TST/CLN: Remove makeUInt/Timedelta/RangeIndex (#56200)

* Remove makeRangeIndex

* Remove makeUInnt64Index

* Remove makeTimedeltaIndex

* Fix typo, address test

Author: mroeschke

pandas/_testing/__init__.py

@@ -47,6 +47,7 @@
     RangeIndex,
     Series,
     bdate_range,
+    timedelta_range,
 )
 from pandas._testing._io import (
     round_trip_localpath,
@@ -111,10 +112,7 @@
         NpDtype,
     )
 
-    from pandas import (
-        PeriodIndex,
-        TimedeltaIndex,
-    )
+    from pandas import PeriodIndex
     from pandas.core.arrays import ArrowExtensionArray
 
 _N = 30
@@ -405,17 +403,6 @@ def makeIntIndex(k: int = 10, *, name=None, dtype: Dtype = "int64") -> Index:
     return makeNumericIndex(k, name=name, dtype=dtype)
 
 
-def makeUIntIndex(k: int = 10, *, name=None, dtype: Dtype = "uint64") -> Index:
-    dtype = pandas_dtype(dtype)
-    if not is_unsigned_integer_dtype(dtype):
-        raise TypeError(f"Wrong dtype {dtype}")
-    return makeNumericIndex(k, name=name, dtype=dtype)
-
-
-def makeRangeIndex(k: int = 10, name=None, **kwargs) -> RangeIndex:
-    return RangeIndex(0, k, 1, name=name, **kwargs)
-
-
 def makeFloatIndex(k: int = 10, *, name=None, dtype: Dtype = "float64") -> Index:
     dtype = pandas_dtype(dtype)
     if not is_float_dtype(dtype):
@@ -431,12 +418,6 @@ def makeDateIndex(
     return DatetimeIndex(dr, name=name, **kwargs)
 
 
-def makeTimedeltaIndex(
-    k: int = 10, freq: Frequency = "D", name=None, **kwargs
-) -> TimedeltaIndex:
-    return pd.timedelta_range(start="1 day", periods=k, freq=freq, name=name, **kwargs)
-
-
 def makePeriodIndex(k: int = 10, name=None, **kwargs) -> PeriodIndex:
     dt = datetime(2000, 1, 1)
     pi = pd.period_range(start=dt, periods=k, freq="D", name=name, **kwargs)
@@ -537,7 +518,7 @@ def makeCustomIndex(
         "f": makeFloatIndex,
         "s": lambda n: Index([f"{i}_{chr(i)}" for i in range(97, 97 + n)]),
         "dt": makeDateIndex,
-        "td": makeTimedeltaIndex,
+        "td": lambda n: timedelta_range("1 day", periods=n),
         "p": makePeriodIndex,
     }
     idx_func = idx_func_dict.get(idx_type)
@@ -1027,11 +1008,8 @@ def shares_memory(left, right) -> bool:
     "makeNumericIndex",
     "makeObjectSeries",
     "makePeriodIndex",
-    "makeRangeIndex",
     "makeTimeDataFrame",
-    "makeTimedeltaIndex",
     "makeTimeSeries",
-    "makeUIntIndex",
     "maybe_produces_warning",
     "NARROW_NP_DTYPES",
     "NP_NAT_OBJECTS",

