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/automatic_eda/chapters/missingness.py

@@ -0,0 +1,594 @@
+"""Missingness chapter (MISSINGNESS) — patterns of missing data.
+
+Complements the CALIDAD chapter: where CALIDAD reports *how much* is missing per
+column (the null percentage that lowers the completeness score), this chapter
+reports the **pattern** of the missing data — whether columns tend to be missing
+*together* (co-occurrence of absences) or independently. That distinction is what
+separates data that is missing completely at random ([[term:mcar]]MCAR[[/term]])
+from data missing as a function of another variable ([[term:mar]]MAR[[/term]]),
+which is the key question to settle before imputing or modelling.
+
+The chapter activates only when the table actually has missing data (at least one
+column with a null in the aggregated profile); otherwise it returns ``None`` and
+disappears from the document.
+
+Sections, in order:
+
+1. **Resumen global** — % of missing cells in the dataset, number of columns with
+   nulls, and complete rows (no missing) vs incomplete rows (≥1 missing).
+2. **Ranking por columna** — columns sorted by their null percentage, with a
+   horizontal bar figure.
+3. **Co-ocurrencia de ausencias** — the correlation of the binary is-null masks
+   between columns (which columns tend to be missing together): a heatmap plus a
+   table of the top column pairs that co-miss.
+4. **Patrones de fila** — the most frequent "which columns are missing together"
+   row patterns, in the style of missingno's pattern matrix.
+5. **Lectura MCAR/MAR** — an interpretive, *exploratory* note (not a confirmatory
+   test such as Little's) reading the absence correlations as a hint of MCAR
+   (independent absences) vs MAR (co-occurring absences).
+
+The aggregate per-column null counts come from the ``eda`` group ``TableProfile``
+(``columns[i]['null_count'] / 'null_pct'`` and the table-level ``null_cell_pct``).
+The per-row is-null mask needed for co-occurrence is built from raw data: a single
+DuckDB push-down over ``ctx['db_path'] / ctx['table']`` (same pattern as the
+AGREGACION chapter) covering ALL columns, with a fallback to the numeric-only
+``ctx['raw_numeric']`` when no database is reachable. All the heavy lifting is
+delegated to pure registry functions (``missingness_overview``,
+``missingness_correlation``, ``missingness_row_patterns``) and two figure helpers
+(``missingness_rank_bar_figure``, ``missingness_corr_heatmap_figure``); every one
+is imported lazily and degrades to an honest note so this chapter never raises.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+"""
+
+from __future__ import annotations
+
+from .. import model
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "missingness"
+CHAPTER_TITLE = "Datos faltantes"
+
+# Sample cap for the per-row is-null mask push-down. Co-occurrence and row
+# patterns are computed on this sample; the global % of missing cells and the
+# per-column ranking come from the (exact) aggregated profile instead.
+MASK_SAMPLE = 5000
+# Thresholds for the MCAR/MAR heuristic note. A pair counts as a *strong*
+# co-occurrence when the absence correlation alone is high; as a *partial*
+# co-occurrence when the absences overlap materially (high Jaccard) even if the
+# Pearson correlation is modest — the usual case when one column is missing far
+# more often than the other (e.g. Cabin 77% vs Age 20% in Titanic), which dilutes
+# the correlation while the rows still co-miss in absolute terms.
+_CORR_STRONG = 0.30
+_JACCARD_NOTABLE = 0.20
+# Rows shown in the top-pairs and row-patterns tables (bounded, never silently
+# truncated: the table note reports the full count).
+_TOP_PAIRS = 12
+_TOP_PATTERNS = 12
+# Truncate long column names in tables (the renderer also wraps).
+_LABEL_MAX = 28
+
+# Glossary terms this chapter explains (contract §11.1). Registered in the shared
+# collector and marked clickable on their first appearance.
+_TERMS = {
+    "missingness": (
+        "Patrón de datos faltantes (missingness)",
+        "El patrón con el que faltan los datos: cuánto falta, en qué columnas y "
+        "si las ausencias de unas columnas coinciden (co-ocurren) con las de "
+        "otras. Analizarlo —no solo contar nulos— distingue datos que faltan al "
+        "azar (MCAR) de los que faltan en función de otra variable (MAR), lo que "
+        "decide cómo imputar o si descartar filas sin sesgar el análisis.",
+    ),
+    "mcar": (
+        "MCAR (Missing Completely At Random)",
+        "Los valores faltan de forma independiente de cualquier dato, observado o "
+        "no: las ausencias de unas columnas no se relacionan entre sí ni con los "
+        "valores. Es el caso más benigno —descartar filas o imputar la media no "
+        "introduce sesgo—, pero rara vez se cumple del todo en datos reales.",
+    ),
+    "mar": (
+        "MAR (Missing At Random)",
+        "La probabilidad de que un valor falte depende de OTRAS variables "
+        "observadas (p. ej. una medición que falta más en cierto grupo). Las "
+        "ausencias co-ocurren entre columnas o se relacionan con los valores de "
+        "otras; imputar exige condicionar en esas variables para no sesgar. La "
+        "co-ocurrencia fuerte de ausencias es un indicio (exploratorio) de MAR.",
+    ),
+}
+
+
+# --------------------------------------------------------------------------- #
+# Small defensive formatters (own copy: the chapter never imports siblings).
+# --------------------------------------------------------------------------- #
+def _fmt_int(value) -> str:
+    if value is None:
+        return "—"
+    try:
+        return f"{int(round(float(value))):,}".replace(",", ".")
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+
+
+def _fmt_pct(value, decimals: int = 1) -> str:
+    """Format an already-0-100 value as a percentage. None -> placeholder."""
+    if value is None:
+        return "—"
+    try:
+        return f"{float(value):.{decimals}f}%"
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+
+
+def _fmt_num(value, decimals: int = 3) -> str:
+    if value is None:
+        return "—"
+    try:
+        f = float(value)
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+    if f != f:  # NaN
+        return "—"
+    text = f"{f:.{decimals}f}".rstrip("0").rstrip(".")
+    return text if text else "0"
+
+
+def _truncate(text, limit: int = _LABEL_MAX) -> str:
+    s = model._safe_str(text)
+    if len(s) <= limit:
+        return s
+    return s[: max(1, limit - 1)].rstrip() + "…"
+
+
+def _term(key: str, label: str, mark: bool) -> str:
+    if mark:
+        return f"[[term:{key}]]**{label}**[[/term]]"
+    return f"**{label}**"
+
+
+# --------------------------------------------------------------------------- #
+# Profile reads (exact, all rows).
+# --------------------------------------------------------------------------- #
+def _null_count_of(col: dict):
+    """Best-effort null count of a column: ``null_count`` or null_pct*n_rows."""
+    nc = col.get("null_count")
+    if isinstance(nc, (int, float)) and not isinstance(nc, bool):
+        return int(nc)
+    np_ = col.get("null_pct")
+    nr = col.get("n_rows")
+    if isinstance(np_, (int, float)) and isinstance(nr, (int, float)):
+        return int(round(float(np_) * float(nr)))
+    return 0
+
+
+def _columns_with_nulls(profile: dict):
+    """Return ``[(name, null_count, null_pct_0_100)]`` for columns with nulls,
+    sorted by null percentage descending. Reads the aggregated profile (exact)."""
+    cols = profile.get("columns") or []
+    out = []
+    for c in cols:
+        if not isinstance(c, dict):
+            continue
+        nc = _null_count_of(c)
+        if nc <= 0:
+            continue
+        np_ = c.get("null_pct")
+        nr = c.get("n_rows") or profile.get("n_rows")
+        if isinstance(np_, (int, float)) and not isinstance(np_, bool):
+            pct = float(np_) * 100.0 if np_ <= 1.0 else float(np_)
+        elif nr:
+            pct = nc / float(nr) * 100.0
+        else:
+            pct = None
+        out.append((c.get("name") or "(col)", nc, pct))
+    out.sort(key=lambda t: (t[2] if t[2] is not None else -1.0), reverse=True)
+    return out
+
+
+def _global_missing_pct(profile: dict):
+    """Table-level % of missing cells (0-100), exact, from the profile."""
+    v = profile.get("null_cell_pct")
+    if isinstance(v, (int, float)) and not isinstance(v, bool):
+        return float(v) * 100.0 if v <= 1.0 else float(v)
+    return None
+
+
+# --------------------------------------------------------------------------- #
+# Per-row is-null mask (sample): DuckDB push-down, fallback to raw_numeric.
+# --------------------------------------------------------------------------- #
+def _build_query_fn(ctx: dict):
+    """Return ``(query_fn, table)`` for a DuckDB-backed ctx, or ``(None, None)``.
+
+    Mirrors build_eda_render_ctx: a read-only closure over the registry wrapper.
+    Only DuckDB is supported here; any other backend degrades to raw_numeric."""
+    db_path = ctx.get("db_path")
+    table = ctx.get("table")
+    if not db_path or not table:
+        return None, None
+    try:
+        from infra import duckdb_query_readonly
+    except Exception:  # noqa: BLE001 — wrapper unavailable -> degrade.
+        return None, None
+
+    def query_fn(sql):
+        return duckdb_query_readonly(db_path, sql)
+
+    return query_fn, table
+
+
+def _null_mask(profile: dict, ctx: dict):
+    """Build the per-row is-null mask ``{col: [0/1, ...]}``.
+
+    Tries a single DuckDB push-down over ALL columns first (so categorical
+    columns like Cabin are covered, not only numeric ones); falls back to the
+    numeric-only ``ctx['raw_numeric']`` (None -> missing); returns ``(None, 0,
+    None)`` when neither is reachable. Never raises.
+    Returns ``(mask, n_sampled, source)`` with source in {"db","raw_numeric"}.
+    """
+    cols = profile.get("columns") or []
+    names = [c.get("name") for c in cols
+             if isinstance(c, dict) and c.get("name")]
+    # 1) DuckDB push-down over every column (covers categoricals too).
+    query_fn, table = _build_query_fn(ctx)
+    if query_fn is not None and names:
+        try:
+            from datascience.extract_null_mask import extract_null_mask
+
+            res = extract_null_mask(query_fn, table, names, max_rows=MASK_SAMPLE)
+            if isinstance(res, dict) and res.get("status") == "ok":
+                mask = res.get("mask") or {}
+                if mask:
+                    return mask, int(res.get("n") or 0), "db"
+        except Exception:  # noqa: BLE001 — degrade to raw_numeric.
+            pass
+    # 2) Fallback: numeric-only mask derived from raw_numeric (None -> missing).
+    rn = ctx.get("raw_numeric")
+    if isinstance(rn, dict) and rn:
+        mask = {}
+        for col, vals in rn.items():
+            if isinstance(vals, (list, tuple)):
+                mask[col] = [1 if v is None else 0 for v in vals]
+        if mask:
+            n = max((len(v) for v in mask.values()), default=0)
+            return mask, n, "raw_numeric"
+    return None, 0, None
+
+
+# --------------------------------------------------------------------------- #
+# Lazy registry delegations (each degrades to None on any failure).
+# --------------------------------------------------------------------------- #
+def _overview(mask: dict):
+    try:
+        from datascience.missingness_overview import missingness_overview
+
+        out = missingness_overview(mask)
+        return out if isinstance(out, dict) else None
+    except Exception:  # noqa: BLE001
+        return None
+
+
+def _correlation(mask: dict, top_k: int):
+    try:
+        from datascience.missingness_correlation import missingness_correlation
+
+        out = missingness_correlation(mask, top_k=top_k)
+        return out if isinstance(out, dict) else None
+    except Exception:  # noqa: BLE001
+        return None
+
+
+def _row_patterns(mask: dict, top_n: int):
+    try:
+        from datascience.missingness_row_patterns import missingness_row_patterns
+
+        out = missingness_row_patterns(mask, top_n=top_n)
+        return out if isinstance(out, dict) else None
+    except Exception:  # noqa: BLE001
+        return None
+
+
+def _rank_bar_make(names, pcts, title):
+    def make():
+        try:
+            from datascience.missingness_rank_bar_figure import (
+                missingness_rank_bar_figure,
+            )
+
+            return missingness_rank_bar_figure(names, pcts, title=title)
+        except Exception:  # noqa: BLE001 — minimal fallback figure.
+            return _fallback_fig("ranking de nulos no disponible")
+
+    return make
+
+
+def _heatmap_make(matrix, labels, title):
+    def make():
+        try:
+            from datascience.missingness_corr_heatmap_figure import (
+                missingness_corr_heatmap_figure,
+            )
+
+            return missingness_corr_heatmap_figure(matrix, labels, title=title)
+        except Exception:  # noqa: BLE001 — minimal fallback figure.
+            return _fallback_fig("heatmap de co-ocurrencia no disponible")
+
+    return make
+
+
+def _fallback_fig(message: str):
+    import matplotlib
+
+    matplotlib.use("Agg")
+    from matplotlib.figure import Figure
+
+    fig = Figure(figsize=(5.0, 2.2))
+    ax = fig.add_subplot(111)
+    ax.text(0.5, 0.5, message, ha="center", va="center")
+    ax.axis("off")
+    return fig
+
+
+# --------------------------------------------------------------------------- #
+# Block builders.
+# --------------------------------------------------------------------------- #
+def _summary_block(profile: dict, with_nulls: list, overview, sampled, n_total):
+    rows = []
+    gpct = _global_missing_pct(profile)
+    rows.append(("Celdas faltantes (global)", _fmt_pct(gpct)))
+    rows.append(("Columnas con faltantes", str(len(with_nulls))))
+    all_null = profile.get("all_null_cols")
+    if isinstance(all_null, (list, tuple)) and all_null:
+        rows.append(("Columnas 100% faltantes", str(len(all_null))))
+    if isinstance(overview, dict):
+        cr = overview.get("complete_rows")
+        ir = overview.get("incomplete_rows")
+        suffix = ""
+        if (isinstance(sampled, int) and isinstance(n_total, (int, float))
+                and sampled and n_total and sampled < n_total):
+            suffix = f" (sobre muestra de {_fmt_int(sampled)} filas)"
+        if cr is not None:
+            rows.append(("Filas completas (sin faltantes)",
+                         f"{_fmt_int(cr)} ({_fmt_pct(overview.get('complete_pct'))})"
+                         + suffix))
+        if ir is not None:
+            rows.append(("Filas con ≥1 faltante",
+                         f"{_fmt_int(ir)} "
+                         f"({_fmt_pct(overview.get('incomplete_pct'))})" + suffix))
+    return model.KVTable(rows=rows, title="Resumen de datos faltantes")
+
+
+def _ranking_block(with_nulls: list):
+    header = ["Columna", "Faltantes", "% faltante"]
+    rows = [[_truncate(n), _fmt_int(c), _fmt_pct(p)] for (n, c, p) in with_nulls]
+    if not rows:
+        return None
+    return model.DataTable(
+        header=header, rows=rows, title="Faltantes por columna",
+        note="ordenado de más a menos faltante")
+
+
+def _ranking_figure(with_nulls: list):
+    names = [n for (n, _, p) in with_nulls if p is not None]
+    pcts = [p for (_, _, p) in with_nulls if p is not None]
+    if not names:
+        return None
+    return model.Figure(
+        make=_rank_bar_make(names, pcts, "% de valores faltantes por columna"),
+        caption="Porcentaje de valores faltantes por columna (barras).")
+
+
+def _pairs_block(corr: dict):
+    """Top column pairs whose absences co-occur, as a table, or None."""
+    pairs = (corr or {}).get("pairs") or []
+    header = ["Columna A", "Columna B", "Corr. ausencia", "Co-faltan", "Jaccard"]
+    rows = []
+    for p in pairs[:_TOP_PAIRS]:
+        if not isinstance(p, dict):
+            continue
+        rows.append([
+            _truncate(p.get("a")),
+            _truncate(p.get("b")),
+            _fmt_num(p.get("corr")),
+            _fmt_int(p.get("co_missing")),
+            _fmt_num(p.get("jaccard")),
+        ])
+    if not rows:
+        return None
+    shown = len(rows)
+    total = len(pairs)
+    note = ("correlación de las máscaras is-null entre columnas; "
+            "«Co-faltan» = nº de filas en que ambas faltan a la vez")
+    if total > shown:
+        note += f" — top {shown} de {total} pares"
+    return model.DataTable(header=header, rows=rows,
+                           title="Pares de columnas que co-faltan", note=note)
+
+
+def _heatmap_block(corr: dict):
+    cols = (corr or {}).get("columns") or []
+    matrix = (corr or {}).get("matrix") or []
+    if len(cols) < 2 or not matrix:
+        return None
+    labels = [_truncate(c, 16) for c in cols]
+    return model.Figure(
+        make=_heatmap_make(matrix, labels, "Co-ocurrencia de ausencias"),
+        caption=("Correlación de las ausencias entre columnas (azul = faltan "
+                 "juntas; rojo = cuando una falta la otra tiende a estar)."))
+
+
+def _patterns_block(patterns_res: dict):
+    patterns = (patterns_res or {}).get("patterns") or []
+    header = ["Columnas que faltan juntas", "Filas", "%"]
+    rows = []
+    for p in patterns[:_TOP_PATTERNS]:
+        if not isinstance(p, dict):
+            continue
+        cols = p.get("missing_cols") or []
+        if cols:
+            label = ", ".join(_truncate(c, 18) for c in cols)
+        else:
+            label = "(fila completa — sin faltantes)"
+        rows.append([label, _fmt_int(p.get("n_rows")), _fmt_pct(p.get("pct"))])
+    if not rows:
+        return None
+    total = (patterns_res or {}).get("n_patterns")
+    shown = len(rows)
+    note = "cada fila es un patrón de «qué columnas faltan juntas»"
+    if isinstance(total, int) and total > shown:
+        note += f" — top {shown} de {total} patrones distintos"
+    return model.DataTable(header=header, rows=rows,
+                           title="Patrones de fila más comunes", note=note)
+
+
+def _mcar_mar_note(corr: dict, mark: bool):
+    """Interpretive, exploratory MCAR/MAR note from the absence correlations.
+
+    Reads the absence correlations at two levels so the verdict never contradicts
+    the visible evidence: a *strong* correlation flags a clear non-random (MAR)
+    pattern; a *partial* overlap (many rows co-miss — high Jaccard — even if the
+    correlation is diluted by one column being missing far more often) flags a
+    localized possible-MAR and cites the concrete co-missing pair; only when
+    neither holds does it read the absences as compatible with MCAR."""
+
+    def _pairs_with(attr_ok):
+        out = []
+        for p in (corr or {}).get("pairs") or []:
+            if isinstance(p, dict) and attr_ok(p):
+                out.append(p)
+        return out
+
+    def _cf(v):
+        try:
+            return float(v)
+        except (TypeError, ValueError):
+            return 0.0
+
+    strong = _pairs_with(lambda p: abs(_cf(p.get("corr"))) >= _CORR_STRONG)
+    partial = _pairs_with(
+        lambda p: _cf(p.get("corr")) > 0 and _cf(p.get("jaccard")) >= _JACCARD_NOTABLE)
+    mcar = _term("mcar", "MCAR", mark)
+    mar = _term("mar", "MAR", mark)
+    head = (
+        "**Lectura exploratoria MCAR/MAR.** Esta es una heurística basada en la "
+        "correlación de las ausencias entre columnas, NO un test confirmatorio "
+        "(como el de Little); orienta, no demuestra. ")
+    if strong:
+        top = strong[0]
+        ev = (f"«{model._safe_str(top.get('a'))}» y "
+              f"«{model._safe_str(top.get('b'))}» "
+              f"(corr {_fmt_num(top.get('corr'))})")
+        body = (
+            f"Hay ausencias que co-ocurren con fuerza —{ev}—: las columnas no "
+            f"faltan de forma independiente, lo que es un indicio de un patrón no "
+            f"aleatorio ({mar}). Antes de imputar o descartar filas conviene "
+            f"comprobar si la ausencia depende de otra variable observada; en ese "
+            f"caso la imputación debería condicionar en ella para no sesgar.")
+    elif partial:
+        top = max(partial, key=lambda p: _cf(p.get("jaccard")))
+        ev = (f"«{model._safe_str(top.get('a'))}» y "
+              f"«{model._safe_str(top.get('b'))}» faltan a la vez en "
+              f"{_fmt_int(top.get('co_missing'))} filas "
+              f"(Jaccard {_fmt_num(top.get('jaccard'))})")
+        body = (
+            f"Hay co-ocurrencia parcial de ausencias —{ev}—: algunas columnas "
+            f"tienden a faltar juntas aunque la correlación global sea modesta "
+            f"(habitual cuando una columna falta mucho más que la otra). Es un "
+            f"indicio de un posible patrón localizado no aleatorio ({mar}); "
+            f"conviene revisar si esa ausencia depende de otra variable observada "
+            f"antes de imputar, en lugar de asumir que faltan al azar.")
+    else:
+        body = (
+            f"Las ausencias entre columnas no muestran correlación ni solape "
+            f"relevante: parecen independientes, lo que es compatible con que "
+            f"falten al azar ({mcar}). Aun así, la ausencia podría depender de "
+            f"variables no observadas (la heurística no lo descarta).")
+    return model.Markdown(text=head + body)
+
+
+def _intro_block(mark: bool, source):
+    missingness = _term("missingness", "missingness", mark)
+    text = (
+        f"Este capítulo analiza el {missingness} de la tabla: no solo cuánto "
+        "falta (eso lo cubre la calidad), sino DÓNDE falta y si las columnas "
+        "faltan juntas. La co-ocurrencia de ausencias se calcula sobre la matriz "
+        "binaria «is-null» por fila.")
+    if source == "raw_numeric":
+        text += (" Nota: no se pudo leer la tabla cruda completa, así que la "
+                 "co-ocurrencia se limita a las columnas numéricas disponibles.")
+    return model.Markdown(text=text)
+
+
+# --------------------------------------------------------------------------- #
+# Entry point.
+# --------------------------------------------------------------------------- #
+def build_missingness(profile: dict, ctx: dict):
+    """Build the missingness Chapter, or None if the table has no missing data."""
+    if not isinstance(profile, dict):
+        profile = {}
+    ctx = ctx or {}
+
+    with_nulls = _columns_with_nulls(profile)
+    if not with_nulls:
+        return None  # no missing data anywhere -> chapter does not apply.
+
+    # Register glossary terms (if a collector is present) and mark them clickable.
+    glossary = ctx.get("glossary")
+    mark = False
+    if isinstance(glossary, model.GlossaryCollector):
+        for key, (label, definition) in _TERMS.items():
+            glossary.add(key, label, definition)
+        mark = True
+
+    # Per-row is-null mask (sample) for co-occurrence and row patterns.
+    mask, sampled, source = _null_mask(profile, ctx)
+    overview = _overview(mask) if mask else None
+    n_total = profile.get("n_rows")
+
+    blocks = [
+        model.Heading(text="Cuánto y dónde faltan datos", level=2),
+        _intro_block(mark, source),
+        _summary_block(profile, with_nulls, overview, sampled, n_total),
+        model.Heading(text="Faltantes por columna", level=2),
+    ]
+    ranking = _ranking_block(with_nulls)
+    if ranking is not None:
+        blocks.append(ranking)
+    rank_fig = _ranking_figure(with_nulls)
+    if rank_fig is not None:
+        blocks.append(rank_fig)
+
+    # Co-occurrence + row patterns need the per-row mask. Without it, say so.
+    if not mask:
+        blocks.append(model.Note(
+            "No se pudo construir la matriz «is-null» por fila (sin acceso a los "
+            "datos crudos), así que no se analiza la co-ocurrencia de ausencias "
+            "ni los patrones de fila en este informe."))
+        return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                             version=CHAPTER_VERSION, blocks=blocks)
+
+    corr = _correlation(mask, _TOP_PAIRS) or {}
+    co_blocks = [model.Heading(text="Co-ocurrencia de ausencias", level=2)]
+    heatmap = _heatmap_block(corr)
+    if heatmap is not None:
+        co_blocks.append(heatmap)
+    pairs = _pairs_block(corr)
+    if pairs is not None:
+        co_blocks.append(pairs)
+    if heatmap is None and pairs is None:
+        co_blocks.append(model.Note(
+            "Ninguna pareja de columnas comparte ausencias con variación "
+            "suficiente para correlacionarlas (p. ej. una sola columna con "
+            "faltantes), así que no hay co-ocurrencia que mostrar."))
+    # Keep the co-occurrence heading next to its heatmap and table.
+    blocks.append(model.Group(blocks=co_blocks))
+
+    patterns_res = _row_patterns(mask, _TOP_PATTERNS) or {}
+    patterns = _patterns_block(patterns_res)
+    if patterns is not None:
+        blocks.append(model.Heading(text="Patrones de fila", level=2))
+        blocks.append(patterns)
+
+    blocks.append(model.Heading(text="Lectura MCAR / MAR", level=2))
+    blocks.append(_mcar_mar_note(corr, mark))
+
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/missingness_test.py

