merge: Fase 3 AutomaticEDA wiring (verificado met)

- build_eda_render_ctx: arma ctx (raw_numeric, timeseries_raw, geo_points, db_path+table) desde tabla DuckDB
- pipeline render_automatic_eda: perfila + ctx + build_document -> PDF + PPTX (11 capitulos poblados)
- profile_table: flag emit_automatic emite el report AutomaticEDA (PDF+PPT) sin romper render_eda_pdf
- text_layout: render real de **negrita** en PDF y PPTX
- .claude/commands/eda.md actualizado

Los 4 capitulos que degradaban (modelos/timeseries/geospatial/agregacion) ahora salen POBLADOS end-to-end.

.claude/commands/eda.md

@@ -25,9 +25,10 @@ Página madre del grupo: `docs/capabilities/eda.md` (léela primero para cargar
   - `--models` → `run_models=True` (PCA/KMeans/IsolationForest/normalidad).
   - `--llm` → `run_llm=True` (1 call LLM sobre el perfil agregado).
   - `--series` → `run_series=True` (estacionariedad ADF+KPSS, ACF/PACF, STL, retornos por columna numérica).
-  - `--pdf` → `emit_pdf=True` (PDF A5 vertical legible en móvil).
+  - `--pdf` → `emit_pdf=True` (PDF A5 legacy de `render_eda_pdf`, legible en móvil).
+  - `--legacy-only` → emite SOLO el PDF legacy (sin AutomaticEDA), para casos en que solo se quiera el PDF rápido.
 
-Por defecto, para un EDA "completo" cuando el usuario no especifica, activa `run_models`, `run_series` y `emit_pdf`; deja `run_llm` para cuando lo pida o cuando interese la interpretación semántica (es la única parte que gasta tokens del modelo).
+Por defecto, **un EDA completo emite SIEMPRE el informe AutomaticEDA en sus dos formatos: PDF (A5 móvil) Y PPTX (16:9 para compartir)** con los 11 capítulos poblados (portada, overview, distribuciones, calidad, correlaciones, modelos, series, geoespacial, agregación, interpretación LLM). Usa el pipeline `render_automatic_eda` (o `profile_table(emit_automatic=True)`), que activa `run_models` y `run_series` para que los capítulos de modelos/series/geoespacial/agregación salgan poblados. Deja `run_llm` para cuando el usuario lo pida o interese la interpretación semántica + narrativa por capítulo (es la única parte que gasta tokens del modelo).
 
 ## Reglas duras
 
@@ -35,7 +36,7 @@ Por defecto, para un EDA "completo" cuando el usuario no especifica, activa `run
 2. **CSV/Parquet/Excel** entran cargándolos antes a DuckDB (`read_csv_auto`/`read_parquet`/`read_xlsx`) — DuckDB es el motor por defecto. No traigas la tabla entera a RAM.
 3. **Secretos**: si la fuente es un DSN PostgreSQL con credenciales, NO las imprimas en los reports ni en el notebook; resuélvelas vía `resolve_pg_dsn`/`pass` cuando aplique.
 4. **El report es un artefacto local**: vive en `reports/` (gitignored), no se sube a Gitea ni se versiona. Compartir = pasar la ruta (regla `reports.md`).
-5. **Entrega las 4 salidas**: JSON sidecar + Markdown + **PDF móvil** + **notebook Jupyter colaborativo ejecutado en vivo**.
+5. **Entrega las salidas**: el informe **AutomaticEDA PDF + PPTX** (siempre, con `render_automatic_eda` / `emit_automatic=True`) + (opcional) JSON sidecar + Markdown + PDF legacy + **notebook Jupyter colaborativo ejecutado en vivo**. Comparte las rutas de PDF y PPTX.
 
 ## Paso 1 — Perfilar y escribir los reports
 
@@ -43,18 +44,26 @@ Una tabla (caso normal):
 
 ```bash
 PYTHONPATH=python/functions python/.venv/bin/python3 - <<'PYEOF'
-from pipelines.profile_table import profile_table
-r = profile_table(
+from pipelines.render_automatic_eda import render_automatic_eda
+# Informe AutomaticEDA COMPLETO one-shot: perfil + ctx (datos crudos) + PDF + PPTX
+# con los 11 capítulos poblados (clusters pintados, evolución temporal, mapa,
+# tablas de agregación). run_llm=True añade la narrativa LLM por capítulo.
+r = render_automatic_eda(
     "/ruta/datos.duckdb", "ventas",
-    run_models=True, run_series=True, emit_pdf=True, run_llm=False,
+    run_models=True, run_series=True, run_llm=False, out_dir="reports",
 )
 print("status:", r["status"])
-print("md:   ", r["report_md_path"])
-print("json: ", r["report_json_path"])
-print("pdf:  ", r["pdf_path"])
+print("pdf:   ", r["pdf_path"], "(", r["n_pages"], "págs )")
+print("pptx:  ", r["pptx_path"], "(", r["n_slides"], "slides )")
+print("manifest:", r["manifest_path"])
 PYEOF
 ```
 
+Si además quieres el report Markdown + JSON sidecar y/o el PDF legacy junto al
+AutomaticEDA, usa `profile_table(emit_automatic=True, emit_pdf=True, write_report=True)`:
+emite todo a la vez (`report_md_path`, `report_json_path`, `pdf_path` legacy,
+`aeda_pdf_path`, `aeda_pptx_path`, `aeda_manifest_path`).
+
 Una base entera (todas las tablas + relaciones FK):
 
 ```bash
@@ -90,6 +99,7 @@ Sigue la memoria `eda-workflow-registry` y la regla `notebook_collaboration.md`:
 ## Notas
 
 - El `TableProfile` lleva ahora, además del perfilado base y las correlaciones con FDR: `series` (por columna numérica, con `run_series`), `reexpression` por columna numérica (escalera de Tukey) y `caveats` (siempre, avisos exploratorios). El Markdown y el PDF renderizan estas secciones automáticamente cuando están presentes.
-- El PDF (`emit_pdf`) está pensado para leerse en el móvil (A5 vertical, tipografía grande, gráficos Tufte). Se escribe junto al Markdown en `reports/`.
+- El informe **AutomaticEDA** (`render_automatic_eda` / `emit_automatic=True`) emite el MISMO documento por capítulos a **PDF (A5 móvil)** y **PPTX (16:9)** con garantía de no-corte (texto envuelto, tablas partidas repitiendo cabecera, figuras escaladas) y negrita real (`**texto**`). Escribe `automatic_eda_manifest.json` con la versión de cada capítulo. Los capítulos modelos/series/geoespacial/agregación se pueblan con los datos crudos que `build_eda_render_ctx` muestrea de la base (no se traen tablas enteras a RAM).
+- El PDF legacy (`emit_pdf`, `render_eda_pdf`) sigue disponible y es independiente del AutomaticEDA (A5 vertical, gráficos Tufte). Se escribe junto al Markdown en `reports/`.
 - `run_series` ordena por la primera columna datetime si existe; si no, por el orden físico de filas. Necesita ≥8 puntos válidos por columna.
 - Fuentes: DuckDB (CSV/Parquet/Excel cargados antes) y PostgreSQL (`backend="postgres"`). `profile_database` (multi-tabla + FK) es solo DuckDB por ahora.

python/functions/datascience/__init__.py

@@ -25,6 +25,7 @@ from .describe_numeric import describe_numeric
 from .summarize_categorical import summarize_categorical
 from .infer_semantic_type import infer_semantic_type
 from .column_quality_score import column_quality_score
+from .select_groupby_keys import select_groupby_keys
 from .render_eda_markdown import render_eda_markdown
 from .detect_distribution_type import detect_distribution_type
 from .spearman_corr import spearman_corr
@@ -36,6 +37,8 @@ from .infer_fk_containment_duckdb import infer_fk_containment_duckdb
 from .build_join_graph import build_join_graph
 from .association_matrix import association_matrix
 from .correlation_matrix_duckdb import correlation_matrix_duckdb
+from .pivot_table_duckdb import pivot_table_duckdb
+from .groupby_stats_duckdb import groupby_stats_duckdb
 from .pca_explained import pca_explained
 from .kmeans_segments import kmeans_segments
 from .isolation_forest_outliers import isolation_forest_outliers
@@ -44,6 +47,9 @@ from .trend_slope import trend_slope
 from .run_eda_models import run_eda_models
 from .project_clusters_2d import project_clusters_2d
 from .describe_clusters_llm import describe_clusters_llm
+from .detect_latlon_columns import detect_latlon_columns
+from .analyze_geo_extent import analyze_geo_extent
+from .build_geo_scatter import build_geo_scatter
 from .eda_llm_insights import eda_llm_insights
 from .build_eda_notebook import build_eda_notebook
 from .decode_qr_image import decode_qr_image
@@ -59,12 +65,14 @@ from .render_automatic_eda_pdf import render_automatic_eda_pdf
 from .render_automatic_eda_pptx import render_automatic_eda_pptx
 from .detect_time_column import detect_time_column
 from .extract_timeseries_raw import extract_timeseries_raw
+from .build_eda_render_ctx import build_eda_render_ctx
 from .profile_datetime import profile_datetime
 from .resample_timeseries import resample_timeseries
 
 __all__ = [
     "detect_time_column",
     "extract_timeseries_raw",
+    "build_eda_render_ctx",
     "profile_datetime",
     "resample_timeseries",
     "render_automatic_eda_pdf",
@@ -90,6 +98,8 @@ __all__ = [
     "build_join_graph",
     "association_matrix",
     "correlation_matrix_duckdb",
+    "pivot_table_duckdb",
+    "groupby_stats_duckdb",
     "pca_explained",
     "kmeans_segments",
     "isolation_forest_outliers",
@@ -98,12 +108,16 @@ __all__ = [
     "run_eda_models",
     "project_clusters_2d",
     "describe_clusters_llm",
+    "detect_latlon_columns",
+    "analyze_geo_extent",
+    "build_geo_scatter",
     "eda_llm_insights",
     "build_eda_notebook",
     "describe_numeric",
     "summarize_categorical",
     "infer_semantic_type",
     "column_quality_score",
+    "select_groupby_keys",
     "render_eda_markdown",
     "detect_distribution_type",
     "pull_gsc_search_analytics",

python/functions/datascience/analyze_geo_extent.md

@@ -0,0 +1,61 @@
+---
+name: analyze_geo_extent
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def analyze_geo_extent(lats: list, lons: list) -> dict"
+description: "Calcula la extension geografica de una nube de coordenadas (lat/lon) y asigna cada punto a un pais/region mediante un lookup OFFLINE contra una tabla de bounding boxes embebida como constante. Devuelve bounding box, centroide, span de la diagonal (haversine), conteo por region (top-8 + Otros), reparto por hemisferios y una frase resumen en ES. Lectura defensiva: descarta pares None/NaN/fuera de rango y NUNCA lanza. Solo stdlib (math); sin geopandas/shapely. Las cajas de paises son rectangulos aproximados, no reverse-geocoding exacto."
+tags: [eda, geospatial, geo, coordinates, bounding-box, haversine, datascience]
+params:
+  - name: lats
+    desc: "Lista de latitudes en grados, rango valido [-90, 90]. Se empareja por indice con lons (gana la longitud minima comun si difieren). Cada valor puede ser None/NaN/no-numerico/fuera de rango: se lee defensivo y se descarta el par."
+  - name: lons
+    desc: "Lista de longitudes en grados, rango valido [-180, 180]. Paralela a lats, emparejada por indice. Valores None/NaN/no-numericos/fuera de rango se descartan junto con su par."
+output: "Dict con el resumen geografico: {n_points=pares validos usados, bbox={lat_min,lat_max,lon_min,lon_max} o None, centroid={lat,lon}=media de lat/lon validos o None, span_km=distancia haversine (radio 6371 km) de la diagonal SO->NE del bbox, by_region=[{region,count}] descendente por count limitado a top-8 con el resto agregado en 'Otros', hemisphere={north,south,east,west} (ecuador->norte, meridiano 0->este), note=frase ES resumen}. Si no hay pares validos devuelve la forma cero: n_points 0, bbox None, centroid None, span_km 0.0, by_region [], hemisphere a ceros y note 'sin coordenadas validas'. Puntos que no caen en ninguna caja -> region 'Oceano/Otros'."
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [math]
+tested: true
+tests: ["test_nube_en_espana", "test_dos_paises_distintos", "test_listas_vacias", "test_pares_invalidos_filtrados", "test_longitudes_desbalanceadas", "test_span_km_haversine_par_conocido", "test_no_lanza_con_entradas_raras"]
+test_file_path: "python/functions/datascience/analyze_geo_extent_test.py"
+file_path: "python/functions/datascience/analyze_geo_extent.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience.analyze_geo_extent import analyze_geo_extent
+
+# Nube de puntos alrededor de Madrid + un punto en Paris.
+lats = [40.4, 40.0, 41.0, 48.8]
+lons = [-3.7, -3.5, -4.0, 2.3]
+res = analyze_geo_extent(lats, lons)
+
+print(res["n_points"])          # 4
+print(res["by_region"])         # [{'region': 'España', 'count': 3}, {'region': 'Francia', 'count': 1}]
+print(round(res["span_km"], 1)) # diagonal SO->NE del bbox en km
+print(res["hemisphere"])        # {'north': 4, 'south': 0, 'east': 1, 'west': 3}
+print(res["note"])              # los puntos se concentran en España (3 de 4)
+```
+
+## Cuando usarla
+
+- Usala en el perfilado EDA (grupo `eda`) cuando una tabla tenga columnas de latitud y longitud y quieras un resumen geografico rapido: donde se concentran los puntos, cuanto territorio cubren y a que paises/regiones caen, sin montar geopandas ni un reverse-geocoder.
+- Cuando necesites un capitulo `geospatial` del `AutomaticEDA`: alimenta el bbox + centroide para centrar un mapa, el `span_km` para elegir el zoom, y `by_region` para una tabla de conteos por pais.
+- Cuando quieras detectar datos sucios de coordenadas (mezcla de hemisferios inesperada, puntos en `Oceano/Otros`, span enorme) antes de seguir el analisis.
+
+## Gotchas
+
+- Funcion pura, sin I/O ni red y determinista: mismas entradas -> misma salida. Lectura defensiva, NUNCA lanza; pares con None/NaN o fuera de rango ([-90,90] lat, [-180,180] lon) se descartan en silencio.
+- El lookup de region es una **aproximacion rectangular**: cada pais/region es un bounding box, NO su frontera real. Un punto en el mar cerca de una costa, o en una esquina del rectangulo, puede asignarse a un pais vecino. No es reverse-geocoding exacto — para precision real hace falta un shapefile (fuera de scope por KISS).
+- Cajas solapadas se resuelven por orden: gana la PRIMERA que contiene el punto. Los paises se listan antes que los continentes (fallback), y entre vecinos el mas estrecho/occidental va primero (Portugal antes que España, Chile antes que Argentina, EEUU contiguo antes que Canada). Un punto que no cae en ninguna caja -> `Oceano/Otros`.
+- La tabla cubre ~24 paises grandes + 6 regiones continentales; paises pequeños o no listados caen a su continente o a `Oceano/Otros`. No incluye territorios insulares lejanos (Canarias, Hawaii, etc.).
+- `span_km` es la diagonal del bounding box (esquina SO a NE), no la dispersion real de la nube ni el area; con un solo punto valido el bbox es degenerado y `span_km` es 0.0.
+- El ecuador (`lat == 0`) cuenta como hemisferio norte y el meridiano 0 (`lon == 0`) como este, por convencion `>= 0`.

python/functions/datascience/analyze_geo_extent.py

@@ -0,0 +1,209 @@
+"""analyze_geo_extent — geographic extent of a cloud of coordinates (EDA `geospatial`).
+
+Pure function: no I/O, no network, deterministic. Given two parallel lists of
+latitudes and longitudes it derives the bounding box, centroid, diagonal span
+(haversine), per-region counts and hemisphere split of the points, and assigns
+each point to a country/region via an OFFLINE lookup against a table of
+rectangular bounding boxes embedded as a constant (`_REGION_BBOXES`).
+
+It never reads files, never hits the network and depends only on `math`. The
+country boxes are deliberately coarse rectangles (a KISS approximation, NOT a
+reverse-geocoder). Reading is defensive throughout and the function NEVER
+raises: invalid pairs (None / NaN / out of range) are silently discarded and an
+empty cloud yields a zeroed result the caller can skip.
+"""
+
+import math
+
+# Earth mean radius in km used by the haversine formula.
+_EARTH_RADIUS_KM = 6371.0
+
+# How many distinct regions to surface in `by_region` before collapsing the
+# remainder into a single "Otros" bucket.
+_TOP_REGIONS = 8
+
+# Offline region lookup: (name, lat_min, lat_max, lon_min, lon_max).
+#
+# Specific countries are listed FIRST and continental fallbacks LAST: each point
+# is assigned to the FIRST box that contains it, so the more specific country box
+# wins over the broad continent box. Boxes are coarse rectangles approximating
+# the mainland extent of each region; overlapping neighbours are ordered so the
+# narrower/more-western country claims its coastal points (e.g. Portugal before
+# Spain, Chile before Argentina, the contiguous US before Canada).
+_REGION_BBOXES = (
+    # --- countries (specific) ---
+    ("Portugal", 36.9, 42.2, -9.6, -6.2),
+    ("España", 36.0, 43.8, -9.4, 3.4),
+    ("Francia", 41.3, 51.1, -5.2, 9.6),
+    ("Reino Unido", 49.9, 58.7, -8.6, 1.8),
+    ("Irlanda", 51.4, 55.4, -10.6, -5.9),
+    ("Países Bajos", 50.7, 53.6, 3.3, 7.2),
+    ("Bélgica", 49.5, 51.5, 2.5, 6.4),
+    ("Suiza", 45.8, 47.8, 5.9, 10.5),
+    ("Alemania", 47.3, 55.1, 5.9, 15.0),
+    ("Italia", 36.6, 47.1, 6.6, 18.5),
+    ("Marruecos", 27.7, 35.9, -13.2, -1.0),
+    ("Egipto", 22.0, 31.7, 25.0, 35.0),
+    ("Sudáfrica", -34.8, -22.1, 16.5, 32.9),
+    ("China", 18.0, 53.6, 73.5, 135.1),
+    ("Japón", 24.0, 45.6, 122.9, 145.9),
+    ("India", 6.7, 35.5, 68.1, 97.4),
+    ("Australia", -43.7, -10.0, 112.9, 153.7),
+    ("México", 14.5, 32.7, -118.4, -86.7),
+    ("Estados Unidos", 24.4, 49.4, -125.0, -66.9),
+    ("Canadá", 41.7, 83.1, -141.0, -52.6),
+    ("Chile", -55.9, -17.5, -75.6, -66.4),
+    ("Argentina", -55.1, -21.8, -73.6, -53.6),
+    ("Brasil", -33.8, 5.3, -74.0, -34.8),
+    ("Rusia", 41.2, 77.0, 19.6, 180.0),
+    # --- continental fallbacks (broad) ---
+    ("Europa", 34.0, 72.0, -25.0, 45.0),
+    ("África", -35.0, 37.5, -18.0, 52.0),
+    ("Asia", 5.0, 78.0, 26.0, 180.0),
+    ("América del Norte", 7.0, 84.0, -168.0, -52.0),
+    ("América del Sur", -56.0, 13.0, -82.0, -34.0),
+    ("Oceanía", -50.0, 0.0, 110.0, 180.0),
+)
+
+
+def _coord(value, limit):
+    """Coerce a coordinate to a valid float in [-limit, limit] or None.
+
+    bool is a subclass of int but never a real coordinate, so True/False are
+    treated as missing. NaN and out-of-range values are rejected.
+    """
+    if value is None or isinstance(value, bool):
+        return None
+    try:
+        f = float(value)
+    except (TypeError, ValueError):
+        return None
+    # NaN is the only value that is not equal to itself.
+    if f != f or f < -limit or f > limit:
+        return None
+    return f
+
+
+def _haversine_km(lat1, lon1, lat2, lon2):
+    """Great-circle distance in km between two (lat, lon) points in degrees."""
+    rlat1, rlat2 = math.radians(lat1), math.radians(lat2)
+    dlat = math.radians(lat2 - lat1)
+    dlon = math.radians(lon2 - lon1)
+    a = math.sin(dlat / 2.0) ** 2 + math.cos(rlat1) * math.cos(rlat2) * math.sin(dlon / 2.0) ** 2
+    return 2.0 * _EARTH_RADIUS_KM * math.asin(min(1.0, math.sqrt(a)))
+
+
+def _region_of(lat, lon):
+    """Return the name of the first embedded box containing (lat, lon)."""
+    for name, lat_min, lat_max, lon_min, lon_max in _REGION_BBOXES:
+        if lat_min <= lat <= lat_max and lon_min <= lon <= lon_max:
+            return name
+    return "Océano/Otros"
+
+
+def _empty_result():
+    """Result shape when there are no valid coordinate pairs."""
+    return {
+        "n_points": 0,
+        "bbox": None,
+        "centroid": None,
+        "span_km": 0.0,
+        "by_region": [],
+        "hemisphere": {"north": 0, "south": 0, "east": 0, "west": 0},
+        "note": "sin coordenadas validas",
+    }
+
+
+def analyze_geo_extent(lats: list, lons: list) -> dict:
+    """Summarise the geographic extent of a cloud of lat/lon coordinates.
+
+    Pairs `lats[i]` with `lons[i]` by index (over the common length when the two
+    lists differ in size), discards any pair where either value is None / NaN or
+    outside [-90, 90] (lat) / [-180, 180] (lon), and derives the bounding box,
+    centroid, diagonal span, per-region counts and hemisphere split. Each valid
+    point is matched to a country/region by an offline lookup against coarse
+    rectangular bounding boxes (`_REGION_BBOXES`).
+
+    Args:
+        lats: List of latitudes in degrees ([-90, 90]); read defensively.
+        lons: List of longitudes in degrees ([-180, 180]); read defensively.
+            Paired with `lats` by index; the shorter length wins when they differ.
+
+    Returns:
+        Dict with the geographic summary:
+        {n_points, bbox={lat_min,lat_max,lon_min,lon_max}, centroid={lat,lon},
+         span_km (haversine of the SW->NE bbox diagonal), by_region=[{region,count}]
+         (descending, top-8 with the rest folded into "Otros"),
+         hemisphere={north,south,east,west}, note (Spanish summary phrase)}.
+        With no valid pairs returns the zeroed shape: n_points 0, bbox None,
+        centroid None, span_km 0.0, empty by_region, zeroed hemisphere and the
+        note "sin coordenadas validas". Never raises.
+    """
+    if not isinstance(lats, (list, tuple)) or not isinstance(lons, (list, tuple)):
+        return _empty_result()
+
+    valid = []
+    # zip already stops at the shorter list -> unbalanced lengths are handled.
+    for raw_lat, raw_lon in zip(lats, lons):
+        lat = _coord(raw_lat, 90.0)
+        lon = _coord(raw_lon, 180.0)
+        if lat is None or lon is None:
+            continue
+        valid.append((lat, lon))
+
+    if not valid:
+        return _empty_result()
+
+    n = len(valid)
+    lat_vals = [p[0] for p in valid]
+    lon_vals = [p[1] for p in valid]
+
+    lat_min, lat_max = min(lat_vals), max(lat_vals)
+    lon_min, lon_max = min(lon_vals), max(lon_vals)
+
+    centroid_lat = sum(lat_vals) / n
+    centroid_lon = sum(lon_vals) / n
+
+    # Diagonal span: SW corner (lat_min, lon_min) to NE corner (lat_max, lon_max).
+    span_km = _haversine_km(lat_min, lon_min, lat_max, lon_max)
+
+    # Hemisphere split: the equator/prime-meridian go to north/east respectively.
+    north = sum(1 for lat in lat_vals if lat >= 0.0)
+    south = n - north
+    east = sum(1 for lon in lon_vals if lon >= 0.0)
+    west = n - east
+
+    # Count points per region (offline bbox lookup).
+    counts = {}
+    for lat, lon in valid:
+        region = _region_of(lat, lon)
+        counts[region] = counts.get(region, 0) + 1
+
+    # Descending by count, then by name for a deterministic tie-break.
+    ranked = sorted(counts.items(), key=lambda kv: (-kv[1], kv[0]))
+    by_region = [{"region": name, "count": count} for name, count in ranked[:_TOP_REGIONS]]
+    rest = sum(count for _, count in ranked[_TOP_REGIONS:])
+    if rest > 0:
+        by_region.append({"region": "Otros", "count": rest})
+
+    top_region, top_count = ranked[0]
+    note = (
+        "los puntos se concentran en {region} ({count} de {n})".format(
+            region=top_region, count=top_count, n=n
+        )
+    )
+
+    return {
+        "n_points": n,
+        "bbox": {
+            "lat_min": lat_min,
+            "lat_max": lat_max,
+            "lon_min": lon_min,
+            "lon_max": lon_max,
+        },
+        "centroid": {"lat": centroid_lat, "lon": centroid_lon},
+        "span_km": span_km,
+        "by_region": by_region,
+        "hemisphere": {"north": north, "south": south, "east": east, "west": west},
+        "note": note,
+    }

python/functions/datascience/analyze_geo_extent_test.py

@@ -0,0 +1,126 @@
+"""Tests para analyze_geo_extent."""
+
+import math
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from analyze_geo_extent import analyze_geo_extent, _haversine_km
+
+# Keys that a non-empty result dict must always contain.
+_EXPECTED_KEYS = {
+    "n_points", "bbox", "centroid", "span_km",
+    "by_region", "hemisphere", "note",
+}
+
+
+def test_nube_en_espana():
+    """Golden: nube de puntos alrededor de Madrid -> region top = España."""
+    # Cuatro puntos en torno a Madrid (lat ~40, lon ~-3.7), con algo de spread.
+    lats = [40.4, 40.0, 41.0, 39.5]
+    lons = [-3.7, -3.5, -4.0, -3.2]
+    res = analyze_geo_extent(lats, lons)
+
+    assert set(res.keys()) == _EXPECTED_KEYS
+    assert res["n_points"] == 4
+
+    # Todos caen en España -> by_region una sola entrada.
+    assert res["by_region"][0]["region"] == "España"
+    assert res["by_region"][0]["count"] == 4
+
+    # Centroide coherente: media de lat y lon.
+    assert math.isclose(res["centroid"]["lat"], sum(lats) / 4, rel_tol=1e-9)
+    assert math.isclose(res["centroid"]["lon"], sum(lons) / 4, rel_tol=1e-9)
+
+    # bbox correcto.
+    assert res["bbox"]["lat_min"] == 39.5
+    assert res["bbox"]["lat_max"] == 41.0
+    assert res["bbox"]["lon_min"] == -4.0
+    assert res["bbox"]["lon_max"] == -3.2
+
+    # Hay spread -> diagonal > 0.
+    assert res["span_km"] > 0.0
+
+    # Hemisferio norte (lat>0) y oeste (lon<0).
+    assert res["hemisphere"]["north"] == 4
+    assert res["hemisphere"]["south"] == 0
+    assert res["hemisphere"]["east"] == 0
+    assert res["hemisphere"]["west"] == 4
+
+    assert "España" in res["note"]
+
+
+def test_dos_paises_distintos():
+    """Golden: puntos en España y Francia -> by_region con 2 entradas."""
+    # Madrid (España) x2 y Paris (Francia) x1.
+    lats = [40.4, 40.0, 48.8]
+    lons = [-3.7, -3.5, 2.3]
+    res = analyze_geo_extent(lats, lons)
+
+    assert res["n_points"] == 3
+    regions = {entry["region"]: entry["count"] for entry in res["by_region"]}
+    assert regions == {"España": 2, "Francia": 1}
+    # Orden descendente por count: España (2) antes que Francia (1).
+    assert res["by_region"][0]["region"] == "España"
+    assert res["by_region"][0]["count"] == 2
+
+    # Madrid y Paris ambos hemisferio norte; Paris lon>0 -> 1 east, 2 west.
+    assert res["hemisphere"]["north"] == 3
+    assert res["hemisphere"]["east"] == 1
+    assert res["hemisphere"]["west"] == 2
+
+
+def test_listas_vacias():
+    """Edge: listas vacias -> n_points 0, bbox None, sin lanzar."""
+    res = analyze_geo_extent([], [])
+    assert res["n_points"] == 0
+    assert res["bbox"] is None
+    assert res["centroid"] is None
+    assert res["span_km"] == 0.0
+    assert res["by_region"] == []
+    assert res["hemisphere"] == {"north": 0, "south": 0, "east": 0, "west": 0}
+    assert res["note"] == "sin coordenadas validas"
+
+
+def test_pares_invalidos_filtrados():
+    """Edge: None / NaN / fuera de rango se descartan, no lanza."""
+    nan = float("nan")
+    lats = [40.4, None, nan, 91.0, -200.0, 40.0]
+    lons = [-3.7, -3.5, -3.0, 2.0, 5.0, -3.5]
+    # Validos: indices 0 y 5 (lat 91 fuera de rango, lon -200 fuera de rango,
+    # None y NaN descartados).
+    res = analyze_geo_extent(lats, lons)
+    assert res["n_points"] == 2
+    assert res["by_region"][0]["region"] == "España"
+    assert res["by_region"][0]["count"] == 2
+
+
+def test_longitudes_desbalanceadas():
+    """Edge: len(lats) != len(lons) usa el minimo comun sin lanzar."""
+    lats = [40.4, 40.0, 41.0, 39.5]  # 4 elementos
+    lons = [-3.7, -3.5]              # 2 elementos
+    res = analyze_geo_extent(lats, lons)
+    # Solo se emparejan los 2 primeros.
+    assert res["n_points"] == 2
+    assert res["bbox"]["lat_min"] == 40.0
+    assert res["bbox"]["lat_max"] == 40.4
+
+
+def test_span_km_haversine_par_conocido():
+    """Edge: span_km coincide con haversine de la diagonal del bbox."""
+    # Dos puntos: (0, 0) y (0, 1). bbox diagonal = mismos dos puntos.
+    res = analyze_geo_extent([0.0, 0.0], [0.0, 1.0])
+    # 1 grado de longitud en el ecuador ~ 111.19 km.
+    expected = _haversine_km(0.0, 0.0, 0.0, 1.0)
+    assert math.isclose(res["span_km"], expected, rel_tol=1e-9)
+    assert math.isclose(res["span_km"], 111.19, abs_tol=0.5)
+
+
+def test_no_lanza_con_entradas_raras():
+    """Edge: tipos no-lista o None devuelven la forma vacia sin lanzar."""
+    assert analyze_geo_extent(None, None)["n_points"] == 0
+    assert analyze_geo_extent("foo", "bar")["n_points"] == 0
+    # Strings dentro de las listas se descartan como invalidos.
+    res = analyze_geo_extent(["x", 40.0], [None, -3.5])
+    assert res["n_points"] == 1

python/functions/datascience/automatic_eda/bold_render_test.py

@@ -0,0 +1,113 @@
+"""Tests for inline-bold rendering (**bold**) in the AutomaticEDA engine.
+
+Covers the pure helpers (parse_inline_bold / wrap_rich) and an end-to-end PPTX
+check that a ``**bold**`` span is rendered with NATIVE PowerPoint bold
+(``run.font.bold is True``) while no line overflows the wrap width (no-cut).
+"""
+
+import os
+import sys
+
+import pytest
+
+# Make the engine importable as a package (datascience.automatic_eda).
+_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 import text_layout as tl  # noqa: E402
+from datascience.automatic_eda import render_pptx  # noqa: E402
+
+
+# --------------------------------------------------------------------------- #
+# Pure helpers.
+# --------------------------------------------------------------------------- #
+def test_parse_inline_bold_marks_spans_and_preserves_visible_text():
+    src = "**Estacionariedad:** serie no estacionaria con `code` y normal."
+    segs = tl.parse_inline_bold(src)
+    # Visible text equals strip_inline_md (no characters lost, markers removed).
+    visible = "".join(s for s, _ in segs)
+    assert visible == tl.strip_inline_md(src)
+    # The span "Estacionariedad:" is flagged bold; the rest is not.
+    bold_text = "".join(s for s, b in segs if b)
+    assert "Estacionariedad:" in bold_text
+    assert "serie no estacionaria" not in bold_text
+
+
+def test_parse_inline_bold_handles_unbalanced_markers():
+    # An unbalanced ** must not crash and must be stripped (matches strip_inline_md).
+    segs = tl.parse_inline_bold("texto **sin cierre aqui")
+    visible = "".join(s for s, _ in segs)
+    assert visible == "texto sin cierre aqui"
+    assert not any(b for _, b in segs)  # nothing rendered bold.
+
+
+def test_wrap_rich_never_overflows_and_keeps_bold():
+    text = ("**Segmento premium.** Clientes de alto gasto y baja frecuencia con "
+            "ticket medio elevado y recurrencia anual estable a lo largo del año.")
+    max_chars = 30
+    lines = tl.wrap_rich(text, max_chars)
+    # No visible line exceeds max_chars (no-cut: the renderer measures these).
+    for ln in lines:
+        visible = "".join(s for s, _ in ln)
+        assert len(visible) <= max_chars, f"línea desborda: {visible!r}"
+    # At least one segment is bold and it is the span content.
+    bold_segs = [s for ln in lines for s, b in ln if b]
+    assert any("Segmento premium." in s for s in bold_segs)
+
+
+def test_wrap_rich_hard_splits_long_token():
+    long = "x" * 50
+    lines = tl.wrap_rich(f"**{long}**", 20)
+    for ln in lines:
+        assert len("".join(s for s, _ in ln)) <= 20
+    # The whole long token is preserved across the split lines.
+    joined = "".join(s for ln in lines for s, _ in ln)
+    assert joined == long
+
+
+# --------------------------------------------------------------------------- #
+# End-to-end: PPTX renders **bold** as a real bold run.
+# --------------------------------------------------------------------------- #
+def _has_pptx():
+    try:
+        import pptx  # noqa: F401
+        return True
+    except Exception:  # noqa: BLE001
+        return False
+
+
+@pytest.mark.skipif(not _has_pptx(), reason="python-pptx no instalado")
+def test_pptx_renders_bold_span_as_native_bold_run(tmp_path):
+    from pptx import Presentation
+
+    doc = [model.Chapter(
+        id="t", title="Negrita", version="1.0.0",
+        blocks=[model.Markdown(
+            text="Frase con **PALABRACLAVE** resaltada y texto normal después.")],
+    )]
+    out = str(tmp_path / "bold.pptx")
+    res = render_pptx(doc, out, {"title": "T"})
+    assert res.get("path") == out
+    assert os.path.exists(out)
+
+    prs = Presentation(out)
+    bold_texts = []
+    all_text = []
+    for slide in prs.slides:
+        for shape in slide.shapes:
+            if not shape.has_text_frame:
+                continue
+            for para in shape.text_frame.paragraphs:
+                for run in para.runs:
+                    all_text.append(run.text)
+                    if run.font.bold:
+                        bold_texts.append(run.text)
+    # The bold span text appears in a run with font.bold True (native bold).
+    assert any("PALABRACLAVE" in t for t in bold_texts), \
+        f"no se encontró run bold con el span; bold={bold_texts}"
+    # And the surrounding plain text is NOT bold (markers did not bleed).
+    assert any("resaltada" in t for t in all_text)
+    assert not any("resaltada" in t for t in bold_texts)

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

@@ -0,0 +1,592 @@
+"""Aggregation chapter (AGREGACION) — group analysis / OLAP of the EDA.
+
+This chapter is the group-by / pivot ("OLAP") section of an AutomaticEDA report
+and is meant to be present **whenever the dataset has at least one low-cardinality
+categorical column to group by**. For the most interesting categoricals (chosen
+by their cardinality/relevance, optionally with an LLM) it renders, as blocks the
+core paginator never cuts:
+
+1. **Per-group statistics** (split-apply-combine) — for each interesting
+   categorical key, the count of rows per group and, for each numeric measure,
+   its mean/median/std/min/max. One compact summary table (mean of every measure
+   per group) plus a per-measure detail table.
+2. **Bar charts** — a vertical bar chart of a measure's mean per group, bars from
+   zero (Tufte Lie-Factor = 1).
+3. **Pivot tables** — categorical A x categorical B -> aggregate of a measure,
+   limited to the top rows/cols so it fits a mobile page/slide, with a grouped
+   bar chart of the same pivot.
+
+The raw data needed to aggregate is **not** in the TableProfile, so — exactly
+like ``modelos`` reads its cluster projection from ``ctx`` — this chapter gets
+the aggregation results in one of two ways and degrades honestly when neither is
+available:
+
+ctx keys this chapter consumes (all optional):
+    aggregations : dict — pre-computed results, used directly (offline / tests /
+        forward-compatible with a calculation phase). Shape::
+
+            {"groupby": [{"group_by": str, "measures": [str], "why": str,
+                          "result": <groupby_stats_duckdb-shaped dict>}],
+             "pivots":  [{"index": str, "columns": str, "value": str, "agg": str,
+                          "why": str, "result": <pivot_table_duckdb-shaped dict>}]}
+
+    db_path, table : str — when ``aggregations`` is absent, the chapter selects
+        the interesting keys (``select_groupby_keys``), optionally asks an LLM
+        which to show (``suggest_aggregations_llm`` when ``run_agg_llm`` is True)
+        and computes the group-by/pivot results live via the push-down registry
+        functions ``groupby_stats_duckdb`` / ``pivot_table_duckdb``.
