feat(eda): capítulo AutomaticEDA CAT DISTR + funciones cardinalidad/pie

Capítulo cat_distr del motor AutomaticEDA: distribuciones categóricas con
explicación de entropía de Shannon, métricas de cardinalidad por columna
(valores distintos, % distintos, total de filas, valores únicos, entropía y
su máximo log2(k) + normalizada), tabla top-k y un donut de las categorías
más comunes (top-k + «Otros»). Marca columnas id-like y dominadas.

Delegadas a fn-constructor (grupo eda):
- categorical_cardinality_block: deriva métricas de cardinalidad/entropía.
- categorical_top_pie_figure: figura donut top-k + «Otros», leyenda lateral.

Defensivo (dict-no-throw): None si no hay columnas categóricas; normaliza
mode_pct a escala 0-100 (summarize_categorical lo emite como fracción).
Tablas vía DataTable y figura perezosa: el paginador del núcleo garantiza
no-corte en PDF y PPTX. Tests: golden + edge (sin categóricas) + anti-corte
(label largo / muchas columnas) en ambos renderers.

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

python/functions/datascience/categorical_cardinality_block.py

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+"""Pure EDA helper: cardinality metrics block from a `summarize_categorical` output.
+
+Part of the `eda` capability group. Consumes the per-column dict produced by
+``summarize_categorical`` (for a single categorical/text column) plus the total
+row count of the dataset and derives render-ready cardinality metrics: distinct
+ratio, normalized entropy, singleton count, and the ``id_like`` / ``dominated``
+flags.
+
+It does NOT recompute the entropy nor reimplement ``summarize_categorical`` — it
+only reads that function's output. Dict-no-throw style of the `eda` group: it
+never raises. Missing or malformed inputs yield ``None``/``False``/``0`` for the
+affected keys, never an exception. Stdlib only (``math.log2``).
+"""
+
+from math import log2
+
+
+def _num(value):
+    """Return ``value`` unchanged if it is a real (non-bool) number, else ``None``.
+
+    ``bool`` is rejected on purpose: in Python ``True`` is an ``int`` but it is
+    never a meaningful count/ratio here.
+    """
+    if isinstance(value, bool):
+        return None
+    if isinstance(value, (int, float)):
+        return value
+    return None
+
+
+def categorical_cardinality_block(cat: dict, n_rows: int) -> dict:
+    """Derive cardinality metrics for one categorical column.
+
+    Args:
+        cat: The per-column dict produced by ``summarize_categorical`` for a
+            single categorical/text column. Expected (all optional, read
+            defensively) keys: ``top`` (list of ``{value, count, pct}``),
+            ``mode``, ``mode_pct``, ``n_distinct``, ``entropy`` (Shannon, bits),
+            ``imbalance``, ``len_min``, ``len_mean``, ``len_max``. ``None`` or a
+            non-dict is treated as ``{}``.
+        n_rows: Total number of rows in the dataset (used for ``pct_distinct``).
+
+    Returns:
+        Dict with exactly these keys, every one always present:
+        ``n_distinct``, ``n_rows``, ``pct_distinct``, ``entropy``,
+        ``entropy_max``, ``entropy_norm``, ``mode``, ``mode_pct``,
+        ``imbalance``, ``n_singletons``, ``n_singletons_partial``, ``len_min``,
+        ``len_mean``, ``len_max``, ``id_like``, ``dominated``. Values are
+        ``None``/``False`` when not derivable; the function never raises.
+    """
+    cat = cat if isinstance(cat, dict) else {}
+
+    # --- passthroughs (numeric-validated, type preserved) ---
+    n_distinct = _num(cat.get("n_distinct"))
+    n_rows_out = _num(n_rows)
+    entropy = _num(cat.get("entropy"))
+    imbalance = _num(cat.get("imbalance"))
+    len_min = _num(cat.get("len_min"))
+    len_mean = _num(cat.get("len_mean"))
+    len_max = _num(cat.get("len_max"))
+    mode = cat.get("mode")  # any value (or None); passthrough as-is
+
+    # --- pct_distinct ---
+    if n_distinct is None or n_rows_out is None or n_rows_out == 0:
+        pct_distinct = None
+    else:
+        pct_distinct = n_distinct / n_rows_out * 100.0
+
+    # --- entropy_max = log2(n_distinct) ---
+    if n_distinct is None:
+        entropy_max = None
+    elif n_distinct > 1:
+        entropy_max = log2(n_distinct)
+    else:  # n_distinct in {0, 1}
+        entropy_max = 0.0
+
+    # --- entropy_norm = entropy / entropy_max, clipped to [0, 1] ---
+    if entropy_max is not None and entropy_max > 0 and entropy is not None:
+        entropy_norm = entropy / entropy_max
+        entropy_norm = max(0.0, min(1.0, entropy_norm))
+    else:
+        entropy_norm = None
+
+    # --- mode_pct: prefer cat['mode_pct']; else derive from top[0].pct ---
+    mode_pct = _num(cat.get("mode_pct"))
+    top = cat.get("top")
+    has_top = isinstance(top, (list, tuple)) and len(top) > 0
+    if mode_pct is None and has_top:
+        first = top[0]
+        if isinstance(first, dict):
+            first_pct = _num(first.get("pct"))
+            if first_pct is not None:
+                # Normalize to 0-100: a fraction (<= 1) becomes a percentage.
+                mode_pct = first_pct * 100.0 if first_pct <= 1 else first_pct
+
+    # --- singletons (count == 1) within the visible top ---
+    if has_top:
+        n_singletons = sum(
+            1
+            for item in top
+            if isinstance(item, dict) and _num(item.get("count")) == 1
+        )
+    else:
+        n_singletons = None
+
+    # The singleton count only covers the visible top; there may be more
+    # distinct values (and thus more singletons) outside it.
+    top_len = len(top) if isinstance(top, (list, tuple)) else 0
+    n_singletons_partial = bool(n_distinct is not None and n_distinct > top_len)
+
+    # --- derived flags ---
+    id_like = pct_distinct is not None and pct_distinct >= 99.0
+    dominated = mode_pct is not None and mode_pct >= 90.0
+
+    return {
+        "n_distinct": n_distinct,
+        "n_rows": n_rows_out,
+        "pct_distinct": pct_distinct,
+        "entropy": entropy,
+        "entropy_max": entropy_max,
+        "entropy_norm": entropy_norm,
+        "mode": mode,
+        "mode_pct": mode_pct,
+        "imbalance": imbalance,
+        "n_singletons": n_singletons,
+        "n_singletons_partial": n_singletons_partial,
+        "len_min": len_min,
+        "len_mean": len_mean,
+        "len_max": len_max,
+        "id_like": id_like,
+        "dominated": dominated,
+    }