feat(eda): capítulo MISSINGNESS — patrones de datos faltantes (co-ocurrencia + MCAR/MAR)

Añade el capítulo `missingness` al motor AutomaticEDA, complemento natural de
`calidad`: donde calidad reporta cuánto falta por columna, este capítulo analiza
el PATRÓN de los nulos — dónde faltan y si las columnas faltan juntas
(co-ocurrencia de ausencias), la señal que distingue MCAR de MAR antes de imputar.

Capítulo (`chapters/missingness.py`), registrado en `chapters_registry.py` justo
tras `calidad`:
- Resumen global: % de celdas faltantes, columnas con nulos, filas completas vs
  incompletas.
- Ranking por columna (tabla + barras horizontales).
- Co-ocurrencia: correlación de las máscaras is-null entre columnas (heatmap +
  tabla de los pares que co-faltan, con co-faltantes y Jaccard).
- Patrones de fila más frecuentes (estilo matriz de missingno).
- Lectura MCAR/MAR exploratoria (heurística por correlación/solape de ausencias,
  no confirmatoria), que cita la evidencia concreta.
- Términos de glosario clicables: missingness, MCAR, MAR.

La máscara is-null por fila de TODAS las columnas (numéricas y categóricas) se
construye con un push-down DuckDB sobre ctx['db_path']/table (mismo patrón que el
capítulo agregación), con fallback a ctx['raw_numeric'] cuando no hay BD. Activa
solo si la tabla tiene nulos; si no, devuelve None.

Funciones nuevas del grupo `eda` (dominio datascience):
- extract_null_mask (impura): máscara is-null por fila vía query_fn.
- missingness_overview (pura): resumen global + filas completas/incompletas.
- missingness_correlation (pura): correlación de ausencias + pares + Jaccard,
  reutiliza pearson.
- missingness_row_patterns (pura): patrones de fila más comunes.
- missingness_corr_heatmap_figure / missingness_rank_bar_figure (impuras): figuras.

Verificado: EDA de titanic genera el capítulo en PDF + PPTX + MD con Cabin 77.1%,
Age 19.9% y la co-ocurrencia Age↔Cabin (158 filas). Suite completa de AutomaticEDA
+ render_automatic_eda en verde (125 passed); tests por función y por capítulo;
fn index sin error.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

