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�

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An interface for extending pandas with custom arrays.

.. warning::

   This is an experimental API and subject to breaking changes
   without warning.
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    Abstract base class for custom 1-D array types.

    pandas will recognize instances of this class as proper arrays
    with a custom type and will not attempt to coerce them to objects. They
    may be stored directly inside a :class:`DataFrame` or :class:`Series`.

    Attributes
    ----------
    dtype
    nbytes
    ndim
    shape

    Methods
    -------
    argsort
    astype
    copy
    dropna
    duplicated
    factorize
    fillna
    equals
    insert
    interpolate
    isin
    isna
    ravel
    repeat
    searchsorted
    shift
    take
    tolist
    unique
    view
    _accumulate
    _concat_same_type
    _explode
    _formatter
    _from_factorized
    _from_sequence
    _from_sequence_of_strings
    _hash_pandas_object
    _pad_or_backfill
    _reduce
    _values_for_argsort
    _values_for_factorize

    Notes
    -----
    The interface includes the following abstract methods that must be
    implemented by subclasses:

    * _from_sequence
    * _from_factorized
    * __getitem__
    * __len__
    * __eq__
    * dtype
    * nbytes
    * isna
    * take
    * copy
    * _concat_same_type
    * interpolate

    A default repr displaying the type, (truncated) data, length,
    and dtype is provided. It can be customized or replaced by
    by overriding:

    * __repr__ : A default repr for the ExtensionArray.
    * _formatter : Print scalars inside a Series or DataFrame.

    Some methods require casting the ExtensionArray to an ndarray of Python
    objects with ``self.astype(object)``, which may be expensive. When
    performance is a concern, we highly recommend overriding the following
    methods:

    * fillna
    * _pad_or_backfill
    * dropna
    * unique
    * factorize / _values_for_factorize
    * argsort, argmax, argmin / _values_for_argsort
    * searchsorted
    * map

    The remaining methods implemented on this class should be performant,
    as they only compose abstract methods. Still, a more efficient
    implementation may be available, and these methods can be overridden.

    One can implement methods to handle array accumulations or reductions.

    * _accumulate
    * _reduce

    One can implement methods to handle parsing from strings that will be used
    in methods such as ``pandas.io.parsers.read_csv``.

    * _from_sequence_of_strings

    This class does not inherit from 'abc.ABCMeta' for performance reasons.
    Methods and properties required by the interface raise
    ``pandas.errors.AbstractMethodError`` and no ``register`` method is
    provided for registering virtual subclasses.

    ExtensionArrays are limited to 1 dimension.

    They may be backed by none, one, or many NumPy arrays. For example,
    ``pandas.Categorical`` is an extension array backed by two arrays,
    one for codes and one for categories. An array of IPv6 address may
    be backed by a NumPy structured array with two fields, one for the
    lower 64 bits and one for the upper 64 bits. Or they may be backed
    by some other storage type, like Python lists. Pandas makes no
    assumptions on how the data are stored, just that it can be converted
    to a NumPy array.
    The ExtensionArray interface does not impose any rules on how this data
    is stored. However, currently, the backing data cannot be stored in
    attributes called ``.values`` or ``._values`` to ensure full compatibility
    with pandas internals. But other names as ``.data``, ``._data``,
    ``._items``, ... can be freely used.

    If implementing NumPy's ``__array_ufunc__`` interface, pandas expects
    that

    1. You defer by returning ``NotImplemented`` when any Series are present
       in `inputs`. Pandas will extract the arrays and call the ufunc again.
    2. You define a ``_HANDLED_TYPES`` tuple as an attribute on the class.
       Pandas inspect this to determine whether the ufunc is valid for the
       types present.

    See :ref:`extending.extension.ufunc` for more.

    By default, ExtensionArrays are not hashable.  Immutable subclasses may
    override this behavior.

    Examples
    --------
    Please see the following:

    https://github.com/pandas-dev/pandas/blob/main/pandas/tests/extension/list/array.py
    �	extensioni�NF��dtype�copyc��t|��)a�
        Construct a new ExtensionArray from a sequence of scalars.

        Parameters
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        scalars : Sequence
            Each element will be an instance of the scalar type for this
            array, ``cls.dtype.type`` or be converted into this type in this method.
        dtype : dtype, optional
            Construct for this particular dtype. This should be a Dtype
            compatible with the ExtensionArray.
        copy : bool, default False
            If True, copy the underlying data.

        Returns
        -------
        ExtensionArray

        Examples
        --------
        >>> pd.arrays.IntegerArray._from_sequence([4, 5])
        <IntegerArray>
        [4, 5]
        Length: 2, dtype: Int64
        r)�cls�scalarsrIrJs    �9C:\Python312\Lib\site-packages\pandas/core/arrays/base.py�_from_sequencezExtensionArray._from_sequence
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        Strict analogue to _from_sequence, allowing only sequences of scalars
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        Parameters
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        scalars : sequence
        dtype : ExtensionDtype

        Raises
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        TypeError or ValueError

        Notes
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        FrHzm_from_scalars should only raise ValueError or TypeError. Consider overriding _from_scalars where appropriate.��
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        Construct a new ExtensionArray from a sequence of strings.

        Parameters
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            Each element will be an instance of the scalar type for this
            array, ``cls.dtype.type``.
        dtype : dtype, optional
            Construct for this particular dtype. This should be a Dtype
            compatible with the ExtensionArray.
        copy : bool, default False
            If True, copy the underlying data.

        Returns
        -------
        ExtensionArray

        Examples
        --------
        >>> pd.arrays.IntegerArray._from_sequence_of_strings(["1", "2", "3"])
        <IntegerArray>
        [1, 2, 3]
        Length: 3, dtype: Int64
        r)rL�stringsrIrJs    rN�_from_sequence_of_stringsz(ExtensionArray._from_sequence_of_stringsJs��:"�#�&�&rQc��t|��)a�
        Reconstruct an ExtensionArray after factorization.

        Parameters
        ----------
        values : ndarray
            An integer ndarray with the factorized values.
        original : ExtensionArray
            The original ExtensionArray that factorize was called on.

        See Also
        --------
        factorize : Top-level factorize method that dispatches here.
        ExtensionArray.factorize : Encode the extension array as an enumerated type.

        Examples
        --------
        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1),
        ...                                      pd.Interval(1, 5), pd.Interval(1, 5)])
        >>> codes, uniques = pd.factorize(interv_arr)
        >>> pd.arrays.IntervalArray._from_factorized(uniques, interv_arr)
        <IntervalArray>
        [(0, 1], (1, 5]]
        Length: 2, dtype: interval[int64, right]
        r)rL�values�originals   rN�_from_factorizedzExtensionArray._from_factorizedirPrQc��y�N���self�items  rN�__getitem__zExtensionArray.__getitem__����rQc��yrcrdres  rNrhzExtensionArray.__getitem__�rirQc��t|��)an
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            * slice: A slice object, where 'start', 'stop', and 'step' are
              integers or None

            * ndarray: A 1-d boolean NumPy ndarray the same length as 'self'

            * list[int]:  A list of int

        Returns
        -------
        item : scalar or ExtensionArray

        Notes
        -----
        For scalar ``item``, return a scalar value suitable for the array's
        type. This should be an instance of ``self.dtype.type``.

        For slice ``key``, return an instance of ``ExtensionArray``, even
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        For a boolean mask, return an instance of ``ExtensionArray``, filtered
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        rres  rNrhzExtensionArray.__getitem__�s��>"�$�'�'rQc�0�tt|��d���)a^
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            When called from, e.g. ``Series.__setitem__``, ``key`` will be
            one of

            * scalar int
            * ndarray of integers.
            * boolean ndarray
            * slice object

        value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
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        Returns
        -------
        None
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        r�rfs rN�__len__zExtensionArray.__len__�s��"�$�'�'rQc#�LK�tt|��D]	}||���y�w)z5
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        Return for `item in self`.
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�4������0Q��{�{�"���D�L�%�%�'�'rQc��t|��)zE
        Return for `self == other` (element-wise equality).
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        Return for `self != other` (element-wise in-equality).
        rdr�s  rN�__ne__zExtensionArray.__ne__s��
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        Convert to a NumPy ndarray.

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        Parameters
        ----------
        dtype : str or numpy.dtype, optional
            The dtype to pass to :meth:`numpy.asarray`.
        copy : bool, default False
            Whether to ensure that the returned value is a not a view on
            another array. Note that ``copy=False`` does not *ensure* that
            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
            a copy is made, even if not strictly necessary.
        na_value : Any, optional
            The value to use for missing values. The default value depends
            on `dtype` and the type of the array.

        Returns
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        numpy.ndarray
        �rI)�np�asarrayr�
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        An instance of ExtensionDtype.

        Examples
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        rrss rNrIzExtensionArray.dtypeCr�rQc��t|�fS)z�
        Return a tuple of the array dimensions.

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        The number of elements in the array.
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        Extension Arrays are only allowed to be 1-dimensional.

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        >>> arr.ndim
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        �rdrss rN�ndimzExtensionArray.ndimes��rQc��t|��)z�
        The number of bytes needed to store this object in memory.

        Examples
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        >>> pd.array([1, 2, 3]).nbytes
        27
        rrss rN�nbyteszExtensionArray.nbytesrs��"�$�'�'rQc��yrcrd�rfrIrJs   rN�astypezExtensionArray.astype�rirQc��yrcrdr�s   rNr�zExtensionArray.astype�rirQc��yrcrdr�s   rNr�zExtensionArray.astype�rirQTc���t|�}||jk(r|s|S|j�St|t�r$|j�}|j
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        Cast to a NumPy array or ExtensionArray with 'dtype'.

        Parameters
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        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.

        Returns
        -------
        np.ndarray or pandas.api.extensions.ExtensionArray
            An ``ExtensionArray`` if ``dtype`` is ``ExtensionDtype``,
            otherwise a Numpy ndarray with ``dtype`` for its dtype.

        Examples
        --------
        >>> arr = pd.array([1, 2, 3])
        >>> arr
        <IntegerArray>
        [1, 2, 3]
        Length: 3, dtype: Int64

        Casting to another ``ExtensionDtype`` returns an ``ExtensionArray``:

        >>> arr1 = arr.astype('Float64')
        >>> arr1
        <FloatingArray>
        [1.0, 2.0, 3.0]
        Length: 3, dtype: Float64
        >>> arr1.dtype
        Float64Dtype()

        Otherwise, we will get a Numpy ndarray:

        >>> arr2 = arr.astype('float64')
        >>> arr2
        array([1., 2., 3.])
        >>> arr2.dtype
        dtype('float64')
        rH�Mr)�
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(�9�!�0�0��U��0�N�N���:�:�d�%�0�0��8�8�D��D�9�9rQc��t|��)a'
        A 1-D array indicating if each value is missing.

        Returns
        -------
        numpy.ndarray or pandas.api.extensions.ExtensionArray
            In most cases, this should return a NumPy ndarray. For
            exceptional cases like ``SparseArray``, where returning
            an ndarray would be expensive, an ExtensionArray may be
            returned.

        Notes
        -----
        If returning an ExtensionArray, then

        * ``na_values._is_boolean`` should be True
        * `na_values` should implement :func:`ExtensionArray._reduce`
        * ``na_values.any`` and ``na_values.all`` should be implemented

        Examples
        --------
        >>> arr = pd.array([1, 2, np.nan, np.nan])
        >>> arr.isna()
        array([False, False,  True,  True])
        rrss rNr!zExtensionArray.isna�s��4"�$�'�'rQc�P�t|j�j��S)z�
        Equivalent to `self.isna().any()`.

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        Return values for sorting.

        Returns
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        ndarray
            The transformed values should maintain the ordering between values
            within the array.

        See Also
        --------
        ExtensionArray.argsort : Return the indices that would sort this array.

        Notes
        -----
        The caller is responsible for *not* modifying these values in-place, so
        it is safe for implementers to give views on ``self``.

        Functions that use this (e.g. ``ExtensionArray.argsort``) should ignore
        entries with missing values in the original array (according to
        ``self.isna()``). This means that the corresponding entries in the returned
        array don't need to be modified to sort correctly.

        Examples
        --------
        In most cases, this is the underlying Numpy array of the ``ExtensionArray``:

        >>> arr = pd.array([1, 2, 3])
        >>> arr._values_for_argsort()
        array([1, 2, 3])
        )r�r�rss rN�_values_for_argsortz"ExtensionArray._values_for_argsort�s��B�x�x��~�rQ�	quicksort�last)�	ascending�kind�na_positionc
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        Return the indices that would sort this array.

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            Whether the indices should result in an ascending
            or descending sort.
        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
            Sorting algorithm.
        na_position : {'first', 'last'}, default 'last'
            If ``'first'``, put ``NaN`` values at the beginning.
            If ``'last'``, put ``NaN`` values at the end.
        *args, **kwargs:
            Passed through to :func:`numpy.argsort`.

        Returns
        -------
        np.ndarray[np.intp]
            Array of indices that sort ``self``. If NaN values are contained,
            NaN values are placed at the end.

        See Also
        --------
        numpy.argsort : Sorting implementation used internally.

        Examples
        --------
        >>> arr = pd.array([3, 1, 2, 5, 4])
        >>> arr.argsort()
        array([1, 2, 0, 4, 3])
        rd)r�r�r��mask)�nv�validate_argsort_with_ascendingr�r/r�r�r!)rfr�r�r��kwargsr_s      rN�argsortzExtensionArray.argsort sR��Z�6�6�y�"�f�M�	��)�)�+������#����D�I�I�K�(�
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        Return the index of minimum value.

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        Parameters
        ----------
        skipna : bool, default True

        Returns
        -------
        int

        See Also
        --------
        ExtensionArray.argmax : Return the index of the maximum value.

        Examples
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        >>> arr = pd.array([3, 1, 2, 5, 4])
        >>> arr.argmin()
        1
        �skipna�argmin�rr~rmr.�rfr�s  rNr�zExtensionArray.argminX�*��:	�F�H�-��$�+�+�%�%��$��)�)rQc�Z�t|d�|s|jrt�t|d�S)a
        Return the index of maximum value.

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        Parameters
        ----------
        skipna : bool, default True

        Returns
        -------
        int

        See Also
        --------
        ExtensionArray.argmin : Return the index of the minimum value.

        Examples
        --------
        >>> arr = pd.array([3, 1, 2, 5, 4])
        >>> arr.argmax()
        3
        r��argmaxr�r�s  rNr�zExtensionArray.argmaxzr�rQc�D�tt|�j�d���)a�
        See DataFrame.interpolate.__doc__.

        Examples
        --------
        >>> arr = pd.arrays.NumpyExtensionArray(np.array([0, 1, np.nan, 3]))
        >>> arr.interpolate(method="linear",
        ...                 limit=3,
        ...                 limit_direction="forward",
        ...                 index=pd.Index([1, 2, 3, 4]),
        ...                 fill_value=1,
        ...                 copy=False,
        ...                 axis=0,
        ...                 limit_area="inside"
        ...                 )
        <NumpyExtensionArray>
        [0.0, 1.0, 2.0, 3.0]
        Length: 4, dtype: float64
        z does not implement interpolate)rmrn�__name__)	rf�method�axis�index�limit�limit_direction�
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        Pad or backfill values, used by Series/DataFrame ffill and bfill.

        Parameters
        ----------
        method : {'backfill', 'bfill', 'pad', 'ffill'}
            Method to use for filling holes in reindexed Series:

            * pad / ffill: propagate last valid observation forward to next valid.
            * backfill / bfill: use NEXT valid observation to fill gap.

        limit : int, default None
            This is the maximum number of consecutive
            NaN values to forward/backward fill. In other words, if there is
            a gap with more than this number of consecutive NaNs, it will only
            be partially filled. If method is not specified, this is the
            maximum number of entries along the entire axis where NaNs will be
            filled.

        copy : bool, default True
            Whether to make a copy of the data before filling. If False, then
            the original should be modified and no new memory should be allocated.
            For ExtensionArray subclasses that cannot do this, it is at the
            author's discretion whether to ignore "copy=False" or to raise.
            The base class implementation ignores the keyword if any NAs are
            present.

        Returns
        -------
        Same type as self

        Examples
        --------
        >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan])
        >>> arr._pad_or_backfill(method="backfill", limit=1)
        <IntegerArray>
        [<NA>, 2, 2, 3, <NA>, <NA>]
        Length: 6, dtype: Int64
        z�ExtensionArray.fillna 'method' keyword is deprecated. In a future version. arr._pad_or_backfill will be called instead. 3rd-party ExtensionArray authors need to implement _pad_or_backfill.rSNz� does not implement limit_area (added in pandas 2.2). 3rd-party ExtnsionArray authors need to add this argument to _pad_or_backfill.)r�r��pad�r�T��
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||�\}}|j�}tj||t|��}|j�r�|��tj|�}tj|�}|dk(r*tj ||��}|j#|d��Stj |ddd	�|��ddd	�}|ddd	�j#|d��S|s|dd}	n|j%�}	||	|<|	S|s|dd}	|	S|j%�}	|	S)
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        Fill NA/NaN values using the specified method.

        Parameters
        ----------
        value : scalar, array-like
            If a scalar value is passed it is used to fill all missing values.
            Alternatively, an array-like "value" can be given. It's expected
            that the array-like have the same length as 'self'.
        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
            Method to use for filling holes in reindexed Series:

            * pad / ffill: propagate last valid observation forward to next valid.
            * backfill / bfill: use NEXT valid observation to fill gap.

            .. deprecated:: 2.1.0

        limit : int, default None
            If method is specified, this is the maximum number of consecutive
            NaN values to forward/backward fill. In other words, if there is
            a gap with more than this number of consecutive NaNs, it will only
            be partially filled. If method is not specified, this is the
            maximum number of entries along the entire axis where NaNs will be
            filled.

            .. deprecated:: 2.1.0

        copy : bool, default True
            Whether to make a copy of the data before filling. If False, then
            the original should be modified and no new memory should be allocated.
            For ExtensionArray subclasses that cannot do this, it is at the
            author's discretion whether to ignore "copy=False" or to raise.
            The base class implementation ignores the keyword in pad/backfill
            cases.

        Returns
        -------
        ExtensionArray
            With NA/NaN filled.

        Examples
        --------
        >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan])
        >>> arr.fillna(0)
        <IntegerArray>
        [0, 0, 2, 3, 0, 0]
        Length: 6, dtype: Int64
        NzThe 'method' keyword in z>.fillna is deprecated and will be removed in a future version.rSr�r�Tr�r�)rXrYrnr��
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���8�8�:��!��0�0��8�����D�)���5�=�&�7�7��e�L�G��9�9�W��9�>�>�'�7�7��t��t��E�R�SW�UW�SW�X�G���"��:�?�?�7�t�?�D�D��!%�a��J�!%����J�#(�
�4� ���	�!�!�W�
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        Return ExtensionArray without NA values.

        Returns
        -------

        Examples
        --------
        >>> pd.array([1, 2, np.nan]).dropna()
        <IntegerArray>
        [1, 2]
        Length: 2, dtype: Int64
        r rss rN�dropnazExtensionArray.dropna�s���T�Y�Y�[�L�!�!rQ�keepc�|�|j�jtjd��}t	|||��S)aU
        Return boolean ndarray denoting duplicate values.

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        ----------
        keep : {'first', 'last', False}, default 'first'
            - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
            - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
            - False : Mark all duplicates as ``True``.

        Returns
        -------
        ndarray[bool]

        Examples
        --------
        >>> pd.array([1, 1, 2, 3, 3], dtype="Int64").duplicated()
        array([False,  True, False, False,  True])
        F)rJ)r_r�r�)r!r�r��bool_r%)rfr�r�s   rNr%zExtensionArray.duplicated�s2��,�y�y�{�!�!�"�(�(��!�7����D�t�<�<rQc	�`�t|�r|dk(r|j�St|�r|jj}|j|gt
t|�t|��z|j��}|dkDr	|}|d|}n|t|�d}|}|j||g�S)a@
        Shift values by desired number.

        Newly introduced missing values are filled with
        ``self.dtype.na_value``.

        Parameters
        ----------
        periods : int, default 1
            The number of periods to shift. Negative values are allowed
            for shifting backwards.

        fill_value : object, optional
            The scalar value to use for newly introduced missing values.
            The default is ``self.dtype.na_value``.

        Returns
        -------
        ExtensionArray
            Shifted.

        Notes
        -----
        If ``self`` is empty or ``periods`` is 0, a copy of ``self`` is
        returned.

        If ``periods > len(self)``, then an array of size
        len(self) is returned, with all values filled with
        ``self.dtype.na_value``.

        For 2-dimensional ExtensionArrays, we are always shifting along axis=0.

        Examples
        --------
        >>> arr = pd.array([1, 2, 3])
        >>> arr.shift(2)
        <IntegerArray>
        [<NA>, <NA>, 1]
        Length: 3, dtype: Int64
        rr�N)	rwrJr!rIr|rO�min�abs�_concat_same_type)rf�periods�
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���Q�;��A��Y�w�h��A��S��\�^�$�A��A��%�%�q�!�f�-�-rQc�x�t|jt��}|j||j��S)a@
        Compute the ExtensionArray of unique values.

        Returns
        -------
        pandas.api.extensions.ExtensionArray

        Examples
        --------
        >>> arr = pd.array([1, 2, 3, 1, 2, 3])
        >>> arr.unique()
        <IntegerArray>
        [1, 2, 3]
        Length: 3, dtype: Int64
        r�)r+r��objectrOrI)rf�uniquess  rNr+zExtensionArray.unique�s1�� ����V�,�-���"�"�7�$�*�*�"�=�=rQc��|jt�}t|t�r|jt�}|j	|||��S)a8
        Find indices where elements should be inserted to maintain order.

        Find the indices into a sorted array `self` (a) such that, if the
        corresponding elements in `value` were inserted before the indices,
        the order of `self` would be preserved.

        Assuming that `self` is sorted:

        ======  ================================
        `side`  returned index `i` satisfies
        ======  ================================
        left    ``self[i-1] < value <= self[i]``
        right   ``self[i-1] <= value < self[i]``
        ======  ================================

        Parameters
        ----------
        value : array-like, list or scalar
            Value(s) to insert into `self`.
        side : {'left', 'right'}, optional
            If 'left', the index of the first suitable location found is given.
            If 'right', return the last such index.  If there is no suitable
            index, return either 0 or N (where N is the length of `self`).
        sorter : 1-D array-like, optional
            Optional array of integer indices that sort array a into ascending
            order. They are typically the result of argsort.

        Returns
        -------
        array of ints or int
            If value is array-like, array of insertion points.
            If value is scalar, a single integer.

        See Also
        --------
        numpy.searchsorted : Similar method from NumPy.

        Examples
        --------
        >>> arr = pd.array([1, 2, 3, 5])
        >>> arr.searchsorted([4])
        array([3])
        )�side�sorter)r�r�r}rF�searchsorted)rfrpr�r��arrs     rNr�zExtensionArray.searchsorted�sC��n�k�k�&�!���e�^�,��L�L��(�E�����D���@�@rQc�|�t|�t|�k7rytt|�}|j|jk7ryt	|�t	|�k7ry||k(}t|t�r|j
d�}|j�|j�z}t||zj��S)a�
        Return if another array is equivalent to this array.

        Equivalent means that both arrays have the same shape and dtype, and
        all values compare equal. Missing values in the same location are
        considered equal (in contrast with normal equality).

        Parameters
        ----------
        other : ExtensionArray
            Array to compare to this Array.

        Returns
        -------
        boolean
            Whether the arrays are equivalent.

        Examples
        --------
        >>> arr1 = pd.array([1, 2, np.nan])
        >>> arr2 = pd.array([1, 2, np.nan])
        >>> arr1.equals(arr2)
        True
        F)
rnr	rFrIrwr}r�r!r�r�)rfr��equal_values�equal_nas    rN�equalszExtensionArray.equals6s���2��:��e��$���^�U�+���:�:����$��
��Y�#�e�*�
$���5�=�L��,��7�+�2�2�5�9���y�y�{�U�Z�Z�\�1�H����0�5�5�7�8�8rQc�@�ttj|�|�S)a�
        Pointwise comparison for set containment in the given values.

        Roughly equivalent to `np.array([x in values for x in self])`

        Parameters
        ----------
        values : np.ndarray or ExtensionArray

        Returns
        -------
        np.ndarray[bool]

        Examples
        --------
        >>> arr = pd.array([1, 2, 3])
        >>> arr.isin([1])
        <BooleanArray>
        [True, False, False]
        Length: 3, dtype: boolean
        )r'r�r�)rfr_s  rNr'zExtensionArray.isin_s��,�B�J�J�t�$�f�-�-rQc�L�|jt�tjfS)a�
        Return an array and missing value suitable for factorization.

        Returns
        -------
        values : ndarray
            An array suitable for factorization. This should maintain order
            and be a supported dtype (Float64, Int64, UInt64, String, Object).
            By default, the extension array is cast to object dtype.
        na_value : object
            The value in `values` to consider missing. This will be treated
            as NA in the factorization routines, so it will be coded as
            `-1` and not included in `uniques`. By default,
            ``np.nan`` is used.

        Notes
        -----
        The values returned by this method are also used in
        :func:`pandas.util.hash_pandas_object`. If needed, this can be
        overridden in the ``self._hash_pandas_object()`` method.

        Examples
        --------
        >>> pd.array([1, 2, 3])._values_for_factorize()
        (array([1, 2, 3], dtype=object), nan)
        )r�r�r��nanrss rN�_values_for_factorizez$ExtensionArray._values_for_factorizews��6�{�{�6�"�B�F�F�*�*rQc�v�|j�\}}t|||��\}}|j||�}||fS)a�
        Encode the extension array as an enumerated type.

        Parameters
        ----------
        use_na_sentinel : bool, default True
            If True, the sentinel -1 will be used for NaN values. If False,
            NaN values will be encoded as non-negative integers and will not drop the
            NaN from the uniques of the values.

            .. versionadded:: 1.5.0

        Returns
        -------
        codes : ndarray
            An integer NumPy array that's an indexer into the original
            ExtensionArray.
        uniques : ExtensionArray
            An ExtensionArray containing the unique values of `self`.

            .. note::

               uniques will *not* contain an entry for the NA value of
               the ExtensionArray if there are any missing values present
               in `self`.

        See Also
        --------
        factorize : Top-level factorize method that dispatches here.

        Notes
        -----
        :meth:`pandas.factorize` offers a `sort` keyword as well.

        Examples
        --------
        >>> idx1 = pd.PeriodIndex(["2014-01", "2014-01", "2014-02", "2014-02",
        ...                       "2014-03", "2014-03"], freq="M")
        >>> arr, idx = idx1.factorize()
        >>> arr
        array([0, 0, 1, 1, 2, 2])
        >>> idx
        PeriodIndex(['2014-01', '2014-02', '2014-03'], dtype='period[M]')
        )�use_na_sentinelr|)r�r&ra)rfr�r�r|�codesr��
uniques_eas       rN�	factorizezExtensionArray.factorize�sK��p�2�2�4�
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        Repeat elements of a %(klass)s.

        Returns a new %(klass)s where each element of the current %(klass)s
        is repeated consecutively a given number of times.

        Parameters
        ----------
        repeats : int or array of ints
            The number of repetitions for each element. This should be a
            non-negative integer. Repeating 0 times will return an empty
            %(klass)s.
        axis : None
            Must be ``None``. Has no effect but is accepted for compatibility
            with numpy.

        Returns
        -------
        %(klass)s
            Newly created %(klass)s with repeated elements.

        See Also
        --------
        Series.repeat : Equivalent function for Series.
        Index.repeat : Equivalent function for Index.
        numpy.repeat : Similar method for :class:`numpy.ndarray`.
        ExtensionArray.take : Take arbitrary positions.

        Examples
        --------
        >>> cat = pd.Categorical(['a', 'b', 'c'])
        >>> cat
        ['a', 'b', 'c']
        Categories (3, object): ['a', 'b', 'c']
        >>> cat.repeat(2)
        ['a', 'a', 'b', 'b', 'c', 'c']
        Categories (3, object): ['a', 'b', 'c']
        >>> cat.repeat([1, 2, 3])
        ['a', 'b', 'b', 'c', 'c', 'c']
        Categories (3, object): ['a', 'b', 'c']
        �repeat)�klassc��tjdd|i�tjt	|��j|�}|j
|�S)Nrdr�)r��validate_repeatr��arangerwrr�)rf�repeatsr��inds    rNrzExtensionArray.repeatsC��	���2���~�.��i�i��D�	�"�)�)�'�2���y�y��~�rQ)r�r�c��t|��)a�
        Take elements from an array.

        Parameters
        ----------
        indices : sequence of int or one-dimensional np.ndarray of int
            Indices to be taken.
        allow_fill : bool, default False
            How to handle negative values in `indices`.

            * False: negative values in `indices` indicate positional indices
              from the right (the default). This is similar to
              :func:`numpy.take`.

            * True: negative values in `indices` indicate
              missing values. These values are set to `fill_value`. Any other
              other negative values raise a ``ValueError``.

        fill_value : any, optional
            Fill value to use for NA-indices when `allow_fill` is True.
            This may be ``None``, in which case the default NA value for
            the type, ``self.dtype.na_value``, is used.

            For many ExtensionArrays, there will be two representations of
            `fill_value`: a user-facing "boxed" scalar, and a low-level
            physical NA value. `fill_value` should be the user-facing version,
            and the implementation should handle translating that to the
            physical version for processing the take if necessary.

        Returns
        -------
        ExtensionArray

        Raises
        ------
        IndexError
            When the indices are out of bounds for the array.
        ValueError
            When `indices` contains negative values other than ``-1``
            and `allow_fill` is True.

        See Also
        --------
        numpy.take : Take elements from an array along an axis.
        api.extensions.take : Take elements from an array.

        Notes
        -----
        ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
        ``iloc``, when `indices` is a sequence of values. Additionally,
        it's called by :meth:`Series.reindex`, or any other method
        that causes realignment, with a `fill_value`.

        Examples
        --------
        Here's an example implementation, which relies on casting the
        extension array to object dtype. This uses the helper method
        :func:`pandas.api.extensions.take`.

        .. code-block:: python

           def take(self, indices, allow_fill=False, fill_value=None):
               from pandas.core.algorithms import take

               # If the ExtensionArray is backed by an ndarray, then
               # just pass that here instead of coercing to object.
               data = self.astype(object)

               if allow_fill and fill_value is None:
                   fill_value = self.dtype.na_value

               # fill value should always be translated from the scalar
               # type for the array, to the physical storage type for
               # the data, before passing to take.

               result = take(data, indices, fill_value=fill_value,
                             allow_fill=allow_fill)
               return self._from_sequence(result, dtype=self.dtype)
        r)rf�indicesr�r�s    rNr�zExtensionArray.takes��z"�$�'�'rQc��t|��)a=
        Return a copy of the array.

        Returns
        -------
        ExtensionArray

        Examples
        --------
        >>> arr = pd.array([1, 2, 3])
        >>> arr2 = arr.copy()
        >>> arr[0] = 2
        >>> arr2
        <IntegerArray>
        [1, 2, 3]
        Length: 3, dtype: Int64
        rrss rNrJzExtensionArray.copyks��$"�$�'�'rQc�&�|�t|��|ddS)a�
        Return a view on the array.

        Parameters
        ----------
        dtype : str, np.dtype, or ExtensionDtype, optional
            Default None.

        Returns
        -------
        ExtensionArray or np.ndarray
            A view on the :class:`ExtensionArray`'s data.

        Examples
        --------
        This gives view on the underlying data of an ``ExtensionArray`` and is not a
        copy. Modifications on either the view or the original ``ExtensionArray``
        will be reflectd on the underlying data:

        >>> arr = pd.array([1, 2, 3])
        >>> arr2 = arr.view()
        >>> arr[0] = 2
        >>> arr2
        <IntegerArray>
        [2, 2, 3]
        Length: 3, dtype: Int64
        N)rm)rfrIs  rN�viewzExtensionArray.views��@��%�e�,�,��A�w�rQc��|jdkDr|j�Sddlm}|||j	�d��jd�}dt
|�j�d�}|j�}|�|�d	|��S)
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�<z>
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%��$�/�/�#�U�
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�&��.�	
���d��,�,�-�S�1�
��&�&�(����d�V�2�f�X�.�.rQc��|jdkDrd|j�d|j��Sdt|��d|j��S)Nr�zShape: z	, dtype: zLength: )r�r�rIrwrss rNrzExtensionArray._get_repr_footer�sC���9�9�q�=��T�Z�Z�L�	�$�*�*��>�>��#�d�)��I�d�j�j�\�:�:rQc�
�ddlm}|D�cgc]*}|||j�d��jd���,}}dj	|�}dt|�j�d�}|j�}|�d	|�d
|��Scc}w)NrrFrrz,
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"�!�T�_�_�%6��N�U�U��
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��z�z�%� ����d��,�,�-�Q�/�
��&�&�(����U�4�&��f�X�6�6��
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        Formatting function for scalar values.

        This is used in the default '__repr__'. The returned formatting
        function receives instances of your scalar type.

        Parameters
        ----------
        boxed : bool, default False
            An indicated for whether or not your array is being printed
            within a Series, DataFrame, or Index (True), or just by
            itself (False). This may be useful if you want scalar values
            to appear differently within a Series versus on its own (e.g.
            quoted or not).

        Returns
        -------
        Callable[[Any], str]
            A callable that gets instances of the scalar type and
            returns a string. By default, :func:`repr` is used
            when ``boxed=False`` and :func:`str` is used when
            ``boxed=True``.

        Examples
        --------
        >>> class MyExtensionArray(pd.arrays.NumpyExtensionArray):
        ...     def _formatter(self, boxed=False):
        ...         return lambda x: '*' + str(x) + '*' if boxed else repr(x) + '*'
        >>> MyExtensionArray(np.array([1, 2, 3, 4]))
        <MyExtensionArray>
        [1*, 2*, 3*, 4*]
        Length: 4, dtype: int64
        )�str�repr)rf�boxeds  rNrzExtensionArray._formatter�s��D��J��rQc��|ddS)a�
        Return a transposed view on this array.

        Because ExtensionArrays are always 1D, this is a no-op.  It is included
        for compatibility with np.ndarray.

        Returns
        -------
        ExtensionArray

        Examples
        --------
        >>> pd.array([1, 2, 3]).transpose()
        <IntegerArray>
        [1, 2, 3]
        Length: 3, dtype: Int64
        Nrd)rf�axess  rN�	transposezExtensionArray.transpose�s
��$�A�w�rQc�"�|j�Src)r+rss rN�TzExtensionArray.Ts���~�~��rQc��|S)a
        Return a flattened view on this array.

        Parameters
        ----------
        order : {None, 'C', 'F', 'A', 'K'}, default 'C'

        Returns
        -------
        ExtensionArray

        Notes
        -----
        - Because ExtensionArrays are 1D-only, this is a no-op.
        - The "order" argument is ignored, is for compatibility with NumPy.

        Examples
        --------
        >>> pd.array([1, 2, 3]).ravel()
        <IntegerArray>
        [1, 2, 3]
        Length: 3, dtype: Int64
        rd)rf�orders  rN�ravelzExtensionArray.ravels	��0�rQc��t|��)a�
        Concatenate multiple array of this dtype.

        Parameters
        ----------
        to_concat : sequence of this type

        Returns
        -------
        ExtensionArray

        Examples
        --------
        >>> arr1 = pd.array([1, 2, 3])
        >>> arr2 = pd.array([4, 5, 6])
        >>> pd.arrays.IntegerArray._concat_same_type([arr1, arr2])
        <IntegerArray>
        [1, 2, 3, 4, 5, 6]
        Length: 6, dtype: Int64
        r)rL�	to_concats  rNr�z ExtensionArray._concat_same_type*rPrQc�.�|jjSrc)rIr{rss rNr{zExtensionArray._can_hold_naLs���z�z�&�&�&rQ�r�c�8�td|�d|j����)as
        Return an ExtensionArray performing an accumulation operation.

        The underlying data type might change.

        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            - cummin
            - cummax
            - cumsum
            - cumprod
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        NotImplementedError : subclass does not define accumulations

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            If True, skip NaN values.
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            Hash_key for string key to encode.
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        operator for an ExtensionArray subclass, by dispatching to the
        relevant operator defined on the individual elements of the
        ExtensionArray.

        Parameters
        ----------
        op : function
            An operator that takes arguments op(a, b)
        coerce_to_dtype : bool, default True
            boolean indicating whether to attempt to convert
            the result to the underlying ExtensionArray dtype.
            If it's not possible to create a new ExtensionArray with the
            values, an ndarray is returned instead.

        Returns
        -------
        Callable[[Any, Any], Union[ndarray, ExtensionArray]]
            A method that can be bound to a class. When used, the method
            receives the two arguments, one of which is the instance of
            this class, and should return an ExtensionArray or an ndarray.

            Returning an ndarray may be necessary when the result of the
            `op` cannot be stored in the ExtensionArray. The dtype of the
            ndarray uses NumPy's normal inference rules.

        Examples
        --------
        Given an ExtensionArray subclass called MyExtensionArray, use

            __add__ = cls._create_method(operator.add)

        in the class definition of MyExtensionArray to create the operator
        for addition, that will be based on the operator implementation
        of the underlying elements of the ExtensionArray
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