diff --git a/asv_bench/benchmarks/algorithms.py b/asv_bench/benchmarks/algorithms.py
index 3c78b0a9a60c8..933e8fbc175d8 100644
--- a/asv_bench/benchmarks/algorithms.py
+++ b/asv_bench/benchmarks/algorithms.py
@@ -4,8 +4,6 @@
 
 import pandas as pd
 
-from .pandas_vb_common import tm
-
 for imp in ["pandas.util", "pandas.tools.hashing"]:
     try:
         hashing = import_module(imp)
@@ -47,9 +45,12 @@ def setup(self, unique, sort, dtype):
         elif dtype == "datetime64[ns, tz]":
             data = pd.date_range("2011-01-01", freq="h", periods=N, tz="Asia/Tokyo")
         elif dtype == "object_str":
-            data = tm.makeStringIndex(N)
+            data = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
         elif dtype == "string[pyarrow]":
-            data = pd.array(tm.makeStringIndex(N), dtype="string[pyarrow]")
+            data = pd.array(
+                pd.Index([f"i-{i}" for i in range(N)], dtype=object),
+                dtype="string[pyarrow]",
+            )
         else:
             raise NotImplementedError
 
@@ -88,7 +89,7 @@ def setup(self, unique, keep, dtype):
         elif dtype == "float64":
             data = pd.Index(np.random.randn(N), dtype="float64")
         elif dtype == "string":
-            data = tm.makeStringIndex(N)
+            data = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
         elif dtype == "datetime64[ns]":
             data = pd.date_range("2011-01-01", freq="h", periods=N)
         elif dtype == "datetime64[ns, tz]":
@@ -136,7 +137,9 @@ def setup_cache(self):
         df = pd.DataFrame(
             {
                 "strings": pd.Series(
-                    tm.makeStringIndex(10000).take(np.random.randint(0, 10000, size=N))
+                    pd.Index([f"i-{i}" for i in range(10000)], dtype=object).take(
+                        np.random.randint(0, 10000, size=N)
+                    )
                 ),
                 "floats": np.random.randn(N),
                 "ints": np.arange(N),
diff --git a/asv_bench/benchmarks/algos/isin.py b/asv_bench/benchmarks/algos/isin.py
index 92797425b2c30..2b3d32fb579dc 100644
--- a/asv_bench/benchmarks/algos/isin.py
+++ b/asv_bench/benchmarks/algos/isin.py
@@ -8,8 +8,6 @@
     date_range,
 )
 
-from ..pandas_vb_common import tm
-
 
 class IsIn:
     params = [
@@ -60,7 +58,9 @@ def setup(self, dtype):
 
         elif dtype in ["str", "string[python]", "string[pyarrow]"]:
             try:
-                self.series = Series(tm.makeStringIndex(N), dtype=dtype)
+                self.series = Series(
+                    Index([f"i-{i}" for i in range(N)], dtype=object), dtype=dtype
+                )
             except ImportError:
                 raise NotImplementedError
             self.values = list(self.series[:2])
diff --git a/asv_bench/benchmarks/ctors.py b/asv_bench/benchmarks/ctors.py
index 2db00cc7f2ad9..77c9faf3d3a87 100644
--- a/asv_bench/benchmarks/ctors.py
+++ b/asv_bench/benchmarks/ctors.py
@@ -9,8 +9,6 @@
     date_range,
 )
 
-from .pandas_vb_common import tm
-
 
 def no_change(arr):
     return arr
@@ -115,7 +113,7 @@ def time_dtindex_from_index_with_series(self):
 class MultiIndexConstructor:
     def setup(self):
         N = 10**4
-        self.iterables = [tm.makeStringIndex(N), range(20)]
+        self.iterables = [Index([f"i-{i}" for i in range(N)], dtype=object), range(20)]
 
     def time_multiindex_from_iterables(self):
         MultiIndex.from_product(self.iterables)
diff --git a/asv_bench/benchmarks/dtypes.py b/asv_bench/benchmarks/dtypes.py
index c33043c0eddc1..7f3429b5e3882 100644
--- a/asv_bench/benchmarks/dtypes.py
+++ b/asv_bench/benchmarks/dtypes.py
@@ -3,7 +3,10 @@
 import numpy as np
 
 import pandas as pd
-from pandas import DataFrame
+from pandas import (
+    DataFrame,
+    Index,
+)
 import pandas._testing as tm
 from pandas.api.types import (
     is_extension_array_dtype,
@@ -73,8 +76,8 @@ class SelectDtypes:
 
     def setup(self, dtype):
         N, K = 5000, 50
-        self.index = tm.makeStringIndex(N)
-        self.columns = tm.makeStringIndex(K)
+        self.index = Index([f"i-{i}" for i in range(N)], dtype=object)
+        self.columns = Index([f"i-{i}" for i in range(K)], dtype=object)
 
         def create_df(data):
             return DataFrame(data, index=self.index, columns=self.columns)
diff --git a/asv_bench/benchmarks/frame_ctor.py b/asv_bench/benchmarks/frame_ctor.py
index 7092a679b8cf0..f938f7eb0d951 100644
--- a/asv_bench/benchmarks/frame_ctor.py
+++ b/asv_bench/benchmarks/frame_ctor.py
@@ -12,8 +12,6 @@
     date_range,
 )
 
-from .pandas_vb_common import tm
-
 try:
     from pandas.tseries.offsets import (
         Hour,
@@ -30,8 +28,8 @@
 class FromDicts:
     def setup(self):
         N, K = 5000, 50
-        self.index = tm.makeStringIndex(N)
-        self.columns = tm.makeStringIndex(K)
+        self.index = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
+        self.columns = pd.Index([f"i-{i}" for i in range(K)], dtype=object)
         frame = DataFrame(np.random.randn(N, K), index=self.index, columns=self.columns)
         self.data = frame.to_dict()
         self.dict_list = frame.to_dict(orient="records")
diff --git a/asv_bench/benchmarks/frame_methods.py b/asv_bench/benchmarks/frame_methods.py
index f22a261041e17..a283afd1f0f1e 100644
--- a/asv_bench/benchmarks/frame_methods.py
+++ b/asv_bench/benchmarks/frame_methods.py
@@ -5,6 +5,7 @@
 
 from pandas import (
     DataFrame,
+    Index,
     MultiIndex,
     NaT,
     Series,
@@ -14,8 +15,6 @@
     timedelta_range,
 )
 
-from .pandas_vb_common import tm
-
 
 class AsType:
     params = [
@@ -703,8 +702,12 @@ def setup(self, monotonic):
         K = 10
         df = DataFrame(
             {
-                "key1": tm.makeStringIndex(N).values.repeat(K),
-                "key2": tm.makeStringIndex(N).values.repeat(K),
+                "key1": Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(
+                    K
+                ),
+                "key2": Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(
+                    K
+                ),
                 "value": np.random.randn(N * K),
             }
         )
diff --git a/asv_bench/benchmarks/gil.py b/asv_bench/benchmarks/gil.py
index c78819f75c52a..a0c4189c72d0e 100644
--- a/asv_bench/benchmarks/gil.py
+++ b/asv_bench/benchmarks/gil.py
@@ -5,6 +5,7 @@
 
 from pandas import (
     DataFrame,
+    Index,
     Series,
     date_range,
     factorize,
@@ -12,8 +13,6 @@
 )
 from pandas.core.algorithms import take_nd
 
-from .pandas_vb_common import tm
-
 try:
     from pandas import (
         rolling_kurt,
@@ -34,7 +33,6 @@
 except ImportError:
     from pandas import algos
 
-
 from .pandas_vb_common import BaseIO  # isort:skip
 
 
@@ -305,7 +303,7 @@ class ParallelFactorize:
     param_names = ["threads"]
 
     def setup(self, threads):
-        strings = tm.makeStringIndex(100000)
+        strings = Index([f"i-{i}" for i in range(100000)], dtype=object)
 
         @test_parallel(num_threads=threads)
         def parallel():
diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py
index 202ba6e981b70..abffa1f702b9c 100644
--- a/asv_bench/benchmarks/groupby.py
+++ b/asv_bench/benchmarks/groupby.py
@@ -17,8 +17,6 @@
     to_timedelta,
 )
 
