0, use_eng_prefix=True)
+        >>> format_eng(0)
         ' 0'
-
-        >>> format_eng(1000000) # for self.accuracy = 1,
-                                #     self.use_eng_prefix = True
+        >>> format_eng = EngFormatter(accuracy=1, use_eng_prefix=True)
+        >>> format_eng(1_000_000)
         ' 1.0M'
-
-        >>> format_eng("-1e-6") # for self.accuracy = 2
-                                #     self.use_eng_prefix = False
+        >>> format_eng = EngFormatter(accuracy=2, use_eng_prefix=False)
+        >>> format_eng("-1e-6")
         '-1.00E-06'
 
         @param num: the value to represent
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index af1c0ca34ec0f..3d6705ed593d2 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -341,9 +341,10 @@ def set_tooltips(
         >>> df.style.set_tooltips(ttips, css_class='tt-add', props=[
         ...     ('visibility', 'hidden'),
         ...     ('position', 'absolute'),
-        ...     ('z-index', 1)])
+        ...     ('z-index', 1)])  # doctest: +SKIP
         >>> df.style.set_tooltips(ttips, css_class='tt-add',
         ...     props='visibility:hidden; position:absolute; z-index:1;')
+        ... # doctest: +SKIP
         """
         if not self.cell_ids:
             # tooltips not optimised for individual cell check. requires reasonable
@@ -553,7 +554,7 @@ def to_latex(
 
         >>> s = df.style.highlight_max(axis=None,
         ...                            props='cellcolor:{red}; bfseries: ;')
-        >>> s.to_latex()
+        >>> s.to_latex()  # doctest: +SKIP
 
         Internally these structured LaTeX ``(<command>, <options>)`` pairs
         are translated to the
@@ -592,7 +593,7 @@ def to_latex(
         ...     props='cellcolor:[HTML]{FFFF00}; color:{red};'
         ...           'textit:--rwrap; textbf:--rwrap;'
         ... )
-        >>> s.to_latex()
+        >>> s.to_latex()  # doctest: +SKIP
 
         .. figure:: ../../_static/style/latex_1.png
 
@@ -653,7 +654,7 @@ def to_latex(
         ...     column_format="rrrrr", position="h", position_float="centering",
         ...     hrules=True, label="table:5", caption="Styled LaTeX Table",
         ...     multirow_align="t", multicol_align="r"
-        ... )
+        ... )  # doctest: +SKIP
 
         .. figure:: ../../_static/style/latex_2.png
 
@@ -670,8 +671,14 @@ def to_latex(
         ...    ("Numeric", "Integers"): '\${}',
         ...    ("Numeric", "Floats"): '{:.3f}',
         ...    ("Non-Numeric", "Strings"): str.upper
-        ... })
-        >>> s.to_latex()
+        ... })  # doctest: +SKIP
+                        Numeric      Non-Numeric
+                  Integers   Floats    Strings
+        L0    ix1       $1   2.200      DOGS
+              ix2       $3   4.400      CATS
+        L1    ix3       $2   6.600      COWS
+
+        >>> s.to_latex()  # doctest: +SKIP
         \begin{tabular}{llrrl}
         {} & {} & \multicolumn{2}{r}{Numeric} & {Non-Numeric} \\
         {} & {} & {Integers} & {Floats} & {Strings} \\
@@ -713,7 +720,7 @@ def to_latex(
         >>> df = pd.DataFrame([[1]])
         >>> df.style.set_properties(
         ...     **{"font-weight": "bold /* --dwrap */", "Huge": "--latex--rwrap"}
-        ... ).to_latex(convert_css=True)
+        ... ).to_latex(convert_css=True)  # doctest: +SKIP
         \begin{tabular}{lr}
         {} & {0} \\
         0 & {\bfseries}{\Huge{1}} \\
@@ -934,7 +941,7 @@ def set_td_classes(self, classes: DataFrame) -> Styler:
         ...     ["min-val red", "", "blue"],
         ...     ["red", None, "blue max-val"]
         ... ], index=df.index, columns=df.columns)
-        >>> df.style.set_td_classes(classes)
+        >>> df.style.set_td_classes(classes)  # doctest: +SKIP
 
