TYP: enable disallow_untyped_decorators

pandas/_libs/properties.pyi

@@ -0,0 +1,9 @@
+# pyright: reportIncompleteStub = false
+from typing import Any
+
+# note: this is a lie to make type checkers happy (they special
+# case property). cache_readonly uses attribute names similar to
+# property (fget) but it does not provide fset and fdel.
+cache_readonly = property
+
+def __getattr__(name: str) -> Any: ...  # incomplete

pandas/_libs/properties.pyx

@@ -10,12 +10,12 @@ from cpython.dict cimport (
 cdef class CachedProperty:
 
     cdef readonly:
-        object func, name, __doc__
+        object fget, name, __doc__
 
-    def __init__(self, func):
-        self.func = func
-        self.name = func.__name__
-        self.__doc__ = getattr(func, '__doc__', None)
+    def __init__(self, fget):
+        self.fget = fget
+        self.name = fget.__name__
+        self.__doc__ = getattr(fget, '__doc__', None)
 
     def __get__(self, obj, typ):
         if obj is None:
@@ -34,7 +34,7 @@ cdef class CachedProperty:
             # not necessary to Py_INCREF
             val = <object>PyDict_GetItem(cache, self.name)
         else:
-            val = self.func(obj)
+            val = self.fget(obj)
             PyDict_SetItem(cache, self.name, val)
         return val
 

pandas/conftest.py

@@ -17,6 +17,7 @@
 - Dtypes
 - Misc
 """
+# pyright: reportUntypedFunctionDecorator = false
 
 from collections import abc
 from datetime import (

pandas/core/_numba/executor.py

@@ -44,7 +44,8 @@ def generate_shared_aggregator(
 
     numba = import_optional_dependency("numba")
 
-    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    # error: Untyped decorator makes function "column_looper" untyped
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)  # type: ignore[misc]
     def column_looper(
         values: np.ndarray,
         start: np.ndarray,

pandas/core/_numba/kernels/mean_.py

@@ -14,7 +14,8 @@
 from pandas.core._numba.kernels.shared import is_monotonic_increasing
 
 
-@numba.jit(nopython=True, nogil=True, parallel=False)
+# error: Untyped decorator makes function "add_mean" untyped
+@numba.jit(nopython=True, nogil=True, parallel=False)  # type: ignore[misc]
 def add_mean(
     val: float, nobs: int, sum_x: float, neg_ct: int, compensation: float
 ) -> tuple[int, float, int, float]:
@@ -29,7 +30,8 @@ def add_mean(
     return nobs, sum_x, neg_ct, compensation
 
 
-@numba.jit(nopython=True, nogil=True, parallel=False)
+# error: Untyped decorator makes function "remove_mean" untyped
+@numba.jit(nopython=True, nogil=True, parallel=False)  # type: ignore[misc]
 def remove_mean(
     val: float, nobs: int, sum_x: float, neg_ct: int, compensation: float
 ) -> tuple[int, float, int, float]:
@@ -44,7 +46,8 @@ def remove_mean(
     return nobs, sum_x, neg_ct, compensation
 
 
-@numba.jit(nopython=True, nogil=True, parallel=False)
+# error: Untyped decorator makes function "sliding_mean" untyped
+@numba.jit(nopython=True, nogil=True, parallel=False)  # type: ignore[misc]
 def sliding_mean(
     values: np.ndarray,
     start: np.ndarray,

pandas/core/_numba/kernels/shared.py

@@ -2,7 +2,10 @@
 import numpy as np
 
 
-@numba.jit(numba.boolean(numba.int64[:]), nopython=True, nogil=True, parallel=False)
+# error: Untyped decorator makes function "is_monotonic_increasing" untyped
+@numba.jit(  # type: ignore[misc]
+    numba.boolean(numba.int64[:]), nopython=True, nogil=True, parallel=False
+)
 def is_monotonic_increasing(bounds: np.ndarray) -> bool:
     """Check if int64 values are monotonically increasing."""
     n = len(bounds)

pandas/core/_numba/kernels/sum_.py

@@ -14,7 +14,8 @@
 from pandas.core._numba.kernels.shared import is_monotonic_increasing
 
