Fix prop_above_and_below typing and empty concat handling

CHANGELOG.md

@@ -66,6 +66,11 @@
     when the input sample has zero rows, instead of relying on an assertion.
   - This aligns runtime behavior with documented exceptions and avoids
     optimization-dependent assert behavior.
+- **Stabilized `prop_above_and_below()` return paths**
+  - `prop_above_and_below()` now builds concatenated outputs only from present
+    Series objects and returns `None` when both `below` and `above` are `None`,
+    avoiding ambiguous concat inputs while preserving existing behavior for valid
+    threshold sets.
 
 ## Tests
 
@@ -79,6 +84,8 @@
   - Added tests for `link_transform()`, and `calc_dev()` to validate behavior for extreme probabilities, and finite 10-fold deviance summaries.
 - **Expanded ASCII plot dispatcher edge-case coverage**
   - Added tests for `ascii_plot_dist` with `comparative=False` to verify direct dispatch to `ascii_plot_hist` and mixed categorical+numeric routing in a single call.
+- **Expanded `prop_above_and_below()` edge-case coverage**
+  - Added focused tests for empty threshold iterables, mixed `None` threshold groups in dict mode, and explicit all-`None` threshold handling across return formats.
 
 # 0.16.0 (2026-02-09)
 

balance/stats_and_plots/weights_stats.py

@@ -8,7 +8,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any
+from typing import Any, Literal, overload, TypedDict
 
 import numpy as np
 import numpy.typing as npt
@@ -17,6 +17,11 @@
 logger: logging.Logger = logging.getLogger(__package__)
 
 
+class PropAboveBelowResult(TypedDict):
+    below: pd.Series | None
+    above: pd.Series | None
+
+
 ##########################################
 # Weights diagnostics - functions for analyzing weights
 # These functions provide statistical measures for evaluating
@@ -199,12 +204,33 @@ def nonparametric_skew(
     return (w.mean() - w.median()) / w.std()
 
 
+@overload
+def prop_above_and_below(
+    w: list[Any] | pd.Series | npt.NDArray | pd.DataFrame,
+    below: tuple[float, ...] | list[float] | None = (1 / 10, 1 / 5, 1 / 3, 1 / 2, 1),
+    above: tuple[float, ...] | list[float] | None = (1, 2, 3, 5, 10),
+    return_as_series: Literal[True] = True,
+) -> pd.Series | None:
+    pass
+
+
+@overload
+def prop_above_and_below(
+    w: list[Any] | pd.Series | npt.NDArray | pd.DataFrame,
+    below: tuple[float, ...] | list[float] | None = (1 / 10, 1 / 5, 1 / 3, 1 / 2, 1),
+    above: tuple[float, ...] | list[float] | None = (1, 2, 3, 5, 10),
+    *,
+    return_as_series: Literal[False],
+) -> PropAboveBelowResult | None:
+    pass
+
+
 def prop_above_and_below(
     w: list[Any] | pd.Series | npt.NDArray | pd.DataFrame,
     below: tuple[float, ...] | list[float] | None = (1 / 10, 1 / 5, 1 / 3, 1 / 2, 1),
     above: tuple[float, ...] | list[float] | None = (1, 2, 3, 5, 10),
     return_as_series: bool = True,
-) -> pd.Series | dict[Any, Any] | None:
+) -> pd.Series | PropAboveBelowResult | None:
     # TODO (p2): look more in the literature (are there references for using this vs another, or none at all?)
     #            update the doc with insights, once done.
     """
@@ -224,14 +250,15 @@ def prop_above_and_below(
             DataFrame, only the first column is used.
         below (tuple[float, ...] | list[float] | None, optional):
             values to check which proportion of normalized weights are *below* them.
-            Using None returns None.
+            Using None omits below-threshold calculations.
             Defaults to (1/10, 1/5, 1/3, 1/2, 1).
         above (tuple[float, ...] | list[float] | None, optional):
             values to check which proportion of normalized weights are *above* (or equal) to them.
-            Using None returns None.
+            Using None omits above-threshold calculations.
             Defaults to (1, 2, 3, 5, 10).
         return_as_series (bool, optional): If true returns one pd.Series of values.
-            If False will return a dict with two pd.Series (one for below and one for above).
+            If False returns ``PropAboveBelowResult`` with ``below``/``above`` entries
+            containing a ``pd.Series`` or ``None`` for omitted groups.
             Defaults to True.
 
     Returns:
@@ -317,16 +344,12 @@ def prop_above_and_below(
 
     # decide if to return one series or a dict
     if return_as_series:
-        out = pd.concat(
-            [  # pyre-ignore[6]: pd.concat supports Series.
-                prop_below_series,
-                prop_above_series,
-            ]
-        )
+        pieces = [s for s in (prop_below_series, prop_above_series) if s is not None]
+        out = pd.concat(pieces) if pieces else None
     else:
-        out = {"below": prop_below_series, "above": prop_above_series}
+        out = PropAboveBelowResult(below=prop_below_series, above=prop_above_series)
 
-    return out  # pyre-ignore[7]:  TODO: see if we can fix this pyre
+    return out
 
 
 def weighted_median_breakdown_point(

tests/test_stats_and_plots.py

@@ -217,6 +217,58 @@ def test_prop_above_and_below(self) -> None:
         }
         self.assertEqual({k: v.to_list() for k, v in result_dict.items()}, expected)
 
+    def test_prop_above_and_below_edge_cases(self) -> None:
+        """Cover edge combinations for thresholds and return formats."""
+        from balance.stats_and_plots.weights_stats import prop_above_and_below
+
+        weights = pd.Series((1.0, 2.0, 3.0, 4.0))
+
+        # Empty threshold iterables should return an empty Series in series mode.
+        result_empty = prop_above_and_below(weights, below=(), above=())
+        self.assertIsNotNone(result_empty)
+        result_empty = _assert_type(result_empty, pd.Series)
+        self.assertEqual(result_empty.to_list(), [])
+        self.assertEqual(result_empty.index.to_list(), [])
+
+        # Dict mode should preserve None for omitted threshold groups.
+        result_dict_only_above = prop_above_and_below(
+            weights,
+            below=None,
+            above=(1, 2),
+            return_as_series=False,
+        )
+        self.assertIsNotNone(result_dict_only_above)
+        result_dict_only_above = _assert_type(result_dict_only_above)
+        self.assertIsNone(result_dict_only_above["below"])
+        self.assertEqual(
+            result_dict_only_above["above"].index.to_list(),
+            ["prop(w >= 1)", "prop(w >= 2)"],
+        )
+
+        result_dict_only_below = prop_above_and_below(
+            weights,
+            below=(0.5, 1),
+            above=None,
+            return_as_series=False,
+        )
+        self.assertIsNotNone(result_dict_only_below)
+        result_dict_only_below = _assert_type(result_dict_only_below)
+        self.assertEqual(
+            result_dict_only_below["below"].index.to_list(),
+            ["prop(w < 0.5)", "prop(w < 1)"],
+        )
+        self.assertIsNone(result_dict_only_below["above"])
+
+        # If both groups are omitted, function should return None in all modes.
+        self.assertIsNone(
+            prop_above_and_below(
+                weights,
+                below=None,
+                above=None,
+                return_as_series=False,
+            )
+        )
+
     def test_weights_diagnostics_accept_list_and_ndarray_input(self) -> None:
         """Ensure diagnostics are equivalent across list/ndarray/Series inputs."""
         from balance.stats_and_plots.weights_stats import (