-from .pandas_vb_common import tm
-
 method_blocklist = {
     "object": {
         "diff",
@@ -167,10 +165,14 @@ def setup_cache(self):
             "int64_small": Series(np.random.randint(0, 100, size=size)),
             "int64_large": Series(np.random.randint(0, 10000, size=size)),
             "object_small": Series(
-                tm.makeStringIndex(100).take(np.random.randint(0, 100, size=size))
+                Index([f"i-{i}" for i in range(100)], dtype=object).take(
+                    np.random.randint(0, 100, size=size)
+                )
             ),
             "object_large": Series(
-                tm.makeStringIndex(10000).take(np.random.randint(0, 10000, size=size))
+                Index([f"i-{i}" for i in range(10000)], dtype=object).take(
+                    np.random.randint(0, 10000, size=size)
+                )
             ),
         }
         return data
@@ -912,7 +914,7 @@ def setup(self):
         n1 = 400
         n2 = 250
         index = MultiIndex(
-            levels=[np.arange(n1), tm.makeStringIndex(n2)],
+            levels=[np.arange(n1), Index([f"i-{i}" for i in range(n2)], dtype=object)],
             codes=[np.repeat(range(n1), n2).tolist(), list(range(n2)) * n1],
             names=["lev1", "lev2"],
         )
diff --git a/asv_bench/benchmarks/index_object.py b/asv_bench/benchmarks/index_object.py
index 7e33223260e0f..637b1b40f59a3 100644
--- a/asv_bench/benchmarks/index_object.py
+++ b/asv_bench/benchmarks/index_object.py
@@ -12,8 +12,6 @@
     date_range,
 )
 
-from .pandas_vb_common import tm
-
 
 class SetOperations:
     params = (
@@ -30,7 +28,7 @@ def setup(self, index_structure, dtype, method):
         date_str_left = Index(dates_left.strftime(fmt))
         int_left = Index(np.arange(N))
         ea_int_left = Index(np.arange(N), dtype="Int64")
-        str_left = tm.makeStringIndex(N)
+        str_left = Index([f"i-{i}" for i in range(N)], dtype=object)
 
         data = {
             "datetime": dates_left,
@@ -155,7 +153,12 @@ class Indexing:
 
     def setup(self, dtype):
         N = 10**6
-        self.idx = getattr(tm, f"make{dtype}Index")(N)
+        if dtype == "String":
+            self.idx = Index([f"i-{i}" for i in range(N)], dtype=object)
+        elif dtype == "Float":
+            self.idx = Index(np.arange(N), dtype=np.float64)
+        elif dtype == "Int":
+            self.idx = Index(np.arange(N), dtype=np.int64)
         self.array_mask = (np.arange(N) % 3) == 0
         self.series_mask = Series(self.array_mask)
         self.sorted = self.idx.sort_values()
diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py
index 4961722c0e9cd..9ad1f5b31016d 100644
--- a/asv_bench/benchmarks/indexing.py
+++ b/asv_bench/benchmarks/indexing.py
@@ -22,8 +22,6 @@
     period_range,
 )
 
-from .pandas_vb_common import tm
-
 
 class NumericSeriesIndexing:
     params = [
@@ -124,7 +122,7 @@ class NonNumericSeriesIndexing:
     def setup(self, index, index_structure):
         N = 10**6
         if index == "string":
-            index = tm.makeStringIndex(N)
+            index = Index([f"i-{i}" for i in range(N)], dtype=object)
         elif index == "datetime":
             index = date_range("1900", periods=N, freq="s")
         elif index == "period":
@@ -156,8 +154,8 @@ def time_getitem_list_like(self, index, index_structure):
 
 class DataFrameStringIndexing:
     def setup(self):
-        index = tm.makeStringIndex(1000)
-        columns = tm.makeStringIndex(30)
+        index = Index([f"i-{i}" for i in range(1000)], dtype=object)
+        columns = Index([f"i-{i}" for i in range(30)], dtype=object)
         with warnings.catch_warnings(record=True):
             self.df = DataFrame(np.random.randn(1000, 30), index=index, columns=columns)
         self.idx_scalar = index[100]
diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py
index 805b0c807452c..d5c58033c1157 100644
--- a/asv_bench/benchmarks/inference.py
+++ b/asv_bench/benchmarks/inference.py
@@ -9,6 +9,7 @@
 import numpy as np
 
 from pandas import (
+    Index,
     NaT,
     Series,
     date_range,
@@ -17,10 +18,7 @@
     to_timedelta,
 )
 
-from .pandas_vb_common import (
-    lib,
-    tm,
-)
+from .pandas_vb_common import lib
 
 
 class ToNumeric:
@@ -31,7 +29,7 @@ def setup(self, errors):
         N = 10000
         self.float = Series(np.random.randn(N))
         self.numstr = self.float.astype("str")
-        self.str = Series(tm.makeStringIndex(N))
+        self.str = Series(Index([f"i-{i}" for i in range(N)], dtype=object))
 
     def time_from_float(self, errors):
         to_numeric(self.float, errors=errors)
diff --git a/asv_bench/benchmarks/io/csv.py b/asv_bench/benchmarks/io/csv.py
index a45315f63d62e..9ac83db4f85b9 100644
--- a/asv_bench/benchmarks/io/csv.py
+++ b/asv_bench/benchmarks/io/csv.py
@@ -10,6 +10,7 @@
 from pandas import (
     Categorical,
     DataFrame,
+    Index,
     concat,
     date_range,
     period_range,
@@ -17,10 +18,7 @@
     to_datetime,
 )
 
-from ..pandas_vb_common import (
-    BaseIO,
-    tm,
-)
+from ..pandas_vb_common import BaseIO
 
 
 class ToCSV(BaseIO):
@@ -288,7 +286,7 @@ class ReadCSVSkipRows(BaseIO):
 
     def setup(self, skiprows, engine):
         N = 20000
-        index = tm.makeStringIndex(N)
+        index = Index([f"i-{i}" for i in range(N)], dtype=object)
         df = DataFrame(
             {
                 "float1": np.random.randn(N),
diff --git a/asv_bench/benchmarks/io/excel.py b/asv_bench/benchmarks/io/excel.py
index f8d81b0f6a699..902a61be901bd 100644
--- a/asv_bench/benchmarks/io/excel.py
+++ b/asv_bench/benchmarks/io/excel.py
@@ -12,12 +12,11 @@
 from pandas import (
     DataFrame,
     ExcelWriter,
+    Index,
     date_range,
     read_excel,
 )
 
-from ..pandas_vb_common import tm
-
 
 def _generate_dataframe():
     N = 2000
@@ -27,7 +26,7 @@ def _generate_dataframe():
         columns=[f"float{i}" for i in range(C)],
         index=date_range("20000101", periods=N, freq="h"),
     )
-    df["object"] = tm.makeStringIndex(N)
+    df["object"] = Index([f"i-{i}" for i in range(N)], dtype=object)
     return df
 
 
diff --git a/asv_bench/benchmarks/io/hdf.py b/asv_bench/benchmarks/io/hdf.py
index 195aaa158e178..acf0ec4b9d359 100644
--- a/asv_bench/benchmarks/io/hdf.py
+++ b/asv_bench/benchmarks/io/hdf.py
@@ -3,20 +3,18 @@
 from pandas import (
     DataFrame,
     HDFStore,
+    Index,
     date_range,
     read_hdf,
 )
 
-from ..pandas_vb_common import (
-    BaseIO,
-    tm,
-)
+from ..pandas_vb_common import BaseIO
 
 
 class HDFStoreDataFrame(BaseIO):
     def setup(self):
         N = 25000
-        index = tm.makeStringIndex(N)
+        index = Index([f"i-{i}" for i in range(N)], dtype=object)
         self.df = DataFrame(
             {"float1": np.random.randn(N), "float2": np.random.randn(N)}, index=index
         )
@@ -124,7 +122,7 @@ def setup(self, format):
             columns=[f"float{i}" for i in range(C)],
             index=date_range("20000101", periods=N, freq="h"),
         )
-        self.df["object"] = tm.makeStringIndex(N)
+        self.df["object"] = Index([f"i-{i}" for i in range(N)], dtype=object)
         self.df.to_hdf(self.fname, "df", format=format)
 
         # Numeric df
diff --git a/asv_bench/benchmarks/io/json.py b/asv_bench/benchmarks/io/json.py
index 8a2e3fa87eb37..bcbfcdea42dd9 100644
--- a/asv_bench/benchmarks/io/json.py
+++ b/asv_bench/benchmarks/io/json.py
@@ -4,6 +4,7 @@
 
 from pandas import (
     DataFrame,
+    Index,
     concat,
     date_range,
     json_normalize,
@@ -11,10 +12,7 @@
     timedelta_range,
 )
 
