TYP: untyped decorators

pandas/core/arrays/_mixins.py

@@ -98,7 +98,7 @@ def ndim(self) -> int:
     def size(self) -> int:
         return np.prod(self.shape)
 
-    @cache_readonly
+    @property
     def nbytes(self) -> int:
         return self._ndarray.nbytes
 

pandas/core/arrays/base.py

@@ -16,7 +16,7 @@
 from pandas.compat import set_function_name
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
-from pandas.util._decorators import Appender, Substitution
+from pandas.util._decorators import Appender, Substitution, cache_readonly
 from pandas.util._validators import validate_fillna_kwargs
 
 from pandas.core.dtypes.cast import maybe_cast_to_extension_array
@@ -395,7 +395,7 @@ def to_numpy(
     # Required attributes
     # ------------------------------------------------------------------------
 
-    @property
+    @cache_readonly
     def dtype(self) -> ExtensionDtype:
         """
         An instance of 'ExtensionDtype'.
@@ -409,14 +409,14 @@ def shape(self) -> Tuple[int, ...]:
         """
         return (len(self),)
 
-    @property
+    @cache_readonly
     def size(self) -> int:
         """
         The number of elements in the array.
         """
         return np.prod(self.shape)
 
-    @property
+    @cache_readonly
     def ndim(self) -> int:
         """
         Extension Arrays are only allowed to be 1-dimensional.

pandas/core/arrays/boolean.py

@@ -8,6 +8,7 @@
 from pandas._typing import ArrayLike
 from pandas.compat import set_function_name
 from pandas.compat.numpy import function as nv
+from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.common import (
     is_bool_dtype,
@@ -68,7 +69,7 @@ def type(self) -> Type:  # type: ignore[override]
     def kind(self) -> str:
         return "b"
 
-    @property
+    @cache_readonly
     def numpy_dtype(self) -> np.dtype:
         return np.dtype("bool")
 
@@ -269,7 +270,7 @@ def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
         self._dtype = BooleanDtype()
         super().__init__(values, mask, copy=copy)
 
-    @property
+    @cache_readonly
     def dtype(self) -> BooleanDtype:
         return self._dtype
 

pandas/core/arrays/categorical.py

@@ -373,7 +373,7 @@ def __init__(
         self._dtype = self._dtype.update_dtype(dtype)
         self._codes = coerce_indexer_dtype(codes, dtype.categories)
 
-    @property
+    @cache_readonly
     def dtype(self) -> CategoricalDtype:
         """
         The :class:`~pandas.api.types.CategoricalDtype` for this instance.

pandas/core/arrays/datetimes.py

@@ -23,6 +23,7 @@
     tzconversion,
 )
 from pandas.errors import PerformanceWarning
+from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.common import (
     DT64NS_DTYPE,
@@ -484,7 +485,7 @@ def _maybe_clear_freq(self):
     def _box_func(self, x) -> Union[Timestamp, NaTType]:
         return Timestamp(x, freq=self.freq, tz=self.tz)
 
-    @property
+    @cache_readonly
     def dtype(self) -> Union[np.dtype, DatetimeTZDtype]:
         """
         The dtype for the DatetimeArray.

pandas/core/arrays/interval.py

@@ -12,7 +12,7 @@
     intervals_to_interval_bounds,
 )
 from pandas.compat.numpy import function as nv
-from pandas.util._decorators import Appender
+from pandas.util._decorators import Appender, cache_readonly
 
 from pandas.core.dtypes.cast import maybe_convert_platform
 from pandas.core.dtypes.common import (
@@ -144,7 +144,6 @@
     )
 )
 class IntervalArray(IntervalMixin, ExtensionArray):
-    ndim = 1
     can_hold_na = True
     _na_value = _fill_value = np.nan
 
@@ -522,15 +521,15 @@ def _shallow_copy(self, left, right):
     # ---------------------------------------------------------------------
     # Descriptive
 
-    @property
+    @cache_readonly
     def dtype(self):
         return IntervalDtype(self.left.dtype)
 
     @property
     def nbytes(self) -> int:
         return self.left.nbytes + self.right.nbytes
 
