DEPR: remove Int64Index, UInt64Index, Float64Index from public namespace

asv_bench/benchmarks/hash_functions.py

@@ -57,14 +57,14 @@ def time_unique(self, exponent):
 class NumericSeriesIndexing:
 
     params = [
-        (pd.Int64Index, pd.UInt64Index, pd.Float64Index),
+        (np.int64, np.uint64, np.float64),
         (10**4, 10**5, 5 * 10**5, 10**6, 5 * 10**6),
     ]
-    param_names = ["index_dtype", "N"]
+    param_names = ["dtype", "N"]
 
-    def setup(self, index, N):
-        vals = np.array(list(range(55)) + [54] + list(range(55, N - 1)))
-        indices = index(vals)
+    def setup(self, dtype, N):
+        vals = np.array(list(range(55)) + [54] + list(range(55, N - 1)), dtype=dtype)
+        indices = pd.Index(vals)
         self.data = pd.Series(np.arange(N), index=indices)
 
     def time_loc_slice(self, index, N):
@@ -75,15 +75,15 @@ def time_loc_slice(self, index, N):
 class NumericSeriesIndexingShuffled:
 
     params = [
-        (pd.Int64Index, pd.UInt64Index, pd.Float64Index),
+        (np.int64, np.uint64, np.float64),
         (10**4, 10**5, 5 * 10**5, 10**6, 5 * 10**6),
     ]
-    param_names = ["index_dtype", "N"]
+    param_names = ["dtype", "N"]
 
-    def setup(self, index, N):
-        vals = np.array(list(range(55)) + [54] + list(range(55, N - 1)))
+    def setup(self, dtype, N):
+        vals = np.array(list(range(55)) + [54] + list(range(55, N - 1)), dtype=dtype)
         np.random.shuffle(vals)
-        indices = index(vals)
+        indices = pd.Index(vals)
         self.data = pd.Series(np.arange(N), index=indices)
 
     def time_loc_slice(self, index, N):

asv_bench/benchmarks/index_cached_properties.py

@@ -30,7 +30,7 @@ def setup(self, index_type):
         elif index_type == "DatetimeIndex":
             self.idx = pd.date_range("1/1/2000", freq="T", periods=N)
         elif index_type == "Int64Index":
-            self.idx = pd.Index(range(N))
+            self.idx = pd.Index(range(N), dtype="int64")
         elif index_type == "PeriodIndex":
             self.idx = pd.period_range("1/1/2000", freq="T", periods=N)
         elif index_type == "RangeIndex":
@@ -40,9 +40,9 @@ def setup(self, index_type):
         elif index_type == "TimedeltaIndex":
             self.idx = pd.TimedeltaIndex(range(N))
         elif index_type == "Float64Index":
-            self.idx = pd.Float64Index(range(N))
+            self.idx = pd.Index(range(N), dtype="float64")
         elif index_type == "UInt64Index":
-            self.idx = pd.UInt64Index(range(N))
+            self.idx = pd.Index(range(N), dtype="uint64")
         elif index_type == "CategoricalIndex":
             self.idx = pd.CategoricalIndex(range(N), range(N))
         else:

asv_bench/benchmarks/index_object.py

@@ -4,7 +4,6 @@
 
 from pandas import (
     DatetimeIndex,
-    Float64Index,
     Index,
     IntervalIndex,
     MultiIndex,
@@ -202,8 +201,8 @@ class Float64IndexMethod:
     # GH 13166
     def setup(self):
         N = 100_000
-        a = np.arange(N)
-        self.ind = Float64Index(a * 4.8000000418824129e-08)
+        a = np.arange(N, dtype=np.float64)
+        self.ind = Index(a * 4.8000000418824129e-08)
 
     def time_get_loc(self):
         self.ind.get_loc(0)

asv_bench/benchmarks/indexing.py

@@ -12,12 +12,10 @@
 from pandas import (
     CategoricalIndex,
     DataFrame,
-    Float64Index,
-    Int64Index,
+    Index,
     IntervalIndex,
     MultiIndex,
     Series,
-    UInt64Index,
     concat,
     date_range,
     option_context,
@@ -30,17 +28,17 @@
 class NumericSeriesIndexing:
 
     params = [
-        (Int64Index, UInt64Index, Float64Index),
+        (np.int64, np.uint64, np.float64),
         ("unique_monotonic_inc", "nonunique_monotonic_inc"),
     ]
-    param_names = ["index_dtype", "index_structure"]
+    param_names = ["dtype", "index_structure"]
 