-from ..pandas_vb_common import (
-    BaseIO,
-    tm,
-)
+from ..pandas_vb_common import BaseIO
 
 
 class ReadJSON(BaseIO):
@@ -114,7 +112,7 @@ def setup(self, orient, frame):
         ints = np.random.randint(100000000, size=N)
         longints = sys.maxsize * np.random.randint(100000000, size=N)
         floats = np.random.randn(N)
-        strings = tm.makeStringIndex(N)
+        strings = Index([f"i-{i}" for i in range(N)], dtype=object)
         self.df = DataFrame(np.random.randn(N, ncols), index=np.arange(N))
         self.df_date_idx = DataFrame(np.random.randn(N, ncols), index=index)
         self.df_td_int_ts = DataFrame(
@@ -220,7 +218,7 @@ def setup(self):
         ints = np.random.randint(100000000, size=N)
         longints = sys.maxsize * np.random.randint(100000000, size=N)
         floats = np.random.randn(N)
-        strings = tm.makeStringIndex(N)
+        strings = Index([f"i-{i}" for i in range(N)], dtype=object)
         self.df = DataFrame(np.random.randn(N, ncols), index=np.arange(N))
         self.df_date_idx = DataFrame(np.random.randn(N, ncols), index=index)
         self.df_td_int_ts = DataFrame(
diff --git a/asv_bench/benchmarks/io/pickle.py b/asv_bench/benchmarks/io/pickle.py
index 54631d9236887..4787b57b54756 100644
--- a/asv_bench/benchmarks/io/pickle.py
+++ b/asv_bench/benchmarks/io/pickle.py
@@ -2,14 +2,12 @@
 
 from pandas import (
     DataFrame,
+    Index,
     date_range,
     read_pickle,
 )
 
-from ..pandas_vb_common import (
-    BaseIO,
-    tm,
-)
+from ..pandas_vb_common import BaseIO
 
 
 class Pickle(BaseIO):
@@ -22,7 +20,7 @@ def setup(self):
             columns=[f"float{i}" for i in range(C)],
             index=date_range("20000101", periods=N, freq="h"),
         )
-        self.df["object"] = tm.makeStringIndex(N)
+        self.df["object"] = Index([f"i-{i}" for i in range(N)], dtype=object)
         self.df.to_pickle(self.fname)
 
     def time_read_pickle(self):
diff --git a/asv_bench/benchmarks/io/sql.py b/asv_bench/benchmarks/io/sql.py
index 6f893ee72d918..e87cc4aaa80c7 100644
--- a/asv_bench/benchmarks/io/sql.py
+++ b/asv_bench/benchmarks/io/sql.py
@@ -5,13 +5,12 @@
 
 from pandas import (
     DataFrame,
+    Index,
     date_range,
     read_sql_query,
     read_sql_table,
 )
 
-from ..pandas_vb_common import tm
-
 
 class SQL:
     params = ["sqlalchemy", "sqlite"]
@@ -35,7 +34,7 @@ def setup(self, connection):
                 "int": np.random.randint(0, N, size=N),
                 "datetime": date_range("2000-01-01", periods=N, freq="s"),
             },
-            index=tm.makeStringIndex(N),
+            index=Index([f"i-{i}" for i in range(N)], dtype=object),
         )
         self.df.iloc[1000:3000, 1] = np.nan
         self.df["date"] = self.df["datetime"].dt.date
@@ -84,7 +83,7 @@ def setup(self, connection, dtype):
                 "int": np.random.randint(0, N, size=N),
                 "datetime": date_range("2000-01-01", periods=N, freq="s"),
             },
-            index=tm.makeStringIndex(N),
+            index=Index([f"i-{i}" for i in range(N)], dtype=object),
         )
         self.df.iloc[1000:3000, 1] = np.nan
         self.df["date"] = self.df["datetime"].dt.date
@@ -113,7 +112,7 @@ def setup(self):
                 "int": np.random.randint(0, N, size=N),
                 "datetime": date_range("2000-01-01", periods=N, freq="s"),
             },
-            index=tm.makeStringIndex(N),
+            index=Index([f"i-{i}" for i in range(N)], dtype=object),
         )
         self.df.iloc[1000:3000, 1] = np.nan
         self.df["date"] = self.df["datetime"].dt.date
@@ -159,7 +158,7 @@ def setup(self, dtype):
                 "int": np.random.randint(0, N, size=N),
                 "datetime": date_range("2000-01-01", periods=N, freq="s"),
             },
-            index=tm.makeStringIndex(N),
+            index=Index([f"i-{i}" for i in range(N)], dtype=object),
         )
         self.df.iloc[1000:3000, 1] = np.nan
         self.df["date"] = self.df["datetime"].dt.date
diff --git a/asv_bench/benchmarks/io/stata.py b/asv_bench/benchmarks/io/stata.py
index 750bcf4ccee5c..ff33ededdfed9 100644
--- a/asv_bench/benchmarks/io/stata.py
+++ b/asv_bench/benchmarks/io/stata.py
@@ -2,14 +2,12 @@
 
 from pandas import (
     DataFrame,
+    Index,
     date_range,
     read_stata,
 )
 
-from ..pandas_vb_common import (
-    BaseIO,
-    tm,
-)
+from ..pandas_vb_common import BaseIO
 
 
 class Stata(BaseIO):
@@ -25,7 +23,7 @@ def setup(self, convert_dates):
             columns=[f"float{i}" for i in range(C)],
             index=date_range("20000101", periods=N, freq="h"),
         )
-        self.df["object"] = tm.makeStringIndex(self.N)
+        self.df["object"] = Index([f"i-{i}" for i in range(self.N)], dtype=object)
         self.df["int8_"] = np.random.randint(
             np.iinfo(np.int8).min, np.iinfo(np.int8).max - 27, N
         )
diff --git a/asv_bench/benchmarks/join_merge.py b/asv_bench/benchmarks/join_merge.py
index 23824c2c748df..6f494562103c2 100644
--- a/asv_bench/benchmarks/join_merge.py
+++ b/asv_bench/benchmarks/join_merge.py
@@ -14,8 +14,6 @@
     merge_asof,
 )
 
-from .pandas_vb_common import tm
-
 try:
     from pandas import merge_ordered
 except ImportError:
@@ -28,7 +26,7 @@ class Concat:
 
     def setup(self, axis):
         N = 1000
-        s = Series(N, index=tm.makeStringIndex(N))
+        s = Series(N, index=Index([f"i-{i}" for i in range(N)], dtype=object))
         self.series = [s[i:-i] for i in range(1, 10)] * 50
         self.small_frames = [DataFrame(np.random.randn(5, 4))] * 1000
         df = DataFrame(
@@ -94,7 +92,7 @@ def setup(self, dtype, structure, axis, sort):
         elif dtype in ("int64", "Int64", "int64[pyarrow]"):
             vals = np.arange(N, dtype=np.int64)
         elif dtype in ("string[python]", "string[pyarrow]"):
-            vals = tm.makeStringIndex(N)
+            vals = Index([f"i-{i}" for i in range(N)], dtype=object)
         else:
             raise NotImplementedError
 
@@ -122,8 +120,8 @@ class Join:
     param_names = ["sort"]
 
     def setup(self, sort):
-        level1 = tm.makeStringIndex(10).values
-        level2 = tm.makeStringIndex(1000).values
+        level1 = Index([f"i-{i}" for i in range(10)], dtype=object).values
+        level2 = Index([f"i-{i}" for i in range(1000)], dtype=object).values
         codes1 = np.arange(10).repeat(1000)
         codes2 = np.tile(np.arange(1000), 10)
         index2 = MultiIndex(levels=[level1, level2], codes=[codes1, codes2])
@@ -231,8 +229,8 @@ class Merge:
 
     def setup(self, sort):
         N = 10000
-        indices = tm.makeStringIndex(N).values
-        indices2 = tm.makeStringIndex(N).values
+        indices = Index([f"i-{i}" for i in range(N)], dtype=object).values
+        indices2 = Index([f"i-{i}" for i in range(N)], dtype=object).values
         key = np.tile(indices[:8000], 10)
         key2 = np.tile(indices2[:8000], 10)
         self.left = DataFrame(
@@ -400,7 +398,7 @@ def time_merge_on_cat_idx(self):
 
 class MergeOrdered:
     def setup(self):
-        groups = tm.makeStringIndex(10).values
+        groups = Index([f"i-{i}" for i in range(10)], dtype=object).values
         self.left = DataFrame(
             {
                 "group": groups.repeat(5000),
diff --git a/asv_bench/benchmarks/libs.py b/asv_bench/benchmarks/libs.py
index f041499c9c622..3419163bcfe09 100644
--- a/asv_bench/benchmarks/libs.py
+++ b/asv_bench/benchmarks/libs.py
@@ -15,13 +15,11 @@
 
 from pandas import (
     NA,
+    Index,
     NaT,
 )
 
