TYP: indexes

pandas/core/arrays/datetimes.py

@@ -190,7 +190,7 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):
     _infer_matches = ("datetime", "datetime64", "date")
 
     # define my properties & methods for delegation
-    _bool_ops = [
+    _bool_ops: list[str] = [
         "is_month_start",
         "is_month_end",
         "is_quarter_start",
@@ -199,8 +199,8 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):
         "is_year_end",
         "is_leap_year",
     ]
-    _object_ops = ["freq", "tz"]
-    _field_ops = [
+    _object_ops: list[str] = ["freq", "tz"]
+    _field_ops: list[str] = [
         "year",
         "month",
         "day",
@@ -220,9 +220,9 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):
         "microsecond",
         "nanosecond",
     ]
-    _other_ops = ["date", "time", "timetz"]
-    _datetimelike_ops = _field_ops + _object_ops + _bool_ops + _other_ops
-    _datetimelike_methods = [
+    _other_ops: list[str] = ["date", "time", "timetz"]
+    _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops + _other_ops
+    _datetimelike_methods: list[str] = [
         "to_period",
         "tz_localize",
         "tz_convert",

pandas/core/arrays/interval.py

@@ -87,7 +87,7 @@
 
 IntervalArrayT = TypeVar("IntervalArrayT", bound="IntervalArray")
 
-_interval_shared_docs = {}
+_interval_shared_docs: dict[str, str] = {}
 
 _shared_docs_kwargs = {
     "klass": "IntervalArray",

pandas/core/arrays/period.py

@@ -157,9 +157,9 @@ class PeriodArray(PeriodMixin, dtl.DatelikeOps):
 
     # Names others delegate to us
     _other_ops: list[str] = []
-    _bool_ops = ["is_leap_year"]
-    _object_ops = ["start_time", "end_time", "freq"]
-    _field_ops = [
+    _bool_ops: list[str] = ["is_leap_year"]
+    _object_ops: list[str] = ["start_time", "end_time", "freq"]
+    _field_ops: list[str] = [
         "year",
         "month",
         "day",

pandas/core/generic.py

@@ -1797,9 +1797,9 @@ def _drop_labels_or_levels(self, keys, axis: int = 0):
 
         Parameters
         ----------
-        keys: str or list of str
+        keys : str or list of str
             labels or levels to drop
-        axis: int, default 0
+        axis : int, default 0
             Axis that levels are associated with (0 for index, 1 for columns)
 
         Returns

pandas/core/groupby/base.py

@@ -3,8 +3,9 @@
 hold the allowlist of methods that are exposed on the
 SeriesGroupBy and the DataFrameGroupBy objects.
 """
+from __future__ import annotations
+
 import collections
-from typing import List
 
 from pandas._typing import final
 
@@ -19,7 +20,7 @@
 
 
 class ShallowMixin(PandasObject):
-    _attributes: List[str] = []
+    _attributes: list[str] = []
 
     @final
     def _shallow_copy(self, obj, **kwargs):
@@ -39,7 +40,7 @@ class GotItemMixin(PandasObject):
     Provide the groupby facilities to the mixed object.
     """
 
-    _attributes: List[str]
+    _attributes: list[str]
 
     @final
     def _gotitem(self, key, ndim, subset=None):
@@ -106,12 +107,16 @@ def _gotitem(self, key, ndim, subset=None):
     | plotting_methods
 )
 
-series_apply_allowlist = (
+series_apply_allowlist: frozenset[str] = (
     common_apply_allowlist
-    | {"nlargest", "nsmallest", "is_monotonic_increasing", "is_monotonic_decreasing"}
+    | frozenset(
+        {"nlargest", "nsmallest", "is_monotonic_increasing", "is_monotonic_decreasing"}
+    )
 ) | frozenset(["dtype", "unique"])
 
-dataframe_apply_allowlist = common_apply_allowlist | frozenset(["dtypes", "corrwith"])
+dataframe_apply_allowlist: frozenset[str] = common_apply_allowlist | frozenset(
+    ["dtypes", "corrwith"]
+)
 
 # cythonized transformations or canned "agg+broadcast", which do not
 # require postprocessing of the result by transform.

pandas/core/indexes/base.py

@@ -173,15 +173,15 @@
 
 _unsortable_types = frozenset(("mixed", "mixed-integer"))
 
-_index_doc_kwargs = {
+_index_doc_kwargs: dict[str, str] = {
     "klass": "Index",
     "inplace": "",
     "target_klass": "Index",
     "raises_section": "",
     "unique": "Index",
     "duplicated": "np.ndarray",
 }
-_index_shared_docs = {}
+_index_shared_docs: dict[str, str] = {}
 str_t = str
 
 
@@ -1189,7 +1189,7 @@ def _format_with_header(
         return header + result
 
     @final
-    def to_native_types(self, slicer=None, **kwargs):
+    def to_native_types(self, slicer=None, **kwargs) -> np.ndarray:
         """
         Format specified values of `self` and return them.
 
@@ -4390,7 +4390,7 @@ def memory_usage(self, deep: bool = False) -> int:
         return result
 
     @final
-    def where(self, cond, other=None):
+    def where(self, cond, other=None) -> Index:
         """
         Replace values where the condition is False.
 