-    @property
+    @cache_readonly
     def size(self) -> int:
         # Avoid materializing self.values
         return self.left.size

pandas/core/arrays/masked.py

@@ -45,7 +45,7 @@ def numpy_dtype(self) -> np.dtype:
         """ Return an instance of our numpy dtype """
         return np.dtype(self.type)
 
-    @cache_readonly
+    @property
     def kind(self) -> str:
         return self.numpy_dtype.kind
 
@@ -95,7 +95,7 @@ def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
         self._data = values
         self._mask = mask
 
-    @property
+    @cache_readonly
     def dtype(self) -> BaseMaskedDtype:
         raise AbstractMethodError(self)
 

pandas/core/arrays/numpy_.py

@@ -7,6 +7,7 @@
 from pandas._libs import lib
 from pandas._typing import Scalar
 from pandas.compat.numpy import function as nv
+from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.missing import isna
@@ -194,7 +195,7 @@ def _from_backing_data(self, arr: np.ndarray) -> "PandasArray":
     # ------------------------------------------------------------------------
     # Data
 
-    @property
+    @cache_readonly
     def dtype(self) -> PandasDtype:
         return self._dtype
 

pandas/core/arrays/sparse/array.py

@@ -17,6 +17,7 @@
 import pandas.compat as compat
 from pandas.compat.numpy import function as nv
 from pandas.errors import PerformanceWarning
+from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.cast import (
     astype_nansafe,
@@ -515,7 +516,7 @@ def sp_values(self) -> np.ndarray:
         """
         return self._sparse_values
 
-    @property
+    @cache_readonly
     def dtype(self):
         return self._dtype
 

pandas/core/arrays/timedeltas.py

@@ -19,6 +19,7 @@
 from pandas._libs.tslibs.fields import get_timedelta_field
 from pandas._libs.tslibs.timedeltas import array_to_timedelta64, parse_timedelta_unit
 from pandas.compat.numpy import function as nv
+from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.common import (
     DT64NS_DTYPE,
@@ -122,7 +123,7 @@ class TimedeltaArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps):
     def _box_func(self, x) -> Union[Timedelta, NaTType]:
         return Timedelta(x, unit="ns")
 
-    @property
+    @cache_readonly
     def dtype(self) -> np.dtype:
         """
         The dtype for the TimedeltaArray.

pandas/core/base.py

@@ -198,7 +198,7 @@ def _selected_obj(self):
         else:
             return self.obj[self._selection]
 
-    @cache_readonly
+    @property
     def ndim(self) -> int:
         return self._selected_obj.ndim
 
@@ -1315,53 +1315,6 @@ def nunique(self, dropna: bool = True) -> int:
             n -= 1
         return n
 
-    @property
-    def is_unique(self) -> bool:
-        """
-        Return boolean if values in the object are unique.
-
-        Returns
-        -------
-        bool
-        """
-        return self.nunique(dropna=False) == len(self)
-
-    @property
-    def is_monotonic(self) -> bool:
-        """
-        Return boolean if values in the object are
-        monotonic_increasing.
-
-        Returns
-        -------
-        bool
-        """
-        from pandas import Index
-
-        return Index(self).is_monotonic
-
-    @property
-    def is_monotonic_increasing(self) -> bool:
-        """
-        Alias for is_monotonic.
-        """
-        # mypy complains if we alias directly
-        return self.is_monotonic
-
-    @property
-    def is_monotonic_decreasing(self) -> bool:
-        """
-        Return boolean if values in the object are
-        monotonic_decreasing.
-
-        Returns
-        -------
-        bool
-        """
-        from pandas import Index
-
-        return Index(self).is_monotonic_decreasing
-
     def memory_usage(self, deep=False):
         """
         Memory usage of the values.

pandas/core/indexes/base.py

@@ -1643,7 +1643,7 @@ def is_monotonic(self) -> bool:
         """
         return self.is_monotonic_increasing
 
-    @property
+    @cache_readonly
     def is_monotonic_increasing(self) -> bool:
         """
         Return if the index is monotonic increasing (only equal or
@@ -1660,7 +1660,7 @@ def is_monotonic_increasing(self) -> bool:
         """
         return self._engine.is_monotonic_increasing
 
