update

Author: zhangbowen-coder

pandas/core/indexes/base.py

@@ -72,7 +72,9 @@
     Pandas4Warning,
 )
 from pandas.util._decorators import (
+    Appender,
     cache_readonly,
+    doc,
     set_module,
 )
 from pandas.util._exceptions import (
@@ -5014,73 +5016,8 @@ def values(self) -> ArrayLike:
         return data
 
     @cache_readonly
+    @doc(IndexOpsMixin.array)
     def array(self) -> ExtensionArray:
-        """
-        The ExtensionArray of the data backing this Series or Index.
-
-        This property provides direct access to the underlying array data of a
-        Series or Index without requiring conversion to a NumPy array. It
-        returns an ExtensionArray, which is the native storage format for
-        pandas extension dtypes.
-
-        Returns
-        -------
-        ExtensionArray
-            An ExtensionArray of the values stored within. For extension
-            types, this is the actual array. For NumPy native types, this
-            is a thin (no copy) wrapper around :class:`numpy.ndarray`.
-
-            ``.array`` differs from ``.values``, which may require converting
-            the data to a different form.
-
-        See Also
-        --------
-        Index.to_numpy : Similar method that always returns a NumPy array.
-        Series.to_numpy : Similar method that always returns a NumPy array.
-
-        Notes
-        -----
-        This table lays out the different array types for each extension
-        dtype within pandas.
-
-        ================== =============================
-        dtype              array type
-        ================== =============================
-        category           Categorical
-        period             PeriodArray
-        interval           IntervalArray
-        IntegerNA          IntegerArray
-        string             StringArray
-        boolean            BooleanArray
-        datetime64[ns, tz] DatetimeArray
-        ================== =============================
-
-        For any 3rd-party extension types, the array type will be an
-        ExtensionArray.
-
-        For all remaining dtypes ``.array`` will be a
-        :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
-        stored within. If you absolutely need a NumPy array (possibly with
-        copying / coercing data), then use :meth:`Series.to_numpy` instead.
-
-        Examples
-        --------
-        For regular NumPy types like int, and float, a NumpyExtensionArray
-        is returned.
-
-        >>> pd.Series([1, 2, 3]).array
-        <NumpyExtensionArray>
-        [1, 2, 3]
-        Length: 3, dtype: int64
-
-        For extension types, like Categorical, the actual ExtensionArray
-        is returned
-
-        >>> ser = pd.Series(pd.Categorical(["a", "b", "a"]))
-        >>> ser.array
-        ['a', 'b', 'a']
-        Categories (2, str): ['a', 'b']
-        """
         array = self._data
         if isinstance(array, np.ndarray):
             from pandas.core.arrays.numpy_ import NumpyExtensionArray
@@ -5186,37 +5123,8 @@ def _from_join_target(self, result: np.ndarray) -> ArrayLike:
             return type(self.values)._from_sequence(result, dtype=self.dtype)
         return result
 
+    @doc(IndexOpsMixin._memory_usage)
     def memory_usage(self, deep: bool = False) -> int:
-        """
-        Memory usage of the values.
-
-        Parameters
-        ----------
-        deep : bool, default False
-            Introspect the data deeply, interrogate
-            `object` dtypes for system-level memory consumption.
-
-        Returns
-        -------
-        bytes used
-            Returns memory usage of the values in the Index in bytes.
-
-        See Also
-        --------
-        numpy.ndarray.nbytes : Total bytes consumed by the elements of the
-            array.
-
-        Notes
-        -----
-        Memory usage does not include memory consumed by elements that
-        are not components of the array if deep=False or if used on PyPy
-
-        Examples
-        --------
-        >>> idx = pd.Index([1, 2, 3])
-        >>> idx.memory_usage()
-        24
-        """
         result = self._memory_usage(deep=deep)
 
         # include our engine hashtable, only if it's already cached
@@ -7535,67 +7443,10 @@ def _maybe_disable_logical_methods(self, opname: str_t) -> None:
         if isinstance(self, ABCMultiIndex):
             raise TypeError(f"cannot perform {opname} with {type(self).__name__}")
 
+    @Appender(IndexOpsMixin.argmin.__doc__)
     def argmin(
         self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
     ) -> int:
-        """
-        Return int position of the smallest value in the Series.
-
-        If the minimum is achieved in multiple locations,
-        the first row position is returned.
-
-        Parameters
-        ----------
-        axis : {None}
-            Unused. Parameter needed for compatibility with DataFrame.
-        skipna : bool, default True
-            Exclude NA/null values. If the entire Series is NA, or if ``skipna=False``
-            and there is an NA value, this method will raise a ``ValueError``.
-        *args, **kwargs
-            Additional arguments and keywords for compatibility with NumPy.
-
-        Returns
-        -------
-        int
-            Row position of the minimum value.
-
-        See Also
-        --------
-        Series.argmin : Return position of the minimum value.
-        Series.argmax : Return position of the maximum value.
-        numpy.ndarray.argmin : Equivalent method for numpy arrays.
-        Series.idxmax : Return index label of the maximum values.
-        Series.idxmin : Return index label of the minimum values.
-
-        Examples
-        --------
-        Consider dataset containing cereal calories
-
-        >>> s = pd.Series(
-        ...     [100.0, 110.0, 120.0, 110.0],
-        ...     index=[
-        ...         "Corn Flakes",
-        ...         "Almond Delight",
-        ...         "Cinnamon Toast Crunch",
-        ...         "Cocoa Puff",
-        ...     ],
-        ... )
-        >>> s
-        Corn Flakes              100.0
-        Almond Delight           110.0
-        Cinnamon Toast Crunch    120.0
-        Cocoa Puff               110.0
-        dtype: float64
-
-        >>> s.argmax()
-        np.int64(2)
-        >>> s.argmin()
-        np.int64(0)
-
-        The maximum cereal calories is the third element and
-        the minimum cereal calories is the first element,
-        since series is zero-indexed.
-        """
         nv.validate_argmin(args, kwargs)
         nv.validate_minmax_axis(axis)
 
@@ -7607,67 +7458,10 @@ def argmin(
 
         return super().argmin(skipna=skipna)
 
+    @Appender(IndexOpsMixin.argmax.__doc__)
     def argmax(
         self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
     ) -> int:
-        """
-        Return int position of the largest value in the Series.
-
-        If the maximum is achieved in multiple locations,
-        the first row position is returned.
-
-        Parameters
-        ----------
-        axis : {None}
-            Unused. Parameter needed for compatibility with DataFrame.
-        skipna : bool, default True
-            Exclude NA/null values. If the entire Series is NA, or if ``skipna=False``
-            and there is an NA value, this method will raise a ``ValueError``.
-        *args, **kwargs
-            Additional arguments and keywords for compatibility with NumPy.
-
-        Returns
-        -------
-        int
-            Row position of the maximum value.
-
-        See Also
-        --------
-        Series.argmax : Return position of the maximum value.
-        Series.argmin : Return position of the minimum value.
-        numpy.ndarray.argmax : Equivalent method for numpy arrays.
-        Series.idxmax : Return index label of the maximum values.
-        Series.idxmin : Return index label of the minimum values.
-
-        Examples
-        --------
-        Consider dataset containing cereal calories
-
-        >>> s = pd.Series(
-        ...     [100.0, 110.0, 120.0, 110.0],
-        ...     index=[
-        ...         "Corn Flakes",
-        ...         "Almond Delight",
-        ...         "Cinnamon Toast Crunch",
-        ...         "Cocoa Puff",
-        ...     ],
-        ... )
-        >>> s
-        Corn Flakes              100.0
-        Almond Delight           110.0
-        Cinnamon Toast Crunch    120.0
-        Cocoa Puff               110.0
-        dtype: float64
-
-        >>> s.argmax()
-        np.int64(2)
-        >>> s.argmin()
-        np.int64(0)
-
-        The maximum cereal calories is the third element and
-        the minimum cereal calories is the first element,
-        since series is zero-indexed.
-        """
         nv.validate_argmax(args, kwargs)
         nv.validate_minmax_axis(axis)