@@ -4606,7 +4606,7 @@ def _can_hold_identifiers_and_holds_name(self, name) -> bool:
             return name in self
         return False
 
-    def append(self, other):
+    def append(self, other) -> Index:
         """
         Append a collection of Index options together.
 
@@ -4616,7 +4616,7 @@ def append(self, other):
 
         Returns
         -------
-        appended : Index
+        Index
         """
         to_concat = [self]
 
@@ -4846,7 +4846,7 @@ def asof(self, label):
                 loc = loc.indices(len(self))[-1]
             return self[loc]
 
-    def asof_locs(self, where: Index, mask) -> np.ndarray:
+    def asof_locs(self, where: Index, mask: np.ndarray) -> np.ndarray:
         """
         Return the locations (indices) of labels in the index.
 
@@ -4863,21 +4863,21 @@ def asof_locs(self, where: Index, mask) -> np.ndarray:
         ----------
         where : Index
             An Index consisting of an array of timestamps.
-        mask : array-like
+        mask : np.ndarray[bool]
             Array of booleans denoting where values in the original
             data are not NA.
 
         Returns
         -------
-        numpy.ndarray
+        np.ndarray[np.intp]
             An array of locations (indices) of the labels from the Index
             which correspond to the return values of the `asof` function
             for every element in `where`.
         """
         locs = self._values[mask].searchsorted(where._values, side="right")
         locs = np.where(locs > 0, locs - 1, 0)
 
-        result = np.arange(len(self))[mask].take(locs)
+        result = np.arange(len(self), dtype=np.intp)[mask].take(locs)
 
         # TODO: overload return type of ExtensionArray.__getitem__
         first_value = cast(Any, self._values[mask.argmax()])
@@ -5050,7 +5050,7 @@ def argsort(self, *args, **kwargs) -> np.ndarray:
 
         Returns
         -------
-        numpy.ndarray
+        np.ndarray[np.intp]
             Integer indices that would sort the index if used as
             an indexer.
 
@@ -5838,7 +5838,7 @@ def delete(self, loc) -> Index:
         Returns
         -------
         Index
-            New Index with passed location(-s) deleted.
+            Will be same type as self, except for RangeIndex.
 
         See Also
         --------
@@ -6352,8 +6352,8 @@ def _maybe_cast_data_without_dtype(subarr):
 
     elif inferred == "interval":
         try:
-            data = IntervalArray._from_sequence(subarr, copy=False)
-            return data
+            ia_data = IntervalArray._from_sequence(subarr, copy=False)
+            return ia_data
         except (ValueError, TypeError):
             # GH27172: mixed closed Intervals --> object dtype
             pass

pandas/core/indexes/category.py

@@ -1,8 +1,8 @@
+from __future__ import annotations
+
 from typing import (
     Any,
     Hashable,
-    List,
-    Optional,
 )
 import warnings
 
@@ -50,7 +50,7 @@
     inherit_names,
 )
 
-_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+_index_doc_kwargs: dict[str, str] = dict(ibase._index_doc_kwargs)
 _index_doc_kwargs.update({"target_klass": "CategoricalIndex"})
 
 
@@ -216,10 +216,10 @@ def __new__(
         data=None,
         categories=None,
         ordered=None,
-        dtype: Optional[Dtype] = None,
-        copy=False,
-        name=None,
-    ):
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable = None,
+    ) -> CategoricalIndex:
 
         name = maybe_extract_name(name, data, cls)
 
@@ -239,7 +239,7 @@ def _shallow_copy(
         self,
         values: Categorical,
         name: Hashable = no_default,
-    ):
+    ) -> CategoricalIndex:
         name = self._name if name is no_default else name
 
         if values is not None:
@@ -349,7 +349,7 @@ def _format_attrs(self):
             attrs.append(("length", len(self)))
         return attrs
 
-    def _format_with_header(self, header: List[str], na_rep: str = "NaN") -> List[str]:
+    def _format_with_header(self, header: list[str], na_rep: str = "NaN") -> list[str]:
         from pandas.io.formats.printing import pprint_thing
 
         result = [
@@ -422,10 +422,9 @@ def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
 
         target = ibase.ensure_index(target)
 
-        missing: List[int]
         if self.equals(target):
             indexer = None
-            missing = []
+            missing = np.array([], dtype=np.intp)
         else:
             indexer, missing = self.get_indexer_non_unique(np.array(target))
 
@@ -494,8 +493,8 @@ def _maybe_cast_indexer(self, key) -> int:
     def _get_indexer(
         self,
         target: Index,
-        method: Optional[str] = None,
-        limit: Optional[int] = None,
+        method: str | None = None,
+        limit: int | None = None,
         tolerance=None,
     ) -> np.ndarray:
 
@@ -626,7 +625,7 @@ def map(self, mapper):
         mapped = self._values.map(mapper)
         return Index(mapped, name=self.name)
 
-    def _concat(self, to_concat: List[Index], name: Hashable) -> Index:
+    def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
         # if calling index is category, don't check dtype of others
         try:
             codes = np.concatenate([self._is_dtype_compat(c).codes for c in to_concat])