def _load_nonlink_level(handler, level, pathtable, pathname):
    """
    Loads level and builds appropriate type, without handling softlinks
    """
    if isinstance(level, tables.Group):
        if _sns and (level._v_title.startswith('SimpleNamespace:') or
                     DEEPDISH_IO_ROOT_IS_SNS in level._v_attrs):
            val = SimpleNamespace()
            dct = val.__dict__
        elif level._v_title.startswith('list:'):
            dct = {}
            val = []
        else:
            dct = {}
            val = dct
        # in case of recursion, object needs to be put in pathtable
        # before trying to fully load it
        pathtable[pathname] = val

        # Load sub-groups
        for grp in level:
            lev = _load_level(handler, grp, pathtable)
            n = grp._v_name
            # Check if it's a complicated pair or a string-value pair
            if n.startswith('__pair'):
                dct[lev['key']] = lev['value']
            else:
                dct[n] = lev

        # Load attributes
        for name in level._v_attrs._f_list():
            if name.startswith(DEEPDISH_IO_PREFIX):
                continue
            v = level._v_attrs[name]
            dct[name] = v

        if level._v_title.startswith('list:'):
            N = int(level._v_title[len('list:'):])
            for i in range(N):
                val.append(dct['i{}'.format(i)])
            return val
        elif level._v_title.startswith('tuple:'):
            N = int(level._v_title[len('tuple:'):])
            lst = []
            for i in range(N):
                lst.append(dct['i{}'.format(i)])
            return tuple(lst)
        elif level._v_title.startswith('nonetype:'):
            return None
        elif is_pandas_dataframe(level):
            assert _pandas, "pandas is required to read this file"
            store = _HDFStoreWithHandle(handler)
            return store.get(level._v_pathname)
        elif level._v_title.startswith('sparse:'):
            frm = level._v_attrs.format
            if frm in ('csr', 'csc', 'bsr'):
                shape = tuple(level.shape[:])
                cls = {'csr': sparse.csr_matrix,
                       'csc': sparse.csc_matrix,
                       'bsr': sparse.bsr_matrix}
                matrix = cls[frm](shape)
                matrix.data = level.data[:]
                matrix.indices = level.indices[:]
                matrix.indptr = level.indptr[:]
                matrix.maxprint = level._v_attrs.maxprint
                return matrix
            elif frm == 'dia':
                shape = tuple(level.shape[:])
                matrix = sparse.dia_matrix(shape)
                matrix.data = level.data[:]
                matrix.offsets = level.offsets[:]
                matrix.maxprint = level._v_attrs.maxprint
                return matrix
            elif frm == 'coo':
                shape = tuple(level.shape[:])
                matrix = sparse.coo_matrix(shape)
                matrix.data = level.data[:]
                matrix.col = level.col[:]
                matrix.row = level.row[:]
                matrix.maxprint = level._v_attrs.maxprint
                return matrix
            else:
                raise ValueError('Unknown sparse matrix type: {}'.format(frm))
        else:
            return val

    elif isinstance(level, tables.VLArray):
        if level.shape == (1,):
            return _load_pickled(level)
        else:
            return level[:]

    elif isinstance(level, tables.Array):
        if 'zeroarray_dtype' in level._v_attrs:
            # Unpack zero-size arrays (shape is stored in an HDF5 array and
            # type is stored in the attibute 'zeroarray_dtype')
            dtype = level._v_attrs.zeroarray_dtype
            sh = level[:]
            return np.zeros(tuple(sh), dtype=dtype)

        if 'strtype' in level._v_attrs:
            strtype = level._v_attrs.strtype
            itemsize = level._v_attrs.itemsize
            if strtype == b'unicode':
                return level[:].view(dtype=(np.unicode_, itemsize))
            elif strtype == b'ascii':
                return level[:].view(dtype=(np.string_, itemsize))
        # This serves two purposes:
        # (1) unpack big integers: the only time we save arrays like this
        # (2) unpack non-deepdish "scalars"
        if level.shape == ():
            return level[()]

        return level[:]