String-based ExtensionArrays force cast to numpy in list based construction

[xhochy]

Code Sample, a copy-pastable example if possible

pd.Series(['E'], dtype=FletcherDType(pa.string())

Problem description

This leads to the following traceback but should actually create a Series object of a string-based type.

    def test_constructor_sanitation():
>       data = pd.Series(["E"], index=["F"], dtype=ArrowStringDtype())

pandas/tests/extension/arrow/test_string.py:11:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/core/series.py:310: in __init__
    data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

data = ['E'], index = Index(['F'], dtype='object'), dtype = <pandas.tests.extension.arrow.arrays.ArrowStringDtype object at 0x120913cf8>, copy = False, raise_cast_failure = True

    def sanitize_array(data, index, dtype=None, copy=False, raise_cast_failure=False):
        """
        Sanitize input data to an ndarray, copy if specified, coerce to the
        dtype if specified.
        """
        if dtype is not None:
            dtype = pandas_dtype(dtype)

        if isinstance(data, ma.MaskedArray):
            mask = ma.getmaskarray(data)
            if mask.any():
                data, fill_value = maybe_upcast(data, copy=True)
                data.soften_mask()  # set hardmask False if it was True
                data[mask] = fill_value
            else:
                data = data.copy()

        # extract ndarray or ExtensionArray, ensure we have no PandasArray
        data = extract_array(data, extract_numpy=True)

        # GH#846
        if isinstance(data, np.ndarray):

            if dtype is not None and is_float_dtype(data.dtype) and is_integer_dtype(dtype):
                # possibility of nan -> garbage
                try:
                    subarr = _try_cast(data, dtype, copy, True)
                except ValueError:
                    if copy:
                        subarr = data.copy()
                    else:
                        subarr = np.array(data, copy=False)
            else:
                # we will try to copy be-definition here
                subarr = _try_cast(data, dtype, copy, raise_cast_failure)

        elif isinstance(data, ABCExtensionArray):
            # it is already ensured above this is not a PandasArray
            subarr = data

            if dtype is not None:
                subarr = subarr.astype(dtype, copy=copy)
            elif copy:
                subarr = subarr.copy()
            return subarr

        elif isinstance(data, (list, tuple)) and len(data) > 0:
            if dtype is not None:
                try:
                    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
                except Exception:
                    if raise_cast_failure:  # pragma: no cover
                        raise
                    subarr = np.array(data, dtype=object, copy=copy)
                    subarr = lib.maybe_convert_objects(subarr)

            else:
                subarr = maybe_convert_platform(data)

            subarr = maybe_cast_to_datetime(subarr, dtype)

        elif isinstance(data, range):
            # GH#16804
            arr = np.arange(data.start, data.stop, data.step, dtype="int64")
            subarr = _try_cast(arr, dtype, copy, raise_cast_failure)
        else:
            subarr = _try_cast(data, dtype, copy, raise_cast_failure)

        # scalar like, GH
        if getattr(subarr, "ndim", 0) == 0:
            if isinstance(data, list):  # pragma: no cover
                subarr = np.array(data, dtype=object)
            elif index is not None:
                value = data

                # figure out the dtype from the value (upcast if necessary)
                if dtype is None:
                    dtype, value = infer_dtype_from_scalar(value)
                else:
                    # need to possibly convert the value here
                    value = maybe_cast_to_datetime(value, dtype)

                subarr = construct_1d_arraylike_from_scalar(value, len(index), dtype)

            else:
                return subarr.item()

        # the result that we want
        elif subarr.ndim == 1:
            if index is not None:

                # a 1-element ndarray
                if len(subarr) != len(index) and len(subarr) == 1:
                    subarr = construct_1d_arraylike_from_scalar(
                        subarr[0], len(index), subarr.dtype
                    )

        elif subarr.ndim > 1:
            if isinstance(data, np.ndarray):
                raise Exception("Data must be 1-dimensional")
            else:
                subarr = com.asarray_tuplesafe(data, dtype=dtype)

        # This is to prevent mixed-type Series getting all casted to
        # NumPy string type, e.g. NaN --> '-1#IND'.
        if issubclass(subarr.dtype.type, str):
            import ipdb; ipdb.set_trace()
            # GH#16605
            # If not empty convert the data to dtype
            # GH#19853: If data is a scalar, subarr has already the result
>           if not lib.is_scalar(data):
E           TypeError: data type not understood

pandas/core/construction.py:478: TypeError

Problem exists in 0.25 and master, I'm working on a fix and will submit a PR later.