-    def setup(self, index, index_structure):
+    def setup(self, dtype, index_structure):
         N = 10**6
         indices = {
-            "unique_monotonic_inc": index(range(N)),
-            "nonunique_monotonic_inc": index(
-                list(range(55)) + [54] + list(range(55, N - 1))
+            "unique_monotonic_inc": Index(range(N), dtype=dtype),
+            "nonunique_monotonic_inc": Index(
+                list(range(55)) + [54] + list(range(55, N - 1)), dtype=dtype
             ),
         }
         self.data = Series(np.random.rand(N), index=indices[index_structure])
@@ -159,17 +157,17 @@ def time_boolean_rows_boolean(self):
 class DataFrameNumericIndexing:
 
     params = [
-        (Int64Index, UInt64Index, Float64Index),
+        (np.int64, np.uint64, np.float64),
         ("unique_monotonic_inc", "nonunique_monotonic_inc"),
     ]
-    param_names = ["index_dtype", "index_structure"]
+    param_names = ["dtype", "index_structure"]
 
-    def setup(self, index, index_structure):
+    def setup(self, dtype, index_structure):
         N = 10**5
         indices = {
-            "unique_monotonic_inc": index(range(N)),
-            "nonunique_monotonic_inc": index(
-                list(range(55)) + [54] + list(range(55, N - 1))
+            "unique_monotonic_inc": Index(range(N), dtype=dtype),
+            "nonunique_monotonic_inc": Index(
+                list(range(55)) + [54] + list(range(55, N - 1)), dtype=dtype
             ),
         }
         self.idx_dupe = np.array(range(30)) * 99
@@ -201,7 +199,7 @@ class Take:
     def setup(self, index):
         N = 100000
         indexes = {
-            "int": Int64Index(np.arange(N)),
+            "int": Index(np.arange(N), dtype=np.int64),
             "datetime": date_range("2011-01-01", freq="S", periods=N),
         }
         index = indexes[index]

doc/source/reference/indexing.rst

@@ -166,9 +166,6 @@ Numeric Index
    :template: autosummary/class_without_autosummary.rst
 
    RangeIndex
-   Int64Index
-   UInt64Index
-   Float64Index
 
 .. We need this autosummary so that the methods are generated.
 .. Separate block, since they aren't classes.

pandas/__init__.py

@@ -183,38 +183,6 @@
 __git_version__ = v.get("full-revisionid")
 del get_versions, v
 
-# GH 27101
-__deprecated_num_index_names = ["Float64Index", "Int64Index", "UInt64Index"]
-
-
-def __dir__() -> list[str]:
-    # GH43028
-    # Int64Index etc. are deprecated, but we still want them to be available in the dir.
-    # Remove in Pandas 2.0, when we remove Int64Index etc. from the code base.
-    return list(globals().keys()) + __deprecated_num_index_names
-
-
-def __getattr__(name):
-    import warnings
-
-    if name in __deprecated_num_index_names:
-        warnings.warn(
-            f"pandas.{name} is deprecated "
-            "and will be removed from pandas in a future version. "
-            "Use pandas.Index with the appropriate dtype instead.",
-            FutureWarning,
-            stacklevel=2,
-        )
-        from pandas.core.api import Float64Index, Int64Index, UInt64Index
-
-        return {
-            "Float64Index": Float64Index,
-            "Int64Index": Int64Index,
-            "UInt64Index": UInt64Index,
-        }[name]
-
-    raise AttributeError(f"module 'pandas' has no attribute '{name}'")
-
 
