diff --git a/.gitignore b/.gitignore
index 07b1f056d511b..88ed58b70925d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -53,6 +53,9 @@ dist
 # type checkers
 pandas/py.typed
 
+# pyenv
+.python-version
+
 # tox testing tool
 .tox
 # rope
diff --git a/pandas/_libs/tslibs/src/datetime/np_datetime.c b/pandas/_libs/tslibs/src/datetime/np_datetime.c
index fa6fc75366b79..038957eda5b6e 100644
--- a/pandas/_libs/tslibs/src/datetime/np_datetime.c
+++ b/pandas/_libs/tslibs/src/datetime/np_datetime.c
@@ -713,338 +713,338 @@ void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
     switch (base) {
         case NPY_FR_ns:
 
-        per_day = 86400000000000LL;
-        per_sec = 1000LL * 1000LL * 1000LL;
-
-        // put frac in seconds
-        if (td < 0 && td % per_sec != 0)
-            frac = td / per_sec - 1;
-        else
-            frac = td / per_sec;
-
-        if (frac < 0) {
-            sign = -1;
-
-            // even fraction
-            if ((-frac % 86400LL) != 0) {
-              out->days = -frac / 86400LL + 1;
-              frac += 86400LL * out->days;
+            per_day = 86400000000000LL;
+            per_sec = 1000LL * 1000LL * 1000LL;
+
+            // put frac in seconds
+            if (td < 0 && td % per_sec != 0)
+                frac = td / per_sec - 1;
+            else
+                frac = td / per_sec;
+
+            if (frac < 0) {
+                sign = -1;
+
+                // even fraction
+                if ((-frac % 86400LL) != 0) {
+                  out->days = -frac / 86400LL + 1;
+                  frac += 86400LL * out->days;
+                } else {
+                  frac = -frac;
+                }
             } else {
-              frac = -frac;
+                sign = 1;
+                out->days = 0;
             }
-        } else {
-            sign = 1;
-            out->days = 0;
-        }
 
-        if (frac >= 86400) {
-            out->days += frac / 86400LL;
-            frac -= out->days * 86400LL;
-        }
+            if (frac >= 86400) {
+                out->days += frac / 86400LL;
+                frac -= out->days * 86400LL;
+            }
 
-        if (frac >= 3600) {
-            out->hrs = frac / 3600LL;
-            frac -= out->hrs * 3600LL;
-        } else {
-            out->hrs = 0;
-        }
+            if (frac >= 3600) {
+                out->hrs = frac / 3600LL;
+                frac -= out->hrs * 3600LL;
+            } else {
+                out->hrs = 0;
+            }
 
-        if (frac >= 60) {
-            out->min = frac / 60LL;
-            frac -= out->min * 60LL;
-        } else {
-            out->min = 0;
-        }
+            if (frac >= 60) {
+                out->min = frac / 60LL;
+                frac -= out->min * 60LL;
+            } else {
+                out->min = 0;
+            }
 
-        if (frac >= 0) {
-            out->sec = frac;
-            frac -= out->sec;
-        } else {
-            out->sec = 0;
-        }
+            if (frac >= 0) {
+                out->sec = frac;
+                frac -= out->sec;
+            } else {
+                out->sec = 0;
+            }
 
-        sfrac = (out->hrs * 3600LL + out->min * 60LL
-                 + out->sec) * per_sec;
+            sfrac = (out->hrs * 3600LL + out->min * 60LL
+                     + out->sec) * per_sec;
 
-        if (sign < 0)
-            out->days = -out->days;
+            if (sign < 0)
+                out->days = -out->days;
 
-        ifrac = td - (out->days * per_day + sfrac);
+            ifrac = td - (out->days * per_day + sfrac);
 
-        if (ifrac != 0) {
-            out->ms = ifrac / (1000LL * 1000LL);
-            ifrac -= out->ms * 1000LL * 1000LL;
-            out->us = ifrac / 1000LL;
-            ifrac -= out->us * 1000LL;
-            out->ns = ifrac;
-        } else {
-            out->ms = 0;
-            out->us = 0;
-            out->ns = 0;
-        }
-        break;
+            if (ifrac != 0) {
+                out->ms = ifrac / (1000LL * 1000LL);
+                ifrac -= out->ms * 1000LL * 1000LL;
+                out->us = ifrac / 1000LL;
+                ifrac -= out->us * 1000LL;
+                out->ns = ifrac;
+            } else {
+                out->ms = 0;
+                out->us = 0;
+                out->ns = 0;
+            }
+            break;
 
         case NPY_FR_us:
 
-        per_day = 86400000000LL;
-        per_sec = 1000LL * 1000LL;
+            per_day = 86400000000LL;
+            per_sec = 1000LL * 1000LL;
 
-        // put frac in seconds
-        if (td < 0 && td % per_sec != 0)
-            frac = td / per_sec - 1;
-        else
-            frac = td / per_sec;
+            // put frac in seconds
+            if (td < 0 && td % per_sec != 0)
+                frac = td / per_sec - 1;
+            else
+                frac = td / per_sec;
 
-        if (frac < 0) {
-            sign = -1;
+            if (frac < 0) {
+                sign = -1;
 
-            // even fraction
-            if ((-frac % 86400LL) != 0) {
-              out->days = -frac / 86400LL + 1;
-              frac += 86400LL * out->days;
+                // even fraction
+                if ((-frac % 86400LL) != 0) {
+                  out->days = -frac / 86400LL + 1;
+                  frac += 86400LL * out->days;
+                } else {
+                  frac = -frac;
+                }
             } else {
-              frac = -frac;
+                sign = 1;
+                out->days = 0;
             }
-        } else {
-            sign = 1;
-            out->days = 0;
-        }
 
-        if (frac >= 86400) {
-            out->days += frac / 86400LL;
-            frac -= out->days * 86400LL;
-        }
+            if (frac >= 86400) {
+                out->days += frac / 86400LL;
+                frac -= out->days * 86400LL;
+            }
 
-        if (frac >= 3600) {
-            out->hrs = frac / 3600LL;
-            frac -= out->hrs * 3600LL;
-        } else {
-            out->hrs = 0;
-        }
+            if (frac >= 3600) {
+                out->hrs = frac / 3600LL;
+                frac -= out->hrs * 3600LL;
+            } else {
+                out->hrs = 0;
+            }
 
