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()

    data = extract_array(data, extract_numpy=True)

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

        if dtype is not None:
            subarr = np.array(data, copy=False)

            # possibility of nan -> garbage
            if is_float_dtype(data.dtype) and is_integer_dtype(dtype):
                try:
                    subarr = _try_cast(data, True, dtype, copy,
                                       True)
                except ValueError:
                    if copy:
                        subarr = data.copy()
            else:
                subarr = _try_cast(data, True, dtype, copy, raise_cast_failure)
        elif isinstance(data, Index):
            # don't coerce Index types
            # e.g. indexes can have different conversions (so don't fast path
            # them)
            # GH#6140
            subarr = sanitize_index(data, index, copy=copy)
        else:

            # we will try to copy be-definition here
            subarr = _try_cast(data, True, dtype, copy, raise_cast_failure)

    elif isinstance(data, ExtensionArray):
        if isinstance(data, ABCPandasArray):
            # We don't want to let people put our PandasArray wrapper
            # (the output of Series/Index.array), into a Series. So
            # we explicitly unwrap it here.
            subarr = data.to_numpy()
        else:
            subarr = data

        # everything else in this block must also handle ndarray's,
        # becuase we've unwrapped PandasArray into an ndarray.

        if dtype is not None:
            subarr = data.astype(dtype)

        if copy:
            subarr = data.copy()
        return subarr

    elif isinstance(data, (list, tuple)) and len(data) > 0:
        if dtype is not None:
            try:
                subarr = _try_cast(data, False, 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, False, dtype, copy, raise_cast_failure)
    else:
        subarr = _try_cast(data, False, 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):
        # 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):
            if not np.all(isna(data)):
                data = np.array(data, dtype=dtype, copy=False)
            subarr = np.array(data, dtype=object, copy=copy)

    if is_object_dtype(subarr.dtype) and dtype != 'object':
        inferred = lib.infer_dtype(subarr, skipna=False)
        if inferred == 'period':
            try:
                subarr = period_array(subarr)
            except IncompatibleFrequency:
                pass

    return subarr
Sanitize input data to an ndarray, copy if specified, coerce to the
    dtype if specified.