rrays.StringArray` or
                                       :class:`pandas.arrays.ArrowStringArray`
        :class:`bool`                  :class:`pandas.arrays.BooleanArray`
        ============================== =======================================

        The ExtensionArray created when the scalar type is :class:`str` is determined by
        ``pd.options.mode.string_storage`` if the dtype is not explicitly given.

        For all other cases, NumPy's usual inference rules will be used.

        .. versionchanged:: 1.2.0

            Pandas now also infers nullable-floating dtype for float-like
            input data

    copy : bool, default True
        Whether to copy the data, even if not necessary. Depending
        on the type of `data`, creating the new array may require
        copying data, even if ``copy=False``.

    Returns
    -------
    ExtensionArray
        The newly created array.

    Raises
    ------
    ValueError
        When `data` is not 1-dimensional.

    See Also
    --------
    numpy.array : Construct a NumPy array.
    Series : Construct a pandas Series.
    Index : Construct a pandas Index.
    arrays.NumpyExtensionArray : ExtensionArray wrapping a NumPy array.
    Series.array : Extract the array stored within a Series.

    Notes
    -----
    Omitting the `dtype` argument means pandas will attempt to infer the
    best array type from the values in the data. As new array types are
    added by pandas and 3rd party libraries, the "best" array type may
    change. We recommend specifying `dtype` to ensure that

    1. the correct array type for the data is returned
    2. the returned array type doesn't change as new extension types
       are added by pandas and third-party libraries

    Additionally, if the underlying memory representation of the returned
    array matters, we recommend specifying the `dtype` as a concrete object
    rather than a string alias or allowing it to be inferred. For example,
    a future version of pandas or a 3rd-party library may include a
    dedicated ExtensionArray for string data. In this event, the following
    would no longer return a :class:`arrays.NumpyExtensionArray` backed by a
    NumPy array.

    >>> pd.array(['a', 'b'], dtype=str)
    <NumpyExtensionArray>
    ['a', 'b']
    Length: 2, dtype: str32

    This would instead return the new ExtensionArray dedicated for string
    data. If you really need the new array to be backed by a  NumPy array,
    specify that in the dtype.

    >>> pd.array(['a', 'b'], dtype=np.dtype("<U1"))
    <NumpyExtensionArray>
    ['a', 'b']
    Length: 2, dtype: str32

    Finally, Pandas has arrays that mostly overlap with NumPy

      * :class:`arrays.DatetimeArray`
      * :class:`arrays.TimedeltaArray`

    When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
    passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
    rather than a ``NumpyExtensionArray``. This is for symmetry with the case of
    timezone-aware data, which NumPy does not natively support.

    >>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
    <DatetimeArray>
    ['2015-01-01 00:00:00', '2016-01-01 00:00:00']
    Length: 2, dtype: datetime64[ns]

    >>> pd.array(["1H", "2H"], dtype='timedelta64[ns]')
    <TimedeltaArray>
    ['0 days 01:00:00', '0 days 02:00:00']
    Length: 2, dtype: timedelta64[ns]

    Examples
    --------
    If a dtype is not specified, pandas will infer the best dtype from the values.
    See the description of `dtype` for the types pandas infers for.

    >>> pd.array([1, 2])
    <IntegerArray>
    [1, 2]
    Length: 2, dtype: Int64

    >>> pd.array([1, 2, np.nan])
    <IntegerArray>
    [1, 2, <NA>]
    Length: 3, dtype: Int64

    >>> pd.array([1.1, 2.2])
    <FloatingArray>
    [1.1, 2.2]
    Length: 2, dtype: Float64

    >>> pd.array(["a", None, "c"])
    <StringArray>
    ['a', <NA>, 'c']
    Length: 3, dtype: string

    >>> with pd.option_context("string_storage", "pyarrow"):
    ...     arr = pd.array(["a", None, "c"])
    ...
    >>> arr
    <ArrowStringArray>
    ['a', <NA>, 'c']
    Length: 3, dtype: string

    >>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
    <PeriodArray>
    ['2000-01-01', '2000-01-01']
    Length: 2, dtype: period[D]

    You can use the string alias for `dtype`

    >>> pd.array(['a', 'b', 'a'], dtype='category')
    ['a', 'b', 'a']
    Categories (2, object): ['a', 'b']

    Or specify the actual dtype

    >>> pd.array(['a', 'b', 'a'],
    ...          dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
    ['a', 'b', 'a']
    Categories (3, object): ['a' < 'b' < 'c']

    If pandas does not infer a dedicated extension type a
    :class:`arrays.NumpyExtensionArray` is returned.

    >>> pd.array([1 + 1j, 3 + 2j])
    <NumpyExtensionArray>
    [(1+1j), (3+2j)]
    Length: 2, dtype: complex128

    As mentioned in the "Notes" section, new extension types may be added
    in the future (by pandas or 3rd party libraries), causing the return
    value to no longer be a :class:`arrays.NumpyExtensionArray`. Specify the
    `dtype` as a NumPy dtype if you need to ensure there's no future change in
    behavior.

    >>> pd.array([1, 2], dtype=np.dtype("int32"))
    <NumpyExtensionArray>
    [1, 2]
    Length: 2, dtype: int32

    `data` must be 1-dimensional. A ValueError is raised when the input
    has the wrong dimensionality.

    >>> pd.array(1)
    Traceback (most recent call last):
      ...
    ValueError: Cannot pass scalar '1' to 'pandas.array'.
    r