feat(eda): NUM DISTR muestra el valor de σ (std) en la leyenda del histograma

La leyenda de cada histograma del capítulo de distribuciones numéricas ya
reporta el valor de la media y la mediana; ahora también reporta el valor de
la desviación estándar σ. La entrada de leyenda de la banda ±1σ pasa a incluir
el número (±1σ (σ = X)) y, cuando la banda no puede dibujarse (sin media o
std<=0) pero σ es conocido, se añade una entrada de leyenda mediante un handle
proxy sin trazo, de modo que el valor de σ se reporta siempre.

No se altera el boxplot de Tukey ni el keep-together (Group) por columna.
Se añaden tests de la leyenda: golden (σ con valor junto a media y mediana),
edge sin banda (proxy) y edge sin std (no revienta). Bump 1.1.0 -> 1.2.0.

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

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

@@ -1,9 +1,10 @@
 """Numeric distributions chapter (NUM DISTR) for AutomaticEDA.
 
 For every numeric column the chapter draws, as a single indivisible figure, a
-histogram with the **mean, median and ±1σ band drawn as reference lines** and a
-**Tukey boxplot right below it** sharing the same X axis — exactly the user
-requirement for this chapter. Each figure is emitted as a lazy ``Figure`` block
+histogram with the **mean, median and ±1σ band drawn as reference lines** (the
+legend reports the numeric value of the mean, the median **and the standard
+deviation σ**) and a **Tukey boxplot right below it** sharing the same X axis —
+exactly the user requirement for this chapter. Each figure is emitted as a lazy ``Figure`` block
 so the renderers rasterize and scale it to fit a whole page/slide and nothing is
 ever cut; columns with many numerics simply flow across pages as small
 multiples.
@@ -34,7 +35,7 @@ try:
 except Exception:  # noqa: BLE001 — keep the chapter importable no matter what.
     build_boxplot_stats = None  # type: ignore[assignment]
 
-CHAPTER_VERSION = "1.1.0"
+CHAPTER_VERSION = "1.2.0"
 CHAPTER_ID = "num_distr"
 CHAPTER_TITLE = "Distribuciones numéricas"
 
@@ -140,9 +141,11 @@ def _make_hist_box(name: str, numeric: dict, box: dict):
     std = numeric.get("std")
 
     # ±1σ band first (behind the lines), then median (solid) and mean (dashed).
+    # The band's legend entry also reports the numeric value of the standard
+    # deviation, so the reader sees mean, median AND σ at a glance.
     if mean is not None and std is not None and std > 0:
         ax_h.axvspan(mean - std, mean + std, color="#f0c27b", alpha=0.22,
-                     zorder=1, label="±1σ")
+                     zorder=1, label=f"±1σ (σ = {_fmt_num(std)})")
     if median is not None:
         ax_h.axvline(median, color="#2e8b57", linestyle="-", linewidth=1.6,
                      zorder=4, label=f"mediana = {_fmt_num(median)}")
@@ -152,7 +155,19 @@ def _make_hist_box(name: str, numeric: dict, box: dict):
 
     ax_h.set_ylabel("frecuencia", fontsize=8)
     ax_h.tick_params(labelsize=7)
-    ax_h.legend(fontsize=6.5, loc="upper right", framealpha=0.85)
+    # Always surface σ in the legend: if the ±1σ band could not be drawn (no mean
+    # or std<=0) but σ is still known, add a label-only proxy handle so the value
+    # of the standard deviation is reported regardless of the band.
+    handles, labels = ax_h.get_legend_handles_labels()
+    if std is not None and not any("σ =" in lbl for lbl in labels):
+        from matplotlib.lines import Line2D
+        proxy = Line2D([], [], linestyle="none", marker="",
+                       label=f"σ = {_fmt_num(std)}")
+        handles.append(proxy)
+        labels.append(f"σ = {_fmt_num(std)}")
+    if handles:
+        ax_h.legend(handles, labels, fontsize=6.5, loc="upper right",
+                    framealpha=0.85)
     for spine in ("top", "right"):
         ax_h.spines[spine].set_visible(False)
 

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

@@ -159,6 +159,50 @@ def test_anti_corte_muchas_columnas_pdf_y_pptx():
         assert res_pptx["n_slides"] >= 8  # at least one slide per column figure.
 
 
+def _hist_legend_texts(numeric, box=None):
+    """Build the per-column figure and return its histogram-legend label texts."""
+    from datascience.automatic_eda.chapters.num_distr import _make_hist_box
+    import matplotlib.pyplot as plt
+    fig = _make_hist_box("col", numeric, box or {})
+    ax_h = fig.axes[0]  # the histogram is the top axis.
+    leg = ax_h.get_legend()
+    texts = [t.get_text() for t in leg.get_texts()] if leg else []
+    plt.close(fig)
+    return texts
+
+
+def test_golden_leyenda_histograma_reporta_valor_std():
+    # The histogram legend must report the numeric value of the standard
+    # deviation σ next to mean and median.
+    numeric = _numeric_block(42.5, 40.0, 12.3, 1.0, 100.0, "right-skewed", 5)
+    texts = _hist_legend_texts(numeric)
+    joined = " ".join(texts)
+    assert any("σ =" in t for t in texts), f"σ value missing in legend: {texts}"
+    assert "12.3" in joined, f"std value 12.3 not in legend: {texts}"
+    assert any("media =" in t for t in texts)
+    assert any("mediana =" in t for t in texts)
+
+
+def test_edge_std_en_leyenda_aunque_no_haya_banda():
+    # When the ±1σ band cannot be drawn (no mean) but σ is known, the legend
+    # still surfaces the σ value via a label-only proxy handle.
+    numeric = _numeric_block(42.5, 40.0, 7.5, 1.0, 100.0, "right-skewed", 0)
+    numeric["mean"] = None  # forces the band off; σ must still appear.
+    texts = _hist_legend_texts(numeric)
+    assert any("σ = 7.5" in t for t in texts), f"σ proxy missing: {texts}"
+
+
+def test_edge_sin_std_no_revienta_la_figura():
+    # A numeric block without σ must not raise and simply omits the σ entry.
+    import matplotlib.pyplot as plt
+    numeric = _numeric_block(42.5, 40.0, 0.0, 1.0, 100.0, "discrete", 0)
+    numeric["std"] = None
+    texts = _hist_legend_texts(numeric)
+    assert not any("σ =" in t for t in texts)
+    # mean/median lines still produce their own legend entries.
+    assert any("media =" in t for t in texts)
+
+
 def test_distribution_gloss_cubre_todas_las_etiquetas():
     # Every label detect_distribution_type can emit has a Spanish gloss.
     for label in ("normal-ish", "right-skewed", "left-skewed", "heavy-tail",