-        if (frac >= 60) {
-            out->min = frac / 60LL;
-            frac -= out->min * 60LL;
-        } else {
-            out->min = 0;
-        }
+            if (frac >= 60) {
+                out->min = frac / 60LL;
+                frac -= out->min * 60LL;
+            } else {
+                out->min = 0;
+            }
 
-        if (frac >= 0) {
-            out->sec = frac;
-            frac -= out->sec;
-        } else {
-            out->sec = 0;
-        }
+            if (frac >= 0) {
+                out->sec = frac;
+                frac -= out->sec;
+            } else {
+                out->sec = 0;
+            }
 
-        sfrac = (out->hrs * 3600LL + out->min * 60LL
-                 + out->sec) * per_sec;
+            sfrac = (out->hrs * 3600LL + out->min * 60LL
+                     + out->sec) * per_sec;
 
-        if (sign < 0)
-            out->days = -out->days;
+            if (sign < 0)
+                out->days = -out->days;
 
-        ifrac = td - (out->days * per_day + sfrac);
+            ifrac = td - (out->days * per_day + sfrac);
 
-        if (ifrac != 0) {
-            out->ms = ifrac / 1000LL;
-            ifrac -= out->ms * 1000LL;
-            out->us = ifrac / 1L;
-            ifrac -= out->us * 1L;
-            out->ns = ifrac;
-        } else {
-            out->ms = 0;
-            out->us = 0;
-            out->ns = 0;
-        }
-        break;
+            if (ifrac != 0) {
+                out->ms = ifrac / 1000LL;
+                ifrac -= out->ms * 1000LL;
+                out->us = ifrac / 1L;
+                ifrac -= out->us * 1L;
+                out->ns = ifrac;
+            } else {
+                out->ms = 0;
+                out->us = 0;
+                out->ns = 0;
+            }
+            break;
 
         case NPY_FR_ms:
 
-        per_day = 86400000LL;
-        per_sec = 1000LL;
+            per_day = 86400000LL;
+            per_sec = 1000LL;
 
-        // put frac in seconds
-        if (td < 0 && td % per_sec != 0)
-            frac = td / per_sec - 1;
-        else
-            frac = td / per_sec;
+            // put frac in seconds
+            if (td < 0 && td % per_sec != 0)
+                frac = td / per_sec - 1;
+            else
+                frac = td / per_sec;
 
-        if (frac < 0) {
-            sign = -1;
+            if (frac < 0) {
+                sign = -1;
 
-            // even fraction
-            if ((-frac % 86400LL) != 0) {
-              out->days = -frac / 86400LL + 1;
-              frac += 86400LL * out->days;
+                // even fraction
+                if ((-frac % 86400LL) != 0) {
+                  out->days = -frac / 86400LL + 1;
+                  frac += 86400LL * out->days;
+                } else {
+                  frac = -frac;
+                }
             } else {
-              frac = -frac;
+                sign = 1;
+                out->days = 0;
             }
-        } else {
-            sign = 1;
-            out->days = 0;
-        }
 
-        if (frac >= 86400) {
-            out->days += frac / 86400LL;
-            frac -= out->days * 86400LL;
-        }
+            if (frac >= 86400) {
+                out->days += frac / 86400LL;
+                frac -= out->days * 86400LL;
+            }
 
-        if (frac >= 3600) {
-            out->hrs = frac / 3600LL;
-            frac -= out->hrs * 3600LL;
-        } else {
-            out->hrs = 0;
-        }
+            if (frac >= 3600) {
+                out->hrs = frac / 3600LL;
+                frac -= out->hrs * 3600LL;
+            } else {
+                out->hrs = 0;
+            }
 
-        if (frac >= 60) {
-            out->min = frac / 60LL;
-            frac -= out->min * 60LL;
-        } else {
-            out->min = 0;
-        }
+            if (frac >= 60) {
+                out->min = frac / 60LL;
+                frac -= out->min * 60LL;
+            } else {
+                out->min = 0;
+            }
 
-        if (frac >= 0) {
-            out->sec = frac;
-            frac -= out->sec;
-        } else {
-            out->sec = 0;
-        }
+            if (frac >= 0) {
+                out->sec = frac;
+                frac -= out->sec;
+            } else {
+                out->sec = 0;
+            }
 
-        sfrac = (out->hrs * 3600LL + out->min * 60LL
-                 + out->sec) * per_sec;
+            sfrac = (out->hrs * 3600LL + out->min * 60LL
+                     + out->sec) * per_sec;
 
-        if (sign < 0)
-            out->days = -out->days;
+            if (sign < 0)
+                out->days = -out->days;
 
-        ifrac = td - (out->days * per_day + sfrac);
+            ifrac = td - (out->days * per_day + sfrac);
 
-        if (ifrac != 0) {
-            out->ms = ifrac;
-            out->us = 0;
-            out->ns = 0;
-        } else {
-            out->ms = 0;
-            out->us = 0;
-            out->ns = 0;
-        }
-        break;
+            if (ifrac != 0) {
+                out->ms = ifrac;
+                out->us = 0;
+                out->ns = 0;
+            } else {
+                out->ms = 0;
+                out->us = 0;
+                out->ns = 0;
+            }
+            break;
 
         case NPY_FR_s:
-        // special case where we can simplify many expressions bc per_sec=1
+            // special case where we can simplify many expressions bc per_sec=1
 
-        per_day = 86400LL;
-        per_sec = 1L;
+            per_day = 86400LL;
+            per_sec = 1L;
 
-        // put frac in seconds
-        if (td < 0 && td % per_sec != 0)
-            frac = td / per_sec - 1;
-        else
-            frac = td / per_sec;
+            // put frac in seconds
+            if (td < 0 && td % per_sec != 0)
+                frac = td / per_sec - 1;
+            else
+                frac = td / per_sec;
 
-        if (frac < 0) {
-            sign = -1;
+            if (frac < 0) {
+                sign = -1;
 
-            // even fraction
-            if ((-frac % 86400LL) != 0) {
-              out->days = -frac / 86400LL + 1;
-              frac += 86400LL * out->days;
+                // even fraction
+                if ((-frac % 86400LL) != 0) {
+                  out->days = -frac / 86400LL + 1;
+                  frac += 86400LL * out->days;
+                } else {
+                  frac = -frac;
+                }
             } else {
-              frac = -frac;
+                sign = 1;
+                out->days = 0;
             }
-        } else {
-            sign = 1;
-            out->days = 0;
-        }
 