+    run_agg_llm : bool — when True (and ``db_path``/``table`` present), let the
+        LLM pick the interesting aggregations; otherwise the deterministic
+        quantitative selection is used.
+    agg_llm_model : str — model id for the optional LLM selection.
+    agg_max_keys, agg_max_card, agg_max_measures, agg_top_n : int — limits.
+    agg_insights : list — optional pre-computed micro-analysis entries
+        (``[{"title": str, "text": str}]``) rendered as an interpretation section.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+Reads everything defensively (``.get``) and never raises: anything missing
+degrades to a note instead of aborting the chapter; the chapter returns ``None``
+only when the dataset has no categorical column to group by.
+"""
+
+from __future__ import annotations
+
+from .. import model
+
+# Pure/impure registry functions (group ``eda``) this chapter composes. Imported
+# defensively so the chapter still builds (degrading the affected part to a note)
+# if a function is somehow unavailable / not indexed yet.
+try:
+    from datascience.select_groupby_keys import select_groupby_keys
+except Exception:  # noqa: BLE001 — keep the chapter importable no matter what.
+    select_groupby_keys = None  # type: ignore[assignment]
+try:
+    from datascience.groupby_stats_duckdb import groupby_stats_duckdb
+except Exception:  # noqa: BLE001
+    groupby_stats_duckdb = None  # type: ignore[assignment]
+try:
+    from datascience.pivot_table_duckdb import pivot_table_duckdb
+except Exception:  # noqa: BLE001
+    pivot_table_duckdb = None  # type: ignore[assignment]
+try:
+    from datascience.suggest_aggregations_llm import suggest_aggregations_llm
+except Exception:  # noqa: BLE001
+    suggest_aggregations_llm = None  # type: ignore[assignment]
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "agregacion"
+CHAPTER_TITLE = "Agregación por grupos"
+
+# Tableau-10 palette — stable colours for the pivot's grouped-bar series.
+_SERIES_COLORS = [
+    "#4e79a7", "#f28e2b", "#e15759", "#76b7b2", "#59a14f",
+    "#edc948", "#b07aa1", "#ff9da7", "#9c755f", "#bab0ac",
+]
+
+# Defaults for the live selection/aggregation (overridable via ctx).
+_DEF_MAX_KEYS = 3
+_DEF_MAX_CARD = 20
+_DEF_MAX_MEASURES = 4
+_DEF_TOP_N = 12
+
+
+# --------------------------------------------------------------------------- #
+# Formatting helpers (mirror the other chapters' defensive style).
+# --------------------------------------------------------------------------- #
+def _fmt_num(value, decimals: int = 3) -> str:
+    if value is None:
+        return "—"
+    if isinstance(value, bool):
+        return "sí" if value else "no"
+    if isinstance(value, int):
+        return f"{value:,}".replace(",", ".")
+    if isinstance(value, float):
+        if value != value:  # NaN
+            return "NaN"
+        if value in (float("inf"), float("-inf")):
+            return str(value)
+        text = f"{value:.{decimals}f}".rstrip("0").rstrip(".")
+        return text if text else "0"
+    return model._safe_str(value)
+
+
+def _is_dict(v) -> bool:
+    return isinstance(v, dict)
+
+
+def _measure_mean(group: dict, measure: str):
+    """Pull the mean of one measure out of a groupby-result group entry."""
+    stats = group.get("stats") if _is_dict(group.get("stats")) else {}
+    ms = stats.get(measure) if _is_dict(stats.get(measure)) else {}
+    return ms.get("mean")
+
+
+# --------------------------------------------------------------------------- #
+# Plan + data resolution. Either a pre-computed ctx['aggregations'] is used
+# verbatim, or the plan is selected and the results are computed live.
+# --------------------------------------------------------------------------- #
+def _resolve_candidates(profile: dict, ctx: dict) -> dict:
+    """Return {group_keys, measures, pivots, note} of interesting columns."""
+    pre = ctx.get("agg_candidates")
+    if _is_dict(pre) and pre.get("group_keys") is not None:
+        return pre
+    if select_groupby_keys is not None:
+        try:
+            out = select_groupby_keys(
+                profile,
+                max_keys=int(ctx.get("agg_max_keys", _DEF_MAX_KEYS)),
+                max_card=int(ctx.get("agg_max_card", _DEF_MAX_CARD)),
+                max_measures=int(ctx.get("agg_max_measures", _DEF_MAX_MEASURES)),
+            )
+            if _is_dict(out):
+                return out
+        except Exception:  # noqa: BLE001 — fall through to the inline fallback.
+            pass
+    return _inline_candidates(profile, ctx)
+
+
+def _inline_candidates(profile: dict, ctx: dict) -> dict:
+    """Minimal defensive selection when select_groupby_keys is unavailable."""
+    max_card = int(ctx.get("agg_max_card", _DEF_MAX_CARD))
+    max_keys = int(ctx.get("agg_max_keys", _DEF_MAX_KEYS))
+    max_measures = int(ctx.get("agg_max_measures", _DEF_MAX_MEASURES))
+    keys = profile.get("key_candidates") or []
+    group_keys, measures = [], []
+    for col in profile.get("columns") or []:
+        if not _is_dict(col):
+            continue
+        name = col.get("name")
+        it = col.get("inferred_type")
+        flags = col.get("flags") or []
+        dc = col.get("distinct_count")
+        if it in ("categorical", "boolean") and name not in keys:
+            if ("possible_id" not in flags and "high_cardinality" not in flags
+                    and "constant" not in flags
+                    and isinstance(dc, int) and 2 <= dc <= max_card):
+                group_keys.append({"col": name, "cardinality": dc, "score": 0.0})
+        elif it == "numeric":
+            num = col.get("numeric") or {}
+            if num.get("std") not in (None, 0) and not (
+                    "possible_id" in flags and (col.get("unique_pct") or 0) >= 0.99):
+                measures.append(name)
+    group_keys = group_keys[:max_keys]
+    measures = measures[:max_measures]
+    pivots = []
+    if len(group_keys) >= 2:
+        pivots.append({"index": group_keys[0]["col"],
+                       "columns": group_keys[1]["col"],
+                       "value": measures[0] if measures else None})
+    return {"group_keys": group_keys, "measures": measures, "pivots": pivots,
+            "note": "selección cuantitativa básica"}
+
+
+def _resolve_plan(profile: dict, ctx: dict, candidates: dict) -> dict:
+    """Return {aggregations:[{group_by,measures,why}], pivots:[...], source}."""
+    group_keys = candidates.get("group_keys") or []
+    measures = candidates.get("measures") or []
+
+    if ctx.get("run_agg_llm") and suggest_aggregations_llm is not None:
+        try:
+            plan = suggest_aggregations_llm(
+                profile, candidates,
+                max_aggs=int(ctx.get("agg_max_keys", _DEF_MAX_KEYS)),
+                model=ctx.get("agg_llm_model", "claude-haiku-4-5-20251001"))
+            if _is_dict(plan) and plan.get("aggregations"):
+                return {"aggregations": plan.get("aggregations") or [],
+                        "pivots": plan.get("pivots") or [],
+                        "source": plan.get("source", "llm")}
+        except Exception:  # noqa: BLE001 — fall back to the quantitative plan.
+            pass
+
+    aggregations = [{
+        "group_by": gk.get("col"),
+        "measures": measures,
+        "why": f"categórica de {_fmt_num(gk.get('cardinality'))} niveles",
+    } for gk in group_keys if _is_dict(gk) and gk.get("col")]
+    pivots = []
+    for pv in candidates.get("pivots") or []:
+        if _is_dict(pv) and pv.get("index") and pv.get("columns"):
+            pivots.append({"index": pv.get("index"), "columns": pv.get("columns"),
+                           "value": pv.get("value") or (measures[0] if measures else None),
+                           "agg": "mean", "why": "cruce de dos categóricas"})
+    return {"aggregations": aggregations, "pivots": pivots, "source": "quantitative"}
+
+
+def _live_groupby(ctx: dict, group_by: str, measures: list, top_n: int):
+    """Compute one group-by result live via the push-down registry function."""
+    db_path = ctx.get("db_path")
+    table = ctx.get("table")
+    if not db_path or not table or groupby_stats_duckdb is None:
+        return None
+    try:
+        out = groupby_stats_duckdb(db_path, table, group_by, list(measures or []),
+                                   top_n=top_n)
+        if _is_dict(out) and out.get("status") == "ok":
+            return out
+    except Exception:  # noqa: BLE001
+        return None
+    return None
+
+
+def _live_pivot(ctx: dict, index: str, columns: str, value, agg: str):
+    """Compute one pivot live via the push-down registry function."""
+    db_path = ctx.get("db_path")
+    table = ctx.get("table")
+    if not db_path or not table or pivot_table_duckdb is None or not value:
+        return None
+    try:
+        out = pivot_table_duckdb(db_path, table, index, columns, value,
+                                 agg=agg or "mean")
+        if _is_dict(out) and out.get("status") == "ok":
+            return out
+    except Exception:  # noqa: BLE001
+        return None
+    return None
+
+
+# --------------------------------------------------------------------------- #
+# Figure builders (lazy: matplotlib only imported when the renderer draws them).
+# --------------------------------------------------------------------------- #
+def _make_group_bars(group_by: str, measure: str, groups: list):
+    """Vertical bars: mean of ``measure`` per group, bars from zero."""
+    labels, values = [], []
+    for g in groups:
+        if not _is_dict(g):
+            continue
+        mean = _measure_mean(g, measure)
+        if mean is None:
+            continue
+        labels.append(model._safe_str(g.get("key")))
+        values.append(float(mean))
+    if not labels:
+        return None
+
+    def _draw():
+        import matplotlib
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        fig, ax = plt.subplots(figsize=(6.6, 3.6))
+        xs = list(range(len(labels)))
+        ax.bar(xs, values, color="#4e79a7", alpha=0.9, edgecolor="#2f4d6e",
+               linewidth=0.4)
+        ax.set_xticks(xs)
+        short = [(s[:18] + "…") if len(s) > 19 else s for s in labels]
+        rot = 30 if max((len(s) for s in short), default=0) > 6 else 0
+        ax.set_xticklabels(short, rotation=rot, ha="right" if rot else "center",
+                           fontsize=7)
+        ax.set_ylabel(f"media de {measure}", fontsize=8)
+        ax.set_xlabel(group_by, fontsize=8)
+        ax.set_title(f"Media de «{measure}» por «{group_by}»", fontsize=10)
+        ax.grid(axis="y", color="#dddddd", linewidth=0.6)
+        for spine in ("top", "right"):
+            ax.spines[spine].set_visible(False)
+        # Value labels above each bar.
+        vmax = max(values) if values else 0
+        for x, v in zip(xs, values):
+            ax.text(x, v + (abs(vmax) * 0.01 if vmax else 0.01),
+                    _fmt_num(v, 2), ha="center", va="bottom", fontsize=6.5)
+        fig.tight_layout()
+        return fig
+
+    return _draw
+
+
+def _make_pivot_bars(pivot: dict):
+    """Grouped bars of a pivot: x = row_labels, one series per col_label."""
+    row_labels = pivot.get("row_labels") or []
+    col_labels = pivot.get("col_labels") or []
+    matrix = pivot.get("matrix") or []
+    if not row_labels or not col_labels or not matrix:
+        return None
+
+    def _draw():
+        import matplotlib
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        n_rows = len(row_labels)
+        n_cols = len(col_labels)
+        fig, ax = plt.subplots(figsize=(6.8, 3.8))
+        total_w = 0.8
+        bar_w = total_w / max(n_cols, 1)
+        base = list(range(n_rows))
+        for j, clabel in enumerate(col_labels):
+            offs = [b - total_w / 2 + bar_w * (j + 0.5) for b in base]
+            vals = []
+            for i in range(n_rows):
+                cell = matrix[i][j] if (i < len(matrix) and j < len(matrix[i])) else None
+                vals.append(float(cell) if isinstance(cell, (int, float)) else 0.0)
+            color = _SERIES_COLORS[j % len(_SERIES_COLORS)]
+            ax.bar(offs, vals, width=bar_w, color=color, alpha=0.9,
+                   label=model._safe_str(clabel))
+        ax.set_xticks(base)
+        short = [(s[:16] + "…") if len(s) > 17 else s
+                 for s in (model._safe_str(r) for r in row_labels)]
+        rot = 30 if max((len(s) for s in short), default=0) > 6 else 0
+        ax.set_xticklabels(short, rotation=rot, ha="right" if rot else "center",
+                           fontsize=7)
+        ax.set_xlabel(model._safe_str(pivot.get("index")), fontsize=8)
+        ax.set_ylabel(f"{pivot.get('agg','mean')} de {pivot.get('value')}",
+                      fontsize=8)
+        ax.set_title(f"{pivot.get('index')} × {pivot.get('columns')}", fontsize=10)
+        ax.grid(axis="y", color="#dddddd", linewidth=0.6)
+        ax.legend(title=model._safe_str(pivot.get("columns")), fontsize=6.5,
+                  title_fontsize=7, frameon=True, framealpha=0.9, loc="best")
+        for spine in ("top", "right"):
+            ax.spines[spine].set_visible(False)
+        fig.tight_layout()
+        return fig
+
+    return _draw
+
+
+def _group_bars_maker(group_by: str, measure: str, groups: list):
+    """Bind per-aggregation args so the lazy closure is loop-safe."""
+    def _make():
+        return _make_group_bars(group_by, measure, groups)()
+    return _make
+
+
+def _pivot_bars_maker(pivot: dict):
+    def _make():
+        return _make_pivot_bars(pivot)()
+    return _make
+
+
+# --------------------------------------------------------------------------- #
+# Section builders. Each returns a list of blocks (possibly empty).
+# --------------------------------------------------------------------------- #
+def _groupby_section(group_by: str, measures: list, result: dict, why: str) -> list:
+    """Build the blocks for one group-by aggregation, or [] if unusable."""
+    if not _is_dict(result) or not result.get("groups"):
+        return []
+    groups = [g for g in result.get("groups") or [] if _is_dict(g)]
+    if not groups:
+        return []
+    eff_measures = result.get("measures") or measures or []
+
+    blocks = [model.Heading(text=f"Agrupado por «{group_by}»", level=2)]
+    intro = f"**{why}.** " if why else ""
+    intro += (f"{_fmt_num(result.get('n_groups') or len(groups))} grupos"
+              f"{' (top por tamaño)' if result.get('truncated') else ''}.")
+    blocks.append(model.Markdown(text=intro))
+
+    # Summary table: one row per group, count + mean of every measure.
+    header = ["Grupo", "n"] + [f"{m} (media)" for m in eff_measures]
+    rows = []
+    for g in groups:
+        row = [model._safe_str(g.get("key")), _fmt_num(g.get("n"))]
+        for m in eff_measures:
+            row.append(_fmt_num(_measure_mean(g, m), 2))
+        rows.append(row)
+    blocks.append(model.DataTable(
+        header=header, rows=rows, title=f"Resumen por «{group_by}»",
+        note="Conteo de filas y media de cada medida por grupo."))
+
+    if not eff_measures:
+        return blocks
+
+    # Primary measure: a bar chart + a detail table (mean/median/std/min/max).
+    primary = eff_measures[0]
+    bars = _make_group_bars(group_by, primary, groups)
+    if bars is not None:
+        blocks.append(model.Figure(
+            make=_group_bars_maker(group_by, primary, groups),
+            caption=f"Media de «{primary}» por «{group_by}» (barras desde cero)."))
+
+    det_header = ["Grupo", "n", "media", "mediana", "σ", "mín", "máx"]
+    det_rows = []
+    for g in groups:
+        stats = g.get("stats") if _is_dict(g.get("stats")) else {}
+        ms = stats.get(primary) if _is_dict(stats.get(primary)) else {}
+        det_rows.append([
+            model._safe_str(g.get("key")), _fmt_num(g.get("n")),
+            _fmt_num(ms.get("mean"), 2), _fmt_num(ms.get("median"), 2),
+            _fmt_num(ms.get("std"), 2), _fmt_num(ms.get("min"), 2),
+            _fmt_num(ms.get("max"), 2),
+        ])
+    blocks.append(model.DataTable(
+        header=det_header, rows=det_rows,
+        title=f"Detalle de «{primary}» por «{group_by}»"))
+    return blocks
+
+
+def _pivot_section(pivot_spec: dict, result: dict) -> list:
+    """Build the blocks for one pivot table, or [] if unusable."""
+    if not _is_dict(result) or not result.get("row_labels"):
+        return []
+    row_labels = result.get("row_labels") or []
+    col_labels = result.get("col_labels") or []
+    matrix = result.get("matrix") or []
+    if not row_labels or not col_labels or not matrix:
+        return []
+
+    index = result.get("index") or pivot_spec.get("index")
+    columns = result.get("columns") or pivot_spec.get("columns")
+    value = result.get("value") or pivot_spec.get("value")
+    agg = result.get("agg") or pivot_spec.get("agg") or "mean"
+    why = pivot_spec.get("why") or ""
+
+    blocks = [model.Heading(text=f"Pivot: «{index}» × «{columns}»", level=2)]
+    intro = f"**{why}.** " if why else ""
+    intro += (f"{agg} de «{value}» cruzando «{index}» (filas) y «{columns}» "
+              f"(columnas).")
+    if result.get("truncated_rows") or result.get("truncated_cols"):
+        intro += " Limitado a las filas/columnas más frecuentes."
+    blocks.append(model.Markdown(text=intro))
+
+    header = [model._safe_str(index)] + [model._safe_str(c) for c in col_labels]
+    rows = []
+    for i, rlabel in enumerate(row_labels):
+        row = [model._safe_str(rlabel)]
+        cells = matrix[i] if i < len(matrix) else []
+        for j in range(len(col_labels)):
+            cell = cells[j] if j < len(cells) else None
+            row.append(_fmt_num(cell, 2))
+        rows.append(row)
+    blocks.append(model.DataTable(
+        header=header, rows=rows,
+        title=f"{agg} de «{value}»",
+        note=f"Cada celda es {agg} de «{value}» para esa combinación."))
+
+    fig_pivot = {"row_labels": row_labels, "col_labels": col_labels,
+                 "matrix": matrix, "index": index, "columns": columns,
+                 "value": value, "agg": agg}
+    if _make_pivot_bars(fig_pivot) is not None:
+        blocks.append(model.Figure(
+            make=_pivot_bars_maker(fig_pivot),
+            caption=f"{agg} de «{value}» por «{index}» y «{columns}» "
+                    f"(barras agrupadas)."))
+    return blocks
+
+
+def _insights_section(ctx: dict) -> list:
+    """Optional pre-computed micro-analysis of the aggregations (SHOULD-11.4)."""
+    entries = ctx.get("agg_insights")
+    if not isinstance(entries, list) or not entries:
+        return []
+    blocks = [model.Heading(text="Interpretación de los grupos", level=2)]
+    for e in entries:
+        if not _is_dict(e):
+            continue
+        title = model._safe_str(e.get("title"))
+        text = model._safe_str(e.get("text"))
+        line = (f"**{title}.** " if title else "") + text
+        if line.strip():
+            blocks.append(model.Markdown(text=line))
+    return blocks if len(blocks) > 1 else []
+
+
+# --------------------------------------------------------------------------- #
+# Pre-computed path: ctx['aggregations'] already carries the results.
+# --------------------------------------------------------------------------- #
+def _sections_from_precomputed(agg: dict) -> list:
+    sections = []
+    for entry in agg.get("groupby") or []:
+        if not _is_dict(entry):
+            continue
+        sections += _groupby_section(
+            entry.get("group_by"), entry.get("measures") or [],
+            entry.get("result") or {}, entry.get("why") or "")
+    for entry in agg.get("pivots") or []:
+        if not _is_dict(entry):
+            continue
+        sections += _pivot_section(entry, entry.get("result") or {})
+    return sections
+
+
+# --------------------------------------------------------------------------- #
+# Live path: select keys, pick a plan, compute results via push-down functions.
+# --------------------------------------------------------------------------- #
+def _sections_live(profile: dict, ctx: dict, candidates: dict) -> list:
+    top_n = int(ctx.get("agg_top_n", _DEF_TOP_N))
+    plan = _resolve_plan(profile, ctx, candidates)
+    sections = []
+    for agg in plan.get("aggregations") or []:
+        if not _is_dict(agg) or not agg.get("group_by"):
+            continue
+        result = _live_groupby(ctx, agg.get("group_by"),
+                               agg.get("measures") or [], top_n)
+        if result is not None:
+            sections += _groupby_section(agg.get("group_by"),
+                                         agg.get("measures") or [], result,
+                                         agg.get("why") or "")
+    for pv in plan.get("pivots") or []:
+        if not _is_dict(pv) or not pv.get("index") or not pv.get("columns"):
+            continue
+        result = _live_pivot(ctx, pv.get("index"), pv.get("columns"),
+                             pv.get("value"), pv.get("agg") or "mean")
+        if result is not None:
+            sections += _pivot_section(pv, result)
+    return sections
+
+
+# --------------------------------------------------------------------------- #
+# Entry point.
+# --------------------------------------------------------------------------- #
+def _intro_blocks() -> list:
+    text = (
+        "Este capítulo analiza la tabla **por grupos** (split-apply-combine): "
+        "elige las columnas categóricas más informativas — por su cardinalidad "
+        "y relevancia, no todas contra todas, para no inflar comparaciones "
+        "espurias — y resume las variables numéricas dentro de cada grupo "
+        "(conteo, media, mediana, desviación). Las **tablas dinámicas** (pivot) "
+        "cruzan dos categóricas sobre una medida, y los **gráficos de barras** "
+        "(siempre desde cero) comparan los grupos de un vistazo."
+    )
+    return [model.Heading(text=CHAPTER_TITLE, level=1),
+            model.Markdown(text=text)]
+
+
+def build_agregacion(profile: dict, ctx: dict):
+    """Build the AGREGACION Chapter, or None if the dataset can't be grouped.
+
+    Args:
+        profile: the ``eda`` group TableProfile dict.
+        ctx: presentation context (see module docstring for the keys consumed).
+
+    Returns:
+        A ``model.Chapter`` with per-group stats, pivots and bar charts; or
+        ``None`` when the dataset has no low-cardinality categorical column to
+        group by (the chapter does not apply).
+    """
+    profile = profile or {}
+    ctx = ctx or {}
+    if not isinstance(profile, dict):
+        return None
+
+    # Pre-computed results take precedence (offline / tests / forward-compat).
+    pre = ctx.get("aggregations")
+    if _is_dict(pre) and (pre.get("groupby") or pre.get("pivots")):
+        sections = _sections_from_precomputed(pre)
+        if not sections:
+            return None
+        blocks = _intro_blocks() + sections + _insights_section(ctx)
+        return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                             version=CHAPTER_VERSION, blocks=blocks)
+
+    # Live path: needs at least one categorical key to group by.
+    candidates = _resolve_candidates(profile, ctx)
+    if not _is_dict(candidates) or not (candidates.get("group_keys")):
+        return None  # chapter does not apply: nothing to group by.
+
+    sections = _sections_live(profile, ctx, candidates)
+    if not sections:
+        # Applies (there are categorical keys) but no aggregation data is
+        # reachable: emit an honest note instead of fabricating numbers.
+        keys = ", ".join(model._safe_str((k or {}).get("col"))
+                         for k in candidates.get("group_keys") or []
+                         if _is_dict(k))
+        note = model.Note(
+            "No se pudo calcular la agregación: el capítulo necesita los datos "
+            "crudos. Pasa ctx['db_path'] + ctx['table'] (para el cálculo "
+            "push-down en DuckDB) o ctx['aggregations'] ya precalculado. "
+            f"Columnas categóricas candidatas: {keys or '—'}.")
+        blocks = _intro_blocks() + [note] + _insights_section(ctx)
+        return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                             version=CHAPTER_VERSION, blocks=blocks)
+
+    blocks = _intro_blocks() + sections + _insights_section(ctx)
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

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

@@ -0,0 +1,256 @@
+"""Tests for the AGREGACION chapter — DoD: golden + edges + error/no-cut path.
+
+Self-contained and deterministic: no DuckDB and no LLM. The aggregation results
+are passed pre-computed via ``ctx['aggregations']`` (the same shape the push-down
+registry functions ``groupby_stats_duckdb`` / ``pivot_table_duckdb`` produce), so
+the chapter's rendering logic is exercised without touching disk or the network.
+Live push-down + LLM selection are covered separately by the golden script.
+
+Verifies:
+- Golden: a profile with categoricals + numerics builds a Chapter with per-group
+  stats tables, a pivot table and bar-chart figures, and it renders to PDF AND
+  PPTX showing the group keys, values and pivot — nothing cut.
+- Edges: a dataset with no low-cardinality categorical returns None; an empty
+  profile returns None; a profile that *could* be grouped but has no reachable
+  data degrades to an honest note instead of raising.
+- No-cut: many groups (30) + a long interpretation paragraph survive intact in
+  the rendered PDF (table split by rows, text wrapped whole).
+"""
+
+import os
+import re
+import tempfile
+
+from pptx import Presentation
+from pypdf import PdfReader
+
+from datascience.automatic_eda.chapters.agregacion import build_agregacion
+from datascience.automatic_eda.model import Chapter
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+# --------------------------------------------------------------------------- #
+# Synthetic fixtures.
+# --------------------------------------------------------------------------- #
+def _profile() -> dict:
+    """A titanic-like profile: 2 categoricals + 2 numeric measures + 1 id."""
+    return {
+        "table": "titanic",
+        "source": "/data/titanic.csv",
+        "n_rows": 891,
+        "n_cols": 5,
+        "key_candidates": ["passenger_id"],
+        "columns": [
+            {"name": "passenger_id", "inferred_type": "numeric",
+             "unique_pct": 1.0, "flags": ["possible_id"],
+             "numeric": {"mean": 446.0, "std": 257.0}},
+            {"name": "sex", "inferred_type": "categorical", "distinct_count": 2,
+             "flags": [], "categorical": {"n_distinct": 2, "imbalance": 0.1,
+                                          "top": [{"value": "male", "count": 577}]}},
+            {"name": "pclass", "inferred_type": "categorical", "distinct_count": 3,
+             "flags": [], "categorical": {"n_distinct": 3, "imbalance": 0.2}},
+            {"name": "fare", "inferred_type": "numeric", "flags": [],
+             "numeric": {"mean": 32.2, "std": 49.7, "cv": 1.54}},
+            {"name": "age", "inferred_type": "numeric", "flags": [],
+             "numeric": {"mean": 29.7, "std": 14.5, "cv": 0.49}},
+        ],
+    }
+
+
+def _groupby_result(group_by: str, keys_n: list) -> dict:
+    """A groupby_stats_duckdb-shaped result for `fare` and `age`."""
+    groups = []
+    for i, (key, n) in enumerate(keys_n):
+        groups.append({
+            "key": key, "n": n,
+            "stats": {
+                "fare": {"mean": 20.0 + i * 15, "median": 10.0 + i * 8,
+                         "std": 40.0 + i, "min": 0.0, "max": 512.3},
+                "age": {"mean": 28.0 + i, "median": 27.0 + i, "std": 14.0,
+                        "min": 0.42, "max": 80.0},
+            },
+        })
+    return {"status": "ok", "group_by": group_by, "measures": ["fare", "age"],
+            "aggs": ["count", "mean", "median", "std", "min", "max"],
+            "n_groups": len(groups), "truncated": False, "groups": groups}
+
+
+def _pivot_result() -> dict:
+    return {"status": "ok", "index": "sex", "columns": "pclass", "value": "fare",
+            "agg": "mean", "row_labels": ["male", "female"],
+            "col_labels": ["1", "2", "3"],
+            "matrix": [[62.0, 19.0, 12.0], [110.0, 22.0, 15.0]],
+            "truncated_rows": False, "truncated_cols": False}
+
+
+def _ctx_precomputed() -> dict:
+    return {
+        "aggregations": {
+            "groupby": [
+                {"group_by": "sex", "measures": ["fare", "age"],
+                 "why": "sexo del pasajero",
+                 "result": _groupby_result("sex", [("male", 577), ("female", 314)])},
+                {"group_by": "pclass", "measures": ["fare", "age"],
+                 "why": "clase del billete",
+                 "result": _groupby_result(
+                     "pclass", [("3", 491), ("1", 216), ("2", 184)])},
+            ],
+            "pivots": [
+                {"index": "sex", "columns": "pclass", "value": "fare",
+                 "agg": "mean", "why": "tarifa por sexo y clase",
+                 "result": _pivot_result()},
+            ],
+        },
+        "agg_insights": [
+            {"title": "Tarifa por sexo",
+             "text": "Las mujeres pagaron de media casi el doble que los hombres."},
+        ],
+    }
+
+
+def _pdf_text(path: str) -> str:
+    txt = "".join((pg.extract_text() or "") for pg in PdfReader(path).pages)
+    return re.sub(r"\s+", " ", txt)
+
+
+def _pptx_text(path: str) -> str:
+    prs = Presentation(path)
+    parts = []
+    for sl in prs.slides:
+        for sh in sl.shapes:
+            if sh.has_text_frame:
+                parts.append(sh.text_frame.text)
+            if sh.has_table:
+                tb = sh.table
+                for r in range(len(tb.rows)):
+                    for c in range(len(tb.columns)):
+                        parts.append(tb.cell(r, c).text)
+    return re.sub(r"\s+", " ", " ".join(parts))
+
+
+# --------------------------------------------------------------------------- #
+# Golden: builds a Chapter and renders to both formats.
+# --------------------------------------------------------------------------- #
+def test_golden_chapter_blocks_present():
+    ch = build_agregacion(_profile(), _ctx_precomputed())
+    assert isinstance(ch, Chapter)
+    assert ch.id == "agregacion"
+    kinds = [b.kind for b in ch.blocks]
+    assert "heading" in kinds
+    assert kinds.count("data_table") >= 3   # 2 group summaries + pivot (+details)
+    assert "figure" in kinds                 # at least one bar chart.
+    # Headings mention the group keys and the pivot.
+    htext = " ".join(b.text for b in ch.blocks if b.kind == "heading")
+    assert "sex" in htext and "pclass" in htext and "Pivot" in htext
+
+
+def test_golden_render_pdf():
+    ch = build_agregacion(_profile(), _ctx_precomputed())
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "agg.pdf")
+        res = render_automatic_eda_pdf([ch], out, {"write_manifest": False})
+        assert res["path"] == out and os.path.exists(out)
+        assert res["n_pages"] >= 1
+        txt = _pdf_text(out)
+        assert "Agregación por grupos" in txt
+        assert "male" in txt and "female" in txt        # group + pivot labels.
+        assert "Pivot" in txt
+        assert "mediana" in txt                           # per-measure detail.
+        assert "casi el doble" in txt                     # interpretation kept.
+
+
+def test_golden_render_pptx():
+    ch = build_agregacion(_profile(), _ctx_precomputed())
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "agg.pptx")
+        res = render_automatic_eda_pptx([ch], out, {"write_manifest": False})
+        assert res["path"] == out and os.path.exists(out)
+        assert res["n_slides"] >= 1
+        txt = _pptx_text(out)
+        assert "male" in txt and "pclass" in txt
+        assert "Pivot" in txt or "sex" in txt
+
+
+# --------------------------------------------------------------------------- #
+# Edges.
+# --------------------------------------------------------------------------- #
+def test_edge_no_categorical_returns_none():
+    # Only numerics + an id: nothing to group by -> chapter does not apply.
+    prof = {
+        "table": "t", "n_rows": 100, "key_candidates": ["id"],
+        "columns": [
+            {"name": "id", "inferred_type": "numeric", "unique_pct": 1.0,
+             "flags": ["possible_id"], "numeric": {"std": 10.0}},
+            {"name": "x", "inferred_type": "numeric", "flags": [],
+             "numeric": {"mean": 1.0, "std": 2.0}},
+        ],
+    }
+    assert build_agregacion(prof, {}) is None
+
+
+def test_edge_empty_profile_returns_none():
+    assert build_agregacion({}, {}) is None
+    assert build_agregacion(None, None) is None
+
+
+def test_edge_high_cardinality_only_returns_none():
+    # The single categorical is id-like (high cardinality) -> not groupable.
+    prof = {
+        "table": "t", "n_rows": 100, "key_candidates": ["uuid"],
+        "columns": [
+            {"name": "uuid", "inferred_type": "categorical", "distinct_count": 100,
+             "flags": ["high_cardinality", "possible_id"]},
+            {"name": "x", "inferred_type": "numeric", "flags": [],
+             "numeric": {"mean": 1.0, "std": 2.0}},
+        ],
+    }
+    assert build_agregacion(prof, {}) is None
+
+
+def test_live_without_data_degrades_to_note():
+    # Has a categorical to group by but no db_path / no precomputed results:
+    # must NOT raise and must emit an honest note (chapter still applies).
+    prof = {
+        "table": "t", "n_rows": 100, "key_candidates": [],
+        "columns": [
+            {"name": "grp", "inferred_type": "categorical", "distinct_count": 3,
+             "flags": [], "categorical": {"n_distinct": 3}},
+            {"name": "v", "inferred_type": "numeric", "flags": [],
+             "numeric": {"mean": 1.0, "std": 2.0}},
+        ],
+    }
+    ch = build_agregacion(prof, {})
+    assert isinstance(ch, Chapter)
+    notes = [b.text for b in ch.blocks if b.kind == "note"]
+    assert any("datos crudos" in n for n in notes)
+
+
+# --------------------------------------------------------------------------- #
+# No-cut: many groups + long text survive intact in the PDF.
+# --------------------------------------------------------------------------- #
+def test_anti_corte_muchos_grupos_y_texto_largo():
+    keys_n = [(f"grupo_{i:02d}", 30 - (i % 5)) for i in range(30)]
+    long_text = " ".join(f"palabra{i}" for i in range(120))
+    ctx = {
+        "aggregations": {
+            "groupby": [
+                {"group_by": "cat", "measures": ["fare"], "why": "muchos niveles",
+                 "result": _groupby_result("cat", keys_n)},
+            ],
+            "pivots": [],
+        },
+        "agg_insights": [{"title": "Nota larga", "text": long_text}],
+    }
+    ch = build_agregacion(_profile(), ctx)
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "big.pdf")
+        res = render_automatic_eda_pdf([ch], out, {"write_manifest": False})
+        assert res["path"] == out
+        assert res["n_pages"] > 1  # 30-row table + figure spill across pages.
+        txt = _pdf_text(out)
+        # First and last group labels both survive (table not truncated).
+        assert "grupo_00" in txt and "grupo_29" in txt
+        # First, middle and last words of the long paragraph all present.
+        for i in (0, 60, 119):
+            assert f"palabra{i}" in txt

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

@@ -0,0 +1,221 @@
+"""LLM analysis chapter (ANÁLISIS LLM) — the interpretive layer, next to overview.
+
+Third reference chapter for AutomaticEDA. Renders the ``llm`` block that the
+``eda`` group function ``eda_llm_insights`` already produced and stored in the
+``TableProfile`` — it does NOT call the LLM nor recompute anything. The block is
+turned into clean, markdown-style document blocks so it reads as a real chapter
+(table summary, row meaning, data dictionary, suggested analyses, cleaning
+suggestions, PII findings) and, crucially, **nothing is ever cut** in PDF or
+PPTX:
+
+* Prose (summary, row meaning) → ``Markdown`` blocks the renderers wrap to whole
+  lines, so no word is lost no matter how long the text is.
+* The data dictionary and PII findings → ``DataTable`` blocks the paginator
+  splits by rows (repeating the header) and whose long cells wrap inside their
+  column — wide, multi-row tables never overflow a page/slide.
+* Cleaning suggestions and suggested analyses → ``Markdown`` bullet lists; each
+  item is a whole line the renderer wraps, never truncated mid-entry.
+
+Position: this chapter is declared in ``chapters_registry.CHAPTER_ORDER`` right
+after ``overview`` so the interpretation sits next to the table preview, as the
+user asked ("va junto al overview").
+
+Data source: the ``llm`` dict produced by ``eda_llm_insights`` (group ``eda``),
+read from ``profile['llm']`` (or ``ctx['llm']`` as a fallback). Shape::
+
+    {
+      "summary": str,            # what the table is, 2-3 sentences
+      "row_meaning": str,        # what one row represents / granularity
+      "dictionary": [ {"column","description","business_meaning","unit"} ],
+      "pii": [ {"column","kind","severity"} ],
+      "cleaning": [str],         # cleaning / transformation suggestions
+      "analyses": [str],         # suggested questions / analyses / hypotheses
+    }
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+Reads everything defensively (``.get``) and NEVER raises; returns ``None`` when
+the profile carries no LLM block (e.g. ``profile_table`` ran without
+``run_llm``), so the chapter is simply omitted from the document.
+"""
+
+from __future__ import annotations
+
+from .. import model
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "analisis_llm"
+CHAPTER_TITLE = "Análisis LLM"
+
+# Key under which eda_llm_insights stores its interpretive block in the profile.
+LLM_KEY = "llm"
+
+
+def _clean_text(value) -> str:
+    """Coerce a value to a single trimmed line (collapse inner newlines).
