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/describe_numeric_test.py

@@ -13,6 +13,7 @@ _EXPECTED_KEYS = {
     "p1", "p5", "p25", "p50", "p75", "p95", "p99", "iqr",
     "skew", "kurtosis", "n_outliers", "outlier_pct",
     "zero_pct", "negative_pct", "distribution_type", "histogram",
+    "histogram_clipped",
 }
 
 
@@ -61,9 +62,10 @@ def test_lista_vacia_todo_none():
     result = describe_numeric([None, "abc", float("nan")])
 
     assert set(result.keys()) == _EXPECTED_KEYS
-    for key in _EXPECTED_KEYS - {"histogram"}:
+    for key in _EXPECTED_KEYS - {"histogram", "histogram_clipped"}:
         assert result[key] is None, f"{key} debe ser None"
     assert result["histogram"] == []
+    assert result["histogram_clipped"] == []
 
 
 def test_cv_none_cuando_mean_cero():
@@ -83,3 +85,56 @@ def test_iqr_y_percentiles():
     assert result["p1"] <= result["p25"] <= result["p50"] <= result["p75"] <= result["p99"]
     assert result["min"] == 1.0
     assert result["max"] == 100.0
+
+
+# --------------------------------------------------------------------------- #
+# histogram_clipped: second view of the central mass, outliers trimmed.
+# --------------------------------------------------------------------------- #
+def test_histogram_clipped_trims_the_tail():
+    """Golden: with a long high tail, the clipped histogram excludes the outliers.
+
+    A tight cluster in [1, 5] plus a handful of extreme values. The full histogram
+    stretches to the extreme (min..max); the clipped one is re-binned over the
+    Tukey inner fences, so its upper edge stays far below the extreme and it holds
+    fewer values than the full sample.
+    """
+    cluster = [1, 2, 3, 4, 5] * 20          # 100 values in [1, 5]
+    values = cluster + [500, 800, 1000]     # 3 far outliers
+    result = describe_numeric(values)
+
+    full = result["histogram"]
+    clipped = result["histogram_clipped"]
+    assert full and clipped                                   # both present
+    for bucket in clipped:
+        assert "lo" in bucket and "hi" in bucket and "count" in bucket
+
+    # The full histogram reaches the extreme; the clipped one does not.
+    assert full[-1]["hi"] >= 900
+    assert clipped[-1]["hi"] < 100
+
+    # The clip removed the tail: fewer values counted than the full sample.
+    total_full = sum(b["count"] for b in full)
+    total_clipped = sum(b["count"] for b in clipped)
+    assert total_full == 103
+    assert total_clipped < total_full
+    assert total_clipped >= 100               # the whole cluster survives the clip
+
+
+def test_histogram_clipped_empty_when_no_outliers():
+    """Edge: a clean spread with no fence outliers yields an empty clipped view.
+
+    When the inner-fence range already covers every value, there is nothing to
+    trim, so histogram_clipped is [] and the renderer skips the redundant second
+    view instead of duplicating the full histogram.
+    """
+    result = describe_numeric(list(range(1, 101)))  # uniform 1..100, no outliers
+    assert result["n_outliers"] == 0
+    assert result["histogram"]                       # full histogram present
+    assert result["histogram_clipped"] == []         # nothing trimmed
+
+
+def test_histogram_clipped_empty_when_constant():
+    """Edge: a constant column (iqr == 0) never produces a clipped view."""
+    result = describe_numeric([7] * 30)
+    assert result["iqr"] == 0
+    assert result["histogram_clipped"] == []