-        if (frac >= 86400) {
-            out->days += frac / 86400LL;
-            frac -= out->days * 86400LL;
-        }
+            if (frac >= 86400) {
+                out->days += frac / 86400LL;
+                frac -= out->days * 86400LL;
+            }
 
-        if (frac >= 3600) {
-            out->hrs = frac / 3600LL;
-            frac -= out->hrs * 3600LL;
-        } else {
-            out->hrs = 0;
-        }
+            if (frac >= 3600) {
+                out->hrs = frac / 3600LL;
+                frac -= out->hrs * 3600LL;
+            } else {
+                out->hrs = 0;
+            }
 
-        if (frac >= 60) {
-            out->min = frac / 60LL;
-            frac -= out->min * 60LL;
-        } else {
-            out->min = 0;
-        }
+            if (frac >= 60) {
+                out->min = frac / 60LL;
+                frac -= out->min * 60LL;
+            } else {
+                out->min = 0;
+            }
 
-        if (frac >= 0) {
-            out->sec = frac;
-            frac -= out->sec;
-        } else {
-            out->sec = 0;
-        }
+            if (frac >= 0) {
+                out->sec = frac;
+                frac -= out->sec;
+            } else {
+                out->sec = 0;
+            }
 
-        sfrac = (out->hrs * 3600LL + out->min * 60LL
-                 + out->sec) * per_sec;
+            sfrac = (out->hrs * 3600LL + out->min * 60LL
+                     + out->sec) * per_sec;
 
-        if (sign < 0)
-            out->days = -out->days;
+            if (sign < 0)
+                out->days = -out->days;
 
-        ifrac = td - (out->days * per_day + sfrac);
+            ifrac = td - (out->days * per_day + sfrac);
 
-        if (ifrac != 0) {
-            out->ms = 0;
-            out->us = 0;
-            out->ns = 0;
-        } else {
-            out->ms = 0;
-            out->us = 0;
-            out->ns = 0;
-        }
-        break;
+            if (ifrac != 0) {
+                out->ms = 0;
+                out->us = 0;
+                out->ns = 0;
+            } else {
+                out->ms = 0;
+                out->us = 0;
+                out->ns = 0;
+            }
+            break;
 
         case NPY_FR_m:
 
-        out->days = td / 1440LL;
-        td -= out->days * 1440LL;
-        out->hrs = td / 60LL;
-        td -= out->hrs * 60LL;
-        out->min = td;
+            out->days = td / 1440LL;
+            td -= out->days * 1440LL;
+            out->hrs = td / 60LL;
+            td -= out->hrs * 60LL;
+            out->min = td;
 
-        out->sec = 0;
-        out->ms = 0;
-        out->us = 0;
-        out->ns = 0;
-        break;
+            out->sec = 0;
+            out->ms = 0;
+            out->us = 0;
+            out->ns = 0;
+            break;
 
         case NPY_FR_h:
-        out->days = td / 24LL;
-        td -= out->days * 24LL;
-        out->hrs = td;
+            out->days = td / 24LL;
+            td -= out->days * 24LL;
+            out->hrs = td;
 
-        out->min = 0;
-        out->sec = 0;
-        out->ms = 0;
-        out->us = 0;
-        out->ns = 0;
-        break;
+            out->min = 0;
+            out->sec = 0;
+            out->ms = 0;
+            out->us = 0;
+            out->ns = 0;
+            break;
 
         case NPY_FR_D:
-        out->days = td;
-        out->hrs = 0;
-        out->min = 0;
-        out->sec = 0;
-        out->ms = 0;
-        out->us = 0;
-        out->ns = 0;
-        break;
+            out->days = td;
+            out->hrs = 0;
+            out->min = 0;
+            out->sec = 0;
+            out->ms = 0;
+            out->us = 0;
+            out->ns = 0;
+            break;
 
         case NPY_FR_W:
-        out->days = 7 * td;
-        out->hrs = 0;
-        out->min = 0;
-        out->sec = 0;
-        out->ms = 0;
-        out->us = 0;
-        out->ns = 0;
-        break;
+            out->days = 7 * td;
+            out->hrs = 0;
+            out->min = 0;
+            out->sec = 0;
+            out->ms = 0;
+            out->us = 0;
+            out->ns = 0;
+            break;
 
         default:
             PyErr_SetString(PyExc_RuntimeError,
diff --git a/pandas/core/arrays/arrow/extension_types.py b/pandas/core/arrays/arrow/extension_types.py
index 05b70afc2e24c..3249c1c829546 100644
--- a/pandas/core/arrays/arrow/extension_types.py
+++ b/pandas/core/arrays/arrow/extension_types.py
@@ -5,6 +5,11 @@
 
 import pyarrow
 
+from pandas.core.dtypes.dtypes import (
+    IntervalDtype,
+    PeriodDtype,
+)
+
 from pandas.core.arrays.interval import VALID_CLOSED
 
 if TYPE_CHECKING:
@@ -44,9 +49,7 @@ def __hash__(self) -> int:
         return hash((str(self), self.freq))
 
     def to_pandas_dtype(self):
-        import pandas as pd
-
-        return pd.PeriodDtype(freq=self.freq)
+        return PeriodDtype(freq=self.freq)
 
 
 # register the type with a dummy instance
@@ -103,9 +106,7 @@ def __hash__(self) -> int:
         return hash((str(self), str(self.subtype), self.closed))
 
     def to_pandas_dtype(self):
-        import pandas as pd
-
-        return pd.IntervalDtype(self.subtype.to_pandas_dtype(), self.closed)
+        return IntervalDtype(self.subtype.to_pandas_dtype(), self.closed)
 
 
 # register the type with a dummy instance
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 9ecba12d26beb..9de83933690f4 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1901,7 +1901,7 @@ def _repr_categories_info(self) -> str:
         category_strs = self._repr_categories()
         dtype = str(self.categories.dtype)
         levheader = f"Categories ({len(self.categories)}, {dtype}): "
-        width, height = get_terminal_size()
+        width, _ = get_terminal_size()
         max_width = get_option("display.width") or width
         if console.in_ipython_frontend():
             # 0 = no breaks
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index fcfafd83b16f4..2be1c62cde2ec 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -8030,116 +8030,118 @@ def rpow(self, other, axis: Axis = "columns", level=None, fill_value=None):
 
     @doc(
         _shared_docs["compare"],
-        """
-Returns
--------
-DataFrame
-    DataFrame that shows the differences stacked side by side.
+        dedent(
+            """
+        Returns
+        -------
+        DataFrame
+            DataFrame that shows the differences stacked side by side.
 