         Using `MultiIndex` columns and a `classes` `DataFrame` as a subset of the
         underlying,
@@ -943,14 +950,14 @@ def set_td_classes(self, classes: DataFrame) -> Styler:
         ...     columns=[["level0", "level0"], ["level1a", "level1b"]])
         >>> classes = pd.DataFrame(["min-val"], index=["a"],
         ...     columns=[["level0"],["level1a"]])
-        >>> df.style.set_td_classes(classes)
+        >>> df.style.set_td_classes(classes)  # doctest: +SKIP
 
         Form of the output with new additional css classes,
 
         >>> df = pd.DataFrame([[1]])
         >>> css = pd.DataFrame([["other-class"]])
         >>> s = Styler(df, uuid="_", cell_ids=False).set_td_classes(css)
-        >>> s.hide_index().render()
+        >>> s.hide_index().render()  # doctest: +SKIP
         '<style type="text/css"></style>'
         '<table id="T__">'
         '  <thead>'
@@ -1178,19 +1185,26 @@ def apply(
         >>> def highlight_max(x, color):
         ...     return np.where(x == np.nanmax(x.to_numpy()), f"color: {color};", None)
         >>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"])
-        >>> df.style.apply(highlight_max, color='red')
-        >>> df.style.apply(highlight_max, color='blue', axis=1)
-        >>> df.style.apply(highlight_max, color='green', axis=None)
+        >>> df.style.apply(highlight_max, color='red')  # doctest: +SKIP
+        >>> df.style.apply(highlight_max, color='blue', axis=1)  # doctest: +SKIP
+        >>> df.style.apply(highlight_max, color='green', axis=None)  # doctest: +SKIP
 
         Using ``subset`` to restrict application to a single column or multiple columns
 
         >>> df.style.apply(highlight_max, color='red', subset="A")
+        ...  # doctest: +SKIP
         >>> df.style.apply(highlight_max, color='red', subset=["A", "B"])
+        ...  # doctest: +SKIP
 
         Using a 2d input to ``subset`` to select rows in addition to columns
 
-        >>> df.style.apply(highlight_max, color='red', subset=([0,1,2], slice(None))
-        >>> df.style.apply(highlight_max, color='red', subset=(slice(0,5,2), "A")
+        >>> df.style.apply(highlight_max, color='red', subset=([0,1,2], slice(None)))
+        ...  # doctest: +SKIP
+        >>> df.style.apply(highlight_max, color='red', subset=(slice(0,5,2), "A"))
+        ...  # doctest: +SKIP
+
+        See `Table Visualization <../../user_guide/style.ipynb>`_ user guide for
+        more details.
         """
         self._todo.append(
             (lambda instance: getattr(instance, "_apply"), (func, axis, subset), kwargs)
@@ -1246,17 +1260,24 @@ def applymap(
         >>> def color_negative(v, color):
         ...     return f"color: {color};" if v < 0 else None
         >>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"])
-        >>> df.style.applymap(color_negative, color='red')
+        >>> df.style.applymap(color_negative, color='red')  # doctest: +SKIP
 
         Using ``subset`` to restrict application to a single column or multiple columns
 
         >>> df.style.applymap(color_negative, color='red', subset="A")
+        ...  # doctest: +SKIP
         >>> df.style.applymap(color_negative, color='red', subset=["A", "B"])
+        ...  # doctest: +SKIP
 
         Using a 2d input to ``subset`` to select rows in addition to columns
 
-        >>> df.style.applymap(color_negative, color='red', subset=([0,1,2], slice(None))
-        >>> df.style.applymap(color_negative, color='red', subset=(slice(0,5,2), "A")
+        >>> df.style.applymap(color_negative, color='red',
+        ...  subset=([0,1,2], slice(None)))  # doctest: +SKIP
+        >>> df.style.applymap(color_negative, color='red', subset=(slice(0,5,2), "A"))
+        ...  # doctest: +SKIP
+
+        See `Table Visualization <../../user_guide/style.ipynb>`_ user guide for
+        more details.
         """
         self._todo.append(
             (lambda instance: getattr(instance, "_applymap"), (func, subset), kwargs)
@@ -1317,6 +1338,7 @@ def where(
         >>> def cond(v, limit=4):
         ...     return v > 1 and v != limit
         >>> df.style.where(cond, value='color:green;', other='color:red;')
+        ...  # doctest: +SKIP
 
         should be refactored to:
 
