REF: simplify ensure_index

pandas/_libs/lib.pyi

@@ -190,11 +190,7 @@ def maybe_indices_to_slice(
     max_len: int,
 ) -> slice | np.ndarray: ...  # np.ndarray[np.uint8]
 
-def clean_index_list(obj: list) -> tuple[
-    list | np.ndarray,  # np.ndarray[object | np.int64 | np.uint64]
-    bool,
-]: ...
-
+def is_all_arraylike(obj: list) -> bool: ...
 
 # -----------------------------------------------------------------
 # Functions which in reality take memoryviews

pandas/_libs/lib.pyx

@@ -740,19 +740,15 @@ cpdef ndarray[object] ensure_string_array(
     return result
 
 
-@cython.wraparound(False)
-@cython.boundscheck(False)
-def clean_index_list(obj: list):
+def is_all_arraylike(obj: list) -> bool:
     """
-    Utility used in ``pandas.core.indexes.api.ensure_index``.
+    Should we treat these as levels of a MultiIndex, as opposed to Index items?
     """
     cdef:
         Py_ssize_t i, n = len(obj)
         object val
         bint all_arrays = True
 
-    # First check if we have a list of arraylikes, in which case we will
-    #  pass them to MultiIndex.from_arrays
     for i in range(n):
         val = obj[i]
         if not (isinstance(val, list) or
@@ -762,31 +758,7 @@ def clean_index_list(obj: list):
             all_arrays = False
             break
 
-    if all_arrays:
-        return obj, all_arrays
-
-    # don't force numpy coerce with nan's
-    inferred = infer_dtype(obj, skipna=False)
-    if inferred in ['string', 'bytes', 'mixed', 'mixed-integer']:
-        return np.asarray(obj, dtype=object), 0
-    elif inferred in ['integer']:
-        # we infer an integer but it *could* be a uint64
-
-        arr = np.asarray(obj)
-        if arr.dtype.kind not in ["i", "u"]:
-            # eg [0, uint64max] gets cast to float64,
-            #  but then we know we have either uint64 or object
-            if (arr < 0).any():
-                # TODO: similar to maybe_cast_to_integer_array
-                return np.asarray(obj, dtype="object"), 0
-
-            # GH#35481
-            guess = np.asarray(obj, dtype="uint64")
-            return guess, 0
-
-        return arr, 0
-
-    return np.asarray(obj), 0
+    return all_arrays
 
 
 # ------------------------------------------------------------------------------

pandas/core/indexes/base.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-from copy import copy as copy_func
 from datetime import datetime
 import functools
 from itertools import zip_longest
@@ -6312,21 +6311,15 @@ def ensure_index(index_like: AnyArrayLike | Sequence, copy: bool = False) -> Ind
             # check in clean_index_list
             index_like = list(index_like)
 
-        converted, all_arrays = lib.clean_index_list(index_like)
-
-        if len(converted) > 0 and all_arrays:
+        if len(index_like) and lib.is_all_arraylike(index_like):
             from pandas.core.indexes.multi import MultiIndex
 
-            return MultiIndex.from_arrays(converted)
+            return MultiIndex.from_arrays(index_like)
         else:
-            index_like = converted
+            return Index(index_like, copy=copy, tupleize_cols=False)
     else:
-        # clean_index_list does the equivalent of copying
-        # so only need to do this if not list instance
-        if copy:
-            index_like = copy_func(index_like)
 
-    return Index(index_like)
+        return Index(index_like, copy=copy)
 
 
 def ensure_has_len(seq):

pandas/tests/indexes/numeric/test_numeric.py

@@ -531,6 +531,14 @@ def test_constructor(self, dtype):
         res = Index([1, 2 ** 63 + 1], dtype=dtype)
         tm.assert_index_equal(res, idx)
 
+    @pytest.mark.xfail(reason="https://github.com/numpy/numpy/issues/19146")
+    def test_constructor_does_not_cast_to_float(self):
+        # https://github.com/numpy/numpy/issues/19146
+        values = [0, np.iinfo(np.uint64).max]
+
+        result = UInt64Index(values)
+        assert list(result) == values
+
 
 @pytest.mark.parametrize(
     "box",

pandas/tests/indexes/test_base.py

@@ -1622,6 +1622,18 @@ def test_ensure_index_mixed_closed_intervals(self):
         expected = Index(intervals, dtype=object)
         tm.assert_index_equal(result, expected)
 
+    def test_ensure_index_uint64(self):
+        # with both 0 and a large-uint64, np.array will infer to float64
+        #  https://github.com/numpy/numpy/issues/19146
+        #  but a more accurate choice would be uint64
+        values = [0, np.iinfo(np.uint64).max]
+
+        result = ensure_index(values)
+        assert list(result) == values
+
+        expected = Index(values, dtype="uint64")
+        tm.assert_index_equal(result, expected)
+
     def test_get_combined_index(self):
         result = _get_combined_index([])
         expected = Index([])

pandas/tests/libs/test_lib.py

@@ -206,15 +206,3 @@ def test_no_default_pickle():
     # GH#40397
     obj = tm.round_trip_pickle(lib.no_default)
     assert obj is lib.no_default
-
-
-def test_clean_index_list():
-    # with both 0 and a large-uint64, np.array will infer to float64
-    #  https://github.com/numpy/numpy/issues/19146
-    #  but a more accurate choice would be uint64
-    values = [0, np.iinfo(np.uint64).max]
-
-    result, _ = lib.clean_index_list(values)
-
-    expected = np.array(values, dtype="uint64")
-    tm.assert_numpy_array_equal(result, expected, check_dtype=True)