@@ -0,0 +1,162 @@
+"""Tests for the MISSINGNESS chapter.
+
+Covers the Definition of Done for this chapter:
+  * Activates (non-None Chapter with the expected sections) when the profile has
+    missing data, building the co-occurrence from the per-row is-null mask.
+  * Returns None when the table has no missing data at all (edge case).
+  * Registers the MCAR/MAR/missingness glossary terms.
+  * The DuckDB push-down path covers categorical columns (not only numeric),
+    so a categorical column that co-misses with a numeric one is detected.
+"""
+
+import os
+import sys
+
+_HERE = os.path.dirname(os.path.abspath(__file__))
+_FUNCTIONS = os.path.abspath(os.path.join(_HERE, "..", "..", ".."))  # python/functions
+if _FUNCTIONS not in sys.path:
+    sys.path.insert(0, _FUNCTIONS)
+
+from datascience.automatic_eda import model  # noqa: E402
+from datascience.automatic_eda.chapters.missingness import (  # noqa: E402
+    build_missingness,
+)
+
+
+def _titles(chapter):
+    """Collect heading texts and table/figure titles for assertions."""
+    out = []
+    for b in chapter.blocks:
+        kind = getattr(b, "kind", None)
+        if kind == "heading":
+            out.append(("heading", getattr(b, "text", "")))
+        elif kind in ("data_table", "kv_table"):
+            out.append((kind, getattr(b, "title", "")))
+        elif kind == "group":
+            for inner in getattr(b, "blocks", []):
+                ik = getattr(inner, "kind", None)
+                if ik == "heading":
+                    out.append(("heading", getattr(inner, "text", "")))
+                elif ik in ("data_table", "kv_table"):
+                    out.append((ik, getattr(inner, "title", "")))
+                elif ik == "figure":
+                    out.append(("figure", getattr(inner, "caption", "")))
+        elif kind == "figure":
+            out.append(("figure", getattr(b, "caption", "")))
+    return out
+
+
+def _all_text(chapter):
+    parts = []
+    def walk(blocks):
+        for b in blocks:
+            for attr in ("text", "title", "note", "caption"):
+                v = getattr(b, attr, None)
+                if v:
+                    parts.append(str(v))
+            if getattr(b, "kind", None) == "group":
+                walk(getattr(b, "blocks", []))
+    walk(chapter.blocks)
+    return "\n".join(parts)
+
+
+def test_returns_none_when_no_missing_data():
+    profile = {
+        "n_rows": 4,
+        "null_cell_pct": 0.0,
+        "columns": [
+            {"name": "a", "null_count": 0, "null_pct": 0.0, "n_rows": 4},
+            {"name": "b", "null_count": 0, "null_pct": 0.0, "n_rows": 4},
+        ],
+    }
+    assert build_missingness(profile, {}) is None
+
+
+def test_activates_with_cooccurrence_via_raw_numeric():
+    # a and b are missing in EXACTLY the same rows (0,1,2) -> perfect absence
+    # correlation. c has no nulls. No db_path -> the chapter falls back to the
+    # numeric raw_numeric mask.
+    profile = {
+        "n_rows": 6,
+        "null_cell_pct": (0.5 + 0.5 + 0.0) / 3.0,
+        "columns": [
+            {"name": "a", "null_count": 3, "null_pct": 0.5, "n_rows": 6},
+            {"name": "b", "null_count": 3, "null_pct": 0.5, "n_rows": 6},
+            {"name": "c", "null_count": 0, "null_pct": 0.0, "n_rows": 6},
+        ],
+    }
+    glossary = model.GlossaryCollector()
+    ctx = {
+        "raw_numeric": {
+            "a": [None, None, None, 1.0, 2.0, 3.0],
+            "b": [None, None, None, 4.0, 5.0, 6.0],
+        },
+        "glossary": glossary,
+    }
+    ch = build_missingness(profile, ctx)
+    assert ch is not None
+    assert ch.id == "missingness"
+    assert ch.blocks
+
+    titles = _titles(ch)
+    headings = {t for (k, t) in titles if k == "heading"}
+    # Core sections present.
+    assert any("Cuánto y dónde" in h for h in headings)
+    assert any("Faltantes por columna" in h for h in headings)
+    assert any("Co-ocurrencia" in h for h in headings)
+    assert any("MCAR" in h for h in headings)
+    # A summary KVTable, a ranking DataTable, a co-occurrence figure and the
+    # pairs table all exist.
+    kinds = {k for (k, _) in titles}
+    assert "kv_table" in kinds
+    assert "data_table" in kinds
+    assert "figure" in kinds
+
+    # Glossary terms registered.
+    keys = {t["key"] for t in glossary.terms()}
+    assert {"missingness", "mcar", "mar"} <= keys
+
+    # The MCAR/MAR note reads the co-occurrence; with a perfect overlap it must
+    # flag the non-random (MAR) reading.
+    text = _all_text(ch)
+    assert "MAR" in text
+
+
+def test_db_pushdown_covers_categorical_column(tmp_path):
+    """The is-null mask push-down must cover a categorical column, so a
+    categorical that co-misses with a numeric one shows up in the pairs."""
+    import duckdb
+
+    db = str(tmp_path / "miss.duckdb")
+    con = duckdb.connect(db)
+    con.execute("CREATE TABLE t (num1 DOUBLE, num2 DOUBLE, cat VARCHAR)")
+    # num1 and cat are NULL together in the first 4 of 10 rows; num2 never null.
+    rows = []
+    for i in range(10):
+        if i < 4:
+            rows.append((None, float(i), None))
+        else:
+            rows.append((float(i), float(i), f"c{i}"))
+    con.executemany("INSERT INTO t VALUES (?,?,?)", rows)
+    con.close()
+
+    profile = {
+        "n_rows": 10,
+        "null_cell_pct": (0.4 + 0.0 + 0.4) / 3.0,
+        "columns": [
+            {"name": "num1", "null_count": 4, "null_pct": 0.4, "n_rows": 10},
+            {"name": "num2", "null_count": 0, "null_pct": 0.0, "n_rows": 10},
+            {"name": "cat", "null_count": 4, "null_pct": 0.4, "n_rows": 10},
+        ],
+    }
+    ctx = {"db_path": db, "table": "t", "glossary": model.GlossaryCollector()}
+    ch = build_missingness(profile, ctx)
+    assert ch is not None
+
+    # The pairs table must mention both num1 and cat (they co-miss perfectly),
+    # which is only possible if the mask covered the categorical column.
+    text = _all_text(ch)
+    assert "num1" in text and "cat" in text
+    # Co-occurrence section + a pairs data table exist.
+    titles = _titles(ch)
+    assert any("co-faltan" in (t or "").lower() for (k, t) in titles)

python/functions/datascience/automatic_eda/chapters_registry.py

@@ -32,6 +32,7 @@ CHAPTER_ORDER = [
     "num_distr",     # numeric distributions
     "cat_distr",     # categorical distributions
     "calidad",       # data quality
+    "missingness",   # missing-data patterns (co-occurrence of absences; MCAR/MAR)
     "correlacion",   # correlations / associations
     "relaciones",    # key relations: declared/candidate PK + FK (inter/intra-table)
     "modelos",       # cheap models (PCA/KMeans/outliers)