python/functions/datascience/missingness_overview_test.py

@@ -0,0 +1,146 @@
+"""Tests para missingness_overview."""
+
+import sys
+import os
+
+import pytest
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from missingness_overview import missingness_overview
+
+
+# Output contract: every call returns exactly these 9 keys.
+EXPECTED_KEYS = {
+    "n_rows",
+    "n_cols",
+    "n_cols_with_null",
+    "n_missing_cells",
+    "missing_cell_pct",
+    "complete_rows",
+    "incomplete_rows",
+    "complete_pct",
+    "incomplete_pct",
+}
+
+
+def test_cooccurrence_three_cols_exact():
+    # 3 columns, 5 rows. Hand-computed expectations:
+    #   col a missing at rows 0, 4      -> 2
+    #   col b missing at rows 0, 2      -> 2
+    #   col c missing at row  4         -> 1
+    #   n_missing_cells = 5, total_cells = 5*3 = 15 -> 33.333...%
+    #   row 0 (a&b co-occur)  -> incomplete
+    #   row 1 (all present)   -> complete
+    #   row 2 (b only)        -> incomplete
+    #   row 3 (all present)   -> complete
+    #   row 4 (a&c co-occur)  -> incomplete
+    mask = {
+        "a": [1, 0, 0, 0, 1],
+        "b": [1, 0, 1, 0, 0],
+        "c": [0, 0, 0, 0, 1],
+    }
+    out = missingness_overview(mask)
+    assert out["n_rows"] == 5
+    assert out["n_cols"] == 3
+    assert out["n_cols_with_null"] == 3
+    assert out["n_missing_cells"] == 5
+    assert out["missing_cell_pct"] == pytest.approx(33.33333333, abs=1e-6)
+    assert out["complete_rows"] == 2
+    assert out["incomplete_rows"] == 3
+    assert out["complete_pct"] == pytest.approx(40.0)
+    assert out["incomplete_pct"] == pytest.approx(60.0)
+
+
+def test_empty_dict_all_zero():
+    out = missingness_overview({})
+    assert out == {
+        "n_rows": 0,
+        "n_cols": 0,
+        "n_cols_with_null": 0,
+        "n_missing_cells": 0,
+        "missing_cell_pct": 0.0,
+        "complete_rows": 0,
+        "incomplete_rows": 0,
+        "complete_pct": 0.0,
+        "incomplete_pct": 0.0,
+    }
+
+
+def test_output_keys_contract():
+    # The 9-key contract holds even for the garbage/zero path.
+    assert set(missingness_overview({}).keys()) == EXPECTED_KEYS
+    assert set(missingness_overview({"a": [1, 0]}).keys()) == EXPECTED_KEYS
+
+
+def test_not_a_dict_returns_zero():
+    for bad in (None, [1, 0, 1], 42, "nope", 3.14):
+        out = missingness_overview(bad)
+        assert out["n_rows"] == 0
+        assert out["n_cols"] == 0
+        assert out["n_missing_cells"] == 0
+        assert out["missing_cell_pct"] == 0.0
+
+
+def test_no_nulls_all_complete():
+    mask = {"a": [0, 0, 0], "b": [0, 0, 0]}
+    out = missingness_overview(mask)
+    assert out["n_rows"] == 3
+    assert out["n_cols"] == 2
+    assert out["n_cols_with_null"] == 0
+    assert out["n_missing_cells"] == 0
+    assert out["missing_cell_pct"] == 0.0
+    assert out["complete_rows"] == 3
+    assert out["incomplete_rows"] == 0
+    assert out["complete_pct"] == pytest.approx(100.0)
+    assert out["incomplete_pct"] == pytest.approx(0.0)
+
+
+def test_none_values_treated_as_present():
+    # None and other non-1 values count as present (0).
+    mask = {"a": [None, 1, None, "x", 0]}
+    out = missingness_overview(mask)
+    assert out["n_rows"] == 5
+    assert out["n_cols"] == 1
+    assert out["n_missing_cells"] == 1  # only the explicit 1 at row 1
+    assert out["n_cols_with_null"] == 1
+    assert out["complete_rows"] == 4
+    assert out["incomplete_rows"] == 1
+
+
+def test_unequal_lengths_pad_with_max():
+    # Ragged lists: n_rows = max length; shorter column padded as present.
+    #   a = [1, 1] -> missing at rows 0, 1
+    #   b = [0]    -> row 1 padded to present
+    #   n_rows = 2, n_cols = 2, total_cells = 4, n_missing_cells = 2 -> 50%
+    mask = {"a": [1, 1], "b": [0]}
+    out = missingness_overview(mask)
+    assert out["n_rows"] == 2
+    assert out["n_cols"] == 2
+    assert out["n_cols_with_null"] == 1
+    assert out["n_missing_cells"] == 2
+    assert out["missing_cell_pct"] == pytest.approx(50.0)
+    assert out["complete_rows"] == 0
+    assert out["incomplete_rows"] == 2
+    assert out["incomplete_pct"] == pytest.approx(100.0)
+
+
+def test_columns_present_but_no_rows():
+    # Columns exist but all empty -> zero metrics, n_cols preserved.
+    out = missingness_overview({"a": [], "b": []})
+    assert out["n_rows"] == 0
+    assert out["n_cols"] == 2
+    assert out["n_missing_cells"] == 0
+    assert out["missing_cell_pct"] == 0.0
+    assert out["complete_pct"] == 0.0
+
+
+def test_never_raises_on_garbage():
+    # Non-list column values, mixed junk -> must not raise.
+    mask = {"a": "not a list", "b": 123, "c": [1, 0, 1]}
+    out = missingness_overview(mask)
+    assert set(out.keys()) == EXPECTED_KEYS
+    assert out["n_rows"] == 3
+    assert out["n_cols"] == 3
+    assert out["n_missing_cells"] == 2  # only col c contributes
+    assert out["n_cols_with_null"] == 1