@@ -1324,6 +1346,7 @@ def where(
         ...     cond = v > 1 and v != limit
         ...     return value if cond else other
         >>> df.style.applymap(style_func, value='color:green;', other='color:red;')
+        ...  # doctest: +SKIP
         """
         warnings.warn(
             "this method is deprecated in favour of `Styler.applymap()`",
@@ -1389,7 +1412,7 @@ def set_table_attributes(self, attributes: str) -> Styler:
         Examples
         --------
         >>> df = pd.DataFrame(np.random.randn(10, 4))
-        >>> df.style.set_table_attributes('class="pure-table"')
+        >>> df.style.set_table_attributes('class="pure-table"')  # doctest: +SKIP
         # ... <table class="pure-table"> ...
         """
         self.table_attributes = attributes
@@ -1637,14 +1660,14 @@ def set_table_styles(
         >>> df.style.set_table_styles(
         ...     [{'selector': 'tr:hover',
         ...       'props': [('background-color', 'yellow')]}]
-        ... )
+        ... )  # doctest: +SKIP
 
         Or with CSS strings
 
         >>> df.style.set_table_styles(
         ...     [{'selector': 'tr:hover',
-        ...       'props': 'background-color: yellow; font-size: 1em;']}]
-        ... )
+        ...       'props': 'background-color: yellow; font-size: 1em;'}]
+        ... )  # doctest: +SKIP
 
         Adding column styling by name
 
@@ -1652,15 +1675,18 @@ def set_table_styles(
         ...     'A': [{'selector': '',
         ...            'props': [('color', 'red')]}],
         ...     'B': [{'selector': 'td',
-        ...            'props': 'color: blue;']}]
-        ... }, overwrite=False)
+        ...            'props': 'color: blue;'}]
+        ... }, overwrite=False)  # doctest: +SKIP
 
         Adding row styling
 
         >>> df.style.set_table_styles({
         ...     0: [{'selector': 'td:hover',
         ...          'props': [('font-size', '25px')]}]
-        ... }, axis=1, overwrite=False)
+        ... }, axis=1, overwrite=False)  # doctest: +SKIP
+
+        See `Table Visualization <../../user_guide/style.ipynb>`_ user guide for
+        more details.
         """
         if isinstance(table_styles, dict):
             if axis in [0, "index"]:
@@ -1753,7 +1779,7 @@ def hide_index(self, subset: Subset | None = None) -> Styler:
         Simple application hiding specific rows:
 
         >>> df = pd.DataFrame([[1,2], [3,4], [5,6]], index=["a", "b", "c"])
-        >>> df.style.hide_index(["a", "b"])
+        >>> df.style.hide_index(["a", "b"])  # doctest: +SKIP
              0    1
         c    5    6
 
@@ -1761,7 +1787,7 @@ def hide_index(self, subset: Subset | None = None) -> Styler:
 
         >>> midx = pd.MultiIndex.from_product([["x", "y"], ["a", "b", "c"]])
         >>> df = pd.DataFrame(np.random.randn(6,6), index=midx, columns=midx)
-        >>> df.style.format("{:.1f}").hide_index()
+        >>> df.style.format("{:.1f}").hide_index()  # doctest: +SKIP
                          x                    y
            a      b      c      a      b      c
          0.1    0.0    0.4    1.3    0.6   -1.4
@@ -1774,6 +1800,7 @@ def hide_index(self, subset: Subset | None = None) -> Styler:
         Hide specific rows but retain the index:
 
         >>> df.style.format("{:.1f}").hide_index(subset=(slice(None), ["a", "c"]))
+        ...   # doctest: +SKIP
                                  x                    y
                    a      b      c      a      b      c
         x   b    0.7    1.0    1.3    1.5   -0.0   -0.2
@@ -1781,8 +1808,8 @@ def hide_index(self, subset: Subset | None = None) -> Styler:
 
         Hide specific rows and the index:
 
-        >>> df.style.format("{:.1f}").hide_index(subset=(slice(None), ["a", "c"]))
-        ...     .hide_index()
+        >>> df.style.format("{:.1f}").hide_index(
+        ...     subset=(slice(None), ["a", "c"])).hide_index()  # doctest: +SKIP
                          x                    y
            a      b      c      a      b      c
          0.7    1.0    1.3    1.5   -0.0   -0.2
@@ -1833,7 +1860,7 @@ def hide_columns(self, subset: Subset | None = None) -> Styler:
         Simple application hiding specific columns:
 