-    The resulting index will be a MultiIndex with 'self' and 'other'
-    stacked alternately at the inner level.
+            The resulting index will be a MultiIndex with 'self' and 'other'
+            stacked alternately at the inner level.
 
-Raises
-------
-ValueError
-    When the two DataFrames don't have identical labels or shape.
+        Raises
+        ------
+        ValueError
+            When the two DataFrames don't have identical labels or shape.
 
-See Also
---------
-Series.compare : Compare with another Series and show differences.
-DataFrame.equals : Test whether two objects contain the same elements.
+        See Also
+        --------
+        Series.compare : Compare with another Series and show differences.
+        DataFrame.equals : Test whether two objects contain the same elements.
 
-Notes
------
-Matching NaNs will not appear as a difference.
+        Notes
+        -----
+        Matching NaNs will not appear as a difference.
 
-Can only compare identically-labeled
-(i.e. same shape, identical row and column labels) DataFrames
+        Can only compare identically-labeled
+        (i.e. same shape, identical row and column labels) DataFrames
 
-Examples
---------
->>> df = pd.DataFrame(
-...     {{
-...         "col1": ["a", "a", "b", "b", "a"],
-...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
-...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
-...     }},
-...     columns=["col1", "col2", "col3"],
-... )
->>> df
-  col1  col2  col3
-0    a   1.0   1.0
-1    a   2.0   2.0
-2    b   3.0   3.0
-3    b   NaN   4.0
-4    a   5.0   5.0
-
->>> df2 = df.copy()
->>> df2.loc[0, 'col1'] = 'c'
->>> df2.loc[2, 'col3'] = 4.0
->>> df2
-  col1  col2  col3
-0    c   1.0   1.0
-1    a   2.0   2.0
-2    b   3.0   4.0
-3    b   NaN   4.0
-4    a   5.0   5.0
-
-Align the differences on columns
-
->>> df.compare(df2)
-  col1       col3
-  self other self other
-0    a     c  NaN   NaN
-2  NaN   NaN  3.0   4.0
-
-Assign result_names
-
->>> df.compare(df2, result_names=("left", "right"))
-  col1       col3
-  left right left right
-0    a     c  NaN   NaN
-2  NaN   NaN  3.0   4.0
-
-Stack the differences on rows
-
->>> df.compare(df2, align_axis=0)
-        col1  col3
-0 self     a   NaN
-  other    c   NaN
-2 self   NaN   3.0
-  other  NaN   4.0
-
-Keep the equal values
-
->>> df.compare(df2, keep_equal=True)
-  col1       col3
-  self other self other
-0    a     c  1.0   1.0
-2    b     b  3.0   4.0
-
-Keep all original rows and columns
-
->>> df.compare(df2, keep_shape=True)
-  col1       col2       col3
-  self other self other self other
-0    a     c  NaN   NaN  NaN   NaN
-1  NaN   NaN  NaN   NaN  NaN   NaN
-2  NaN   NaN  NaN   NaN  3.0   4.0
-3  NaN   NaN  NaN   NaN  NaN   NaN
-4  NaN   NaN  NaN   NaN  NaN   NaN
-
-Keep all original rows and columns and also all original values
-
->>> df.compare(df2, keep_shape=True, keep_equal=True)
-  col1       col2       col3
-  self other self other self other
-0    a     c  1.0   1.0  1.0   1.0
-1    a     a  2.0   2.0  2.0   2.0
-2    b     b  3.0   3.0  3.0   4.0
-3    b     b  NaN   NaN  4.0   4.0
-4    a     a  5.0   5.0  5.0   5.0
-""",
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {{
+        ...         "col1": ["a", "a", "b", "b", "a"],
+        ...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
+        ...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
+        ...     }},
+        ...     columns=["col1", "col2", "col3"],
+        ... )
+        >>> df
+          col1  col2  col3
+        0    a   1.0   1.0
+        1    a   2.0   2.0
+        2    b   3.0   3.0
+        3    b   NaN   4.0
+        4    a   5.0   5.0
+
+        >>> df2 = df.copy()
+        >>> df2.loc[0, 'col1'] = 'c'
+        >>> df2.loc[2, 'col3'] = 4.0
+        >>> df2
+          col1  col2  col3
+        0    c   1.0   1.0
+        1    a   2.0   2.0
+        2    b   3.0   4.0
+        3    b   NaN   4.0
+        4    a   5.0   5.0
+
+        Align the differences on columns
+
+        >>> df.compare(df2)
+          col1       col3
+          self other self other
+        0    a     c  NaN   NaN
+        2  NaN   NaN  3.0   4.0
+
+        Assign result_names
+
+        >>> df.compare(df2, result_names=("left", "right"))
+          col1       col3
+          left right left right
+        0    a     c  NaN   NaN
+        2  NaN   NaN  3.0   4.0
+
+        Stack the differences on rows
+
+        >>> df.compare(df2, align_axis=0)
+                col1  col3
+        0 self     a   NaN
+          other    c   NaN
+        2 self   NaN   3.0
+          other  NaN   4.0
+
+        Keep the equal values
+
+        >>> df.compare(df2, keep_equal=True)
+          col1       col3
+          self other self other
+        0    a     c  1.0   1.0
+        2    b     b  3.0   4.0
+
+        Keep all original rows and columns
+
+        >>> df.compare(df2, keep_shape=True)
+          col1       col2       col3
+          self other self other self other
+        0    a     c  NaN   NaN  NaN   NaN
+        1  NaN   NaN  NaN   NaN  NaN   NaN
+        2  NaN   NaN  NaN   NaN  3.0   4.0
+        3  NaN   NaN  NaN   NaN  NaN   NaN
+        4  NaN   NaN  NaN   NaN  NaN   NaN
+
+        Keep all original rows and columns and also all original values
+
+        >>> df.compare(df2, keep_shape=True, keep_equal=True)
+          col1       col2       col3
+          self other self other self other
+        0    a     c  1.0   1.0  1.0   1.0
+        1    a     a  2.0   2.0  2.0   2.0
+        2    b     b  3.0   3.0  3.0   4.0
+        3    b     b  NaN   NaN  4.0   4.0
+        4    a     a  5.0   5.0  5.0   5.0
+        """
+        ),
         klass=_shared_doc_kwargs["klass"],
     )
     def compare(
@@ -8568,108 +8570,110 @@ def update(
     # ----------------------------------------------------------------------
     # Data reshaping
     @Appender(
-        """
-Examples
---------
->>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
-...                               'Parrot', 'Parrot'],
-...                    'Max Speed': [380., 370., 24., 26.]})
->>> df
-   Animal  Max Speed
-0  Falcon      380.0
-1  Falcon      370.0
-2  Parrot       24.0
-3  Parrot       26.0
->>> df.groupby(['Animal']).mean()