-from .pandas_vb_common import (
-    lib,
-    tm,
-)
+from .pandas_vb_common import lib
 
 try:
     from pandas.util import cache_readonly
@@ -61,8 +59,8 @@ class FastZip:
     def setup(self):
         N = 10000
         K = 10
-        key1 = tm.makeStringIndex(N).values.repeat(K)
-        key2 = tm.makeStringIndex(N).values.repeat(K)
+        key1 = Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(K)
+        key2 = Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(K)
         col_array = np.vstack([key1, key2, np.random.randn(N * K)])
         col_array2 = col_array.copy()
         col_array2[:, :10000] = np.nan
diff --git a/asv_bench/benchmarks/multiindex_object.py b/asv_bench/benchmarks/multiindex_object.py
index 87dcdb16fa647..54788d41d83fe 100644
--- a/asv_bench/benchmarks/multiindex_object.py
+++ b/asv_bench/benchmarks/multiindex_object.py
@@ -5,6 +5,7 @@
 from pandas import (
     NA,
     DataFrame,
+    Index,
     MultiIndex,
     RangeIndex,
     Series,
@@ -12,8 +13,6 @@
     date_range,
 )
 
-from .pandas_vb_common import tm
-
 
 class GetLoc:
     def setup(self):
@@ -144,7 +143,11 @@ def time_is_monotonic(self):
 class Duplicated:
     def setup(self):
         n, k = 200, 5000
-        levels = [np.arange(n), tm.makeStringIndex(n).values, 1000 + np.arange(n)]
+        levels = [
+            np.arange(n),
+            Index([f"i-{i}" for i in range(n)], dtype=object).values,
+            1000 + np.arange(n),
+        ]
         codes = [np.random.choice(n, (k * n)) for lev in levels]
         self.mi = MultiIndex(levels=levels, codes=codes)
 
@@ -249,7 +252,7 @@ def setup(self, index_structure, dtype, method, sort):
         level2 = range(N // 1000)
         int_left = MultiIndex.from_product([level1, level2])
 
-        level2 = tm.makeStringIndex(N // 1000).values
+        level2 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
         str_left = MultiIndex.from_product([level1, level2])
 
         level2 = range(N // 1000)
@@ -293,7 +296,7 @@ def setup(self, dtype):
         level2[0] = NA
         ea_int_left = MultiIndex.from_product([level1, level2])
 
-        level2 = tm.makeStringIndex(N // 1000).values
+        level2 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
         str_left = MultiIndex.from_product([level1, level2])
 
         data = {
@@ -354,7 +357,7 @@ def setup(self, dtype):
         level2 = range(N // 1000)
         int_midx = MultiIndex.from_product([level1, level2])
 
-        level2 = tm.makeStringIndex(N // 1000).values
+        level2 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
         str_midx = MultiIndex.from_product([level1, level2])
 
         data = {
@@ -411,7 +414,7 @@ def setup(self, dtype):
         elif dtype == "int64":
             level2 = range(N2)
         elif dtype == "string":
-            level2 = tm.makeStringIndex(N2)
+            level2 = Index([f"i-{i}" for i in range(N2)], dtype=object)
         else:
             raise NotImplementedError
 
diff --git a/asv_bench/benchmarks/reindex.py b/asv_bench/benchmarks/reindex.py
index 1c5e6050db275..3d22bfce7e2b2 100644
--- a/asv_bench/benchmarks/reindex.py
+++ b/asv_bench/benchmarks/reindex.py
@@ -9,8 +9,6 @@
     period_range,
 )
 
-from .pandas_vb_common import tm
-
 
 class Reindex:
     def setup(self):
@@ -23,8 +21,8 @@ def setup(self):
         )
         N = 5000
         K = 200
-        level1 = tm.makeStringIndex(N).values.repeat(K)
-        level2 = np.tile(tm.makeStringIndex(K).values, N)
+        level1 = Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(K)
+        level2 = np.tile(Index([f"i-{i}" for i in range(K)], dtype=object).values, N)
         index = MultiIndex.from_arrays([level1, level2])
         self.s = Series(np.random.randn(N * K), index=index)
         self.s_subset = self.s[::2]
@@ -93,8 +91,8 @@ class DropDuplicates:
     def setup(self, inplace):
         N = 10000
         K = 10
-        key1 = tm.makeStringIndex(N).values.repeat(K)
-        key2 = tm.makeStringIndex(N).values.repeat(K)
+        key1 = Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(K)
+        key2 = Index([f"i-{i}" for i in range(N)], dtype=object).values.repeat(K)
         self.df = DataFrame(
             {"key1": key1, "key2": key2, "value": np.random.randn(N * K)}
         )
@@ -102,7 +100,9 @@ def setup(self, inplace):
         self.df_nan.iloc[:10000, :] = np.nan
 
         self.s = Series(np.random.randint(0, 1000, size=10000))
-        self.s_str = Series(np.tile(tm.makeStringIndex(1000).values, 10))
+        self.s_str = Series(
+            np.tile(Index([f"i-{i}" for i in range(1000)], dtype=object).values, 10)
+        )
 
         N = 1000000
         K = 10000
@@ -133,7 +133,7 @@ class Align:
     # blog "pandas escaped the zoo"
     def setup(self):
         n = 50000
-        indices = tm.makeStringIndex(n)
+        indices = Index([f"i-{i}" for i in range(n)], dtype=object)
         subsample_size = 40000
         self.x = Series(np.random.randn(n), indices)
         self.y = Series(
diff --git a/asv_bench/benchmarks/series_methods.py b/asv_bench/benchmarks/series_methods.py
index 79cf8f9cd2048..b021af4694d7d 100644
--- a/asv_bench/benchmarks/series_methods.py
+++ b/asv_bench/benchmarks/series_methods.py
@@ -10,8 +10,6 @@
     date_range,
 )
 
-from .pandas_vb_common import tm
-
 
 class SeriesConstructor:
     def setup(self):
@@ -253,7 +251,7 @@ def time_mode(self, N):
 
 class Dir:
     def setup(self):
-        self.s = Series(index=tm.makeStringIndex(10000))
+        self.s = Series(index=Index([f"i-{i}" for i in range(10000)], dtype=object))
 
     def time_dir_strings(self):
         dir(self.s)
diff --git a/asv_bench/benchmarks/strings.py b/asv_bench/benchmarks/strings.py
index 6682a60f42997..1f4a104255057 100644
--- a/asv_bench/benchmarks/strings.py
+++ b/asv_bench/benchmarks/strings.py
@@ -6,12 +6,11 @@
     NA,
     Categorical,
     DataFrame,
+    Index,
     Series,
 )
 from pandas.arrays import StringArray
 
-from .pandas_vb_common import tm
-
 
 class Dtypes:
     params = ["str", "string[python]", "string[pyarrow]"]
@@ -19,7 +18,9 @@ class Dtypes:
 
     def setup(self, dtype):
         try:
-            self.s = Series(tm.makeStringIndex(10**5), dtype=dtype)
+            self.s = Series(
+                Index([f"i-{i}" for i in range(10000)], dtype=object), dtype=dtype
+            )
         except ImportError:
             raise NotImplementedError
 
@@ -172,7 +173,7 @@ class Repeat:
 
     def setup(self, repeats):
         N = 10**5
-        self.s = Series(tm.makeStringIndex(N))
+        self.s = Series(Index([f"i-{i}" for i in range(N)], dtype=object))
         repeat = {"int": 1, "array": np.random.randint(1, 3, N)}
         self.values = repeat[repeats]
 
