TYP: Add type annotations to Index (pandas/core/indexes/base.py)

pandas/core/apply.py

@@ -1896,7 +1896,7 @@ def normalize_keyword_aggregation(
     uniquified_aggspec = _make_unique_kwarg_list(aggspec_order)
 
     # get the new index of columns by comparison
-    col_idx_order = Index(uniquified_aggspec).get_indexer(uniquified_order)
+    col_idx_order = Index(uniquified_aggspec).get_indexer(uniquified_order)  # type: ignore[arg-type]
     return aggspec, columns, col_idx_order
 
 

pandas/core/frame.py

@@ -4233,15 +4233,15 @@ def __getitem__(self, key):
                 return self._getitem_multilevel(key)
             indexer = self.columns.get_loc(key)
             if is_integer(indexer):
-                indexer = [indexer]
+                indexer = [indexer]  # type: ignore[assignment]
         else:
             if is_iterator(key):
                 key = list(key)
             indexer = self.columns._get_indexer_strict(key, "columns")[1]
 
         # take() does not accept boolean indexers
         if getattr(indexer, "dtype", None) == bool:
-            indexer = np.where(indexer)[0]
+            indexer = np.where(indexer)[0]  # type: ignore[arg-type, assignment]
 
         if isinstance(indexer, slice):
             return self._slice(indexer, axis=1)
@@ -4673,7 +4673,7 @@ def _set_item_frame_value(self, key, value: DataFrame) -> None:
             elif is_scalar(loc):
                 locs = [loc]
             else:
-                locs = loc.nonzero()[0]
+                locs = loc.nonzero()[0]  # type: ignore[union-attr]
 
             return self.isetitem(locs, value)
 
@@ -4763,7 +4763,7 @@ def _set_value(
                 iindex = cast("int", index)
             else:
                 icol = self.columns.get_loc(col)
-                iindex = self.index.get_loc(index)
+                iindex = self.index.get_loc(index)  # type: ignore[assignment]
             self._mgr.column_setitem(icol, iindex, value, inplace_only=True)
 
         except (KeyError, TypeError, ValueError, LossySetitemError):
@@ -4821,7 +4821,7 @@ def _box_col_values(self, values: SingleBlockManager, loc: int) -> Series:
 
     def _get_item(self, item: Hashable) -> Series:
         loc = self.columns.get_loc(item)
-        return self._ixs(loc, axis=1)
+        return self._ixs(loc, axis=1)  # type: ignore[arg-type]
 
     # ----------------------------------------------------------------------
     # Unsorted
@@ -7130,13 +7130,13 @@ class    max    type
                 level = [level]
             level = [self.index._get_level_number(lev) for lev in level]
             if len(level) < self.index.nlevels:
-                new_index = self.index.droplevel(level)
+                new_index = self.index.droplevel(level)  # type: ignore[assignment]
 
         if not drop:
             to_insert: Iterable[tuple[Any, Any | None]]
 
             default = "index" if "index" not in self else "level_0"
-            names = self.index._get_default_index_names(names, default)
+            names = self.index._get_default_index_names(names, default)  # type: ignore[arg-type]
 
             if isinstance(self.index, MultiIndex):
                 to_insert = zip(
@@ -7642,7 +7642,7 @@ def dropna(
             if not is_list_like(subset):
                 subset = [cast("Hashable", subset)]
             ax = self._get_axis(agg_axis)
-            indices = ax.get_indexer_for(subset)
+            indices = ax.get_indexer_for(subset)  # type: ignore[arg-type]
             check = indices == -1
             if check.any():
                 raise KeyError(np.array(subset)[check].tolist())
@@ -13366,7 +13366,7 @@ def explode(
         if ignore_index:
             result.index = default_index(len(result))
         else:
-            result.index = self.index.take(result.index)
+            result.index = self.index.take(result.index)  # type: ignore[arg-type]
         result = result.reindex(columns=self.columns)
 
         return result.__finalize__(self, method="explode")

pandas/core/generic.py

@@ -1033,9 +1033,9 @@ def _rename(
             # GH 13473
             if not callable(replacements):
                 if ax._is_multi and level is not None:
-                    indexer = ax.get_level_values(level).get_indexer_for(replacements)
+                    indexer = ax.get_level_values(level).get_indexer_for(replacements)  # type: ignore[arg-type]
                 else:
-                    indexer = ax.get_indexer_for(replacements)
+                    indexer = ax.get_indexer_for(replacements)  # type: ignore[arg-type]
 
                 if errors == "raise" and len(indexer[indexer == -1]):
                     missing_labels = [

pandas/core/groupby/grouper.py

@@ -363,9 +363,9 @@ def _set_grouper(
                 if self._indexer is not None:
                     reverse_indexer = self._indexer.argsort()
                     unsorted_ax = self._grouper.take(reverse_indexer)
-                    ax = unsorted_ax.take(obj.index)
+                    ax = unsorted_ax.take(obj.index)  # type: ignore[arg-type]
                 else:
-                    ax = self._grouper.take(obj.index)
+                    ax = self._grouper.take(obj.index)  # type: ignore[arg-type]
             else:
                 if key not in obj._info_axis:
                     raise KeyError(f"The grouper name {key} is not found")
@@ -686,7 +686,7 @@ def _codes_and_uniques(self) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]:
     @cache_readonly
     def groups(self) -> dict[Hashable, Index]:
         codes, uniques = self._codes_and_uniques
-        uniques = Index._with_infer(uniques, name=self.name, copy=False)
+        uniques = Index._with_infer(uniques, name=self.name, copy=False)  # type: ignore[assignment]
 
         r, counts = libalgos.groupsort_indexer(ensure_platform_int(codes), len(uniques))
         counts = ensure_int64(counts).cumsum()

pandas/core/groupby/ops.py

@@ -798,7 +798,7 @@ def result_index_and_ids(self) -> tuple[Index, npt.NDArray[np.intp]]:
         # When passed a categorical grouping, keep all categories
         for k, (ping, level) in enumerate(zip(self.groupings, levels, strict=True)):
             if ping._passed_categorical:
-                levels[k] = level.set_categories(ping._orig_cats)
+                levels[k] = level.set_categories(ping._orig_cats)  # type: ignore[attr-defined]
 
         if len(self.groupings) == 1:
             result_index = levels[0]