 
-@numba.jit(nopython=True, nogil=True, parallel=False)
+# error: Untyped decorator makes function "add_sum" untyped
+@numba.jit(nopython=True, nogil=True, parallel=False)  # type: ignore[misc]
 def add_sum(
     val: float, nobs: int, sum_x: float, compensation: float
 ) -> tuple[int, float, float]:
@@ -27,7 +28,8 @@ def add_sum(
     return nobs, sum_x, compensation
 
 
-@numba.jit(nopython=True, nogil=True, parallel=False)
+# error: Untyped decorator makes function "remove_sum" untyped
+@numba.jit(nopython=True, nogil=True, parallel=False)  # type: ignore[misc]
 def remove_sum(
     val: float, nobs: int, sum_x: float, compensation: float
 ) -> tuple[int, float, float]:
@@ -40,7 +42,8 @@ def remove_sum(
     return nobs, sum_x, compensation
 
 
-@numba.jit(nopython=True, nogil=True, parallel=False)
+# error: Untyped decorator makes function "sliding_sum" untyped
+@numba.jit(nopython=True, nogil=True, parallel=False)  # type: ignore[misc]
 def sliding_sum(
     values: np.ndarray,
     start: np.ndarray,

pandas/core/arrays/categorical.py

@@ -2699,7 +2699,7 @@ def _get_codes_for_values(values, categories: Index) -> np.ndarray:
     """
     dtype_equal = is_dtype_equal(values.dtype, categories.dtype)
 
-    if is_extension_array_dtype(categories.dtype) and is_object_dtype(values):
+    if isinstance(categories.dtype, ExtensionDtype) and is_object_dtype(values):
         # Support inferring the correct extension dtype from an array of
         # scalar objects. e.g.
         # Categorical(array[Period, Period], categories=PeriodIndex(...))

pandas/core/arrays/period.py

@@ -669,7 +669,9 @@ def fillna(self, value=None, method=None, limit=None) -> PeriodArray:
             # view as dt64 so we get treated as timelike in core.missing
             dta = self.view("M8[ns]")
             result = dta.fillna(value=value, method=method, limit=limit)
-            return result.view(self.dtype)
+            # error: Incompatible return value type (got "Union[ExtensionArray,
+            # ndarray[Any, Any]]", expected "PeriodArray")
+            return result.view(self.dtype)  # type: ignore[return-value]
         return super().fillna(value=value, method=method, limit=limit)
 
     # ------------------------------------------------------------------

pandas/core/groupby/generic.py

@@ -675,7 +675,11 @@ def apply_series_value_counts():
         # multi-index components
         codes = self.grouper.reconstructed_codes
         codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
-        levels = [ping.group_index for ping in self.grouper.groupings] + [lev]
+        # error: List item 0 has incompatible type "Union[ndarray[Any, Any], Index]";
+        # expected "Index"
+        levels = [ping.group_index for ping in self.grouper.groupings] + [
+            lev  # type: ignore[list-item]
+        ]
         names = self.grouper.names + [self.obj.name]
 
         if dropna:

pandas/core/groupby/groupby.py

@@ -3449,7 +3449,9 @@ def _reindex_output(
         levels_list = [ping.group_index for ping in groupings]
         names = self.grouper.names
         if qs is not None:
-            levels_list.append(qs)
+            # error: Argument 1 to "append" of "list" has incompatible type
+            # "ndarray[Any, dtype[floating[_64Bit]]]"; expected "Index"
+            levels_list.append(qs)  # type: ignore[arg-type]
             names = names + [None]
         index, _ = MultiIndex.from_product(levels_list, names=names).sortlevel()
 

pandas/core/groupby/numba_.py

@@ -92,7 +92,8 @@ def generate_numba_agg_func(
     numba_func = jit_user_function(func, nopython, nogil, parallel)
     numba = import_optional_dependency("numba")
 
-    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    # error: Untyped decorator makes function "group_agg" untyped
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)  # type: ignore[misc]
     def group_agg(
         values: np.ndarray,
         index: np.ndarray,
@@ -153,7 +154,8 @@ def generate_numba_transform_func(
     numba_func = jit_user_function(func, nopython, nogil, parallel)
     numba = import_optional_dependency("numba")
 