-        Max Speed
-Animal
-Falcon      375.0
-Parrot       25.0
-
-**Hierarchical Indexes**
-
-We can groupby different levels of a hierarchical index
-using the `level` parameter:
-
->>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
-...           ['Captive', 'Wild', 'Captive', 'Wild']]
->>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
->>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
-...                   index=index)
->>> df
+        dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
+        ...                               'Parrot', 'Parrot'],
+        ...                    'Max Speed': [380., 370., 24., 26.]})
+        >>> df
+           Animal  Max Speed
+        0  Falcon      380.0
+        1  Falcon      370.0
+        2  Parrot       24.0
+        3  Parrot       26.0
+        >>> df.groupby(['Animal']).mean()
                 Max Speed
-Animal Type
-Falcon Captive      390.0
-       Wild         350.0
-Parrot Captive       30.0
-       Wild          20.0
->>> df.groupby(level=0).mean()
-        Max Speed
-Animal
-Falcon      370.0
-Parrot       25.0
->>> df.groupby(level="Type").mean()
-         Max Speed
-Type
-Captive      210.0
-Wild         185.0
-
-We can also choose to include NA in group keys or not by setting
-`dropna` parameter, the default setting is `True`.
-
->>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
->>> df = pd.DataFrame(l, columns=["a", "b", "c"])
-
->>> df.groupby(by=["b"]).sum()
-    a   c
-b
-1.0 2   3
-2.0 2   5
-
->>> df.groupby(by=["b"], dropna=False).sum()
-    a   c
-b
-1.0 2   3
-2.0 2   5
-NaN 1   4
-
->>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
->>> df = pd.DataFrame(l, columns=["a", "b", "c"])
-
->>> df.groupby(by="a").sum()
-    b     c
-a
-a   13.0   13.0
-b   12.3  123.0
-
->>> df.groupby(by="a", dropna=False).sum()
-    b     c
-a
-a   13.0   13.0
-b   12.3  123.0
-NaN 12.3   33.0
-
-When using ``.apply()``, use ``group_keys`` to include or exclude the group keys.
-The ``group_keys`` argument defaults to ``True`` (include).
-
->>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
-...                               'Parrot', 'Parrot'],
-...                    'Max Speed': [380., 370., 24., 26.]})
->>> df.groupby("Animal", group_keys=True).apply(lambda x: x)
-          Animal  Max Speed
-Animal
-Falcon 0  Falcon      380.0
-       1  Falcon      370.0
-Parrot 2  Parrot       24.0
-       3  Parrot       26.0
-
->>> df.groupby("Animal", group_keys=False).apply(lambda x: x)
-   Animal  Max Speed
-0  Falcon      380.0
-1  Falcon      370.0
-2  Parrot       24.0
-3  Parrot       26.0
-"""
+        Animal
+        Falcon      375.0
+        Parrot       25.0
+
+        **Hierarchical Indexes**
+
+        We can groupby different levels of a hierarchical index
+        using the `level` parameter:
+
+        >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...           ['Captive', 'Wild', 'Captive', 'Wild']]
+        >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
+        >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
+        ...                   index=index)
+        >>> df
+                        Max Speed
+        Animal Type
+        Falcon Captive      390.0
+               Wild         350.0
+        Parrot Captive       30.0
+               Wild          20.0
+        >>> df.groupby(level=0).mean()
+                Max Speed
+        Animal
+        Falcon      370.0
+        Parrot       25.0
+        >>> df.groupby(level="Type").mean()
+                 Max Speed
+        Type
+        Captive      210.0
+        Wild         185.0
+
+        We can also choose to include NA in group keys or not by setting
+        `dropna` parameter, the default setting is `True`.
+
+        >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
+        >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
+
+        >>> df.groupby(by=["b"]).sum()
+            a   c
+        b
+        1.0 2   3
+        2.0 2   5
+
+        >>> df.groupby(by=["b"], dropna=False).sum()
+            a   c
+        b
+        1.0 2   3
+        2.0 2   5
+        NaN 1   4
+
+        >>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
+        >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
+
+        >>> df.groupby(by="a").sum()
+            b     c
+        a
+        a   13.0   13.0
+        b   12.3  123.0
+
+        >>> df.groupby(by="a", dropna=False).sum()
+            b     c
+        a
+        a   13.0   13.0
+        b   12.3  123.0
+        NaN 12.3   33.0
+
+        When using ``.apply()``, use ``group_keys`` to include or exclude the
+        group keys. The ``group_keys`` argument defaults to ``True`` (include).
+
+        >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
+        ...                               'Parrot', 'Parrot'],
+        ...                    'Max Speed': [380., 370., 24., 26.]})
+        >>> df.groupby("Animal", group_keys=True).apply(lambda x: x)
+                  Animal  Max Speed
+        Animal
+        Falcon 0  Falcon      380.0
+               1  Falcon      370.0
+        Parrot 2  Parrot       24.0
+               3  Parrot       26.0
+
+        >>> df.groupby("Animal", group_keys=False).apply(lambda x: x)
+           Animal  Max Speed
+        0  Falcon      380.0
+        1  Falcon      370.0
+        2  Parrot       24.0
+        3  Parrot       26.0
+        """
+        )
     )
     @Appender(_shared_docs["groupby"] % _shared_doc_kwargs)
     def groupby(
@@ -10176,26 +10180,6 @@ def join(
         4  K0  A4   B0
         5  K1  A5   B1
         """
-        return self._join_compat(
-            other,
-            on=on,
-            how=how,
-            lsuffix=lsuffix,
-            rsuffix=rsuffix,
-            sort=sort,
-            validate=validate,
-        )
-
-    def _join_compat(
-        self,
-        other: DataFrame | Series | Iterable[DataFrame | Series],
-        on: IndexLabel | None = None,
-        how: MergeHow = "left",
-        lsuffix: str = "",
-        rsuffix: str = "",
-        sort: bool = False,
-        validate: str | None = None,
-    ):
         from pandas.core.reshape.concat import concat
         from pandas.core.reshape.merge import merge
 