@@ -187,13 +188,20 @@ class Cat:
     def setup(self, other_cols, sep, na_rep, na_frac):
         N = 10**5
         mask_gen = lambda: np.random.choice([True, False], N, p=[1 - na_frac, na_frac])
-        self.s = Series(tm.makeStringIndex(N)).where(mask_gen())
+        self.s = Series(Index([f"i-{i}" for i in range(N)], dtype=object)).where(
+            mask_gen()
+        )
         if other_cols == 0:
             # str.cat self-concatenates only for others=None
             self.others = None
         else:
             self.others = DataFrame(
-                {i: tm.makeStringIndex(N).where(mask_gen()) for i in range(other_cols)}
+                {
+                    i: Index([f"i-{i}" for i in range(N)], dtype=object).where(
+                        mask_gen()
+                    )
+                    for i in range(other_cols)
+                }
             )
 
     def time_cat(self, other_cols, sep, na_rep, na_frac):
@@ -254,7 +262,7 @@ def time_get_dummies(self, dtype):
 
 class Encode:
     def setup(self):
-        self.ser = Series(tm.makeStringIndex())
+        self.ser = Series(Index([f"i-{i}" for i in range(10_000)], dtype=object))
 
     def time_encode_decode(self):
         self.ser.str.encode("utf-8").str.decode("utf-8")
diff --git a/pandas/_testing/__init__.py b/pandas/_testing/__init__.py
index a74fb2bf48bc4..c73d869b6c39c 100644
--- a/pandas/_testing/__init__.py
+++ b/pandas/_testing/__init__.py
@@ -370,11 +370,6 @@ def getCols(k) -> str:
     return string.ascii_uppercase[:k]
 
 
-# make index
-def makeStringIndex(k: int = 10, name=None) -> Index:
-    return Index(rands_array(nchars=10, size=k), name=name)
-
-
 def makeCategoricalIndex(
     k: int = 10, n: int = 3, name=None, **kwargs
 ) -> CategoricalIndex:
@@ -385,14 +380,6 @@ def makeCategoricalIndex(
     )
 
 
-def makeBoolIndex(k: int = 10, name=None) -> Index:
-    if k == 1:
-        return Index([True], name=name)
-    elif k == 2:
-        return Index([False, True], name=name)
-    return Index([False, True] + [False] * (k - 2), name=name)
-
-
 def makeNumericIndex(k: int = 10, *, name=None, dtype: Dtype | None) -> Index:
     dtype = pandas_dtype(dtype)
     assert isinstance(dtype, np.dtype)
@@ -457,14 +444,13 @@ def makePeriodIndex(k: int = 10, name=None, **kwargs) -> PeriodIndex:
 
 
 def makeObjectSeries(name=None) -> Series:
-    data = makeStringIndex(_N)
-    data = Index(data, dtype=object)
-    index = makeStringIndex(_N)
-    return Series(data, index=index, name=name)
+    data = [f"foo_{i}" for i in range(_N)]
+    index = Index([f"bar_{i}" for i in range(_N)])
+    return Series(data, index=index, name=name, dtype=object)
 
 
 def getSeriesData() -> dict[str, Series]:
-    index = makeStringIndex(_N)
+    index = Index([f"foo_{i}" for i in range(_N)])
     return {
         c: Series(np.random.default_rng(i).standard_normal(_N), index=index)
         for i, c in enumerate(getCols(_K))
@@ -566,7 +552,7 @@ def makeCustomIndex(
     idx_func_dict: dict[str, Callable[..., Index]] = {
         "i": makeIntIndex,
         "f": makeFloatIndex,
-        "s": makeStringIndex,
+        "s": lambda n: Index([f"{i}_{chr(i)}" for i in range(97, 97 + n)]),
         "dt": makeDateIndex,
         "td": makeTimedeltaIndex,
         "p": makePeriodIndex,
@@ -1049,7 +1035,6 @@ def shares_memory(left, right) -> bool:
     "iat",
     "iloc",
     "loc",
-    "makeBoolIndex",
     "makeCategoricalIndex",
     "makeCustomDataframe",
     "makeCustomIndex",
@@ -1062,7 +1047,6 @@ def shares_memory(left, right) -> bool:
     "makeObjectSeries",
     "makePeriodIndex",
     "makeRangeIndex",
-    "makeStringIndex",
     "makeTimeDataFrame",
     "makeTimedeltaIndex",
     "makeTimeSeries",
diff --git a/pandas/conftest.py b/pandas/conftest.py
index 350871c3085c1..3205b6657439f 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -610,7 +610,7 @@ def _create_mi_with_dt64tz_level():
 
 
 indices_dict = {
-    "string": tm.makeStringIndex(100),
+    "string": Index([f"pandas_{i}" for i in range(100)]),
     "datetime": tm.makeDateIndex(100),
     "datetime-tz": tm.makeDateIndex(100, tz="US/Pacific"),
     "period": tm.makePeriodIndex(100),
@@ -626,7 +626,7 @@ def _create_mi_with_dt64tz_level():
     "uint64": tm.makeUIntIndex(100, dtype="uint64"),
     "float32": tm.makeFloatIndex(100, dtype="float32"),
     "float64": tm.makeFloatIndex(100, dtype="float64"),
-    "bool-object": tm.makeBoolIndex(10).astype(object),
+    "bool-object": Index([True, False] * 5, dtype=object),
     "bool-dtype": Index(np.random.default_rng(2).standard_normal(10) < 0),
     "complex64": tm.makeNumericIndex(100, dtype="float64").astype("complex64"),
     "complex128": tm.makeNumericIndex(100, dtype="float64").astype("complex128"),
@@ -641,10 +641,12 @@ def _create_mi_with_dt64tz_level():
     "nullable_uint": Index(np.arange(100), dtype="UInt16"),
     "nullable_float": Index(np.arange(100), dtype="Float32"),
     "nullable_bool": Index(np.arange(100).astype(bool), dtype="boolean"),
-    "string-python": Index(pd.array(tm.makeStringIndex(100), dtype="string[python]")),
+    "string-python": Index(
+        pd.array([f"pandas_{i}" for i in range(100)], dtype="string[python]")
+    ),
 }
 if has_pyarrow:
-    idx = Index(pd.array(tm.makeStringIndex(100), dtype="string[pyarrow]"))
+    idx = Index(pd.array([f"pandas_{i}" for i in range(100)], dtype="string[pyarrow]"))
     indices_dict["string-pyarrow"] = idx
 
 
diff --git a/pandas/tests/arithmetic/test_numeric.py b/pandas/tests/arithmetic/test_numeric.py
index f89711c0edee7..c3c4a8b4fc6c0 100644
--- a/pandas/tests/arithmetic/test_numeric.py
+++ b/pandas/tests/arithmetic/test_numeric.py
@@ -916,7 +916,7 @@ def test_add_frames(self, first, second, expected):
     # TODO: This came from series.test.test_operators, needs cleanup
     def test_series_frame_radd_bug(self, fixed_now_ts):
         # GH#353
-        vals = Series(tm.makeStringIndex())
+        vals = Series([str(i) for i in range(5)])
         result = "foo_" + vals
         expected = vals.map(lambda x: "foo_" + x)
         tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/arithmetic/test_object.py b/pandas/tests/arithmetic/test_object.py
index 33953900c2006..6b36f447eb7d5 100644
--- a/pandas/tests/arithmetic/test_object.py
+++ b/pandas/tests/arithmetic/test_object.py
@@ -299,7 +299,7 @@ def test_iadd_string(self):
 
     @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="add doesn't work")
     def test_add(self):
-        index = tm.makeStringIndex(100)
+        index = pd.Index([str(i) for i in range(10)])
         expected = pd.Index(index.values * 2)
         tm.assert_index_equal(index + index, expected)
         tm.assert_index_equal(index + index.tolist(), expected)
@@ -313,7 +313,7 @@ def test_add(self):
         tm.assert_index_equal("1" + index, expected)
 
     def test_sub_fail(self, using_infer_string):
-        index = tm.makeStringIndex(100)
+        index = pd.Index([str(i) for i in range(10)])
 
         if using_infer_string:
             import pyarrow as pa
diff --git a/pandas/tests/frame/methods/test_first_valid_index.py b/pandas/tests/frame/methods/test_first_valid_index.py
index a448768f4173d..2e27f1aa71700 100644
--- a/pandas/tests/frame/methods/test_first_valid_index.py
+++ b/pandas/tests/frame/methods/test_first_valid_index.py
@@ -6,9 +6,10 @@
 
 from pandas import (
     DataFrame,
+    Index,
     Series,
+    date_range,
 )
-import pandas._testing as tm
 
 
 class TestFirstValidIndex:
@@ -44,11 +45,12 @@ def test_first_last_valid_frame(self, data, idx, expected_first, expected_last):
         assert expected_first == df.first_valid_index()
         assert expected_last == df.last_valid_index()
 
-    @pytest.mark.parametrize("index_func", [tm.makeStringIndex, tm.makeDateIndex])
-    def test_first_last_valid(self, index_func):
-        N = 30
-        index = index_func(N)
-        mat = np.random.default_rng(2).standard_normal(N)
+    @pytest.mark.parametrize(
+        "index",
+        [Index([str(i) for i in range(20)]), date_range("2020-01-01", periods=20)],
+    )
+    def test_first_last_valid(self, index):
+        mat = np.random.default_rng(2).standard_normal(len(index))
         mat[:5] = np.nan
         mat[-5:] = np.nan
 