         >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "b", "c"])
-        >>> df.style.hide_columns(["a", "b"])
+        >>> df.style.hide_columns(["a", "b"])  # doctest: +SKIP
              c
         0    3
         1    6
@@ -1842,17 +1869,18 @@ def hide_columns(self, subset: Subset | None = None) -> Styler:
 
         >>> midx = pd.MultiIndex.from_product([["x", "y"], ["a", "b", "c"]])
         >>> df = pd.DataFrame(np.random.randn(6,6), index=midx, columns=midx)
-        >>> df.style.format("{:.1f}").hide_columns()
-        x   d    0.1    0.0    0.4    1.3    0.6   -1.4
-            e    0.7    1.0    1.3    1.5   -0.0   -0.2
-            f    1.4   -0.8    1.6   -0.2   -0.4   -0.3
-        y   d    0.4    1.0   -0.2   -0.8   -1.2    1.1
-            e   -0.6    1.2    1.8    1.9    0.3    0.3
-            f    0.8    0.5   -0.3    1.2    2.2   -0.8
+        >>> df.style.format("{:.1f}").hide_columns()  # doctest: +SKIP
+        x   a    0.1    0.0    0.4    1.3    0.6   -1.4
+            b    0.7    1.0    1.3    1.5   -0.0   -0.2
+            c    1.4   -0.8    1.6   -0.2   -0.4   -0.3
+        y   a    0.4    1.0   -0.2   -0.8   -1.2    1.1
+            b   -0.6    1.2    1.8    1.9    0.3    0.3
+            c    0.8    0.5   -0.3    1.2    2.2   -0.8
 
         Hide specific columns but retain the column headers:
 
         >>> df.style.format("{:.1f}").hide_columns(subset=(slice(None), ["a", "c"]))
+        ...   # doctest: +SKIP
                    x      y
                    b      b
         x   a    0.0    0.6
@@ -1864,8 +1892,8 @@ def hide_columns(self, subset: Subset | None = None) -> Styler:
 
         Hide specific columns and the column headers:
 
-        >>> df.style.format("{:.1f}").hide_columns(subset=(slice(None), ["a", "c"]))
-        ...     .hide_columns()
+        >>> df.style.format("{:.1f}").hide_columns(
+        ...    subset=(slice(None), ["a", "c"])).hide_columns()  # doctest: +SKIP
         x   a    0.0    0.6
             b    1.0   -0.0
             c   -0.8   -0.4
@@ -1995,31 +2023,32 @@ def background_gradient(
 
         Shading the values column-wise, with ``axis=0``, preselecting numeric columns
 
-        >>> df.style.{name}_gradient(axis=0)
+        >>> df.style.{name}_gradient(axis=0)  # doctest: +SKIP
 
         .. figure:: ../../_static/style/{image_prefix}_ax0.png
 
         Shading all values collectively using ``axis=None``
 
-        >>> df.style.{name}_gradient(axis=None)
+        >>> df.style.{name}_gradient(axis=None)  # doctest: +SKIP
 
         .. figure:: ../../_static/style/{image_prefix}_axNone.png
 
         Compress the color map from the both ``low`` and ``high`` ends
 
-        >>> df.style.{name}_gradient(axis=None, low=0.75, high=1.0)
+        >>> df.style.{name}_gradient(axis=None, low=0.75, high=1.0)  # doctest: +SKIP
 
         .. figure:: ../../_static/style/{image_prefix}_axNone_lowhigh.png
 
         Manually setting ``vmin`` and ``vmax`` gradient thresholds
 
-        >>> df.style.{name}_gradient(axis=None, vmin=6.7, vmax=21.6)
+        >>> df.style.{name}_gradient(axis=None, vmin=6.7, vmax=21.6)  # doctest: +SKIP
 
         .. figure:: ../../_static/style/{image_prefix}_axNone_vminvmax.png
 
         Setting a ``gmap`` and applying to all columns with another ``cmap``
 
         >>> df.style.{name}_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd')
+        ...  # doctest: +SKIP
 