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 5f56623efdc21..d761bc132b89e 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -497,8 +497,6 @@ def _data(self):
 
     # ----------------------------------------------------------------------
     # Axis
-    _stat_axis_number = 0
-    _stat_axis_name = "index"
     _AXIS_ORDERS: list[Literal["index", "columns"]]
     _AXIS_TO_AXIS_NUMBER: dict[Axis, AxisInt] = {0: 0, "index": 0, "rows": 0}
     _info_axis_number: int
@@ -608,10 +606,6 @@ def _get_cleaned_column_resolvers(self) -> dict[Hashable, Series]:
     def _info_axis(self) -> Index:
         return getattr(self, self._info_axis_name)
 
-    @property
-    def _stat_axis(self) -> Index:
-        return getattr(self, self._stat_axis_name)
-
     @property
     def shape(self) -> tuple[int, ...]:
         """
@@ -5853,7 +5847,7 @@ def sample(
         fish           0          0                  8
         """  # noqa:E501
         if axis is None:
-            axis = self._stat_axis_number
+            axis = 0
 
         axis = self._get_axis_number(axis)
         obj_len = self.shape[axis]
@@ -8461,7 +8455,7 @@ def at_time(self, time, asof: bool_t = False, axis: Axis | None = None) -> Self:
         2018-04-10 12:00:00  4
         """
         if axis is None:
-            axis = self._stat_axis_number
+            axis = 0
         axis = self._get_axis_number(axis)
 
         index = self._get_axis(axis)
@@ -8541,7 +8535,7 @@ def between_time(
         2018-04-12 01:00:00  4
         """
         if axis is None:
-            axis = self._stat_axis_number
+            axis = 0
         axis = self._get_axis_number(axis)
 
         index = self._get_axis(axis)
@@ -10373,7 +10367,7 @@ def truncate(
         2016-01-10 23:59:59  1
         """
         if axis is None:
-            axis = self._stat_axis_number
+            axis = 0
         axis = self._get_axis_number(axis)
         ax = self._get_axis(axis)
 
@@ -11045,7 +11039,7 @@ def pct_change(
         GOOG  0.179241  0.094112   NaN
         APPL -0.252395 -0.011860   NaN
         """
-        axis = self._get_axis_number(kwargs.pop("axis", self._stat_axis_name))
+        axis = self._get_axis_number(kwargs.pop("axis", "index"))
         if fill_method is None:
             data = self
         else:
@@ -11140,7 +11134,7 @@ def _accum_func(
     ):
         skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
         if axis is None:
-            axis = self._stat_axis_number
+            axis = 0
         else:
             axis = self._get_axis_number(axis)
 
@@ -11195,7 +11189,7 @@ def _stat_function_ddof(
         nv.validate_stat_ddof_func((), kwargs, fname=name)
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
         if axis is None:
-            axis = self._stat_axis_number
+            axis = 0
 
         return self._reduce(
             func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof
@@ -11357,7 +11351,7 @@ def _min_count_stat_function(
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
 
         if axis is None:
-            axis = self._stat_axis_number
+            axis = 0
 
         return self._reduce(
             func,
diff --git a/pandas/core/interchange/utils.py b/pandas/core/interchange/utils.py
index 7faef09d11239..eb24a7a672ebd 100644
--- a/pandas/core/interchange/utils.py
+++ b/pandas/core/interchange/utils.py
@@ -9,7 +9,8 @@
 
 import numpy as np
 
-import pandas as pd
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
 from pandas.api.types import is_datetime64_dtype
 
 if typing.TYPE_CHECKING:
@@ -72,7 +73,7 @@ def dtype_to_arrow_c_fmt(dtype: DtypeObj) -> str:
     str
         Format string in Apache Arrow C notation of the given `dtype`.
     """
-    if isinstance(dtype, pd.CategoricalDtype):
+    if isinstance(dtype, CategoricalDtype):
         return ArrowCTypes.INT64
     elif dtype == np.dtype("O"):
         return ArrowCTypes.STRING
diff --git a/pandas/core/methods/describe.py b/pandas/core/methods/describe.py
index 33afbfe6489a6..2fa059178d238 100644
--- a/pandas/core/methods/describe.py
+++ b/pandas/core/methods/describe.py
@@ -37,7 +37,7 @@
     is_timedelta64_dtype,
 )
 
-import pandas as pd
+from pandas.core.arrays.floating import Float64Dtype
 from pandas.core.reshape.concat import concat
 
 from pandas.io.formats.format import format_percentiles
@@ -230,7 +230,7 @@ def describe_numeric_1d(series: Series, percentiles: Sequence[float]) -> Series:
     # GH#48340 - always return float on non-complex numeric data
     dtype: DtypeObj | None
     if is_extension_array_dtype(series):
-        dtype = pd.Float64Dtype()
+        dtype = Float64Dtype()
     elif is_numeric_dtype(series) and not is_complex_dtype(series):
         dtype = np.dtype("float")
     else:
diff --git a/pandas/core/ops/array_ops.py b/pandas/core/ops/array_ops.py
index 87b3091fca75a..bae2ab15f3696 100644
--- a/pandas/core/ops/array_ops.py
+++ b/pandas/core/ops/array_ops.py
@@ -106,6 +106,7 @@ def fill_binop(left, right, fill_value):
 
 def comp_method_OBJECT_ARRAY(op, x, y):
     if isinstance(y, list):
+        # e.g. test_tuple_categories
         y = construct_1d_object_array_from_listlike(y)
 
     if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 50eae11be99eb..d44facdcc5382 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -413,11 +413,9 @@ def _groupby_and_aggregate(self, how, *args, **kwargs):
         """
         grouper = self.grouper
 
