_is_num_index -> _is_numeric_index + Index.union

Author: topper-123

pandas/core/indexes/base.py

@@ -439,7 +439,11 @@ def __new__(
             return Index._simple_new(data, name=name)
 
         # index-like
-        elif isinstance(data, NumericIndex) and data._is_num_index() and dtype is None:
+        elif (
+            isinstance(data, NumericIndex)
+            and data._is_numeric_index()
+            and dtype is None
+        ):
             return NumericIndex(data, name=name, copy=copy)
         elif isinstance(data, (np.ndarray, Index, ABCSeries)):
 
@@ -2418,7 +2422,7 @@ def _is_multi(self) -> bool:
 
     @final
     @classmethod
-    def _is_num_index(cls) -> bool:
+    def _is_numeric_index(cls) -> bool:
         """
         Check if this is a NumericIndex, but *not* Int64Index, UInt64Index, FloatIndex.
 
@@ -5514,7 +5518,7 @@ def map(self, mapper, na_action=None):
             # empty
             attributes["dtype"] = self.dtype
 
-        if self._is_num_index() and is_numeric_dtype(new_values.dtype):
+        if self._is_numeric_index() and is_numeric_dtype(new_values.dtype):
             return NumericIndex(new_values, **attributes)
 
         return Index(new_values, **attributes)

pandas/core/indexes/category.py

@@ -297,7 +297,7 @@ def astype(self, dtype, copy: bool = True) -> Index:
         # the super method always returns Int64Index, UInt64Index and Float64Index
         # but if e.g. the categories are a NumIndex with dtype float32, we want to
         # return an index with the same dtype as self.categories.
-        if categories._is_num_index():
+        if categories._is_numeric_index():
             assert isinstance(categories, NumericIndex)  # mypy complaint fix
             try:
                 categories._validate_dtype(dtype)

pandas/tests/base/test_unique.py

@@ -25,7 +25,7 @@ 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_num_index():
+    elif isinstance(obj, pd.Index) and obj._is_numeric_index():
         expected = NumericIndex(unique_values, dtype=obj.dtype)
         tm.assert_index_equal(result, expected, exact=True)
     elif isinstance(obj, pd.Index):
@@ -66,7 +66,7 @@ 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_num_index():
+    if isinstance(obj, pd.Index) and obj._is_numeric_index():
         expected = NumericIndex(unique_values, dtype=obj.dtype)
         tm.assert_index_equal(result, expected, exact=True)
     elif isinstance(obj, pd.Index):

pandas/tests/indexes/common.py

@@ -664,7 +664,7 @@ def test_map_dictlike(self, mapper, simple_index):
         tm.assert_index_equal(result, expected)
 
         # empty mappable
-        if idx._is_num_index():
+        if idx._is_numeric_index():
             new_index_cls = NumericIndex
         else:
             new_index_cls = Float64Index

pandas/tests/indexes/test_base.py

@@ -713,7 +713,7 @@ def test_map_dictlike(self, index, mapper):
         if index.empty:
             # to match proper result coercion for uints
             expected = Index([])
-        elif index._is_num_index():
+        elif index._is_numeric_index():
             expected = type(index)(np.arange(len(index), 0, -1), dtype=index.dtype)
         else:
             expected = Index(np.arange(len(index), 0, -1))