@@ -60,10 +62,12 @@ def test_first_last_valid(self, index_func):
         assert ser.first_valid_index() == frame.index[5]
         assert ser.last_valid_index() == frame.index[-6]
 
-    @pytest.mark.parametrize("index_func", [tm.makeStringIndex, tm.makeDateIndex])
-    def test_first_last_valid_all_nan(self, index_func):
+    @pytest.mark.parametrize(
+        "index",
+        [Index([str(i) for i in range(10)]), date_range("2020-01-01", periods=10)],
+    )
+    def test_first_last_valid_all_nan(self, index):
         # GH#17400: no valid entries
-        index = index_func(30)
         frame = DataFrame(np.nan, columns=["foo"], index=index)
 
         assert frame.last_valid_index() is None
diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index bf17b61b0e3f3..3111075c5c1a7 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -784,7 +784,7 @@ def test_constructor_dict_cast(self):
     def test_constructor_dict_cast2(self):
         # can't cast to float
         test_data = {
-            "A": dict(zip(range(20), tm.makeStringIndex(20))),
+            "A": dict(zip(range(20), [f"word_{i}" for i in range(20)])),
             "B": dict(zip(range(15), np.random.default_rng(2).standard_normal(15))),
         }
         with pytest.raises(ValueError, match="could not convert string"):
diff --git a/pandas/tests/frame/test_repr.py b/pandas/tests/frame/test_repr.py
index 98962b3003b6d..eed48b9db116b 100644
--- a/pandas/tests/frame/test_repr.py
+++ b/pandas/tests/frame/test_repr.py
@@ -165,7 +165,7 @@ def test_repr_mixed_big(self):
         biggie = DataFrame(
             {
                 "A": np.random.default_rng(2).standard_normal(200),
-                "B": tm.makeStringIndex(200),
+                "B": [str(i) for i in range(200)],
             },
             index=range(200),
         )
diff --git a/pandas/tests/groupby/test_grouping.py b/pandas/tests/groupby/test_grouping.py
index 3c1a35c984031..c401762dace23 100644
--- a/pandas/tests/groupby/test_grouping.py
+++ b/pandas/tests/groupby/test_grouping.py
@@ -19,6 +19,7 @@
     Series,
     Timestamp,
     date_range,
+    period_range,
 )
 import pandas._testing as tm
 from pandas.core.groupby.grouper import Grouping
@@ -174,23 +175,21 @@ class TestGrouping:
     @pytest.mark.parametrize(
         "index",
         [
-            tm.makeFloatIndex,
-            tm.makeStringIndex,
-            tm.makeIntIndex,
-            tm.makeDateIndex,
-            tm.makePeriodIndex,
+            Index(list("abcde")),
+            Index(np.arange(5)),
+            Index(np.arange(5, dtype=float)),
+            date_range("2020-01-01", periods=5),
+            period_range("2020-01-01", periods=5),
         ],
     )
-    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
     def test_grouper_index_types(self, index):
         # related GH5375
         # groupby misbehaving when using a Floatlike index
-        df = DataFrame(np.arange(10).reshape(5, 2), columns=list("AB"))
+        df = DataFrame(np.arange(10).reshape(5, 2), columns=list("AB"), index=index)
 
-        df.index = index(len(df))
         df.groupby(list("abcde"), group_keys=False).apply(lambda x: x)
 
-        df.index = list(reversed(df.index.tolist()))
+        df.index = df.index[::-1]
         df.groupby(list("abcde"), group_keys=False).apply(lambda x: x)
 
     def test_grouper_multilevel_freq(self):
diff --git a/pandas/tests/indexes/multi/test_duplicates.py b/pandas/tests/indexes/multi/test_duplicates.py
index a69248cf038f8..6c6d9022b1af3 100644
--- a/pandas/tests/indexes/multi/test_duplicates.py
+++ b/pandas/tests/indexes/multi/test_duplicates.py
@@ -257,7 +257,7 @@ def test_duplicated(idx_dup, keep, expected):
 def test_duplicated_hashtable_impl(keep, monkeypatch):
     # GH 9125
     n, k = 6, 10
-    levels = [np.arange(n), tm.makeStringIndex(n), 1000 + np.arange(n)]
+    levels = [np.arange(n), [str(i) for i in range(n)], 1000 + np.arange(n)]
     codes = [np.random.default_rng(2).choice(n, k * n) for _ in levels]
     with monkeypatch.context() as m:
         m.setattr(libindex, "_SIZE_CUTOFF", 50)
diff --git a/pandas/tests/indexing/test_floats.py b/pandas/tests/indexing/test_floats.py
index c9fbf95751dfe..cf9966145afce 100644
--- a/pandas/tests/indexing/test_floats.py
+++ b/pandas/tests/indexing/test_floats.py
@@ -6,6 +6,9 @@
     Index,
     RangeIndex,
     Series,
+    date_range,
+    period_range,
+    timedelta_range,
 )
 import pandas._testing as tm
 
@@ -39,22 +42,21 @@ def check(self, result, original, indexer, getitem):
         tm.assert_almost_equal(result, expected)
 
     @pytest.mark.parametrize(
-        "index_func",
+        "index",
         [
-            tm.makeStringIndex,
-            tm.makeCategoricalIndex,
-            tm.makeDateIndex,
-            tm.makeTimedeltaIndex,
-            tm.makePeriodIndex,
+            Index(list("abcde")),
+            Index(list("abcde"), dtype="category"),
+            date_range("2020-01-01", periods=5),
+            timedelta_range("1 day", periods=5),
+            period_range("2020-01-01", periods=5),
         ],
     )
-    def test_scalar_non_numeric(self, index_func, frame_or_series, indexer_sl):
+    def test_scalar_non_numeric(self, index, frame_or_series, indexer_sl):
         # GH 4892
         # float_indexers should raise exceptions
         # on appropriate Index types & accessors
 
-        i = index_func(5)
-        s = gen_obj(frame_or_series, i)
+        s = gen_obj(frame_or_series, index)
 
         # getting
         with pytest.raises(KeyError, match="^3.0$"):
@@ -75,19 +77,18 @@ def test_scalar_non_numeric(self, index_func, frame_or_series, indexer_sl):
             assert 3.0 not in s2.axes[-1]
 
     @pytest.mark.parametrize(
-        "index_func",
+        "index",
         [
-            tm.makeStringIndex,
-            tm.makeCategoricalIndex,
-            tm.makeDateIndex,
-            tm.makeTimedeltaIndex,
-            tm.makePeriodIndex,
+            Index(list("abcde")),
+            Index(list("abcde"), dtype="category"),
+            date_range("2020-01-01", periods=5),
+            timedelta_range("1 day", periods=5),
+            period_range("2020-01-01", periods=5),
         ],
     )
-    def test_scalar_non_numeric_series_fallback(self, index_func):
+    def test_scalar_non_numeric_series_fallback(self, index):
         # fallsback to position selection, series only
-        i = index_func(5)
-        s = Series(np.arange(len(i)), index=i)
+        s = Series(np.arange(len(index)), index=index)
 
         msg = "Series.__getitem__ treating keys as positions is deprecated"
         with tm.assert_produces_warning(FutureWarning, match=msg):
@@ -214,21 +215,20 @@ def test_scalar_float(self, frame_or_series):
         self.check(result, s, 3, False)
 
     @pytest.mark.parametrize(
-        "index_func",
+        "index",
         [
-            tm.makeStringIndex,
-            tm.makeDateIndex,
-            tm.makeTimedeltaIndex,
-            tm.makePeriodIndex,
+            Index(list("abcde"), dtype=object),
+            date_range("2020-01-01", periods=5),
+            timedelta_range("1 day", periods=5),
+            period_range("2020-01-01", periods=5),
         ],
     )
     @pytest.mark.parametrize("idx", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)])
-    def test_slice_non_numeric(self, index_func, idx, frame_or_series, indexer_sli):
+    def test_slice_non_numeric(self, index, idx, frame_or_series, indexer_sli):
         # GH 4892
         # float_indexers should raise exceptions
         # on appropriate Index types & accessors
 
-        index = index_func(5)
         s = gen_obj(frame_or_series, index)
 
