DEPR: remove Index._is backward_compat_public_numeric_index (#50052)

* DEPR: remove Index._is_backward_compat_public_numeric_index

* remove comments

Co-authored-by: Terji Petersen <[email protected]>

Author: topper-123

pandas/core/indexes/base.py

@@ -90,7 +90,6 @@
     ensure_platform_int,
     is_bool_dtype,
     is_categorical_dtype,
-    is_complex_dtype,
     is_dtype_equal,
     is_ea_or_datetimelike_dtype,
     is_extension_array_dtype,
@@ -107,7 +106,6 @@
     is_scalar,
     is_signed_integer_dtype,
     is_string_dtype,
-    is_unsigned_integer_dtype,
     needs_i8_conversion,
     pandas_dtype,
     validate_all_hashable,
@@ -125,7 +123,6 @@
     ABCDatetimeIndex,
     ABCMultiIndex,
     ABCPeriodIndex,
-    ABCRangeIndex,
     ABCSeries,
     ABCTimedeltaIndex,
 )
@@ -392,11 +389,6 @@ def _outer_indexer(
     _attributes: list[str] = ["name"]
     _can_hold_strings: bool = True
 
-    # Whether this index is a NumericIndex, but not a Int64Index, Float64Index,
-    # UInt64Index or RangeIndex. Needed for backwards compat. Remove this attribute and
-    # associated code in pandas 2.0.
-    _is_backward_compat_public_numeric_index: bool = False
-
     @property
     def _engine_type(
         self,
@@ -446,13 +438,6 @@ def __new__(
         elif is_ea_or_datetimelike_dtype(data_dtype):
             pass
 
-        # index-like
-        elif (
-            isinstance(data, Index)
-            and data._is_backward_compat_public_numeric_index
-            and dtype is None
-        ):
-            return data._constructor(data, name=name, copy=copy)
         elif isinstance(data, (np.ndarray, Index, ABCSeries)):
 
             if isinstance(data, ABCMultiIndex):
@@ -981,34 +966,6 @@ def astype(self, dtype, copy: bool = True):
                 new_values = astype_array(values, dtype=dtype, copy=copy)
 
         # pass copy=False because any copying will be done in the astype above
-        if not self._is_backward_compat_public_numeric_index and not isinstance(
-            self, ABCRangeIndex
-        ):
-            # this block is needed so e.g. Int64Index.astype("int32") returns
-            # Int64Index and not a NumericIndex with dtype int32.
-            # When Int64Index etc. are removed from the code base, removed this also.
-            if (
-                isinstance(dtype, np.dtype)
-                and is_numeric_dtype(dtype)
-                and not is_complex_dtype(dtype)
-            ):
-                from pandas.core.api import (
-                    Float64Index,
-                    Int64Index,
-                    UInt64Index,
-                )
-
-                klass: type[Index]
-                if is_signed_integer_dtype(dtype):
-                    klass = Int64Index
-                elif is_unsigned_integer_dtype(dtype):
-                    klass = UInt64Index
-                elif is_float_dtype(dtype):
-                    klass = Float64Index
-                else:
-                    klass = Index
-                return klass(new_values, name=self.name, dtype=dtype, copy=False)
-
         return Index(new_values, name=self.name, dtype=new_values.dtype, copy=False)
 
     _index_shared_docs[
@@ -5089,10 +5046,6 @@ def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
 
         result = concat_compat(to_concat_vals)
 
-        is_numeric = result.dtype.kind in ["i", "u", "f"]
-        if self._is_backward_compat_public_numeric_index and is_numeric:
-            return type(self)._simple_new(result, name=name)
-
         return Index._with_infer(result, name=name)
 
     def putmask(self, mask, value) -> Index:
@@ -6490,12 +6443,7 @@ def insert(self, loc: int, item) -> Index:
             loc = loc if loc >= 0 else loc - 1
             new_values[loc] = item
 
-        if self._typ == "numericindex":
-            # Use self._constructor instead of Index to retain NumericIndex GH#43921
-            # TODO(2.0) can use Index instead of self._constructor
-            return self._constructor(new_values, name=self.name)
-        else:
-            return Index._with_infer(new_values, name=self.name)
+        return Index._with_infer(new_values, name=self.name)
 
     def drop(
         self,

pandas/core/indexes/category.py

@@ -21,7 +21,6 @@
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
     is_scalar,
-    pandas_dtype,
 )
 from pandas.core.dtypes.missing import (
     is_valid_na_for_dtype,
@@ -274,30 +273,6 @@ def _is_dtype_compat(self, other) -> Categorical:
 
         return other
 
-    @doc(Index.astype)
-    def astype(self, dtype: Dtype, copy: bool = True) -> Index:
-        from pandas.core.api import NumericIndex
-
-        dtype = pandas_dtype(dtype)
-
-        categories = self.categories
-        # the super method always returns Int64Index, UInt64Index and Float64Index
-        # but if the categories are a NumericIndex with dtype float32, we want to
-        # return an index with the same dtype as self.categories.
-        if categories._is_backward_compat_public_numeric_index:
-            assert isinstance(categories, NumericIndex)  # mypy complaint fix
-            try:
-                categories._validate_dtype(dtype)
-            except ValueError:
-                pass
-            else:
-                new_values = self._data.astype(dtype, copy=copy)
-                # pass copy=False because any copying has been done in the
-                #  _data.astype call above
-                return categories._constructor(new_values, name=self.name, copy=False)
-
-        return super().astype(dtype, copy=copy)
-
     def equals(self, other: object) -> bool:
         """
         Determine if two CategoricalIndex objects contain the same elements.