-        if self._selected_obj.ndim == 1:
-            obj = self._selected_obj
-        else:
-            # Excludes `on` column when provided
-            obj = self._obj_with_exclusions
+        # Excludes `on` column when provided
+        obj = self._obj_with_exclusions
+
         grouped = get_groupby(
             obj, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys
         )
@@ -1269,11 +1267,9 @@ def _downsample(self, how, **kwargs):
         """
         how = com.get_cython_func(how) or how
         ax = self.ax
-        if self._selected_obj.ndim == 1:
-            obj = self._selected_obj
-        else:
-            # Excludes `on` column when provided
-            obj = self._obj_with_exclusions
+
+        # Excludes `on` column when provided
+        obj = self._obj_with_exclusions
 
         if not len(ax):
             # reset to the new freq
diff --git a/pandas/core/series.py b/pandas/core/series.py
index fa5673b1c6326..05f9eb9c5d5d6 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1918,86 +1918,89 @@ def _set_name(self, name, inplace: bool = False) -> Series:
         return ser
 
     @Appender(
+        dedent(
+            """
+        Examples
+        --------
+        >>> ser = pd.Series([390., 350., 30., 20.],
+        ...                 index=['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...                 name="Max Speed")
+        >>> ser
+        Falcon    390.0
+        Falcon    350.0
+        Parrot     30.0
+        Parrot     20.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(["a", "b", "a", "b"]).mean()
+        a    210.0
+        b    185.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).mean()
+        Falcon    370.0
+        Parrot     25.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(ser > 100).mean()
+        Max Speed
+        False     25.0
+        True     370.0
+        Name: Max Speed, dtype: float64
+
+        **Grouping by Indexes**
+
+        We can groupby different levels of a hierarchical index
+        using the `level` parameter:
+
+        >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...           ['Captive', 'Wild', 'Captive', 'Wild']]
+        >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
+        >>> ser = pd.Series([390., 350., 30., 20.], index=index, name="Max Speed")
+        >>> ser
+        Animal  Type
+        Falcon  Captive    390.0
+                Wild       350.0
+        Parrot  Captive     30.0
+                Wild        20.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).mean()
+        Animal
+        Falcon    370.0
+        Parrot     25.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level="Type").mean()
+        Type
+        Captive    210.0
+        Wild       185.0
+        Name: Max Speed, dtype: float64
+
+        We can also choose to include `NA` in group keys or not by defining
+        `dropna` parameter, the default setting is `True`.
+
+        >>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
+        >>> ser.groupby(level=0).sum()
+        a    3
+        b    3
+        dtype: int64
+
+        >>> ser.groupby(level=0, dropna=False).sum()
+        a    3
+        b    3
+        NaN  3
+        dtype: int64
+
+        >>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
+        >>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
+        >>> ser.groupby(["a", "b", "a", np.nan]).mean()
+        a    210.0
+        b    350.0
+        Name: Max Speed, dtype: float64
+
+        >>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()
+        a    210.0
+        b    350.0
+        NaN   20.0
+        Name: Max Speed, dtype: float64
         """
-Examples
---------
->>> ser = pd.Series([390., 350., 30., 20.],
-...                 index=['Falcon', 'Falcon', 'Parrot', 'Parrot'], name="Max Speed")
->>> ser
-Falcon    390.0
-Falcon    350.0
-Parrot     30.0
-Parrot     20.0
-Name: Max Speed, dtype: float64
->>> ser.groupby(["a", "b", "a", "b"]).mean()
-a    210.0
-b    185.0
-Name: Max Speed, dtype: float64
->>> ser.groupby(level=0).mean()
-Falcon    370.0
-Parrot     25.0
-Name: Max Speed, dtype: float64
->>> ser.groupby(ser > 100).mean()
-Max Speed
-False     25.0
-True     370.0
-Name: Max Speed, dtype: float64
-
-**Grouping by Indexes**
-
-We can groupby different levels of a hierarchical index
-using the `level` parameter:
-
->>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
-...           ['Captive', 'Wild', 'Captive', 'Wild']]
->>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
->>> ser = pd.Series([390., 350., 30., 20.], index=index, name="Max Speed")
->>> ser
-Animal  Type
-Falcon  Captive    390.0
-        Wild       350.0
-Parrot  Captive     30.0
-        Wild        20.0
-Name: Max Speed, dtype: float64
->>> ser.groupby(level=0).mean()
-Animal
-Falcon    370.0
-Parrot     25.0
-Name: Max Speed, dtype: float64
->>> ser.groupby(level="Type").mean()
-Type
-Captive    210.0
-Wild       185.0
-Name: Max Speed, dtype: float64
-
-We can also choose to include `NA` in group keys or not by defining
-`dropna` parameter, the default setting is `True`.
-
->>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
->>> ser.groupby(level=0).sum()
-a    3
-b    3
-dtype: int64
-
->>> ser.groupby(level=0, dropna=False).sum()
-a    3
-b    3
-NaN  3
-dtype: int64
-
->>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
->>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
->>> ser.groupby(["a", "b", "a", np.nan]).mean()
-a    210.0
-b    350.0
-Name: Max Speed, dtype: float64
-
->>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()
-a    210.0
-b    350.0
-NaN   20.0
-Name: Max Speed, dtype: float64
-"""
+        )
     )
     @Appender(_shared_docs["groupby"] % _shared_doc_kwargs)
     def groupby(
@@ -3002,66 +3005,68 @@ def _append(
 
