PERF: Pass copy=False to Index constructor (#63451)

Co-authored-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>

Author: rhshadrach

pandas/_testing/__init__.py

@@ -290,7 +290,7 @@ def box_expected(expected, box_cls, transpose: bool = True):
         else:
             expected = pd.array(expected, copy=False)
     elif box_cls is Index:
-        expected = Index(expected)
+        expected = Index(expected, copy=False)
     elif box_cls is Series:
         expected = Series(expected)
     elif box_cls is DataFrame:

pandas/conftest.py

@@ -769,7 +769,7 @@ def index_with_missing(request):
         vals = ind.values.copy()
         vals[0] = None
         vals[-1] = None
-        return type(ind)(vals)
+        return type(ind)(vals, copy=False)
 
 
 # ----------------------------------------------------------------

pandas/core/algorithms.py

@@ -926,7 +926,7 @@ def value_counts_internal(
 
             # Starting in 3.0, we no longer perform dtype inference on the
             #  Index object we construct here, xref GH#56161
-            idx = Index(keys, dtype=keys.dtype, name=index_name)
+            idx = Index(keys, dtype=keys.dtype, name=index_name, copy=False)
 
             if (
                 not sort

pandas/core/apply.py

@@ -1972,7 +1972,7 @@ def relabel_result(
             fun = [
                 com.get_callable_name(f) if not isinstance(f, str) else f for f in fun
             ]
-            col_idx_order = Index(s.index).get_indexer(fun)
+            col_idx_order = Index(s.index, copy=False).get_indexer(fun)
             valid_idx = col_idx_order != -1
             if valid_idx.any():
                 s = s.iloc[col_idx_order[valid_idx]]

pandas/core/arrays/_mixins.py

@@ -496,7 +496,7 @@ def value_counts(self, dropna: bool = True) -> Series:
         result = value_counts(values, sort=False, dropna=dropna)
 
         index_arr = self._from_backing_data(np.asarray(result.index._data))
-        index = Index(index_arr, name=result.index.name)
+        index = Index(index_arr, name=result.index.name, copy=False)
         return Series(result._values, index=index, name=result.name, copy=False)
 
     def _quantile(

pandas/core/arrays/arrow/array.py

@@ -1804,7 +1804,7 @@ def value_counts(self, dropna: bool = True) -> Series:
 
         counts = ArrowExtensionArray(counts)
 
-        index = Index(self._from_pyarrow_array(values))
+        index = Index(self._from_pyarrow_array(values), copy=False)
 
         return Series(counts, index=index, name="count", copy=False)
 

pandas/core/arrays/categorical.py

@@ -670,7 +670,7 @@ def _from_inferred_categories(
             to_timedelta,
         )
 
-        cats = Index(inferred_categories)
+        cats = Index(inferred_categories, copy=False)
         known_categories = (
             isinstance(dtype, CategoricalDtype) and dtype.categories is not None
         )
@@ -2397,7 +2397,7 @@ def _validate_listlike(self, value):
         from pandas import Index
 
         # tupleize_cols=False for e.g. test_fillna_iterable_category GH#41914
-        to_add = Index._with_infer(value, tupleize_cols=False).difference(
+        to_add = Index._with_infer(value, tupleize_cols=False, copy=False).difference(
             self.categories
         )
 

pandas/core/arrays/masked.py

@@ -1422,7 +1422,8 @@ def value_counts(self, dropna: bool = True) -> Series:
             self.dtype.construct_array_type()(
                 keys,  # type: ignore[arg-type]
                 mask_index,
-            )
+            ),
+            copy=False,
         )
         return Series(arr, index=index, name="count", copy=False)
 

pandas/core/arrays/sparse/array.py

@@ -961,7 +961,7 @@ def value_counts(self, dropna: bool = True) -> Series:
                 counts = np.insert(counts, 0, fcounts)
 
         if not isinstance(keys, ABCIndex):
-            index = Index(keys)
+            index = Index(keys, copy=False)
         else:
             index = keys
         return Series(counts, index=index, copy=False)

pandas/core/base.py

@@ -1312,11 +1312,11 @@ def factorize(
             from pandas import Index
 
             try:
-                uniques = Index(uniques, dtype=self.dtype)
+                uniques = Index(uniques, dtype=self.dtype, copy=False)
             except NotImplementedError:
                 # not all dtypes are supported in Index that are allowed for Series
                 # e.g. float16 or bytes
-                uniques = Index(uniques)
+                uniques = Index(uniques, copy=False)
         return codes, uniques
 
     _shared_docs["searchsorted"] = """