+
+    Used for bullet items so each suggestion stays a single markdown bullet the
+    renderer wraps; never drops content, only normalizes whitespace.
+    """
+    text = model._safe_str(value).strip()
+    if not text:
+        return ""
+    return " ".join(text.split())
+
+
+def _para(value) -> str:
+    """Coerce a value to trimmed prose, preserving paragraph breaks."""
+    text = model._safe_str(value).strip()
+    if not text:
+        return ""
+    # Keep blank-line paragraph breaks; collapse runs of spaces/tabs per line.
+    lines = [" ".join(ln.split()) for ln in text.splitlines()]
+    out: list = []
+    for ln in lines:
+        if ln or (out and out[-1] != ""):
+            out.append(ln)
+    return "\n".join(out).strip()
+
+
+def _bullets(items) -> str:
+    """Build a markdown bullet list from a sequence of strings.
+
+    Each item becomes one ``- ...`` line (a whole, wrappable unit). Empty items
+    and non-list inputs are handled gracefully; returns "" when there is nothing.
+    """
+    if isinstance(items, str):
+        items = [items]
+    if not isinstance(items, (list, tuple)):
+        return ""
+    lines = []
+    for it in items:
+        text = _clean_text(it)
+        if text:
+            lines.append(f"- {text}")
+    return "\n".join(lines)
+
+
+def _summary_blocks(llm: dict) -> list:
+    """Heading + prose for the table summary, or [] if absent."""
+    text = _para(llm.get("summary"))
+    if not text:
+        return []
+    return [model.Heading(text="Resumen de la tabla", level=2),
+            model.Markdown(text=text)]
+
+
+def _row_meaning_blocks(llm: dict) -> list:
+    """Heading + prose for what one row represents, or [] if absent."""
+    text = _para(llm.get("row_meaning"))
+    if not text:
+        return []
+    return [model.Heading(text="Significado de una fila", level=2),
+            model.Markdown(text=text)]
+
+
+def _dictionary_block(llm: dict):
+    """DataTable for the data dictionary, or None if absent/empty.
+
+    Columns: Columna / Descripción / Significado de negocio / Unidad. The
+    paginator splits this by rows repeating the header and wraps long cells, so a
+    long dictionary (many columns) never gets cut.
+    """
+    entries = llm.get("dictionary")
+    if not isinstance(entries, (list, tuple)) or not entries:
+        return None
+    header = ["Columna", "Descripción", "Significado de negocio", "Unidad"]
+    rows = []
+    for e in entries:
+        if not isinstance(e, dict):
+            # Be tolerant: a bare string still shows up as a description row.
+            rows.append(["—", _clean_text(e), "", ""])
+            continue
+        rows.append([
+            _clean_text(e.get("column")) or "—",
+            _clean_text(e.get("description")),
+            _clean_text(e.get("business_meaning")),
+            _clean_text(e.get("unit")),
+        ])
+    if not rows:
+        return None
+    return model.DataTable(header=header, rows=rows, title="Diccionario de datos")
+
+
+def _analyses_blocks(llm: dict) -> list:
+    """Heading + bullet list of suggested analyses, or [] if absent."""
+    bullets = _bullets(llm.get("analyses"))
+    if not bullets:
+        return []
+    return [model.Heading(text="Análisis sugeridos", level=2),
+            model.Markdown(text=bullets)]
+
+
+def _cleaning_blocks(llm: dict) -> list:
+    """Heading + bullet list of cleaning suggestions, or [] if absent."""
+    bullets = _bullets(llm.get("cleaning"))
+    if not bullets:
+        return []
+    return [model.Heading(text="Limpieza sugerida", level=2),
+            model.Markdown(text=bullets)]
+
+
+def _pii_block(llm: dict):
+    """DataTable for PII/GDPR findings, or None if absent/empty."""
+    entries = llm.get("pii")
+    if not isinstance(entries, (list, tuple)) or not entries:
+        return None
+    header = ["Columna", "Tipo", "Severidad"]
+    rows = []
+    for e in entries:
+        if not isinstance(e, dict):
+            continue
+        rows.append([
+            _clean_text(e.get("column")) or "—",
+            _clean_text(e.get("kind")),
+            _clean_text(e.get("severity")),
+        ])
+    if not rows:
+        return None
+    return model.DataTable(
+        header=header, rows=rows, title="Datos personales (PII / RGPD)",
+        note="detección automática orientativa — revisar antes de tratar los datos")
+
+
+def build_analisis_llm(profile: dict, ctx: dict):
+    """Build the LLM analysis Chapter, or None if there is no LLM block.
+
+    Consumes ``profile['llm']`` (the block produced by ``eda_llm_insights``,
+    group ``eda``); falls back to ``ctx['llm']``. Returns ``None`` when no LLM
+    block is present or it carries no usable content, so the chapter is omitted
+    rather than rendering an empty section.
+    """
+    profile = profile or {}
+    ctx = ctx or {}
+
+    llm = profile.get(LLM_KEY)
+    if not isinstance(llm, dict):
+        llm = ctx.get(LLM_KEY)
+    if not isinstance(llm, dict) or not llm:
+        return None
+
+    blocks: list = []
+    blocks += _summary_blocks(llm)
+    blocks += _row_meaning_blocks(llm)
+
+    dict_block = _dictionary_block(llm)
+    if dict_block is not None:
+        blocks.append(model.Heading(text="Diccionario de datos", level=2))
+        blocks.append(dict_block)
+
+    blocks += _analyses_blocks(llm)
+    blocks += _cleaning_blocks(llm)
+
+    pii_block = _pii_block(llm)
+    if pii_block is not None:
+        blocks.append(model.Heading(text="Datos personales (PII / RGPD)", level=2))
+        blocks.append(pii_block)
+
+    if not blocks:
+        return None  # LLM block present but every field empty → omit chapter.
+
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

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

@@ -0,0 +1,190 @@
+"""Tests for the ANÁLISIS LLM chapter — DoD: golden + edges + anti-cut.
+
+Self-contained: builds a synthetic TableProfile carrying an ``llm`` block (the
+shape ``eda_llm_insights`` produces) so the suite is fast and deterministic — no
+DuckDB and no LLM call. Verifies:
+
+* golden — ``build_analisis_llm`` yields the chapter and the full document
+  renders to PDF *and* PPTX with the summary, a suggested analysis, a cleaning
+  suggestion and a dictionary column all present;
+* order — the chapter sits immediately after ``overview`` (user requirement);
+* edges — a profile with no ``llm`` block (or None/empty/malformed) returns
+  ``None`` and never raises;
+* anti-cut — a long dictionary (40 rows) and a 150-char cleaning suggestion are
+  rendered to PDF and PPTX without losing a single row or word.
+"""
+
+import os
+import re
+import tempfile
+
+from pypdf import PdfReader
+from pptx import Presentation
+
+from datascience.automatic_eda.chapters.analisis_llm import (
+    build_analisis_llm, CHAPTER_VERSION)
+from datascience.automatic_eda.chapters_registry import build_document
+from datascience.automatic_eda.model import Chapter, DataTable
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+def _profile() -> dict:
+    return {
+        "table": "ventas",
+        "source": "/data/ventas.csv",
+        "profiled_at": "2026-06-30T10:00:00+00:00",
+        "n_rows": 1000,
+        "n_cols": 2,
+        "quality_score": 92.5,
+        "columns": [
+            {"name": "precio", "inferred_type": "numeric", "null_pct": 0.0,
+             "null_count": 0,
+             "numeric": {"mean": 42.5, "median": 40.0, "min": 1.0,
+                         "max": 100.0, "std": 12.3}},
+            {"name": "categoria", "inferred_type": "categorical",
+             "null_pct": 0.0, "null_count": 0,
+             "categorical": {"top": [{"value": "neumaticos", "count": 500}]}},
+        ],
+        "llm": {
+            "summary": "Tabla de ventas por producto. Token SUMMARYTOKEN.",
+            "row_meaning": "Cada fila es una venta. Token ROWTOKEN.",
+            "dictionary": [
+                {"column": "precio", "description": "Precio unitario DESCTOKEN",
+                 "business_meaning": "Ingreso por unidad", "unit": "EUR"},
+                {"column": "categoria", "description": "Familia de producto",
+                 "business_meaning": "Segmento comercial", "unit": ""},
+            ],
+            "pii": [{"column": "categoria", "kind": "ninguno", "severity": "low"}],
+            "cleaning": ["Quitar nulos de precio CLEANTOKEN",
+                         "Normalizar mayusculas en categoria"],
+            "analyses": ["Estudiar relacion precio-categoria ANALYSISTOKEN",
+                         "Detectar outliers de precio"],
+        },
+    }
+
+
+def _pdf_text(path: str) -> str:
+    txt = "".join((pg.extract_text() or "") for pg in PdfReader(path).pages)
+    return re.sub(r"\s+", " ", txt)
+
+
+def _pptx_text(path: str) -> str:
+    prs = Presentation(path)
+    parts = []
+    for sl in prs.slides:
+        for sh in sl.shapes:
+            if sh.has_text_frame:
+                parts.append(sh.text_frame.text)
+            if sh.has_table:
+                tb = sh.table
+                for r in range(len(tb.rows)):
+                    for c in range(len(tb.columns)):
+                        parts.append(tb.cell(r, c).text)
+    return re.sub(r"\s+", " ", " ".join(parts))
+
+
+def test_golden_build_y_render_pdf_pptx():
+    prof = _profile()
+    ch = build_analisis_llm(prof, {})
+    assert ch is not None
+    assert ch.id == "analisis_llm"
+    assert ch.version == CHAPTER_VERSION
+    assert ch.blocks  # non-empty.
+
+    with tempfile.TemporaryDirectory() as d:
+        out_pdf = os.path.join(d, "eda.pdf")
+        res = render_automatic_eda_pdf(prof, out_pdf, {"title": "EDA — ventas"})
+        assert res["path"] == out_pdf and os.path.exists(out_pdf)
+        ids = [c["id"] for c in res["chapters"]]
+        assert "analisis_llm" in ids
+        txt = _pdf_text(out_pdf)
+        # The user's required content: summary, suggested analyses, cleaning.
+        assert "SUMMARYTOKEN" in txt
+        assert "ANALYSISTOKEN" in txt
+        assert "CLEANTOKEN" in txt
+        assert "DESCTOKEN" in txt  # data dictionary cell.
+
+        out_pptx = os.path.join(d, "eda.pptx")
+        res2 = render_automatic_eda_pptx(prof, out_pptx, {"title": "EDA — ventas"})
+        assert res2["path"] == out_pptx and os.path.exists(out_pptx)
+        ids2 = [c["id"] for c in res2["chapters"]]
+        assert "analisis_llm" in ids2
+        ptx = _pptx_text(out_pptx)
+        assert "SUMMARYTOKEN" in ptx
+        assert "ANALYSISTOKEN" in ptx
+        assert "CLEANTOKEN" in ptx
+        assert "DESCTOKEN" in ptx
+
+
+def test_orden_capitulo_junto_a_overview():
+    chapters = build_document(_profile(), {})
+    ids = [c.id for c in chapters]
+    assert "overview" in ids and "analisis_llm" in ids
+    # User requirement: the LLM chapter sits right after overview.
+    assert ids.index("analisis_llm") == ids.index("overview") + 1
+
+
+def test_edge_sin_llm_devuelve_none():
+    # No llm block at all.
+    prof = {k: v for k, v in _profile().items() if k != "llm"}
+    assert build_analisis_llm(prof, {}) is None
+    # None / empty / malformed never raise and yield None.
+    assert build_analisis_llm(None, None) is None
+    assert build_analisis_llm({}, {}) is None
+    assert build_analisis_llm({"llm": {}}, {}) is None
+    assert build_analisis_llm({"llm": "not-a-dict"}, {}) is None
+    # All-empty fields → omitted (no blocks).
+    empty = {"llm": {"summary": "", "dictionary": [], "cleaning": [],
+                     "analyses": [], "pii": [], "row_meaning": ""}}
+    assert build_analisis_llm(empty, {}) is None
+
+
+def test_edge_llm_via_ctx_fallback():
+    # The block may arrive in ctx instead of the profile.
+    prof = {k: v for k, v in _profile().items() if k != "llm"}
+    ctx = {"llm": {"summary": "Resumen via ctx CTXTOKEN."}}
+    ch = build_analisis_llm(prof, ctx)
+    assert ch is not None and ch.id == "analisis_llm"
+
+
+def test_anti_cortes_diccionario_largo_y_limpieza_larga():
+    long_clean = ("Lorem ipsum dolor sit amet consectetur adipiscing elit sed do "
+                  "eiusmod tempor incididunt ut labore et dolore magna aliqua "
+                  "reprehenderit voluptate velit esse cillum dolore")
+    dictionary = [
+        {"column": f"col_{i}",
+         "description": f"Descripcion larga numero {i} con bastante texto para "
+                        f"forzar el wrap dentro de la celda fila{i}",
+         "business_meaning": f"Significado de negocio {i}", "unit": "u"}
+        for i in range(40)
+    ]
+    prof = {
+        "table": "t", "n_rows": 1, "n_cols": 1, "columns": [],
+        "llm": {"summary": "S", "dictionary": dictionary,
+                "cleaning": [long_clean], "analyses": ["A"]},
+    }
+    ch = build_analisis_llm(prof, {})
+    assert ch is not None
+    # Structure: the dictionary DataTable keeps ALL 40 rows — none dropped on
+    # construction (the renderers then split it by rows, repeating the header).
+    dts = [b for b in ch.blocks if isinstance(b, DataTable)]
+    assert any(len(dt.rows) == 40 for dt in dts)
+
+    with tempfile.TemporaryDirectory() as d:
+        out_pdf = os.path.join(d, "x.pdf")
+        render_automatic_eda_pdf([ch], out_pdf, {"write_manifest": False})
+        # 40 wide rows + a long cleaning line cannot fit one page → it spills,
+        # which is exactly the no-cut behaviour (paginate, never truncate).
+        assert len(PdfReader(out_pdf).pages) > 1
+        txt = _pdf_text(out_pdf)
+        # The long cleaning suggestion is wrapped word-by-word, not truncated.
+        for word in ("Lorem", "incididunt", "reprehenderit", "voluptate", "cillum"):
+            assert word in txt
+
+        out_pptx = os.path.join(d, "x.pptx")
+        res2 = render_automatic_eda_pptx([ch], out_pptx, {"write_manifest": False})
+        assert res2["n_slides"] > 1  # table + long text spill across slides.
+        ptx = _pptx_text(out_pptx)
+        for word in ("Lorem", "reprehenderit", "voluptate"):
+            assert word in ptx

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

@@ -0,0 +1,402 @@
+"""Categorical distributions chapter (CAT DISTR).
+
+Third reference chapter for AutomaticEDA. For every categorical column it shows,
+fulfilling the user's request:
+
+1. A short opening explanation of **Shannon entropy** (what it measures, its 0
+   and log2(k) bounds, the normalized 0–1 version) and the dataset row total used
+   as a comparison baseline.
+2. Per column, a cardinality key/value table: distinct values, ``% distinct``
+   (distinct / total rows), total dataset rows, singleton values (frequency 1),
+   entropy with its theoretical maximum and the normalized ratio, mode, imbalance
+   and string-length stats.
+3. A short note flagging problematic cardinality (id-like ≈100% distinct, or a
+   single dominating category).
+4. A ``top-k`` table (value / count / %).
+5. A **donut pie chart** of the most common categories (top-k + an "Otros"
+   bucket), drawn lazily so the renderers scale it to fit entirely.
+
+Data comes from the ``eda`` group: each ``columns[i]['categorical']`` is the
+output of ``summarize_categorical`` (``top[{value,count,pct}]``, ``mode``,
+``n_distinct``, ``entropy``, ``imbalance``, ``len_min/mean/max``). The derived
+cardinality metrics and the pie figure are delegated to two registry functions
+(``categorical_cardinality_block`` and ``categorical_top_pie_figure``); both are
+imported lazily and degrade to a minimal inline fallback so this chapter never
+raises even if they are unavailable.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+"""
+
+from __future__ import annotations
+
+import math
+
+from .. import model
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "cat_distr"
+CHAPTER_TITLE = "Distribuciones categóricas"
+
+# Cap the number of categorical columns rendered to keep the document bounded;
+# the rest are summarized in a closing note (no silent truncation).
+MAX_COLS = 40
+# Rows shown in each top-k table and explicit slices in the pie.
+TOP_TABLE_ROWS = 15
+PIE_TOP_K = 6
+# Truncate very long category labels in tables (the renderer also wraps).
+LABEL_MAX = 48
+
+
+def _fmt_int(value) -> str:
+    if value is None:
+        return "—"
+    try:
+        return f"{int(value):,}".replace(",", ".")
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def _fmt_num(value, decimals: int = 3) -> str:
+    if value is None:
+        return "—"
+    if isinstance(value, bool):
+        return str(value)
+    if isinstance(value, int):
+        return f"{value:,}".replace(",", ".")
+    if isinstance(value, float):
+        if value != value:  # NaN
+            return "NaN"
+        if value in (float("inf"), float("-inf")):
+            return str(value)
+        text = f"{value:.{decimals}f}".rstrip("0").rstrip(".")
+        return text if text else "0"
+    return str(value)
+
+
+def _fmt_pct_value(value, decimals: int = 1) -> str:
+    """Format an already-in-percent value (0–100). None -> placeholder."""
+    if value is None:
+        return "—"
+    try:
+        return f"{float(value):.{decimals}f}%"
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def _pct_from_maybe_fraction(value, decimals: int = 1) -> str:
+    """Format a percentage that may arrive as a 0–1 fraction or a 0–100 number."""
+    if value is None:
+        return "—"
+    try:
+        v = float(value)
+    except (TypeError, ValueError):
+        return str(value)
+    if v <= 1.0:
+        v *= 100.0
+    return f"{v:.{decimals}f}%"
+
+
+def _truncate(text: str, limit: int = LABEL_MAX) -> str:
+    s = model._safe_str(text)
+    if len(s) <= limit:
+        return s
+    return s[: max(1, limit - 1)].rstrip() + "…"
+
+
+def _is_categorical(col: dict) -> bool:
+    """A column is treated as categorical when it carries a non-empty top list
+    and is not a pure numeric column (numeric columns may still expose a top)."""
+    if not isinstance(col, dict):
+        return False
+    cat = col.get("categorical")
+    if not (isinstance(cat, dict) and cat.get("top")):
+        return False
+    if col.get("inferred_type") == "numeric":
+        return False
+    return True
+
+
+def _cardinality(cat: dict, n_rows) -> dict:
+    """Derive cardinality metrics for a column, via the registry function when
+    available, otherwise a minimal inline fallback. Never raises."""
+    try:
+        from datascience.categorical_cardinality_block import (
+            categorical_cardinality_block,
+        )
+
+        out = categorical_cardinality_block(cat=cat, n_rows=n_rows)
+        if isinstance(out, dict):
+            return out
+    except Exception:  # noqa: BLE001 — fall back to the inline derivation.
+        pass
+    return _fallback_cardinality(cat, n_rows)
+
+
+def _fallback_cardinality(cat: dict, n_rows) -> dict:
+    cat = cat or {}
+    top = cat.get("top") or []
+    n_distinct = cat.get("n_distinct")
+    entropy = cat.get("entropy")
+    try:
+        nr = int(n_rows) if n_rows is not None else None
+    except (TypeError, ValueError):
+        nr = None
+    pct_distinct = None
+    if isinstance(n_distinct, (int, float)) and nr:
+        pct_distinct = float(n_distinct) / nr * 100.0
+    entropy_max = None
+    if isinstance(n_distinct, (int, float)):
+        entropy_max = math.log2(n_distinct) if n_distinct > 1 else 0.0
+    entropy_norm = None
+    if isinstance(entropy, (int, float)) and entropy_max:
+        entropy_norm = max(0.0, min(1.0, float(entropy) / entropy_max))
+    mode_pct = cat.get("mode_pct")
+    if mode_pct is None and top and isinstance(top[0], dict):
+        mode_pct = top[0].get("pct")
+    # Normalize to a 0–100 scale: summarize_categorical emits a 0–1 fraction.
+    if isinstance(mode_pct, (int, float)) and not isinstance(mode_pct, bool):
+        mode_pct = float(mode_pct) * 100.0 if mode_pct <= 1.0 else float(mode_pct)
+    else:
+        mode_pct = None
+    n_singletons = None
+    if top:
+        n_singletons = sum(
+            1 for t in top if isinstance(t, dict) and t.get("count") == 1)
+    return {
+        "n_distinct": n_distinct,
+        "n_rows": nr,
+        "pct_distinct": pct_distinct,
+        "entropy": entropy,
+        "entropy_max": entropy_max,
+        "entropy_norm": entropy_norm,
+        "mode": cat.get("mode"),
+        "mode_pct": mode_pct,
+        "imbalance": cat.get("imbalance"),
+        "n_singletons": n_singletons,
+        "n_singletons_partial": (
+            isinstance(n_distinct, (int, float)) and n_distinct > len(top)),
+        "len_min": cat.get("len_min"),
+        "len_mean": cat.get("len_mean"),
+        "len_max": cat.get("len_max"),
+        "id_like": pct_distinct is not None and pct_distinct >= 99.0,
+        "dominated": mode_pct is not None and mode_pct >= 90.0,
+    }
+
+
+def _pie_make(top, n_distinct, title, n_rows):
+    """Return a zero-arg callable that builds the donut figure lazily."""
+
+    def make():
+        try:
+            from datascience.categorical_top_pie_figure import (
+                categorical_top_pie_figure,
+            )
+
+            return categorical_top_pie_figure(
+                top=top, n_distinct=n_distinct or 0, title=title,
+                top_k=PIE_TOP_K, n_rows=n_rows)
+        except Exception:  # noqa: BLE001 — minimal local fallback figure.
+            return _fallback_pie(top, title)
+
+    return make
+
+
+def _fallback_pie(top, title):
+    """Minimal donut figure used only if the registry function is unavailable."""
+    import matplotlib
+
+    matplotlib.use("Agg")
+    from matplotlib.figure import Figure
+
+    fig = Figure(figsize=(5.0, 3.2))
+    ax = fig.add_subplot(111)
+    items = [t for t in (top or [])
+             if isinstance(t, dict) and isinstance(t.get("count"), (int, float))]
+    items = sorted(items, key=lambda t: t.get("count") or 0, reverse=True)
+    head = items[:PIE_TOP_K]
+    rest = items[PIE_TOP_K:]
+    labels = [_truncate(t.get("value"), 20) for t in head]
+    sizes = [float(t.get("count") or 0) for t in head]
+    if rest:
+        labels.append(f"Otros ({len(rest)})")
+        sizes.append(sum(float(t.get("count") or 0) for t in rest))
+    if not sizes or sum(sizes) <= 0:
+        ax.text(0.5, 0.5, "sin datos categóricos", ha="center", va="center")
+        ax.axis("off")
+        return fig
+    ax.pie(sizes, labels=None, wedgeprops={"width": 0.42},
+           autopct=lambda p: f"{p:.0f}%" if p >= 4 else "")
+    ax.legend(labels, loc="center left", bbox_to_anchor=(1.0, 0.5),
+              fontsize=7, frameon=False)
+    ax.set_title(_truncate(title, 40))
+    fig.tight_layout()
+    return fig
+
+
+def _normalize_card(card: dict) -> dict:
+    """Make the cardinality dict robust regardless of the upstream scale.
+
+    ``summarize_categorical`` emits ``mode_pct`` as a 0–1 fraction; bring it to a
+    0–100 scale and recompute the ``dominated`` flag here so the chapter is
+    correct whether it consumed the registry function or the inline fallback.
+    """
+    card = dict(card or {})
+    mp = card.get("mode_pct")
+    if isinstance(mp, (int, float)) and not isinstance(mp, bool):
+        mp = float(mp) * 100.0 if mp <= 1.0 else float(mp)
+    else:
+        mp = None
+    card["mode_pct"] = mp
+    card["dominated"] = mp is not None and mp >= 90.0
+    pd = card.get("pct_distinct")
+    card["id_like"] = isinstance(pd, (int, float)) and pd >= 99.0
+    return card
+
+
+def _cardinality_block(card: dict):
+    """KVTable with the cardinality / entropy metrics for one column."""
+    n_singletons = card.get("n_singletons")
+    if n_singletons is not None and card.get("n_singletons_partial"):
+        singletons = f"≥{_fmt_int(n_singletons)} (en top mostrado)"
+    elif n_singletons is not None:
+        singletons = _fmt_int(n_singletons)
+    else:
+        singletons = "—"
+
+    entropy_ref = _fmt_num(card.get("entropy"))
+    emax = card.get("entropy_max")
+    if emax is not None:
+        entropy_ref = f"{entropy_ref} (máx {_fmt_num(emax)})"
+
+    mode = card.get("mode")
+    mode_pct = card.get("mode_pct")
+    mode_str = "—" if mode is None else model._safe_str(mode)
+    if mode is not None and mode_pct is not None:
+        mode_str = f"{mode_str} ({_fmt_pct_value(mode_pct)})"
+
+    rows = [
+        ("Valores distintos", _fmt_int(card.get("n_distinct"))),
+        ("% distintos", _fmt_pct_value(card.get("pct_distinct"))),
+        ("Total filas (dataset)", _fmt_int(card.get("n_rows"))),
+        ("Valores únicos (frecuencia 1)", singletons),
+        ("Entropía (bits)", entropy_ref),
+        ("Entropía normalizada (0–1)", _fmt_num(card.get("entropy_norm"))),
+        ("Moda", mode_str),
+    ]
+    imbalance = card.get("imbalance")
+    if imbalance is not None:
+        rows.append(("Desbalance", _fmt_num(imbalance)))
+    lm = card.get("len_min")
+    lmean = card.get("len_mean")
+    lmax = card.get("len_max")
+    if any(v is not None for v in (lm, lmean, lmax)):
+        rows.append((
+            "Longitud (mín/media/máx)",
+            f"{_fmt_num(lm)} / {_fmt_num(lmean)} / {_fmt_num(lmax)}"))
+    return model.KVTable(rows=rows, title="Cardinalidad")
+
+
+def _flag_note(card: dict):
+    """Return a Note flagging problematic cardinality, or None."""
+    if card.get("id_like"):
+        return model.Note(
+            "Casi todos los valores son distintos (≈100% distintos): la columna "
+            "se comporta como un identificador y aporta poco para agrupar o "
+            "comparar categorías.")
+    if card.get("dominated"):
+        mp = card.get("mode_pct")
+        mp_str = _fmt_pct_value(mp) if mp is not None else "muy alta"
+        return model.Note(
+            f"Una sola categoría domina la columna (moda {mp_str}): la "
+            "distribución está muy desbalanceada.")
+    return None
+
+
+def _topk_table(cat: dict):
+    """DataTable value / count / % for the top categories."""
+    top = cat.get("top") or []
+    n_distinct = cat.get("n_distinct")
+    header = ["Valor", "Conteo", "%"]
+    rows = []
+    for t in top[:TOP_TABLE_ROWS]:
+        if not isinstance(t, dict):
+            continue
+        rows.append([
+            model._safe_str(t.get("value")),
+            _fmt_int(t.get("count")),
+            _pct_from_maybe_fraction(t.get("pct")),
+        ])
+    if not rows:
+        return None
+    shown = len(rows)
+    if isinstance(n_distinct, (int, float)) and n_distinct > shown:
+        note = f"top {shown} de {_fmt_int(n_distinct)} categorías distintas"
+    else:
+        note = f"{shown} categorías"
+    return model.DataTable(header=header, rows=rows, title="Top categorías",
+                           note=note)
+
+
+def _intro_blocks(n_rows):
+    total = _fmt_int(n_rows)
+    text = (
+        "La **entropía de Shannon** mide cómo de repartidos están los valores de "
+        "una columna categórica, en bits. Vale 0 cuando una sola categoría "
+        "concentra todas las filas (máxima previsibilidad) y alcanza su máximo, "
+        "log2(k) para k categorías distintas, cuando todas aparecen por igual "
+        "(máxima diversidad). La **entropía normalizada** (entropía dividida por "
+        "su máximo) la lleva al rango 0–1 para comparar columnas con distinto "
+        "número de categorías. Para cada columna se muestran los valores "
+        "distintos, el porcentaje que representan sobre el total de filas, los "
+        "valores únicos (que aparecen una sola vez), la tabla de las categorías "
+        "más frecuentes y un gráfico de tarta (donut) de las más comunes."
+    )
+    if n_rows is not None:
+        text += f" El dataset tiene {total} filas en total como referencia."
+    return [
+        model.Heading(text="Entropía y cardinalidad", level=2),
+        model.Markdown(text=text),
+    ]
+
+
+def build_cat_distr(profile: dict, ctx: dict):
+    """Build the categorical-distributions Chapter, or None if the dataset has
+    no categorical columns."""
+    profile = profile or {}
+    ctx = ctx or {}
+    cols = profile.get("columns") or []
+    cat_cols = [c for c in cols if _is_categorical(c)]
+    if not cat_cols:
+        return None
+
+    n_rows = profile.get("n_rows")
+    blocks = list(_intro_blocks(n_rows))
+
+    rendered = cat_cols[:MAX_COLS]
+    for col in rendered:
+        name = col.get("name") or "(columna)"
+        cat = col.get("categorical") or {}
+        card = _normalize_card(_cardinality(cat, n_rows))
+
+        blocks.append(model.Heading(text=str(name), level=2))
+        blocks.append(_cardinality_block(card))
+        note = _flag_note(card)
+        if note is not None:
+            blocks.append(note)
+        topk = _topk_table(cat)
+        if topk is not None:
+            blocks.append(topk)
+        blocks.append(model.Figure(
+            make=_pie_make(cat.get("top") or [], card.get("n_distinct"),
+                           str(name), n_rows),
+            caption=(f"Categorías más comunes de «{_truncate(name, 32)}» "
+                     "(donut: top-k + «Otros»)")))
+
+    if len(cat_cols) > len(rendered):
+        omitted = len(cat_cols) - len(rendered)
+        blocks.append(model.Note(
+            f"Se muestran las primeras {len(rendered)} columnas categóricas; "
+            f"quedan {omitted} sin mostrar para mantener acotado el informe."))
+
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

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

@@ -0,0 +1,186 @@
+"""Tests for the CAT DISTR chapter — DoD: golden + edges + anti-cut.
+
+Self-contained: builds synthetic TableProfiles (no DuckDB) so the suite is fast
+and deterministic. Verifies that ``build_cat_distr`` emits the blocks the user
+asked for (entropy intro, distinct/total/%-distinct/unique metrics, top-k table
+and a donut figure), that the chapter renders inside the full document to both
+PDF and PPTX showing that content, that a profile with no categorical columns
+yields ``None`` without raising, and that long labels / many columns are never
+cut in either output.
+"""
+
+import os
+import re
+import tempfile
+
+from pypdf import PdfReader
+from pptx import Presentation
+
+from datascience.automatic_eda.model import (
+    DataTable, Figure, Heading, KVTable, Note,
+)
+from datascience.automatic_eda.chapters.cat_distr import (
+    CHAPTER_ID, CHAPTER_VERSION, build_cat_distr,
+)
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+def _profile() -> dict:
+    return {
+        "table": "productos",
+        "source": "/data/productos.csv",
+        "profiled_at": "2026-06-30T10:00:00+00:00",
+        "n_rows": 1000,
+        "n_cols": 3,
+        "quality_score": 90.0,
+        "columns": [
+            {"name": "precio", "inferred_type": "numeric", "null_pct": 0.0,
+             "null_count": 0,
+             "numeric": {"mean": 42.5, "median": 40.0, "min": 1.0,
+                         "max": 100.0, "std": 12.3}},
+            {"name": "categoria", "inferred_type": "categorical",
+             "null_pct": 0.0, "null_count": 0, "distinct_count": 8,
+             "categorical": {
+                 "top": [
+                     {"value": "neumaticos", "count": 500, "pct": 0.5},
+                     {"value": "aceite", "count": 300, "pct": 0.3},
+                     {"value": "filtros", "count": 120, "pct": 0.12},
+                     {"value": "frenos", "count": 80, "pct": 0.08},
+                 ],
+                 "mode": "neumaticos", "n_distinct": 8, "entropy": 1.6,
+                 "imbalance": 6.25, "len_min": 6, "len_mean": 7.5,
+                 "len_max": 10}},
+            {"name": "uuid", "inferred_type": "categorical",
+             "null_pct": 0.0, "null_count": 0, "distinct_count": 1000,
+             "categorical": {
+                 "top": [{"value": f"id-{i}", "count": 1} for i in range(5)],
+                 "mode": "id-0", "n_distinct": 1000, "entropy": 9.97,
+                 "imbalance": 1.0}},
+        ],
+    }
+
+
+def _pdf_text(path: str) -> str:
+    txt = "".join((pg.extract_text() or "") for pg in PdfReader(path).pages)
+    return re.sub(r"\s+", " ", txt)
+
+
+def _pptx_text(path: str) -> str:
+    prs = Presentation(path)
+    parts = []
+    for sl in prs.slides:
+        for sh in sl.shapes:
+            if sh.has_text_frame:
+                parts.append(sh.text_frame.text)
+            if sh.has_table:
+                tb = sh.table
+                for r in range(len(tb.rows)):
+                    for c in range(len(tb.columns)):
+                        parts.append(tb.cell(r, c).text)
+    return re.sub(r"\s+", " ", " ".join(parts))
+
+
+def _kinds(chapter):
+    return [b.kind for b in chapter.blocks]
+
+
+def test_golden_build_cat_distr_emite_bloques_pedidos():
+    ch = build_cat_distr(_profile(), {})
+    assert ch is not None
+    assert ch.id == CHAPTER_ID
+    assert ch.version == CHAPTER_VERSION
+    kinds = _kinds(ch)
+    # Entropy intro present.
+    headings = [b.text for b in ch.blocks if isinstance(b, Heading)]
+    assert any("Entrop" in h for h in headings)
+    md = next(b for b in ch.blocks if b.kind == "markdown")
+    assert "entropía" in md.text.lower() and "log2" in md.text
+    # Cardinality metrics: distinct, total rows, %-distinct, unique values.
+    kv = next(b for b in ch.blocks if isinstance(b, KVTable))
+    labels = [r[0] for r in kv.rows]
+    assert "Valores distintos" in labels
+    assert "% distintos" in labels
+    assert "Total filas (dataset)" in labels
+    assert "Valores únicos (frecuencia 1)" in labels
+    assert any("Entropía" in lbl for lbl in labels)
+    # Top-k table + pie figure.
+    dt = next(b for b in ch.blocks if isinstance(b, DataTable))
+    assert dt.header == ["Valor", "Conteo", "%"]
+    assert any("neumaticos" in str(cell) for row in dt.rows for cell in row)
+    assert any(isinstance(b, Figure) for b in ch.blocks)
+    # id-like column flagged with a Note.
+    assert any(isinstance(b, Note) and "identificador" in b.text
+               for b in ch.blocks)
+
+
+def test_golden_render_pdf_muestra_categoricas():
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "eda.pdf")
+        res = render_automatic_eda_pdf(_profile(), out, {"title": "EDA"})
+        assert res["path"] == out and os.path.exists(out)
+        assert CHAPTER_ID in [c["id"] for c in res["chapters"]]
+        txt = _pdf_text(out)
+        assert "Entrop" in txt
+        assert "distintos" in txt
+        assert "categoria" in txt and "neumaticos" in txt
+        assert "donut" in txt           # figure caption rendered as text.
+        assert "identificador" in txt   # id-like note rendered.
+
+
+def test_golden_render_pptx_muestra_categoricas():
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "eda.pptx")
+        res = render_automatic_eda_pptx(_profile(), out, {"title": "EDA"})
+        assert res["path"] == out and os.path.exists(out)
+        assert CHAPTER_ID in [c["id"] for c in res["chapters"]]
+        txt = _pptx_text(out)
+        assert "Entrop" in txt
+        assert "categoria" in txt and "neumaticos" in txt
+        assert "distintos" in txt
+
+
+def test_edge_sin_categoricas_devuelve_none():
+    only_numeric = {
+        "n_rows": 10, "columns": [
+            {"name": "x", "inferred_type": "numeric",
+             "numeric": {"mean": 1.0}}]}
+    assert build_cat_distr(only_numeric, {}) is None
+    # None / empty / no-columns never raise and yield None.
+    assert build_cat_distr(None, None) is None
+    assert build_cat_distr({}, {}) is None
+    assert build_cat_distr({"columns": []}, {}) is None
+
+
+def test_anti_corte_label_largo_y_muchas_columnas():
+    long_label = ("Lorem ipsum dolor sit amet consectetur adipiscing elit sed "
+                  "do eiusmod tempor incididunt ut labore reprehenderit voluptate")
+    cols = []
+    for i in range(30):
+        cols.append({
+            "name": f"cat_{i}", "inferred_type": "categorical",
+            "distinct_count": 3,
+            "categorical": {
+                "top": [{"value": long_label, "count": 60},
+                        {"value": "b", "count": 30},
+                        {"value": "c", "count": 10}],
+                "mode": long_label, "n_distinct": 3, "entropy": 1.2}})
+    profile = {"table": "t", "source": "t.csv", "n_rows": 100,
+               "n_cols": len(cols), "columns": cols}
+
+    ch = build_cat_distr(profile, {})
+    assert ch is not None
+    with tempfile.TemporaryDirectory() as d:
+        pdf = os.path.join(d, "anti.pdf")
+        res = render_automatic_eda_pdf(profile, pdf, {"write_manifest": False})
+        assert res["path"] == pdf
+        assert res["n_pages"] > 1       # many columns spilled across pages, OK.