-    @property
+    @cache_readonly
     def is_monotonic_decreasing(self) -> bool:
         """
         Return if the index is monotonic decreasing (only equal or

pandas/core/indexes/category.py

@@ -333,7 +333,7 @@ def _format_with_header(self, header: List[str], na_rep: str = "NaN") -> List[st
 
     # --------------------------------------------------------------------
 
-    @property
+    @cache_readonly
     def inferred_type(self) -> str:
         return "categorical"
 

pandas/core/indexes/datetimelike.py

@@ -95,13 +95,13 @@ class DatetimeIndexOpsMixin(ExtensionIndex):
     _bool_ops: List[str] = []
     _field_ops: List[str] = []
 
-    # error: "Callable[[Any], Any]" has no attribute "fget"
-    hasnans = cache_readonly(
-        DatetimeLikeArrayMixin._hasnans.fget  # type: ignore[attr-defined]
-    )
+    @cache_readonly
+    def hasnans(self) -> bool:
+        return self._data._hasnans
+
     _hasnans = hasnans  # for index / array -agnostic code
 
-    @property
+    @cache_readonly
     def _is_all_dates(self) -> bool:
         return True
 

pandas/core/indexes/datetimes.py

@@ -799,7 +799,7 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None):
     def is_type_compatible(self, typ) -> bool:
         return typ == self.inferred_type or typ == "datetime"
 
-    @property
+    @cache_readonly
     def inferred_type(self) -> str:
         # b/c datetime is represented as microseconds since the epoch, make
         # sure we can't have ambiguous indexing

pandas/core/indexes/interval.py

@@ -385,7 +385,7 @@ def __contains__(self, key: Any) -> bool:
     def _multiindex(self) -> MultiIndex:
         return MultiIndex.from_arrays([self.left, self.right], names=["left", "right"])
 
-    @cache_readonly
+    @property
     def values(self) -> IntervalArray:
         """
         Return the IntervalIndex's data as an IntervalArray.
@@ -414,7 +414,7 @@ def astype(self, dtype, copy: bool = True):
             return self._shallow_copy(new_values)
         return Index.astype(self, dtype, copy=copy)
 
-    @property
+    @cache_readonly
     def inferred_type(self) -> str:
         """Return a string of the type inferred from the values"""
         return "interval"
@@ -1096,7 +1096,7 @@ def func(self, other, sort=sort):
 
     # --------------------------------------------------------------------
 
-    @property
+    @cache_readonly
     def _is_all_dates(self) -> bool:
         """
         This is False even when left/right contain datetime-like objects,

pandas/core/indexes/multi.py

@@ -1228,7 +1228,7 @@ def memory_usage(self, deep: bool = False) -> int:
         # a tuple representation unnecessarily
         return self._nbytes(deep)
 
-    @cache_readonly
+    @property
     def nbytes(self) -> int:
         """ return the number of bytes in the underlying data """
         return self._nbytes(False)
@@ -1748,7 +1748,7 @@ def to_flat_index(self):
         """
         return Index(self._values, tupleize_cols=False)
 
-    @property
+    @cache_readonly
     def _is_all_dates(self) -> bool:
         return False
 

pandas/core/indexes/numeric.py

@@ -148,7 +148,7 @@ def _assert_safe_casting(cls, data, subarr):
         """
         pass
 
-    @property
+    @cache_readonly
     def _is_all_dates(self) -> bool:
         """
         Checks that all the labels are datetime objects.
@@ -240,7 +240,7 @@ def __contains__(self, key) -> bool:
         except (OverflowError, TypeError, ValueError):
             return False
 
-    @property
+    @cache_readonly
     def inferred_type(self) -> str:
         """
         Always 'integer' for ``Int64Index`` and ``UInt64Index``
@@ -335,7 +335,7 @@ class Float64Index(NumericIndex):
     _engine_type = libindex.Float64Engine
     _default_dtype = np.float64
 
-    @property
+    @cache_readonly
     def inferred_type(self) -> str:
         """
         Always 'floating' for ``Float64Index``