DEPR: to_native_types, set_value, iteritems, union_many, to_perioddelta

doc/redirects.csv

@@ -401,10 +401,8 @@ generated/pandas.DataFrame.isna,../reference/api/pandas.DataFrame.isna
 generated/pandas.DataFrame.isnull,../reference/api/pandas.DataFrame.isnull
 generated/pandas.DataFrame.items,../reference/api/pandas.DataFrame.items
 generated/pandas.DataFrame.__iter__,../reference/api/pandas.DataFrame.__iter__
-generated/pandas.DataFrame.iteritems,../reference/api/pandas.DataFrame.iteritems
 generated/pandas.DataFrame.iterrows,../reference/api/pandas.DataFrame.iterrows
 generated/pandas.DataFrame.itertuples,../reference/api/pandas.DataFrame.itertuples
-generated/pandas.DataFrame.ix,../reference/api/pandas.DataFrame.ix
 generated/pandas.DataFrame.join,../reference/api/pandas.DataFrame.join
 generated/pandas.DataFrame.keys,../reference/api/pandas.DataFrame.keys
 generated/pandas.DataFrame.kurt,../reference/api/pandas.DataFrame.kurt
@@ -575,7 +573,6 @@ generated/pandas.DatetimeIndex.strftime,../reference/api/pandas.DatetimeIndex.st
 generated/pandas.DatetimeIndex.time,../reference/api/pandas.DatetimeIndex.time
 generated/pandas.DatetimeIndex.timetz,../reference/api/pandas.DatetimeIndex.timetz
 generated/pandas.DatetimeIndex.to_frame,../reference/api/pandas.DatetimeIndex.to_frame
-generated/pandas.DatetimeIndex.to_perioddelta,../reference/api/pandas.DatetimeIndex.to_perioddelta
 generated/pandas.DatetimeIndex.to_period,../reference/api/pandas.DatetimeIndex.to_period
 generated/pandas.DatetimeIndex.to_pydatetime,../reference/api/pandas.DatetimeIndex.to_pydatetime
 generated/pandas.DatetimeIndex.to_series,../reference/api/pandas.DatetimeIndex.to_series
@@ -708,7 +705,6 @@ generated/pandas.Index.rename,../reference/api/pandas.Index.rename
 generated/pandas.Index.repeat,../reference/api/pandas.Index.repeat
 generated/pandas.Index.searchsorted,../reference/api/pandas.Index.searchsorted
 generated/pandas.Index.set_names,../reference/api/pandas.Index.set_names
-generated/pandas.Index.set_value,../reference/api/pandas.Index.set_value
 generated/pandas.Index.shape,../reference/api/pandas.Index.shape
 generated/pandas.Index.shift,../reference/api/pandas.Index.shift
 generated/pandas.Index.size,../reference/api/pandas.Index.size
@@ -727,7 +723,6 @@ generated/pandas.Index.to_flat_index,../reference/api/pandas.Index.to_flat_index
 generated/pandas.Index.to_frame,../reference/api/pandas.Index.to_frame
 generated/pandas.Index.to_list,../reference/api/pandas.Index.to_list
 generated/pandas.Index.tolist,../reference/api/pandas.Index.tolist
-generated/pandas.Index.to_native_types,../reference/api/pandas.Index.to_native_types
 generated/pandas.Index.to_numpy,../reference/api/pandas.Index.to_numpy
 generated/pandas.Index.to_series,../reference/api/pandas.Index.to_series
 generated/pandas.Index.transpose,../reference/api/pandas.Index.transpose
@@ -1087,8 +1082,6 @@ generated/pandas.Series.is_unique,../reference/api/pandas.Series.is_unique
 generated/pandas.Series.item,../reference/api/pandas.Series.item
 generated/pandas.Series.items,../reference/api/pandas.Series.items
 generated/pandas.Series.__iter__,../reference/api/pandas.Series.__iter__
-generated/pandas.Series.iteritems,../reference/api/pandas.Series.iteritems
-generated/pandas.Series.ix,../reference/api/pandas.Series.ix
 generated/pandas.Series.keys,../reference/api/pandas.Series.keys
 generated/pandas.Series.kurt,../reference/api/pandas.Series.kurt
 generated/pandas.Series.kurtosis,../reference/api/pandas.Series.kurtosis

doc/source/reference/frame.rst

@@ -63,7 +63,6 @@ Indexing, iteration
    DataFrame.insert
    DataFrame.__iter__
    DataFrame.items
-   DataFrame.iteritems
    DataFrame.keys
    DataFrame.iterrows
    DataFrame.itertuples

doc/source/reference/indexing.rst

@@ -110,7 +110,6 @@ Conversion
    Index.map
    Index.ravel
    Index.to_list
-   Index.to_native_types
    Index.to_series
    Index.to_frame
    Index.view
@@ -393,7 +392,6 @@ Conversion
    :toctree: api/
 
    DatetimeIndex.to_period
-   DatetimeIndex.to_perioddelta
    DatetimeIndex.to_pydatetime
    DatetimeIndex.to_series
    DatetimeIndex.to_frame

doc/source/reference/series.rst

@@ -66,7 +66,6 @@ Indexing, iteration
    Series.iloc
    Series.__iter__
    Series.items
-   Series.iteritems
    Series.keys
    Series.pop
    Series.item

doc/source/whatsnew/v2.0.0.rst

@@ -164,6 +164,13 @@ Removal of prior version deprecations/changes
 - 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`)
+- Removed deprecated :meth:`DataFrame._AXIS_NUMBERS`, :meth:`DataFrame._AXIS_NAMES`, :meth:`Series._AXIS_NUMBERS`, :meth:`Series._AXIS_NAMES` (:issue:`33637)
+- Removed deprecated :meth:`Index.to_native_types`, use ``obj.astype(str)`` instead (:issue:`36418`)
+- Removed deprecated :meth:`Index.set_value` (:issue:`28621`)
+- Removed deprecated :meth:`Series.iteritems`, :meth:`DataFrame.iteritems`, use ``obj.items`` instead (:issue:`45321`)
+- Removed deprecated :meth:`DatetimeIndex.union_many` (:issue:`45018`)
+- Removed deprecated :meth:`RangeIndex._start`, :meth:`RangeIndex._stop`, :meth:`RangeIndex._step`, use ``start``, ``stop``, ``step`` instead (:issue:`30482`)
+- Removed deprecated :meth:`DatetimeIndex.to_perioddelta`, Use ``dtindex - dtindex.to_period(freq).to_timestamp()`` instead (:issue:`34853`)
 - 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/core/arrays/datetimes.py

@@ -89,10 +89,7 @@
 if TYPE_CHECKING:
 
     from pandas import DataFrame
-    from pandas.core.arrays import (
-        PeriodArray,
-        TimedeltaArray,
-    )
+    from pandas.core.arrays import PeriodArray
 
 _midnight = time(0, 0)
 
@@ -1194,38 +1191,6 @@ def to_period(self, freq=None) -> PeriodArray:
 
         return PeriodArray._from_datetime64(self._ndarray, freq, tz=self.tz)
 
-    def to_perioddelta(self, freq) -> TimedeltaArray:
-        """
-        Calculate deltas between self values and self converted to Periods at a freq.
-
-        Used for vectorized offsets.
-
-        Parameters
-        ----------
-        freq : Period frequency
-
-        Returns
-        -------
-        TimedeltaArray/Index
-        """
-        # Deprecaation GH#34853
-        warnings.warn(
-            "to_perioddelta is deprecated and will be removed in a "
-            "future version. "
-            "Use `dtindex - dtindex.to_period(freq).to_timestamp()` instead.",
-            FutureWarning,
-            # stacklevel chosen to be correct for when called from DatetimeIndex
-            stacklevel=find_stack_level(),
-        )
-        from pandas.core.arrays.timedeltas import TimedeltaArray
-
-        if self._ndarray.dtype != "M8[ns]":
-            raise NotImplementedError("Only supported for nanosecond resolution.")
-
-        i8delta = self.asi8 - self.to_period(freq).to_timestamp().asi8
-        m8delta = i8delta.view("m8[ns]")
-        return TimedeltaArray(m8delta)
-
     # -----------------------------------------------------------------
     # Properties - Vectorized Timestamp Properties/Methods
 

pandas/core/frame.py

@@ -1328,44 +1328,6 @@ def items(self) -> Iterable[tuple[Hashable, Series]]:
             for i, k in enumerate(self.columns):
                 yield k, self._ixs(i, axis=1)
 
-    _shared_docs[
-        "iteritems"
-    ] = r"""
-        Iterate over (column name, Series) pairs.
-
-        .. deprecated:: 1.5.0
-            iteritems is deprecated and will be removed in a future version.
-            Use .items instead.
-
-        Iterates over the DataFrame columns, returning a tuple with
-        the column name and the content as a Series.
-
-        Yields
-        ------
-        label : object
-            The column names for the DataFrame being iterated over.
-        content : Series
-            The column entries belonging to each label, as a Series.
-
-        See Also
-        --------
-        DataFrame.iter : Recommended alternative.
-        DataFrame.iterrows : Iterate over DataFrame rows as
-            (index, Series) pairs.
-        DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
-            of the values.
-        """
-
-    @Appender(_shared_docs["iteritems"])
-    def iteritems(self) -> Iterable[tuple[Hashable, Series]]:
-        warnings.warn(
-            "iteritems is deprecated and will be removed in a future version. "
-            "Use .items instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        yield from self.items()
-
     def iterrows(self) -> Iterable[tuple[Hashable, Series]]:
         """
         Iterate over DataFrame rows as (index, Series) pairs.
@@ -11628,18 +11590,6 @@ def isin(self, values: Series | DataFrame | Sequence | Mapping) -> DataFrame:
     )
     columns = properties.AxisProperty(axis=0, doc="The column labels of the DataFrame.")
 
-    @property
-    def _AXIS_NUMBERS(self) -> dict[str, int]:
-        """.. deprecated:: 1.1.0"""
-        super()._AXIS_NUMBERS
-        return {"index": 0, "columns": 1}
-
-    @property
-    def _AXIS_NAMES(self) -> dict[int, str]:
-        """.. deprecated:: 1.1.0"""
-        super()._AXIS_NAMES
-        return {0: "index", 1: "columns"}
-
     # ----------------------------------------------------------------------
     # Add plotting methods to DataFrame
     plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)

pandas/core/generic.py

@@ -494,25 +494,6 @@ def _data(self):
     _info_axis_name: Literal["index", "columns"]
     _AXIS_LEN: int
 
-    @property
-    def _AXIS_NUMBERS(self) -> dict[str, int]:
-        """.. deprecated:: 1.1.0"""
-        warnings.warn(
-            "_AXIS_NUMBERS has been deprecated.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        return {"index": 0}
-
-    @property
-    def _AXIS_NAMES(self) -> dict[int, str]:
-        """.. deprecated:: 1.1.0"""
-        level = self.ndim + 1
-        warnings.warn(
-            "_AXIS_NAMES has been deprecated.", FutureWarning, stacklevel=level
-        )
-        return {0: "index"}
-
     @final
     def _construct_axes_dict(self, axes: Sequence[Axis] | None = None, **kwargs):
         """Return an axes dictionary for myself."""

pandas/core/indexes/base.py

@@ -1460,45 +1460,6 @@ def _format_with_header(self, header: list[str_t], na_rep: str_t) -> list[str_t]
             result = trim_front(format_array(values, None, justify="left"))
         return header + result
 
-    @final
-    def to_native_types(self, slicer=None, **kwargs) -> np.ndarray:
-        """
-        Format specified values of `self` and return them.
-
-        .. deprecated:: 1.2.0
-
-        Parameters
-        ----------
-        slicer : int, array-like
-            An indexer into `self` that specifies which values
-            are used in the formatting process.
-        kwargs : dict
-            Options for specifying how the values should be formatted.
-            These options include the following:
-
-            1) na_rep : str
-                The value that serves as a placeholder for NULL values
-            2) quoting : bool or None
-                Whether or not there are quoted values in `self`
-            3) date_format : str
-                The format used to represent date-like values.
-
-        Returns
-        -------
-        numpy.ndarray
-            Formatted values.
-        """
-        warnings.warn(
-            "The 'to_native_types' method is deprecated and will be removed in "
-            "a future version. Use 'astype(str)' instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        values = self
-        if slicer is not None:
-            values = values[slicer]
-        return values._format_native_types(**kwargs)
-
     def _format_native_types(
         self, *, na_rep: str_t = "", quoting=None, **kwargs
     ) -> npt.NDArray[np.object_]:
@@ -5905,30 +5866,6 @@ def _get_values_for_loc(self, series: Series, loc, key):
 
         return series.iloc[loc]
 
-    @final
-    def set_value(self, arr, key, value) -> None:
-        """
-        Fast lookup of value from 1-dimensional ndarray.
-
-        .. deprecated:: 1.0
-
-        Notes
-        -----
-        Only use this if you know what you're doing.
-        """
-        warnings.warn(
-            (
-                "The 'set_value' method is deprecated, and "
-                "will be removed in a future version."
-            ),
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        loc = self._engine.get_loc(key)
-        if not can_hold_element(arr, value):
-            raise ValueError
-        arr[loc] = value
-
     _index_shared_docs[
         "get_indexer_non_unique"
     ] = """

pandas/core/indexes/datetimes.py

@@ -63,7 +63,6 @@
 import pandas.core.common as com
 from pandas.core.indexes.base import (
     Index,
-    get_unanimous_names,
     maybe_extract_name,
 )
 from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin
@@ -75,7 +74,6 @@
         DataFrame,
         Float64Index,
         PeriodIndex,
-        TimedeltaIndex,
     )
 
 
@@ -229,7 +227,6 @@ class DatetimeIndex(DatetimeTimedeltaMixin):
     floor
     ceil
     to_period
-    to_perioddelta
     to_pydatetime
     to_series
     to_frame
@@ -295,13 +292,6 @@ def to_period(self, freq=None) -> PeriodIndex:
         arr = self._data.to_period(freq)
         return PeriodIndex._simple_new(arr, name=self.name)
 
-    @doc(DatetimeArray.to_perioddelta)
-    def to_perioddelta(self, freq) -> TimedeltaIndex:
-        from pandas.core.indexes.api import TimedeltaIndex
-
-        arr = self._data.to_perioddelta(freq)
-        return TimedeltaIndex._simple_new(arr, name=self.name)
-
     @doc(DatetimeArray.to_julian_date)
     def to_julian_date(self) -> Float64Index:
         from pandas.core.indexes.api import Float64Index
@@ -439,43 +429,6 @@ def _can_range_setop(self, other) -> bool:
             return False
         return super()._can_range_setop(other)
 
-    def union_many(self, others):
-        """
-        A bit of a hack to accelerate unioning a collection of indexes.
-        """
-        warnings.warn(
-            "DatetimeIndex.union_many is deprecated and will be removed in "
-            "a future version. Use obj.union instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-
-        this = self
-
-        for other in others:
-            if not isinstance(this, DatetimeIndex):
-                this = Index.union(this, other)
-                continue
-
-            if not isinstance(other, DatetimeIndex):
-                try:
-                    other = DatetimeIndex(other)
-                except TypeError:
-                    pass
-
-            this, other = this._maybe_utc_convert(other)
-
-            if len(self) and len(other) and this._can_fast_union(other):
-                # union already has fastpath handling for empty cases
-                this = this._fast_union(other)
-            else:
-                this = Index.union(this, other)
-
-        res_name = get_unanimous_names(self, *others)[0]
-        if this.name != res_name:
-            return this.rename(res_name)
-        return this
-
     def _maybe_utc_convert(self, other: Index) -> tuple[DatetimeIndex, Index]:
         this = self