DEPR: Categorical, Index (#49220)

* DEPR: Categorical, Index

* mypy fixup

Author: jbrockmendel

doc/redirects.csv

@@ -637,7 +637,6 @@ generated/pandas.Index.argmax,../reference/api/pandas.Index.argmax
 generated/pandas.Index.argmin,../reference/api/pandas.Index.argmin
 generated/pandas.Index.argsort,../reference/api/pandas.Index.argsort
 generated/pandas.Index.array,../reference/api/pandas.Index.array
-generated/pandas.Index.asi8,../reference/api/pandas.Index.asi8
 generated/pandas.Index.asof,../reference/api/pandas.Index.asof
 generated/pandas.Index.asof_locs,../reference/api/pandas.Index.asof_locs
 generated/pandas.Index.astype,../reference/api/pandas.Index.astype
@@ -680,15 +679,13 @@ generated/pandas.Index.isin,../reference/api/pandas.Index.isin
 generated/pandas.Index.is_integer,../reference/api/pandas.Index.is_integer
 generated/pandas.Index.is_interval,../reference/api/pandas.Index.is_interval
 generated/pandas.Index.is_lexsorted_for_tuple,../reference/api/pandas.Index.is_lexsorted_for_tuple
-generated/pandas.Index.is_mixed,../reference/api/pandas.Index.is_mixed
 generated/pandas.Index.is_monotonic_decreasing,../reference/api/pandas.Index.is_monotonic_decreasing
 generated/pandas.Index.is_monotonic,../reference/api/pandas.Index.is_monotonic
 generated/pandas.Index.is_monotonic_increasing,../reference/api/pandas.Index.is_monotonic_increasing
 generated/pandas.Index.isna,../reference/api/pandas.Index.isna
 generated/pandas.Index.isnull,../reference/api/pandas.Index.isnull
 generated/pandas.Index.is_numeric,../reference/api/pandas.Index.is_numeric
 generated/pandas.Index.is_object,../reference/api/pandas.Index.is_object
-generated/pandas.Index.is_type_compatible,../reference/api/pandas.Index.is_type_compatible
 generated/pandas.Index.is_unique,../reference/api/pandas.Index.is_unique
 generated/pandas.Index.item,../reference/api/pandas.Index.item
 generated/pandas.Index.join,../reference/api/pandas.Index.join

doc/source/reference/index.rst

@@ -47,9 +47,7 @@ public functions related to data types in pandas.
 ..
     .. toctree::
 
-        api/pandas.Index.asi8
         api/pandas.Index.holds_integer
-        api/pandas.Index.is_type_compatible
         api/pandas.Index.nlevels
         api/pandas.Index.sort
 

doc/source/reference/indexing.rst

@@ -68,7 +68,6 @@ Modifying and computations
    Index.is_floating
    Index.is_integer
    Index.is_interval
-   Index.is_mixed
    Index.is_numeric
    Index.is_object
    Index.min

doc/source/whatsnew/v2.0.0.rst

@@ -156,6 +156,14 @@ Deprecations
 
 Removal of prior version deprecations/changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+- Removed deprecated :meth:`Categorical.to_dense`, use ``np.asarray(cat)`` instead (:issue:`32639`)
+- Removed deprecated :meth:`Categorical.take_nd` (:issue:`27745`)
+- Removed deprecated :meth:`Categorical.mode`, use ``Series(cat).mode()`` instead (:issue:`45033`)
+- Removed deprecated :meth:`Categorical.is_dtype_equal` and :meth:`CategoricalIndex.is_dtype_equal` (:issue:`37545`)
+- Removed deprecated :meth:`CategoricalIndex.take_nd` (:issue:`30702`)
+- Removed deprecated :meth:`Index.is_type_compatible` (:issue:`42113`)
+- Removed deprecated :meth:`Index.is_mixed`, check ``index.inferred_type`` directly instead (:issue:`32922`)
+- Removed deprecated :meth:`Index.asi8` (:issue:`37877`)
 - Enforced deprecation disallowing passing a timezone-aware :class:`Timestamp` and ``dtype="datetime64[ns]"`` to :class:`Series` or :class:`DataFrame` constructors (:issue:`41555`)
 - Enforced deprecation disallowing passing a sequence of timezone-aware values and ``dtype="datetime64[ns]"`` to to :class:`Series` or :class:`DataFrame` constructors (:issue:`41555`)
 - Enforced deprecation disallowing using ``.astype`` to convert a ``datetime64[ns]`` :class:`Series`, :class:`DataFrame`, or :class:`DatetimeIndex` to timezone-aware dtype, use ``obj.tz_localize`` or ``ser.dt.tz_localize`` instead (:issue:`39258`)

pandas/conftest.py

@@ -155,7 +155,6 @@ def pytest_collection_modifyitems(items, config) -> None:
         ("Series.append", "The series.append method is deprecated"),
         ("dtypes.common.is_categorical", "is_categorical is deprecated"),
         ("Categorical.replace", "Categorical.replace is deprecated"),
-        ("Index.is_mixed", "Index.is_mixed is deprecated"),
         ("MultiIndex._is_lexsorted", "MultiIndex.is_lexsorted is deprecated"),
         # Docstring divides by zero to show behavior difference
         ("missing.mask_zero_div_zero", "divide by zero encountered"),

pandas/core/arrays/categorical.py

@@ -2052,24 +2052,6 @@ def _values_for_rank(self):
             )
         return values
 
-    def to_dense(self) -> np.ndarray:
-        """
-        Return my 'dense' representation
-
-        For internal compatibility with numpy arrays.
-
-        Returns
-        -------
-        dense : array
-        """
-        warn(
-            "Categorical.to_dense is deprecated and will be removed in "
-            "a future version.  Use np.asarray(cat) instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        return np.asarray(self)
-
     # ------------------------------------------------------------------
     # NDArrayBackedExtensionArray compat
 
@@ -2101,17 +2083,6 @@ def _unbox_scalar(self, key) -> int:
 
     # ------------------------------------------------------------------
 
-    def take_nd(
-        self, indexer, allow_fill: bool = False, fill_value=None
-    ) -> Categorical:
-        # GH#27745 deprecate alias that other EAs dont have
-        warn(
-            "Categorical.take_nd is deprecated, use Categorical.take instead",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        return self.take(indexer, allow_fill=allow_fill, fill_value=fill_value)
-
     def __iter__(self) -> Iterator:
         """
         Returns an Iterator over the values of this Categorical.
@@ -2539,18 +2510,6 @@ def _categories_match_up_to_permutation(self, other: Categorical) -> bool:
         """
         return hash(self.dtype) == hash(other.dtype)
 
-    def is_dtype_equal(self, other) -> bool:
-        warn(
-            "Categorical.is_dtype_equal is deprecated and will be removed "
-            "in a future version",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        try:
-            return self._categories_match_up_to_permutation(other)
-        except (AttributeError, TypeError):
-            return False
-
     def describe(self) -> DataFrame:
         """
         Describes this Categorical

pandas/core/indexes/base.py

@@ -680,23 +680,6 @@ def _dtype_to_subclass(cls, dtype: DtypeObj):
     See each method's docstring.
     """
 
-    @property
-    def asi8(self):
-        """
-        Integer representation of the values.
-
-        Returns
-        -------
-        ndarray
-            An ndarray with int64 dtype.
-        """
-        warnings.warn(
-            "Index.asi8 is deprecated and will be removed in a future version.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        return None
-
     @classmethod
     def _simple_new(cls: type[_IndexT], values, name: Hashable = None) -> _IndexT:
         """
@@ -2451,7 +2434,6 @@ def is_boolean(self) -> bool:
         is_object : Check if the Index is of the object dtype.
         is_categorical : Check if the Index holds categorical data.
         is_interval : Check if the Index holds Interval objects.
-        is_mixed : Check if the Index holds data with mixed data types.
 
         Examples
         --------
@@ -2487,7 +2469,6 @@ def is_integer(self) -> bool:
         is_object : Check if the Index is of the object dtype.
         is_categorical : Check if the Index holds categorical data.
         is_interval : Check if the Index holds Interval objects.
-        is_mixed : Check if the Index holds data with mixed data types.
 
         Examples
         --------
@@ -2527,7 +2508,6 @@ def is_floating(self) -> bool:
         is_object : Check if the Index is of the object dtype.
         is_categorical : Check if the Index holds categorical data.
         is_interval : Check if the Index holds Interval objects.
-        is_mixed : Check if the Index holds data with mixed data types.
 
         Examples
         --------
@@ -2567,7 +2547,6 @@ def is_numeric(self) -> bool:
         is_object : Check if the Index is of the object dtype.
         is_categorical : Check if the Index holds categorical data.
         is_interval : Check if the Index holds Interval objects.
-        is_mixed : Check if the Index holds data with mixed data types.
 
         Examples
         --------
@@ -2611,7 +2590,6 @@ def is_object(self) -> bool:
         is_numeric : Check if the Index only consists of numeric data.
         is_categorical : Check if the Index holds categorical data.
         is_interval : Check if the Index holds Interval objects.
-        is_mixed : Check if the Index holds data with mixed data types.
 
         Examples
         --------
@@ -2653,7 +2631,6 @@ def is_categorical(self) -> bool:
         is_numeric : Check if the Index only consists of numeric data.
         is_object : Check if the Index is of the object dtype.
         is_interval : Check if the Index holds Interval objects.
-        is_mixed : Check if the Index holds data with mixed data types.
 
         Examples
         --------
@@ -2697,7 +2674,6 @@ def is_interval(self) -> bool:
         is_numeric : Check if the Index only consists of numeric data.
         is_object : Check if the Index is of the object dtype.
         is_categorical : Check if the Index holds categorical data.
-        is_mixed : Check if the Index holds data with mixed data types.
 
         Examples
         --------
@@ -2712,44 +2688,6 @@ def is_interval(self) -> bool:
         """
         return self.inferred_type in ["interval"]
 
-    @final
-    def is_mixed(self) -> bool:
-        """
-        Check if the Index holds data with mixed data types.
-
-        Returns
-        -------
-        bool
-            Whether or not the Index holds data with mixed data types.
-
-        See Also
-        --------
-        is_boolean : Check if the Index only consists of booleans.
-        is_integer : Check if the Index only consists of integers.
-        is_floating : Check if the Index is a floating type.
-        is_numeric : Check if the Index only consists of numeric data.
-        is_object : Check if the Index is of the object dtype.
-        is_categorical : Check if the Index holds categorical data.
-        is_interval : Check if the Index holds Interval objects.
-
-        Examples
-        --------
-        >>> idx = pd.Index(['a', np.nan, 'b'])
-        >>> idx.is_mixed()
-        True
-
-        >>> idx = pd.Index([1.0, 2.0, 3.0, 5.0])
-        >>> idx.is_mixed()
-        False
-        """
-        warnings.warn(
-            "Index.is_mixed is deprecated and will be removed in a future version. "
-            "Check index.inferred_type directly instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        return self.inferred_type in ["mixed"]
-
     @final
     def holds_integer(self) -> bool:
         """
@@ -5312,18 +5250,6 @@ def _is_memory_usage_qualified(self) -> bool:
         """
         return self.is_object()
 
-    def is_type_compatible(self, kind: str_t) -> bool:
-        """
-        Whether the index type is compatible with the provided type.
-        """
-        warnings.warn(
-            "Index.is_type_compatible is deprecated and will be removed in a "
-            "future version.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        return kind == self.inferred_type
-
     def __contains__(self, key: Any) -> bool:
         """
         Return a boolean indicating whether the provided key is in the index.