         .. figure:: ../../_static/style/{image_prefix}_gmap.png
 
@@ -2029,7 +2058,7 @@ def background_gradient(
         >>> gmap = np.array([[1,2,3], [2,3,4], [3,4,5]])
         >>> df.style.{name}_gradient(axis=None, gmap=gmap,
         ...     cmap='YlOrRd', subset=['Temp (c)', 'Rain (mm)', 'Wind (m/s)']
-        ... )
+        ... )  # doctest: +SKIP
 
         .. figure:: ../../_static/style/{image_prefix}_axNone_gmap.png
         """
@@ -2111,8 +2140,11 @@ def set_properties(self, subset: Subset | None = None, **kwargs) -> Styler:
         Examples
         --------
         >>> df = pd.DataFrame(np.random.randn(10, 4))
-        >>> df.style.set_properties(color="white", align="right")
-        >>> df.style.set_properties(**{'background-color': 'yellow'})
+        >>> df.style.set_properties(color="white", align="right")  # doctest: +SKIP
+        >>> df.style.set_properties(**{'background-color': 'yellow'})  # doctest: +SKIP
+
+        See `Table Visualization <../../user_guide/style.ipynb>`_ user guide for
+        more details.
         """
         values = "".join([f"{p}: {v};" for p, v in kwargs.items()])
         return self.applymap(lambda x: values, subset=subset)
@@ -2447,7 +2479,7 @@ def highlight_between(
         ...     'Two': [2.9, 2.1, 2.5],
         ...     'Three': [3.1, 3.2, 3.8],
         ... })
-        >>> df.style.highlight_between(left=2.1, right=2.9)
+        >>> df.style.highlight_between(left=2.1, right=2.9)  # doctest: +SKIP
 
         .. figure:: ../../_static/style/hbetw_basic.png
 
@@ -2455,7 +2487,7 @@ def highlight_between(
         and ``right`` for each column individually
 
         >>> df.style.highlight_between(left=[1.4, 2.4, 3.4], right=[1.6, 2.6, 3.6],
-        ...     axis=1, color="#fffd75")
+        ...     axis=1, color="#fffd75")  # doctest: +SKIP
 
         .. figure:: ../../_static/style/hbetw_seq.png
 
@@ -2463,14 +2495,14 @@ def highlight_between(
         matches the input DataFrame, with a constant ``right``
 
         >>> df.style.highlight_between(left=[[2,2,3],[2,2,3],[3,3,3]], right=3.5,
-        ...     axis=None, color="#fffd75")
+        ...     axis=None, color="#fffd75")  # doctest: +SKIP
 
         .. figure:: ../../_static/style/hbetw_axNone.png
 
         Using ``props`` instead of default background coloring
 
         >>> df.style.highlight_between(left=1.5, right=3.5,
-        ...     props='font-weight:bold;color:#e83e8c')
+        ...     props='font-weight:bold;color:#e83e8c')  # doctest: +SKIP
 
         .. figure:: ../../_static/style/hbetw_props.png
         """
@@ -2547,19 +2579,21 @@ def highlight_quantile(
 
         >>> df = pd.DataFrame(np.arange(10).reshape(2,5) + 1)
         >>> df.style.highlight_quantile(axis=None, q_left=0.8, color="#fffd75")
+        ...  # doctest: +SKIP
 
         .. figure:: ../../_static/style/hq_axNone.png
 
         Or highlight quantiles row-wise or column-wise, in this case by row-wise
 
         >>> df.style.highlight_quantile(axis=1, q_left=0.8, color="#fffd75")
+        ...  # doctest: +SKIP
 
         .. figure:: ../../_static/style/hq_ax1.png
 
         Use ``props`` instead of default background coloring
 
         >>> df.style.highlight_quantile(axis=None, q_left=0.2, q_right=0.8,
-        ...     props='font-weight:bold;color:#e83e8c')
+        ...     props='font-weight:bold;color:#e83e8c')  # doctest: +SKIP
 