python/functions/datascience/automatic_eda/md_completeness_test.py

@@ -1,253 +0,0 @@
-"""Tests for the Markdown completeness appendix (report 2053).
-
-The AutomaticEDA Markdown is the output meant to be *pasted into an LLM*, so it
-must carry EVERYTHING the engine computed — even the numbers the human-facing
-chapters (shared with the PDF/PPTX) drop for readability. ``render_md`` appends a
-full-data appendix built from ``meta['profile']`` that closes the six losses the
-evaluation found:
-
-1. the complete association matrix (every pair, incl. correlation_ratio /
-   cramers_v) — not just the top extremes;
-2. every numeric statistic for every numeric column (skew/kurtosis/percentiles);
-3. the concrete recommended re-expression;
-4. KMeans ``scores_by_k``;
-5. the normality test statistics;
-6. correct headers for bar/scree figure tables (not ``Desde/Hasta/Frecuencia``).
-
-Self-contained: a synthetic profile, no DuckDB, no heavy renderer.
-"""
-
-import os
-import sys
-
-import pytest  # noqa: F401
-
-_HERE = os.path.dirname(os.path.abspath(__file__))
-_FUNCTIONS = os.path.abspath(os.path.join(_HERE, "..", "..", ".."))  # python/functions
-if _FUNCTIONS not in sys.path:
-    sys.path.insert(0, _FUNCTIONS)
-
-from datascience.automatic_eda import model  # noqa: E402
-from datascience.automatic_eda.render_md_impl import (  # noqa: E402
-    _bars_table,
-    _is_histogram_caption,
-    _profile_appendix,
-    render_md,
-)
-
-
-# --------------------------------------------------------------------------- #
-# Synthetic profile fixtures.
-# --------------------------------------------------------------------------- #
-def _numeric(skew, kurtosis):
-    """A numeric stat block with every key the appendix serializes."""
-    return {
-        "count": 100, "min": 0.0, "max": 10.0, "mean": 5.0, "median": 5.0,
-        "mode": 4.0, "std": 2.0, "variance": 4.0, "cv": 0.4,
-        "p1": 0.1, "p5": 0.5, "p25": 2.5, "p50": 5.0, "p75": 7.5,
-        "p95": 9.5, "p99": 9.9, "iqr": 5.0, "skew": skew, "kurtosis": kurtosis,
-        "n_outliers": 1, "distribution_type": "normal",
-    }
-
-
-def _profile():
-    """A small but structurally faithful TableProfile (3 numeric, 2 categorical)."""
-    pairs = [
-        {"a": "A", "b": "B", "a_type": "numeric", "b_type": "numeric",
-         "method": "pearson/spearman", "value": 0.8,
-         "p_value": 1e-9, "p_value_adjusted": 2e-9, "significant": True},
-        {"a": "A", "b": "C", "a_type": "numeric", "b_type": "numeric",
-         "method": "pearson/spearman", "value": -0.3,
-         "p_value": 0.01, "p_value_adjusted": 0.02, "significant": True},
-        {"a": "A", "b": "Cat1", "a_type": "numeric", "b_type": "categorical",
-         "method": "correlation_ratio", "value": 0.45,
-         "p_value": 0.001, "p_value_adjusted": 0.002, "significant": True},
-        # The single cat-cat pair the human chapter never shows.
-        {"a": "Cat1", "b": "Cat2", "a_type": "categorical",
-         "b_type": "categorical", "method": "cramers_v", "value": 0.11,
-         "p_value": 0.04, "p_value_adjusted": 0.05, "significant": False},
-    ]
-    return {
-        "correlations": {
-            "pairs": pairs,
-            "multiple_testing": {"method": "bh", "n_tests": 4, "n_rejected": 3},
-        },
-        "columns": [
-            {"name": "A", "count": 100, "numeric": _numeric(0.0, -1.2),
-             "reexpression": {"recommended": "none", "ladder_power": 1.0,
-                              "reason": "symmetric", "alternatives": []}},
-            {"name": "B", "count": 100, "numeric": _numeric(4.77, 33.1),
-             "reexpression": {"recommended": "log1p", "ladder_power": 0.0,
-                              "reason": "skew 4.77 with zeros",
-                              "alternatives": [{"transform": "yeo-johnson"},
-                                               {"transform": "sqrt"}]}},
-            {"name": "C", "count": 100, "numeric": _numeric(-0.6, 0.2)},
-            {"name": "Cat1", "categorical": {"top": [], "mode": "x"}},
-            {"name": "Cat2", "categorical": {"top": [], "mode": "y"}},
-        ],
-        "models": {
-            "kmeans": {
-                "best_k": 3,
-                "scores_by_k": [
-                    {"k": 2, "silhouette": 0.46, "inertia": 900.0},
-                    {"k": 3, "silhouette": 0.50, "inertia": 550.0},
-                    {"k": 4, "silhouette": 0.38, "inertia": 430.0},
-                ],
-                "cluster_sizes": [40, 35, 25],
-            },
-            "normality": {
-                "A": {"n": 100,
-                      "jarque_bera": {"stat": 18.7, "p": 8e-5, "normal": False},
-                      "dagostino": {"stat": 18.1, "p": 1e-4, "normal": False},
-                      "shapiro": {"stat": 0.98, "p": 7e-8, "normal": False},
-                      "is_normal": False},
-                "C": {"n": 100,
-                      "jarque_bera": {"stat": 2.1, "p": 0.35, "normal": True},
-                      "dagostino": {"stat": 1.9, "p": 0.38, "normal": True},
-                      "shapiro": {"stat": 0.99, "p": 0.12, "normal": True},
-                      "is_normal": True},
-            },
-        },
-    }
-
-
-def _dummy_chapters():
-    """A minimal one-chapter document so render_md does not early-return empty."""
-    return model.as_chapters([
-        {"id": "intro", "title": "Intro",
-         "blocks": [{"kind": "markdown", "text": "cuerpo del informe"}]},
-    ])
-
-
-def _render(tmp_path, profile):
-    out = os.path.join(str(tmp_path), "out.md")
-    res = render_md(_dummy_chapters(), out, {"title": "EDA — t", "profile": profile})
-    assert res["path"] == out
-    return open(out, encoding="utf-8").read()
-
-
-def _table_rows(md, section_title):
-    """Count data rows of the first Markdown table under ``section_title``."""
-    seg = md.split(section_title, 1)[1]
-    rows, in_t, seen_sep = 0, False, False
-    for ln in seg.splitlines():
-        if ln.startswith("|"):
-            in_t = True
-            stripped = ln.replace("|", "").replace(" ", "")
-            if stripped and set(stripped) == {"-"}:
-                seen_sep = True
-                continue
-            if seen_sep:
-                rows += 1
-        elif in_t and not ln.strip():
-            break
-    return rows
-
-
-# --------------------------------------------------------------------------- #
-# Golden: every datum the profile holds reaches the .md.
-# --------------------------------------------------------------------------- #
-def test_appendix_lists_all_correlation_pairs(tmp_path):
-    md = _render(tmp_path, _profile())
-    assert "## Apéndice — Datos completos del perfil" in md
-    # All 4 pairs (the real titanic profile has 28; here 4 synthetic).
-    assert _table_rows(md, "### Matriz de asociación") == 4
-    # The cat-cat Cramér's V pair the human chapter drops is present.
-    assert "Cat1 ↔ Cat2" in md
-    assert "cramers_v" in md
-    assert "correlation_ratio" in md
-
-
-def test_appendix_has_skew_kurtosis_for_every_numeric(tmp_path):
-    md = _render(tmp_path, _profile())
-    seg = md.split("### Estadísticos numéricos completos", 1)[1].split("###", 1)[0]
-    lines = [l for l in seg.splitlines() if l.startswith("|")]
-    header = [h.strip() for h in lines[0].strip("|").split("|")]
-    assert "skew" in header and "kurtosis" in header
-    ski, kui = header.index("skew"), header.index("kurtosis")
-    data = lines[2:]  # skip header + separator
-    assert len(data) == 3  # exactly the 3 numeric columns
-    for row in data:
-        cells = [c.strip() for c in row.strip("|").split("|")]
-        assert cells[ski] != "", f"missing skew in {cells[0]}"
-        assert cells[kui] != "", f"missing kurtosis in {cells[0]}"
-
-
-def test_appendix_has_extended_percentiles(tmp_path):
-    md = _render(tmp_path, _profile())
-    seg = md.split("### Estadísticos numéricos completos", 1)[1]
-    header = [h.strip() for h in seg.splitlines()[2].strip("|").split("|")]
-    for p in ("p1", "p5", "p25", "p75", "p95", "p99"):
-        assert p in header, f"percentile {p} missing from describe header"
-
-
-def test_appendix_names_concrete_reexpression(tmp_path):
-    md = _render(tmp_path, _profile())
-    assert "### Re-expresión recomendada" in md
-    assert "log1p" in md  # the concrete transform, not just "consider re-expressing"
-    assert "yeo-johnson" in md  # alternatives listed too
-
-
-def test_appendix_has_kmeans_scores_by_k(tmp_path):
-    md = _render(tmp_path, _profile())
-    assert "scores_by_k" in md
-    assert _table_rows(md, "#### KMeans — selección de k") == 3  # k=2,3,4
-
-
-def test_appendix_has_normality_statistics(tmp_path):
-    md = _render(tmp_path, _profile())
-    assert "JB stat" in md  # the statistic, not only the p-value
-    assert "Shapiro stat" in md
-    assert _table_rows(md, "#### Tests de normalidad") == 2  # cols A and C
-
-
-# --------------------------------------------------------------------------- #
-# Edge: a profile missing models / correlations degrades, never raises.
-# --------------------------------------------------------------------------- #
-def test_lite_profile_without_models(tmp_path):
-    prof = _profile()
-    prof.pop("models")  # lite: no KMeans/normality
-    md = _render(tmp_path, prof)
-    assert "scores_by_k" not in md  # section skipped
-    assert "Matriz de asociación" in md  # correlations still dumped
-    assert "## Apéndice" in md
-
-
-def test_profile_without_correlations(tmp_path):
-    prof = _profile()
-    prof.pop("correlations")
-    md = _render(tmp_path, prof)  # must not raise
-    assert "Matriz de asociación" not in md
-    assert "Estadísticos numéricos completos" in md  # numeric section still there
-
-
-def test_no_profile_means_no_appendix(tmp_path):
-    out = os.path.join(str(tmp_path), "noprof.md")
-    res = render_md(_dummy_chapters(), out, {"title": "x"})
-    assert res["path"] == out
-    assert "## Apéndice" not in open(out, encoding="utf-8").read()
-
-
-def test_appendix_helper_is_defensive():
-    assert _profile_appendix(None) == ""
-    assert _profile_appendix({}) == ""
-    assert _profile_appendix({"columns": []}) == ""
-
-
-# --------------------------------------------------------------------------- #
-# Loss #6: bar/scree figure tables get a non-misleading header.
-# --------------------------------------------------------------------------- #
-def test_histogram_caption_detection():
-    assert _is_histogram_caption("Histograma de Age")
-    assert _is_histogram_caption("Distribución de Fare")
-    assert not _is_histogram_caption("Media de Survived por Sex")
-    assert not _is_histogram_caption("Varianza explicada (scree PCA)")
-
-
-def test_bars_table_custom_header():
-    bars = [(0.0, 1.0, 5.0), (1.0, 2.0, 3.0)]
-    hist = _bars_table(bars)  # default histogram header
-    assert "| Desde | Hasta | Frecuencia |" in hist
-    bar = _bars_table(bars, ("Inicio", "Fin", "Valor"))
-    assert "| Inicio | Fin | Valor |" in bar
-    assert "Frecuencia" not in bar

python/functions/datascience/automatic_eda/render_md_impl.py

@@ -178,17 +178,9 @@ def _md_data_table(block) -> str:
     return "\n".join(lines)
 
 
-def _bars_table(bars: list, header: tuple = ("Desde", "Hasta", "Frecuencia")) -> str:
-    """Render extracted bar/histogram data as a Markdown table.
-
-    ``header`` is the 3-column header to use. Histogram bars are
-    ``(Desde, Hasta, Frecuencia)``; bar/scree charts (means by group, PCA
-    explained variance) are *not* bins, so the caller passes a semantically
-    correct header (e.g. ``(Inicio, Fin, Valor)``) to avoid the misleading
-    "Frecuencia" label — see report 2053, loss #6.
-    """
-    h0, h1, h2 = header
-    lines = [f"| {h0} | {h1} | {h2} |", "| --- | --- | --- |"]
+def _bars_table(bars: list) -> str:
+    """Render extracted bar/histogram data as a Markdown table (Desde/Hasta/Frec)."""
+    lines = ["| Desde | Hasta | Frecuencia |", "| --- | --- | --- |"]
     shown = bars[:_MAX_BAR_ROWS]
     for x0, x1, h in shown:
         lines.append(f"| {_fmt_num(x0)} | {_fmt_num(x1)} | {_fmt_num(h)} |")
@@ -199,18 +191,6 @@ def _bars_table(bars: list, header: tuple = ("Desde", "Hasta", "Frecuencia")) ->
     return out
 
 
-def _is_histogram_caption(caption: str) -> bool:
-    """True when a figure caption describes a histogram (genuine numeric bins).
-
-    Histograms are the only figures whose bars are real ``[Desde, Hasta)`` bins
-    with a frequency count. Bar charts (means by group) and the PCA scree plot
-    carry per-category / per-component values, not bins — they must not inherit
-    the ``Desde/Hasta/Frecuencia`` header.
-    """
-    c = (caption or "").lower()
-    return "histograma" in c or "distribución" in c or "distribucion" in c
-
-
 def _extract_bars(fig) -> list:
     """Collect (x_from, x_to, height) of the rectangular bars of a matplotlib fig.
 
@@ -273,13 +253,7 @@ def _md_figure(block, meta: dict, out_path: str, counter: list) -> str:
         if fig is not None:
             bars = _extract_bars(fig)
             if bars:
-                # A histogram's bars are genuine numeric bins (Desde/Hasta/
-                # Frecuencia). Bar charts and the PCA scree plot are not bins —
-                # give them a header that does not lie about "Frecuencia".
-                header = (("Desde", "Hasta", "Frecuencia")
-                          if _is_histogram_caption(caption)
-                          else ("Inicio", "Fin", "Valor"))
-                parts.append(_bars_table(bars, header))
+                parts.append(_bars_table(bars))
             if meta.get("embed_figures"):
                 png = _embed_png(fig, out_path, counter)
                 if png:
@@ -380,258 +354,6 @@ def _serialize_block(block, meta: dict, out_path: str, counter: list) -> str:
     return _md_note(model.Note(text=model._safe_str(block)))
 
 
-# --------------------------------------------------------------------------- #
-# Profile appendix — the data the human-facing chapters drop.
-#
-# The chapter document (shared with the PDF/PPTX renderers) is designed for human
-# reading and intentionally omits raw numbers: the correlation matrix shows only
-# the top extremes, the numeric blocks skip skew/kurtosis/extended percentiles,
-# the model chapter does not list ``scores_by_k`` or the normality test
-# statistics. But the Markdown is meant to be *pasted into an LLM*, so it should
-# carry EVERYTHING the engine computed. This appendix serializes the full
-# ``profile`` (passed via ``meta['profile']``) as Markdown tables, additively:
-# the PDF/PPTX are untouched, the .md simply has more than they do. Each section
-# is emitted only when its source data is present, so a ``lite`` profile (no
-# models) or a profile without correlations degrades cleanly instead of raising.
-# See report 2053 for the six losses this closes.
-# --------------------------------------------------------------------------- #
-def _pair_types(a_type, b_type) -> str:
-    """Short ``num↔cat`` label for an association pair's variable types."""
-    def short(t):
-        t = model._safe_str(t).lower()
-        if t.startswith("num"):
-            return "num"
-        if t.startswith("cat"):
-            return "cat"
-        return t or "?"
-    return f"{short(a_type)}↔{short(b_type)}"
-
-
-def _app_correlations(corr: dict) -> str:
-    """Loss #1 — every association pair (not just the top extremes).
-
-    Dumps all of ``correlations['pairs']`` as a table (pair · types · method ·
-    value · p · p-FDR · significant), ordered by |value| desc so the strongest
-    associations lead while nothing is cut. Includes the ``correlation_ratio``
-    (num↔cat) and ``cramers_v`` (cat↔cat) pairs the human chapter never shows.
-    """
-    pairs = list(corr.get("pairs", []) or [])
-    if not pairs:
-        return ""
-    def keyfn(p):
-        try:
-            return -abs(float(p.get("value")))
-        except Exception:  # noqa: BLE001
-            return 0.0
-    pairs_sorted = sorted(pairs, key=keyfn)
-    lines = ["### Matriz de asociación — todos los pares",
-             "",
-             ("| Par | Tipos | Método | Valor | p-value | p-ajustado (FDR) "
-              "| ¿Sig? |"),
-             "| --- | --- | --- | --- | --- | --- | --- |"]
-    for p in pairs_sorted:
-        par = f"{_cell(p.get('a'))} ↔ {_cell(p.get('b'))}"
-        types = _pair_types(p.get("a_type"), p.get("b_type"))
-        method = _cell(p.get("method"))
-        val = _fmt_num(p.get("value"))
-        pv = _fmt_num(p.get("p_value")) if p.get("p_value") is not None else ""
-        padj = (_fmt_num(p.get("p_value_adjusted"))
-                if p.get("p_value_adjusted") is not None else "")
-        sig = "sí" if p.get("significant") else "no"
-        lines.append(
-            f"| {par} | {types} | {method} | {val} | {pv} | {padj} | {sig} |")
-    mt = corr.get("multiple_testing") or {}
-    n_tests = mt.get("n_tests", corr.get("n_tests"))
-    n_rej = mt.get("n_rejected")
-    note_bits = [f"{len(pairs)} pares en total"]
-    if n_tests is not None and n_rej is not None:
-        note_bits.append(
-            f"{n_rej} de {n_tests} significativos tras corrección "
-            f"{model._safe_str(mt.get('method', 'FDR')).upper()}")
-    lines.append("")
-    lines.append(f"*{'; '.join(note_bits)}.*")
-    return "\n".join(lines)
-
-
-# Numeric statistics, in serialization order: (profile key, column header).
-_NUM_STATS = [
-    ("count", "n"), ("mean", "mean"), ("median", "median"), ("mode", "mode"),
-    ("std", "std"), ("variance", "variance"), ("cv", "cv"),
-    ("skew", "skew"), ("kurtosis", "kurtosis"),
-    ("min", "min"), ("p1", "p1"), ("p5", "p5"), ("p25", "p25"), ("p50", "p50"),
-    ("p75", "p75"), ("p95", "p95"), ("p99", "p99"), ("iqr", "iqr"),
-    ("max", "max"), ("n_outliers", "outliers"),
-    ("distribution_type", "distribución"),
-]
-
-
-def _app_numeric_describe(columns: list) -> str:
-    """Loss #2 — every numeric statistic for every numeric column.
-
-    One row per numeric column with the full describe: mean/median/mode/std/
-    variance/cv, skew & kurtosis (for ALL columns, not only the skewed ones),
-    p1/p5/p25/p50/p75/p95/p99, iqr, min/max, outliers and distribution_type.
-    """
-    rows = []
-    for info in (columns or []):
-        num = info.get("numeric") if isinstance(info, dict) else None
-        if not num:
-            continue
-        name = _cell(info.get("name"))
-        cells = [name]
-        for key, _hdr in _NUM_STATS:
-            v = num.get("count" if key == "count" else key)
-            if key == "count":
-                v = num.get("count", info.get("count"))
-            if key == "distribution_type":
-                cells.append(_cell(v))
-            else:
-                cells.append(_fmt_num(v) if v is not None else "")
-        rows.append(cells)
-    if not rows:
-        return ""
-    header = ["Columna"] + [hdr for _k, hdr in _NUM_STATS]
-    lines = ["### Estadísticos numéricos completos (describe)",
-             "",
-             "| " + " | ".join(header) + " |",
-             "| " + " | ".join(["---"] * len(header)) + " |"]
-    for cells in rows:
-        lines.append("| " + " | ".join(cells) + " |")
-    return "\n".join(lines)
-
-
-def _app_reexpression(columns: list) -> str:
-    """Loss #3 — the concrete recommended re-expression per column.
-
-    Names the transform (log1p/sqrt/yeo-johnson/none) instead of a vague
-    "consider re-expressing", with the ladder power, reason and alternatives.
-    """
-    rows = []
-    for info in (columns or []):
-        rx = info.get("reexpression") if isinstance(info, dict) else None
-        if not rx or not isinstance(rx, dict):
-            continue
-        rec = model._safe_str(rx.get("recommended")).strip()
-        if not rec:
-            continue
-        alts = rx.get("alternatives") or []
-        alt_txt = ", ".join(
-            model._safe_str(a.get("transform")) for a in alts
-            if isinstance(a, dict) and a.get("transform")) or "—"
-        rows.append([
-            _cell(info.get("name")), _cell(rec),
-            _fmt_num(rx.get("ladder_power")) if rx.get("ladder_power") is not None else "",
-            _cell(rx.get("reason")), _cell(alt_txt),
-        ])
-    if not rows:
-        return ""
-    lines = ["### Re-expresión recomendada (escalera de Tukey)",
-             "",
-             "| Columna | Recomendada | Potencia | Razón | Alternativas |",
-             "| --- | --- | --- | --- | --- |"]
-    for r in rows:
-        lines.append("| " + " | ".join(r) + " |")
-    return "\n".join(lines)
-
-
-def _app_kmeans_scores(kmeans: dict) -> str:
-    """Loss #4 — KMeans silhouette + inertia per k (justifies the chosen k)."""
-    scores = list(kmeans.get("scores_by_k", []) or [])
-    if not scores:
-        return ""
-    best_k = kmeans.get("best_k")
-    lines = ["#### KMeans — selección de k (`scores_by_k`)",
-             "",
-             "| k | Silhouette | Inercia | Elegido |",
-             "| --- | --- | --- | --- |"]
-    for s in scores:
-        if not isinstance(s, dict):
-            continue
-        k = s.get("k")
-        chosen = "✓" if best_k is not None and k == best_k else ""
-        lines.append(
-            f"| {_fmt_num(k)} | {_fmt_num(s.get('silhouette'))} "
-            f"| {_fmt_num(s.get('inertia'))} | {chosen} |")
-    return "\n".join(lines)
-
-
-def _app_normality(normality: dict) -> str:
-    """Loss #5 — each normality test's statistic next to its p-value."""
-    if not isinstance(normality, dict) or not normality:
-        return ""
-    lines = ["#### Tests de normalidad (estadístico + p-value)",
-             "",
-             ("| Columna | n | JB stat | JB p | D'Agostino stat | D'Agostino p "
-              "| Shapiro stat | Shapiro p | ¿Normal? |"),
-             "| --- | --- | --- | --- | --- | --- | --- | --- | --- |"]
-    any_row = False
-    for col, res in normality.items():
-        if not isinstance(res, dict):
-            continue
-        jb = res.get("jarque_bera") or {}
-        da = res.get("dagostino") or {}
-        sh = res.get("shapiro") or {}
-        is_norm = "sí" if res.get("is_normal") else "no"
-        lines.append(
-            f"| {_cell(col)} | {_fmt_num(res.get('n')) if res.get('n') is not None else ''} "
-            f"| {_fmt_num(jb.get('stat'))} | {_fmt_num(jb.get('p'))} "
-            f"| {_fmt_num(da.get('stat'))} | {_fmt_num(da.get('p'))} "
-            f"| {_fmt_num(sh.get('stat'))} | {_fmt_num(sh.get('p'))} | {is_norm} |")
-        any_row = True
-    return "\n".join(lines) if any_row else ""
-
-
-def _profile_appendix(profile: dict) -> str:
-    """Build the full-data appendix from a TableProfile dict (additive).
-
-    Returns a Markdown ``## Apéndice`` section with one sub-table per loss the
-    human chapters drop, or ``""`` when the profile carries none of them. Never
-    raises: a missing/oddly-shaped section is skipped, not fatal.
-    """
-    if not isinstance(profile, dict):
-        return ""
-    sections: list = []
-    try:
-        corr = profile.get("correlations") or {}
-        seg = _app_correlations(corr) if isinstance(corr, dict) else ""
-        if seg:
-            sections.append(seg)
-    except Exception:  # noqa: BLE001
-        pass
-    try:
-        columns = profile.get("columns") or []
-        seg = _app_numeric_describe(columns)
-        if seg:
-            sections.append(seg)
-        seg = _app_reexpression(columns)
-        if seg:
-            sections.append(seg)
-    except Exception:  # noqa: BLE001
-        pass
-    try:
-        models = profile.get("models") or {}
-        if isinstance(models, dict):
-            model_segs = []
-            seg = _app_kmeans_scores(models.get("kmeans") or {})
-            if seg:
-                model_segs.append(seg)
-            seg = _app_normality(models.get("normality") or {})
-            if seg:
-                model_segs.append(seg)
-            if model_segs:
-                sections.append(
-                    "### Modelos — detalle\n\n" + "\n\n".join(model_segs))
-    except Exception:  # noqa: BLE001
-        pass
-    if not sections:
-        return ""
-    intro = ("Volcado completo de los datos que el motor computó y que los "
-             "capítulos (pensados para lectura humana / PDF) resumen. "
-             "Pensado para que un LLM reconstruya el análisis entero.")
-    return ("## Apéndice — Datos completos del perfil\n\n"
-            f"*{intro}*\n\n" + "\n\n".join(sections))
-
-
 # --------------------------------------------------------------------------- #
 # Entry point.
 # --------------------------------------------------------------------------- #
@@ -715,18 +437,6 @@ def render_md(chapters: list, out_path: str, meta: dict = None) -> dict:
                 segments.append(seg)
         chapters_meta.append({"id": ch.id, "version": ch.version})
 