+        txt = _pdf_text(pdf)
+        # Long label wrapped (not truncated): every word survives.
+        for word in ("Lorem", "incididunt", "reprehenderit", "voluptate"):
+            assert word in txt
+        # PPTX path must not raise either.
+        pptx = os.path.join(d, "anti.pptx")
+        res2 = render_automatic_eda_pptx(profile, pptx,
+                                         {"write_manifest": False})
+        assert res2["path"] == pptx and os.path.exists(pptx)

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

@@ -0,0 +1,352 @@
+"""Correlation chapter — association matrix plus top positive/negative pairs.
+
+Builds the CORRELACION chapter of an AutomaticEDA document from a TableProfile.
+It renders exactly what the user asked for:
+
+1. A correlation/association **matrix** (heatmap) reconstructed from the evaluated
+   pairs, signed for numeric-numeric pairs (Pearson/Spearman, ``[-1, 1]``) and as
+   magnitude for the mixed-type metrics (Cramér's V, correlation ratio, mutual
+   information, ``[0, 1]``). Labels are ordered by total connectivity so strong
+   associations cluster together instead of being scattered alphabetically.
+2. The **TOP positive** pairs and the **TOP negative** pairs as two separate
+   tables. Only numeric-numeric metrics carry a sign, so negative pairs are by
+   construction Pearson/Spearman; positive pairs may use any method.
+3. The methods legend and the multiple-testing (FDR) summary, so the reader sees
+   how many pairs survive the correction.
+4. A spuriousness caveat when the profile flags level-based correlations on
+   non-stationary series (Granger–Newbold).
+
+All data comes from ``profile['correlations']`` — the output of the ``eda`` group
+function ``association_matrix`` (optionally enriched by ``profile_table``). The
+chapter never recomputes any statistic; it only lays the existing values out as
+format-independent blocks. The renderers paginate tables (repeating the header)
+and scale the heatmap to fit entirely, so nothing is ever cut.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+"""
+
+from __future__ import annotations
+
+import math
+
+from .. import model
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "correlacion"
+CHAPTER_TITLE = "Correlación"
+
+# Methods whose value carries a sign (direction). Everything else is a magnitude
+# in [0, 1] and therefore only ever contributes to the positive side.
+_SIGNED_METHODS = ("pearson", "spearman")
+
+# Cap the heatmap to the most-connected variables so it stays legible on a phone
+# screen / a slide. The renderer would scale a bigger matrix to fit, but the
+# cells become unreadable; we instead show the top-N and say so.
+_MAX_MATRIX_LABELS = 16
+
+# How many pairs to show in each of the top-positive / top-negative tables.
+_TOP_N = 10
+
+
+def _is_num(v) -> bool:
+    """True for a real, finite int/float (not bool, not NaN/inf)."""
+    return (
+        isinstance(v, (int, float))
+        and not isinstance(v, bool)
+        and not (isinstance(v, float) and (math.isnan(v) or math.isinf(v)))
+    )
+
+
+def _fmt_val(value, decimals: int = 2) -> str:
+    """Format an association value compactly, signed, with a fixed width feel."""
+    if not _is_num(value):
+        return "—"
+    text = f"{float(value):+.{decimals}f}"
+    # Strip a trailing -0.00 / +0.00 into a clean 0.00 for readability.
+    if text in ("+0.00", "-0.00"):
+        return "0.00"
+    return text
+
+
+def _fmt_p(value) -> str:
+    """Format an adjusted p-value; tiny values collapse to a '<' threshold."""
+    if not _is_num(value):
+        return "—"
+    p = float(value)
+    if p < 0.001:
+        return "<0.001"
+    return f"{p:.3f}"
+
+
+def _is_signed(pair: dict) -> bool:
+    """True if the pair's method reports a directional (signed) value."""
+    method = str(pair.get("method") or "").lower()
+    return any(m in method for m in _SIGNED_METHODS)
+
+
+def _significant(pair: dict) -> bool:
+    """True if the pair is significant after FDR (or has no test to correct)."""
+    if pair.get("significant") is True:
+        return True
+    # Pairs without an applicable test (p_value None) are not penalised: they are
+    # admitted on magnitude alone upstream, so treat missing as "not rejected".
+    return pair.get("p_value") is None and pair.get("significant") is None
+
+
+def _label(pair: dict) -> str:
+    """Human label for a pair, e.g. 'alcohol ↔ density'."""
+    return f"{model._safe_str(pair.get('a'))} ↔ {model._safe_str(pair.get('b'))}"
+
+
+def _split_top(pairs: list, top_n: int = _TOP_N):
+    """Split evaluated pairs into ranked top-positive and top-negative lists.
+
+    Positive: any pair with a positive value, ranked by value descending.
+    Negative: only signed (numeric-numeric) pairs with a negative value, ranked
+    by value ascending (most negative first). Non-finite values are dropped.
+    """
+    positive = []
+    negative = []
+    for pair in pairs:
+        if not isinstance(pair, dict):
+            continue
+        value = pair.get("value")
+        if not _is_num(value):
+            continue
+        if value > 0:
+            positive.append(pair)
+        elif value < 0 and _is_signed(pair):
+            negative.append(pair)
+    positive.sort(key=lambda p: float(p.get("value", 0.0)), reverse=True)
+    negative.sort(key=lambda p: float(p.get("value", 0.0)))
+    return positive[:top_n], negative[:top_n]
+
+
+def _top_table(pairs: list, title: str):
+    """Build a DataTable for a list of pairs, or None if there are none."""
+    if not pairs:
+        return None
+    header = ["Par", "Método", "Valor", "p (FDR)", "Sig."]
+    rows = []
+    for pair in pairs:
+        method = model._safe_str(pair.get("method")) or "—"
+        rows.append([
+            _label(pair),
+            method,
+            _fmt_val(pair.get("value")),
+            _fmt_p(pair.get("p_value_adjusted")),
+            "sí" if _significant(pair) else "no",
+        ])
+    return model.DataTable(header=header, rows=rows, title=title)
+
+
+def _ordered_labels(pairs: list):
+    """Pick and order the matrix labels by total connectivity (descending).
+
+    Returns the list of variable names to place on the axes, capped at
+    ``_MAX_MATRIX_LABELS`` (the most-connected ones), plus a boolean saying
+    whether the cap trimmed anything.
+    """
+    strength = {}
+    for pair in pairs:
+        if not isinstance(pair, dict):
+            continue
+        value = pair.get("value")
+        if not _is_num(value):
+            continue
+        mag = abs(float(value))
+        for key in ("a", "b"):
+            name = pair.get(key)
+            if name is None:
+                continue
+            strength[name] = strength.get(name, 0.0) + mag
+    if not strength:
+        return [], False
+    ordered = sorted(strength, key=lambda n: strength[n], reverse=True)
+    trimmed = len(ordered) > _MAX_MATRIX_LABELS
+    return ordered[:_MAX_MATRIX_LABELS], trimmed
+
+
+def _matrix_figure(pairs: list, labels: list):
+    """Return a Figure (lazy) with the signed association heatmap, or None.
+
+    The matplotlib figure is built lazily inside ``make`` so importing this
+    module never requires matplotlib and a malformed plot degrades to nothing
+    instead of aborting the chapter.
+    """
+    if len(labels) < 2:
+        return None
+
+    index = {name: i for i, name in enumerate(labels)}
+
+    def make():
+        import numpy as np
+        from matplotlib.figure import Figure
+
+        n = len(labels)
+        grid = np.full((n, n), np.nan, dtype=float)
+        for i in range(n):
+            grid[i, i] = 1.0
+        for pair in pairs:
+            if not isinstance(pair, dict):
+                continue
+            a = pair.get("a")
+            b = pair.get("b")
+            value = pair.get("value")
+            if a not in index or b not in index or not _is_num(value):
+                continue
+            v = float(value)
+            # Mixed-type magnitudes are non-negative; keep them as-is on [0, 1].
+            ia, ib = index[a], index[b]
+            grid[ia, ib] = v
+            grid[ib, ia] = v
+
+        import matplotlib
+
+        masked = np.ma.masked_invalid(grid)
+        fig = Figure(figsize=(6.2, 5.6))
+        ax = fig.add_subplot(111)
+        cmap = matplotlib.colormaps["RdBu_r"].copy()
+        cmap.set_bad(color="#eeeeee")
+        im = ax.imshow(masked, cmap=cmap, vmin=-1.0, vmax=1.0, aspect="auto")
+        ax.set_xticks(range(n))
+        ax.set_yticks(range(n))
+        short = [str(s)[:14] for s in labels]
+        ax.set_xticks(range(n))
+        ax.set_xticklabels(short, rotation=90, fontsize=7)
+        ax.set_yticklabels(short, fontsize=7)
+        # Annotate cells only when the matrix is small enough to stay legible.
+        if n <= 8:
+            for i in range(n):
+                for j in range(n):
+                    cell = grid[i, j]
+                    if _is_num(cell):
+                        ax.text(j, i, f"{cell:+.2f}".replace("+", "") if cell < 0
+                                else f"{cell:.2f}",
+                                ha="center", va="center", fontsize=6,
+                                color="#222222")
+        fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04,
+                     label="asociación (signo en num-num)")
+        fig.tight_layout()
+        return fig
+
+    return model.Figure(make=make,
+                        caption="Matriz de asociación. Azul = positiva, rojo = "
+                                "negativa (sólo num-num lleva signo); gris = par "
+                                "no evaluado.")
+
+
+def _methods_block(corr: dict):
+    """Build a KVTable with the legend of the methods actually present."""
+    legend = corr.get("methods_legend")
+    if not isinstance(legend, dict) or not legend:
+        return None
+    rows = [(model._safe_str(k), model._safe_str(v)) for k, v in legend.items()]
+    return model.KVTable(rows=rows, title="Métodos de asociación")
+
+
+def _fdr_text(corr: dict) -> str | None:
+    """One-line summary of the multiple-testing (FDR) correction, or None."""
+    mt = corr.get("multiple_testing")
+    if not isinstance(mt, dict) or not mt:
+        return None
+    method = model._safe_str(mt.get("method")).upper() or "FDR"
+    alpha = mt.get("alpha")
+    n_tests = mt.get("n_tests")
+    n_rej = mt.get("n_rejected")
+    parts = [f"Corrección por comparaciones múltiples ({method}"]
+    if _is_num(alpha):
+        parts[0] += f", α={float(alpha):g}"
+    parts[0] += ")."
+    if _is_num(n_tests):
+        rej = n_rej if _is_num(n_rej) else "—"
+        parts.append(
+            f"De {int(n_tests)} pares con test, {rej} siguen siendo "
+            f"significativos tras la corrección.")
+    return " ".join(parts)
+
+
+def build_correlacion(profile: dict, ctx: dict):
+    """Build the Correlation Chapter, or None if there are no pairs to show.
+
+    Reads ``profile['correlations']`` (the ``association_matrix`` output). Returns
+    ``None`` when the dataset has fewer than two associable columns (no evaluated
+    pairs), so the chapter is omitted instead of showing an empty section. Never
+    raises: every access is defensive.
+
+    ctx keys consumed: none specific (presentation metadata is inherited from the
+    document). The chapter reads everything it needs from the profile.
+    """
+    profile = profile or {}
+    ctx = ctx or {}
+
+    corr = profile.get("correlations")
+    if not isinstance(corr, dict):
+        return None
+    pairs = corr.get("pairs")
+    if not isinstance(pairs, list) or not pairs:
+        return None
+
+    blocks: list = []
+
+    # Intro: what this chapter shows and how to read the sign.
+    blocks.append(model.Markdown(text=(
+        "Asociación entre columnas. Cada par se evalúa con la métrica adecuada a "
+        "sus tipos (Pearson/Spearman entre numéricas — con **signo**; Cramér's V "
+        "entre categóricas; razón de correlación num-categórica; información mutua "
+        "como medida común no lineal). Sólo las correlaciones **num-num** tienen "
+        "dirección: por eso los pares **negativos** son siempre num-num.")))
+
+    # 1) Association matrix (heatmap).
+    labels, trimmed = _ordered_labels(pairs)
+    fig = _matrix_figure(pairs, labels)
+    if fig is not None:
+        blocks.append(model.Heading(text="Matriz de asociación", level=2))
+        blocks.append(fig)
+        if trimmed:
+            blocks.append(model.Note(text=(
+                f"Se muestran las {len(labels)} variables más conectadas de la "
+                "matriz para mantenerla legible; el resto de pares siguen en las "
+                "tablas de abajo.")))
+
+    # 2) Top positive / top negative pairs.
+    positive, negative = _split_top(pairs, _TOP_N)
+    pos_table = _top_table(positive, f"Top {len(positive)} positivas")
+    neg_table = _top_table(negative, f"Top {len(negative)} negativas")
+    if pos_table is not None:
+        blocks.append(model.Heading(text="Pares más correlacionados (positivos)",
+                                    level=2))
+        blocks.append(pos_table)
+    if neg_table is not None:
+        blocks.append(model.Heading(text="Pares más correlacionados (negativos)",
+                                    level=2))
+        blocks.append(neg_table)
+    elif pos_table is not None:
+        # No signed-negative pairs at all: say so honestly rather than omit.
+        blocks.append(model.Note(text=(
+            "No se han hallado correlaciones negativas significativas entre "
+            "columnas numéricas.")))
+
+    # 3) Spuriousness caveat for level-based correlations (Granger–Newbold).
+    caveat = corr.get("levels_caveat")
+    if isinstance(caveat, str) and caveat.strip():
+        blocks.append(model.Note(text=caveat.strip()))
+    elif corr.get("levels_possible_spurious"):
+        blocks.append(model.Note(text=(
+            "Aviso: algunas correlaciones se calcularon sobre niveles de series "
+            "no estacionarias y pueden ser espurias (Granger–Newbold). Compáralas "
+            "sobre los retornos/diferencias antes de interpretarlas.")))
+
+    # 4) FDR summary + methods legend.
+    fdr_text = _fdr_text(corr)
+    if fdr_text:
+        blocks.append(model.Markdown(text=fdr_text))
+    methods = _methods_block(corr)
+    if methods is not None:
+        blocks.append(model.Heading(text="Métodos y leyenda", level=2))
+        blocks.append(methods)
+
+    if not blocks:
+        return None
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

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

@@ -0,0 +1,175 @@
+"""Tests for the CORRELACION chapter — DoD: golden + edges + error/anti-cut.
+
+Self-contained: builds a synthetic TableProfile carrying a ``correlations`` block
+shaped exactly like ``association_matrix`` output (no DuckDB), so the suite is
+fast and deterministic. Verifies that the chapter emits the association-matrix
+figure plus separate top-positive / top-negative tables with the right pairs,
+that it returns None when the profile has no pairs, that a None/empty profile
+does not raise, and that a wide matrix with long labels renders to PDF *and* PPTX
+without cutting anything.
+"""
+
+import os
+import re
+import tempfile
+
+from pypdf import PdfReader
+
+from datascience.automatic_eda.chapters.correlacion import (
+    CHAPTER_VERSION,
+    build_correlacion,
+)
+from datascience.automatic_eda.model import DataTable, Figure
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+def _pair(a, b, value, method, padj, sig, p=0.0001):
+    return {
+        "a": a, "b": b, "a_type": "numeric", "b_type": "numeric",
+        "method": method, "value": value, "extra": {"mi": abs(value) * 0.5},
+        "p_value": p, "p_value_adjusted": padj, "significant": sig,
+    }
+
+
+def _profile() -> dict:
+    """Synthetic wine-like profile with signed and unsigned associations."""
+    pairs = [
+        _pair("alcohol", "quality", 0.48, "pearson/spearman", 0.0005, True),
+        _pair("density", "alcohol", -0.78, "pearson/spearman", 0.0001, True),
+        _pair("ph", "fixed_acidity", -0.68, "pearson/spearman", 0.0002, True),
+        _pair("sulphates", "quality", 0.25, "pearson/spearman", 0.03, True),
+        # Unsigned mixed-type metrics: only ever positive, never in the neg table.
+        {"a": "region", "b": "type", "a_type": "categorical",
+         "b_type": "categorical", "method": "cramers_v", "value": 0.55,
+         "extra": {"mi": 0.3}, "p_value": 0.001, "p_value_adjusted": 0.004,
+         "significant": True},
+    ]
+    return {
+        "table": "wine",
+        "source": "/data/wine.csv",
+        "n_rows": 1599,
+        "n_cols": 12,
+        "correlations": {
+            "pairs": pairs,
+            "strong": [p for p in pairs if abs(p["value"]) >= 0.5],
+            "methods_legend": {
+                "pearson": "num-num lineal (Pearson r), [-1, 1]",
+                "cramers_v": "cat-cat simétrica (Cramér's V), [0, 1]",
+            },
+            "multiple_testing": {"method": "bh", "alpha": 0.05,
+                                 "n_tests": 5, "n_rejected": 5},
+        },
+    }
+
+
+def _pdf_text(path: str) -> str:
+    txt = "".join((pg.extract_text() or "") for pg in PdfReader(path).pages)
+    return re.sub(r"\s+", " ", txt)
+
+
+def test_golden_chapter_tiene_matriz_y_top_positivos_y_negativos():
+    ch = build_correlacion(_profile(), {})
+    assert ch is not None
+    assert ch.id == "correlacion"
+    assert ch.version == CHAPTER_VERSION
+    kinds = [b.kind for b in ch.blocks]
+    assert "figure" in kinds  # association matrix heatmap.
+    figs = [b for b in ch.blocks if isinstance(b, Figure)]
+    assert figs and figs[0].make is not None  # lazy figure.
+
+    tables = [b for b in ch.blocks if isinstance(b, DataTable)]
+    assert len(tables) >= 2  # top positive + top negative.
+    flat = " ".join(str(c) for t in tables for r in t.rows for c in r)
+    # Strongest positive present and signed +, strongest negative present and -.
+    assert "alcohol" in flat and "quality" in flat
+    assert "+0.48" in flat
+    assert "density" in flat and "-0.78" in flat
+
+
+def test_golden_render_pdf_y_pptx_muestran_lo_exigido():
+    prof = _profile()
+    with tempfile.TemporaryDirectory() as d:
+        pdf = os.path.join(d, "corr.pdf")
+        pptx = os.path.join(d, "corr.pptx")
+        rp = render_automatic_eda_pdf(prof, pdf, {"title": "EDA — wine"})
+        rx = render_automatic_eda_pptx(prof, pptx, {"title": "EDA — wine"})
+        assert rp["path"] == pdf and rp["n_pages"] >= 1
+        assert rx["path"] == pptx and rx["n_slides"] >= 1
+        assert "correlacion" in [c["id"] for c in rp["chapters"]]
+        assert "correlacion" in [c["id"] for c in rx["chapters"]]
+        txt = _pdf_text(pdf)
+        # The requirement: matrix + top positive/negative pairs, all visible.
+        assert "Correlaci" in txt  # chapter title (accents may vary in extract).
+        assert "density" in txt and "alcohol" in txt and "quality" in txt
+        assert "0.78" in txt and "0.48" in txt
+        # Both signs surfaced as separate sections.
+        assert "positiv" in txt.lower() and "negativ" in txt.lower()
+
+
+def test_edge_sin_pares_devuelve_none():
+    # No correlations key, empty pairs, and wrong types all yield None, not error.
+    assert build_correlacion({"table": "x"}, {}) is None
+    assert build_correlacion({"correlations": {}}, {}) is None
+    assert build_correlacion({"correlations": {"pairs": []}}, {}) is None
+    assert build_correlacion({"correlations": {"pairs": "nope"}}, {}) is None
+    assert build_correlacion(None, None) is None
+    assert build_correlacion({}, {}) is None
+
+
+def test_edge_solo_positivos_emite_nota_sin_tabla_negativa():
+    prof = {
+        "correlations": {
+            "pairs": [
+                _pair("a", "b", 0.6, "pearson/spearman", 0.001, True),
+                {"a": "c", "b": "d", "a_type": "categorical",
+                 "b_type": "categorical", "method": "cramers_v", "value": 0.7,
+                 "extra": {"mi": 0.4}, "p_value": 0.001,
+                 "p_value_adjusted": 0.003, "significant": True},
+            ],
+        },
+    }
+    ch = build_correlacion(prof, {})
+    assert ch is not None
+    tables = [b for b in ch.blocks if isinstance(b, DataTable)]
+    assert len(tables) == 1  # only the positive table.
+    notes = " ".join(b.text for b in ch.blocks if b.kind == "note")
+    assert "negativas" in notes  # honest "no negative correlations" note.
+
+
+def test_anticorte_matriz_ancha_y_etiquetas_largas_no_se_cortan():
+    # 20 numeric vars with long names -> matrix trimmed to top-N + both renderers
+    # must lay the chapter out without raising and keep a long label intact.
+    long_a = "concentracion_de_dioxido_de_azufre_libre"
+    long_b = "concentracion_de_dioxido_de_azufre_total"
+    pairs = [_pair(long_a, long_b, -0.72, "pearson/spearman", 0.0001, True)]
+    for i in range(20):
+        pairs.append(_pair(f"variable_numerica_larga_{i:02d}",
+                           f"variable_numerica_larga_{(i + 1) % 20:02d}",
+                           0.55 - i * 0.02, "pearson/spearman", 0.01, True))
+    prof = {"correlations": {"pairs": pairs,
+                             "multiple_testing": {"method": "bh", "alpha": 0.05,
+                                                  "n_tests": len(pairs),
+                                                  "n_rejected": len(pairs)}}}
+    ch = build_correlacion(prof, {})
+    assert ch is not None
+    # A "showing top-N most connected" note appears when the matrix is trimmed.
+    notes = " ".join(b.text for b in ch.blocks if b.kind == "note")
+    assert "más conectadas" in notes
+    # Anti-cut guarantee at the block level: the long pair reaches the renderer
+    # whole (the block never truncates); the renderer then wraps the cell inside
+    # its column. Both long labels are present, intact, in a table cell.
+    tables = [b for b in ch.blocks if isinstance(b, DataTable)]
+    cells = [str(c) for t in tables for r in t.rows for c in r]
+    assert any(long_a in c and long_b in c for c in cells)
+    with tempfile.TemporaryDirectory() as d:
+        pdf = os.path.join(d, "wide.pdf")
+        pptx = os.path.join(d, "wide.pptx")
+        rp = render_automatic_eda_pdf(prof, pdf, {"write_manifest": False})
+        rx = render_automatic_eda_pptx(prof, pptx, {"write_manifest": False})
+        # Both renderers lay the wide chapter out without raising and produce a
+        # non-empty document (nothing dropped, just wrapped/scaled to fit).
+        assert rp["path"] == pdf and os.path.exists(pdf) and rp["n_pages"] >= 1
+        assert rx["path"] == pptx and os.path.exists(pptx) and rx["n_slides"] >= 1
+        # A short, unbreakable fragment of the long label survives the wrap.
+        assert "azufre" in _pdf_text(pdf)

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

@@ -0,0 +1,477 @@
+"""Geospatial chapter (GEOSPATIAL) for AutomaticEDA.
+
+When the dataset carries a coordinate pair (latitude/longitude), this chapter
+draws the points on a **geographic scatter** in an equirectangular projection
+(scaled so degrees of longitude are not stretched at the data's latitude) and
+analyses the **zone / country** the points fall in: bounding box, centroid,
+geographic span, and a per-region count. When there is **no** coordinate pair the
+chapter returns ``None`` — exactly the user requirement.
+
+Detection and the heavy lifting are delegated to pure ``eda``-group registry
+functions, never reimplemented here:
+
+- ``detect_latlon_columns`` — finds the (lat, lon) column pair by name + value
+  range from the ``profile['columns']`` metadata.
+- ``analyze_geo_extent`` — bbox, centroid, haversine span, per-region counts and
+  hemisphere from the raw coordinate arrays.
+- ``build_geo_scatter`` — deterministically down-sampled points + bbox + the
+  aspect ratio for the equirectangular projection. This chapter only draws the
+  matplotlib figure from that prepared data (same split as ``num_distr`` does
+  with ``build_boxplot_stats``).
+
+The raw coordinate arrays are **not** in a standard TableProfile (it stores only
+per-column aggregates), so — exactly like ``modelos`` reads ``raw_numeric`` from
+``ctx`` — this chapter looks for the coordinates in ``ctx`` (or ``profile``) and
+degrades honestly when they are absent: it still detects the columns and shows an
+approximate bounding box derived from the per-column ``numeric.min/max``, with a
+note that the raw points are needed for the map.
+
+ctx keys this chapter consumes (all optional):
+    geo_points : dict — ``{"lats": [...], "lons": [...]}`` raw coordinate arrays.
+        Used directly when present (forward-compatible with a calculation phase
+        that samples them from the table).
+    raw_numeric : dict — ``{col: [values]}`` raw numeric columns; when present
+        and ``geo_points`` is not, the detected lat/lon columns are read from it.
+    run_geo_llm : bool — when True, call ``ask_llm`` for a one-line narrative of
+        where the points concentrate (otherwise a derived note is used).
+    geo_llm_model : str — model id for the optional live LLM call.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+Reads everything defensively (``.get``) and never raises.
+"""
+
+from __future__ import annotations
+
+import math
+
+from .. import model
+
+# Pure registry functions (group ``eda``) delegated to. Imported defensively so
+# the chapter stays importable (degrading gracefully) if one is unavailable.
+try:
+    from datascience.detect_latlon_columns import detect_latlon_columns
+except Exception:  # noqa: BLE001 — keep the chapter importable no matter what.
+    detect_latlon_columns = None  # type: ignore[assignment]
+try:
+    from datascience.analyze_geo_extent import analyze_geo_extent
+except Exception:  # noqa: BLE001
+    analyze_geo_extent = None  # type: ignore[assignment]
+try:
+    from datascience.build_geo_scatter import build_geo_scatter
+except Exception:  # noqa: BLE001
+    build_geo_scatter = None  # type: ignore[assignment]
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "geospatial"
+CHAPTER_TITLE = "Análisis geoespacial"
+
+
+# --------------------------------------------------------------------------- #
+# Formatting helpers (mirror the other chapters' defensive style).
+# --------------------------------------------------------------------------- #
+def _fmt_num(value, decimals: int = 4) -> str:
+    if value is None:
+        return "—"
+    if isinstance(value, bool):
+        return "sí" if value else "no"
+    if isinstance(value, int):
+        return f"{value:,}".replace(",", ".")
+    if isinstance(value, float):
+        if value != value:  # NaN
+            return "NaN"
+        if value in (float("inf"), float("-inf")):
+            return str(value)
+        text = f"{value:.{decimals}f}".rstrip("0").rstrip(".")
+        return text if text else "0"
+    return model._safe_str(value)
+
+
+def _fmt_coord(value, decimals: int = 4) -> str:
+    """Format a coordinate degree value, defensively."""
+    try:
+        return f"{float(value):.{decimals}f}°"
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+
+
+def _fmt_km(value) -> str:
+    if value is None:
+        return "—"
+    try:
+        v = float(value)
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+    if v >= 100:
+        return f"{v:,.0f} km".replace(",", ".")
+    return f"{v:.1f} km"
+
+
+def _is_dict(v) -> bool:
+    return isinstance(v, dict)
+
+
+def _clean_floats(seq) -> list:
+    """Return a list of floats from an arbitrary sequence (drop None/NaN)."""
+    out = []
+    if not isinstance(seq, (list, tuple)):
+        return out
+    for v in seq:
+        try:
+            f = float(v)
+        except (TypeError, ValueError):
+            out.append(None)
+            continue
+        out.append(f if f == f else None)  # NaN -> None
+    return out
+
+
+# --------------------------------------------------------------------------- #
+# Resolve the (lat, lon) columns and the raw coordinate arrays.
+# --------------------------------------------------------------------------- #
+def _detect_columns(profile: dict) -> dict:
+    """Detect the lat/lon column pair from the profile metadata, or {}."""
+    cols = profile.get("columns")
+    if not isinstance(cols, list) or not cols or detect_latlon_columns is None:
+        return {}
+    try:
+        det = detect_latlon_columns(cols)
+    except Exception:  # noqa: BLE001 — never break the chapter.
+        return {}
+    return det if _is_dict(det) else {}
+
+
+def _resolve_coords(profile: dict, ctx: dict, detected: dict):
+    """Return (lats, lons, source_label).
+
+    Order: ctx/profile['geo_points'] (explicit arrays) → ctx/profile
+    ['raw_numeric'] keyed by the detected lat/lon column names → (None, None).
+    """
+    gp = ctx.get("geo_points") or profile.get("geo_points")
+    if _is_dict(gp):
+        lats = gp.get("lats")
+        if lats is None:
+            lats = gp.get("lat")
+        lons = gp.get("lons")
+        if lons is None:
+            lons = gp.get("lon")
+        if lats and lons:
+            return list(lats), list(lons), "geo_points"
+
+    lat_col = (detected or {}).get("lat_col")
+    lon_col = (detected or {}).get("lon_col")
+    if lat_col and lon_col:
+        raw = ctx.get("raw_numeric") or profile.get("raw_numeric")
+        if _is_dict(raw):
+            lats = raw.get(lat_col)
+            lons = raw.get(lon_col)
+            if lats and lons:
+                return list(lats), list(lons), "raw_numeric"
+    return None, None, "none"
+
+
+def _column_by_name(profile: dict, name):
+    if not name:
+        return None
+    for col in profile.get("columns") or []:
+        if isinstance(col, dict) and col.get("name") == name:
+            return col
+    return None
+
+
+def _bbox_from_profile(profile: dict, detected: dict):
+    """Approximate bbox from the per-column numeric.min/max (no raw points)."""
+    lat_c = _column_by_name(profile, (detected or {}).get("lat_col"))
+    lon_c = _column_by_name(profile, (detected or {}).get("lon_col"))
+    lat_n = lat_c.get("numeric") if _is_dict(lat_c) else None
+    lon_n = lon_c.get("numeric") if _is_dict(lon_c) else None
+    if not _is_dict(lat_n) or not _is_dict(lon_n):
+        return None
+    try:
+        return {
+            "lat_min": float(lat_n.get("min")),
+            "lat_max": float(lat_n.get("max")),
+            "lon_min": float(lon_n.get("min")),
+            "lon_max": float(lon_n.get("max")),
+        }
+    except (TypeError, ValueError):
+        return None
+
+
+# --------------------------------------------------------------------------- #
+# Figure builder (lazy: matplotlib only imported when the renderer draws it).
+# --------------------------------------------------------------------------- #
+def _make_geo_scatter(scatter: dict, lat_col: str, lon_col: str):
+    """Return a zero-arg callable drawing the geographic scatter, or None."""
+    points = scatter.get("points") or []
+    if not points:
+        return None
+    bbox = scatter.get("bbox") if _is_dict(scatter.get("bbox")) else {}
+    aspect = scatter.get("aspect") or 1.0
+    pad = scatter.get("pad") if _is_dict(scatter.get("pad")) else {}
+    n_total = scatter.get("n_total")
+    n_shown = scatter.get("n_shown")
+
+    def _draw():
+        import matplotlib
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        xs = [p[0] for p in points if isinstance(p, (list, tuple)) and len(p) >= 2]
+        ys = [p[1] for p in points if isinstance(p, (list, tuple)) and len(p) >= 2]
+
+        fig, ax = plt.subplots(figsize=(6.6, 5.0))
+        # More points -> smaller markers + lower alpha so dense clouds read as
+        # density without saturating the page with ink (Tufte).
+        n = max(len(xs), 1)
+        size = 18 if n <= 200 else (8 if n <= 1000 else 4)
+        alpha = 0.75 if n <= 200 else (0.5 if n <= 1000 else 0.35)
+        ax.scatter(xs, ys, s=size, c="#2a6f97", alpha=alpha, linewidths=0,
+                   zorder=3)
+
+        # Bounding box rectangle for orientation.
+        if bbox:
+            try:
+                lo_x, hi_x = float(bbox["lon_min"]), float(bbox["lon_max"])
+                lo_y, hi_y = float(bbox["lat_min"]), float(bbox["lat_max"])
+                ax.plot([lo_x, hi_x, hi_x, lo_x, lo_x],
+                        [lo_y, lo_y, hi_y, hi_y, lo_y],
+                        color="#e15759", linewidth=1.0, linestyle="--",
+                        alpha=0.8, zorder=4, label="Bounding box")
+                px = float(pad.get("lon", 0.0) or 0.0)
+                py = float(pad.get("lat", 0.0) or 0.0)
+                ax.set_xlim(lo_x - px, hi_x + px)
+                ax.set_ylim(lo_y - py, hi_y + py)
+            except (TypeError, ValueError, KeyError):
+                pass
+
+        # Equirectangular: scale Y/X so longitude is not stretched at this
+        # latitude (integridad de proyección, Tufte). aspect = 1/cos(lat).
+        try:
+            ax.set_aspect(float(aspect))
+        except (TypeError, ValueError):
+            pass
+
+        ax.set_xlabel(f"Longitud ({lon_col})", fontsize=8)
+        ax.set_ylabel(f"Latitud ({lat_col})", fontsize=8)
+        ax.tick_params(labelsize=7)
+        ax.grid(color="#e6e6e6", linewidth=0.5, zorder=0)
+        title = "Distribución geográfica de las coordenadas"
+        if n_shown is not None and n_total is not None and n_shown < n_total:
+            title += f"\n(mostrando {n_shown:,} de {n_total:,} puntos)".replace(",", ".")
+        ax.set_title(title, fontsize=10)
+        ax.legend(loc="best", fontsize=7, frameon=True, framealpha=0.9)
+        fig.tight_layout()
+        return fig
+
+    return _draw
+
+
+# --------------------------------------------------------------------------- #
+# Section builders.
+# --------------------------------------------------------------------------- #
+def _intro_block(detected: dict, lat_col: str, lon_col: str) -> list:
+    conf = (detected or {}).get("confidence")
+    reason = model._safe_str((detected or {}).get("reason"))
+    conf_txt = ""
+    if conf is not None:
+        try:
+            conf_txt = f" (confianza {float(conf) * 100:.0f}%)"
+        except (TypeError, ValueError):
+            conf_txt = ""
+    text = (
+        "Este dataset contiene **coordenadas geográficas**: se identificó el par "
+        f"**latitud = «{lat_col}»** y **longitud = «{lon_col}»**{conf_txt}. La "
+        "detección combina el nombre de la columna y el rango de sus valores "
+        "(latitud en [−90, 90], longitud en [−180, 180])."
+    )
+    if reason:
+        text += f"\n\n*Criterio de detección:* {reason}."
+    return [model.Heading(text=CHAPTER_TITLE, level=1),
+            model.Markdown(text=text)]
+
+
+def _extent_blocks(extent: dict) -> list:
+    """KVTable with bbox/centroid/span + DataTable with the per-region counts."""
+    if not _is_dict(extent) or not extent.get("n_points"):
+        return []
+    blocks = []
+    bbox = extent.get("bbox") if _is_dict(extent.get("bbox")) else {}
+    centroid = extent.get("centroid") if _is_dict(extent.get("centroid")) else {}
+    hemi = extent.get("hemisphere") if _is_dict(extent.get("hemisphere")) else {}
+
+    rows = [("Puntos con coordenadas", _fmt_num(extent.get("n_points")))]
+    if bbox:
+        rows.append(("Latitud (mín. / máx.)",
+                     f"{_fmt_coord(bbox.get('lat_min'))} a "
+                     f"{_fmt_coord(bbox.get('lat_max'))}"))
+        rows.append(("Longitud (mín. / máx.)",
+                     f"{_fmt_coord(bbox.get('lon_min'))} a "
+                     f"{_fmt_coord(bbox.get('lon_max'))}"))
+    if centroid:
+        rows.append(("Centroide",
+                     f"{_fmt_coord(centroid.get('lat'))}, "
+                     f"{_fmt_coord(centroid.get('lon'))}"))
+    if extent.get("span_km") is not None:
+        rows.append(("Extensión (diagonal)", _fmt_km(extent.get("span_km"))))
+    if hemi:
+        n, s = hemi.get("north"), hemi.get("south")
+        e, w = hemi.get("east"), hemi.get("west")
+        rows.append(("Hemisferios",
+                     f"N {_fmt_num(n)} / S {_fmt_num(s)} · "
+                     f"E {_fmt_num(e)} / O {_fmt_num(w)}"))
+    blocks.append(model.KVTable(rows=rows, title="Extensión geográfica"))
+
+    by_region = extent.get("by_region")
+    if isinstance(by_region, list) and by_region:
+        total = sum(r.get("count", 0) for r in by_region if _is_dict(r)) or 0
+        rrows = []
+        for r in by_region:
+            if not _is_dict(r):
+                continue
+            cnt = r.get("count", 0)
+            pct = (cnt / total) if total else None
+            pct_txt = f"{pct * 100:.1f}%" if pct is not None else "—"
+            rrows.append([model._safe_str(r.get("region")), _fmt_num(cnt),
+                          pct_txt])
+        if rrows:
+            blocks.append(model.DataTable(
+                header=["Zona / país", "Puntos", "% del total"], rows=rrows,
+                title="Distribución por zona",
+                note="Asignación aproximada por bounding box de cada región "
+                     "(no es reverse-geocoding exacto de fronteras)."))
+    return blocks
+
+
+def _narrative_block(profile: dict, ctx: dict, extent: dict) -> list:
+    """A one-line narrative of where the points concentrate.
+
+    Uses the derived ``note`` from analyze_geo_extent by default; optionally
+    calls an LLM (ctx['run_geo_llm']) for a richer one-liner.
+    """
+    note = model._safe_str((extent or {}).get("note"))
+    if ctx.get("run_geo_llm"):
+        by_region = (extent or {}).get("by_region") or []
+        bbox = (extent or {}).get("bbox") or {}
+        try:
+            from core.ask_llm import ask_llm
+            prompt = (
+                "Eres un analista de datos. En UNA frase en español, describe "
+                "dónde se concentran geográficamente estos puntos. Sé concreto "
+                "y no inventes precisión que los datos no tienen.\n"
+                f"Conteo por zona: {by_region}\nBounding box: {bbox}."
+            )
+            out = ask_llm(prompt,
+                          model=ctx.get("geo_llm_model",
+                                        "claude-haiku-4-5-20251001"),
+                          echo=False)
+            if out and isinstance(out, str) and out.strip():
+                note = out.strip()
+        except Exception:  # noqa: BLE001 — degrade to the derived note.
+            pass
+    if not note:
+        return []
+    return [model.Markdown(text=f"**Interpretación.** {note}")]
+
+
+def _no_points_block(profile: dict, detected: dict) -> list:
+    """Degrade honestly when the raw coordinate arrays are not available."""
+    blocks = []
+    bbox = _bbox_from_profile(profile, detected)
+    if bbox:
+        rows = [
+            ("Latitud (mín. / máx.)",
+             f"{_fmt_coord(bbox.get('lat_min'))} a "
+             f"{_fmt_coord(bbox.get('lat_max'))}"),
+            ("Longitud (mín. / máx.)",
+             f"{_fmt_coord(bbox.get('lon_min'))} a "
+             f"{_fmt_coord(bbox.get('lon_max'))}"),
+        ]
+        blocks.append(model.KVTable(
+            rows=rows, title="Extensión geográfica (aproximada)"))
+    blocks.append(model.Note(
+        "No se incluyeron las coordenadas crudas en el contexto, por lo que el "
+        "mapa y el análisis por zona no se han dibujado. El bounding box "
+        "mostrado se deriva de los mínimos y máximos por columna. Para el "
+        "scatter geográfico completo, pasa los arrays en "
+        "ctx['geo_points'] = {'lats': [...], 'lons': [...]} o las columnas en "
+        "ctx['raw_numeric']."))
+    return blocks
+
+
+# --------------------------------------------------------------------------- #
+# Entry point.
+# --------------------------------------------------------------------------- #
+def build_geospatial(profile: dict, ctx: dict):
+    """Build the GEOSPATIAL Chapter, or None if the dataset has no coordinates.
+
+    Args:
+        profile: the ``eda`` group TableProfile dict.
+        ctx: presentation context; may carry ``geo_points``/``raw_numeric`` with
+            the raw coordinate arrays and the ``run_geo_llm`` flag.
+
+    Returns:
+        A ``model.Chapter`` with the geographic scatter + zone/country analysis,
+        or ``None`` when no latitude/longitude column pair is detected.
+    """
+    profile = profile or {}
+    ctx = ctx or {}
+    if not isinstance(profile, dict):
+        return None
+
+    detected = _detect_columns(profile)
+    lats, lons, source = _resolve_coords(profile, ctx, detected)
+
+    has_detection = bool((detected or {}).get("lat_col") and
+                         (detected or {}).get("lon_col"))
+    has_points = bool(lats and lons)
+    if not has_detection and not has_points:
+        return None  # chapter does not apply: no coordinates in this dataset.
+
+    # Labels for axes / intro. When only raw arrays were given (no detection),
+    # fall back to generic names.
+    lat_col = (detected or {}).get("lat_col") or "lat"
+    lon_col = (detected or {}).get("lon_col") or "lon"
+
+    blocks = _intro_block(detected, lat_col, lon_col)
+
+    if has_points:
+        clean_lats = _clean_floats(lats)
+        clean_lons = _clean_floats(lons)
+
+        # Zone / country analysis.
+        extent = {}
+        if analyze_geo_extent is not None:
+            try:
+                extent = analyze_geo_extent(clean_lats, clean_lons) or {}
+            except Exception:  # noqa: BLE001
+                extent = {}
+
+        # The geographic scatter figure (its own page/slide).
+        scatter = {}
+        if build_geo_scatter is not None:
+            try:
+                scatter = build_geo_scatter(clean_lats, clean_lons) or {}
+            except Exception:  # noqa: BLE001
+                scatter = {}
+        maker = _make_geo_scatter(scatter, lat_col, lon_col) if scatter else None
+        if maker is not None:
+            blocks.append(model.Figure(
+                make=maker,
+                caption="Cada punto es una observación situada por sus "
+                        "coordenadas; el recuadro rojo es el bounding box. La "
+                        "escala respeta la latitud (proyección equirectangular)."))
+        else:
+            blocks.append(model.Note(
+                "No se pudo construir el scatter geográfico a partir de las "
+                "coordenadas proporcionadas."))
+
+        blocks += _extent_blocks(extent)
+        blocks += _narrative_block(profile, ctx, extent)
+    else:
+        # Columns detected but no raw points available — degrade honestly.
+        blocks += _no_points_block(profile, detected)
+
+    if not blocks:
+        return None
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

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

@@ -0,0 +1,245 @@
+"""Tests for the GEOSPATIAL chapter — DoD: golden + edges + anti-cut.
+
+Self-contained: builds synthetic TableProfiles (no DuckDB) so the suite is fast
+and deterministic. The raw coordinate arrays are passed through ``ctx`` exactly
+as the chapter's contract documents (``ctx['geo_points']`` / ``ctx['raw_numeric']``).
+
+Verifies that the chapter detects the lat/lon pair, draws the geographic scatter
+figure, analyses the zone/country (bounding box + per-region counts), returns
+None when there are no coordinates, degrades honestly when the raw points are
+absent, and that a profile with long column names + many points + several
+regions renders to PDF and PPTX without cutting any text (long content wraps, it
+is never truncated).
+"""
+
+import os
+import re
+import tempfile
+
+from pypdf import PdfReader
+from pptx import Presentation
+
+from datascience.automatic_eda.chapters.geospatial import (
+    build_geospatial,
+    CHAPTER_VERSION,
+)
+from datascience.automatic_eda import build_document, render_pdf, render_pptx
+
+
+# --------------------------------------------------------------------------- #
+# Synthetic data helpers
+# --------------------------------------------------------------------------- #
+def _grid(lat0: float, lon0: float, n: int, spread: float = 1.0):
+    """A small deterministic cloud of n points around (lat0, lon0)."""
+    lats, lons = [], []
+    for i in range(n):
+        # deterministic pseudo-spread, no randomness.
+        f = (i % 11) / 11.0 - 0.5
+        g = (i % 7) / 7.0 - 0.5
+        lats.append(lat0 + f * spread)
+        lons.append(lon0 + g * spread)
+    return lats, lons
+
+
+def _profile_with_coords(lat_name="lat", lon_name="lon", lats=None, lons=None):
+    """A profile carrying a lat/lon column pair with valid ranges."""
+    lats = lats if lats is not None else [40.4, 41.0, 39.8, 40.1]
+    lons = lons if lons is not None else [-3.7, -3.6, -4.0, -3.9]
+    return {
+        "table": "lugares",
+        "columns": [
+            {"name": lat_name, "inferred_type": "numeric",
+             "numeric": {"min": min(lats), "max": max(lats),
+                         "mean": sum(lats) / len(lats)}},
+            {"name": lon_name, "inferred_type": "numeric",
+             "numeric": {"min": min(lons), "max": max(lons),
+                         "mean": sum(lons) / len(lons)}},
+            {"name": "valor", "inferred_type": "numeric",
+             "numeric": {"min": 0, "max": 100, "mean": 50}},
+        ],
+    }
+
+
+def _ctx_points(lats, lons):
+    return {"geo_points": {"lats": lats, "lons": lons}}
+
+
+def _kinds(chapter):
+    return [getattr(b, "kind", None) for b in chapter.blocks]
+
+
+def _tables(chapter):
+    return [b for b in chapter.blocks if getattr(b, "kind", None) == "data_table"]
+
+
+def _figures(chapter):
+    return [b for b in chapter.blocks if getattr(b, "kind", None) == "figure"]
+
+
+# --------------------------------------------------------------------------- #
+# Golden
+# --------------------------------------------------------------------------- #
+def test_golden_estructura_y_version():
+    lats, lons = [40.4, 41.0, 39.8, 40.1], [-3.7, -3.6, -4.0, -3.9]
+    ch = build_geospatial(_profile_with_coords(lats=lats, lons=lons),
+                          _ctx_points(lats, lons))
+    assert ch is not None
+    assert ch.id == "geospatial"
+    assert ch.version == CHAPTER_VERSION
+    kinds = _kinds(ch)
+    # intro heading + markdown + scatter figure + extent kv + per-region table.
+    assert "heading" in kinds
+    assert "markdown" in kinds
+    assert "figure" in kinds, "falta el scatter geográfico"
+    assert "kv_table" in kinds, "falta la tabla de extensión"
+
+
+def test_golden_detecta_columnas_y_nombra_ejes():
+    lats, lons = _grid(40.4, -3.7, 30, spread=0.8)
+    prof = _profile_with_coords("latitude", "longitude", lats, lons)
+    ch = build_geospatial(prof, _ctx_points(lats, lons))
+    intro = [b for b in ch.blocks if b.kind == "markdown"][0].text
+    assert "latitude" in intro and "longitude" in intro
+
+
+def test_golden_figura_es_perezosa_y_dibujable():
+    lats, lons = _grid(40.4, -3.7, 50, spread=0.6)
+    ch = build_geospatial(_profile_with_coords(lats=lats, lons=lons),
+                          _ctx_points(lats, lons))
+    fig_block = _figures(ch)[0]
+    assert fig_block.make is not None and fig_block.fig is None  # lazy
+    fig = fig_block.make()  # must draw without raising
+    assert fig is not None
+    import matplotlib.pyplot as plt
+    plt.close(fig)
+
+
+def test_golden_analisis_por_zona_espana():
+    lats, lons = _grid(40.4, -3.7, 40, spread=0.5)  # Madrid area
+    ch = build_geospatial(_profile_with_coords(lats=lats, lons=lons),
+                          _ctx_points(lats, lons))
+    tables = _tables(ch)
+    region_tbl = [t for t in tables if "zona" in (t.title or "").lower()]
+    assert region_tbl, "falta la tabla por zona/país"
+    flat = " ".join(" ".join(str(c) for c in r) for r in region_tbl[0].rows)
+    # Spain-area points must resolve to a Spain/European region, not empty.
+    assert region_tbl[0].rows
+    assert any(c for c in (region_tbl[0].rows[0]))
+
+
+def test_golden_raw_numeric_source():
+    """Coordinates can also come from ctx['raw_numeric'] keyed by detected cols."""
+    lats, lons = _grid(48.85, 2.35, 25, spread=0.4)  # Paris area
+    prof = _profile_with_coords("lat", "lon", lats, lons)
+    ctx = {"raw_numeric": {"lat": lats, "lon": lons}}
+    ch = build_geospatial(prof, ctx)
+    assert ch is not None
+    assert _figures(ch), "el scatter debe construirse desde raw_numeric"
+
+
+# --------------------------------------------------------------------------- #
+# Edges
+# --------------------------------------------------------------------------- #
+def test_edge_sin_coordenadas_devuelve_none():
+    prof = {
+        "table": "ventas",
+        "columns": [
+            {"name": "precio", "inferred_type": "numeric",
+             "numeric": {"min": 0, "max": 1000}},
+            {"name": "categoria", "inferred_type": "text"},
+        ],
+    }
+    assert build_geospatial(prof, {}) is None
+
+
+def test_edge_none_y_vacio_no_rompen():
+    assert build_geospatial(None, None) is None
+    assert build_geospatial({}, {}) is None
+    assert build_geospatial({"columns": []}, {}) is None
+    assert build_geospatial("not a dict", {}) is None
+
+
+def test_edge_nombre_lat_pero_rango_invalido_no_aplica():
+    """A column named 'lat' whose values are out of [-90,90] is NOT a coordinate."""
+    prof = {
+        "table": "x",
+        "columns": [
+            {"name": "lat", "inferred_type": "numeric",
+             "numeric": {"min": 1000, "max": 9999}},
+            {"name": "lon", "inferred_type": "numeric",
+             "numeric": {"min": 1000, "max": 9999}},
+        ],
+    }
+    assert build_geospatial(prof, {}) is None
+
+
+def test_edge_columnas_detectadas_sin_puntos_degrada():
+    """Detected lat/lon but no raw arrays -> honest note + approx bbox, no crash."""
+    prof = _profile_with_coords(lats=[40.0, 41.0], lons=[-3.0, -4.0])
+    ch = build_geospatial(prof, {})  # no geo_points / raw_numeric
+    assert ch is not None
+    assert not _figures(ch), "sin puntos no debe dibujarse el scatter"
+    notes = [b for b in ch.blocks if b.kind == "note"]
+    assert notes and "coordenadas crudas" in notes[0].text
+
+
+def test_edge_coordenadas_con_nan_se_filtran():
+    lats = [40.4, float("nan"), 41.0, None, 39.8]
+    lons = [-3.7, -3.6, float("nan"), -3.9, -4.0]
+    ch = build_geospatial(_profile_with_coords(lats=[39.8, 41.0],
+                                               lons=[-4.0, -3.6]),
+                          _ctx_points(lats, lons))
+    assert ch is not None  # must not raise on NaN/None
+
+
+# --------------------------------------------------------------------------- #
+# Anti-cut: long names + many points + several regions render without truncation
+# --------------------------------------------------------------------------- #
+def _multiregion_points(per: int = 700):
+    """Points spread across Spain, France and the USA to fill the region table."""
+    lats, lons = [], []
+    for (la, lo) in ((40.4, -3.7), (48.85, 2.35), (39.0, -98.0)):
+        gl, gn = _grid(la, lo, per, spread=2.0)
+        lats += gl
+        lons += gn
+    return lats, lons
+
+
+def test_anticut_pdf_y_pptx_no_truncan():
+    lat_name = "latitud_geografica_del_punto_de_observacion_registrado"
+    lon_name = "longitud_geografica_del_punto_de_observacion_registrado"
+    lats, lons = _multiregion_points(700)
+    prof = _profile_with_coords(lat_name, lon_name, lats, lons)
+    ctx = {"geo_points": {"lats": lats, "lons": lons}}
+
+    full = build_document(prof, ctx)
+    assert any(c.id == "geospatial" for c in full)
+    chapters = [c for c in full if c.id == "geospatial"]
+
+    with tempfile.TemporaryDirectory() as d:
+        pdf = os.path.join(d, "g.pdf")
+        pptx = os.path.join(d, "g.pptx")
+        rp = render_pdf(chapters, pdf, {"title": "EDA"})
+        rx = render_pptx(chapters, pptx, {"title": "EDA"})
+        assert os.path.exists(pdf) and os.path.exists(pptx)
+        assert (rp or {}).get("n_pages", 0) >= 1
+
+        # PDF: the long lat column name survives whole (wraps, not cut) and there
+        # is no truncation marker in this chapter.
+        pdf_txt = "".join((pg.extract_text() or "") for pg in PdfReader(pdf).pages)
+        assert "…" not in pdf_txt and "..." not in pdf_txt
+        norm = re.sub(r"\s+", "", pdf_txt)
+        assert lat_name in norm, "el nombre largo de la columna se cortó en el PDF"
+
+        # PPTX: long name present in some shape/cell, untruncated.
+        allt = []
+        for s in Presentation(pptx).slides:
+            for sh in s.shapes:
+                if sh.has_text_frame:
+                    allt.append(sh.text_frame.text)
+                if sh.has_table:
+                    for row in sh.table.rows:
+                        for c in row.cells:
+                            allt.append(c.text)
+        joined = re.sub(r"\s+", "", "\n".join(allt))
+        assert lat_name in joined, "el nombre largo de la columna se cortó en el PPTX"

python/functions/datascience/automatic_eda/chapters_registry.py

@@ -28,12 +28,12 @@ from . import model
 CHAPTER_ORDER = [
     "portada",       # cover
     "overview",      # df.head + columns/types/nulls/examples + describe
+    "analisis_llm",  # LLM interpretation — sits next to overview (user request)
     "num_distr",     # numeric distributions
     "cat_distr",     # categorical distributions
     "calidad",       # data quality
     "correlacion",   # correlations / associations
     "modelos",       # cheap models (PCA/KMeans/outliers)
-    "analisis_llm",  # LLM interpretation
     "timeseries",    # time-series analysis
     "geospatial",    # geospatial
     "agregacion",    # aggregations / pivots

python/functions/datascience/automatic_eda/render_pdf_impl.py

@@ -169,6 +169,38 @@ def _place_text_lines(st: _PdfState, lines: list, fs: float, color: str,
         st.y += lh
 
 
+def _place_rich_lines(st: _PdfState, rich_lines: list, fs: float, color: str,
+                      indent: float = 0.0, prefixes=None) -> None:
+    """Draw pre-wrapped lines of styled segments (bold spans rendered bold).
+
+    Each line is ``[(text, is_bold), ...]``. Segments are placed left-to-right,
+    advancing x by the deterministic character grid (same metric the wrapper
+    used), so a bold span is rendered with ``fontweight='bold'`` without
+    changing the line's measured width — the no-cut guarantee is preserved.
+    ``prefixes`` is an optional ``(first_line, other_lines)`` pair (e.g. a
+    bullet) drawn before the segments.
+    """
+    lh = tl.line_height_in(fs)
+    cw = tl.avg_char_width_in(fs)
+    for idx, segs in enumerate(rich_lines):
+        _ensure_space(st, lh)
+        x = _ML + indent
+        if prefixes is not None:
+            prefix = prefixes[0] if idx == 0 else prefixes[1]
+            if prefix:
+                st.fig.text(_xf(x), _yf(st.y), prefix, fontsize=fs, color=color,
+                            ha="left", va="top")
+                x += cw * len(prefix)
+        for seg_text, is_bold in segs:
+            if seg_text == "":
+                continue
+            st.fig.text(_xf(x), _yf(st.y), seg_text, fontsize=fs, color=color,
+                        ha="left", va="top",
+                        fontweight="bold" if is_bold else "normal")
+            x += cw * len(seg_text)
+        st.y += lh
+
+
 def _place_markdown(st: _PdfState, block) -> None:
     raw = getattr(block, "text", "") or ""
     md_lines = str(raw).split("\n")
@@ -208,29 +240,25 @@ def _place_markdown(st: _PdfState, block) -> None:
             i += 1
             continue
         if stripped.startswith("- ") or stripped.startswith("* "):
-            content = tl.strip_inline_md(stripped[2:])
+            content = stripped[2:]  # keep inline markers for bold rendering.
             bullet_chars = tl.chars_per_line(_USABLE_W - 0.22, _FS_BODY)
-            wrapped = tl.wrap(content, bullet_chars)
-            first = True
-            for w in wrapped:
-                prefix = "•  " if first else "   "
-                _place_text_lines(st, [prefix + w], _FS_BODY, _INK,
-                                  indent=0.0)
-                first = False
+            rich = tl.wrap_rich(content, bullet_chars)
+            _place_rich_lines(st, rich, _FS_BODY, _INK,
+                              prefixes=("•  ", "   "))
             i += 1
             continue
         # Plain paragraph (gather following plain lines into one paragraph).
-        para = [tl.strip_inline_md(stripped)]
+        para = [stripped]  # keep inline markers; wrap_rich renders **bold**.
         j = i + 1
         while j < n:
             nxt = md_lines[j].strip()
             if nxt == "" or nxt.startswith(("|", "#", "- ", "* ")):
                 break
-            para.append(tl.strip_inline_md(nxt))
+            para.append(nxt)
             j += 1
         text = " ".join(para)
         max_chars = tl.chars_per_line(_USABLE_W, _FS_BODY)
-        _place_text_lines(st, tl.wrap(text, max_chars), _FS_BODY, _INK)
+        _place_rich_lines(st, tl.wrap_rich(text, max_chars), _FS_BODY, _INK)
         i = j
     st.y += _GAP
 

python/functions/datascience/automatic_eda/render_pptx_impl.py

@@ -151,6 +151,42 @@ def _add_text(st: _PptxState, lines: list, fs: float, color, bold=False,
     st.y += height
 
 
+def _add_rich_text(st: _PptxState, rich_lines: list, fs: float, color,
+                   indent=0.0, bullet=False) -> None:
+    """Add pre-wrapped lines of styled segments as one paragraph per line.
+
+    Each line is ``[(text, is_bold), ...]``; every segment becomes its own run
+    so ``**bold**`` spans render with native PowerPoint bold (``run.font.bold``)
+    without affecting the measured height (one paragraph per pre-wrapped line).
+    """
+    lh = tl.line_height_in(fs)
+    height = lh * len(rich_lines) + 0.05
+    _ensure(st, height)
+    box = st.slide.shapes.add_textbox(
+        Inches(_ML + indent), Inches(st.y), Inches(_USABLE_W - indent),
+        Inches(height))
+    tf = box.text_frame
+    tf.word_wrap = True
+    first = True
+    for segs in rich_lines:
+        p = tf.paragraphs[0] if first else tf.add_paragraph()
+        first = False
+        if bullet:
+            r0 = p.add_run()
+            r0.text = "•  "
+            r0.font.size = Pt(fs)
+            r0.font.color.rgb = _rgb(color)
+        for seg_text, is_bold in segs:
+            if seg_text == "":
+                continue
+            run = p.add_run()
+            run.text = seg_text
+            run.font.size = Pt(fs)
+            run.font.bold = bool(is_bold)
+            run.font.color.rgb = _rgb(color)
+    st.y += height
+
+
 def _place_heading(st: _PptxState, block) -> None:
     level = max(1, min(3, int(getattr(block, "level", 1) or 1)))
     fs = {1: _FS_H1, 2: _FS_H2, 3: _FS_H3}[level]
@@ -196,22 +232,23 @@ def _place_markdown(st: _PptxState, block) -> None:
             i += 1
             continue
         if stripped.startswith("- ") or stripped.startswith("* "):
-            content = tl.strip_inline_md(stripped[2:])
-            lines = tl.wrap(content, tl.chars_per_line(_USABLE_W - 0.3, _FS_BODY))
-            _add_text(st, lines, _FS_BODY, _INK, bullet=True)
+            content = stripped[2:]  # keep inline markers for bold rendering.
+            rich = tl.wrap_rich(content,
+                                tl.chars_per_line(_USABLE_W - 0.3, _FS_BODY))
+            _add_rich_text(st, rich, _FS_BODY, _INK, bullet=True)
             i += 1
             continue
-        para = [tl.strip_inline_md(stripped)]
+        para = [stripped]  # keep inline markers; wrap_rich renders **bold**.
         j = i + 1
         while j < n:
             nxt = md_lines[j].strip()
             if nxt == "" or nxt.startswith(("|", "#", "- ", "* ")):
                 break
-            para.append(tl.strip_inline_md(nxt))
+            para.append(nxt)
             j += 1
         text = " ".join(para)
-        _add_text(st, tl.wrap(text, tl.chars_per_line(_USABLE_W, _FS_BODY)),
-                  _FS_BODY, _INK)
+        _add_rich_text(st, tl.wrap_rich(text, tl.chars_per_line(_USABLE_W, _FS_BODY)),
+                       _FS_BODY, _INK)
         i = j
     st.y += _GAP
 

python/functions/datascience/automatic_eda/text_layout.py

@@ -15,8 +15,15 @@ overflowing — that is wrapping, not loss: every character is still rendered.
 
 from __future__ import annotations
 
+import re
 import textwrap
 
+# Inline span markers: ``**bold**`` / ``__bold__`` (rendered bold) and
+# `` `code` `` (markers removed, not styled). Matched non-greedily so the
+# shortest balanced pair wins. Unbalanced leftovers are stripped afterwards so
+# the visible text matches ``strip_inline_md`` exactly.
+_INLINE_SPAN_RE = re.compile(r"(\*\*.+?\*\*|__.+?__|`.+?`)")
+
 
 def avg_char_width_in(fontsize_pt: float) -> float:
     """Approximate average glyph width in inches for a sans-serif font.
@@ -84,6 +91,137 @@ def strip_inline_md(text: str) -> str:
     return s
 
 
+def _strip_leftover_markers(s: str) -> str:
+    """Drop any unbalanced inline markers from a plain (non-span) fragment.
+
+    Keeps the visible text identical to :func:`strip_inline_md` even when a
+    ``**`` / ``__`` / `` ` `` has no matching closing marker.
+    """
+    for marker in ("**", "__", "`"):
+        s = s.replace(marker, "")
+    return s
+
+
+def parse_inline_bold(text: str):
+    """Split ``text`` into ``[(fragment, is_bold), ...]`` preserving order.
+
+    ``**...**`` and ``__...__`` spans become bold fragments (markers removed);
+    `` `code` `` keeps its text without the backticks and is not bold; any other
+    text is emitted verbatim with unbalanced markers stripped. The concatenation
+    of all fragment texts equals :func:`strip_inline_md` of the input — so the
+    *visible* characters (and therefore line wrapping) are unchanged; only the
+    bold flag is added. Adjacent fragments of the same weight are merged.
+    """
+    s = "" if text is None else str(text)
+    if not s:
+        return []
+    out = []
+
+    def _emit(fragment: str, bold: bool) -> None:
+        if fragment == "":
+            return
+        if out and out[-1][1] == bold:
+            out[-1] = (out[-1][0] + fragment, bold)
+        else:
+            out.append((fragment, bold))
+
+    pos = 0
+    for m in _INLINE_SPAN_RE.finditer(s):
+        if m.start() > pos:
+            _emit(_strip_leftover_markers(s[pos:m.start()]), False)
+        tok = m.group(0)
+        if tok.startswith("**") and tok.endswith("**"):
+            _emit(tok[2:-2], True)
+        elif tok.startswith("__") and tok.endswith("__"):
+            _emit(tok[2:-2], True)
+        else:  # `code`
+            _emit(tok[1:-1], False)
+        pos = m.end()
+    if pos < len(s):
+        _emit(_strip_leftover_markers(s[pos:]), False)
+    return out
+
+
+def _hard_split(word: str, max_chars: int):
+    """Split a single long token into <= max_chars chunks (never loses chars)."""
+    return [word[i:i + max_chars] for i in range(0, len(word), max_chars)] or [""]
+
+
+def wrap_rich(text: str, max_chars: int):
+    """Word-wrap ``text`` to ``max_chars`` while preserving inline bold spans.
+
+    Returns ``list[list[(fragment, is_bold)]]`` — one inner list of styled
+    fragments per output line; concatenating an inner list's fragment texts is
+    the visible line. Wrapping is word-aware and hard-splits over-long tokens, so
+    no line exceeds ``max_chars`` (the renderers measure these very lines, so the
+    no-cut guarantee holds). Bold spans never widen a line: only the bold flag is
+    carried, the visible width is identical to :func:`wrap`.
+    """
+    if max_chars < 1:
+        max_chars = 1
+    spans = parse_inline_bold(text)
+    if not spans:
+        return [[("", False)]]
+
+    # Flatten to (word, is_bold) tokens, honoring hard newlines as line breaks.
+    # A token list of None marks a forced line break.
+    tokens = []  # each: (word, bold) or ("\n", None)
+    for frag, bold in spans:
+        parts = frag.split("\n")
+        for pi, part in enumerate(parts):
+            if pi > 0:
+                tokens.append(("\n", None))
+            for word in part.split(" "):
+                if word == "":
+                    continue
+                tokens.append((word, bold))
+
+    lines = []          # list[list[(seg, bold)]]
+    cur = []            # list[(word, bold)]
+    cur_len = 0
+
+    def _flush():
+        nonlocal cur, cur_len
+        # Merge adjacent same-weight words (with separating spaces) into segments.
+        merged = []
+        for k, (word, bold) in enumerate(cur):
+            piece = word if k == 0 else " " + word
+            if merged and merged[-1][1] == bold:
+                merged[-1] = (merged[-1][0] + piece, bold)
+            else:
+                merged.append((piece, bold))
+        lines.append(merged or [("", False)])
+        cur = []
+        cur_len = 0
+
+    for word, bold in tokens:
+        if bold is None:  # forced newline
+            _flush()
+            continue
+        if len(word) > max_chars:
+            if cur:
+                _flush()
+            chunks = _hard_split(word, max_chars)
+            for ci, chunk in enumerate(chunks):
+                if ci < len(chunks) - 1:
+                    lines.append([(chunk, bold)])
+                else:
+                    cur = [(chunk, bold)]
+                    cur_len = len(chunk)
+            continue
+        add = len(word) if cur_len == 0 else cur_len + 1 + len(word)
+        if cur_len != 0 and add > max_chars:
+            _flush()
+            cur = [(word, bold)]
+            cur_len = len(word)
+        else:
+            cur.append((word, bold))
+            cur_len = add
+    if cur:
+        _flush()
+    return lines or [[("", False)]]
+
+
 def parse_md_table(lines: list):
     """Parse consecutive ``| a | b |`` lines into ``(header, rows)`` or None.
 

python/functions/datascience/build_eda_render_ctx.md

@@ -0,0 +1,114 @@
+---
+name: build_eda_render_ctx
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def build_eda_render_ctx(db_path: str, table: str, profile: dict, backend: str = 'duckdb', sample: int = 5000, base_ctx: dict = None) -> dict"
+description: "Constructor del `ctx` de datos crudos del motor AutomaticEDA. Dado un db_path+table (DuckDB o Postgres) y el TableProfile AGREGADO ya calculado por profile_table, produce el dict ctx que los renderers (render_automatic_eda_pdf/_pptx -> build_document(profile, ctx)) pasan a los capitulos que necesitan DATOS CRUDOS no presentes en el perfil agregado: modelos (project_clusters_2d en vivo), timeseries, geospatial y agregacion (groupby/pivot push-down). NO trae tablas enteras a RAM: muestrea con LIMIT sample y delega el push-down de la serie en extract_timeseries_raw. Construye el lector read-only query_fn(sql)->dict igual que profile_table (closure sobre duckdb_query_readonly / pg_query). Estilo dict-no-throw del grupo eda: NUNCA lanza; si una pieza falla, degrada esa clave a ausente/[] y sigue. Devuelve el ctx dict directamente (NO un wrapper {status,...}); se pasa tal cual como meta={'ctx': <ese dict>}. Claves de datos que produce: raw_numeric (muestra cruda alineada por fila), timeseries_raw (fechas+series), geo_points (lats/lons) y db_path+table para el push-down de agregacion. Respeta base_ctx: parte de una copia y solo AÑADE las claves de datos; las de presentacion (dataset_name, source_origin, ...) no se pisan."
+tags: [eda, datascience, automatic-eda, render, ctx, extraction, read-only, duckdb, postgres, python]
+uses_functions: [detect_time_column_py_datascience, extract_timeseries_raw_py_datascience, detect_latlon_columns_py_datascience, duckdb_query_readonly_py_infra, pg_query_py_infra]
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: []
+params:
+  - name: db_path
+    desc: "ruta al archivo DuckDB, o DSN PostgreSQL si backend='postgres'. Se guarda tal cual en ctx['db_path'] (el capitulo agregacion lo usa para el groupby/pivot push-down via DuckDB) y se inyecta en el closure query_fn. No se valida aqui: si la base no existe, las queries devuelven status error y las claves de datos se omiten."
+  - name: table
+    desc: "nombre de la tabla. Se escapa con comillas dobles en las queries (raw_numeric y timeseries) y se guarda en ctx['table']."
+  - name: profile
+    desc: "TableProfile AGREGADO producido por profile_table. Solo se lee su clave `columns` (lista de ColumnProfile dict con name / inferred_type / numeric.{min,max} / semantic_type). Lectura defensiva: si no es dict o no tiene columns, se trata como []. NO se traen las filas crudas de aqui — se muestrean de la base."
+  - name: backend
+    desc: "'duckdb' (default) o 'postgres'. Selecciona el lector read-only del registry (duckdb_query_readonly / pg_query). Cualquier otro valor devuelve el base_ctx tal cual, SIN añadir claves de datos (ni siquiera db_path/table)."
+  - name: sample
+    desc: "maximo de filas a muestrear (clausula LIMIT) tanto para raw_numeric (una sola query SELECT de las numericas) como para timeseries_raw (max_rows de extract_timeseries_raw). Default 5000. Acota memoria y tiempo de render."
+  - name: base_ctx
+    desc: "dict opcional con claves de PRESENTACION ya preparadas (dataset_name, source_origin, ...). Se parte de una copia y NO se pisan sus claves; solo se añaden las de datos. Default None -> {}."
+output: "El dict `ctx` directamente (NO un wrapper {status,...}); se pasa tal cual como meta={'ctx': <ese dict>} a render_automatic_eda_pdf/pptx. Nunca lanza. Para backends validos contiene SIEMPRE db_path + table, y opcionalmente: raw_numeric {col:[float|None,...]} (muestra cruda alineada por fila; omitida si no hay numericas o falla la query), timeseries_raw {time_col, t:[iso...], series:{col:[float|None,...]}} (solo si hay columna temporal + numericas y trae filas), geo_points {lats:[...], lons:[...]} (solo si se detecta par lat/lon y ambas estan en raw_numeric). Ante fallo global devuelve al menos {**base_ctx, 'db_path': db_path, 'table': table}. Backend desconocido -> base_ctx tal cual sin claves de datos."
+tested: true
+tests: ["test_db_path_y_table_en_ctx", "test_raw_numeric_con_columnas_numericas", "test_timeseries_raw_con_fecha", "test_geo_points_con_latlon", "test_sin_fecha_no_hay_timeseries", "test_base_ctx_preservado"]
+test_file_path: "python/functions/datascience/build_eda_render_ctx_test.py"
+file_path: "python/functions/datascience/build_eda_render_ctx.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience import build_eda_render_ctx, render_automatic_eda_pdf
+from datascience import profile_table  # opcional: para obtener el TableProfile
+
+# 1) Perfil agregado de la tabla (push-down, sin RAM).
+prof = profile_table("data/ventas.duckdb", "ventas_geo", write_report=False)["profile"]
+
+# 2) ctx de datos crudos para los capitulos (muestrea con LIMIT, no carga todo).
+ctx = build_eda_render_ctx(
+    "data/ventas.duckdb", "ventas_geo", prof,
+    backend="duckdb", sample=5000,
+    base_ctx={"dataset_name": "Ventas con geolocalizacion"},
+)
+# ctx == {
+#   "dataset_name": "Ventas con geolocalizacion",   # preservado del base_ctx
+#   "db_path": "data/ventas.duckdb", "table": "ventas_geo",
+#   "raw_numeric": {"ventas": [1200.5, ...], "lat": [40.41, ...], "lon": [-3.70, ...]},
+#   "timeseries_raw": {"time_col": "fecha", "t": ["2024-01-01", ...], "series": {...}},
+#   "geo_points": {"lats": [40.41, ...], "lons": [-3.70, ...]},
+# }
+
+# 3) Se entrega tal cual a los renderers via meta={"ctx": ctx}.
+render_automatic_eda_pdf(prof, "reports/eda.pdf", meta={"ctx": ctx})
+```
+
+## Cuando usarla
+
+Justo antes de renderizar un AutomaticEDA (PDF o PPTX), cuando ya tienes el
+TableProfile AGREGADO de `profile_table` pero los capitulos de modelos,
+timeseries, geospatial y agregacion necesitan DATOS CRUDOS que el perfil
+agregado no lleva (la muestra numerica alineada por fila, la serie cronologica,
+el par lat/lon, y el db_path/table para el push-down del groupby/pivot). Es el
+puente entre el perfil agregado y `build_document(profile, ctx)`: una sola
+llamada produce el `ctx` completo muestreando con `LIMIT` en vez de cargar la
+tabla entera en memoria.
+
+## Gotchas
+
+- **Impura**: lee de la base de datos a traves de `query_fn` (closure sobre
+  `duckdb_query_readonly` / `pg_query`). No abre conexiones fuera de esos
+  wrappers del registry. Estilo dict-no-throw del grupo `eda`: NUNCA lanza; ante
+  cualquier fallo (query, deteccion, render de una clave) degrada esa clave a
+  ausente/`[]` y sigue. Ante un fallo global devuelve al menos
+  `{**base_ctx, "db_path": db_path, "table": table}`.
+- **`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 ctx parcial. Es metadata, no
+  comportamiento.
+- **Devuelve el ctx dict directamente, NO un wrapper `{status,...}`**: a
+  diferencia de `extract_timeseries_raw` / `profile_table`, esta funcion es el
+  ultimo eslabon antes del render y su salida se pasa tal cual como
+  `meta={"ctx": <ese dict>}`. No envuelvas su retorno.