         .. figure:: ../../_static/style/hq_props.png
         """
@@ -2703,6 +2737,9 @@ def pipe(self, func: Callable, *args, **kwargs):
         ...    .highlight_min(subset=['conversion'], color='yellow')
         ...    .pipe(format_conversion)
         ...    .set_caption("Results with minimum conversion highlighted."))
+        ...  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/df_pipe.png
         """
         return com.pipe(self, func, *args, **kwargs)
 
diff --git a/pandas/tests/extension/base/getitem.py b/pandas/tests/extension/base/getitem.py
index 96833a2e49fa1..ac181af7875b5 100644
--- a/pandas/tests/extension/base/getitem.py
+++ b/pandas/tests/extension/base/getitem.py
@@ -425,3 +425,23 @@ def test_item(self, data):
 
         with pytest.raises(ValueError, match=msg):
             s.item()
+
+    def test_ellipsis_index(self):
+        # GH42430 1D slices over extension types turn into N-dimensional slices over
+        #  ExtensionArrays
+        class CapturingStringArray(pd.arrays.StringArray):
+            """Extend StringArray to capture arguments to __getitem__"""
+
+            def __getitem__(self, item):
+                self.last_item_arg = item
+                return super().__getitem__(item)
+
+        df = pd.DataFrame(
+            {"col1": CapturingStringArray(np.array(["hello", "world"], dtype=object))}
+        )
+        _ = df.iloc[:1]
+
+        # String comparison because there's no native way to compare slices.
+        # Before the fix for GH42430, last_item_arg would get set to the 2D slice
+        # (Ellipsis, slice(None, 1, None))
+        self.assert_equal(str(df["col1"].array.last_item_arg), "slice(None, 1, None)")
diff --git a/pandas/tests/frame/methods/test_drop.py b/pandas/tests/frame/methods/test_drop.py
index be29a3e50b9fa..fa658d87c3ca0 100644
--- a/pandas/tests/frame/methods/test_drop.py
+++ b/pandas/tests/frame/methods/test_drop.py
@@ -479,6 +479,17 @@ def test_drop_with_non_unique_multiindex(self):
         expected = DataFrame([2], index=MultiIndex.from_arrays([["y"], ["j"]]))
         tm.assert_frame_equal(result, expected)
 
+    @pytest.mark.parametrize("indexer", [("a", "a"), [("a", "a")]])
+    def test_drop_tuple_with_non_unique_multiindex(self, indexer):
+        # GH#42771
+        idx = MultiIndex.from_product([["a", "b"], ["a", "a"]])
+        df = DataFrame({"x": range(len(idx))}, index=idx)
+        result = df.drop(index=[("a", "a")])
+        expected = DataFrame(
+            {"x": [2, 3]}, index=MultiIndex.from_tuples([("b", "a"), ("b", "a")])
+        )
+        tm.assert_frame_equal(result, expected)
+
     def test_drop_with_duplicate_columns(self):
         df = DataFrame(
             [[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "a", "a"]
diff --git a/pandas/tests/frame/methods/test_sort_values.py b/pandas/tests/frame/methods/test_sort_values.py
index d46796bcd978b..e104617552efc 100644
--- a/pandas/tests/frame/methods/test_sort_values.py
+++ b/pandas/tests/frame/methods/test_sort_values.py
@@ -868,3 +868,23 @@ def test_sort_values_pos_args_deprecation(self):
             result = df.sort_values("a", 0)
         expected = DataFrame({"a": [1, 2, 3]})
         tm.assert_frame_equal(result, expected)
+
+    def test_sort_values_validate_ascending_for_value_error(self):
+        # GH41634
+        df = DataFrame({"D": [23, 7, 21]})
+
+        msg = 'For argument "ascending" expected type bool, received type str.'
+        with pytest.raises(ValueError, match=msg):
+            df.sort_values(by="D", ascending="False")
+
+    @pytest.mark.parametrize("ascending", [False, 0, 1, True])
+    def test_sort_values_validate_ascending_functional(self, ascending):
+        df = DataFrame({"D": [23, 7, 21]})
+        indexer = df["D"].argsort().values
+
+        if not ascending:
+            indexer = indexer[::-1]
+
+        expected = df.loc[df.index[indexer]]
+        result = df.sort_values(by="D", ascending=ascending)
+        tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index 5434fc49e2174..77e5e9ba133f5 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -803,6 +803,39 @@ def test_cummax(dtypes_for_minmax):
     tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.parametrize("method", ["cummin", "cummax"])
+@pytest.mark.parametrize("dtype", ["float", "Int64", "Float64"])
+@pytest.mark.parametrize(
+    "groups,expected_data",
+    [
+        ([1, 1, 1], [1, None, None]),
+        ([1, 2, 3], [1, None, 2]),
+        ([1, 3, 3], [1, None, None]),
+    ],
+)
+def test_cummin_max_skipna(method, dtype, groups, expected_data):
+    # GH-34047
+    df = DataFrame({"a": Series([1, None, 2], dtype=dtype)})
+    gb = df.groupby(groups)["a"]
+
+    result = getattr(gb, method)(skipna=False)
+    expected = Series(expected_data, dtype=dtype, name="a")
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["cummin", "cummax"])
+def test_cummin_max_skipna_multiple_cols(method):
+    # Ensure missing value in "a" doesn't cause "b" to be nan-filled
+    df = DataFrame({"a": [np.nan, 2.0, 2.0], "b": [2.0, 2.0, 2.0]})
+    gb = df.groupby([1, 1, 1])[["a", "b"]]
+
+    result = getattr(gb, method)(skipna=False)
+    expected = DataFrame({"a": [np.nan, np.nan, np.nan], "b": [2.0, 2.0, 2.0]})
+
+    tm.assert_frame_equal(result, expected)
+
+
 @td.skip_if_32bit
 @pytest.mark.parametrize("method", ["cummin", "cummax"])
 @pytest.mark.parametrize(
diff --git a/pandas/tests/resample/test_resample_api.py b/pandas/tests/resample/test_resample_api.py
index 76ac86d798086..addd6c17809a2 100644
--- a/pandas/tests/resample/test_resample_api.py
+++ b/pandas/tests/resample/test_resample_api.py
@@ -349,7 +349,12 @@ def test_agg():
     expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
     expected.columns = pd.MultiIndex.from_product([["A", "B"], ["mean", "std"]])
     for t in cases:
-        result = t.aggregate([np.mean, np.std])
+        warn = FutureWarning if t in cases[1:3] else None
+        with tm.assert_produces_warning(
+            warn, match="Dropping invalid columns", check_stacklevel=False
+        ):
+            # .var on dt64 column raises and is dropped
+            result = t.aggregate([np.mean, np.std])
         tm.assert_frame_equal(result, expected)
 