pandas/core/indexes/numeric.py

@@ -87,7 +87,6 @@ class NumericIndex(Index):
         "numeric type",
     )
     _can_hold_strings = False
-    _is_backward_compat_public_numeric_index: bool = True
 
     _engine_types: dict[np.dtype, type[libindex.IndexEngine]] = {
         np.dtype(np.int8): libindex.Int8Engine,
@@ -214,12 +213,7 @@ def _ensure_dtype(cls, dtype: Dtype | None) -> np.dtype | None:
             # float16 not supported (no indexing engine)
             raise NotImplementedError("float16 indexes are not supported")
 
-        if cls._is_backward_compat_public_numeric_index:
-            # dtype for NumericIndex
-            return dtype
-        else:
-            # dtype for Int64Index, UInt64Index etc. Needed for backwards compat.
-            return cls._default_dtype
+        return dtype
 
     # ----------------------------------------------------------------
     # Indexing Methods
@@ -415,7 +409,6 @@ class Float64Index(NumericIndex):
     _typ = "float64index"
     _default_dtype = np.dtype(np.float64)
     _dtype_validation_metadata = (is_float_dtype, "float")
-    _is_backward_compat_public_numeric_index: bool = False
 
     @property
     def _engine_type(self) -> type[libindex.Float64Engine]:

pandas/core/indexes/range.py

@@ -103,7 +103,6 @@ class RangeIndex(NumericIndex):
     _typ = "rangeindex"
     _dtype_validation_metadata = (is_signed_integer_dtype, "signed integer")
     _range: range
-    _is_backward_compat_public_numeric_index: bool = False
 
     @property
     def _engine_type(self) -> type[libindex.Int64Engine]:

pandas/tests/base/test_unique.py

@@ -5,7 +5,6 @@
 
 import pandas as pd
 import pandas._testing as tm
-from pandas.core.api import NumericIndex
 from pandas.tests.base.common import allow_na_ops
 
 
@@ -20,9 +19,6 @@ def test_unique(index_or_series_obj):
         expected = pd.MultiIndex.from_tuples(unique_values)
         expected.names = obj.names
         tm.assert_index_equal(result, expected, exact=True)
-    elif isinstance(obj, pd.Index) and obj._is_backward_compat_public_numeric_index:
-        expected = NumericIndex(unique_values, dtype=obj.dtype)
-        tm.assert_index_equal(result, expected, exact=True)
     elif isinstance(obj, pd.Index):
         expected = pd.Index(unique_values, dtype=obj.dtype)
         if is_datetime64tz_dtype(obj.dtype):
@@ -58,10 +54,7 @@ def test_unique_null(null_obj, index_or_series_obj):
     unique_values_not_null = [val for val in unique_values_raw if not pd.isnull(val)]
     unique_values = [null_obj] + unique_values_not_null
 
-    if isinstance(obj, pd.Index) and obj._is_backward_compat_public_numeric_index:
-        expected = NumericIndex(unique_values, dtype=obj.dtype)
-        tm.assert_index_equal(result, expected, exact=True)
-    elif isinstance(obj, pd.Index):
+    if isinstance(obj, pd.Index):
         expected = pd.Index(unique_values, dtype=obj.dtype)
         if is_datetime64tz_dtype(obj.dtype):
             result = result.normalize()

pandas/tests/indexes/common.py

@@ -624,14 +624,10 @@ def test_map_dictlike(self, mapper, simple_index):
 
         # empty mappable
         dtype = None
-        if idx._is_backward_compat_public_numeric_index:
-            new_index_cls = NumericIndex
-            if idx.dtype.kind == "f":
-                dtype = idx.dtype
-        else:
-            new_index_cls = Float64Index
+        if idx.dtype.kind == "f":
+            dtype = idx.dtype
 
-        expected = new_index_cls([np.nan] * len(idx), dtype=dtype)
+        expected = Index([np.nan] * len(idx), dtype=dtype)
         result = idx.map(mapper(expected, idx))
         tm.assert_index_equal(result, expected)
 
@@ -896,13 +892,9 @@ def test_insert_na(self, nulls_fixture, simple_index):
             expected = Index([index[0], pd.NaT] + list(index[1:]), dtype=object)
         else:
             expected = Index([index[0], np.nan] + list(index[1:]))
-
-            if index._is_backward_compat_public_numeric_index:
-                # GH#43921 we preserve NumericIndex
-                if index.dtype.kind == "f":
-                    expected = NumericIndex(expected, dtype=index.dtype)
-                else:
-                    expected = NumericIndex(expected)
+            # GH#43921 we preserve float dtype
+            if index.dtype.kind == "f":
+                expected = Index(expected, dtype=index.dtype)
 
         result = index.insert(1, na_val)
         tm.assert_index_equal(result, expected, exact=True)

pandas/tests/indexes/test_base.py

@@ -18,6 +18,8 @@
 )
 from pandas.util._test_decorators import async_mark
 
+from pandas.core.dtypes.common import is_numeric_dtype
+
 import pandas as pd
 from pandas import (
     CategoricalIndex,
@@ -592,13 +594,11 @@ def test_map_dictlike(self, index, mapper, request):
         if index.empty:
             # to match proper result coercion for uints
             expected = Index([])
-        elif index._is_backward_compat_public_numeric_index:
+        elif is_numeric_dtype(index.dtype):
             expected = index._constructor(rng, dtype=index.dtype)
         elif type(index) is Index and index.dtype != object:
             # i.e. EA-backed, for now just Nullable
             expected = Index(rng, dtype=index.dtype)
-        elif index.dtype.kind == "u":
-            expected = Index(rng, dtype=index.dtype)
         else:
             expected = Index(rng)