axis_name = 'obs', join_type = 'outer', merge_strategy = 'only', shape = (8, 0)

    @pytest.mark.parametrize(
        "shape", [pytest.param((8, 0), id="no_var"), pytest.param((0, 10), id="no_obs")]
    )
    def test_concat_size_0_axis(axis_name, join_type, merge_strategy, shape):
        """Regression test for https://github.com/scverse/anndata/issues/526"""
        axis, axis_name = merge._resolve_axis(axis_name)
        alt_axis = 1 - axis
        col_dtypes = (*DEFAULT_COL_TYPES, pd.StringDtype)
        a = gen_adata((5, 7), obs_dtypes=col_dtypes, var_dtypes=col_dtypes)
        b = gen_adata(shape, obs_dtypes=col_dtypes, var_dtypes=col_dtypes)
    
        expected_size = expected_shape(a, b, axis=axis, join=join_type)
    
        ctx_concat_empty = (
            pytest.warns(
                FutureWarning,
                match=r"The behavior of DataFrame concatenation with empty or all-NA entries is deprecated",
            )
            if shape[axis] == 0 and Version(pd.__version__) >= Version("2.1")
            else nullcontext()
        )
        with ctx_concat_empty:
            result = concat(
                {"a": a, "b": b},
                axis=axis,
                join=join_type,
                merge=merge_strategy,
                pairwise=True,
                index_unique="-",
            )
        assert result.shape == expected_size
    
        if join_type == "outer":
            # Check new entries along axis of concatenation
            axis_new_inds = axis_labels(result, axis).str.endswith("-b")
            altaxis_new_inds = ~axis_labels(result, alt_axis).isin(axis_labels(a, alt_axis))
            axis_idx = make_idx_tuple(axis_new_inds, axis)
            altaxis_idx = make_idx_tuple(altaxis_new_inds, 1 - axis)
    
            check_filled_like(result.X[axis_idx], elem_name="X")
            check_filled_like(result.X[altaxis_idx], elem_name="X")
            for k, elem in getattr(result, "layers").items():
                check_filled_like(elem[axis_idx], elem_name=f"layers/{k}")
                check_filled_like(elem[altaxis_idx], elem_name=f"layers/{k}")
    
            if shape[axis] > 0:
                b_result = result[axis_idx].copy()
                mapping_elem = f"{axis_name}m"
                setattr(b_result, f"{axis_name}_names", getattr(b, f"{axis_name}_names"))
                for k, result_elem in getattr(b_result, mapping_elem).items():
                    elem_name = f"{mapping_elem}/{k}"
                    # pd.concat can have unintuitive return types. is similar to numpy promotion
                    if isinstance(result_elem, pd.DataFrame):
>                       assert_equal(
                            getattr(b, mapping_elem)[k].astype(object),
                            result_elem.astype(object),
                            elem_name=elem_name,
                        )

tests/test_concatenate.py:1425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/functools.py:907: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/anndata/tests/helpers.py:633: in are_equal_dataframe
    report_name(pd.testing.assert_frame_equal)(
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/anndata/tests/helpers.py:550: in func_wrapper
    raise e
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/anndata/tests/helpers.py:539: in func_wrapper
    return func(*args, **kwargs)
testing.pyx:55: in pandas._libs.testing.assert_almost_equal
    ???
testing.pyx:160: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   FutureWarning: Mismatched null-like values <NA> and nan found. In a future version, pandas equality-testing functions (e.g. assert_frame_equal) will consider these not-matching and raise.
E  

Test was limited to 1.5MiB but allocated 2.1MiB