+- **Backend desconocido**: con un `backend` que no sea `duckdb` ni `postgres`
+  devuelve el `base_ctx` tal cual, SIN claves de datos (ni siquiera
+  `db_path`/`table`). Comprueba el backend antes si dependes de esas claves.
+- **Alineacion por fila de `raw_numeric`**: `raw_numeric[col]` tiene una entrada
+  por fila muestreada (un valor no convertible a float queda como `None`, no se
+  descarta la fila) porque `project_clusters_2d` descarta filas listwise: todas
+  las columnas deben tener la MISMA longitud. `geo_points` se construye desde
+  `raw_numeric` para heredar esa alineacion.
+- **`geo_points` exige lat/lon en `raw_numeric`**: el par lat/lon solo se adjunta
+  si ambas columnas se detectaron (nombre+rango) Y figuran en `raw_numeric`
+  (es decir, son numericas en el perfil). Si la tabla guarda lat/lon como texto
+  no promovido a numeric, no apareceran; el capitulo geospatial sabe degradar.
+- **`timeseries_raw` depende del orden del backend**: hereda el `ORDER BY
+  "time_col"` de `extract_timeseries_raw`. Si la columna temporal esta guardada
+  como texto no ordenable lexicograficamente (p.ej. `DD/MM/YYYY`), el orden no
+  sera el cronologico real — normaliza la columna a date/timestamp antes.
+- **`LIMIT sample`**: con tablas grandes obtienes el primer tramo (raw_numeric
+  por orden fisico, timeseries por orden cronologico), no un muestreo uniforme.
+  Sube `sample` si necesitas mas cobertura.
+- **No loguear los datos crudos**: `raw_numeric` / `timeseries_raw` /
+  `geo_points` pueden contener datos sensibles. En trazas usa solo conteos y
+  nombres de columna, no el ctx completo.

python/functions/datascience/build_eda_render_ctx.py

@@ -0,0 +1,200 @@
+"""build_eda_render_ctx — constructor del `ctx` de datos crudos del motor AutomaticEDA.
+
+Funcion impura (lee de la base de datos) del grupo de capacidad `eda`. Dado un
+``db_path`` + ``table`` (DuckDB o PostgreSQL) y el ``TableProfile`` AGREGADO ya
+calculado por ``profile_table``, produce el dict ``ctx`` que los renderers
+(``render_automatic_eda_pdf`` / ``render_automatic_eda_pptx`` ->
+``build_document(profile, ctx)``) pasan a los capitulos que necesitan DATOS
+CRUDOS no presentes en el perfil agregado: modelos (``project_clusters_2d`` en
+vivo), timeseries, geospatial y agregacion (groupby/pivot push-down).
+
+NO trae tablas enteras a RAM: muestrea con ``LIMIT sample`` y, para la serie
+temporal, delega el push-down en ``extract_timeseries_raw`` (una sola query
+ordenada). El lector read-only ``query_fn(sql) -> dict`` se construye igual que
+en ``profile_table`` (un closure sobre ``duckdb_query_readonly`` / ``pg_query``)
+y nunca abre conexiones fuera de esos wrappers.
+
+Estilo dict-no-throw del grupo `eda`: la funcion NUNCA lanza. Si una pieza falla
+(query, deteccion, render de una clave), esa clave se degrada a ausente / lista
+vacia y el resto del ctx se construye igual. Ante un fallo global devuelve al
+menos ``{**base_ctx, "db_path": db_path, "table": table}``.
+
+Claves de DATOS que produce (las consumen los capitulos):
+  - ``raw_numeric``    : {col: [float|None, ...]} muestra cruda de las columnas
+                         numericas, ALINEADA POR FILA (una entrada por fila aunque
+                         sea None). La leen modelos (clustering 2D en vivo) y
+                         geospatial (lat/lon salen de aqui).
+  - ``timeseries_raw`` : {time_col, t: [iso...], series: {col: [float|None, ...]}}.
+                         La lee el capitulo TIMESERIES.
+  - ``geo_points``     : {lats: [...], lons: [...]} listas alineadas (ya floats).
+                         La lee el capitulo GEOSPATIAL.
+  - ``db_path``, ``table`` : los usa el capitulo AGREGACION para el groupby/pivot
+                         push-down via DuckDB.
+
+Las claves de PRESENTACION que traiga ``base_ctx`` (dataset_name, source_origin,
+...) NO se pisan: esta funcion solo AÑADE las claves de datos sobre una copia.
+"""
+
+
+def _to_float(value):
+    """Convierte un valor a float de forma defensiva. None si no es convertible.
+
+    Un bool es subclase de int en Python pero nunca es un valor numerico de
+    serie/coordenada valido, asi que se trata como None (mismo criterio que
+    extract_timeseries_raw / detect_latlon_columns).
+    """
+    if value is None or isinstance(value, bool):
+        return None
+    if isinstance(value, (int, float)):
+        return float(value)
+    s = str(value).strip()
+    if not s:
+        return None
+    try:
+        return float(s)
+    except (TypeError, ValueError):
+        return None
+
+
+def build_eda_render_ctx(db_path, table, profile, backend="duckdb", sample=5000, base_ctx=None):
+    """Construye el ctx de datos crudos para los renderers de AutomaticEDA.
+
+    Args:
+        db_path: ruta al archivo DuckDB, o DSN PostgreSQL si backend="postgres".
+            Se guarda tal cual en ctx["db_path"] (el capitulo agregacion lo usa
+            para el push-down).
+        table: nombre de la tabla. Se escapa con comillas dobles en las queries y
+            se guarda en ctx["table"].
+        profile: TableProfile agregado producido por profile_table. Solo se lee
+            su clave ``columns`` (lista de ColumnProfile dict con name /
+            inferred_type / numeric.{min,max} / semantic_type). Lectura
+            defensiva: si no es dict o no tiene columns, se trata como [].
+        backend: "duckdb" (default) o "postgres". Selecciona el lector read-only
+            (duckdb_query_readonly / pg_query). Cualquier otro valor devuelve el
+            base_ctx tal cual, sin añadir claves de datos.
+        sample: maximo de filas a muestrear (clausula LIMIT) tanto para
+            raw_numeric como para timeseries_raw. Default 5000.
+        base_ctx: dict opcional con claves de presentacion ya preparadas
+            (dataset_name, source_origin, ...). Se parte de una copia y NO se
+            pisan sus claves; solo se añaden las de datos. Default None -> {}.
+
+    Returns:
+        El dict ``ctx`` directamente (NO un wrapper {status,...}): se pasa tal
+        cual como ``meta={"ctx": <ese dict>}`` a render_automatic_eda_pdf/pptx.
+        Nunca lanza. Claves que puede contener: raw_numeric, timeseries_raw,
+        geo_points (omitidas si no aplican o fallan), y siempre db_path + table
+        para backends validos.
+    """
+    # Copia de base_ctx: nunca mutamos el dict del caller. Las claves de
+    # presentacion que ya traiga se conservan; las de datos se añaden encima.
+    ctx = dict(base_ctx) if isinstance(base_ctx, dict) else {}
+
+    try:
+        # 1) Lector read-only del backend activo, construido EXACTAMENTE como en
+        # profile_table (closure sobre el wrapper del registry). Imports perezosos
+        # dentro de la funcion: este modulo vive en el paquete `datascience`, asi
+        # que importar sus hermanas a nivel de modulo crearia un ciclo al cargar
+        # el __init__ del paquete. Lazy import rompe el ciclo y respeta el
+        # contrato (imports explicitos, sin `import *`).
+        if backend == "duckdb":
+            from infra import duckdb_query_readonly
+
+            def query_fn(sql):
+                return duckdb_query_readonly(db_path, sql)
+
+        elif backend == "postgres":
+            from infra import pg_query
+
+            def query_fn(sql):
+                return pg_query(db_path, sql)
+
+        else:
+            # Backend desconocido: devolver base_ctx tal cual, sin claves de datos.
+            return ctx
+
+        # 7) db_path + table SIEMPRE (para backends validos): el capitulo
+        # agregacion los necesita para el groupby/pivot push-down via DuckDB.
+        ctx["db_path"] = db_path
+        ctx["table"] = table
+
+        # 2) Columnas del perfil agregado (lectura defensiva).
+        cols = profile.get("columns") if isinstance(profile, dict) else None
+        cols = cols or []
+
+        # 3) Deteccion temporal/numerica con la funcion PURA del registry.
+        from datascience import detect_time_column
+
+        det = detect_time_column(cols)
+        time_col = det.get("time_col")
+        numeric_cols = det.get("numeric_cols") or []
+
+        # 4) raw_numeric: muestra de las columnas numericas CRUDAS, ALINEADAS POR
+        # FILA en UNA sola query. Cada columna queda con una entrada por fila
+        # (None si no parsea) para no desalinear filas: project_clusters_2d
+        # descarta filas listwise, asi que las listas deben tener igual longitud.
+        raw_numeric = {}
+        if numeric_cols:
+            try:
+                cols_sql = ", ".join(f'"{c}"' for c in numeric_cols)
+                sql = f'SELECT {cols_sql} FROM "{table}" LIMIT {int(sample)}'
+                q = query_fn(sql)
+                if isinstance(q, dict) and q.get("status") == "ok":
+                    rows = q.get("rows", []) or []
+                    raw_numeric = {c: [] for c in numeric_cols}
+                    for row in rows:
+                        for c in numeric_cols:
+                            raw_numeric[c].append(_to_float(row.get(c)))
+            except Exception:  # noqa: BLE001 - dict-no-throw: degradar la clave
+                raw_numeric = {}
+        if raw_numeric:
+            ctx["raw_numeric"] = raw_numeric
+
+        # 5) timeseries_raw: SOLO si hay columna temporal y numericas. Se delega
+        # el push-down en la funcion impura extract_timeseries_raw (una sola query
+        # ordenada cronologicamente). Solo se adjunta si trae filas.
+        if time_col and numeric_cols:
+            try:
+                from datascience import extract_timeseries_raw
+
+                ts = extract_timeseries_raw(
+                    query_fn, table, time_col, numeric_cols, max_rows=sample
+                )
+                if (
+                    isinstance(ts, dict)
+                    and ts.get("status") == "ok"
+                    and (ts.get("n") or 0) > 0
+                ):
+                    ctx["timeseries_raw"] = {
+                        "time_col": ts["time_col"],
+                        "t": ts["t"],
+                        "series": ts["series"],
+                    }
+            except Exception:  # noqa: BLE001 - dict-no-throw: omitir la clave
+                pass
+
+        # 6) geo_points: detecta el par lat/lon con la funcion PURA del registry.
+        # Solo se adjunta si AMBAS columnas estan en raw_numeric (ya floats,
+        # alineadas por fila). Si no hay par o no estan, se omite: el capitulo
+        # geospatial sabe degradar.
+        try:
+            from datascience import detect_latlon_columns
+
+            geo = detect_latlon_columns(cols)
+            lat_col = geo.get("lat_col")
+            lon_col = geo.get("lon_col")
+            if lat_col and lon_col and lat_col in raw_numeric and lon_col in raw_numeric:
+                ctx["geo_points"] = {
+                    "lats": raw_numeric[lat_col],
+                    "lons": raw_numeric[lon_col],
+                }
+        except Exception:  # noqa: BLE001 - dict-no-throw: omitir la clave
+            pass
+
+        return ctx
+    except Exception:  # noqa: BLE001 - dict-no-throw global: nunca reventar.
+        # Fallback minimo: copia de base_ctx + db_path/table para que el capitulo
+        # agregacion siga teniendo lo imprescindible.
+        out = dict(base_ctx) if isinstance(base_ctx, dict) else {}
+        out["db_path"] = db_path
+        out["table"] = table
+        return out

python/functions/datascience/build_eda_render_ctx_test.py

@@ -0,0 +1,153 @@
+"""Tests para build_eda_render_ctx.
+
+Self-contained: crea un DuckDB temporal pequeño con una columna fecha, varias
+numericas y un par lat/lon, construye un TableProfile minimo a mano (con la forma
+de columnas del grupo `eda`: name / inferred_type / numeric.{min,max} /
+semantic_type) y verifica que el ctx producido contiene las claves de datos que
+consumen los capitulos del AutomaticEDA.
+"""
+
+import os
+import sys
+
+# El test importa funciones del registry como una app del registry: inserta el
+# directorio raiz `python/functions` en sys.path y luego `from datascience import`.
+_FUNCTIONS_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
+if _FUNCTIONS_ROOT not in sys.path:
+    sys.path.insert(0, _FUNCTIONS_ROOT)
+
+import duckdb  # noqa: E402
+
+from datascience import build_eda_render_ctx  # noqa: E402
+
+_TABLE = "ventas_geo"
+# Filas: fecha creciente, 2 columnas numericas (ventas, unidades) y un par lat/lon
+# (Madrid -> lat ~40, lon ~-3, dentro de [-90,90] y [-180,180]).
+_ROWS = [
+    ("2024-01-01", 1200.5, 12, 40.41, -3.70),
+    ("2024-01-02", 980.0, 9, 41.38, 2.17),
+    ("2024-01-03", 1500.25, 15, 37.39, -5.99),
+    ("2024-01-04", 1100.0, 11, 39.47, -0.38),
+    ("2024-01-05", 1750.75, 18, 43.26, -2.93),
+]
+
+
+def _make_db(tmp_path):
+    """Crea un DuckDB temporal con la tabla de prueba y devuelve su ruta."""
+    db_path = os.path.join(str(tmp_path), "eda_ctx.duckdb")
+    con = duckdb.connect(db_path)
+    try:
+        con.execute(
+            f'CREATE TABLE "{_TABLE}" '
+            "(fecha DATE, ventas DOUBLE, unidades INTEGER, lat DOUBLE, lon DOUBLE)"
+        )
+        con.executemany(
+            f'INSERT INTO "{_TABLE}" VALUES (?, ?, ?, ?, ?)', _ROWS
+        )
+    finally:
+        con.close()
+    return db_path
+
+
+def _profile_with_date():
+    """TableProfile minimo con columna fecha + numericas + lat/lon."""
+    return {
+        "columns": [
+            {"name": "fecha", "inferred_type": "datetime", "semantic_type": "datetime_iso"},
+            {
+                "name": "ventas",
+                "inferred_type": "numeric",
+                "semantic_type": "decimal",
+                "numeric": {"min": 980.0, "max": 1750.75},
+            },
+            {
+                "name": "unidades",
+                "inferred_type": "numeric",
+                "semantic_type": "integer",
+                "numeric": {"min": 9, "max": 18},
+            },
+            {
+                "name": "lat",
+                "inferred_type": "numeric",
+                "semantic_type": "decimal",
+                "numeric": {"min": 37.39, "max": 43.26},
+            },
+            {
+                "name": "lon",
+                "inferred_type": "numeric",
+                "semantic_type": "decimal",
+                "numeric": {"min": -5.99, "max": 2.17},
+            },
+        ]
+    }
+
+
+def _profile_without_date():
+    """Mismo perfil pero SIN columna temporal (solo numericas)."""
+    prof = _profile_with_date()
+    prof["columns"] = [c for c in prof["columns"] if c["name"] != "fecha"]
+    return prof
+
+
+def test_db_path_y_table_en_ctx(tmp_path):
+    db_path = _make_db(tmp_path)
+    ctx = build_eda_render_ctx(db_path, _TABLE, _profile_with_date())
+    assert ctx["db_path"] == db_path
+    assert ctx["table"] == _TABLE
+
+
+def test_raw_numeric_con_columnas_numericas(tmp_path):
+    db_path = _make_db(tmp_path)
+    ctx = build_eda_render_ctx(db_path, _TABLE, _profile_with_date())
+    raw = ctx["raw_numeric"]
+    # Las 4 columnas numericas (ventas, unidades, lat, lon), listas no vacias y
+    # alineadas por fila (misma longitud == nº de filas).
+    for col in ("ventas", "unidades", "lat", "lon"):
+        assert col in raw
+        assert len(raw[col]) == len(_ROWS)
+    assert raw["ventas"][0] == 1200.5
+    assert raw["unidades"][0] == 12.0  # int promovido a float
+
+
+def test_timeseries_raw_con_fecha(tmp_path):
+    db_path = _make_db(tmp_path)
+    ctx = build_eda_render_ctx(db_path, _TABLE, _profile_with_date())
+    ts = ctx["timeseries_raw"]
+    assert ts["time_col"] == "fecha"
+    assert len(ts["t"]) == len(_ROWS)  # fechas ISO no vacias
+    # Las numericas aparecen como series paralelas a t.
+    for col in ("ventas", "unidades", "lat", "lon"):
+        assert col in ts["series"]
+        assert len(ts["series"][col]) == len(_ROWS)
+
+
+def test_geo_points_con_latlon(tmp_path):
+    db_path = _make_db(tmp_path)
+    ctx = build_eda_render_ctx(db_path, _TABLE, _profile_with_date())
+    geo = ctx["geo_points"]
+    assert len(geo["lats"]) == len(_ROWS)
+    assert len(geo["lons"]) == len(_ROWS)
+    # Listas alineadas, ya floats, leidas de raw_numeric.
+    assert geo["lats"][0] == 40.41
+    assert geo["lons"][0] == -3.70
+
+
+def test_sin_fecha_no_hay_timeseries(tmp_path):
+    db_path = _make_db(tmp_path)
+    ctx = build_eda_render_ctx(db_path, _TABLE, _profile_without_date())
+    assert "timeseries_raw" not in ctx
+    # raw_numeric y geo_points siguen presentes (no dependen de la fecha).
+    assert "raw_numeric" in ctx
+    assert "geo_points" in ctx
+
+
+def test_base_ctx_preservado(tmp_path):
+    db_path = _make_db(tmp_path)
+    base = {"dataset_name": "ventas_geo_demo", "source_origin": "test"}
+    ctx = build_eda_render_ctx(db_path, _TABLE, _profile_with_date(), base_ctx=base)
+    # Las claves de presentacion del base_ctx no se pisan.
+    assert ctx["dataset_name"] == "ventas_geo_demo"
+    assert ctx["source_origin"] == "test"
+    # Y las de datos se añaden encima.
+    assert ctx["db_path"] == db_path
+    assert "raw_numeric" in ctx

python/functions/datascience/build_geo_scatter.md

@@ -0,0 +1,68 @@
+---
+name: build_geo_scatter
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def build_geo_scatter(lats: list, lons: list, max_points: int = 2000) -> dict"
+description: "Prepara los datos de un scatter geografico en proyeccion equirectangular para el grupo eda. Empareja lats/lons por indice, descarta pares None/NaN/inf/bool o fuera de rango (lat en [-90,90], lon en [-180,180]) y aplica downsampling DETERMINISTA por paso fijo (pairs[::step]) cuando hay mas pares validos que max_points, para no saturar el PDF/PPTX en moviles. Devuelve los puntos en orden [lon, lat] listos para ax.scatter, el bbox, el aspect 1/cos(centroid_lat) clampado a [0.3,5.0] y un pad sugerido (~5% del rango con suelo minimo). Lectura defensiva; NUNCA lanza ni dibuja: el capitulo se encarga de matplotlib."
+tags: [eda, geospatial, datascience, scatter, map, downsample, equirectangular, profiling]
+params:
+  - name: lats
+    desc: "Lista (o tupla) de latitudes en grados, paralela a lons. Se empareja por indice. Un valor None, NaN, infinito, bool o fuera de [-90,90] descarta ese par. Lectura defensiva."
+  - name: lons
+    desc: "Lista (o tupla) de longitudes en grados, paralela a lats. Un valor None, NaN, infinito, bool o fuera de [-180,180] descarta ese par."
+  - name: max_points
+    desc: "Tope de puntos a devolver (default 2000). Si los pares validos superan el tope, se hace downsampling determinista por paso fijo step=ceil(n_total/max_points) tomando pairs[::step] (NO aleatorio, reproducible). Un valor no entero o <=0 desactiva el downsampling."
+output: "Dict listo para dibujar: {points: [[lon, lat], ...] en orden x=lon/y=lat para ax.scatter; n_total: pares validos antes del downsample (int); n_shown: puntos devueltos tras el downsample (int); downsampled: bool (n_shown<n_total); bbox: {lat_min, lat_max, lon_min, lon_max} o None si no hay puntos; aspect: 1/cos(centroid_lat) clampado a [0.3,5.0] para no estirar la proyeccion equirectangular; pad: {lon, lat} ~5% del rango respectivo con suelo minimo 0.01 grados}. Si no hay pares validos: points=[], n_total=0, n_shown=0, downsampled=False, bbox=None, aspect=1.0, pad={lon:0.0, lat:0.0}."
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+tested: true
+tests: ["test_geo_scatter_nube_espana", "test_downsampling_determinista_y_reproducible", "test_listas_vacias_no_lanza", "test_un_solo_punto_pad_minimo_y_aspect_finito", "test_filtra_none_nan_y_fuera_de_rango", "test_latitud_alta_aspect_clamped"]
+test_file_path: "python/functions/datascience/build_geo_scatter_test.py"
+file_path: "python/functions/datascience/build_geo_scatter.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience.build_geo_scatter import build_geo_scatter
+
+# Nube de coordenadas (lat, lon) alrededor de Madrid:
+lats = [40.0, 41.0, 39.0, 40.5]
+lons = [-3.7, -3.0, -4.0, -3.5]
+geo = build_geo_scatter(lats, lons, max_points=2000)
+
+print(geo["points"][0])     # [-3.7, 40.0]  -> orden [x=lon, y=lat]
+print(geo["bbox"])          # {'lat_min': 39.0, 'lat_max': 41.0, 'lon_min': -4.0, 'lon_max': -3.0}
+print(round(geo["aspect"], 3))  # 1.308  -> ensancha el eje x en latitudes medias
+print(geo["pad"])           # {'lon': 0.05, 'lat': 0.1}  -> margen ~5%
+
+# El capitulo dibuja con matplotlib (esta funcion NO dibuja):
+#   xs = [p[0] for p in geo["points"]]; ys = [p[1] for p in geo["points"]]
+#   ax.scatter(xs, ys); ax.set_aspect(geo["aspect"])
+#   ax.set_xlim(geo["bbox"]["lon_min"] - geo["pad"]["lon"], geo["bbox"]["lon_max"] + geo["pad"]["lon"])
+#   ax.set_ylim(geo["bbox"]["lat_min"] - geo["pad"]["lat"], geo["bbox"]["lat_max"] + geo["pad"]["lat"])
+```
+
+## Cuando usarla
+
+- Usala antes de dibujar un scatter geografico (mapa de puntos en proyeccion equirectangular) en el capitulo geospatial de `AutomaticEDA`: limpia los pares de coordenadas, los reduce a un tamano razonable para el PDF/PPTX y te da bbox, aspect y pad listos para fijar los ejes.
+- Cuando tengas dos columnas de lat/lon ya extraidas y quieras un punto de entrada determinista (mismo dataset -> mismo dibujo) que no sature el documento en moviles.
+- Cuando necesites el aspect correcto para que un grado de longitud no se vea estirado respecto a uno de latitud (integridad visual, Tufte) sin calcularlo a mano.
+
+## Gotchas
+
+- Funcion pura, sin I/O y determinista. NO dibuja: solo PREPARA los datos; el capitulo se encarga de matplotlib. Lectura defensiva: pares con None/NaN/inf/bool o coordenadas fuera de rango se descartan en silencio y NUNCA lanza.
+- El downsampling es DETERMINISTA por paso fijo (`step = ceil(n_total / max_points)`, `pairs[::step]`), NO aleatorio: la misma entrada produce siempre la misma salida (reproducible en tests). El primer punto mostrado es siempre el primer par valido. No es un muestreo uniforme aleatorio — es un barrido regular del orden de entrada.
+- `points` va en orden `[lon, lat]` (x, y), no `[lat, lon]`: pasalo directo a `ax.scatter(xs, ys)` sin invertir. Confundir el orden espeja el mapa.
+- `aspect = 1/cos(centroid_lat)` se clampa a `[0.3, 5.0]`. En latitudes altas `cos -> 0` y el valor real explota: por encima de ~78 grados el aspect queda fijado en 5.0. Si el centroide cae justo en un polo (`+-90`) se usa el clamp en vez de dividir por cero.
+- `pad` es ~5% del rango de cada eje con un suelo minimo de `0.01` grados: con un solo punto o todos iguales (rango 0) el pad cae al suelo para que el punto no quede en una linea. En el caso sin puntos validos el pad es `{lon:0.0, lat:0.0}` y `bbox` es `None`.
+- `bbox`, `aspect` y `pad` se calculan sobre los puntos YA mostrados (tras el downsample), de modo que los ejes encajan exactamente con lo que se dibuja.

python/functions/datascience/build_geo_scatter.py

@@ -0,0 +1,153 @@
+"""build_geo_scatter — prepare points for a geographic scatter (EDA `geospatial`).
+
+Pure function: no I/O, deterministic. Takes two parallel lists of latitudes and
+longitudes and returns the data a caller needs to draw a geographic scatter in an
+equirectangular projection: cleaned points in [lon, lat] order, a bounding box, a
+projection aspect ratio and a suggested axis padding.
+
+It NEVER draws anything (no matplotlib) — the chapter that consumes this output is
+responsible for the rendering. Reading is defensive throughout and the function
+NEVER raises: malformed pairs (None, NaN, infinity or out-of-range coordinates)
+are silently dropped and an empty/valid result is always returned.
+
+To keep the rendered PDF/PPTX light on phones, when the number of valid pairs
+exceeds `max_points` the points are down-sampled DETERMINISTICALLY by a fixed
+step (`pairs[::step]`), never randomly, so the result is reproducible.
+"""
+
+import math
+
+# Minimum axis padding (in degrees) so a single point or a zero-range cloud is
+# never drawn glued to the axis border (it would collapse to a line).
+_MIN_PAD = 0.01
+
+# Aspect ratio clamp. 1/cos(lat) blows up near the poles; clamp keeps the render
+# sane (Tufte: do not let the projection stretch the cloud out of proportion).
+_ASPECT_MIN = 0.3
+_ASPECT_MAX = 5.0
+
+
+def _coord(value):
+    """Coerce to a finite float defensively; return None for invalid coordinates.
+
+    bool is a subclass of int, but a real latitude/longitude is never a bool, so
+    True/False are treated as missing instead of coercing to 1.0/0.0. NaN and
+    +/-infinity are never valid coordinates either.
+    """
+    if value is None or isinstance(value, bool):
+        return None
+    try:
+        coord = float(value)
+    except (TypeError, ValueError):
+        return None
+    if math.isnan(coord) or math.isinf(coord):
+        return None
+    return coord
+
+
+def build_geo_scatter(lats: list, lons: list, max_points: int = 2000) -> dict:
+    """Prepare the data for a geographic scatter in equirectangular projection.
+
+    Pairs `lats` and `lons` by index, drops invalid pairs, optionally
+    down-samples deterministically, and derives the geometry (bbox, aspect, pad)
+    a caller needs to draw the cloud. No raw rendering is performed.
+
+    Args:
+        lats: List (or tuple) of latitudes in degrees. Paired by index with
+            `lons`. A value that is None, NaN, infinite, bool or outside
+            [-90, 90] discards that pair. Read defensively.
+        lons: List (or tuple) of longitudes in degrees, parallel to `lats`. A
+            value outside [-180, 180] (or None/NaN/inf/bool) discards that pair.
+        max_points: Cap on the number of points returned. When the number of
+            valid pairs exceeds this cap, the points are down-sampled by a fixed
+            step `ceil(n_total / max_points)` taking `pairs[::step]` — DETERMINISTIC,
+            not random, so the output is reproducible. A non-positive or non-int
+            value disables down-sampling.
+
+    Returns:
+        Dict ready for a caller's ax.scatter:
+        {points: [[lon, lat], ...] (x=lon, y=lat order), n_total: valid pairs
+        before down-sampling, n_shown: points returned, downsampled: bool,
+        bbox: {lat_min, lat_max, lon_min, lon_max} or None, aspect: 1/cos(centroid
+        lat) clamped to [0.3, 5.0], pad: {lon, lat} ~5% of each range with a small
+        floor}. When there are no valid pairs returns points=[], n_total=0,
+        n_shown=0, downsampled=False, bbox=None, aspect=1.0, pad={lon:0.0, lat:0.0}.
+    """
+    pairs = []  # each item is (lon, lat) — already in [x, y] order
+    if isinstance(lats, (list, tuple)) and isinstance(lons, (list, tuple)):
+        n = min(len(lats), len(lons))
+        for i in range(n):
+            lat = _coord(lats[i])
+            lon = _coord(lons[i])
+            if lat is None or lon is None:
+                continue
+            if lat < -90.0 or lat > 90.0:
+                continue
+            if lon < -180.0 or lon > 180.0:
+                continue
+            pairs.append((lon, lat))
+
+    n_total = len(pairs)
+    if n_total == 0:
+        return {
+            "points": [],
+            "n_total": 0,
+            "n_shown": 0,
+            "downsampled": False,
+            "bbox": None,
+            "aspect": 1.0,
+            "pad": {"lon": 0.0, "lat": 0.0},
+        }
+
+    # Deterministic down-sampling by a fixed step. Reproducible: same input ->
+    # same output, no randomness.
+    if (
+        isinstance(max_points, int)
+        and not isinstance(max_points, bool)
+        and max_points > 0
+        and n_total > max_points
+    ):
+        step = math.ceil(n_total / max_points)
+        sampled = pairs[::step]
+    else:
+        sampled = pairs
+
+    points = [[lon, lat] for (lon, lat) in sampled]
+    n_shown = len(points)
+    downsampled = n_shown < n_total
+
+    lons_s = [p[0] for p in sampled]
+    lats_s = [p[1] for p in sampled]
+    lon_min, lon_max = min(lons_s), max(lons_s)
+    lat_min, lat_max = min(lats_s), max(lats_s)
+    bbox = {
+        "lat_min": lat_min,
+        "lat_max": lat_max,
+        "lon_min": lon_min,
+        "lon_max": lon_max,
+    }
+
+    # Aspect for an equirectangular projection: stretch the x axis by 1/cos(lat)
+    # at the cloud centroid so a degree of longitude reads at its real width.
+    centroid_lat = sum(lats_s) / len(lats_s)
+    cos_lat = math.cos(math.radians(centroid_lat))
+    if cos_lat < 1e-12:  # centroid at (or numerically at) a pole
+        aspect = _ASPECT_MAX
+    else:
+        aspect = 1.0 / cos_lat
+    aspect = max(_ASPECT_MIN, min(_ASPECT_MAX, aspect))
+
+    # Padding ~5% of each range, with a small floor so a zero-range cloud (single
+    # point / all identical) still gets a non-zero margin.
+    pad_lon = max(0.05 * (lon_max - lon_min), _MIN_PAD)
+    pad_lat = max(0.05 * (lat_max - lat_min), _MIN_PAD)
+
+    return {
+        "points": points,
+        "n_total": n_total,
+        "n_shown": n_shown,
+        "downsampled": downsampled,
+        "bbox": bbox,
+        "aspect": aspect,
+        "pad": {"lon": pad_lon, "lat": pad_lat},
+    }

python/functions/datascience/build_geo_scatter_test.py

@@ -0,0 +1,140 @@
+"""Tests para build_geo_scatter."""
+
+import math
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from build_geo_scatter import build_geo_scatter
+
+# Keys that a non-empty result dict must always contain.
+_EXPECTED_KEYS = {
+    "points", "n_total", "n_shown", "downsampled", "bbox", "aspect", "pad",
+}
+
+
+def test_geo_scatter_nube_espana():
+    """Golden: nube en Espana -> points en orden [lon, lat], bbox, aspect>1, pad 5%."""
+    # Cuatro puntos alrededor de Madrid (lat ~40, lon negativo).
+    lats = [40.0, 41.0, 39.0, 40.5]
+    lons = [-3.7, -3.0, -4.0, -3.5]
+    r = build_geo_scatter(lats, lons)
+
+    assert set(r.keys()) == _EXPECTED_KEYS
+
+    # points en orden [x=lon, y=lat]: primer elemento lon (negativo), segundo lat (~40).
+    assert r["points"] == [[-3.7, 40.0], [-3.0, 41.0], [-4.0, 39.0], [-3.5, 40.5]]
+    for lon, lat in r["points"]:
+        assert lon < 0.0          # longitudes de Espana son negativas
+        assert 36.0 < lat < 44.0  # latitudes peninsulares
+
+    # Sin downsampling: 4 < 2000.
+    assert r["n_total"] == 4
+    assert r["n_shown"] == 4
+    assert r["downsampled"] is False
+
+    # bbox correcto.
+    assert r["bbox"] == {
+        "lat_min": 39.0, "lat_max": 41.0,
+        "lon_min": -4.0, "lon_max": -3.0,
+    }
+
+    # aspect = 1/cos(centroid_lat); centroid = 40.125 -> ~1.31 > 1.
+    centroid_lat = (40.0 + 41.0 + 39.0 + 40.5) / 4.0
+    expected_aspect = 1.0 / math.cos(math.radians(centroid_lat))
+    assert r["aspect"] > 1.0
+    assert abs(r["aspect"] - expected_aspect) < 1e-9
+    assert abs(r["aspect"] - 1.305) < 0.02  # cos(40) ~ 0.77
+
+    # pad 5% del rango (lon_range=1.0 -> 0.05 ; lat_range=2.0 -> 0.1).
+    assert abs(r["pad"]["lon"] - 0.05) < 1e-9
+    assert abs(r["pad"]["lat"] - 0.10) < 1e-9
+
+
+def test_downsampling_determinista_y_reproducible():
+    """Golden: 5000 puntos, max_points=2000 -> n_shown<=2000, downsampled, reproducible."""
+    lats = [40.0 + (i % 100) * 0.01 for i in range(5000)]
+    lons = [-3.0 - (i % 100) * 0.01 for i in range(5000)]
+
+    r1 = build_geo_scatter(lats, lons, max_points=2000)
+
+    assert r1["n_total"] == 5000
+    assert r1["n_shown"] <= 2000
+    assert r1["downsampled"] is True
+    # step = ceil(5000/2000) = 3 -> len(pairs[::3]) = 1667.
+    assert r1["n_shown"] == 1667
+
+    # Determinista: dos llamadas con la misma entrada dan exactamente lo mismo.
+    r2 = build_geo_scatter(lats, lons, max_points=2000)
+    assert r1 == r2
+    assert r1["points"] == r2["points"]
+
+    # El primer punto del downsample es el primer par valido (step parte de 0).
+    assert r1["points"][0] == [lons[0], lats[0]]
+
+
+def test_listas_vacias_no_lanza():
+    """Edge: listas vacias / None -> points [] sin lanzar."""
+    r = build_geo_scatter([], [])
+    assert r["points"] == []
+    assert r["n_total"] == 0
+    assert r["n_shown"] == 0
+    assert r["downsampled"] is False
+    assert r["bbox"] is None
+    assert r["aspect"] == 1.0
+    assert r["pad"] == {"lon": 0.0, "lat": 0.0}
+
+    # None como entrada tampoco lanza.
+    assert build_geo_scatter(None, None)["points"] == []
+    assert build_geo_scatter([40.0], None)["n_total"] == 0
+    assert build_geo_scatter(None, [-3.0])["n_total"] == 0
+
+
+def test_un_solo_punto_pad_minimo_y_aspect_finito():
+    """Edge: un solo punto -> pad minimo no cero, bbox degenerado, aspect finito."""
+    r = build_geo_scatter([40.0], [-3.7])
+
+    assert r["n_total"] == 1
+    assert r["n_shown"] == 1
+    assert r["points"] == [[-3.7, 40.0]]
+    assert r["downsampled"] is False
+    assert r["bbox"] == {
+        "lat_min": 40.0, "lat_max": 40.0,
+        "lon_min": -3.7, "lon_max": -3.7,
+    }
+    # rango 0 -> pad cae al floor minimo (no cero).
+    assert r["pad"]["lon"] == 0.01
+    assert r["pad"]["lat"] == 0.01
+    # aspect finito y dentro del clamp.
+    assert math.isfinite(r["aspect"])
+    assert 0.3 <= r["aspect"] <= 5.0
+
+
+def test_filtra_none_nan_y_fuera_de_rango():
+    """Edge: pares con None/NaN/fuera de rango se descartan por indice."""
+    nan = float("nan")
+    inf = float("inf")
+    #          i=0     i=1   i=2   i=3      i=4     i=5      i=6
+    lats = [40.0, None, nan, 200.0, 41.0, 39.0, inf]
+    lons = [-3.0, -3.5, -3.6, -3.7, 999.0, -4.0, -2.0]
+    r = build_geo_scatter(lats, lons)
+
+    # Validos solo i=0 (40,-3.0) e i=5 (39,-4.0):
+    #   i=1 lat None, i=2 lat NaN, i=3 lat 200 fuera de rango,
+    #   i=4 lon 999 fuera de rango, i=6 lat inf.
+    assert r["n_total"] == 2
+    assert r["points"] == [[-3.0, 40.0], [-4.0, 39.0]]
+    assert r["bbox"] == {
+        "lat_min": 39.0, "lat_max": 40.0,
+        "lon_min": -4.0, "lon_max": -3.0,
+    }
+
+
+def test_latitud_alta_aspect_clamped():
+    """Edge: latitudes ~85 -> aspect clamped <= 5.0."""