         # getitem
diff --git a/pandas/tests/io/formats/test_format.py b/pandas/tests/io/formats/test_format.py
index edac82193d1c8..f5738b83a8e64 100644
--- a/pandas/tests/io/formats/test_format.py
+++ b/pandas/tests/io/formats/test_format.py
@@ -25,7 +25,6 @@
     read_csv,
     reset_option,
 )
-import pandas._testing as tm
 
 from pandas.io.formats import printing
 import pandas.io.formats.format as fmt
@@ -834,19 +833,21 @@ def test_to_string_buffer_all_unicode(self):
         buf.getvalue()
 
     @pytest.mark.parametrize(
-        "index",
+        "index_scalar",
         [
-            tm.makeStringIndex,
-            tm.makeIntIndex,
-            tm.makeDateIndex,
-            tm.makePeriodIndex,
+            "a" * 10,
+            1,
+            Timestamp(2020, 1, 1),
+            pd.Period("2020-01-01"),
         ],
     )
     @pytest.mark.parametrize("h", [10, 20])
     @pytest.mark.parametrize("w", [10, 20])
-    def test_to_string_truncate_indices(self, index, h, w):
+    def test_to_string_truncate_indices(self, index_scalar, h, w):
         with option_context("display.expand_frame_repr", False):
-            df = DataFrame(index=index(h), columns=tm.makeStringIndex(w))
+            df = DataFrame(
+                index=[index_scalar] * h, columns=[str(i) * 10 for i in range(w)]
+            )
             with option_context("display.max_rows", 15):
                 if h == 20:
                     assert has_vertically_truncated_repr(df)
diff --git a/pandas/tests/io/formats/test_to_html.py b/pandas/tests/io/formats/test_to_html.py
index a7648cf1c471a..605e5e182d8cc 100644
--- a/pandas/tests/io/formats/test_to_html.py
+++ b/pandas/tests/io/formats/test_to_html.py
@@ -59,7 +59,7 @@ def biggie_df_fixture(request):
         df = DataFrame(
             {
                 "A": np.random.default_rng(2).standard_normal(200),
-                "B": tm.makeStringIndex(200),
+                "B": Index([f"{i}?!" for i in range(200)]),
             },
             index=np.arange(200),
         )
diff --git a/pandas/tests/io/formats/test_to_string.py b/pandas/tests/io/formats/test_to_string.py
index e607b6eb454a1..02c20b0e25477 100644
--- a/pandas/tests/io/formats/test_to_string.py
+++ b/pandas/tests/io/formats/test_to_string.py
@@ -804,7 +804,7 @@ def test_to_string(self):
         biggie = DataFrame(
             {
                 "A": np.random.default_rng(2).standard_normal(200),
-                "B": tm.makeStringIndex(200),
+                "B": Index([f"{i}?!" for i in range(200)]),
             },
         )
 
diff --git a/pandas/tests/io/pytables/test_put.py b/pandas/tests/io/pytables/test_put.py
index c109b72e9c239..59a05dc9ea546 100644
--- a/pandas/tests/io/pytables/test_put.py
+++ b/pandas/tests/io/pytables/test_put.py
@@ -196,32 +196,27 @@ def test_put_mixed_type(setup_path):
         tm.assert_frame_equal(expected, df)
 
 
+@pytest.mark.parametrize("format", ["table", "fixed"])
 @pytest.mark.parametrize(
-    "format, index",
+    "index",
     [
-        ["table", tm.makeFloatIndex],
-        ["table", tm.makeStringIndex],
-        ["table", tm.makeIntIndex],
-        ["table", tm.makeDateIndex],
-        ["fixed", tm.makeFloatIndex],
-        ["fixed", tm.makeStringIndex],
-        ["fixed", tm.makeIntIndex],
-        ["fixed", tm.makeDateIndex],
-        ["table", tm.makePeriodIndex],  # GH#7796
-        ["fixed", tm.makePeriodIndex],
+        Index([str(i) for i in range(10)]),
+        Index(np.arange(10, dtype=float)),
+        Index(np.arange(10)),
+        date_range("2020-01-01", periods=10),
+        pd.period_range("2020-01-01", periods=10),
     ],
 )
-@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
 def test_store_index_types(setup_path, format, index):
     # GH5386
     # test storing various index types
 
     with ensure_clean_store(setup_path) as store:
         df = DataFrame(
-            np.random.default_rng(2).standard_normal((10, 2)), columns=list("AB")
+            np.random.default_rng(2).standard_normal((10, 2)),
+            columns=list("AB"),
+            index=index,
         )
-        df.index = index(len(df))
-
         _maybe_remove(store, "df")
         store.put("df", df, format=format)
         tm.assert_frame_equal(df, store["df"])
diff --git a/pandas/tests/io/pytables/test_round_trip.py b/pandas/tests/io/pytables/test_round_trip.py
index baac90e52e962..e05e1e96eb11f 100644
--- a/pandas/tests/io/pytables/test_round_trip.py
+++ b/pandas/tests/io/pytables/test_round_trip.py
@@ -51,7 +51,8 @@ def roundtrip(key, obj, **kwargs):
 
 def test_long_strings(setup_path):
     # GH6166
-    df = DataFrame({"a": tm.makeStringIndex(10)}, index=tm.makeStringIndex(10))
+    data = ["a" * 50] * 10
+    df = DataFrame({"a": data}, index=data)
 
     with ensure_clean_store(setup_path) as store:
         store.append("df", df, data_columns=["a"])
@@ -65,7 +66,7 @@ def test_api(tmp_path, setup_path):
     # API issue when to_hdf doesn't accept append AND format args
     path = tmp_path / setup_path
 
-    df = tm.makeDataFrame()
+    df = DataFrame(range(20))
     df.iloc[:10].to_hdf(path, key="df", append=True, format="table")
     df.iloc[10:].to_hdf(path, key="df", append=True, format="table")
     tm.assert_frame_equal(read_hdf(path, "df"), df)
@@ -79,7 +80,7 @@ def test_api(tmp_path, setup_path):
 def test_api_append(tmp_path, setup_path):
     path = tmp_path / setup_path
 
-    df = tm.makeDataFrame()
+    df = DataFrame(range(20))
     df.iloc[:10].to_hdf(path, key="df", append=True)
     df.iloc[10:].to_hdf(path, key="df", append=True, format="table")
     tm.assert_frame_equal(read_hdf(path, "df"), df)
@@ -93,7 +94,7 @@ def test_api_append(tmp_path, setup_path):
 def test_api_2(tmp_path, setup_path):
     path = tmp_path / setup_path
 
-    df = tm.makeDataFrame()
+    df = DataFrame(range(20))
     df.to_hdf(path, key="df", append=False, format="fixed")
     tm.assert_frame_equal(read_hdf(path, "df"), df)
 
@@ -107,7 +108,7 @@ def test_api_2(tmp_path, setup_path):
     tm.assert_frame_equal(read_hdf(path, "df"), df)
 
     with ensure_clean_store(setup_path) as store:
-        df = tm.makeDataFrame()
+        df = DataFrame(range(20))
 
         _maybe_remove(store, "df")
         store.append("df", df.iloc[:10], append=True, format="table")
diff --git a/pandas/tests/io/pytables/test_store.py b/pandas/tests/io/pytables/test_store.py
index 96c160ab40bd8..df8a1e3cb7470 100644
--- a/pandas/tests/io/pytables/test_store.py
+++ b/pandas/tests/io/pytables/test_store.py
@@ -956,10 +956,6 @@ def test_to_hdf_with_object_column_names(tmp_path, setup_path):
         tm.makeTimedeltaIndex,
         tm.makePeriodIndex,
     ]
-    types_should_run = [
-        tm.makeStringIndex,
-        tm.makeCategoricalIndex,
-    ]
 
     for index in types_should_fail:
         df = DataFrame(
@@ -970,14 +966,18 @@ def test_to_hdf_with_object_column_names(tmp_path, setup_path):
         with pytest.raises(ValueError, match=msg):
             df.to_hdf(path, key="df", format="table", data_columns=True)
 