     expected = pd.concat([a_mean, b_std], axis=1)
diff --git a/pandas/tests/series/methods/test_sort_values.py b/pandas/tests/series/methods/test_sort_values.py
index 67f986c0949ca..adc578d948163 100644
--- a/pandas/tests/series/methods/test_sort_values.py
+++ b/pandas/tests/series/methods/test_sort_values.py
@@ -51,7 +51,7 @@ def test_sort_values(self, datetime_series):
         expected = ts.sort_values(ascending=False, na_position="first")
         tm.assert_series_equal(expected, ordered)
 
-        msg = "ascending must be boolean"
+        msg = 'For argument "ascending" expected type bool, received type NoneType.'
         with pytest.raises(ValueError, match=msg):
             ts.sort_values(ascending=None)
         msg = r"Length of ascending \(0\) must be 1 for Series"
@@ -63,7 +63,7 @@ def test_sort_values(self, datetime_series):
         msg = r"Length of ascending \(2\) must be 1 for Series"
         with pytest.raises(ValueError, match=msg):
             ts.sort_values(ascending=[False, False])
-        msg = "ascending must be boolean"
+        msg = 'For argument "ascending" expected type bool, received type str.'
         with pytest.raises(ValueError, match=msg):
             ts.sort_values(ascending="foobar")
 
@@ -206,6 +206,27 @@ def test_mergesort_decending_stability(self):
         expected = Series([3, 2, 1, 1], ["c", "b", "first", "second"])
         tm.assert_series_equal(result, expected)
 
+    def test_sort_values_validate_ascending_for_value_error(self):
+        # GH41634
+        ser = Series([23, 7, 21])
+
+        msg = 'For argument "ascending" expected type bool, received type str.'
+        with pytest.raises(ValueError, match=msg):
+            ser.sort_values(ascending="False")
+
+    @pytest.mark.parametrize("ascending", [False, 0, 1, True])
+    def test_sort_values_validate_ascending_functional(self, ascending):
+        # GH41634
+        ser = Series([23, 7, 21])
+        expected = np.sort(ser.values)
+
+        sorted_ser = ser.sort_values(ascending=ascending)
+        if not ascending:
+            expected = expected[::-1]
+
+        result = sorted_ser.values
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class TestSeriesSortingKey:
     def test_sort_values_key(self):