+    r = build_geo_scatter([85.0, 85.0, 84.0], [10.0, 11.0, 9.0])
+    # cos(~84.7) ~ 0.093 -> 1/0.093 ~ 10.7 -> clamp a 5.0.
+    assert r["aspect"] <= 5.0
+    assert r["aspect"] == 5.0
+    assert math.isfinite(r["aspect"])

python/functions/datascience/categorical_cardinality_block.md

@@ -0,0 +1,115 @@
+---
+id: categorical_cardinality_block_py_datascience
+name: categorical_cardinality_block
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def categorical_cardinality_block(cat: dict, n_rows: int) -> dict"
+description: "Deriva métricas de cardinalidad listas para renderizar a partir de la salida de summarize_categorical para UNA columna categórica más el número total de filas. Calcula pct_distinct, entropy_max=log2(n_distinct), entropy_norm (recortada a [0,1]), n_singletons (sobre el top visible) y los flags id_like / dominated. NO recalcula la entropía ni reimplementa summarize_categorical: la consume. Estilo dict-no-throw del grupo eda — nunca lanza."
+tags: [eda, categorical, cardinality, entropy, profiling, datascience, pure]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [math]
+example: |
+  from categorical_cardinality_block import categorical_cardinality_block
+  cat = {"top": [{"value": "a", "count": 5, "pct": 0.5}], "mode": "a",
+         "mode_pct": 0.5, "n_distinct": 4, "entropy": 1.685, "imbalance": 5.0,
+         "len_min": 1, "len_mean": 1.0, "len_max": 1}
+  block = categorical_cardinality_block(cat, n_rows=10)
+tested: true
+tests:
+  - "test_normal_case"
+  - "test_empty_cat_does_not_raise"
+  - "test_none_cat_does_not_raise"
+  - "test_n_rows_zero_no_zero_division"
+  - "test_id_like_when_distinct_near_rows"
+  - "test_dominated_when_mode_pct_high"
+  - "test_mode_pct_fallback_from_top_fraction"
+  - "test_n_singletons_partial_when_top_truncated"
+  - "test_single_distinct_value_entropy_norm_none"
+test_file_path: "python/functions/datascience/categorical_cardinality_block_test.py"
+file_path: "python/functions/datascience/categorical_cardinality_block.py"
+params:
+  - name: cat
+    desc: "Dict producido por summarize_categorical para UNA columna categórica. Claves leídas (todas opcionales, lectura defensiva): top (list de {value,count,pct}), mode, mode_pct (puede faltar), n_distinct, entropy (Shannon en bits), imbalance, len_min, len_mean, len_max. None o no-dict se tratan como {}."
+  - name: n_rows
+    desc: "Número total de filas del dataset. Usado para pct_distinct. Si es 0 o None, pct_distinct sale None (sin ZeroDivisionError)."
+output: "Dict con exactamente 16 claves, todas siempre presentes: 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. Valores None/False cuando no son derivables; la función nunca lanza. pct_distinct en escala 0-100. entropy_max=log2(n_distinct) (0.0 si n_distinct in {0,1}). entropy_norm=entropy/entropy_max recortada a [0,1]. n_singletons = nº de elementos de top con count==1 (None si top vacío). n_singletons_partial=True si n_distinct>len(top). id_like=pct_distinct>=99. dominated=mode_pct>=90."
+---
+
+## Ejemplo
+
+```python
+from categorical_cardinality_block import categorical_cardinality_block
+
+# Salida típica de summarize_categorical para una columna, con n_rows del dataset.
+cat = {
+    "top": [
+        {"value": "a", "count": 5, "pct": 0.5},
+        {"value": "b", "count": 3, "pct": 0.3},
+        {"value": "c", "count": 1, "pct": 0.1},
+        {"value": "d", "count": 1, "pct": 0.1},
+    ],
+    "mode": "a",
+    "mode_pct": 0.5,
+    "n_distinct": 4,
+    "entropy": 1.685,   # Shannon en bits (<= log2(4) = 2.0)
+    "imbalance": 5.0,
+    "len_min": 1, "len_mean": 1.0, "len_max": 1,
+}
+
+categorical_cardinality_block(cat, n_rows=10)
+# {
+#   "n_distinct": 4, "n_rows": 10,
+#   "pct_distinct": 40.0,            # 4 / 10 * 100
+#   "entropy": 1.685,
+#   "entropy_max": 2.0,             # log2(4)
+#   "entropy_norm": 0.8425,         # 1.685 / 2.0, recortado a [0,1]
+#   "mode": "a", "mode_pct": 0.5,
+#   "imbalance": 5.0,
+#   "n_singletons": 2,             # c y d con count == 1
+#   "n_singletons_partial": False, # top cubre los 4 distintos
+#   "len_min": 1, "len_mean": 1.0, "len_max": 1,
+#   "id_like": False,              # pct_distinct 40 < 99
+#   "dominated": False,            # mode_pct 0.5 < 90
+# }
+```
+
+## Cuando usarla
+
+Úsala justo después de `summarize_categorical`, cuando vayas a renderizar el
+bloque de cardinalidad de una columna categórica en un EDA: necesitas el ratio
+de valores distintos (`pct_distinct`), la entropía normalizada al rango `[0,1]`
+para comparar columnas con cardinalidades distintas, el conteo de singletons, y
+las banderas heurísticas `id_like` (la columna parece un identificador) y
+`dominated` (una sola categoría domina). Pásale el dict crudo de
+`summarize_categorical` para esa columna y el `n_rows` total del dataset. No
+reimplementa nada: solo deriva métricas de presentación a partir de lo ya
+calculado.
+
+## Gotchas
+
+- **`mode_pct` se pasa tal cual viene en `cat`.** `summarize_categorical`
+  produce `mode_pct` como **fracción** (0–1), no como porcentaje. El flag
+  `dominated` compara `mode_pct >= 90.0`, así que con la salida cruda de
+  `summarize_categorical` (fracciones) `dominated` no se dispara: aliméntalo con
+  `mode_pct` en escala 0–100 si quieres usar esa bandera. Solo el camino de
+  *fallback* (cuando `cat` no trae `mode_pct` y se deriva de `top[0]['pct']`)
+  normaliza una fracción `<= 1` multiplicándola por 100.
+- **`n_singletons` solo cubre el `top` visible.** Si `summarize_categorical` se
+  llamó con `top_k` pequeño, hay valores fuera del top; en ese caso
+  `n_singletons_partial` es `True` para avisar de que el conteo es parcial.
+- **`pct_distinct` es `None` si `n_rows` es 0 o `None`** (no lanza
+  `ZeroDivisionError`); por tanto `id_like` queda `False` en ese caso.
+- **`entropy_norm` es `None` cuando `entropy_max <= 0`** (columna constante,
+  `n_distinct in {0,1}`): no hay división por cero y no se inventa un 0/1.
+- **No recalcula la entropía.** Si `cat['entropy']` es incoherente con
+  `n_distinct`, `entropy_norm` se recorta a `[0,1]` pero el valor de entrada no
+  se corrige.
+- **`bool` no cuenta como número.** Un `True`/`False` en una clave numérica de
+  `cat` se trata como ausente (`None`), por la guarda defensiva.

python/functions/datascience/categorical_cardinality_block.py

@@ -0,0 +1,132 @@
+"""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,
+    }

python/functions/datascience/categorical_cardinality_block_test.py

@@ -0,0 +1,216 @@
+"""Tests para categorical_cardinality_block."""
+
+import sys
+import os
+from math import log2
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from categorical_cardinality_block import categorical_cardinality_block
+
+
+# Output contract: every call returns exactly these 16 keys.
+EXPECTED_KEYS = {
+    "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",
+}
+
+
+def _sample_cat():
+    """A realistic summarize_categorical output for one column."""
+    return {
+        "top": [
+            {"value": "a", "count": 5, "pct": 0.5},
+            {"value": "b", "count": 3, "pct": 0.3},
+            {"value": "c", "count": 1, "pct": 0.1},
+            {"value": "d", "count": 1, "pct": 0.1},
+        ],
+        "mode": "a",
+        "mode_pct": 0.5,
+        "n_distinct": 4,
+        "entropy": 1.685,  # <= log2(4) = 2.0
+        "imbalance": 5.0,
+        "len_min": 1,
+        "len_mean": 1.0,
+        "len_max": 1,
+    }
+
+
+def test_normal_case():
+    """Caso normal: pct_distinct, entropy_max=log2(n_distinct), entropy_norm in [0,1], n_singletons."""
+    cat = _sample_cat()
+    result = categorical_cardinality_block(cat, n_rows=10)
+
+    assert set(result.keys()) == EXPECTED_KEYS
+
+    # passthroughs
+    assert result["n_distinct"] == 4
+    assert result["n_rows"] == 10
+    assert result["entropy"] == 1.685
+    assert result["imbalance"] == 5.0
+    assert result["mode"] == "a"
+    assert result["mode_pct"] == 0.5  # passthrough, not normalized
+    assert result["len_min"] == 1
+    assert result["len_max"] == 1
+
+    # pct_distinct = 4 / 10 * 100
+    assert abs(result["pct_distinct"] - 40.0) < 1e-12
+
+    # entropy_max = log2(4) = 2.0
+    assert abs(result["entropy_max"] - log2(4)) < 1e-12
+    assert abs(result["entropy_max"] - 2.0) < 1e-12
+
+    # entropy_norm = 1.685 / 2.0 = 0.8425, within [0, 1]
+    assert abs(result["entropy_norm"] - 1.685 / 2.0) < 1e-12
+    assert 0.0 <= result["entropy_norm"] <= 1.0
+
+    # singletons: c and d have count == 1
+    assert result["n_singletons"] == 2
+    # top covers all distinct values (4 == 4)
+    assert result["n_singletons_partial"] is False
+
+    # neither id-like (40%) nor dominated (mode_pct 0.5)
+    assert result["id_like"] is False
+    assert result["dominated"] is False
+
+
+def test_empty_cat_does_not_raise():
+    """Caso cat={}: no lanza, claves derivadas None y flags False."""
+    result = categorical_cardinality_block({}, n_rows=100)
+
+    assert set(result.keys()) == EXPECTED_KEYS
+    for key in (
+        "n_distinct",
+        "pct_distinct",
+        "entropy",
+        "entropy_max",
+        "entropy_norm",
+        "mode",
+        "mode_pct",
+        "imbalance",
+        "n_singletons",
+        "len_min",
+        "len_mean",
+        "len_max",
+    ):
+        assert result[key] is None
+    assert result["n_singletons_partial"] is False
+    assert result["id_like"] is False
+    assert result["dominated"] is False
+    # n_rows is a passthrough of the argument, still coherent.
+    assert result["n_rows"] == 100
+
+
+def test_none_cat_does_not_raise():
+    """Caso cat=None: tratado como {}, mismas garantias que el dict vacio."""
+    result = categorical_cardinality_block(None, n_rows=None)
+    assert set(result.keys()) == EXPECTED_KEYS
+    assert result["n_distinct"] is None
+    assert result["pct_distinct"] is None
+    assert result["entropy_max"] is None
+    assert result["entropy_norm"] is None
+    assert result["id_like"] is False
+    assert result["dominated"] is False
+
+
+def test_n_rows_zero_no_zero_division():
+    """Caso n_rows=0: pct_distinct None sin ZeroDivisionError."""
+    cat = _sample_cat()
+    result = categorical_cardinality_block(cat, n_rows=0)
+    assert result["pct_distinct"] is None
+    # n_distinct still passes through.
+    assert result["n_distinct"] == 4
+    assert result["id_like"] is False
+
+
+def test_id_like_when_distinct_near_rows():
+    """id_like True cuando n_distinct ~ n_rows (pct_distinct >= 99)."""
+    cat = {"n_distinct": 99, "entropy": 6.6, "top": [], "mode": None}
+    result = categorical_cardinality_block(cat, n_rows=100)
+    assert abs(result["pct_distinct"] - 99.0) < 1e-12
+    assert result["id_like"] is True
+
+    # exact identity column: 100 / 100 = 100%
+    cat_full = {"n_distinct": 100, "top": []}
+    result_full = categorical_cardinality_block(cat_full, n_rows=100)
+    assert result_full["id_like"] is True
+
+
+def test_dominated_when_mode_pct_high():
+    """dominated True cuando mode_pct alto (>= 90)."""
+    cat = {
+        "n_distinct": 3,
+        "entropy": 0.3,
+        "mode": "x",
+        "mode_pct": 95.0,
+        "top": [
+            {"value": "x", "count": 95, "pct": 0.95},
+            {"value": "y", "count": 3, "pct": 0.03},
+            {"value": "z", "count": 2, "pct": 0.02},
+        ],
+        "imbalance": 47.5,
+    }
+    result = categorical_cardinality_block(cat, n_rows=100)
+    assert result["mode_pct"] == 95.0
+    assert result["dominated"] is True
+
+
+def test_mode_pct_fallback_from_top_fraction():
+    """Sin mode_pct: deriva del pct del primer top, fraccion <=1 escala a 0-100."""
+    cat = {
+        "n_distinct": 3,
+        "top": [
+            {"value": "x", "count": 95, "pct": 0.95},
+            {"value": "y", "count": 5, "pct": 0.05},
+        ],
+    }
+    result = categorical_cardinality_block(cat, n_rows=100)
+    # 0.95 (fraction) -> 95.0 (percentage)
+    assert abs(result["mode_pct"] - 95.0) < 1e-12
+    assert result["dominated"] is True
+
+
+def test_n_singletons_partial_when_top_truncated():
+    """n_distinct > len(top): n_singletons cubre solo el top visible, partial True."""
+    cat = {
+        "n_distinct": 10,
+        "top": [
+            {"value": "a", "count": 4, "pct": 0.4},
+            {"value": "b", "count": 1, "pct": 0.1},
+            {"value": "c", "count": 1, "pct": 0.1},
+        ],
+        "entropy": 2.5,
+    }
+    result = categorical_cardinality_block(cat, n_rows=12)
+    assert result["n_singletons"] == 2  # only b, c visible
+    assert result["n_singletons_partial"] is True
+
+
+def test_single_distinct_value_entropy_norm_none():
+    """n_distinct=1: entropy_max=0.0 -> entropy_norm None (no division by zero)."""
+    cat = {
+        "n_distinct": 1,
+        "entropy": 0.0,
+        "mode": "only",
+        "mode_pct": 1.0,
+        "top": [{"value": "only", "count": 7, "pct": 1.0}],
+        "imbalance": 1.0,
+    }
+    result = categorical_cardinality_block(cat, n_rows=7)
+    assert result["entropy_max"] == 0.0
+    assert result["entropy_norm"] is None
+    assert result["n_singletons"] == 0

python/functions/datascience/categorical_top_pie_figure.md

@@ -0,0 +1,108 @@
+---
+id: categorical_top_pie_figure_py_datascience
+name: categorical_top_pie_figure
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def categorical_top_pie_figure(top: list, n_distinct: int = 0, title: str = \"\", top_k: int = 6, n_rows=None) -> \"matplotlib.figure.Figure\""
+description: "Construye una figura matplotlib tipo donut (pie con agujero central) de las top_k categorías más frecuentes de una columna categórica, agregando el resto en un sector gris \"Otros (N categorías)\". Consume el bloque `top` de summarize_categorical y devuelve un matplotlib.figure.Figure listo para rasterizar por el renderer del informe EDA. Backend Agg sin pyplot global; defensivo ante top vacío/None."
+tags: [eda, categorical, pie, donut, matplotlib, figure, visualization, datascience, impure]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [matplotlib]
+example: |
+  from categorical_top_pie_figure import categorical_top_pie_figure
+  top = [
+      {"value": "rojo", "count": 40, "pct": 0.4},
+      {"value": "azul", "count": 30, "pct": 0.3},
+      {"value": "verde", "count": 20, "pct": 0.2},
+  ]
+  fig = categorical_top_pie_figure(top, n_distinct=12, title="color", top_k=6, n_rows=100)
+tested: true
+tests:
+  - "test_returns_figure"
+  - "test_ten_items_topk_six_yields_seven_wedges"
+  - "test_empty_top_does_not_raise_and_returns_figure"
+  - "test_long_value_truncated_in_legend"
+  - "test_none_value_and_none_count_are_handled"
+  - "test_n_rows_adds_exact_others_slice"
+test_file_path: "python/functions/datascience/categorical_top_pie_figure_test.py"
+file_path: "python/functions/datascience/categorical_top_pie_figure.py"
+params:
+  - name: top
+    desc: "Lista de dicts {value, count, pct} ordenada de mayor a menor por count (salida del bloque `top` de summarize_categorical). Puede venir vacía o con dicts incompletos: items no-dict, sin count, con count None o count <= 0 se descartan. value None se admite (sin etiqueta)."
+  - name: n_distinct
+    desc: "Nº total de categorías distintas de la columna. Etiqueta el sector agregado como \"Otros (n_distinct - top_k)\" (mínimo 0). Si no supera el nº de sectores mostrados, se usa el overflow real de `top` como nº de categorías agregadas. Default 0."
+  - name: title
+    desc: "Título de la figura (nombre de la columna). Se trunca a ~48 chars con elipsis si es muy largo. Default \"\" (sin título)."
+  - name: top_k
+    desc: "Nº máximo de sectores explícitos. Default 6. El sector \"Otros\" no cuenta contra este límite. Con top_k <= 0 se muestra al menos la categoría mayor."
+  - name: n_rows
+    desc: "Opcional. Total de filas del dataset. Si se da y la suma de counts mostrados < n_rows, el sector \"Otros\" usa (n_rows - suma_mostrada) como count para que los ángulos sean exactos respecto al total real. Si se omite, \"Otros\" usa la suma de counts fuera del top_k mostrado (solo cuando top trae más de top_k items). Default None."
+output: "Un matplotlib.figure.Figure (figsize 6.4x4.0, dpi 150) con un Axes donut (wedgeprops width 0.42) más una leyenda lateral con value truncado a 20 chars + count; el sector \"Otros\" en gris. Anotación central con el total n. Si no hay counts válidos, devuelve igualmente una Figure con un texto centrado \"sin datos categóricos\" (nunca lanza). El caller rasteriza/cierra la figura; la función no la muestra ni la guarda."
+---
+
+## Ejemplo
+
+```python
+from categorical_top_pie_figure import categorical_top_pie_figure
+
+# `top` es la salida del bloque "top" de summarize_categorical (ya ordenado desc).
+top = [
+    {"value": "rojo", "count": 40, "pct": 0.40},
+    {"value": "azul", "count": 30, "pct": 0.30},
+    {"value": "verde", "count": 20, "pct": 0.20},
+    {"value": "amarillo", "count": 5, "pct": 0.05},
+]
+
+fig = categorical_top_pie_figure(
+    top,
+    n_distinct=12,            # 12 categorías distintas en total
+    title="color_producto",
+    top_k=6,                  # hasta 6 sectores explícitos
+    n_rows=100,               # "Otros" = 100 - 95 = 5, sobre 8 categorías agregadas
+)
+
+# El renderer del informe lo rasteriza; aquí solo persistimos para inspección.
+fig.savefig("/tmp/donut_color.png")
+```
+
+## Cuando usarla
+
+Úsala dentro de un informe EDA cuando quieras visualizar la composición de una
+columna categórica de un vistazo: cuántas filas caen en las categorías
+dominantes frente a la cola larga. Pásale directamente el bloque `top` de
+`summarize_categorical` (ya ordenado de mayor a menor) más `n_distinct` para que
+el sector "Otros" indique cuántas categorías quedan agrupadas. Es la pareja
+"composición" del gráfico de barras top-k: el donut comunica proporciones del
+total, las barras comunican magnitudes comparables.
+
+## 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.** La función devuelve el `Figure` pero no lo
+  muestra ni lo guarda. Quien la consume debe rasterizarla y luego liberarla
+  (`fig.clf()` / `matplotlib.pyplot.close(fig)` si se usó pyplot en el caller)
+  para no acumular memoria en lotes grandes de columnas.
+- **Pie engaña con muchos sectores.** Por eso `top_k` por defecto es 6 y el
+  resto se agrega en "Otros": donuts con 15+ sectores son ilegibles. Para
+  cardinalidad muy alta el donut solo muestra la cabeza de la distribución; la
+  cola vive en el sector gris.
+- **Ángulos exactos solo con `n_rows`.** Sin `n_rows`, el sector "Otros" se
+  calcula con el overflow presente en `top`; si `top` ya viene recortado a
+  `top_k` por el productor, no habrá "Otros" aunque existan más categorías. Pasa
+  `n_rows` (total de filas del dataset) para ángulos correctos respecto al total
+  real.
+- **Defensiva, nunca lanza.** `top=[]`, `value=None`, `count=None` o counts no
+  numéricos se manejan sin error: en el peor caso devuelve una `Figure` con
+  "sin datos categóricos". No envuelvas la llamada en try/except por miedo a un
+  raise — no lo hay.

python/functions/datascience/categorical_top_pie_figure.py

@@ -0,0 +1,230 @@
+"""Impure EDA helper: donut figure of the most common categories (`eda` group).
+
+Builds a matplotlib donut (pie with a central hole) of the ``top_k`` most
+frequent categories of a categorical column, folding everything else into a
+single "Otros (N categorías)" slice. 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
+
+
+# Gray reserved for the aggregated "Otros" slice.
+_OTHER_COLOR = "#9e9e9e"
+# Muted gray for secondary text (title fallback, center annotation, no-data).
+_MUTED_TEXT = "#5f6b7a"
+# Pleasant, colour-blind-friendly qualitative palette for the explicit slices.
+_PALETTE = [
+    "#4C72B0",
+    "#DD8452",
+    "#55A868",
+    "#C44E52",
+    "#8172B3",
+    "#937860",
+    "#DA8BC3",
+    "#8C8C8C",
+    "#CCB974",
+    "#64B5CD",
+]
+
+
+def _truncate(text, width: int = 20) -> 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 categorical_top_pie_figure(
+    top: list,
+    n_distinct: int = 0,
+    title: str = "",
+    top_k: int = 6,
+    n_rows=None,
+) -> "matplotlib.figure.Figure":
+    """Build a donut figure of the most common categories of a column.
+
+    Renders the ``top_k`` most frequent categories as explicit donut slices and
+    aggregates every remaining category into a single gray "Otros (N
+    categorías)" slice. Category names are not painted on the wedges; they are
+    listed in a lateral legend (truncated value + count) to avoid overlap on
+    narrow (mobile) figures.
+
+    The function is fully defensive: empty input, missing/``None`` values or
+    counts never raise. When there is nothing valid to draw it still returns a
+    ``Figure`` carrying a centered "sin datos categóricos" message.
+
+    Args:
+        top: List of ``{value, count, pct}`` dicts, already sorted by ``count``
+            descending (the ``top`` block of ``summarize_categorical``). May be
+            empty or carry incomplete/``None`` entries; non-dict items, items
+            without a positive numeric ``count`` and ``None`` counts are skipped.
+        n_distinct: Total number of distinct categories in the column. Used to
+            label the aggregated slice as "Otros (n_distinct - top_k)" (floored
+            at 0). Ignored when it does not exceed the number of shown slices.
+        title: Figure title (the column name). Truncated when too long.
+        top_k: Maximum number of explicit slices. Default 6. The "Otros" slice
+            does not count against this limit.
+        n_rows: Optional total row count of the dataset. When given and the sum
+            of shown counts is below ``n_rows``, the "Otros" slice uses
+            ``n_rows - sum_shown`` as its count so the wedge angles are exact
+            with respect to the real total. When omitted, "Otros" uses the sum
+            of the counts that fall outside the shown ``top_k`` (only when
+            ``top`` carries more than ``top_k`` items).
+
+    Returns:
+        A ``matplotlib.figure.Figure`` with a single donut Axes plus a lateral
+        legend. The caller is responsible for rasterizing/closing it.
+    """
+    fig = Figure(figsize=(6.4, 4.0), dpi=150)
+    ax = fig.add_subplot(111)
+
+    safe_title = _truncate(title, 48)
+
+    # --- Defensive parse: keep only well-formed {value, count} with count > 0.
+    cleaned = []
+    if isinstance(top, list):
+        for item in top:
+            if not isinstance(item, dict):
+                continue
+            count = item.get("count")
+            if count is None:
+                continue
+            try:
+                count = float(count)
+            except (TypeError, ValueError):
+                continue
+            if count <= 0:
+                continue
+            cleaned.append((item.get("value"), count))
+
+    if not cleaned:
+        ax.axis("off")
+        ax.text(
+            0.5,
+            0.5,
+            "sin datos categóricos",
+            ha="center",
+            va="center",
+            fontsize=12,
+            color=_MUTED_TEXT,
+            transform=ax.transAxes,
+        )
+        if safe_title:
+            ax.set_title(safe_title, fontsize=12, loc="center", pad=8)
+        fig.tight_layout()
+        return fig
+
+    # --- Split into shown slices and the aggregated remainder.
+    shown = cleaned[: max(int(top_k), 0)]
+    if not shown:  # top_k <= 0 — show at least the largest category.
+        shown = cleaned[:1]
+
+    sum_shown = sum(c for _, c in shown)
+    overflow_count = sum(c for _, c in cleaned[len(shown):])
+
+    # How many categories are folded into "Otros".
+    try:
+        nd = int(n_distinct)
+    except (TypeError, ValueError):
+        nd = 0
+    others_categories = max(nd - len(shown), 0)
+    # If n_distinct is unknown/too small, fall back to the overflow we actually
+    # have in `top` beyond the shown slices.
+    overflow_items = len(cleaned) - len(shown)
+    if others_categories == 0 and overflow_items > 0:
+        others_categories = overflow_items
+
+    # Count attributed to the "Otros" slice for exact angles.
+    others_count = 0.0
+    if n_rows is not None:
+        try:
+            total_rows = float(n_rows)
+        except (TypeError, ValueError):
+            total_rows = None
+        if total_rows is not None and total_rows > sum_shown:
+            others_count = total_rows - sum_shown
+    if others_count <= 0:
+        others_count = overflow_count
+
+    labels = [v for v, _ in shown]
+    values = [c for _, c in shown]
+    colors = [_PALETTE[i % len(_PALETTE)] for i in range(len(shown))]
+
+    has_others = others_count > 0 and others_categories > 0
+    if has_others:
+        values.append(others_count)
+        labels.append("Otros")
+        colors.append(_OTHER_COLOR)
+
+    total = sum(values)
+
+    def _autopct(pct: float) -> str:
+        # Hide tiny labels to avoid crowding the wedges.
+        return f"{pct:.0f}%" if pct >= 5 else ""
+
+    wedges, _texts, autotexts = ax.pie(
+        values,
+        colors=colors,
+        startangle=90,
+        counterclock=False,
+        wedgeprops={"width": 0.42, "edgecolor": "white", "linewidth": 1.0},
+        autopct=_autopct,
+        pctdistance=0.79,
+        textprops={"fontsize": 8},
+    )
+    for at in autotexts:
+        at.set_color("white")
+        at.set_fontweight("bold")
+    ax.set_aspect("equal")
+
+    # --- Lateral legend: truncated value + count (+ "(N categorías)" for Otros).
+    legend_labels = []
+    for idx, (lab, val) in enumerate(zip(labels, values)):
+        if has_others and idx == len(labels) - 1:
+            legend_labels.append(
+                f"Otros ({others_categories} categorías) — {int(round(val))}"
+            )
+        else:
+            legend_labels.append(f"{_truncate(lab, 20)} — {int(round(val))}")
+
+    ax.legend(
+        wedges,
+        legend_labels,
+        title="Categorías",
+        loc="center left",
+        bbox_to_anchor=(1.02, 0.5),
+        fontsize=8,
+        title_fontsize=9,
+        frameon=False,
+    )
+
+    if safe_title:
+        ax.set_title(safe_title, fontsize=13, loc="left", pad=10)
+
+    # Center annotation: total count covered by the donut.
+    ax.text(
+        0,
+        0,
+        f"n={int(round(total))}",
+        ha="center",
+        va="center",
+        fontsize=11,
+        color=_MUTED_TEXT,
+        fontweight="bold",
+    )
+
+    # Leave room on the right for the legend (avoid clipping it).
+    fig.subplots_adjust(left=0.02, right=0.62, top=0.88, bottom=0.06)
+    return fig

python/functions/datascience/categorical_top_pie_figure_test.py

@@ -0,0 +1,104 @@
+"""Tests para categorical_top_pie_figure (donut de categorías top, 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 categorical_top_pie_figure import categorical_top_pie_figure
+
+
+def _make_top(n):
+    """n items {value, count, pct} ordenados desc por count."""
+    return [
+        {"value": f"cat_{i}", "count": n - i, "pct": (n - i) / sum(range(1, n + 1))}
+        for i in range(n)
+    ]
+
+
+def _wedges(ax):
+    """Devuelve los wedges (sectores) de un Axes con un pie."""
+    from matplotlib.patches import Wedge
+
+    return [p for p in ax.patches if isinstance(p, Wedge)]
+
+
+def test_returns_figure():
+    fig = categorical_top_pie_figure(_make_top(3), n_distinct=3, title="col")
+    assert isinstance(fig, Figure)
+    plt.close(fig)
+
+
+def test_ten_items_topk_six_yields_seven_wedges():
+    top = _make_top(10)
+    fig = categorical_top_pie_figure(top, n_distinct=10, title="muchas", top_k=6)
+    ax = fig.axes[0]
+    wedges = _wedges(ax)
+    # 6 categorías explícitas + 1 sector "Otros".
+    assert len(wedges) == 7
+    plt.close(fig)
+
+
+def test_empty_top_does_not_raise_and_returns_figure():
+    fig = categorical_top_pie_figure([], n_distinct=0, title="vacía")
+    assert isinstance(fig, Figure)
+    # Sin datos: no debe haber sectores de pie.
+    assert len(_wedges(fig.axes[0])) == 0
+    plt.close(fig)
+
+
+def test_long_value_truncated_in_legend():
+    long_value = "una_categoria_con_un_nombre_larguisimo_que_excede_el_limite"
+    top = [
+        {"value": long_value, "count": 10, "pct": 0.5},
+        {"value": "corta", "count": 10, "pct": 0.5},
+    ]
+    fig = categorical_top_pie_figure(top, n_distinct=2, title="col", top_k=6)
+    ax = fig.axes[0]
+    legend = ax.get_legend()
+    assert legend is not None
+    texts = [t.get_text() for t in legend.get_texts()]
+    # El valor largo aparece truncado con elipsis y NO en su forma completa.
+    assert any("…" in t for t in texts)
+    assert long_value not in " ".join(texts)
+    plt.close(fig)
+
+
+def test_none_value_and_none_count_are_handled():
+    top = [
+        {"value": None, "count": 5, "pct": 0.5},
+        {"value": "b", "count": None, "pct": 0.0},  # count None -> se descarta
+        {"value": "c", "count": 5, "pct": 0.5},
+    ]
+    fig = categorical_top_pie_figure(top, n_distinct=2, title="con nones", top_k=6)
+    assert isinstance(fig, Figure)
+    # Solo 2 items válidos, sin overflow -> 2 wedges, sin "Otros".
+    assert len(_wedges(fig.axes[0])) == 2
+    plt.close(fig)
+
+
+def test_n_rows_adds_exact_others_slice():
+    # 3 categorías mostradas suman 30, dataset real 100 -> "Otros" = 70.
+    top = _make_top(3)  # counts 3,2,1 -> reescalamos abajo
+    top = [
+        {"value": "a", "count": 15, "pct": 0.15},
+        {"value": "b", "count": 10, "pct": 0.10},
+        {"value": "c", "count": 5, "pct": 0.05},
+    ]
+    fig = categorical_top_pie_figure(
+        top, n_distinct=20, title="col", top_k=3, n_rows=100
+    )
+    ax = fig.axes[0]
+    # 3 explícitas + Otros.
+    assert len(_wedges(ax)) == 4
+    legend_texts = [t.get_text() for t in ax.get_legend().get_texts()]
+    # El sector Otros refleja n_distinct - top_k = 17 categorías y count 70.
+    assert any("Otros (17 categorías)" in t and "70" in t for t in legend_texts)
+    plt.close(fig)

python/functions/datascience/detect_latlon_columns.md

@@ -0,0 +1,67 @@
+---
+name: detect_latlon_columns
+id: detect_latlon_columns_py_datascience
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def detect_latlon_columns(columns: list, samples: dict | None = None) -> dict"
+description: "Detecta un par (latitud, longitud) entre las columnas de un TableProfile del grupo eda combinando heuristica de nombre (latitude/longitude/lat/lon/lng + x/y debiles) con validacion de rango obligatoria (latitud en [-90,90], longitud en [-180,180]). Lee defensivamente con .get; NUNCA lanza. Usa el sub-bloque numeric.min/max o, si falta, la lista de samples opcional. Devuelve SIEMPRE un dict {lat_col, lon_col, confidence, reason}; si no hay par valido, las columnas van a None y confidence a 0.0."
+tags: [eda, geospatial, profiling, latlon, coordinates, detection, datascience]
+params:
+  - name: columns
+    desc: "Lista de dicts ColumnProfile (el campo `columns` de un TableProfile del grupo eda). Cada dict se lee con .get; solo `name` (str) es obligatorio. Se consultan `inferred_type` (p.ej. 'numeric') y el sub-dict `numeric` con `min`/`max` (floats) para validar el rango. Entradas no-dict o sin name se ignoran sin lanzar."
+  - name: samples
+    desc: "Opcional {nombre_columna: [valores...]} para validar el rango cuando una columna no trae numeric.min/max. Los valores nulos se ignoran; si algun valor no nulo no es numerico la columna no se considera coordenada. Si es None u omitido, solo se usa el bloque numeric."
+output: "Dict SIEMPRE presente con la forma {lat_col: str|None, lon_col: str|None, confidence: float en [0,1], reason: str en espanol}. En exito, lat_col y lon_col nombran columnas distintas; confidence ~1.0 para par con nombre fuerte (latitude/longitude/lat/lon/lng) + rango valido y ~0.7 para par debil (x/y) + rango. En fallo, ambas columnas None, confidence 0.0 y reason explica por que (sin columnas, nombre sin match, rango fuera de bounds, falta uno de los dos ejes...)."
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+tested: true
+tests: ["test_par_latitude_longitude_fuerte", "test_par_lat_lon_abreviado", "test_par_x_y_debil_con_rango_valido", "test_nombre_lat_lon_pero_rango_fuera_no_detecta", "test_par_fuerte_prevalece_sobre_debil", "test_entradas_vacias_o_invalidas_no_lanzan", "test_solo_latitud_sin_longitud_no_detecta", "test_deteccion_por_samples_cuando_falta_numeric", "test_samples_fuera_de_rango_descarta"]
+test_file_path: "python/functions/datascience/detect_latlon_columns_test.py"
+file_path: "python/functions/datascience/detect_latlon_columns.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience.detect_latlon_columns import detect_latlon_columns
+
+# Columnas tal y como vienen en profile['columns'] de un TableProfile del grupo eda:
+columns = [
+    {"name": "id", "inferred_type": "numeric", "numeric": {"min": 1, "max": 9999}},
+    {"name": "latitude", "inferred_type": "numeric", "numeric": {"min": -45.0, "max": 45.0}},
+    {"name": "longitude", "inferred_type": "numeric", "numeric": {"min": -120.0, "max": 120.0}},
+]
+res = detect_latlon_columns(columns)
+print(res["lat_col"], res["lon_col"], res["confidence"])
+# latitude longitude 1.0
+
+# Sin bloque numeric, validando el rango con samples:
+cols2 = [{"name": "lat"}, {"name": "lon"}]
+samples = {"lat": [10.5, 20.0, 30.25], "lon": [-40.0, 50.5, 60.0]}
+print(detect_latlon_columns(cols2, samples)["lat_col"])  # lat
+```
+
+## Cuando usarla
+
+- Usala al perfilar una tabla en `AutomaticEDA` para decidir si tiene geometria de puntos: cuando `detect_latlon_columns` devuelve un par con `confidence` alta, el capitulo geospatial puede dibujar un mapa, calcular un bounding box o proponer un cluster espacial.
+- Antes de un analisis geoespacial (alpha shape, convex hull, joins por proximidad) para localizar automaticamente que columnas son la latitud y la longitud sin pedirlo al usuario.
+- Cuando recibas un `TableProfile` del grupo `eda` y quieras enrutar columnas a sub-analisis por tipo semantico: este es el detector del par lat/lon, complementario a `infer_semantic_type`.
+
+## Gotchas
+
+- Funcion pura, sin I/O y determinista. Lectura defensiva con `.get`: NUNCA lanza. Cualquier input malformado (None, no-lista, entradas no-dict, claves ausentes) devuelve el dict de fallo con `lat_col`/`lon_col` en None y `confidence` 0.0.
+- **El nombre solo no basta**: una columna `latitude` cuyo rango se sale de `[-90, 90]` se descarta (no es coordenada real). Igual para `longitude` fuera de `[-180, 180]`. La validacion de rango es obligatoria.
+- El rango de latitud `[-90, 90]` es un subconjunto del de longitud `[-180, 180]`, por eso el nombre es necesario para desambiguar cual eje es cual; una columna numerica en `[-90, 90]` sin nombre que sugiera lat/lon no se detecta.