-    for index in types_should_run:
-        df = DataFrame(
-            np.random.default_rng(2).standard_normal((10, 2)), columns=index(2)
-        )
-        path = tmp_path / setup_path
-        df.to_hdf(path, key="df", format="table", data_columns=True)
-        result = read_hdf(path, "df", where=f"index = [{df.index[0]}]")
-        assert len(result)
+
+@pytest.mark.parametrize("dtype", [None, "category"])
+def test_to_hdf_with_object_column_names_should_run(tmp_path, setup_path, dtype):
+    # GH9057
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((10, 2)),
+        columns=Index(["a", "b"], dtype=dtype),
+    )
+    path = tmp_path / setup_path
+    df.to_hdf(path, key="df", format="table", data_columns=True)
+    result = read_hdf(path, "df", where=f"index = [{df.index[0]}]")
+    assert len(result)
 
 
 def test_hdfstore_strides(setup_path):
diff --git a/pandas/tests/reductions/test_reductions.py b/pandas/tests/reductions/test_reductions.py
index 9c4ae92224148..303f8550c5a80 100644
--- a/pandas/tests/reductions/test_reductions.py
+++ b/pandas/tests/reductions/test_reductions.py
@@ -33,13 +33,13 @@
 
 def get_objs():
     indexes = [
-        tm.makeBoolIndex(10, name="a"),
+        Index([True, False] * 5, name="a"),
         tm.makeIntIndex(10, name="a"),
         tm.makeFloatIndex(10, name="a"),
         tm.makeDateIndex(10, name="a"),
         tm.makeDateIndex(10, name="a").tz_localize(tz="US/Eastern"),
         tm.makePeriodIndex(10, name="a"),
-        tm.makeStringIndex(10, name="a"),
+        Index([str(i) for i in range(10)], name="a"),
     ]
 
     arr = np.random.default_rng(2).standard_normal(10)
diff --git a/pandas/tests/resample/test_time_grouper.py b/pandas/tests/resample/test_time_grouper.py
index 1dc25cb9d4c1e..3d9098917a12d 100644
--- a/pandas/tests/resample/test_time_grouper.py
+++ b/pandas/tests/resample/test_time_grouper.py
@@ -87,19 +87,17 @@ def f(df):
 
 
 @pytest.mark.parametrize(
-    "func",
+    "index",
     [
-        tm.makeIntIndex,
-        tm.makeStringIndex,
-        tm.makeFloatIndex,
-        (lambda m: tm.makeCustomIndex(m, 2)),
+        Index([1, 2]),
+        Index(["a", "b"]),
+        Index([1.1, 2.2]),
+        pd.MultiIndex.from_arrays([[1, 2], ["a", "b"]]),
     ],
 )
-def test_fails_on_no_datetime_index(func):
-    n = 2
-    index = func(n)
+def test_fails_on_no_datetime_index(index):
     name = type(index).__name__
-    df = DataFrame({"a": np.random.default_rng(2).standard_normal(n)}, index=index)
+    df = DataFrame({"a": range(len(index))}, index=index)
 
     msg = (
         "Only valid with DatetimeIndex, TimedeltaIndex "
diff --git a/pandas/tests/reshape/merge/test_multi.py b/pandas/tests/reshape/merge/test_multi.py
index f5d78fbd44812..269d3a2b7078e 100644
--- a/pandas/tests/reshape/merge/test_multi.py
+++ b/pandas/tests/reshape/merge/test_multi.py
@@ -188,7 +188,7 @@ def test_merge_multiple_cols_with_mixed_cols_index(self):
 
     def test_compress_group_combinations(self):
         # ~ 40000000 possible unique groups
-        key1 = tm.makeStringIndex(10000)
+        key1 = [str(i) for i in range(10000)]
         key1 = np.tile(key1, 2)
         key2 = key1[::-1]
 
diff --git a/pandas/tests/series/methods/test_combine_first.py b/pandas/tests/series/methods/test_combine_first.py
index 795b2eab82aca..1cbb7c7982802 100644
--- a/pandas/tests/series/methods/test_combine_first.py
+++ b/pandas/tests/series/methods/test_combine_first.py
@@ -51,9 +51,9 @@ def test_combine_first(self):
         tm.assert_series_equal(combined[1::2], series_copy[1::2])
 
         # mixed types
-        index = tm.makeStringIndex(20)
+        index = pd.Index([str(i) for i in range(20)])
         floats = Series(np.random.default_rng(2).standard_normal(20), index=index)
-        strings = Series(tm.makeStringIndex(10), index=index[::2], dtype=object)
+        strings = Series([str(i) for i in range(10)], index=index[::2], dtype=object)
 
         combined = strings.combine_first(floats)
 
diff --git a/pandas/tests/series/test_api.py b/pandas/tests/series/test_api.py
index a39b3ff7e6f2b..c29fe6ba06ab4 100644
--- a/pandas/tests/series/test_api.py
+++ b/pandas/tests/series/test_api.py
@@ -68,7 +68,7 @@ def test_tab_completion_with_categorical(self):
     @pytest.mark.parametrize(
         "index",
         [
-            tm.makeStringIndex(10),
+            Index([str(i) for i in range(10)]),
             tm.makeCategoricalIndex(10),
             Index(["foo", "bar", "baz"] * 2),
             tm.makeDateIndex(10),
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index a6e63dfd5f409..24c4706810154 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -1663,19 +1663,18 @@ def test_unique_complex_numbers(self, array, expected):
 
 class TestHashTable:
     @pytest.mark.parametrize(
-        "htable, tm_dtype",
+        "htable, data",
         [
-            (ht.PyObjectHashTable, "String"),
-            (ht.StringHashTable, "String"),
-            (ht.Float64HashTable, "Float"),
-            (ht.Int64HashTable, "Int"),
-            (ht.UInt64HashTable, "UInt"),
+            (ht.PyObjectHashTable, [f"foo_{i}" for i in range(1000)]),
+            (ht.StringHashTable, [f"foo_{i}" for i in range(1000)]),
+            (ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
+            (ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
+            (ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
         ],
     )
-    def test_hashtable_unique(self, htable, tm_dtype, writable):
+    def test_hashtable_unique(self, htable, data, writable):
         # output of maker has guaranteed unique elements
-        maker = getattr(tm, "make" + tm_dtype + "Index")
-        s = Series(maker(1000))
+        s = Series(data)
         if htable == ht.Float64HashTable:
             # add NaN for float column
             s.loc[500] = np.nan
@@ -1703,19 +1702,18 @@ def test_hashtable_unique(self, htable, tm_dtype, writable):
         tm.assert_numpy_array_equal(reconstr, s_duplicated.values)
 
     @pytest.mark.parametrize(
-        "htable, tm_dtype",
+        "htable, data",
         [
-            (ht.PyObjectHashTable, "String"),
-            (ht.StringHashTable, "String"),
-            (ht.Float64HashTable, "Float"),
-            (ht.Int64HashTable, "Int"),
-            (ht.UInt64HashTable, "UInt"),
+            (ht.PyObjectHashTable, [f"foo_{i}" for i in range(1000)]),
+            (ht.StringHashTable, [f"foo_{i}" for i in range(1000)]),
+            (ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
+            (ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
+            (ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
         ],
     )
-    def test_hashtable_factorize(self, htable, tm_dtype, writable):
+    def test_hashtable_factorize(self, htable, writable, data):
         # output of maker has guaranteed unique elements
-        maker = getattr(tm, "make" + tm_dtype + "Index")
-        s = Series(maker(1000))
+        s = Series(data)
         if htable == ht.Float64HashTable:
             # add NaN for float column
             s.loc[500] = np.nan
diff --git a/pandas/tests/tseries/frequencies/test_inference.py b/pandas/tests/tseries/frequencies/test_inference.py
index 75528a8b99c4d..5d22896d9d055 100644
--- a/pandas/tests/tseries/frequencies/test_inference.py
+++ b/pandas/tests/tseries/frequencies/test_inference.py
@@ -401,7 +401,7 @@ def test_invalid_index_types_unicode():
     msg = "Unknown datetime string format"
 
     with pytest.raises(ValueError, match=msg):
-        frequencies.infer_freq(tm.makeStringIndex(10))
+        frequencies.infer_freq(Index(["ZqgszYBfuL"]))
 
 
 def test_string_datetime_like_compat():
diff --git a/pandas/tests/util/test_hashing.py b/pandas/tests/util/test_hashing.py
index 00dc184a0ac4d..e7c4c27714d5f 100644
--- a/pandas/tests/util/test_hashing.py
+++ b/pandas/tests/util/test_hashing.py
@@ -328,9 +328,9 @@ def test_alternate_encoding(index):
 @pytest.mark.parametrize("l_add", [0, 1])
 def test_same_len_hash_collisions(l_exp, l_add):
     length = 2 ** (l_exp + 8) + l_add
-    s = tm.makeStringIndex(length).to_numpy()
+    idx = np.array([str(i) for i in range(length)], dtype=object)
 
-    result = hash_array(s, "utf8")
+    result = hash_array(idx, "utf8")
     assert not result[0] == result[1]