-    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    # error: Untyped decorator makes function "group_transform" untyped
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)  # type: ignore[misc]
     def group_transform(
         values: np.ndarray,
         index: np.ndarray,

pandas/core/indexes/base.py

@@ -3681,7 +3681,11 @@ def _get_indexer(
         else:
             tgt_values = target._get_engine_target()
             if target._is_multi and self._is_multi:
-                tgt_values = self._engine._extract_level_codes(target)
+                engine = self._engine
+                # error: "IndexEngine" has no attribute "_extract_level_codes"
+                tgt_values = engine._extract_level_codes(  # type: ignore[attr-defined]
+                    target
+                )
 
             indexer = self._engine.get_indexer(tgt_values)
 
@@ -3758,7 +3762,8 @@ def _get_fill_indexer(
         if self._is_multi:
             # TODO: get_indexer_with_fill docstring says values must be _sorted_
             #  but that doesn't appear to be enforced
-            return self._engine.get_indexer_with_fill(
+            # error: "IndexEngine" has no attribute "get_indexer_with_fill"
+            return self._engine.get_indexer_with_fill(  # type: ignore[attr-defined]
                 target=target._values, values=self._values, method=method, limit=limit
             )
 
@@ -4677,7 +4682,9 @@ def values(self) -> ArrayLike:
         """
         return self._data
 
-    @cache_readonly
+    # error: Decorated property not supported
+    # https://github.com/python/mypy/issues/1362
+    @cache_readonly  # type: ignore[misc]
     @doc(IndexOpsMixin.array)
     def array(self) -> ExtensionArray:
         array = self._data
@@ -5596,7 +5603,11 @@ def get_indexer_non_unique(
         #  self and non-Multi target
         tgt_values = target._get_engine_target()
         if self._is_multi and target._is_multi:
-            tgt_values = self._engine._extract_level_codes(target)
+            engine = self._engine
+            # error: "IndexEngine" has no attribute "_extract_level_codes"
+            tgt_values = engine._extract_level_codes(  # type: ignore[attr-defined]
+                target
+            )
 
         indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
         return ensure_platform_int(indexer), ensure_platform_int(missing)
@@ -7049,7 +7060,9 @@ def unpack_nested_dtype(other: _IndexT) -> _IndexT:
     if is_categorical_dtype(dtype):
         # If there is ever a SparseIndex, this could get dispatched
         #  here too.
-        return dtype.categories
+        # error: Item  "dtype[Any]"/"ExtensionDtype" of "Union[dtype[Any],
+        # ExtensionDtype]" has no attribute "categories"
+        return dtype.categories  # type: ignore[union-attr]
     return other
 
 

pandas/core/indexes/datetimelike.py

@@ -95,8 +95,11 @@ class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex):
     _field_ops: list[str] = []
 
     # error: "Callable[[Any], Any]" has no attribute "fget"
-    hasnans = cache_readonly(
-        DatetimeLikeArrayMixin._hasnans.fget  # type: ignore[attr-defined]
+    hasnans = cast(
+        bool,
+        cache_readonly(
+            DatetimeLikeArrayMixin._hasnans.fget  # type: ignore[attr-defined]
+        ),
     )
 
     @property

pandas/core/indexes/extension.py

@@ -3,7 +3,10 @@
 """
 from __future__ import annotations
 
-from typing import TypeVar
+from typing import (
+    Callable,
+    TypeVar,
+)
 
 import numpy as np
 
@@ -23,9 +26,12 @@
 from pandas.core.indexes.base import Index
 
 _T = TypeVar("_T", bound="NDArrayBackedExtensionIndex")
+_ExtensionIndexT = TypeVar("_ExtensionIndexT", bound="ExtensionIndex")
 
 
-def _inherit_from_data(name: str, delegate, cache: bool = False, wrap: bool = False):
+def _inherit_from_data(
+    name: str, delegate: type, cache: bool = False, wrap: bool = False
+):
     """
     Make an alias for a method of the underlying ExtensionArray.
 