pandas/conftest.py

@@ -63,9 +63,11 @@
     Interval,
     IntervalIndex,
     Period,
+    RangeIndex,
     Series,
     Timedelta,
     Timestamp,
+    timedelta_range,
 )
 import pandas._testing as tm
 from pandas.core import ops
@@ -614,16 +616,16 @@ def _create_mi_with_dt64tz_level():
     "datetime": tm.makeDateIndex(100),
     "datetime-tz": tm.makeDateIndex(100, tz="US/Pacific"),
     "period": tm.makePeriodIndex(100),
-    "timedelta": tm.makeTimedeltaIndex(100),
-    "range": tm.makeRangeIndex(100),
+    "timedelta": timedelta_range(start="1 day", periods=100, freq="D"),
+    "range": RangeIndex(100),
     "int8": tm.makeIntIndex(100, dtype="int8"),
     "int16": tm.makeIntIndex(100, dtype="int16"),
     "int32": tm.makeIntIndex(100, dtype="int32"),
     "int64": tm.makeIntIndex(100, dtype="int64"),
-    "uint8": tm.makeUIntIndex(100, dtype="uint8"),
-    "uint16": tm.makeUIntIndex(100, dtype="uint16"),
-    "uint32": tm.makeUIntIndex(100, dtype="uint32"),
-    "uint64": tm.makeUIntIndex(100, dtype="uint64"),
+    "uint8": Index(np.arange(100), dtype="uint8"),
+    "uint16": Index(np.arange(100), dtype="uint16"),
+    "uint32": Index(np.arange(100), dtype="uint32"),
+    "uint64": Index(np.arange(100), dtype="uint64"),
     "float32": tm.makeFloatIndex(100, dtype="float32"),
     "float64": tm.makeFloatIndex(100, dtype="float64"),
     "bool-object": Index([True, False] * 5, dtype=object),

pandas/tests/frame/test_reductions.py

@@ -18,7 +18,10 @@
     Categorical,
     CategoricalDtype,
     DataFrame,
+    DatetimeIndex,
     Index,
+    PeriodIndex,
+    RangeIndex,
     Series,
     Timestamp,
     date_range,
@@ -598,7 +601,7 @@ def test_sem(self, datetime_frame):
                     "C": [1.0],
                     "D": ["a"],
                     "E": Categorical(["a"], categories=["a"]),
-                    "F": pd.DatetimeIndex(["2000-01-02"], dtype="M8[ns]"),
+                    "F": DatetimeIndex(["2000-01-02"], dtype="M8[ns]"),
                     "G": to_timedelta(["1 days"]),
                 },
             ),
@@ -610,7 +613,7 @@ def test_sem(self, datetime_frame):
                     "C": [np.nan],
                     "D": np.array([np.nan], dtype=object),
                     "E": Categorical([np.nan], categories=["a"]),
-                    "F": pd.DatetimeIndex([pd.NaT], dtype="M8[ns]"),
+                    "F": DatetimeIndex([pd.NaT], dtype="M8[ns]"),
                     "G": to_timedelta([pd.NaT]),
                 },
             ),
@@ -621,7 +624,7 @@ def test_sem(self, datetime_frame):
                     "I": [8, 9, np.nan, np.nan],
                     "J": [1, np.nan, np.nan, np.nan],
                     "K": Categorical(["a", np.nan, np.nan, np.nan], categories=["a"]),
-                    "L": pd.DatetimeIndex(
+                    "L": DatetimeIndex(
                         ["2000-01-02", "NaT", "NaT", "NaT"], dtype="M8[ns]"
                     ),
                     "M": to_timedelta(["1 days", "nan", "nan", "nan"]),
@@ -635,7 +638,7 @@ def test_sem(self, datetime_frame):
                     "I": [8, 9, np.nan, np.nan],
                     "J": [1, np.nan, np.nan, np.nan],
                     "K": Categorical([np.nan, "a", np.nan, np.nan], categories=["a"]),
-                    "L": pd.DatetimeIndex(
+                    "L": DatetimeIndex(
                         ["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"
                     ),
                     "M": to_timedelta(["nan", "1 days", "nan", "nan"]),
@@ -652,15 +655,13 @@ def test_mode_dropna(self, dropna, expected):
                 "C": [1, np.nan, np.nan, np.nan],
                 "D": [np.nan, np.nan, "a", np.nan],
                 "E": Categorical([np.nan, np.nan, "a", np.nan]),
-                "F": pd.DatetimeIndex(
-                    ["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"
-                ),
+                "F": DatetimeIndex(["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"),
                 "G": to_timedelta(["1 days", "nan", "nan", "nan"]),
                 "H": [8, 8, 9, 9],
                 "I": [9, 9, 8, 8],
                 "J": [1, 1, np.nan, np.nan],
                 "K": Categorical(["a", np.nan, "a", np.nan]),
-                "L": pd.DatetimeIndex(
+                "L": DatetimeIndex(
                     ["2000-01-02", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"
                 ),
                 "M": to_timedelta(["1 days", "nan", "1 days", "nan"]),
@@ -831,15 +832,15 @@ def test_sum_corner(self):
     @pytest.mark.parametrize(
         "index",
         [
-            tm.makeRangeIndex(0),
-            tm.makeDateIndex(0),
-            tm.makeNumericIndex(0, dtype=int),
-            tm.makeNumericIndex(0, dtype=float),
-            tm.makeDateIndex(0, freq="ME"),
-            tm.makePeriodIndex(0),
+            RangeIndex(0),
+            DatetimeIndex([]),
+            Index([], dtype=np.int64),
+            Index([], dtype=np.float64),
+            DatetimeIndex([], freq="ME"),
+            PeriodIndex([], freq="D"),
         ],
     )
-    def test_axis_1_empty(self, all_reductions, index, using_array_manager):
+    def test_axis_1_empty(self, all_reductions, index):
         df = DataFrame(columns=["a"], index=index)
         result = getattr(df, all_reductions)(axis=1)
         if all_reductions in ("any", "all"):

