feat(eda): histograma sin outliers (vista central) en num_distr

describe_numeric emite una nueva clave aditiva histogram_clipped: un segundo histograma re-binado sobre el rango de vallas de Tukey [p25-1.5*IQR, p75+1.5*IQR], reutilizando los percentiles ya calculados. Es [] cuando el recorte no excluye nada (sin outliers), la columna es constante (iqr==0) o la sub-muestra recortada pierde dispersion, de modo que el renderer no duplica el histograma completo.

El capitulo num_distr consume histogram_clipped como una segunda figura DENTRO del mismo grupo keep-together de la columna: la vista central se lee cuando una cola larga aplasta la escala del histograma completo. Bump describe_numeric 1.0.0->1.1.0 (aditivo) y CHAPTER_VERSION num_distr 1.3.0->1.4.0. Tests: golden (recorta la cola), edges (sin outliers -> [], constante -> []), contrato de claves y smoke e2e de render.

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

@@ -35,7 +35,7 @@ try:
 except Exception:  # noqa: BLE001 — keep the chapter importable no matter what.
     build_boxplot_stats = None  # type: ignore[assignment]
 
-CHAPTER_VERSION = "1.3.0"
+CHAPTER_VERSION = "1.4.0"
 CHAPTER_ID = "num_distr"
 CHAPTER_TITLE = "Distribuciones numéricas"
 
@@ -275,6 +275,69 @@ def _make_hist_box(name: str, numeric: dict, box: dict):
     return fig
 
 
+def _make_hist_clipped(name: str, numeric: dict):
+    """Histogram of the central mass with the outliers trimmed away.
+
+    Companion to :func:`_make_hist_box`: same column, re-binned over the Tukey
+    inner-fence range [Q1-1.5*IQR, Q3+1.5*IQR] (precomputed in ``describe_numeric``
+    as ``histogram_clipped``), so the bulk of the distribution stays readable when
+    a long tail would otherwise crush the scale. Only the reference median is drawn
+    — it always falls inside the fence range by construction — because mean/±σ were
+    already shown on the full histogram above and could sit outside the clip.
+    """
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    fig, ax = plt.subplots(figsize=(6.4, 2.6))
+    hist = numeric.get("histogram_clipped") or []
+    drew_bars = False
+    for b in hist:
+        if not isinstance(b, dict):
+            continue
+        lo = b.get("lo")
+        hi = b.get("hi")
+        count = b.get("count") or 0
+        if lo is None or hi is None:
+            continue
+        width = (hi - lo) if hi > lo else max(abs(lo) * 1e-3, 1e-6)
+        ax.bar(lo, count, width=width, align="edge", color="#b7d7a8",
+               edgecolor="#6a9a5b", linewidth=0.4, zorder=2)
+        drew_bars = True
+
+    median = numeric.get("median")
+    if drew_bars and median is not None:
+        lo0 = hist[0].get("lo")
+        hi1 = hist[-1].get("hi")
+        if lo0 is not None and hi1 is not None and lo0 <= median <= hi1:
+            ax.axvline(median, color="#2e8b57", linestyle="-", linewidth=1.4,
+                       zorder=4, label=f"mediana = {_fmt_num(median)}")
+            ax.legend(fontsize=6.5, loc="upper right", framealpha=0.85)
+    if not drew_bars:
+        ax.text(0.5, 0.5, "(sin histograma recortado)", ha="center",
+                va="center", fontsize=9, color="#8a8a8a",
+                transform=ax.transAxes)
+
+    ax.set_ylabel("frecuencia", fontsize=8)
+    ax.set_xlabel(name, fontsize=8)
+    ax.tick_params(labelsize=7)
+    for spine in ("top", "right"):
+        ax.spines[spine].set_visible(False)
+    fig.suptitle(f"{name} — vista central (sin outliers)", fontsize=10,
+                 fontweight="bold", x=0.02, ha="left")
+    fig.tight_layout()
+    return fig
+
+
+def _clipped_figure_maker(name: str, numeric: dict):
+    """Bind the per-column arguments so the lazy closure is loop-safe."""
+    def _make():
+        return _make_hist_clipped(name, numeric)
+
+    return _make
+
+
 def _stats_note(name: str, numeric: dict, box: dict) -> str:
     """One compact line of the key numbers + a plain-Spanish shape gloss."""
     bits = [
@@ -374,6 +437,16 @@ def build_num_distr(profile: dict, ctx: dict):
             make=_figure_maker(name, numeric, box),
             caption=f"Distribución de «{name}» — histograma "
                     f"(media/mediana/±σ) y boxplot."))
+        # Second view: the central mass with the outliers trimmed (Tukey fences).
+        # Only added when describe_numeric produced a non-empty histogram_clipped
+        # (i.e. the clip actually removed tail values), and stays inside the same
+        # keep-together Group so it never drifts to another page from its heading.
+        if numeric.get("histogram_clipped"):
+            col_blocks.append(model.Figure(
+                make=_clipped_figure_maker(name, numeric),
+                caption=f"«{name}» — vista central con los atípicos recortados "
+                        f"(vallas de Tukey 1,5·IQR); útil cuando la cola larga "
+                        f"aplasta la escala del histograma completo."))
         col_blocks.append(model.Markdown(text=_stats_note(name, numeric, box)))
         blocks.append(model.Group(blocks=col_blocks))