     @doc(
         _shared_docs["compare"],
+        dedent(
+            """
+        Returns
+        -------
+        Series or DataFrame
+            If axis is 0 or 'index' the result will be a Series.
+            The resulting index will be a MultiIndex with 'self' and 'other'
+            stacked alternately at the inner level.
+
+            If axis is 1 or 'columns' the result will be a DataFrame.
+            It will have two columns namely 'self' and 'other'.
+
+        See Also
+        --------
+        DataFrame.compare : Compare with another DataFrame and show differences.
+
+        Notes
+        -----
+        Matching NaNs will not appear as a difference.
+
+        Examples
+        --------
+        >>> s1 = pd.Series(["a", "b", "c", "d", "e"])
+        >>> s2 = pd.Series(["a", "a", "c", "b", "e"])
+
+        Align the differences on columns
+
+        >>> s1.compare(s2)
+          self other
+        1    b     a
+        3    d     b
+
+        Stack the differences on indices
+
+        >>> s1.compare(s2, align_axis=0)
+        1  self     b
+           other    a
+        3  self     d
+           other    b
+        dtype: object
+
+        Keep all original rows
+
+        >>> s1.compare(s2, keep_shape=True)
+          self other
+        0  NaN   NaN
+        1    b     a
+        2  NaN   NaN
+        3    d     b
+        4  NaN   NaN
+
+        Keep all original rows and also all original values
+
+        >>> s1.compare(s2, keep_shape=True, keep_equal=True)
+          self other
+        0    a     a
+        1    b     a
+        2    c     c
+        3    d     b
+        4    e     e
         """
-Returns
--------
-Series or DataFrame
-    If axis is 0 or 'index' the result will be a Series.
-    The resulting index will be a MultiIndex with 'self' and 'other'
-    stacked alternately at the inner level.
-
-    If axis is 1 or 'columns' the result will be a DataFrame.
-    It will have two columns namely 'self' and 'other'.
-
-See Also
---------
-DataFrame.compare : Compare with another DataFrame and show differences.
-
-Notes
------
-Matching NaNs will not appear as a difference.
-
-Examples
---------
->>> s1 = pd.Series(["a", "b", "c", "d", "e"])
->>> s2 = pd.Series(["a", "a", "c", "b", "e"])
-
-Align the differences on columns
-
->>> s1.compare(s2)
-  self other
-1    b     a
-3    d     b
-
-Stack the differences on indices
-
->>> s1.compare(s2, align_axis=0)
-1  self     b
-   other    a
-3  self     d
-   other    b
-dtype: object
-
-Keep all original rows
-
->>> s1.compare(s2, keep_shape=True)
-  self other
-0  NaN   NaN
-1    b     a
-2  NaN   NaN
-3    d     b
-4  NaN   NaN
-
-Keep all original rows and also all original values
-
->>> s1.compare(s2, keep_shape=True, keep_equal=True)
-  self other
-0    a     a
-1    b     a
-2    c     c
-3    d     b
-4    e     e
-""",
+        ),
         klass=_shared_doc_kwargs["klass"],
     )
     def compare(
diff --git a/pandas/core/tools/numeric.py b/pandas/core/tools/numeric.py
index 62976f68cbdd4..97900eacd1f5d 100644
--- a/pandas/core/tools/numeric.py
+++ b/pandas/core/tools/numeric.py
@@ -28,8 +28,8 @@
     ABCSeries,
 )
 
-import pandas as pd
 from pandas.core.arrays import BaseMaskedArray
+from pandas.core.arrays.arrow import ArrowDtype
 
 if TYPE_CHECKING:
     from pandas._typing import (
@@ -204,7 +204,7 @@ def to_numeric(
         values = values._data[~mask]
 
     values_dtype = getattr(values, "dtype", None)
-    if isinstance(values_dtype, pd.ArrowDtype):
+    if isinstance(values_dtype, ArrowDtype):
         mask = values.isna()
         values = values.dropna().to_numpy()
     new_mask: np.ndarray | None = None
@@ -290,7 +290,7 @@ def to_numeric(
             klass = FloatingArray
         values = klass(data, mask)
 
-        if dtype_backend == "pyarrow" or isinstance(values_dtype, pd.ArrowDtype):
+        if dtype_backend == "pyarrow" or isinstance(values_dtype, ArrowDtype):
             values = ArrowExtensionArray(values.__arrow_array__())
 
     if is_series:
@@ -298,7 +298,9 @@ def to_numeric(
     elif is_index:
         # because we want to coerce to numeric if possible,
         # do not use _shallow_copy
-        return pd.Index(values, name=arg.name)
+        from pandas import Index
+
+        return Index(values, name=arg.name)
     elif is_scalars:
         return values[0]
     else:
diff --git a/pandas/tests/arrays/test_datetimelike.py b/pandas/tests/arrays/test_datetimelike.py
index 8db056b8fef58..2a300b6a724d0 100644
--- a/pandas/tests/arrays/test_datetimelike.py
+++ b/pandas/tests/arrays/test_datetimelike.py
@@ -755,9 +755,7 @@ def test_to_period(self, datetime_index, freqstr):
         result = arr.to_period(freq=freqstr)
         assert isinstance(result, PeriodArray)
 
-        # placeholder until these become actual EA subclasses and we can use
-        #  an EA-specific tm.assert_ function
-        tm.assert_index_equal(pd.Index(result), pd.Index(expected))
+        tm.assert_equal(result, expected._data)
 
     def test_to_period_2d(self, arr1d):
         arr2d = arr1d.reshape(1, -1)
@@ -1057,9 +1055,7 @@ def test_to_timestamp(self, how, arr1d):
         result = arr.to_timestamp(how=how)
         assert isinstance(result, DatetimeArray)
 
-        # placeholder until these become actual EA subclasses and we can use
-        #  an EA-specific tm.assert_ function
-        tm.assert_index_equal(pd.Index(result), pd.Index(expected))
+        tm.assert_equal(result, expected)
 
     def test_to_timestamp_roundtrip_bday(self):
         # Case where infer_freq inside would choose "D" instead of "B"
diff --git a/pandas/tests/resample/test_period_index.py b/pandas/tests/resample/test_period_index.py
index 6d0a56a947065..17277b0c74568 100644
--- a/pandas/tests/resample/test_period_index.py
+++ b/pandas/tests/resample/test_period_index.py
@@ -826,12 +826,12 @@ def test_resample_with_only_nat(self):
     )
     def test_resample_with_offset(self, start, end, start_freq, end_freq, offset):
         # GH 23882 & 31809
-        s = Series(0, index=period_range(start, end, freq=start_freq))
-        s = s + np.arange(len(s))
-        result = s.resample(end_freq, offset=offset).mean()
+        pi = period_range(start, end, freq=start_freq)
+        ser = Series(np.arange(len(pi)), index=pi)
+        result = ser.resample(end_freq, offset=offset).mean()
         result = result.to_timestamp(end_freq)
 
-        expected = s.to_timestamp().resample(end_freq, offset=offset).mean()
+        expected = ser.to_timestamp().resample(end_freq, offset=offset).mean()
         if end_freq == "M":
             # TODO: is non-tick the relevant characteristic? (GH 33815)
             expected.index = expected.index._with_freq(None)