pandas/tests/indexes/datetimelike_/test_equals.py

@@ -18,6 +18,7 @@
     TimedeltaIndex,
     date_range,
     period_range,
+    timedelta_range,
 )
 import pandas._testing as tm
 
@@ -141,7 +142,7 @@ def test_not_equals_bday(self, freq):
 class TestTimedeltaIndexEquals(EqualsTests):
     @pytest.fixture
     def index(self):
-        return tm.makeTimedeltaIndex(10)
+        return timedelta_range("1 day", periods=10)
 
     def test_equals2(self):
         # GH#13107

pandas/tests/indexes/test_base.py

@@ -30,6 +30,7 @@
     TimedeltaIndex,
     date_range,
     period_range,
+    timedelta_range,
 )
 import pandas._testing as tm
 from pandas.core.indexes.api import (
@@ -92,7 +93,7 @@ def test_constructor_copy(self, index):
                 name="Green Eggs & Ham",
             ),  # DTI with tz
             date_range("2015-01-01 10:00", freq="D", periods=3),  # DTI no tz
-            pd.timedelta_range("1 days", freq="D", periods=3),  # td
+            timedelta_range("1 days", freq="D", periods=3),  # td
             period_range("2015-01-01", freq="D", periods=3),  # period
         ],
     )
@@ -122,7 +123,7 @@ def test_constructor_from_index_dtlike(self, cast_as_obj, index):
                 date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern"),
                 True,
             ),  # datetimetz
-            (pd.timedelta_range("1 days", freq="D", periods=3), False),  # td
+            (timedelta_range("1 days", freq="D", periods=3), False),  # td
             (period_range("2015-01-01", freq="D", periods=3), False),  # period
         ],
     )
@@ -267,7 +268,7 @@ def test_constructor_dtypes_datetime(self, tz_naive_fixture, attr, klass):
     @pytest.mark.parametrize("attr", ["values", "asi8"])
     @pytest.mark.parametrize("klass", [Index, TimedeltaIndex])
     def test_constructor_dtypes_timedelta(self, attr, klass):
-        index = pd.timedelta_range("1 days", periods=5)
+        index = timedelta_range("1 days", periods=5)
         index = index._with_freq(None)  # won't be preserved by constructors
         dtype = index.dtype
 
@@ -526,10 +527,14 @@ def test_map_with_tuples_mi(self):
         tm.assert_index_equal(reduced_index, Index(first_level))
 
     @pytest.mark.parametrize(
-        "attr", ["makeDateIndex", "makePeriodIndex", "makeTimedeltaIndex"]
+        "index",
+        [
+            date_range("2020-01-01", freq="D", periods=10),
+            period_range("2020-01-01", freq="D", periods=10),
+            timedelta_range("1 day", periods=10),
+        ],
     )
-    def test_map_tseries_indices_return_index(self, attr):
-        index = getattr(tm, attr)(10)
+    def test_map_tseries_indices_return_index(self, index):
         expected = Index([1] * 10)
         result = index.map(lambda x: 1)
         tm.assert_index_equal(expected, result)

pandas/tests/indexes/test_setops.py

@@ -139,19 +139,16 @@ def test_union_different_types(index_flat, index_flat2, request):
 