 # module level doc-string
 __doc__ = """

pandas/conftest.py

@@ -62,6 +62,7 @@
     Series,
     Timedelta,
     Timestamp,
+    compat,
 )
 import pandas._testing as tm
 from pandas.core import ops
@@ -79,7 +80,7 @@
     has_pyarrow = True
 
 zoneinfo = None
-if pd.compat.PY39:
+if compat.PY39:
     # Import "zoneinfo" could not be resolved (reportMissingImports)
     import zoneinfo  # type: ignore[no-redef]
 

pandas/core/arrays/datetimelike.py

@@ -1354,8 +1354,9 @@ def _addsub_object_array(self, other: np.ndarray, op):
 
         res_values = op(self.astype("O"), np.asarray(other))
 
-        result = pd_array(res_values.ravel())
-        result = extract_array(result, extract_numpy=True).reshape(self.shape)
+        ext_arr = pd_array(res_values.ravel())
+        result = cast(np.ndarray, extract_array(ext_arr, extract_numpy=True))
+        result = result.reshape(self.shape)
         return result
 
     def _time_shift(

pandas/core/construction.py

@@ -68,10 +68,10 @@
 
 if TYPE_CHECKING:
     from pandas import (
-        ExtensionArray,
         Index,
         Series,
     )
+    from pandas.core.arrays.base import ExtensionArray
 
 
 def array(

pandas/core/dtypes/generic.py

@@ -13,16 +13,13 @@
         CategoricalIndex,
         DataFrame,
         DatetimeIndex,
-        Float64Index,
         Index,
-        Int64Index,
         IntervalIndex,
         MultiIndex,
         PeriodIndex,
         RangeIndex,
         Series,
         TimedeltaIndex,
-        UInt64Index,
     )
     from pandas.core.arrays import (
         DatetimeArray,
@@ -32,6 +29,11 @@
         TimedeltaArray,
     )
     from pandas.core.generic import NDFrame
+    from pandas.core.indexes.api import (
+        Float64Index,
+        Int64Index,
+        UInt64Index,
+    )
 
 
 # define abstract base classes to enable isinstance type checking on our

pandas/core/indexes/datetimes.py

@@ -70,7 +70,7 @@
 from pandas.core.tools.times import to_time
 
 if TYPE_CHECKING:
-    from pandas import (
+    from pandas.core.api import (
         DataFrame,
         Float64Index,
         PeriodIndex,

pandas/core/internals/array_manager.py

@@ -96,7 +96,7 @@
 )
 
 if TYPE_CHECKING:
-    from pandas import Float64Index
+    from pandas.core.api import Float64Index
 
 
 T = TypeVar("T", bound="BaseArrayManager")

pandas/core/internals/blocks.py

@@ -109,7 +109,7 @@
 from pandas.core.indexers import check_setitem_lengths
 
 if TYPE_CHECKING:
-    from pandas import (
+    from pandas.core.api import (
         Float64Index,
         Index,
     )

pandas/tests/api/test_api.py

@@ -55,17 +55,14 @@ class TestPDApi(Base):
         "DatetimeIndex",
         "ExcelFile",
         "ExcelWriter",
-        "Float64Index",
         "Flags",
         "Grouper",
         "HDFStore",
         "Index",
-        "Int64Index",
         "MultiIndex",
         "Period",
         "PeriodIndex",
         "RangeIndex",
-        "UInt64Index",
         "Series",
         "SparseDtype",
         "StringDtype",
@@ -93,7 +90,7 @@ class TestPDApi(Base):
     ]
 
     # these are already deprecated; awaiting removal
-    deprecated_classes: list[str] = ["Float64Index", "Int64Index", "UInt64Index"]
+    deprecated_classes: list[str] = []
 
     # external modules exposed in pandas namespace
     modules: list[str] = []