@@ -81,8 +87,9 @@ def fset(self, value):
         method = attr
 
     else:
-
-        def method(self, *args, **kwargs):
+        # error: Incompatible redefinition (redefinition with type "Callable[[Any,
+        # VarArg(Any), KwArg(Any)], Any]", original type "property")
+        def method(self, *args, **kwargs):  # type: ignore[misc]
             if "inplace" in kwargs:
                 raise ValueError(f"cannot use inplace with {type(self).__name__}")
             result = attr(self._data, *args, **kwargs)
@@ -94,12 +101,15 @@ def method(self, *args, **kwargs):
                 return Index(result, name=self.name)
             return result
 
-        method.__name__ = name
+        # error: "property" has no attribute "__name__"
+        method.__name__ = name  # type: ignore[attr-defined]
         method.__doc__ = attr.__doc__
     return method
 
 
-def inherit_names(names: list[str], delegate, cache: bool = False, wrap: bool = False):
+def inherit_names(
+    names: list[str], delegate: type, cache: bool = False, wrap: bool = False
+) -> Callable[[type[_ExtensionIndexT]], type[_ExtensionIndexT]]:
     """
     Class decorator to pin attributes from an ExtensionArray to a Index subclass.
 
@@ -112,7 +122,7 @@ def inherit_names(names: list[str], delegate, cache: bool = False, wrap: bool =
         Whether to wrap the inherited result in an Index.
     """
 
-    def wrapper(cls):
+    def wrapper(cls: type[_ExtensionIndexT]) -> type[_ExtensionIndexT]:
         for name in names:
             meth = _inherit_from_data(name, delegate, cache=cache, wrap=wrap)
             setattr(cls, name, meth)

pandas/core/indexes/interval.py

@@ -531,7 +531,9 @@ def _maybe_convert_i8(self, key):
                 key_i8 = key_i8.where(~key._isnan)
 
         # ensure consistency with IntervalIndex subtype
-        subtype = self.dtype.subtype
+        # error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any],
+        # ExtensionDtype]" has no attribute "subtype"
+        subtype = self.dtype.subtype  # type: ignore[union-attr]
 
         if not is_dtype_equal(subtype, key_dtype):
             raise ValueError(
@@ -766,7 +768,9 @@ def _convert_slice_indexer(self, key: slice, kind: str):
     def _should_fallback_to_positional(self) -> bool:
         # integer lookups in Series.__getitem__ are unambiguously
         #  positional in this case
-        return self.dtype.subtype.kind in ["m", "M"]
+        # error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any],
+        # ExtensionDtype]" has no attribute "subtype"
+        return self.dtype.subtype.kind in ["m", "M"]  # type: ignore[union-attr]
 
     def _maybe_cast_slice_bound(self, label, side: str, kind=lib.no_default):
         self._deprecated_arg(kind, "kind", "_maybe_cast_slice_bound")

pandas/core/indexes/multi.py

@@ -1095,8 +1095,10 @@ def _engine(self):
             return MultiIndexPyIntEngine(self.levels, self.codes, offsets)
         return MultiIndexUIntEngine(self.levels, self.codes, offsets)
 
+    # Return type "Callable[..., MultiIndex]" of "_constructor" incompatible with return
+    # type "Type[MultiIndex]" in supertype "Index"
     @property
-    def _constructor(self) -> Callable[..., MultiIndex]:
+    def _constructor(self) -> Callable[..., MultiIndex]:  # type: ignore[override]
         return type(self).from_tuples
 
     @doc(Index._shallow_copy)

pandas/core/indexes/numeric.py

@@ -101,8 +101,9 @@ class NumericIndex(Index):
     _can_hold_strings = False
     _is_backward_compat_public_numeric_index: bool = True
 
+    # error: Signature of "_can_hold_na" incompatible with supertype "Index"
     @cache_readonly
-    def _can_hold_na(self) -> bool:
+    def _can_hold_na(self) -> bool:  # type: ignore[override]
         if is_float_dtype(self.dtype):
             return True
         else:
@@ -123,7 +124,9 @@ def _can_hold_na(self) -> bool:
 
     @property
     def _engine_type(self):
-        return self._engine_types[self.dtype]
+        # error: Invalid index type "Union[dtype[Any], ExtensionDtype]" for
+        # "Dict[dtype[Any], Type[IndexEngine]]"; expected type "dtype[Any]"
+        return self._engine_types[self.dtype]  # type: ignore[index]
 
     @cache_readonly
     def inferred_type(self) -> str:
@@ -264,7 +267,8 @@ def astype(self, dtype, copy=True):
     # ----------------------------------------------------------------
     # Indexing Methods
 
-    @cache_readonly
+    # error: Decorated property not supported
+    @cache_readonly  # type: ignore[misc]
     @doc(Index._should_fallback_to_positional)
     def _should_fallback_to_positional(self) -> bool:
         return False