 
 @pytest.mark.parametrize(
-    "idx_fact1,idx_fact2",
+    "idx1,idx2",
     [
-        (tm.makeIntIndex, tm.makeRangeIndex),
-        (tm.makeFloatIndex, tm.makeIntIndex),
-        (tm.makeFloatIndex, tm.makeRangeIndex),
-        (tm.makeFloatIndex, tm.makeUIntIndex),
+        (Index(np.arange(5), dtype=np.int64), RangeIndex(5)),
+        (Index(np.arange(5), dtype=np.float64), Index(np.arange(5), dtype=np.int64)),
+        (Index(np.arange(5), dtype=np.float64), RangeIndex(5)),
+        (Index(np.arange(5), dtype=np.float64), Index(np.arange(5), dtype=np.uint64)),
     ],
 )
-def test_compatible_inconsistent_pairs(idx_fact1, idx_fact2):
+def test_compatible_inconsistent_pairs(idx1, idx2):
     # GH 23525
-    idx1 = idx_fact1(10)
-    idx2 = idx_fact2(20)
-
     res1 = idx1.union(idx2)
     res2 = idx2.union(idx1)
 

pandas/tests/indexing/test_floats.py

@@ -132,14 +132,16 @@ def test_scalar_with_mixed(self, indexer_sl):
         expected = 3
         assert result == expected
 
-    @pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
-    def test_scalar_integer(self, index_func, frame_or_series, indexer_sl):
+    @pytest.mark.parametrize(
+        "index", [Index(np.arange(5), dtype=np.int64), RangeIndex(5)]
+    )
+    def test_scalar_integer(self, index, frame_or_series, indexer_sl):
         getitem = indexer_sl is not tm.loc
 
         # test how scalar float indexers work on int indexes
 
         # integer index
-        i = index_func(5)
+        i = index
         obj = gen_obj(frame_or_series, i)
 
         # coerce to equal int
@@ -169,11 +171,12 @@ def compare(x, y):
         result = indexer_sl(s2)[3]
         compare(result, expected)
 
-    @pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
-    def test_scalar_integer_contains_float(self, index_func, frame_or_series):
+    @pytest.mark.parametrize(
+        "index", [Index(np.arange(5), dtype=np.int64), RangeIndex(5)]
+    )
+    def test_scalar_integer_contains_float(self, index, frame_or_series):
         # contains
         # integer index
-        index = index_func(5)
         obj = gen_obj(frame_or_series, index)
 
         # coerce to equal int
@@ -348,11 +351,11 @@ def test_integer_positional_indexing(self, idx):
         with pytest.raises(TypeError, match=msg):
             s.iloc[idx]
 
-    @pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
-    def test_slice_integer_frame_getitem(self, index_func):
+    @pytest.mark.parametrize(
+        "index", [Index(np.arange(5), dtype=np.int64), RangeIndex(5)]
+    )
+    def test_slice_integer_frame_getitem(self, index):
         # similar to above, but on the getitem dim (of a DataFrame)
-        index = index_func(5)
-
         s = DataFrame(np.random.default_rng(2).standard_normal((5, 2)), index=index)
 
         # getitem
@@ -403,11 +406,11 @@ def test_slice_integer_frame_getitem(self, index_func):
                 s[idx]
 
     @pytest.mark.parametrize("idx", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)])
-    @pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
-    def test_float_slice_getitem_with_integer_index_raises(self, idx, index_func):
+    @pytest.mark.parametrize(
+        "index", [Index(np.arange(5), dtype=np.int64), RangeIndex(5)]
+    )
+    def test_float_slice_getitem_with_integer_index_raises(self, idx, index):
         # similar to above, but on the getitem dim (of a DataFrame)
-        index = index_func(5)
-
         s = DataFrame(np.random.default_rng(2).standard_normal((5, 2)), index=index)
 
         # setitem