pandas/core/indexes/category.py

@@ -61,7 +61,6 @@
         "ordered",
         "_reverse_indexer",
         "searchsorted",
-        "is_dtype_equal",
         "min",
         "max",
     ],
@@ -489,16 +488,6 @@ def _maybe_cast_listlike_indexer(self, values) -> CategoricalIndex:
     def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
         return self.categories._is_comparable_dtype(dtype)
 
-    def take_nd(self, *args, **kwargs) -> CategoricalIndex:
-        """Alias for `take`"""
-        warnings.warn(
-            "CategoricalIndex.take_nd is deprecated, use CategoricalIndex.take "
-            "instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        return self.take(*args, **kwargs)
-
     def map(self, mapper):
         """
         Map values using input an input mapping or function.

pandas/core/indexes/datetimelike.py

@@ -13,7 +13,6 @@
     cast,
     final,
 )
-import warnings
 
 import numpy as np
 
@@ -29,14 +28,16 @@
     parsing,
     to_offset,
 )
-from pandas._typing import Axis
+from pandas._typing import (
+    Axis,
+    npt,
+)
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import (
     Appender,
     cache_readonly,
     doc,
 )
-from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
@@ -80,7 +81,7 @@
     DatetimeLikeArrayMixin,
     cache=True,
 )
-@inherit_names(["mean", "asi8", "freq", "freqstr"], DatetimeLikeArrayMixin)
+@inherit_names(["mean", "freq", "freqstr"], DatetimeLikeArrayMixin)
 class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex):
     """
     Common ops mixin to support a unified interface datetimelike Index.
@@ -93,6 +94,10 @@ class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex):
     freqstr: str | None
     _resolution_obj: Resolution
 
+    @property
+    def asi8(self) -> npt.NDArray[np.int64]:
+        return self._data.asi8
+
     # ------------------------------------------------------------------------
 
     @cache_readonly
@@ -394,15 +399,6 @@ def _with_freq(self, freq):
         arr = self._data._with_freq(freq)
         return type(self)._simple_new(arr, name=self._name)
 
-    def is_type_compatible(self, kind: str) -> bool:
-        warnings.warn(
-            f"{type(self).__name__}.is_type_compatible is deprecated and will be "
-            "removed in a future version.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        return kind in self._data._infer_matches
-
     @property
     def values(self) -> np.ndarray:
         # NB: For Datetime64TZ this is lossy

pandas/core/indexes/period.py

@@ -354,7 +354,7 @@ def is_full(self) -> bool:
         if not self.is_monotonic_increasing:
             raise ValueError("Index is not monotonic")
         values = self.asi8
-        return ((values[1:] - values[:-1]) < 2).all()
+        return bool(((values[1:] - values[:-1]) < 2).all())
 
     @property
     def inferred_type(self) -> str:

pandas/core/tools/numeric.py

@@ -158,7 +158,7 @@ def to_numeric(
     elif isinstance(arg, ABCIndex):
         is_index = True
         if needs_i8_conversion(arg.dtype):
-            values = arg.asi8
+            values = arg.view("i8")
         else:
             values = arg.values
     elif isinstance(arg, (list, tuple)):

pandas/core/window/rolling.py

@@ -16,6 +16,7 @@
     Hashable,
     Iterator,
     Sized,
+    cast,
 )
 import warnings
 
@@ -409,7 +410,8 @@ def _insert_on_column(self, result: DataFrame, obj: DataFrame) -> None:
     def _index_array(self):
         # TODO: why do we get here with e.g. MultiIndex?
         if needs_i8_conversion(self._on.dtype):
-            return self._on.asi8
+            idx = cast("PeriodIndex | DatetimeIndex | TimedeltaIndex", self._on)
+            return idx.asi8
         return None
 
     def _resolve_output(self, out: DataFrame, obj: DataFrame) -> DataFrame: