CLN: Remove pandas.value_counts, Index.format, is_interval, is_period

asv_bench/benchmarks/strftime.py

@@ -79,12 +79,6 @@ def time_frame_period_formatting_default(self, nobs, freq):
     def time_frame_period_formatting_default_explicit(self, nobs, freq):
         self.data["p"].dt.strftime(date_format=self.default_fmt)
 
-    def time_frame_period_formatting_index_default(self, nobs, freq):
-        self.data.index.format()
-
-    def time_frame_period_formatting_index_default_explicit(self, nobs, freq):
-        self.data.index.format(self.default_fmt)
-
     def time_frame_period_formatting_custom(self, nobs, freq):
         self.data["p"].dt.strftime(date_format="%Y-%m-%d --- %H:%M:%S")
 

doc/redirects.csv

@@ -118,7 +118,6 @@ generated/pandas.api.types.is_int64_dtype,../reference/api/pandas.api.types.is_i
 generated/pandas.api.types.is_integer_dtype,../reference/api/pandas.api.types.is_integer_dtype
 generated/pandas.api.types.is_integer,../reference/api/pandas.api.types.is_integer
 generated/pandas.api.types.is_interval_dtype,../reference/api/pandas.api.types.is_interval_dtype
-generated/pandas.api.types.is_interval,../reference/api/pandas.api.types.is_interval
 generated/pandas.api.types.is_iterator,../reference/api/pandas.api.types.is_iterator
 generated/pandas.api.types.is_list_like,../reference/api/pandas.api.types.is_list_like
 generated/pandas.api.types.is_named_tuple,../reference/api/pandas.api.types.is_named_tuple

doc/source/reference/arrays.rst

@@ -700,7 +700,6 @@ Scalar introspection
     api.types.is_float
     api.types.is_hashable
     api.types.is_integer
-    api.types.is_interval
     api.types.is_number
     api.types.is_re
     api.types.is_re_compilable

doc/source/whatsnew/v3.0.0.rst

@@ -106,9 +106,12 @@ Removal of prior version deprecations/changes
 - All arguments except the first ``path``-like argument in IO writers are now keyword only (:issue:`54229`)
 - Removed ``DataFrameGroupBy.grouper`` and ``SeriesGroupBy.grouper`` (:issue:`56521`)
 - Removed ``DataFrameGroupby.fillna`` and ``SeriesGroupBy.fillna``` (:issue:`55719`)
+- Removed ``Index.format``, use :meth:`Index.astype` with ``str`` or :meth:`Index.map` with a ``formatter`` function instead (:issue:`55439`)
 - Removed ``axis`` argument from :meth:`DataFrame.groupby`, :meth:`Series.groupby`, :meth:`DataFrame.rolling`, :meth:`Series.rolling`, :meth:`DataFrame.resample`, and :meth:`Series.resample` (:issue:`51203`)
 - Removed ``axis`` argument from all groupby operations (:issue:`50405`)
+- Removed ``pandas.api.types.is_interval`` and ``pandas.api.types.is_period``, use ``isinstance(obj, pd.Interval)`` and ``isinstance(obj, pd.Period)`` instead (:issue:`55264`)
 - Removed ``pandas.io.sql.execute`` (:issue:`50185`)
+- Removed ``pandas.value_counts``, use :meth:`Series.value_counts` instead (:issue:`53493`)
 - Removed ``read_gbq`` and ``DataFrame.to_gbq``. Use ``pandas_gbq.read_gbq`` and ``pandas_gbq.to_gbq`` instead https://pandas-gbq.readthedocs.io/en/latest/api.html (:issue:`55525`)
 - Removed deprecated argument ``obj`` in :meth:`.DataFrameGroupBy.get_group` and :meth:`.SeriesGroupBy.get_group` (:issue:`53545`)
 - Removed the ``ArrayManager`` (:issue:`55043`)

pandas/__init__.py

@@ -101,7 +101,6 @@
     Grouper,
     factorize,
     unique,
-    value_counts,
     NamedAgg,
     array,
     Categorical,
@@ -378,6 +377,5 @@
     "to_timedelta",
     "tseries",
     "unique",
-    "value_counts",
     "wide_to_long",
 ]

pandas/_libs/lib.pyi

@@ -14,8 +14,6 @@ from typing import (
 
 import numpy as np
 
-from pandas._libs.interval import Interval
-from pandas._libs.tslibs import Period
 from pandas._typing import (
     ArrayLike,
     DtypeObj,
@@ -44,8 +42,6 @@ def is_iterator(obj: object) -> bool: ...
 def is_scalar(val: object) -> bool: ...
 def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
 def is_pyarrow_array(obj: object) -> bool: ...
-def is_period(val: object) -> TypeGuard[Period]: ...
-def is_interval(obj: object) -> TypeGuard[Interval]: ...
 def is_decimal(obj: object) -> TypeGuard[Decimal]: ...
 def is_complex(obj: object) -> TypeGuard[complex]: ...
 def is_bool(obj: object) -> TypeGuard[bool | np.bool_]: ...

pandas/_libs/lib.pyx

@@ -1164,43 +1164,6 @@ cpdef bint is_decimal(object obj):
     return isinstance(obj, Decimal)
 
 
-cpdef bint is_interval(object obj):
-    import warnings
-
-    from pandas.util._exceptions import find_stack_level
-
-    warnings.warn(
-        # GH#55264
-        "is_interval is deprecated and will be removed in a future version. "
-        "Use isinstance(obj, pd.Interval) instead.",
-        FutureWarning,
-        stacklevel=find_stack_level(),
-    )
-    return getattr(obj, "_typ", "_typ") == "interval"
-
-
-def is_period(val: object) -> bool:
-    """
-    Return True if given object is Period.
-
-    Returns
-    -------
-    bool
-    """
-    import warnings
-
-    from pandas.util._exceptions import find_stack_level
-
-    warnings.warn(
-        # GH#55264
-        "is_period is deprecated and will be removed in a future version. "
-        "Use isinstance(obj, pd.Period) instead.",
-        FutureWarning,
-        stacklevel=find_stack_level(),
-    )
-    return is_period_object(val)
-
-
 def is_list_like(obj: object, allow_sets: bool = True) -> bool:
     """
     Check if the object is list-like.

pandas/core/algorithms.py

@@ -818,53 +818,6 @@ def factorize(
     return codes, uniques
 
 
-def value_counts(
-    values,
-    sort: bool = True,
-    ascending: bool = False,
-    normalize: bool = False,
-    bins=None,
-    dropna: bool = True,
-) -> Series:
-    """
-    Compute a histogram of the counts of non-null values.
-
-    Parameters
-    ----------
-    values : ndarray (1-d)
-    sort : bool, default True
-        Sort by values
-    ascending : bool, default False
-        Sort in ascending order
-    normalize: bool, default False
-        If True then compute a relative histogram
-    bins : integer, optional
-        Rather than count values, group them into half-open bins,
-        convenience for pd.cut, only works with numeric data
-    dropna : bool, default True
-        Don't include counts of NaN
-
-    Returns
-    -------
-    Series
-    """
-    warnings.warn(
-        # GH#53493
-        "pandas.value_counts is deprecated and will be removed in a "
-        "future version. Use pd.Series(obj).value_counts() instead.",
-        FutureWarning,
-        stacklevel=find_stack_level(),
-    )
-    return value_counts_internal(
-        values,
-        sort=sort,
-        ascending=ascending,
-        normalize=normalize,
-        bins=bins,
-        dropna=dropna,
-    )
-
-
 def value_counts_internal(
     values,
     sort: bool = True,

pandas/core/api.py

@@ -23,7 +23,6 @@
 from pandas.core.algorithms import (
     factorize,
     unique,
-    value_counts,
 )
 from pandas.core.arrays import Categorical
 from pandas.core.arrays.boolean import BooleanDtype
@@ -136,5 +135,4 @@
     "UInt64Dtype",
     "UInt8Dtype",
     "unique",
-    "value_counts",
 ]

pandas/core/dtypes/api.py

@@ -20,7 +20,6 @@
     is_int64_dtype,
     is_integer,
     is_integer_dtype,
-    is_interval,
     is_interval_dtype,
     is_iterator,
     is_list_like,
@@ -63,7 +62,6 @@
     "is_int64_dtype",
     "is_integer",
     "is_integer_dtype",
-    "is_interval",
     "is_interval_dtype",
     "is_iterator",
     "is_list_like",

pandas/core/dtypes/common.py

@@ -42,7 +42,6 @@
     is_float,
     is_hashable,
     is_integer,
-    is_interval,
     is_iterator,
     is_list_like,
     is_named_tuple,
@@ -1710,7 +1709,6 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_float_dtype",
     "is_int64_dtype",
     "is_integer_dtype",
-    "is_interval",
     "is_interval_dtype",
     "is_iterator",
     "is_named_tuple",

pandas/core/dtypes/inference.py

@@ -29,8 +29,6 @@
 
 is_decimal = lib.is_decimal
 
-is_interval = lib.is_interval
-
 is_list_like = lib.is_list_like
 
 is_iterator = lib.is_iterator

pandas/core/indexes/base.py

@@ -1394,36 +1394,6 @@ def _mpl_repr(self) -> np.ndarray:
             return cast(np.ndarray, self.values)
         return self.astype(object, copy=False)._values
 
-    def format(
-        self,
-        name: bool = False,
-        formatter: Callable | None = None,
-        na_rep: str_t = "NaN",
-    ) -> list[str_t]:
-        """
-        Render a string representation of the Index.
-        """
-        warnings.warn(
-            # GH#55413
-            f"{type(self).__name__}.format is deprecated and will be removed "
-            "in a future version. Convert using index.astype(str) or "
-            "index.map(formatter) instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        header = []
-        if name:
-            header.append(
-                pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
-                if self.name is not None
-                else ""
-            )
-
-        if formatter is not None:
-            return header + list(self.map(formatter))
-
-        return self._format_with_header(header=header, na_rep=na_rep)
-
     _default_na_rep = "NaN"
 
     @final

pandas/core/indexes/datetimelike.py

@@ -10,11 +10,9 @@
 from typing import (
     TYPE_CHECKING,
     Any,
-    Callable,
     cast,
     final,
 )
-import warnings
 
 import numpy as np
 
@@ -43,7 +41,6 @@
     cache_readonly,
     doc,
 )
-from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
     is_integer,
@@ -192,39 +189,6 @@ def _convert_tolerance(self, tolerance, target):
     # Rendering Methods
     _default_na_rep = "NaT"
 
-    def format(
-        self,
-        name: bool = False,
-        formatter: Callable | None = None,
-        na_rep: str = "NaT",
-        date_format: str | None = None,
-    ) -> list[str]:
-        """
-        Render a string representation of the Index.
-        """
-        warnings.warn(
-            # GH#55413
-            f"{type(self).__name__}.format is deprecated and will be removed "
-            "in a future version. Convert using index.astype(str) or "
-            "index.map(formatter) instead.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        header = []
-        if name:
-            header.append(
-                ibase.pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
-                if self.name is not None
-                else ""
-            )
-
-        if formatter is not None:
-            return header + list(self.map(formatter))
-
-        return self._format_with_header(
-            header=header, na_rep=na_rep, date_format=date_format
-        )
-
     def _format_with_header(
         self, *, header: list[str], na_rep: str, date_format: str | None = None
     ) -> list[str]: