e.

    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
    Ú	extensionič