-    # Full-data appendix: dump everything the profile holds that the human
-    # chapters drop (additive — the .md ends up with more than the PDF/PPTX).
-    # Emitted only when a profile is supplied via meta['profile']; never fatal.
-    try:
-        appendix = _profile_appendix(meta.get("profile"))
-    except Exception as e:  # noqa: BLE001
-        appendix = ""
-        notes.append(f"apéndice de perfil omitido: {e}")
-    if appendix:
-        segments.append("---")
-        segments.append(appendix)
-
     content = "\n\n".join(segments) + "\n"
     note = f"{len(content)} caracteres"
     if notes:

python/functions/datascience/extract_null_mask.md

@@ -0,0 +1,97 @@
+---
+name: extract_null_mask
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def extract_null_mask(query_fn, table: str, columns: list, max_rows: int = 5000) -> dict"
+description: "Extrae la mascara de nulos (1=falta / 0=presente) de una muestra de filas de una tabla, una lista 0/1 por columna alineada por fila, para alimentar el capitulo de calidad / patron de nulos de AutomaticEDA sin que el capitulo toque la base de datos. Recibe un lector read-only inyectado `query_fn(sql) -> dict` (mismo contrato que duckdb_query_readonly / pg_query / el `_q` de profile_table) y NO abre ninguna conexion por su cuenta. Construye UNA sola query que proyecta por cada columna `CASE WHEN \"col\" IS NULL THEN 1 ELSE 0 END` con identificadores escapados y LIMIT. Devuelve dict dict-no-throw: columns (efectivamente leidas, en orden), mask (lista int 0/1 por columna, misma longitud todas) y n. Una celda None se cuenta defensivamente como 1 (falta)."
+tags: [eda, nulls, missing, datascience, automatic-eda, extraction, read-only, duckdb, postgres, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: []
+params:
+  - name: query_fn
+    desc: "callable lector read-only del backend activo. Recibe un string SQL y devuelve un dict {'status':'ok','rows':[{col:val,...},...]} (mismo contrato que duckdb_query_readonly o el `_q` de profile_table). NO se abre ninguna conexion dentro de la funcion: toda la lectura pasa por query_fn. Si es None -> error."
+  - name: table
+    desc: "nombre de la tabla de la que muestrear la mascara de nulos. Se escapa con comillas dobles en la query. Vacio o None -> status error."
+  - name: columns
+    desc: "lista de nombres de columna a evaluar. Cada una produce una entrada en `mask` con una lista 0/1 paralela por fila (1=IS NULL, 0=presente). Cada nombre se escapa con comillas dobles. Vacia o None -> status error."
+  - name: max_rows
+    desc: "limite de filas a muestrear (clausula LIMIT). Default 5000. Protege frente a tablas enormes; con LIMIT obtienes el primer tramo, no un muestreo uniforme."
+output: "dict (nunca lanza). En exito: {'status':'ok','table':str,'columns':[str,...] (en orden),'mask':{col:[int 0/1,...],...} (1=falta/IS NULL, 0=presente; todas las listas con misma longitud = n),'n':int}. En error (sin lanzar): {'status':'error','error':str,'table':str,'columns':[],'mask':{},'n':0}. Errores: query_fn None, table vacia, columns vacia, o query_fn devuelve status!='ok' (se propaga su error)."
+tested: true
+tests: ["test_golden_mask_alineada", "test_celda_none_cuenta_como_falta", "test_columns_vacia_status_error", "test_query_fn_status_error_propaga", "test_query_fn_none_da_error_sin_reventar", "test_sql_contiene_case_y_limit"]
+test_file_path: "python/functions/datascience/extract_null_mask_test.py"
+file_path: "python/functions/datascience/extract_null_mask.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience.extract_null_mask import extract_null_mask
+from infra import duckdb_query_readonly
+
+# El lector read-only se inyecta como closure (igual que el `_q` de profile_table).
+db = "data/clientes.duckdb"
+def _q(sql):
+    return duckdb_query_readonly(db, sql)
+
+res = extract_null_mask(_q, "clientes", ["email", "telefono", "edad"])
+# res == {
+#   "status": "ok",
+#   "table": "clientes",
+#   "columns": ["email", "telefono", "edad"],
+#   "mask": {
+#     "email":    [0, 0, 1, 0, ...],   # fila 2 sin email
+#     "telefono": [1, 0, 1, 0, ...],
+#     "edad":     [0, 0, 0, 1, ...],
+#   },
+#   "n": 5000,
+# }
+
+# % de nulos por columna a partir de la muestra:
+pct = {c: 100 * sum(bits) / max(res["n"], 1) for c, bits in res["mask"].items()}
+
+# Se entrega al capitulo de calidad sin que este toque la BD:
+ctx = {"null_mask": res}
+```
+
+## Cuando usarla
+
+Cuando el capitulo de calidad / patron de nulos de AutomaticEDA necesita saber
+DONDE faltan los valores (no solo cuantos) y NO debe abrir la base de datos por
+su cuenta: extraes aqui la mascara 0/1 por columna alineada por fila y se la pasas
+en `ctx['null_mask']`. Usala siempre que quieras detectar co-ocurrencia de nulos
+(filas que fallan en varias columnas a la vez), calcular el % de nulos sobre una
+muestra, o pintar un heatmap de missingness reutilizando un unico lector read-only
+inyectado, en vez de hacer N `COUNT(*) WHERE col IS NULL` por separado.
+
+## Gotchas
+
+- **Impura**: lee de la base de datos a traves de `query_fn`. No abre conexiones
+  por su cuenta — depende por completo del lector inyectado. Sigue el estilo
+  dict-no-throw del grupo `eda`: nunca lanza; ante cualquier fallo devuelve
+  `{"status":"error","error":...}` con `columns=[]`, `mask={}`, `n=0`.
+- **`error_type` en el frontmatter es `error_go_core` por convencion del registry**
+  (toda funcion impura debe declararlo y el indexer lo exige), pero el codigo
+  NO lanza esa excepcion: degrada al dict de error. Es metadata, no comportamiento.
+- **Muestra, no censo**: con `LIMIT max_rows` obtienes el primer tramo de filas que
+  devuelva el backend, no un muestreo uniforme ni la tabla entera. El % de nulos
+  derivado es una estimacion sobre esa muestra; para el conteo exacto usa un
+  agregado `COUNT(*)`/`COUNT(col)` aparte.
+- **Alineacion por fila**: `mask[col][i]` corresponde a la misma fila `i` que
+  `mask[otra_col][i]`. Todas las listas tienen longitud `n`, asi que puedes cruzar
+  columnas por indice (co-ocurrencia de nulos) sin re-alinear.
+- **Defensa None -> 1**: el SQL ya devuelve 0/1, pero si una celda llega como `None`
+  (CASE no aplicado, columna ausente en la fila, backend que nulifica) se cuenta
+  como 1 (falta). Un valor inesperado no convertible a int se trata como presente (0).
+- **No loguear los datos crudos**: aunque `mask` es solo 0/1, los nombres de columna
+  pueden revelar el esquema. En trazas usa `n` y el numero de columnas, no el dict
+  completo.

python/functions/datascience/extract_null_mask.py

@@ -0,0 +1,101 @@
+"""extract_null_mask — extrae la mascara de nulos (1=falta / 0=presente) de una tabla.
+
+Lector read-only inyectado: recibe `query_fn(sql) -> dict` con el mismo contrato
+que duckdb_query_readonly / pg_query (y que el `_q` de profile_table):
+`{"status": "ok", "rows": [{col: val, ...}, ...]}`. Esta funcion NO abre ninguna
+conexion por su cuenta — solo usa `query_fn`. Construye UNA sola query que, por
+cada columna pedida, evalua `CASE WHEN "col" IS NULL THEN 1 ELSE 0 END` y devuelve
+una muestra de filas con esos bits. El resultado es un dict `mask` con una lista
+0/1 por columna, alineada por fila (1 = el valor falta / IS NULL, 0 = presente),
+listo para alimentar el capitulo de calidad / patron de nulos de AutomaticEDA sin
+que el capitulo toque la base de datos.
+
+Estilo dict-no-throw del grupo `eda`: nunca lanza; captura cualquier excepcion y
+degrada a `{"status": "error", "error": str, ...}`.
+"""
+
+
+def _to_bit(value):
+    """Coacciona el valor 0/1 del CASE a int de forma defensiva.
+
+    El SQL ya devuelve 0 (presente) o 1 (falta). Por si una celda llega como None
+    (el CASE no se aplico o el backend la nulifico), se cuenta como 1 (falta). El
+    resto se reduce a int: un entero distinto de 0 cuenta como 1 (falta), 0 como
+    presente. Un valor no convertible se trata como presente (0) — nunca lanza.
+    """
+    if value is None:
+        return 1
+    try:
+        return 1 if int(value) != 0 else 0
+    except (TypeError, ValueError):
+        return 0
+
+
+def extract_null_mask(query_fn, table, columns, max_rows=5000):
+    """Extrae la mascara de nulos (1=falta / 0=presente) de una muestra de la tabla.
+
+    Args:
+        query_fn: callable lector read-only del backend activo. Recibe un string
+            SQL y devuelve un dict {"status": "ok", "rows": [{col: val, ...}]}
+            (mismo contrato que duckdb_query_readonly / el `_q` de profile_table).
+            No se abre ninguna conexion aqui: toda la lectura pasa por query_fn.
+        table: nombre de la tabla. Se escapa con comillas dobles en la query.
+        columns: lista de nombres de columna a evaluar. Cada una produce una
+            entrada en `mask` con una lista 0/1 paralela por fila. Vacia o None ->
+            status error.
+        max_rows: limite de filas a muestrear (clausula LIMIT). Default 5000.
+
+    Returns:
+        dict (nunca lanza):
+            {
+              "status": "ok" | "error",
+              "error": str,                 # solo si status == "error"
+              "table": str,
+              "columns": [str, ...],        # columnas efectivamente leidas, en orden
+              "mask": {col: [int 0/1, ...], ...},  # alineada por fila, 1=falta, 0=presente
+              "n": int                      # nº de filas muestreadas
+            }
+        Todas las listas de `mask` tienen la misma longitud (= n).
+    """
+    base = {"status": "ok", "table": table, "columns": [], "mask": {}, "n": 0}
+    try:
+        if query_fn is None:
+            return {**base, "status": "error", "error": "query_fn es None"}
+        if not table:
+            return {**base, "status": "error", "error": "table es obligatorio"}
+        if not columns:
+            return {**base, "status": "error", "error": "columns vacío"}
+
+        # Identificadores escapados con comillas dobles (como hace profile_table)
+        # para tolerar nombres con mayusculas/espacios/palabras reservadas. Cada
+        # columna se proyecta como su propio bit IS NULL conservando el alias.
+        select_sql = ", ".join(
+            f'(CASE WHEN "{c}" IS NULL THEN 1 ELSE 0 END) AS "{c}"' for c in columns
+        )
+        sql = f'SELECT {select_sql} FROM "{table}" LIMIT {int(max_rows)}'
+
+        q = query_fn(sql)
+        if not isinstance(q, dict) or q.get("status") != "ok":
+            err = (
+                q.get("error", "query_fn fallo")
+                if isinstance(q, dict)
+                else "query_fn no devolvio un dict"
+            )
+            return {**base, "status": "error", "error": err}
+
+        rows = q.get("rows", []) or []
+        mask = {c: [] for c in columns}
+        for row in rows:
+            for c in columns:
+                # row.get tolera filas que no traigan la columna (None -> falta).
+                mask[c].append(_to_bit(row.get(c) if isinstance(row, dict) else None))
+
+        return {
+            "status": "ok",
+            "table": table,
+            "columns": list(columns),
+            "mask": mask,
+            "n": len(rows),
+        }
+    except Exception as e:  # noqa: BLE001 - dict-no-throw: degradar, nunca lanzar
+        return {**base, "status": "error", "error": str(e)}

python/functions/datascience/extract_null_mask_test.py

@@ -0,0 +1,116 @@
+"""Tests para extract_null_mask.
+
+No usa DuckDB real: inyecta un query_fn FAKE (closure) que devuelve filas
+predefinidas (simulando el SELECT de bits 0/1) y, opcionalmente, captura el SQL
+recibido para verificar la query generada (CASE WHEN ... IS NULL + LIMIT). Asi el
+test es autocontenido y no depende de ningun backend.
+"""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from extract_null_mask import extract_null_mask
+
+
+def _fake_query(rows, captured=None, status="ok", error=None):
+    """Crea un query_fn FAKE.
+
+    `captured` (lista opcional) recibe el SQL ejecutado para poder inspeccionarlo.
+    `status`/`error` permiten simular un fallo del backend.
+    """
+
+    def _q(sql):
+        if captured is not None:
+            captured.append(sql)
+        if status != "ok":
+            return {"status": "error", "error": error or "boom"}
+        return {"status": "ok", "rows": rows}
+
+    return _q
+
+
+def test_golden_mask_alineada():
+    """Golden: mask 0/1 por columna alineada por fila, n correcto, status ok."""
+    # Cada fila simula el SELECT (CASE WHEN col IS NULL THEN 1 ELSE 0 END) AS col.
+    rows = [
+        {"email": 0, "telefono": 1, "edad": 0},
+        {"email": 0, "telefono": 0, "edad": 1},
+        {"email": 1, "telefono": 1, "edad": 0},
+    ]
+    res = extract_null_mask(_fake_query(rows), "clientes", ["email", "telefono", "edad"])
+    assert res["status"] == "ok"
+    assert res["table"] == "clientes"
+    assert res["columns"] == ["email", "telefono", "edad"]
+    assert res["n"] == 3
+    assert res["mask"]["email"] == [0, 0, 1]
+    assert res["mask"]["telefono"] == [1, 0, 1]
+    assert res["mask"]["edad"] == [0, 1, 0]
+    # Todas las listas con la misma longitud.
+    assert all(len(v) == res["n"] for v in res["mask"].values())
+
+
+def test_celda_none_cuenta_como_falta():
+    """Una celda None se cuenta defensivamente como 1 (falta)."""
+    rows = [
+        {"email": 0, "telefono": None},
+        {"email": None, "telefono": 1},
+        {"email": 1, "telefono": 0},
+    ]
+    res = extract_null_mask(_fake_query(rows), "clientes", ["email", "telefono"])
+    assert res["status"] == "ok"
+    assert res["mask"]["email"] == [0, 1, 1]
+    assert res["mask"]["telefono"] == [1, 1, 0]
+    assert res["n"] == 3
+
+
+def test_columns_vacia_status_error():
+    """columns vacia -> status error con columns/mask/n vacios."""
+    res = extract_null_mask(_fake_query([]), "clientes", [])
+    assert res["status"] == "error"
+    assert "columns" in res["error"]
+    assert res["table"] == "clientes"
+    assert res["columns"] == []
+    assert res["mask"] == {}
+    assert res["n"] == 0
+
+
+def test_query_fn_status_error_propaga():
+    """query_fn que devuelve status != ok -> se propaga como error, mask {}."""
+    res = extract_null_mask(
+        _fake_query([], status="error", error="db locked"),
+        "clientes",
+        ["email"],
+    )
+    assert res["status"] == "error"
+    assert "db locked" in res["error"]
+    assert res["mask"] == {}
+    assert res["n"] == 0
+
+
+def test_query_fn_none_da_error_sin_reventar():
+    """query_fn None -> error degradado, sin excepcion."""
+    res = extract_null_mask(None, "clientes", ["email"])
+    assert res["status"] == "error"
+    assert res["columns"] == []
+    assert res["mask"] == {}
+    assert res["n"] == 0
+
+
+def test_sql_contiene_case_y_limit():
+    """La query genera un CASE WHEN IS NULL por columna escapada + LIMIT sobre la tabla."""
+    captured = []
+    rows = [{"email": 0}]
+    extract_null_mask(
+        _fake_query(rows, captured),
+        "clientes_tbl",
+        ["email"],
+        max_rows=123,
+    )
+    assert len(captured) == 1
+    sql = captured[0]
+    assert 'CASE WHEN "email" IS NULL THEN 1 ELSE 0 END' in sql
+    assert 'AS "email"' in sql
+    assert 'FROM "clientes_tbl"' in sql
+    assert "LIMIT 123" in sql

python/functions/datascience/missingness_corr_heatmap_figure.md

@@ -0,0 +1,103 @@
+---
+id: missingness_corr_heatmap_figure_py_datascience
+name: missingness_corr_heatmap_figure
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def missingness_corr_heatmap_figure(matrix, labels, title=\"Co-ocurrencia de ausencias\") -> \"matplotlib.figure.Figure\""
+description: "Construye una figura matplotlib (heatmap) de la matriz NxN de correlación de ausencias entre columnas: +1 = dos columnas suelen ser nulas a la vez, -1 = cuando una falta la otra está presente, 0 = ausencias independientes. Usa ax.imshow con coolwarm fijado a [-1,1], ticks con los labels truncados (X rotados 45º), colorbar y anota el valor de cada celda si N<=12. Devuelve un matplotlib.figure.Figure listo para rasterizar por el renderer del informe EDA (capítulo de datos faltantes). Backend Agg sin pyplot global; defensivo ante matrix/labels vacíos o celdas no numéricas (nunca lanza)."
+tags: [eda, missing, missingness, correlation, heatmap, matplotlib, figure, visualization, datascience, impure]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [matplotlib]
+example: |
+  from datascience.missingness_corr_heatmap_figure import missingness_corr_heatmap_figure
+  matrix = [
+      [1.0, 0.82, -0.10],
+      [0.82, 1.0, 0.05],
+      [-0.10, 0.05, 1.0],
+  ]
+  labels = ["telefono", "movil", "email"]
+  fig = missingness_corr_heatmap_figure(matrix, labels, title="Co-ocurrencia de ausencias")
+tested: true
+tests:
+  - "test_returns_figure_with_axes"
+  - "test_empty_matrix_does_not_raise_and_returns_figure"
+  - "test_empty_labels_returns_message_figure"
+  - "test_large_matrix_omits_annotations"
+  - "test_ragged_and_non_numeric_cells_are_handled"
+test_file_path: "python/functions/datascience/missingness_corr_heatmap_figure_test.py"
+file_path: "python/functions/datascience/missingness_corr_heatmap_figure.py"
+params:
+  - name: matrix
+    desc: "Lista de listas (NxN) de floats en [-1,1]: la correlación de ausencias por pares de columnas. Puede venir vacía. Filas de longitud desigual se toleran (se rellenan/recortan a N); celdas None, NaN o no numéricas se coercen a 0.0. No se muta el original."
+  - name: labels
+    desc: "Lista de N nombres de columna, paralela a matrix. Puede venir vacía (devuelve figura \"sin columnas con ausencia variable\"). Se truncan a ~14 chars con elipsis para los ticks; los originales no se mutan."
+  - name: title
+    desc: "Título de la figura. Se trunca a ~60 chars con elipsis si es muy largo. Default \"Co-ocurrencia de ausencias\"."
+output: "Un matplotlib.figure.Figure (figsize 6.4x5.2, dpi 150) con un Axes heatmap (imshow vmin=-1, vmax=1, cmap coolwarm) más una colorbar etiquetada \"correlación de ausencias\". Ticks en ambos ejes con los labels truncados (X rotados 45º). Si N<=12 cada celda lleva su valor numérico anotado (texto blanco sobre celdas saturadas, oscuro sobre pálidas); con N grande se omiten las anotaciones para no saturar. Si matrix o labels vienen vacíos devuelve una Figure con texto centrado \"sin columnas con ausencia variable\"; cualquier error inesperado se captura y devuelve una Figure con el mensaje de error (nunca lanza). El caller rasteriza/cierra la figura; la función no la muestra ni la guarda."
+---
+
+## Ejemplo
+
+```python
+from datascience.missingness_corr_heatmap_figure import missingness_corr_heatmap_figure
+
+# Correlación de ausencias entre 3 columnas de contacto:
+# telefono y movil tienden a faltar juntos (0.82); email es casi independiente.
+matrix = [
+    [1.00, 0.82, -0.10],
+    [0.82, 1.00,  0.05],
+    [-0.10, 0.05, 1.00],
+]
+labels = ["telefono", "movil", "email"]
+
+fig = missingness_corr_heatmap_figure(
+    matrix,
+    labels,
+    title="Co-ocurrencia de ausencias",
+)
+
+# El renderer del informe lo rasteriza; aquí solo persistimos para inspección.
+fig.savefig("/tmp/missingness_heatmap.png")
+```
+
+## Cuando usarla
+
+Úsala en el capítulo de datos faltantes de un informe EDA cuando quieras ver de
+un vistazo qué columnas faltan juntas (mismo formulario sin rellenar, mismo
+proceso roto) frente a columnas cuyas ausencias son independientes. Pásale la
+matriz de correlación de ausencias (calculada sobre la máscara de nulos, p. ej.
+`df.isnull().corr()`) restringida a las columnas que de verdad tienen ausencia
+variable, junto con sus nombres. Es la pareja "estructura" del ranking de % de
+nulos: las barras dicen *cuánto* falta cada columna, este heatmap dice *si las
+ausencias están relacionadas* entre columnas.
+
+## Gotchas
+
+- **Impura por matplotlib.** Toca la maquinaria de render. Usa el backend `Agg`
+  y la API orientada a objetos `Figure`/`add_subplot` — NUNCA `pyplot.*` aquí,
+  para no tocar el estado global ni filtrar figuras entre llamadas. `pyplot` NO
+  es thread-safe; esta función evita ese riesgo construyendo el `Figure`
+  directamente, así que es segura de llamar en bucle desde el renderer.
+- **El caller cierra la figura.** Devuelve el `Figure` pero no lo muestra ni lo
+  guarda. Quien la consume debe rasterizarla y luego liberarla
+  (`matplotlib.pyplot.close(fig)`) para no acumular memoria en lotes grandes.
+- **Escala de color fija en [-1, 1].** `vmin=-1`, `vmax=1` están fijados a
+  propósito para que el color sea comparable entre informes y entre columnas. No
+  se autoescala al rango real de la matriz; valores fuera de `[-1, 1]` se
+  saturan al extremo del colormap.
+- **Anotaciones solo con N<=12.** Por encima de 12 columnas el grid de números
+  se vuelve ilegible y se omite; queda solo el color + la colorbar. Filtra a las
+  columnas con ausencia variable antes de llamar para no llegar a matrices
+  enormes.
+- **Defensiva, nunca lanza.** `matrix=[]`, `labels=[]`, filas cortas, celdas
+  `None`/`NaN`/no numéricas o cualquier error inesperado se manejan sin propagar:
+  en el peor caso devuelve una `Figure` con "sin columnas con ausencia variable"
+  o con el texto del error. No envuelvas la llamada en try/except por miedo a un
+  raise — no lo hay.

python/functions/datascience/missingness_corr_heatmap_figure.py

@@ -0,0 +1,158 @@
+"""Impure EDA helper: heatmap of missingness co-occurrence (`eda` group).
+
+Builds a matplotlib heatmap of the pairwise missingness correlation matrix of a
+dataset: a value near ``+1`` means two columns tend to be null together, near
+``-1`` means when one is null the other tends to be present, and ``0`` means
+their absences are independent. Returns a ready-to-rasterize
+``matplotlib.figure.Figure``; it never shows nor saves it.
+
+Impure because it touches matplotlib's rendering machinery. It uses the headless
+Agg backend and the object-oriented ``Figure`` API (no ``pyplot``) so it leaks no
+global state and is safe to call repeatedly from a report renderer.
+"""
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+from matplotlib.figure import Figure  # noqa: E402
+
+# Muted gray for secondary text (no-data / fallback messages).
+_MUTED_TEXT = "#5f6b7a"
+# Soft red for the error fallback message (kept readable, not alarming).
+_ERROR_TEXT = "#b00020"
+
+
+def _truncate(text, width: int = 14) -> str:
+    """Truncate ``text`` to ``width`` chars, appending an ellipsis if cut."""
+    s = "" if text is None else str(text)
+    if len(s) <= width:
+        return s
+    if width <= 1:
+        return s[:width]
+    return s[: width - 1] + "…"
+
+
+def _message_figure(message: str, color: str = _MUTED_TEXT) -> "Figure":
+    """Return a fallback ``Figure`` carrying a single centered message."""
+    fig = Figure(figsize=(6.4, 4.0), dpi=150)
+    ax = fig.add_subplot(111)
+    ax.axis("off")
+    ax.text(
+        0.5,
+        0.5,
+        message,
+        ha="center",
+        va="center",
+        fontsize=12,
+        color=color,
+        wrap=True,
+        transform=ax.transAxes,
+    )
+    fig.tight_layout()
+    return fig
+
+
+def missingness_corr_heatmap_figure(
+    matrix,
+    labels,
+    title: str = "Co-ocurrencia de ausencias",
+) -> "matplotlib.figure.Figure":
+    """Build a heatmap figure of a missingness correlation matrix.
+
+    Renders an ``NxN`` matrix of missingness correlations in ``[-1, 1]`` with a
+    diverging ``coolwarm`` colormap (fixed ``vmin=-1``, ``vmax=1`` so the color
+    scale is comparable across reports). Both axes are tick-labelled with the
+    column names (truncated to ~14 chars; the X labels rotated 45°). A colorbar
+    is attached. When the matrix is small (``N <= 12``) each cell is annotated
+    with its numeric value; for larger matrices the annotations are omitted to
+    avoid an unreadable grid.
+
+    The function is fully defensive: empty/ragged/non-numeric input never raises.
+    When there is nothing valid to draw it returns a ``Figure`` carrying a
+    centered "sin columnas con ausencia variable" message, and any unexpected
+    error is caught and turned into a fallback ``Figure`` carrying the error text.
+
+    Args:
+        matrix: List of lists (``NxN``) of floats in ``[-1, 1]`` — the pairwise
+            missingness correlation. May be empty; rows of unequal length are
+            tolerated by treating the matrix as invalid only when it is empty or
+            its label count does not match. Non-numeric/``None`` cells are
+            coerced to ``0.0``.
+        labels: List of ``N`` column names, parallel to ``matrix``. May be empty.
+            Truncated for display; the originals are not mutated.
+        title: Figure title. Default "Co-ocurrencia de ausencias".
+
+    Returns:
+        A ``matplotlib.figure.Figure`` with a single heatmap Axes plus a
+        colorbar. The caller is responsible for rasterizing/closing it.
+    """
+    try:
+        # --- Validate shape: need a non-empty square-ish matrix with labels.
+        if (
+            not isinstance(matrix, (list, tuple))
+            or not isinstance(labels, (list, tuple))
+            or len(matrix) == 0
+            or len(labels) == 0
+        ):
+            return _message_figure("sin columnas con ausencia variable")
+
+        n = len(labels)
+        # Build a clean NxN grid: coerce each cell to float, default 0.0, pad/clip
+        # rows so a ragged input never crashes imshow.
+        grid = []
+        for i in range(n):
+            row_src = matrix[i] if i < len(matrix) else []
+            if not isinstance(row_src, (list, tuple)):
+                row_src = []
+            row = []
+            for j in range(n):
+                cell = row_src[j] if j < len(row_src) else 0.0
+                try:
+                    val = float(cell)
+                except (TypeError, ValueError):
+                    val = 0.0
+                if val != val:  # NaN guard.
+                    val = 0.0
+                row.append(val)
+            grid.append(row)
+
+        fig = Figure(figsize=(6.4, 5.2), dpi=150)
+        ax = fig.add_subplot(111)
+
+        im = ax.imshow(grid, vmin=-1, vmax=1, cmap="coolwarm", aspect="equal")
+
+        short = [_truncate(lab, 14) for lab in labels]
+        ax.set_xticks(range(n))
+        ax.set_yticks(range(n))
+        ax.set_xticklabels(short, rotation=45, ha="right", fontsize=8)
+        ax.set_yticklabels(short, fontsize=8)
+
+        # Annotate each cell only when the grid is small enough to stay legible.
+        if n <= 12:
+            for i in range(n):
+                for j in range(n):
+                    val = grid[i][j]
+                    # White text over saturated (dark) cells, dark over pale.
+                    txt_color = "white" if abs(val) >= 0.55 else "#202020"
+                    ax.text(
+                        j,
+                        i,
+                        f"{val:.2f}",
+                        ha="center",
+                        va="center",
+                        fontsize=7,
+                        color=txt_color,
+                    )
+
+        cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+        cbar.ax.tick_params(labelsize=8)
+        cbar.set_label("correlación de ausencias", fontsize=8)
+
+        if title:
+            ax.set_title(_truncate(title, 60), fontsize=12, loc="center", pad=10)
+
+        fig.tight_layout()
+        return fig
+    except Exception as exc:  # noqa: BLE001 — never raise from a figure builder.
+        return _message_figure(f"error al dibujar heatmap: {exc}", color=_ERROR_TEXT)

python/functions/datascience/missingness_corr_heatmap_figure_test.py

@@ -0,0 +1,62 @@
+"""Tests para missingness_corr_heatmap_figure (heatmap de ausencias, grupo eda).
+
+Usa el backend Agg sin pyplot; no muestra ni guarda figuras. Cada test cierra
+explícitamente la Figure construida (matplotlib.pyplot.close) para no acumular
+estado entre tests.
+"""
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.figure import Figure  # noqa: E402
+
+from missingness_corr_heatmap_figure import missingness_corr_heatmap_figure
+
+
+def _identity_matrix(n):
+    """Matriz NxN con diagonal 1.0 y resto 0.0 (correlación de ausencias)."""
+    return [[1.0 if i == j else 0.0 for j in range(n)] for i in range(n)]
+
+
+def test_returns_figure_with_axes():
+    matrix = [[1.0, 0.3, -0.2], [0.3, 1.0, 0.5], [-0.2, 0.5, 1.0]]
+    labels = ["edad", "ingresos", "ciudad"]
+    fig = missingness_corr_heatmap_figure(matrix, labels, title="ausencias")
+    assert isinstance(fig, Figure)
+    # Heatmap (>=1 axes) + colorbar añade su propio Axes -> al menos 1.
+    assert len(fig.axes) >= 1
+    plt.close(fig)
+
+
+def test_empty_matrix_does_not_raise_and_returns_figure():
+    fig = missingness_corr_heatmap_figure([], [], title="vacía")
+    assert isinstance(fig, Figure)
+    assert len(fig.axes) >= 1
+    plt.close(fig)
+
+
+def test_empty_labels_returns_message_figure():
+    fig = missingness_corr_heatmap_figure([[1.0]], [], title="sin labels")
+    assert isinstance(fig, Figure)
+    plt.close(fig)
+
+
+def test_large_matrix_omits_annotations():
+    n = 16
+    fig = missingness_corr_heatmap_figure(
+        _identity_matrix(n), [f"col_{i}" for i in range(n)]
+    )
+    assert isinstance(fig, Figure)
+    assert len(fig.axes) >= 1
+    plt.close(fig)
+
+
+def test_ragged_and_non_numeric_cells_are_handled():
+    # Fila corta + celda None + celda string -> se rellenan/coercen sin lanzar.
+    matrix = [[1.0, None], ["x", 1.0, 0.5]]
+    labels = ["a", "b"]
+    fig = missingness_corr_heatmap_figure(matrix, labels)
+    assert isinstance(fig, Figure)
+    plt.close(fig)

python/functions/datascience/missingness_correlation.md

@@ -0,0 +1,68 @@
+---
+name: missingness_correlation
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def missingness_correlation(null_mask: dict, top_k: int = 20) -> dict"
+description: "Co-ocurrencia de ausencias: nucleo del capitulo de missingness del grupo eda. Recibe la mascara binaria de nulos de una tabla (1 = falta, 0 = presente, alineada por fila) y mide hasta que punto las columnas faltan juntas. Calcula la matriz de correlacion de Pearson entre los vectores binarios de ausencia de las columnas con varianza (al menos un 1 y un 0), mas las cifras de solapamiento de conjuntos por par (co-missing, either-missing, Jaccard). Excluye las columnas constantes en su ausencia (correlacion indefinida) y reporta cuantas. Compone la funcion atomica pearson del registry; no la reimplementa. Lectura defensiva; NUNCA lanza."
+tags: [eda, missingness, correlation, pearson, co-occurrence, jaccard, datascience]
+params:
+  - name: null_mask
+    desc: "dict {col: [int 0/1, ...]} con la mascara de ausencias de la tabla, alineada por fila: 1 = el valor falta en esa fila, 0 = presente. Todas las listas se asumen de la misma longitud (numero de filas). Valores truthy distintos de 0 se tratan como ausencia; entradas no-lista se ignoran sin romper."
+  - name: top_k
+    desc: "Numero maximo de pares a devolver en `pairs`, ordenados por valor absoluto de correlacion descendente. Default 20. Solo limita la lista de pares; la matriz cubre siempre todas las columnas con varianza."
+output: "dict con: columns (columnas con varianza en la ausencia, en orden de entrada); matrix (len(columns) x len(columns) de correlacion de Pearson entre las mascaras binarias, diagonal 1.0); pairs (hasta top_k pares i<j ordenados por |corr| desc, cada uno {a, b, corr, co_missing, either_missing, jaccard} donde co_missing = filas en que ambas faltan, either_missing = filas en que al menos una falta, jaccard = co_missing/either_missing o 0.0 si either_missing=0); n_excluded (nº de columnas con algun nulo pero sin varianza, constantes en la ausencia); excluded_cols (esas columnas en orden de entrada). Si hay <2 columnas con varianza, columns/matrix/pairs van vacios pero n_excluded/excluded_cols se rellenan. NUNCA lanza."
+uses_functions: [pearson_py_datascience]
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+tested: true
+tests: ["test_co_ocurrencia_fuerte_corr_uno_jaccard_uno", "test_ausencias_disjuntas_corr_negativa_jaccard_cero", "test_columna_sin_varianza_se_excluye", "test_menos_de_dos_columnas_con_varianza_vacio_pero_cuenta_excluidas", "test_mask_vacio_todo_vacio", "test_top_k_limita_pares", "test_no_lanza_con_entradas_raras"]
+test_file_path: "python/functions/datascience/missingness_correlation_test.py"
+file_path: "python/functions/datascience/missingness_correlation.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience.missingness_correlation import missingness_correlation
+
+# Mascara de ausencias de 6 filas. 1 = falta, 0 = presente.
+mask = {
+    "ingresos":  [1, 0, 1, 0, 1, 0],   # falta junto a "deducciones"
+    "deducciones": [1, 0, 1, 0, 1, 0], # mismas filas que "ingresos"
+    "telefono":  [0, 0, 0, 1, 0, 0],   # casi siempre presente
+    "verificado": [1, 1, 1, 1, 1, 1],  # siempre ausente -> constante, excluida
+}
+out = missingness_correlation(mask, top_k=10)
+
+print(out["columns"])        # ['ingresos', 'deducciones', 'telefono']
+print(out["n_excluded"])     # 1
+print(out["excluded_cols"])  # ['verificado']
+
+# El par mas fuerte: ingresos y deducciones faltan siempre juntas.
+top = out["pairs"][0]
+print(top["a"], top["b"], round(top["corr"], 3))  # ingresos deducciones 1.0
+print(top["co_missing"], top["either_missing"], top["jaccard"])  # 3 3 1.0
+```
+
+## Cuando usarla
+
+- Usala en el capitulo de **missingness** de `AutomaticEDA` cuando ya tengas la mascara binaria de nulos por columna y quieras detectar **patrones de ausencia conjunta**: que columnas faltan siempre juntas (posible misma fuente/proceso roto) y cuales faltan de forma independiente.
+- Cuando necesites ordenar los pares de columnas por fuerza de co-ocurrencia (|corr|) para priorizar que bloques de ausencia investigar o imputar juntos.
+- Cuando quieras la cifra de solapamiento de conjuntos (Jaccard, co-missing) ademas de la correlacion lineal, para distinguir "faltan juntas" de "estan presentes juntas".
+- Antes de elegir una estrategia de imputacion: dos columnas con corr de ausencia ~1.0 no aportan informacion independiente sobre por que falta la otra.
+
+## Gotchas
+
+- Funcion pura, sin I/O y determinista. Lectura defensiva: entradas no-dict, columnas no-lista o vacias se ignoran sin lanzar.
+- Solo entran al calculo las columnas con **varianza en la ausencia** (al menos un 1 y al menos un 0). Una columna siempre-presente (todo 0) no aporta ausencia y **no** se cuenta como excluida; una columna siempre-ausente o constante con nulos (todo 1) tiene correlacion indefinida y se excluye, sumando a `n_excluded` / `excluded_cols`.
+- Con menos de 2 columnas con varianza, `columns`/`matrix`/`pairs` quedan vacios pero `n_excluded`/`excluded_cols` se rellenan igual — el caller debe contemplar el caso "sin pares".
+- La correlacion es la de Pearson sobre vectores binarios (equivale al coeficiente phi). El signo importa: corr negativa = las ausencias tienden a ser **complementarias** (cuando una falta, la otra suele estar presente).
+- Asume todas las listas alineadas por fila y de la misma longitud. Si vienen de longitudes distintas, `pearson` opera sobre el solapamiento que permita `zip` y degrada a 0.0 cuando no hay varianza efectiva; alinea la mascara antes de llamar.

python/functions/datascience/missingness_correlation.py

@@ -0,0 +1,120 @@
+"""Co-ocurrencia de ausencias: matriz de correlacion de Pearson entre mascaras de nulos.
+
+Funcion pura del grupo eda, nucleo del capitulo de missingness. Recibe la mascara
+binaria de ausencias de una tabla (1 = falta, 0 = presente, alineada por fila) y
+mide hasta que punto las columnas faltan juntas. Para cada par de columnas con
+varianza en su ausencia calcula la correlacion de Pearson entre los vectores
+binarios, mas las cifras de solapamiento de conjuntos (co-missing, either-missing,
+Jaccard). Compone la funcion atomica `pearson` del registry; no reimplementa la
+correlacion. Lectura defensiva; NUNCA lanza.
+"""
+
+from datascience import pearson
+
+
+def missingness_correlation(null_mask, top_k=20) -> dict:
+    """Correlacion de co-ocurrencia de ausencias entre columnas.
+
+    Args:
+        null_mask: dict {col: [int 0/1, ...]} alineado por fila (1 = el valor
+            falta en esa fila). Todas las listas se asumen de la misma longitud.
+        top_k: numero maximo de pares a devolver, ordenados por |corr| desc.
+
+    Returns:
+        dict con:
+          - columns: columnas con varianza en la ausencia (al menos un 1 y al
+            menos un 0), en orden de entrada.
+          - matrix: matriz len(columns) x len(columns) de correlacion de Pearson
+            entre las mascaras binarias, diagonal 1.0.
+          - pairs: lista de hasta top_k pares (i<j) ordenados por |corr| desc.
+            Cada par: {a, b, corr, co_missing, either_missing, jaccard}.
+          - n_excluded: numero de columnas con algun nulo pero sin varianza
+            (constantes en la ausencia: siempre presentes o siempre ausentes).
+          - excluded_cols: lista de esas columnas (en orden de entrada).
+
+        Si hay menos de 2 columnas con varianza, columns/matrix/pairs van vacios
+        pero n_excluded/excluded_cols se rellenan igualmente. NUNCA lanza.
+    """
+    # Salida base, defensiva ante entradas no-dict.
+    result = {
+        "columns": [],
+        "matrix": [],
+        "pairs": [],
+        "n_excluded": 0,
+        "excluded_cols": [],
+    }
+
+    if not isinstance(null_mask, dict) or not null_mask:
+        return result
+
+    varying = []          # columnas con varianza en la ausencia
+    varying_vecs = []     # sus vectores binarios saneados (floats 0.0/1.0)
+    excluded_cols = []    # columnas con nulos pero sin varianza (constantes)
+
+    for col, raw in null_mask.items():
+        if not isinstance(raw, (list, tuple)):
+            continue
+        # Sanea a 0/1: cualquier valor truthy distinto de 0 cuenta como ausencia.
+        vec = [1 if bool(v) else 0 for v in raw]
+        if not vec:
+            continue
+        ones = sum(vec)
+        zeros = len(vec) - ones
+        if ones > 0 and zeros > 0:
+            varying.append(col)
+            varying_vecs.append([float(v) for v in vec])
+        elif ones > 0:
+            # Tiene nulos pero todos (constante en la ausencia): sin varianza.
+            excluded_cols.append(col)
+        # ones == 0 -> columna siempre presente, sin nulos: no se cuenta como
+        # excluida (no aporta ausencia al analisis de co-ocurrencia).
+
+    result["n_excluded"] = len(excluded_cols)
+    result["excluded_cols"] = excluded_cols
+
+    n = len(varying)
+    if n < 2:
+        return result
+
+    result["columns"] = list(varying)
+
+    # Matriz de correlacion de Pearson, diagonal 1.0.
+    matrix = [[0.0] * n for _ in range(n)]
+    for i in range(n):
+        matrix[i][i] = 1.0
+    for i in range(n):
+        for j in range(i + 1, n):
+            r = pearson(varying_vecs[i], varying_vecs[j])
+            matrix[i][j] = r
+            matrix[j][i] = r
+    result["matrix"] = matrix
+
+    # Pares con cifras de solapamiento de conjuntos.
+    pairs = []
+    for i in range(n):
+        vi = varying_vecs[i]
+        for j in range(i + 1, n):
+            vj = varying_vecs[j]
+            co_missing = 0
+            either_missing = 0
+            for a, b in zip(vi, vj):
+                a_miss = a != 0.0
+                b_miss = b != 0.0
+                if a_miss and b_miss:
+                    co_missing += 1
+                if a_miss or b_miss:
+                    either_missing += 1
+            jaccard = co_missing / either_missing if either_missing > 0 else 0.0
+            pairs.append({
+                "a": varying[i],
+                "b": varying[j],
+                "corr": matrix[i][j],
+                "co_missing": co_missing,
+                "either_missing": either_missing,
+                "jaccard": jaccard,
+            })
+
+    pairs.sort(key=lambda p: abs(p["corr"]), reverse=True)
+    result["pairs"] = pairs[:top_k] if top_k is not None and top_k >= 0 else pairs
+
+    return result

python/functions/datascience/missingness_correlation_test.py

@@ -0,0 +1,115 @@
+"""Tests para missingness_correlation."""
+
+from datascience.missingness_correlation import missingness_correlation
+
+
+def test_co_ocurrencia_fuerte_corr_uno_jaccard_uno():
+    # a y b faltan EXACTAMENTE en las mismas filas -> corr 1.0, jaccard 1.0.
+    mask = {
+        "a": [1, 0, 1, 0, 1, 0],
+        "b": [1, 0, 1, 0, 1, 0],
+    }
+    out = missingness_correlation(mask)
+    assert out["columns"] == ["a", "b"]
+    assert out["n_excluded"] == 0
+    # Diagonal 1.0, off-diagonal ~1.0.
+    assert out["matrix"][0][0] == 1.0
+    assert out["matrix"][1][1] == 1.0
+    assert abs(out["matrix"][0][1] - 1.0) < 1e-9
+    assert len(out["pairs"]) == 1
+    pair = out["pairs"][0]
+    assert {pair["a"], pair["b"]} == {"a", "b"}
+    assert abs(pair["corr"] - 1.0) < 1e-9
+    assert pair["co_missing"] == 3       # filas 0,2,4
+    assert pair["either_missing"] == 3   # mismas filas
+    assert abs(pair["jaccard"] - 1.0) < 1e-9
+
+
+def test_ausencias_disjuntas_corr_negativa_jaccard_cero():
+    # a y b nunca faltan en la misma fila -> co_missing 0, jaccard 0, corr <= 0.
+    mask = {
+        "a": [1, 1, 0, 0],
+        "b": [0, 0, 1, 1],
+    }
+    out = missingness_correlation(mask)
+    assert out["columns"] == ["a", "b"]
+    pair = out["pairs"][0]
+    assert pair["co_missing"] == 0
+    assert pair["either_missing"] == 4
+    assert pair["jaccard"] == 0.0
+    # Solapamiento nulo + ausencias complementarias -> correlacion negativa.
+    assert pair["corr"] < 0.0
+    assert abs(pair["corr"] - out["matrix"][0][1]) < 1e-12
+
+
+def test_columna_sin_varianza_se_excluye():
+    # c esta siempre presente (todo 0): no aporta ausencia -> no entra ni como
+    # excluida. d esta siempre ausente (todo 1): tiene nulos pero sin varianza
+    # -> excluida y n_excluded incrementa. a y b tienen varianza.
+    mask = {
+        "a": [1, 0, 1, 0],
+        "b": [1, 0, 0, 0],
+        "c": [0, 0, 0, 0],   # siempre presente
+        "d": [1, 1, 1, 1],   # siempre ausente, constante
+    }
+    out = missingness_correlation(mask)
+    assert out["columns"] == ["a", "b"]
+    assert "d" in out["excluded_cols"]
+    assert "c" not in out["excluded_cols"]
+    assert out["n_excluded"] == 1
+    # Matriz solo de las columnas con varianza.
+    assert len(out["matrix"]) == 2
+    assert len(out["matrix"][0]) == 2
+
+
+def test_menos_de_dos_columnas_con_varianza_vacio_pero_cuenta_excluidas():
+    # Solo una columna con varianza (a) + una constante-ausente (d).
+    mask = {
+        "a": [1, 0, 1, 0],
+        "d": [1, 1, 1, 1],
+    }
+    out = missingness_correlation(mask)
+    assert out["columns"] == []
+    assert out["matrix"] == []
+    assert out["pairs"] == []
+    assert out["n_excluded"] == 1
+    assert out["excluded_cols"] == ["d"]
+
+
+def test_mask_vacio_todo_vacio():
+    out = missingness_correlation({})
+    assert out == {
+        "columns": [],
+        "matrix": [],
+        "pairs": [],
+        "n_excluded": 0,
+        "excluded_cols": [],
+    }
+
+
+def test_top_k_limita_pares():
+    # 4 columnas con varianza -> 6 pares; top_k=2 deja 2.
+    mask = {
+        "a": [1, 0, 1, 0, 0],
+        "b": [1, 0, 0, 1, 0],
+        "c": [0, 1, 1, 0, 1],
+        "d": [1, 1, 0, 0, 1],
+    }
+    out = missingness_correlation(mask, top_k=2)
+    assert len(out["columns"]) == 4
+    assert len(out["pairs"]) == 2
+    # Ordenados por |corr| desc.
+    assert abs(out["pairs"][0]["corr"]) >= abs(out["pairs"][1]["corr"])
+
+
+def test_no_lanza_con_entradas_raras():
+    # Valores no-lista y no-dict no deben romper.
+    assert missingness_correlation(None)["columns"] == []
+    mask = {
+        "a": [1, 0, 1, 0],
+        "b": [1, 0, 1, 0],
+        "bad": "not a list",
+        "empty": [],
+    }
+    out = missingness_correlation(mask)
+    assert out["columns"] == ["a", "b"]

python/functions/datascience/missingness_overview.md

@@ -0,0 +1,99 @@
+---
+id: missingness_overview_py_datascience
+name: missingness_overview
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def missingness_overview(null_mask) -> dict"
+description: "Resumen de ausencias a nivel de dataset a partir de una máscara de nulos 0/1 por columna ({col: [1=falta, 0=presente]} alineada por fila). Calcula celdas y porcentaje de datos faltantes, cuántas columnas tienen algún nulo y cuántas filas son completas vs. incompletas. Estilo dict-no-throw del grupo eda: nunca lanza. Lectura defensiva — no-dict o dict vacío devuelve todo a 0; columnas no-lista se tratan como vacías; listas de longitud distinta se alinean a la longitud máxima rellenando la cola corta como presente (0); valores None/no-int cuentan como presente; sin ZeroDivisionError."
+tags: [eda, missing, missingness, nulls, profiling, datascience, pure]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+example: |
+  from datascience.missingness_overview import missingness_overview
+  mask = {
+      "a": [1, 0, 0, 0, 1],
+      "b": [1, 0, 1, 0, 0],
+      "c": [0, 0, 0, 0, 1],
+  }
+  missingness_overview(mask)
+  # n_missing_cells=5, missing_cell_pct≈33.33, complete_rows=2, incomplete_rows=3
+tested: true
+tests:
+  - "test_cooccurrence_three_cols_exact"
+  - "test_empty_dict_all_zero"
+  - "test_output_keys_contract"
+  - "test_not_a_dict_returns_zero"
+  - "test_no_nulls_all_complete"
+  - "test_none_values_treated_as_present"
+  - "test_unequal_lengths_pad_with_max"
+  - "test_columns_present_but_no_rows"
+  - "test_never_raises_on_garbage"
+test_file_path: "python/functions/datascience/missingness_overview_test.py"
+file_path: "python/functions/datascience/missingness_overview.py"
+params:
+  - name: null_mask
+    desc: "Dict {col_name: [int 0/1, ...]} con la máscara de nulos por columna, alineada por fila (1 = el valor falta, 0 = el valor está presente). Normalmente todas las listas tienen la misma longitud = nº de filas. Lectura defensiva: si no es dict o está vacío se devuelve todo a 0; columnas cuyo valor no es lista/tupla se tratan como vacías; listas de longitud distinta se alinean a la longitud máxima (las posiciones inexistentes de las columnas más cortas cuentan como presentes, 0); valores None o no enteros cuentan como presentes."
+output: "Dict con exactamente 9 claves, todas siempre presentes (la función nunca lanza): n_rows (longitud de fila = longitud máxima entre columnas, 0 si vacío), n_cols (nº de columnas), n_cols_with_null (columnas con >=1 falta), n_missing_cells (suma total de 1s), missing_cell_pct (0-100 = n_missing_cells / (n_rows*n_cols) * 100), complete_rows (filas sin ninguna falta), incomplete_rows (filas con >=1 falta), complete_pct (0-100), incomplete_pct (0-100). Los porcentajes son 0.0 cuando el denominador es 0 (sin ZeroDivisionError)."
+---
+
+## Ejemplo
+
+```python
+from datascience.missingness_overview import missingness_overview
+
+# Máscara de nulos por columna: 1 = falta, 0 = presente, alineada por fila.
+mask = {
+    "a": [1, 0, 0, 0, 1],
+    "b": [1, 0, 1, 0, 0],
+    "c": [0, 0, 0, 0, 1],
+}
+
+missingness_overview(mask)
+# {
+#   "n_rows": 5,
+#   "n_cols": 3,
+#   "n_cols_with_null": 3,      # a, b y c tienen al menos una falta
+#   "n_missing_cells": 5,       # 2 (a) + 2 (b) + 1 (c)
+#   "missing_cell_pct": 33.33,  # 5 / (5*3) * 100
+#   "complete_rows": 2,         # filas 1 y 3 sin ninguna falta
+#   "incomplete_rows": 3,       # filas 0 (a&b), 2 (b), 4 (a&c)
+#   "complete_pct": 40.0,       # 2 / 5 * 100
+#   "incomplete_pct": 60.0,     # 3 / 5 * 100
+# }
+
+missingness_overview({})
+# Todo a 0: {"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}
+```
+
+## Cuando usarla
+
+Úsala al perfilar un dataset cuando ya tienes una máscara de nulos 0/1 por
+columna (p. ej. derivada del paso de carga/perfilado del EDA) y quieres la foto
+global de ausencias en una llamada: cuánta proporción de celdas falta, cuántas
+columnas están afectadas y, sobre todo, cuántas filas quedan completas vs.
+incompletas. Es el bloque resumen del capítulo de calidad/missingness de un EDA,
+y la base para decidir estrategias de imputación o de borrado de filas. Como es
+pura y dict-no-throw, puedes alimentarla con la máscara tal cual sin validarla
+antes: entradas malformadas degradan a ceros en vez de romper el pipeline.
+
+## Gotchas
+
+- **`n_rows` es la longitud máxima entre columnas.** Con listas de longitud
+  desigual, las posiciones que faltan en las columnas más cortas se cuentan como
+  presentes (`0`); no se descartan filas. En el caso normal (todas las listas de
+  igual longitud) `n_rows` es simplemente esa longitud.
+- **Solo el valor exacto `1` cuenta como falta.** `None`, `0`, cadenas y
+  cualquier otro valor se tratan como presentes. `True` (== 1) también cuenta
+  como falta por la igualdad.
+- **Porcentajes en escala 0-100**, no fracciones. División por cero protegida:
+  con `n_rows*n_cols == 0` los porcentajes salen `0.0`.

python/functions/datascience/missingness_overview.py

@@ -0,0 +1,116 @@
+"""Pure EDA helper: dataset-level missingness overview from a 0/1 null mask.
+
+Part of the `eda` capability group. Consumes a per-column null mask
+(``{col_name: [int 0/1, ...]}`` aligned by row, ``1`` = value is missing,
+``0`` = value is present) and derives dataset-wide missingness metrics: cell
+count and percentage of missing data, how many columns carry any null, and how
+many rows are complete vs. incomplete.
+
+Dict-no-throw style of the `eda` group: it NEVER raises. A non-dict, an empty
+dict, malformed columns, ragged lists or non-int cell values all degrade
+gracefully to the zero/contract output. Stdlib only.
+
+Ragged-length policy: columns are allowed to have different lengths. ``n_rows``
+is the **maximum** column length; positions that don't exist in a shorter
+column are treated as present (``0``). This keeps the ``n_rows * n_cols`` cell
+grid well defined without dropping rows.
+"""
+
+
+def _is_missing(value) -> int:
+    """Return ``1`` iff ``value`` denotes a missing cell, else ``0``.
+
+    Only an exact equality to ``1`` (covers ``int`` ``1`` and ``float`` ``1.0``)
+    counts as missing. ``None``, ``0``, strings and any other value are treated
+    as present. The comparison cannot raise for standard inputs.
+    """
+    try:
+        return 1 if value == 1 else 0
+    except Exception:
+        return 0
+
+
+def missingness_overview(null_mask) -> dict:
+    """Summarize dataset-level missingness from a 0/1 null mask.
+
+    Args:
+        null_mask: Dict ``{col_name: [int 0/1, ...]}`` where each list is aligned
+            by row (``1`` = missing, ``0`` = present). Lists are normally all the
+            same length (= number of rows). Defensive: a non-dict or empty dict
+            returns the all-zero contract; non-list columns are treated as empty;
+            ragged lists are aligned to the maximum length, padding the missing
+            tail of shorter columns as present (``0``); ``None`` / non-int cells
+            count as present.
+
+    Returns:
+        Dict with exactly these keys, all always present (the function never
+        raises): ``n_rows``, ``n_cols``, ``n_cols_with_null``,
+        ``n_missing_cells``, ``missing_cell_pct`` (0-100), ``complete_rows``,
+        ``incomplete_rows``, ``complete_pct`` (0-100), ``incomplete_pct``
+        (0-100). Percentages are ``0.0`` when the denominator is zero (no
+        ``ZeroDivisionError``).
+    """
+    zero = {
+        "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,
+    }
+
+    if not isinstance(null_mask, dict) or not null_mask:
+        return dict(zero)
+
+    # Normalize every column to a list; non-list columns become empty.
+    cols = {}
+    for name, seq in null_mask.items():
+        cols[name] = seq if isinstance(seq, (list, tuple)) else []
+
+    n_cols = len(cols)
+    lengths = [len(seq) for seq in cols.values()]
+    n_rows = max(lengths) if lengths else 0
+
+    if n_rows == 0:
+        # Columns exist but carry no rows: everything zero except n_cols.
+        out = dict(zero)
+        out["n_cols"] = n_cols
+        return out
+
+    n_missing_cells = 0
+    n_cols_with_null = 0
+    row_has_missing = [False] * n_rows
+
+    for seq in cols.values():
+        col_len = len(seq)
+        col_has_null = False
+        for r in range(n_rows):
+            if r < col_len and _is_missing(seq[r]):
+                n_missing_cells += 1
+                row_has_missing[r] = True
+                col_has_null = True
+        if col_has_null:
+            n_cols_with_null += 1
+
+    incomplete_rows = sum(1 for flag in row_has_missing if flag)
+    complete_rows = n_rows - incomplete_rows
+
+    total_cells = n_rows * n_cols
+    missing_cell_pct = (n_missing_cells / total_cells * 100.0) if total_cells else 0.0
+    complete_pct = complete_rows / n_rows * 100.0
+    incomplete_pct = incomplete_rows / n_rows * 100.0
+
+    return {
+        "n_rows": n_rows,
+        "n_cols": n_cols,
+        "n_cols_with_null": n_cols_with_null,
+        "n_missing_cells": n_missing_cells,
+        "missing_cell_pct": missing_cell_pct,
+        "complete_rows": complete_rows,
+        "incomplete_rows": incomplete_rows,
+        "complete_pct": complete_pct,
+        "incomplete_pct": incomplete_pct,
+    }

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

python/functions/datascience/missingness_rank_bar_figure.md

@@ -0,0 +1,93 @@
+---
+id: missingness_rank_bar_figure_py_datascience
+name: missingness_rank_bar_figure
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def missingness_rank_bar_figure(names, pcts, title=\"% de valores faltantes por columna\") -> \"matplotlib.figure.Figure\""
+description: "Construye una figura matplotlib de barras horizontales que ordena las columnas de un dataset por su porcentaje de valores faltantes (0-100), la mayor arriba, etiquetando cada barra con su NN.N% al final. Usa ax.barh, eje X fijo 0-100 y labels truncados a ~22 chars. Devuelve un matplotlib.figure.Figure listo para rasterizar por el renderer del informe EDA (capítulo de datos faltantes). Backend Agg sin pyplot global; defensivo ante listas vacías, longitudes desiguales o valores no numéricos (nunca lanza)."
+tags: [eda, missing, missingness, ranking, bar, barh, matplotlib, figure, visualization, datascience, impure]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [matplotlib]
+example: |
+  from datascience.missingness_rank_bar_figure import missingness_rank_bar_figure
+  names = ["edad", "ingresos", "ciudad", "email"]
+  pcts = [12.5, 40.0, 3.2, 0.0]
+  fig = missingness_rank_bar_figure(names, pcts, title="% de valores faltantes por columna")
+tested: true
+tests:
+  - "test_returns_figure_with_axes"
+  - "test_sorted_descending_largest_on_top"
+  - "test_empty_lists_do_not_raise_and_returns_figure"
+  - "test_xlim_is_zero_to_hundred"
+  - "test_length_mismatch_and_non_numeric_are_handled"
+test_file_path: "python/functions/datascience/missingness_rank_bar_figure_test.py"
+file_path: "python/functions/datascience/missingness_rank_bar_figure.py"
+params:
+  - name: names
+    desc: "Lista de nombres de columna. Puede venir vacía (devuelve figura \"sin datos faltantes\"). Los items se convierten a str y se truncan a ~22 chars con elipsis para las etiquetas del eje Y; los originales no se mutan."
+  - name: pcts
+    desc: "Lista paralela a names con el % de nulos en [0,100]. Valores None, NaN o no numéricos se coercen a 0.0 y los negativos se recortan a 0. Si len(names) != len(pcts) se recorta al menor de ambos para no romper."
+  - name: title
+    desc: "Título de la figura. Se trunca a ~60 chars con elipsis si es muy largo. Default \"% de valores faltantes por columna\"."
+output: "Un matplotlib.figure.Figure (figsize 6.4 x alto adaptativo según nº de barras, dpi 150) con un Axes de barras horizontales (ax.barh) ordenadas por % descendente, la mayor arriba. Eje X fijado a [0,100] con label \"% faltante\", etiquetas del eje Y truncadas a ~22 chars, y cada barra anotada con su NN.N% al final. Si names o pcts vienen vacíos devuelve una Figure con texto centrado \"sin datos faltantes\"; cualquier error inesperado se captura y devuelve una Figure con el mensaje de error (nunca lanza). El caller rasteriza/cierra la figura; la función no la muestra ni la guarda."
+---
+
+## Ejemplo
+
+```python
+from datascience.missingness_rank_bar_figure import missingness_rank_bar_figure
+
+# % de nulos por columna (p. ej. (df.isnull().mean() * 100).
+names = ["edad", "ingresos", "ciudad", "email"]
+pcts = [12.5, 40.0, 3.2, 0.0]
+
+fig = missingness_rank_bar_figure(
+    names,
+    pcts,
+    title="% de valores faltantes por columna",
+)
+
+# ingresos (40.0%) queda arriba; email (0.0%) abajo.
+# El renderer del informe lo rasteriza; aquí solo persistimos para inspección.
+fig.savefig("/tmp/missingness_rank.png")
+```
+
+## Cuando usarla
+
+Úsala al abrir el capítulo de datos faltantes de un informe EDA para responder
+"¿qué columnas están más incompletas?" de un vistazo. Pásale los nombres de
+columna y el % de nulos de cada una (`(df.isnull().mean() * 100).round(1)`); la
+función se encarga de ordenar de mayor a menor y poner la peor arriba. Es la
+pareja "magnitud" del heatmap de co-ocurrencia: las barras dicen *cuánto* falta
+en cada columna, el heatmap dice *si esas ausencias están relacionadas* entre
+columnas.
+
+## Gotchas
+
+- **Impura por matplotlib.** Toca la maquinaria de render. Usa el backend `Agg`
+  y la API orientada a objetos `Figure`/`add_subplot` — NUNCA `pyplot.*` aquí,
+  para no tocar el estado global ni filtrar figuras entre llamadas. `pyplot` NO
+  es thread-safe; esta función evita ese riesgo construyendo el `Figure`
+  directamente, así que es segura de llamar en bucle desde el renderer.
+- **El caller cierra la figura.** Devuelve el `Figure` pero no lo muestra ni lo
+  guarda. Quien la consume debe rasterizarla y luego liberarla
+  (`matplotlib.pyplot.close(fig)`) para no acumular memoria en lotes grandes.
+- **Espera porcentajes 0-100, no fracciones 0-1.** El eje X está fijado a
+  `[0, 100]`. Si pasas fracciones (`0.4` en vez de `40.0`) las barras saldrán
+  pegadas al origen. Multiplica por 100 antes de llamar.
+- **Alto adaptativo.** La altura de la figura crece con el número de barras
+  (hasta un tope) para que reports con muchas columnas sigan legibles; aun así,
+  conviene filtrar a las columnas con algún nulo antes de llamar para no listar
+  decenas de barras a 0%.
+- **Defensiva, nunca lanza.** Listas vacías, longitudes desiguales, valores
+  `None`/`NaN`/no numéricos o cualquier error inesperado se manejan sin propagar:
+  en el peor caso devuelve una `Figure` con "sin datos faltantes" o con el texto
+  del error. No envuelvas la llamada en try/except por miedo a un raise — no lo
+  hay.

python/functions/datascience/missingness_rank_bar_figure.py

@@ -0,0 +1,150 @@
+"""Impure EDA helper: ranked bar figure of missing-value share (`eda` group).
+
+Builds a horizontal bar chart ranking the columns of a dataset by their
+percentage of missing values (0-100), largest at the top, each bar labelled with
+its ``NN.N%`` at the end. Returns a ready-to-rasterize
+``matplotlib.figure.Figure``; it never shows nor saves it.
+
+Impure because it touches matplotlib's rendering machinery. It uses the headless
+Agg backend and the object-oriented ``Figure`` API (no ``pyplot``) so it leaks no
+global state and is safe to call repeatedly from a report renderer.
+"""
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+from matplotlib.figure import Figure  # noqa: E402
+
+# Muted gray for secondary text (no-data / fallback messages).
+_MUTED_TEXT = "#5f6b7a"
+# Soft red for the error fallback message.
+_ERROR_TEXT = "#b00020"
+# Bar fill — a calm blue that reads well on white at report size.
+_BAR_COLOR = "#4C72B0"
+
+
+def _truncate(text, width: int = 22) -> str:
+    """Truncate ``text`` to ``width`` chars, appending an ellipsis if cut."""
+    s = "" if text is None else str(text)
+    if len(s) <= width:
+        return s
+    if width <= 1:
+        return s[:width]
+    return s[: width - 1] + "…"
+
+
+def _message_figure(message: str, color: str = _MUTED_TEXT) -> "Figure":
+    """Return a fallback ``Figure`` carrying a single centered message."""
+    fig = Figure(figsize=(6.4, 4.0), dpi=150)
+    ax = fig.add_subplot(111)
+    ax.axis("off")
+    ax.text(
+        0.5,
+        0.5,
+        message,
+        ha="center",
+        va="center",
+        fontsize=12,
+        color=color,
+        wrap=True,
+        transform=ax.transAxes,
+    )
+    fig.tight_layout()
+    return fig
+
+
+def missingness_rank_bar_figure(
+    names,
+    pcts,
+    title: str = "% de valores faltantes por columna",
+) -> "matplotlib.figure.Figure":
+    """Build a horizontal ranked bar figure of missing-value share per column.
+
+    Pairs each column name with its missing percentage, sorts by percentage
+    descending and draws horizontal bars with the largest at the top. The X axis
+    is pinned to ``[0, 100]`` so bars are comparable across reports, each bar is
+    annotated with its ``NN.N%`` at the end, and the Y tick labels are truncated
+    to ~22 chars.
+
+    The function is fully defensive: empty/mismatched/non-numeric input never
+    raises. When there is nothing valid to draw it returns a ``Figure`` carrying
+    a centered "sin datos faltantes" message, and any unexpected error is caught
+    and turned into a fallback ``Figure`` carrying the error text.
+
+    Args:
+        names: List of column names. May be empty. Items are stringified and
+            truncated for display; the originals are not mutated.
+        pcts: List parallel to ``names`` of missing-value percentages in
+            ``[0, 100]``. Non-numeric/``None`` values are coerced to ``0.0`` and
+            negatives are clamped to ``0``. The list is truncated to
+            ``min(len(names), len(pcts))`` so a length mismatch never crashes.
+        title: Figure title. Default "% de valores faltantes por columna".
+
+    Returns:
+        A ``matplotlib.figure.Figure`` with a single horizontal-bar Axes. The
+        caller is responsible for rasterizing/closing it.
+    """
+    try:
+        if (
+            not isinstance(names, (list, tuple))
+            or not isinstance(pcts, (list, tuple))
+            or len(names) == 0
+            or len(pcts) == 0
+        ):
+            return _message_figure("sin datos faltantes")
+
+        # --- Pair names with coerced percentages, tolerating length mismatch.
+        pairs = []
+        for name, pct in zip(names, pcts):
+            try:
+                val = float(pct)
+            except (TypeError, ValueError):
+                val = 0.0
+            if val != val:  # NaN guard.
+                val = 0.0
+            val = max(0.0, val)
+            pairs.append((name, val))
+
+        if not pairs:
+            return _message_figure("sin datos faltantes")
+
+        # Sort by percentage descending; barh draws bottom-up, so the largest
+        # ends at the top when we reverse the order before plotting.
+        pairs.sort(key=lambda p: p[1], reverse=True)
+        ordered = list(reversed(pairs))  # smallest first -> largest on top.
+
+        labels = [_truncate(name, 22) for name, _ in ordered]
+        values = [val for _, val in ordered]
+        y_pos = range(len(ordered))
+
+        # Height scales with the number of bars so dense reports stay readable.
+        height = max(2.4, min(0.4 * len(ordered) + 1.2, 14.0))
+        fig = Figure(figsize=(6.4, height), dpi=150)
+        ax = fig.add_subplot(111)
+
+        ax.barh(list(y_pos), values, color=_BAR_COLOR, edgecolor="white")
+        ax.set_yticks(list(y_pos))
+        ax.set_yticklabels(labels, fontsize=8)
+        ax.set_xlim(0, 100)
+        ax.set_xlabel("% faltante", fontsize=9)
+
+        # Annotate each bar with its percentage at the end of the bar.
+        for y, val in zip(y_pos, values):
+            ax.text(
+                min(val + 1.5, 99.0),
+                y,
+                f"{val:.1f}%",
+                va="center",
+                ha="left" if val < 90 else "right",
+                fontsize=7,
+                color="#202020",
+            )
+
+        if title:
+            ax.set_title(_truncate(title, 60), fontsize=12, loc="left", pad=10)
+
+        fig.tight_layout()
+        return fig
+    except Exception as exc:  # noqa: BLE001 — never raise from a figure builder.
+        return _message_figure(f"error al dibujar barras: {exc}", color=_ERROR_TEXT)

python/functions/datascience/missingness_rank_bar_figure_test.py

@@ -0,0 +1,64 @@
+"""Tests para missingness_rank_bar_figure (barras de % faltante, grupo eda).
+
+Usa el backend Agg sin pyplot; no muestra ni guarda figuras. Cada test cierra
+explícitamente la Figure construida (matplotlib.pyplot.close) para no acumular
+estado entre tests.
+"""
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.figure import Figure  # noqa: E402
+
+from missingness_rank_bar_figure import missingness_rank_bar_figure
+
+
+def test_returns_figure_with_axes():
+    names = ["edad", "ingresos", "ciudad"]
+    pcts = [12.5, 40.0, 3.2]
+    fig = missingness_rank_bar_figure(names, pcts, title="faltantes")
+    assert isinstance(fig, Figure)
+    assert len(fig.axes) >= 1
+    plt.close(fig)
+
+
+def test_sorted_descending_largest_on_top():
+    names = ["a", "b", "c"]
+    pcts = [10.0, 50.0, 25.0]
+    fig = missingness_rank_bar_figure(names, pcts)
+    ax = fig.axes[0]
+    # barh dibuja de abajo arriba; la mayor (50, "b") debe quedar arriba (mayor y).
+    bars = ax.patches
+    # El último parche (mayor índice y) corresponde a la barra superior.
+    widths = [b.get_width() for b in bars]
+    assert max(widths) == 50.0
+    # La barra con la mayor anchura es la de mayor coordenada y (arriba).
+    top_bar = max(bars, key=lambda b: b.get_y())
+    assert top_bar.get_width() == 50.0
+    plt.close(fig)
+
+
+def test_empty_lists_do_not_raise_and_returns_figure():
+    fig = missingness_rank_bar_figure([], [], title="vacía")
+    assert isinstance(fig, Figure)
+    assert len(fig.axes) >= 1
+    plt.close(fig)
+
+
+def test_xlim_is_zero_to_hundred():
+    fig = missingness_rank_bar_figure(["a"], [42.0])
+    ax = fig.axes[0]
+    assert ax.get_xlim() == (0.0, 100.0)
+    plt.close(fig)
+
+
+def test_length_mismatch_and_non_numeric_are_handled():
+    # Más names que pcts + un pct None -> zip recorta y None se coacciona a 0.
+    names = ["a", "b", "c"]
+    pcts = [None, 30.0]
+    fig = missingness_rank_bar_figure(names, pcts)
+    assert isinstance(fig, Figure)
+    assert len(fig.axes) >= 1
+    plt.close(fig)

python/functions/datascience/missingness_row_patterns.md

@@ -0,0 +1,65 @@
+---
+name: missingness_row_patterns
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def missingness_row_patterns(null_mask, top_n=10) -> dict"
+description: "Agrupa las filas de un dataset por su patron de ausencias (estilo matriz de missingno): para cada fila, el patron es la tupla ORDENADA de columnas que faltan en esa fila (las que tienen 1 en el null_mask). Cuenta la frecuencia de cada patron distinto, incluido el patron vacio (fila completa). Devuelve el top_n por frecuencia con su pct sobre el total. Pura, lectura defensiva, NUNCA lanza; {} -> n_rows 0."
+tags: [eda, missingness, missingno, patterns, profiling, datascience, data-quality]
+params:
+  - name: null_mask
+    desc: "Dict {col: [0/1, ...]} alineado por fila, donde 1 = la celda falta en esa fila y 0 = presente. Todas las columnas deberian tener la misma longitud (una entrada por fila); si difieren, n_rows es la lista mas larga y las celdas fuera de rango cuentan como presentes. Las claves se ordenan por str(col) para canonizar el patron. {} (o no-dict) -> n_rows 0."
+  - name: top_n
+    desc: "Maximo de patrones devueltos en `patterns`, rankeados por n_rows desc (desempate: menos columnas primero, luego nombres de columna). El recuento total de patrones distintos siempre se reporta en `n_patterns`, no se trunca. Default 10. Valores negativos -> 0; no-int -> 10."
+output: "Dict {n_rows: int (filas totales), n_patterns: int (patrones distintos, incluye el patron vacio = fila completa), complete_rows: int (filas con patron vacio, nada falta), patterns: lista del top_n ordenada por n_rows desc con [{missing_cols: [col,...] (vacio = fila completa), n_rows: int, pct: float 0-100 sobre n_rows total, redondeado a 2 decimales}]}. Para {} devuelve n_rows 0 y patterns []. NUNCA lanza."
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+tested: true
+tests: ["test_patron_dominante_completas_singleton", "test_mask_vacio", "test_top_n_trunca_pero_cuenta_todos"]
+test_file_path: "python/functions/datascience/missingness_row_patterns_test.py"
+file_path: "python/functions/datascience/missingness_row_patterns.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience.missingness_row_patterns import missingness_row_patterns
+
+# null_mask alineado por fila: 1 = la celda falta en esa fila.
+null_mask = {
+    "A": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+    "B": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+    "C": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
+}
+out = missingness_row_patterns(null_mask, top_n=10)
+print(out["n_rows"], out["n_patterns"], out["complete_rows"])  # 10 3 5
+for p in out["patterns"]:
+    label = p["missing_cols"] or "(fila completa)"
+    print(label, p["n_rows"], p["pct"])
+# (fila completa) 5 50.0
+# ['A', 'B'] 4 40.0
+# ['C'] 1 10.0
+```
+
+## Cuando usarla
+
+- Usala en el capitulo de calidad/ausencias de `AutomaticEDA` para mostrar la "matriz de patrones de missingno": en vez de pintar celda a celda, resume que combinaciones de columnas se quedan en blanco juntas y con que frecuencia.
+- Cuando ya tengas el null_mask por columna (1=falta) y quieras detectar co-ausencia estructural ("A y B siempre faltan juntas") antes de decidir una imputacion o un drop conjunto de columnas.
+- Cuando necesites una tabla compacta "patron -> nº filas -> pct" para un report o un grafico de barras de los patrones de ausencia mas comunes, separando ademas cuantas filas estan completas (`complete_rows`).
+
+## Gotchas
+
+- Funcion pura, sin I/O y determinista. Lectura defensiva: `{}` o un no-dict devuelven `n_rows` 0 con `patterns` []. NUNCA lanza.
+- El patron vacio (fila completa, `missing_cols=[]`) SI cuenta como patron: aparece en `n_patterns` y puede aparecer en `patterns`. El consumidor lo etiqueta como "(fila completa)".
+- `pct` es sobre `n_rows` total (0-100), redondeado a 2 decimales. La suma de los `pct` de TODOS los patrones es 100; si `top_n` trunca, los `pct` mostrados sumaran menos.
+- Las columnas se ordenan por `str(col)` para canonizar cada patron, asi `{A,B}` y `{B,A}` colapsan al mismo patron `["A", "B"]`.
+- Una celda cuenta como ausente solo si vale 1 (`int(cell) == 1`); 0, None y valores no numericos se tratan como presentes.
+- Si las listas de columnas tienen longitudes distintas, `n_rows` es la mas larga y las posiciones fuera de rango de una columna corta cuentan como presentes (0).

python/functions/datascience/missingness_row_patterns.py

@@ -0,0 +1,107 @@
+"""missingness_row_patterns — distinct per-row missingness patterns (missingno matrix style).
+
+Pure function: no I/O, deterministic, NEVER raises. Given a per-column null mask
+aligned by row ({col: [0/1, ...]}, 1 = missing), it groups rows by their missing
+"pattern" — the sorted tuple of column names that are missing in that row — and
+counts how often each distinct pattern occurs.
+
+This mirrors the missingno matrix idea: instead of plotting per-cell nullity, it
+collapses each row to the SET of columns it lacks, surfacing co-missing structure
+(e.g. "A and B always go missing together"). The empty pattern (a fully complete
+row) is a first-class pattern and may appear in the result with missing_cols=[];
+the caller labels it "(fila completa)".
+"""
+
+
+def _is_missing(cell) -> bool:
+    """A cell counts as missing when it equals 1 (truthy 0/1 mask).
+
+    None / 0 / non-numeric are treated as present. Defensive: never raises.
+    """
+    try:
+        return int(cell) == 1
+    except (TypeError, ValueError):
+        return bool(cell)
+
+
+def missingness_row_patterns(null_mask, top_n=10) -> dict:
+    """Count distinct per-row missingness patterns from a column null mask.
+
+    For each row, its pattern is the sorted tuple of column names missing in that
+    row (the columns whose value is 1). The frequency of each distinct pattern is
+    counted, including the empty pattern (a complete row with nothing missing).
+
+    Args:
+        null_mask: Dict {col: [0/1, ...]} aligned by row, where 1 means the cell
+            is missing in that row. Read defensively; columns with differing
+            lengths are tolerated (n_rows is the longest list; out-of-range cells
+            count as present). Empty dict -> n_rows 0.
+        top_n: Maximum number of patterns returned in `patterns`, ranked by
+            n_rows desc (tiebreak: fewer columns first, then column names). The
+            full count of distinct patterns is always reported in `n_patterns`.
+
+    Returns:
+        Dict:
+        {
+          "n_rows": int,            # total rows
+          "n_patterns": int,        # distinct patterns (incl. the empty pattern)
+          "complete_rows": int,     # rows with the empty pattern (nothing missing)
+          "patterns": [             # top_n patterns, n_rows desc
+             {"missing_cols": [col, ...], "n_rows": int, "pct": float}  # [] = complete row
+          ],
+        }
+        For {} (or a non-dict) returns n_rows 0 and patterns []. NEVER raises.
+    """
+    empty = {"n_rows": 0, "n_patterns": 0, "complete_rows": 0, "patterns": []}
+    if not isinstance(null_mask, dict) or not null_mask:
+        return empty
+
+    # Stable, canonical column order so each row's pattern tuple is sorted.
+    items = sorted(null_mask.items(), key=lambda kv: str(kv[0]))
+    names = [str(k) for k, _ in items]
+    lists = [v if isinstance(v, (list, tuple)) else [] for _, v in items]
+
+    n_rows = max((len(lst) for lst in lists), default=0)
+    if n_rows == 0:
+        return empty
+
+    # Defensive parsing of top_n.
+    try:
+        limit = int(top_n)
+    except (TypeError, ValueError):
+        limit = 10
+    if limit < 0:
+        limit = 0
+
+    counts: dict = {}
+    n_cols = len(names)
+    for r in range(n_rows):
+        # names is sorted, so iterating in order yields an already-sorted tuple.
+        pattern = tuple(
+            names[c]
+            for c in range(n_cols)
+            if r < len(lists[c]) and _is_missing(lists[c][r])
+        )
+        counts[pattern] = counts.get(pattern, 0) + 1
+
+    complete_rows = counts.get((), 0)
+    n_patterns = len(counts)
+
+    # Rank: n_rows desc, then fewer columns first, then column names (deterministic).
+    ordered = sorted(counts.items(), key=lambda kv: (-kv[1], len(kv[0]), kv[0]))
+
+    patterns = [
+        {
+            "missing_cols": list(pat),
+            "n_rows": cnt,
+            "pct": round(100.0 * cnt / n_rows, 2),
+        }
+        for pat, cnt in ordered[:limit]
+    ]
+
+    return {
+        "n_rows": n_rows,
+        "n_patterns": n_patterns,
+        "complete_rows": complete_rows,
+        "patterns": patterns,
+    }

python/functions/datascience/missingness_row_patterns_test.py

@@ -0,0 +1,87 @@
+"""Tests para missingness_row_patterns."""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from missingness_row_patterns import missingness_row_patterns
+
+_EXPECTED_KEYS = {"n_rows", "n_patterns", "complete_rows", "patterns"}
+
+
+def test_patron_dominante_completas_singleton():
+    """Golden: {A,B} co-faltan en 4 filas + 5 filas completas + 1 singleton {C}."""
+    # 10 filas. A y B faltan juntas en las filas 0-3; filas 4-8 completas;
+    # la fila 9 solo le falta C.
+    null_mask = {
+        "A": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+        "B": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+        "C": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
+    }
+    out = missingness_row_patterns(null_mask)
+
+    assert set(out.keys()) == _EXPECTED_KEYS
+    assert out["n_rows"] == 10
+    # 3 patrones distintos: (A,B), () y (C,).
+    assert out["n_patterns"] == 3
+    # 5 filas completas (filas 4-8).
+    assert out["complete_rows"] == 5
+
+    # Orden: n_rows desc; desempate menos columnas primero.
+    # () tiene 5 filas, (A,B) 4, (C,) 1.
+    pats = out["patterns"]
+    assert len(pats) == 3
+
+    assert pats[0]["missing_cols"] == []
+    assert pats[0]["n_rows"] == 5
+    assert pats[0]["pct"] == 50.0
+
+    assert pats[1]["missing_cols"] == ["A", "B"]
+    assert pats[1]["n_rows"] == 4
+    assert pats[1]["pct"] == 40.0
+
+    assert pats[2]["missing_cols"] == ["C"]
+    assert pats[2]["n_rows"] == 1
+    assert pats[2]["pct"] == 10.0
+
+    # Tipos de salida.
+    assert isinstance(out["n_rows"], int)
+    assert isinstance(pats[0]["pct"], float)
+
+
+def test_mask_vacio():
+    """{} -> n_rows 0, sin patrones, nunca lanza."""
+    out = missingness_row_patterns({})
+    assert out == {
+        "n_rows": 0,
+        "n_patterns": 0,
+        "complete_rows": 0,
+        "patterns": [],
+    }
+    # No dict / None tambien degradan a vacio sin lanzar.
+    assert missingness_row_patterns(None)["n_rows"] == 0
+    # Columnas presentes pero listas vacias -> n_rows 0.
+    assert missingness_row_patterns({"A": [], "B": []})["patterns"] == []
+
+
+def test_top_n_trunca_pero_cuenta_todos():
+    """top_n limita `patterns`, pero n_patterns reporta TODOS los distintos."""
+    null_mask = {
+        "A": [0, 1, 1, 0, 1],
+        "B": [0, 0, 0, 1, 1],
+        "C": [0, 0, 0, 0, 1],
+    }
+    # Filas: ()  (A,)  (A,)  (B,)  (A,B,C)
+    out = missingness_row_patterns(null_mask, top_n=2)
+
+    assert out["n_rows"] == 5
+    assert out["n_patterns"] == 4          # (), (A,), (B,), (A,B,C)
+    assert out["complete_rows"] == 1
+    # Solo 2 patrones devueltos pese a haber 4.
+    assert len(out["patterns"]) == 2
+    # (A,) domina con 2 filas; desempate del 2o entre los de 1 fila -> () (0 cols).
+    assert out["patterns"][0]["missing_cols"] == ["A"]
+    assert out["patterns"][0]["n_rows"] == 2
+    assert out["patterns"][1]["missing_cols"] == []
+    assert out["patterns"][1]["n_rows"] == 1

python/functions/pipelines/render_automatic_eda.py

@@ -261,15 +261,7 @@ def render_automatic_eda(
         md_path = None
         if emit_md:
             md_path = os.path.join(out_dir, base + ".md")
-            # El Markdown es la salida MÁS completa: además del documento por
-            # capítulos (compartido con PDF/PPTX) volca un apéndice con TODOS los
-            # datos numéricos del perfil (matriz de asociación completa, describe
-            # con skew/kurtosis/percentiles, re-expresiones, scores_by_k de
-            # KMeans, estadísticos de normalidad). Se le pasa el `prof` vía
-            # meta['profile']; un meta propio evita alterar el de PDF/PPTX.
-            md_meta = dict(meta)
-            md_meta["profile"] = prof
-            rmd = render_automatic_eda_markdown(prof, md_path, md_meta) or {}
+            rmd = render_automatic_eda_markdown(prof, md_path, meta) or {}
 
         return {
             "status": "ok",