diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 8b49c2186dde0..697d759206ff9 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -298,11 +298,11 @@ def __arrow_array__(self, type=None):
                 if self.freqstr != type.freq:
                     raise TypeError(
                         "Not supported to convert PeriodArray to array with different"
-                        " 'freq' ({0} vs {1})".format(self.freqstr, type.freq)
+                        f" 'freq' ({self.freqstr} vs {type.freq})"
                     )
             else:
                 raise TypeError(
-                    "Not supported to convert PeriodArray to '{0}' type".format(type)
+                    f"Not supported to convert PeriodArray to '{type}' type"
                 )
 
         period_type = ArrowPeriodType(self.freqstr)
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index 5a007f28d63cb..f62f03be9b732 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -194,12 +194,11 @@ def ensure_python_int(value: Union[int, np.integer]) -> int:
     """
     if not is_scalar(value):
         raise TypeError(f"Value needs to be a scalar value, was type {type(value)}")
-    msg = "Wrong type {} for value {}"
     try:
         new_value = int(value)
         assert new_value == value
     except (TypeError, ValueError, AssertionError):
-        raise TypeError(msg.format(type(value), value))
+        raise TypeError(f"Wrong type {type(value)} for value {value}")
     return new_value
 
 
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 466ed815e8e5a..93522abc3a48f 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -435,12 +435,11 @@ def __eq__(self, other: Any) -> bool:
             return hash(self) == hash(other)
 
     def __repr__(self) -> str_type:
-        tpl = "CategoricalDtype(categories={data}ordered={ordered})"
         if self.categories is None:
             data = "None, "
         else:
             data = self.categories._format_data(name=type(self).__name__)
-        return tpl.format(data=data, ordered=self.ordered)
+        return f"CategoricalDtype(categories={data}ordered={self.ordered})"
 
     @staticmethod
     def _hash_categories(categories, ordered: Ordered = True) -> int:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 5ad133f9e21a4..c91c38f1d9fb4 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2431,7 +2431,7 @@ def _verbose_repr():
                 dtype = self.dtypes.iloc[i]
                 col = pprint_thing(col)
 
-                line_no = _put_str(" {num}".format(num=i), space_num)
+                line_no = _put_str(f" {i}", space_num)
                 count = ""
                 if show_counts:
                     count = counts.iloc[i]
@@ -3792,7 +3792,7 @@ def _reindex_multi(self, axes, copy, fill_value) -> "DataFrame":
                 fill_value=fill_value,
             )
 
-    @Appender(_shared_docs["align"] % _shared_doc_kwargs)
+    @Appender(_shared_docs["align"].format(**_shared_doc_kwargs))
     def align(
         self,
         other,
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 0116207675889..8f9a6261b8569 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8221,7 +8221,7 @@ def ranker(data):
         Parameters
         ----------
         other : DataFrame or Series
-        join : {'outer', 'inner', 'left', 'right'}, default 'outer'
+        join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
         axis : allowed axis of the other object, default None
             Align on index (0), columns (1), or both (None).
         level : int or level name, default None
@@ -8233,7 +8233,7 @@ def ranker(data):
         fill_value : scalar, default np.NaN
             Value to use for missing values. Defaults to NaN, but can be any
             "compatible" value.
-        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+        method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
             Method to use for filling holes in reindexed Series:
 
             - pad / ffill: propagate last valid observation forward to next valid.
@@ -8246,19 +8246,19 @@ def ranker(data):
             be partially filled. If method is not specified, this is the
             maximum number of entries along the entire axis where NaNs will be
             filled. Must be greater than 0 if not None.
-        fill_axis : %(axes_single_arg)s, default 0
+        fill_axis : {axes_single_arg}, default 0
             Filling axis, method and limit.
-        broadcast_axis : %(axes_single_arg)s, default None
+        broadcast_axis : {axes_single_arg}, default None
             Broadcast values along this axis, if aligning two objects of
             different dimensions.
 
         Returns
         -------
-        (left, right) : (%(klass)s, type of other)
+        (left, right) : ({klass}, type of other)
             Aligned objects.
         """
 
-    @Appender(_shared_docs["align"] % _shared_doc_kwargs)
+    @Appender(_shared_docs["align"].format(**_shared_doc_kwargs))
     def align(
         self,
         other,
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 3e1f011fde51a..432f53c8ac5b7 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -3866,7 +3866,7 @@ def _needs_reindex_multi(self, axes, method, level):
         """
         return False
 
-    @Appender(generic._shared_docs["align"] % _shared_doc_kwargs)
+    @Appender(generic._shared_docs["align"].format(**_shared_doc_kwargs))
     def align(
         self,
         other,