DEPR: Remove NumericIndex from pandas/_testing/ (#51098)

* DEPR: Remove NumericIndex from pandas/_testing/

* add GH-number

Author: topper-123

doc/source/whatsnew/v2.0.0.rst

@@ -708,6 +708,7 @@ Other API changes
 - The ``other`` argument in :meth:`DataFrame.mask` and :meth:`Series.mask` now defaults to ``no_default`` instead of ``np.nan`` consistent with :meth:`DataFrame.where` and :meth:`Series.where`. Entries will be filled with the corresponding NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension dtypes). (:issue:`49111`)
 - Changed behavior of :meth:`Series.quantile` and :meth:`DataFrame.quantile` with :class:`SparseDtype` to retain sparse dtype (:issue:`49583`)
 - When creating a :class:`Series` with a object-dtype :class:`Index` of datetime objects, pandas no longer silently converts the index to a :class:`DatetimeIndex` (:issue:`39307`, :issue:`23598`)
+- :func:`pandas.testing.assert_index_equal` with parameter ``exact="equiv"`` now considers two indexes equal when both are either a :class:`RangeIndex` or :class:`Index` with an ``int64`` dtype. Previously it meant either a :class:`RangeIndex` or a :class:`Int64Index` (:issue:`51098`)
 - :meth:`Series.unique` with dtype "timedelta64[ns]" or "datetime64[ns]" now returns :class:`TimedeltaArray` or :class:`DatetimeArray` instead of ``numpy.ndarray`` (:issue:`49176`)
 - :func:`to_datetime` and :class:`DatetimeIndex` now allow sequences containing both ``datetime`` objects and numeric entries, matching :class:`Series` behavior (:issue:`49037`, :issue:`50453`)
 - :func:`pandas.api.dtypes.is_string_dtype` now only returns ``True`` for array-likes with ``dtype=object`` when the elements are inferred to be strings (:issue:`15585`)

pandas/_testing/__init__.py

@@ -108,7 +108,6 @@
     use_numexpr,
     with_csv_dialect,
 )
-from pandas.core.api import NumericIndex
 from pandas.core.arrays import (
     BaseMaskedArray,
     ExtensionArray,
@@ -357,7 +356,7 @@ def makeBoolIndex(k: int = 10, name=None) -> Index:
     return Index([False, True] + [False] * (k - 2), name=name)
 
 
-def makeNumericIndex(k: int = 10, *, name=None, dtype: Dtype | None) -> NumericIndex:
+def makeNumericIndex(k: int = 10, *, name=None, dtype: Dtype | None) -> Index:
     dtype = pandas_dtype(dtype)
     assert isinstance(dtype, np.dtype)
 
@@ -372,17 +371,17 @@ def makeNumericIndex(k: int = 10, *, name=None, dtype: Dtype | None) -> NumericI
     else:
         raise NotImplementedError(f"wrong dtype {dtype}")
 
-    return NumericIndex(values, dtype=dtype, name=name)
+    return Index(values, dtype=dtype, name=name)
 
 
-def makeIntIndex(k: int = 10, *, name=None, dtype: Dtype = "int64") -> NumericIndex:
+def makeIntIndex(k: int = 10, *, name=None, dtype: Dtype = "int64") -> Index:
     dtype = pandas_dtype(dtype)
     if not is_signed_integer_dtype(dtype):
         raise TypeError(f"Wrong dtype {dtype}")
     return makeNumericIndex(k, name=name, dtype=dtype)
 
 
-def makeUIntIndex(k: int = 10, *, name=None, dtype: Dtype = "uint64") -> NumericIndex:
+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}")
@@ -393,7 +392,7 @@ 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") -> NumericIndex:
+def makeFloatIndex(k: int = 10, *, name=None, dtype: Dtype = "float64") -> Index:
     dtype = pandas_dtype(dtype)
     if not is_float_dtype(dtype):
         raise TypeError(f"Wrong dtype {dtype}")

pandas/_testing/asserters.py

@@ -75,7 +75,7 @@ def assert_almost_equal(
     right : object
     check_dtype : bool or {'equiv'}, default 'equiv'
         Check dtype if both a and b are the same type. If 'equiv' is passed in,
-        then `RangeIndex` and `NumericIndex` with int64 dtype are also considered
+        then `RangeIndex` and `Index` with int64 dtype are also considered
         equivalent when doing type checking.
     rtol : float, default 1e-5
         Relative tolerance.
@@ -197,7 +197,7 @@ def assert_index_equal(
     exact : bool or {'equiv'}, default 'equiv'
         Whether to check the Index class, dtype and inferred_type
         are identical. If 'equiv', then RangeIndex can be substituted for
-        NumericIndex with an int64 dtype as well.
+        Index with an int64 dtype as well.
     check_names : bool, default True
         Whether to check the names attribute.
     check_exact : bool, default True
@@ -348,8 +348,6 @@ def assert_class_equal(
     """
     Checks classes are equal.
     """
-    from pandas.core.indexes.numeric import NumericIndex
-
     __tracebackhide__ = True
 
     def repr_class(x):
@@ -359,12 +357,26 @@ def repr_class(x):
 
         return type(x).__name__
 
+    def is_class_equiv(idx: Index) -> bool:
+        """Classes that are a RangeIndex (sub-)instance or exactly an `Index` .
+
+        This only checks class equivalence. There is a separate check that the
+        dtype is int64.
+        """
+        from pandas.core.indexes.numeric import NumericIndex
+
+        if isinstance(idx, RangeIndex):
+            return True
+        elif type(idx) is Index or type(idx) is NumericIndex:
+            return True
+        else:
+            return False
+
     if type(left) == type(right):
         return
 
     if exact == "equiv":
-        # accept equivalence of NumericIndex (sub-)classes
-        if isinstance(left, NumericIndex) and isinstance(right, NumericIndex):
+        if is_class_equiv(left) and is_class_equiv(right):
             return
 
     msg = f"{obj} classes are different"
@@ -470,7 +482,7 @@ def assert_categorical_equal(
     ):
         exact = "equiv"
     else:
-        # We still want to require exact matches for NumericIndex
+        # We still want to require exact matches for Index
         exact = True
 
     if check_category_order:
@@ -511,7 +523,7 @@ def assert_interval_array_equal(
     exact : bool or {'equiv'}, default 'equiv'
         Whether to check the Index class, dtype and inferred_type
         are identical. If 'equiv', then RangeIndex can be substituted for
-        NumericIndex with an int64 dtype as well.
+        Index with an int64 dtype as well.
     obj : str, default 'IntervalArray'
         Specify object name being compared, internally used to show appropriate
         assertion message