+- Los nombres genericos `x`/`y` (y `x_coord`/`y_coord`) son candidatos **debiles**: solo forman par si el rango encaja y existe la otra mitad (un `x`/`lon` para la `y`, un `y`/`lat` para la `x`). Un `y` suelto sin pareja devuelve None.
+- Requiere AMBOS ejes para considerar exito. Si solo encuentra latitud o solo longitud, devuelve el dict de fallo (no media coordenada).
+- `samples` solo se consulta cuando falta `numeric.min`/`numeric.max`. Si una columna trae el bloque numeric, ese manda aunque pases samples para ella.
+- El matching de nombre es por subcadena normalizada (se quitan `_`, `-` y espacios), asi que nombres como `plate` (contiene "lat") podrian marcarse como candidatos por nombre — pero solo pasarian si su rango cae en `[-90, 90]` y hay una longitud pareja, filtro que en la practica descarta los falsos positivos.

python/functions/datascience/detect_latlon_columns.py

@@ -0,0 +1,198 @@
+"""detect_latlon_columns — detect a (latitude, longitude) column pair in an EDA profile.
+
+Pure function: no I/O, deterministic. Takes the `columns` list of a TableProfile
+(group `eda`) and decides whether two of its columns form a geographic coordinate
+pair (latitude + longitude), combining a name heuristic with a value-range check.
+
+The detection is intentionally conservative: a name hint alone is never enough. A
+column is only accepted as latitude/longitude if its numeric range fits inside the
+valid coordinate bounds ([-90, 90] for latitude, [-180, 180] for longitude). When
+the `numeric` sub-block is absent the optional `samples` argument is used instead.
+
+Reading is fully defensive (.get throughout) and the function NEVER raises: any
+malformed input (None, non-list, non-dict entries, missing keys) simply yields a
+no-pair result {"lat_col": None, "lon_col": None, "confidence": 0.0, "reason": ...}.
+"""
+
+import re
+
+# Collapse the separators a column name may use (snake_case, kebab-case, spaces)
+# so that "y_coord", "y-coord" and "y coord" all normalize to the same token.
+_SEP_RE = re.compile(r"[\s_\-]+")
+
+# Name-match strengths: a strong, unambiguous coordinate name vs a weak generic
+# axis name (x / y) that only counts when the range also fits and a partner exists.
+_STRONG = 0.6
+_WEAK = 0.3
+_RANGE_BONUS = 0.4  # added once the mandatory range validation passes
+
+
+def _normalize(name):
+    """Lowercase a column name and strip separator chars (_, -, whitespace)."""
+    if not isinstance(name, str):
+        return ""
+    return _SEP_RE.sub("", name.strip().lower())
+
+
+def _num(value):
+    """Coerce to float defensively; return None for None/bool/non-numeric."""
+    # bool is a subclass of int; a coordinate value is never a real bool, so treat
+    # True/False as missing instead of silently coercing to 1.0/0.0.
+    if value is None or isinstance(value, bool):
+        return None
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _lat_name_strength(nn):
+    """Strength of a normalized name as a latitude candidate (0=no match)."""
+    if not nn:
+        return 0.0
+    # "lat", "latitude", "latitud" all contain the "lat" stem.
+    if "lat" in nn:
+        return _STRONG
+    # Weak generic axis name: only useful when paired with an x/lon partner.
+    if nn in ("y", "ycoord", "ycoordinate", "ycoordinates"):
+        return _WEAK
+    return 0.0
+
+
+def _lon_name_strength(nn):
+    """Strength of a normalized name as a longitude candidate (0=no match)."""
+    if not nn:
+        return 0.0
+    # "lon", "long", "longitude", "longitud" share the "lon" stem; "lng" is separate.
+    if "lon" in nn or "lng" in nn:
+        return _STRONG
+    if nn in ("x", "xcoord", "xcoordinate", "xcoordinates"):
+        return _WEAK
+    return 0.0
+
+
+def _col_range(col, sample_values):
+    """Return (min, max) floats for a column, or (None, None) if not numeric.
+
+    Prefers the `numeric` sub-block min/max (the output of describe_numeric); falls
+    back to the provided sample list. A column is only treated as numeric when both
+    extremes are derivable: from the numeric block, or from samples whose every
+    non-null value coerces to a number.
+    """
+    if isinstance(col, dict):
+        numeric = col.get("numeric")
+        if isinstance(numeric, dict):
+            mn = _num(numeric.get("min"))
+            mx = _num(numeric.get("max"))
+            if mn is not None and mx is not None:
+                return mn, mx
+    # Fall back to samples when the numeric block is missing or incomplete.
+    if isinstance(sample_values, (list, tuple)):
+        non_null = [v for v in sample_values if v is not None]
+        if non_null:
+            coerced = [_num(v) for v in non_null]
+            # Any non-numeric sample means we cannot trust the column as numeric.
+            if all(c is not None for c in coerced):
+                return min(coerced), max(coerced)
+    return None, None
+
+
+def _no_pair(reason):
+    """Canonical empty result: no coordinate pair detected."""
+    return {"lat_col": None, "lon_col": None, "confidence": 0.0, "reason": reason}
+
+
+def detect_latlon_columns(columns: list, samples: dict | None = None) -> dict:
+    """Detect a (latitude, longitude) column pair from an eda TableProfile.
+
+    Combines a name heuristic (latitude/longitude/lat/lon/lng + weak x/y) with a
+    mandatory range validation: the chosen latitude must sit in [-90, 90] and the
+    longitude in [-180, 180]. A name hint whose range does not fit is discarded.
+    Both sides are required for success; if only one is found, no pair is returned.
+
+    Args:
+        columns: List of ColumnProfile dicts (the `columns` of a TableProfile).
+            Each dict is read defensively with .get; only `name` is required.
+            `numeric.min` / `numeric.max` (and optionally `inferred_type`) are used
+            for the range check when present.
+        samples: Optional {column_name: [values...]} used to validate the range
+            when a column lacks `numeric.min`/`numeric.max`. If None/omitted, only
+            the `numeric` sub-block is consulted.
+
+    Returns:
+        Always a dict {"lat_col": str|None, "lon_col": str|None,
+        "confidence": float, "reason": str}. On success lat_col and lon_col name
+        the detected pair (distinct columns) and confidence is in [0, 1]: a pair
+        validated by a strong name on both sides scores ~1.0, a weak x/y pair ~0.7.
+        On failure both columns are None and confidence is 0.0.
+    """
+    if not isinstance(columns, (list, tuple)) or len(columns) == 0:
+        return _no_pair("sin columnas que inspeccionar")
+
+    sample_map = samples if isinstance(samples, dict) else {}
+
+    # (column_name, confidence) for each side. Confidence already includes the
+    # range bonus because membership in the list implies the range was validated.
+    lat_candidates = []
+    lon_candidates = []
+
+    for col in columns:
+        if not isinstance(col, dict):
+            continue
+        name = col.get("name")
+        if not isinstance(name, str) or not name:
+            continue
+
+        nn = _normalize(name)
+        lat_strength = _lat_name_strength(nn)
+        lon_strength = _lon_name_strength(nn)
+        if lat_strength == 0.0 and lon_strength == 0.0:
+            continue  # name gives no coordinate hint; skip.
+
+        mn, mx = _col_range(col, sample_map.get(name))
+        is_numeric = mn is not None and mx is not None
+        if not is_numeric:
+            continue  # range cannot be validated -> not a coordinate.
+
+        if lat_strength > 0.0 and mn >= -90.0 and mx <= 90.0:
+            lat_candidates.append((name, lat_strength + _RANGE_BONUS))
+        if lon_strength > 0.0 and mn >= -180.0 and mx <= 180.0:
+            lon_candidates.append((name, lon_strength + _RANGE_BONUS))
+
+    if not lat_candidates and not lon_candidates:
+        return _no_pair("ninguna columna sugiere latitud ni longitud por nombre+rango")
+    if not lat_candidates:
+        return _no_pair("no se encontro columna de latitud valida (nombre+rango en [-90,90])")
+    if not lon_candidates:
+        return _no_pair("no se encontro columna de longitud valida (nombre+rango en [-180,180])")
+
+    # Pick the distinct pair with the highest combined confidence. First match wins
+    # on ties to keep the result deterministic by input order.
+    best = None  # (combined, lat_name, lon_name, lat_c, lon_c)
+    for lat_name, lat_c in lat_candidates:
+        for lon_name, lon_c in lon_candidates:
+            if lat_name == lon_name:
+                continue  # a column cannot be both axes of the same pair.
+            combined = (lat_c + lon_c) / 2.0
+            if best is None or combined > best[0]:
+                best = (combined, lat_name, lon_name, lat_c, lon_c)
+
+    if best is None:
+        return _no_pair("solo una columna sirve para ambos ejes; no hay par lat/lon distinto")
+
+    combined, lat_name, lon_name, lat_c, lon_c = best
+    confidence = max(0.0, min(1.0, combined))
+
+    lat_label = "fuerte" if lat_c >= 0.9 else "debil"
+    lon_label = "fuerte" if lon_c >= 0.9 else "debil"
+    reason = (
+        f"par lat='{lat_name}' (nombre {lat_label}) / lon='{lon_name}' "
+        f"(nombre {lon_label}) con rango valido"
+    )
+
+    return {
+        "lat_col": lat_name,
+        "lon_col": lon_name,
+        "confidence": confidence,
+        "reason": reason,
+    }

python/functions/datascience/detect_latlon_columns_test.py

@@ -0,0 +1,141 @@
+"""Tests para detect_latlon_columns."""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from detect_latlon_columns import detect_latlon_columns
+
+# Keys that every result dict (success or failure) must expose.
+_EXPECTED_KEYS = {"lat_col", "lon_col", "confidence", "reason"}
+
+
+def _col(name, mn=None, mx=None, inferred="numeric"):
+    """Build a minimal ColumnProfile-like dict for the tests."""
+    col = {"name": name, "inferred_type": inferred}
+    if mn is not None or mx is not None:
+        col["numeric"] = {"min": mn, "max": mx}
+    return col
+
+
+def test_par_latitude_longitude_fuerte():
+    """Golden: nombres latitude/longitude con rango valido -> par con confianza alta."""
+    columns = [
+        _col("id", mn=1, mx=9999, inferred="numeric"),
+        _col("latitude", mn=-45.0, mx=45.0),
+        _col("longitude", mn=-120.0, mx=120.0),
+    ]
+    res = detect_latlon_columns(columns)
+
+    assert set(res.keys()) == _EXPECTED_KEYS
+    assert res["lat_col"] == "latitude"
+    assert res["lon_col"] == "longitude"
+    # Nombre fuerte (0.6) + rango (0.4) en ambos lados -> 1.0.
+    assert abs(res["confidence"] - 1.0) < 1e-9
+    assert "rango valido" in res["reason"]
+
+
+def test_par_lat_lon_abreviado():
+    """Golden: nombres abreviados lat/lon tambien se detectan como fuertes."""
+    columns = [
+        _col("lat", mn=40.0, mx=43.0),
+        _col("lon", mn=-4.0, mx=-1.0),
+        _col("precio", mn=0.0, mx=500.0),
+    ]
+    res = detect_latlon_columns(columns)
+    assert res["lat_col"] == "lat"
+    assert res["lon_col"] == "lon"
+    assert abs(res["confidence"] - 1.0) < 1e-9
+
+
+def test_par_x_y_debil_con_rango_valido():
+    """Edge: x/y genericos solo cuentan como par debil cuando el rango encaja."""
+    columns = [
+        _col("y_coord", mn=-10.0, mx=10.0),   # debil latitud
+        _col("x_coord", mn=-150.0, mx=150.0),  # debil longitud
+    ]
+    res = detect_latlon_columns(columns)
+    assert res["lat_col"] == "y_coord"
+    assert res["lon_col"] == "x_coord"
+    # Nombre debil (0.3) + rango (0.4) -> 0.7 en ambos lados.
+    assert abs(res["confidence"] - 0.7) < 1e-9
+
+
+def test_nombre_lat_lon_pero_rango_fuera_no_detecta():
+    """Edge: nombre lat/lon con rango fuera de bounds -> NO es coordenada."""
+    columns = [
+        _col("latitude", mn=-200.0, mx=200.0),   # fuera de [-90, 90]
+        _col("longitude", mn=-120.0, mx=120.0),  # valido, pero sin par lat
+    ]
+    res = detect_latlon_columns(columns)
+    assert res["lat_col"] is None
+    assert res["lon_col"] is None
+    assert res["confidence"] == 0.0
+    assert isinstance(res["reason"], str) and res["reason"]
+
+
+def test_par_fuerte_prevalece_sobre_debil():
+    """Edge: con candidatos fuertes y debiles, gana el par de mayor confianza."""
+    columns = [
+        _col("latitude", mn=-45.0, mx=45.0),    # fuerte lat
+        _col("y", mn=-30.0, mx=30.0),           # debil lat
+        _col("longitude", mn=-120.0, mx=120.0),  # fuerte lon
+        _col("x", mn=-100.0, mx=100.0),         # debil lon
+    ]
+    res = detect_latlon_columns(columns)
+    assert res["lat_col"] == "latitude"
+    assert res["lon_col"] == "longitude"
+    assert abs(res["confidence"] - 1.0) < 1e-9
+
+
+def test_entradas_vacias_o_invalidas_no_lanzan():
+    """Edge: sin columnas / vacio / no-lista / entradas no-dict -> dict None sin lanzar."""
+    for bad in ([], None, "no soy lista", 42, [1, 2, 3], [{}], [{"foo": "bar"}]):
+        res = detect_latlon_columns(bad)
+        assert set(res.keys()) == _EXPECTED_KEYS
+        assert res["lat_col"] is None
+        assert res["lon_col"] is None
+        assert res["confidence"] == 0.0
+        assert isinstance(res["reason"], str)
+
+
+def test_solo_latitud_sin_longitud_no_detecta():
+    """Edge: solo hay latitud valida, falta la longitud -> sin par."""
+    columns = [
+        _col("latitude", mn=-45.0, mx=45.0),
+        _col("temperatura", mn=-5.0, mx=40.0),
+    ]
+    res = detect_latlon_columns(columns)
+    assert res["lat_col"] is None
+    assert res["lon_col"] is None
+    assert res["confidence"] == 0.0
+
+
+def test_deteccion_por_samples_cuando_falta_numeric():
+    """Edge: sin bloque numeric, el rango se valida con samples."""
+    columns = [
+        {"name": "lat"},  # sin numeric ni inferred_type
+        {"name": "lon"},
+    ]
+    samples = {
+        "lat": [10.5, 20.0, None, 30.25],   # todos dentro de [-90, 90]
+        "lon": [-40.0, 50.5, 60.0],          # todos dentro de [-180, 180]
+    }
+    res = detect_latlon_columns(columns, samples)
+    assert res["lat_col"] == "lat"
+    assert res["lon_col"] == "lon"
+    assert abs(res["confidence"] - 1.0) < 1e-9
+
+
+def test_samples_fuera_de_rango_descarta():
+    """Edge: samples fuera de bounds invalidan la columna pese al nombre fuerte."""
+    columns = [{"name": "lat"}, {"name": "lon"}]
+    samples = {
+        "lat": [10.0, 95.0],   # 95 > 90 -> latitud invalida
+        "lon": [-40.0, 50.0],
+    }
+    res = detect_latlon_columns(columns, samples)
+    assert res["lat_col"] is None
+    assert res["lon_col"] is None
+    assert res["confidence"] == 0.0

python/functions/datascience/groupby_stats_duckdb.md

@@ -0,0 +1,87 @@
+---
+name: groupby_stats_duckdb
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def groupby_stats_duckdb(db_path: str, table: str, group_by: str, measures: list, aggs: list = None, top_n: int = 15) -> dict"
+description: "Agregaciones GROUP BY con push-down SQL en DuckDB: para cada measure numerica calcula mean/median/std/min/max por grupo (split-apply-combine en el motor), trayendo solo una fila por grupo. Nucleo de un capitulo de agregacion/OLAP de un EDA. count = tamanio del grupo, independiente de measures."
+tags: [eda, groupby, aggregation, olap, duckdb, datascience, push-down, split-apply-combine]
+uses_functions: [duckdb_query_readonly_py_infra]
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: []
+params:
+  - name: db_path
+    desc: "Ruta al archivo DuckDB. Debe existir; el modo read_only NO crea la base. Path inexistente -> {status:'error'} sin lanzar."
+  - name: table
+    desc: "Nombre de la tabla. Se interpola citado con dobles comillas (soporta nombres con espacios; las comillas internas se escapan)."
+  - name: group_by
+    desc: "Columna por la que agrupar. Se interpola citada. Sus valores distintos son las claves de los grupos."
+  - name: measures
+    desc: "Lista de columnas numericas a agregar. Lista vacia es valida: cada grupo trae solo su tamanio `n` y `stats` vacio."
+  - name: aggs
+    desc: "Lista de agregaciones. None (default) = ['count','mean','median','std','min','max']. Validas: count (tamanio del grupo, va a `n`), mean->avg, median, std->stddev_samp, min, max (estas cinco por measure). Agg desconocido -> error."
+  - name: top_n
+    desc: "Maximo de grupos a devolver, ordenados por tamanio de grupo descendente (default 15). Internamente se piden top_n+1 para detectar truncado."
+output: "dict. En exito {status:'ok', group_by, measures:[...], aggs:[...], n_groups:int, truncated:bool, groups:[{key:<valor grupo>, n:int, stats:{<measure>:{mean,median,std,min,max}}}], note:str}. Las estadisticas son float o None (p.ej. std de un grupo de 1 fila -> NULL -> None). En error {status:'error', error:str} (no lanza)."
+tested: true
+tests: ["agrega por grupo con valores conocidos", "db inexistente devuelve error sin lanzar", "measures vacias agrega solo count", "columna con espacio agrupa bien"]
+test_file_path: "python/functions/datascience/groupby_stats_duckdb_test.py"
+file_path: "python/functions/datascience/groupby_stats_duckdb.py"
+---
+
+## Ejemplo
+
+```python
+import duckdb
+from datascience import groupby_stats_duckdb
+
+# Cargar el titanic en una tabla DuckDB de prueba.
+db = "/tmp/titanic.duckdb"
+con = duckdb.connect(db)
+con.execute(
+    "CREATE TABLE titanic AS "
+    "SELECT * FROM read_csv_auto('https://raw.githubusercontent.com/"
+    "datasciencedojo/datasets/master/titanic.csv')"
+)
+con.close()
+
+# Agrupar por sexo midiendo edad y tarifa.
+res = groupby_stats_duckdb(db, "titanic", "Sex", ["Age", "Fare"])
+print(res["status"])          # ok
+print(res["n_groups"])        # 2  (male, female)
+for g in res["groups"]:
+    print(g["key"], g["n"], round(g["stats"]["Fare"]["mean"], 2))
+# female 314 44.48
+# male   577 25.52
+```
+
+## Cuando usarla
+
+Cuando en un EDA necesitas el clasico split-apply-combine: "para cada categoria de X,
+¿cuanto vale en media/mediana/desviacion/min/max la metrica Y?". Es el nucleo de un
+capitulo de agregacion/OLAP. Usala antes de pintar barras o boxplots por grupo, para
+detectar segmentos con comportamiento distinto, o para resumir una tabla grande sin
+traer las filas a RAM: todo el GROUP BY ocurre push-down en el motor de DuckDB y solo
+viaja una fila por grupo. `top_n` te deja quedarte con los grupos mas poblados.
+
+## Gotchas
+
+- Funcion impura: lee un archivo DuckDB del disco (read_only, nunca lo modifica). La
+  tabla debe existir ya en el `.db` (no carga CSV; para eso crea la tabla antes).
+- Identificadores (tabla, group_by, measures) se interpolan citados con dobles comillas
+  y escapando las internas: soporta nombres con espacios y evita inyeccion. No pases
+  expresiones SQL como group_by/measure — solo nombres de columna.
+- `count` es el tamanio del grupo (`COUNT(*)`), independiente de las measures: se
+  refleja en el campo `n` de cada grupo, NO como clave dentro de `stats`. Las claves de
+  `stats[measure]` son las measure-aggs efectivas (mean/median/std/min/max menos count).
+- `std` usa `stddev_samp` (muestral, n-1): un grupo con una sola fila da `NULL` -> `None`.
+  Las measures pueden contener NULLs; cada agregada los ignora segun la semantica de DuckDB.
+- `truncated:True` indica que habia mas grupos que `top_n` (se devolvieron los `top_n`
+  mayores por tamanio). Sube `top_n` si necesitas todos los grupos.
+- Si `measures` esta vacio, cada grupo trae solo `n` y `stats == {}` (valido, util para
+  un simple conteo por categoria).

python/functions/datascience/groupby_stats_duckdb.py

@@ -0,0 +1,184 @@
+"""groupby_stats_duckdb — agregaciones GROUP BY con push-down SQL en DuckDB.
+
+Funcion impura: lee de disco a traves de DuckDB (via la primitiva read-only
+`duckdb_query_readonly` del grupo `duckdb`). Pertenece al grupo de capacidad `eda`.
+
+Ejecuta un `GROUP BY <group_by>` en el motor de DuckDB (split-apply-combine con
+push-down) calculando, para cada columna numerica de `measures`, las agregaciones
+pedidas (mean/median/std/min/max). Solo trae al cliente una fila por grupo, nunca
+las filas crudas: apto para tablas grandes. Es el nucleo de un capitulo de
+agregacion/OLAP de un EDA.
+
+Estilo dict-no-throw del grupo duckdb: nunca lanza; captura cualquier error y
+devuelve {status:'error', error:str}.
+"""
+
+from infra import duckdb_query_readonly
+
+# Mapeo agg -> funcion agregada SQL de DuckDB. `count` se trata aparte: es
+# COUNT(*) (tamanio del grupo), independiente de las measures.
+_AGG_SQL = {
+    "mean": "avg",
+    "median": "median",
+    "std": "stddev_samp",
+    "min": "min",
+    "max": "max",
+}
+
+# Aggs por defecto cuando aggs=None. count primero (tamanio del grupo) + las
+# cinco estadisticas por measure.
+_DEFAULT_AGGS = ["count", "mean", "median", "std", "min", "max"]
+
+
+def _quote_ident(ident: str) -> str:
+    """Cita un identificador SQL con dobles comillas, escapando las internas.
+
+    Soporta nombres con espacios o caracteres especiales y evita inyeccion: dentro
+    de un identificador entrecomillado el unico caracter peligroso es la propia
+    comilla doble, que se duplica ("") segun el estandar SQL. DuckDB no admite
+    parametros posicionales para nombres de tabla/columna, asi que esta es la via
+    segura de interpolarlos.
+    """
+    return '"' + str(ident).replace('"', '""') + '"'
+
+
+def groupby_stats_duckdb(
+    db_path: str,
+    table: str,
+    group_by: str,
+    measures: list,
+    aggs: list = None,
+    top_n: int = 15,
+) -> dict:
+    """GROUP BY con agregaciones por measure, todo push-down en DuckDB.
+
+    Args:
+        db_path: ruta al archivo DuckDB. Debe existir; el modo read_only NO crea la
+            base. Un path inexistente devuelve {status:'error', ...} sin lanzar.
+        table: nombre de la tabla. Se interpola citado con dobles comillas (soporta
+            nombres con espacios).
+        group_by: columna por la que agrupar. Se interpola citada.
+        measures: lista de columnas numericas a agregar. Lista vacia es valida:
+            cada grupo trae solo su tamanio `n` y `stats` vacio.
+        aggs: lista de agregaciones a calcular. None (default) =
+            ["count", "mean", "median", "std", "min", "max"]. Valores validos:
+            count (tamanio del grupo, va a `n`), mean, median, std, min, max
+            (estas cinco se calculan por cada measure). Un agg desconocido devuelve
+            error.
+        top_n: numero maximo de grupos a devolver, ordenados por tamanio de grupo
+            descendente (default 15). Se pide top_n+1 internamente para detectar si
+            habia mas grupos y marcar `truncated`.
+
+    Returns:
+        dict. En exito:
+            {status:'ok',
+             group_by:str,
+             measures:[...],
+             aggs:[...],                 # las efectivas (incluye count si se pidio)
+             n_groups:int,               # nº de grupos devueltos (<= top_n)
+             truncated:bool,             # True si habia mas de top_n grupos
+             groups:[{key:<valor grupo>, n:int,
+                      stats:{<measure>:{mean,median,std,min,max}}}, ...],
+             note:str}
+        Las estadisticas son float o None (p.ej. stddev_samp de un grupo de una
+        sola fila -> NULL -> None). En error (sin lanzar): {status:'error', error:str}.
+    """
+    try:
+        # 1. Validar entradas.
+        if not isinstance(table, str) or table == "":
+            return {"status": "error", "error": "table must be a non-empty string"}
+        if not isinstance(group_by, str) or group_by == "":
+            return {"status": "error", "error": "group_by must be a non-empty string"}
+
+        if measures is None:
+            measures = []
+        if not isinstance(measures, list):
+            return {"status": "error", "error": "measures must be a list"}
+        for m in measures:
+            if not isinstance(m, str) or m == "":
+                return {
+                    "status": "error",
+                    "error": f"invalid measure identifier: {m!r}",
+                }
+
+        if aggs is None:
+            aggs = list(_DEFAULT_AGGS)
+        if not isinstance(aggs, list) or len(aggs) == 0:
+            return {
+                "status": "error",
+                "error": "aggs must be a non-empty list or None",
+            }
+        for a in aggs:
+            if a != "count" and a not in _AGG_SQL:
+                return {
+                    "status": "error",
+                    "error": f"unknown agg {a!r}; valid: count, "
+                    + ", ".join(_AGG_SQL),
+                }
+
+        if not isinstance(top_n, int) or isinstance(top_n, bool) or top_n < 1:
+            return {"status": "error", "error": "top_n must be a positive int"}
+
+        # 2. Aggs por measure = todas menos count (count es el tamanio del grupo,
+        # se mapea siempre a la columna `n`).
+        measure_aggs = [a for a in aggs if a != "count"]
+
+        # 3. Construir el SELECT. grp y n primero; luego un termino por measure x agg
+        # con alias posicional (m{idx}_{agg}) para no chocar con nombres de columna
+        # que lleven espacios o caracteres raros.
+        select_terms = [f"{_quote_ident(group_by)} AS grp", "COUNT(*) AS n"]
+        agg_index = []  # (measure_name, agg_name, alias)
+        for mi, m in enumerate(measures):
+            for a in measure_aggs:
+                alias = f"m{mi}_{a}"
+                fn = _AGG_SQL[a]
+                select_terms.append(f"{fn}({_quote_ident(m)}) AS {alias}")
+                agg_index.append((m, a, alias))
+
+        # Pedimos top_n+1 grupos para detectar truncado (habia mas que top_n).
+        sql = (
+            f"SELECT {', '.join(select_terms)} "
+            f"FROM {_quote_ident(table)} "
+            f"GROUP BY {_quote_ident(group_by)} "
+            f"ORDER BY n DESC "
+            f"LIMIT {top_n + 1}"
+        )
+
+        # 4. Ejecutar push-down. sandbox=True (default) basta: la tabla ya existe en
+        # el .db, no necesitamos read_csv/read_blob ni acceso al filesystem.
+        result = duckdb_query_readonly(db_path, sql, max_rows=top_n + 1)
+        if result.get("status") != "ok":
+            return {
+                "status": "error",
+                "error": "groupby query failed: "
+                + str(result.get("error", "unknown")),
+            }
+
+        rows = result.get("rows", [])
+        truncated = len(rows) > top_n
+        if truncated:
+            rows = rows[:top_n]
+
+        # 5. Reconstruir la estructura por grupo.
+        groups = []
+        for row in rows:
+            stats = {m: {} for m in measures}
+            for (m, a, alias) in agg_index:
+                stats[m][a] = row.get(alias)
+            groups.append(
+                {"key": row.get("grp"), "n": row.get("n"), "stats": stats}
+            )
+
+        return {
+            "status": "ok",
+            "group_by": group_by,
+            "measures": list(measures),
+            "aggs": list(aggs),
+            "n_groups": len(groups),
+            "truncated": truncated,
+            "groups": groups,
+            "note": f"GROUP BY {group_by}: top {len(groups)} grupos por tamanio sobre "
+            f"{len(measures)} measure(s)",
+        }
+    except Exception as e:  # noqa: BLE001
+        return {"status": "error", "error": str(e)}

python/functions/datascience/groupby_stats_duckdb_test.py

@@ -0,0 +1,106 @@
+"""Tests para groupby_stats_duckdb."""
+
+import os
+import sys
+
+import duckdb
+
+# Permitir importar funciones del registry (from infra import ..., from datascience import ...).
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "functions"))
+
+from datascience.groupby_stats_duckdb import groupby_stats_duckdb
+
+
+def _make_db(tmp_path, rows):
+    """Crea una DuckDB con tabla t(g VARCHAR, x DOUBLE) e inserta `rows`."""
+    db = os.path.join(str(tmp_path), "t.duckdb")
+    con = duckdb.connect(db)
+    con.execute("CREATE TABLE t(g VARCHAR, x DOUBLE)")
+    con.executemany("INSERT INTO t VALUES (?, ?)", rows)
+    con.close()
+    return db
+
+
+def test_agrega_por_grupo_con_valores_conocidos(tmp_path):
+    # Grupo a: [10, 20, 30] -> n=3, mean=20, min=10, max=30, median=20, std=10.
+    # Grupo b: [5, 15]      -> n=2, mean=10, median=10.
+    # Grupo c: [100]        -> n=1, mean=100, std=None (1 sola fila).
+    rows = [
+        ("a", 10.0), ("a", 20.0), ("a", 30.0),
+        ("b", 5.0), ("b", 15.0),
+        ("c", 100.0),
+    ]
+    db = _make_db(tmp_path, rows)
+    res = groupby_stats_duckdb(db, "t", "g", ["x"])
+    assert res["status"] == "ok", res
+    assert res["n_groups"] == 3
+    assert res["truncated"] is False
+    assert res["aggs"] == ["count", "mean", "median", "std", "min", "max"]
+
+    by_key = {g["key"]: g for g in res["groups"]}
+    assert set(by_key) == {"a", "b", "c"}
+
+    # Grupo a: comprobacion manual de mean/min/max/median/std.
+    sa = by_key["a"]["stats"]["x"]
+    assert by_key["a"]["n"] == 3
+    assert abs(sa["mean"] - 20.0) < 1e-9
+    assert abs(sa["min"] - 10.0) < 1e-9
+    assert abs(sa["max"] - 30.0) < 1e-9
+    assert abs(sa["median"] - 20.0) < 1e-9
+    assert "std" in sa and sa["std"] is not None
+    assert abs(sa["std"] - 10.0) < 1e-9  # stddev_samp([10,20,30]) = 10
+
+    # Grupo b: mean y median conocidas.
+    sb = by_key["b"]["stats"]["x"]
+    assert by_key["b"]["n"] == 2
+    assert abs(sb["mean"] - 10.0) < 1e-9
+    assert abs(sb["median"] - 10.0) < 1e-9
+    assert "median" in sb and "std" in sb
+
+    # Grupo c: una sola fila -> std None (stddev_samp NULL), mean/min/max definidos.
+    sc = by_key["c"]["stats"]["x"]
+    assert by_key["c"]["n"] == 1
+    assert abs(sc["mean"] - 100.0) < 1e-9
+    assert sc["std"] is None
+
+
+def test_db_inexistente_devuelve_error_sin_lanzar(tmp_path):
+    db = os.path.join(str(tmp_path), "no_existe.duckdb")
+    res = groupby_stats_duckdb(db, "t", "g", ["x"])
+    assert res["status"] == "error", res
+    assert isinstance(res["error"], str) and res["error"]
+
+
+def test_measures_vacias_agrega_solo_count(tmp_path):
+    rows = [("a", 1.0), ("a", 2.0), ("b", 3.0)]
+    db = _make_db(tmp_path, rows)
+    res = groupby_stats_duckdb(db, "t", "g", [])
+    assert res["status"] == "ok", res
+    by_key = {g["key"]: g for g in res["groups"]}
+    assert by_key["a"]["n"] == 2
+    assert by_key["b"]["n"] == 1
+    # Sin measures, stats por grupo es un dict vacio (valido).
+    assert by_key["a"]["stats"] == {}
+    assert by_key["b"]["stats"] == {}
+
+
+def test_columna_con_espacio_agrupa_bien(tmp_path):
+    # Tabla con nombres de columna con espacios -> prueba el quoting con dobles
+    # comillas tanto en group_by como en la measure.
+    db = os.path.join(str(tmp_path), "space.duckdb")
+    con = duckdb.connect(db)
+    con.execute('CREATE TABLE t("my col" VARCHAR, "the val" DOUBLE)')
+    con.executemany(
+        'INSERT INTO t VALUES (?, ?)',
+        [("x", 1.0), ("x", 3.0), ("y", 10.0)],
+    )
+    con.close()
+
+    res = groupby_stats_duckdb(db, "t", "my col", ["the val"])
+    assert res["status"] == "ok", res
+    by_key = {g["key"]: g for g in res["groups"]}
+    assert by_key["x"]["n"] == 2
+    assert abs(by_key["x"]["stats"]["the val"]["mean"] - 2.0) < 1e-9
+    assert by_key["y"]["n"] == 1
+    assert abs(by_key["y"]["stats"]["the val"]["mean"] - 10.0) < 1e-9

python/functions/datascience/pivot_table_duckdb.md

@@ -0,0 +1,92 @@
+---
+name: pivot_table_duckdb
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def pivot_table_duckdb(db_path: str, table: str, index: str, columns: str, value: str, agg: str = 'mean', top_rows: int = 10, top_cols: int = 8) -> dict"
+description: "Pivot table (index x columns -> agg(value)) calculada con push-down SQL en DuckDB (GROUP BY en el motor, sin traer filas a RAM) y recortada a las top_rows filas y top_cols columnas con mas observaciones para que quepa entera en un PDF movil / slide PPTX sin cortarse. Version push-down para tablas grandes de la funcion pura `pivot` (que pivota list[dict] en memoria)."
+tags: [eda, pivot, duckdb, aggregate, datascience, push-down, report]
+uses_functions: [duckdb_query_readonly_py_infra]
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: []
+params:
+  - name: db_path
+    desc: "Ruta al archivo DuckDB. Debe existir; el modo read_only NO crea la base."
+  - name: table
+    desc: "Nombre de la tabla a pivotar. Se interpola citado con dobles comillas (DuckDB no admite parametros para identificadores)."
+  - name: index
+    desc: "Columna cuyos valores forman las filas de la pivot (eje vertical)."
+  - name: columns
+    desc: "Columna cuyos valores forman las columnas de la pivot (eje horizontal)."
+  - name: value
+    desc: "Columna numerica a agregar en cada celda. Ignorada cuando agg='count'."
+  - name: agg
+    desc: "Funcion de agregacion: mean, sum, count, min, max, median. mean->avg(), count->COUNT(*). Otro valor devuelve {status:'error'}."
+  - name: top_rows
+    desc: "Numero maximo de filas a conservar, elegidas por mayor numero de observaciones (suma de COUNT(*) por valor de index). Default 10."
+  - name: top_cols
+    desc: "Numero maximo de columnas a conservar, elegidas por mayor numero de observaciones (suma de COUNT(*) por valor de columns). Default 8."
+output: "dict. En exito {status:'ok', index, columns, value, agg, row_labels:[...], col_labels:[...], matrix:[[...]], truncated_rows:bool, truncated_cols:bool, note:str}. matrix tiene len(row_labels) filas y cada fila len(col_labels) celdas (valor agregado o None si la combinacion no existe). truncated_* indica si hubo mas filas/columnas que el top. En error {status:'error', error:str} (no lanza)."
+tested: true
+tests: ["pivot mean labels y celda conocida", "pivot trunca a top rows y top cols", "pivot count no necesita value real", "pivot db inexistente devuelve error sin lanzar", "pivot agg invalido devuelve error"]
+test_file_path: "python/functions/datascience/pivot_table_duckdb_test.py"
+file_path: "python/functions/datascience/pivot_table_duckdb.py"
+---
+
+## Ejemplo
+
+```python
+import duckdb
+from datascience import pivot_table_duckdb
+
+# Tabla DuckDB de prueba estilo titanic: sex x pclass -> mean(fare).
+db = "/tmp/pivot_demo.duckdb"
+con = duckdb.connect(db)
+con.execute(
+    "CREATE TABLE titanic AS SELECT * FROM (VALUES "
+    "('male',1,211.3),('female',1,151.5),('male',3,7.9),"
+    "('female',3,16.7),('male',1,52.0),('female',2,41.6)"
+    ") t(sex, pclass, fare)"
+)
+con.close()
+
+res = pivot_table_duckdb(db, "titanic", index="sex", columns="pclass", value="fare", agg="mean")
+print(res["status"])        # ok
+print(res["row_labels"])    # ['female', 'male']  (orden por nº de observaciones desc; empate -> etiqueta)
+print(res["col_labels"])    # [1, 3, 2]  (pclass=1 tiene 3 obs, pclass=3 -> 2, pclass=2 -> 1)
+print(res["matrix"])        # [[151.5, 16.7, 41.6], [131.65, 7.9, None]]  (male/pclass=2 no existe -> None)
+```
+
+## Cuando usarla
+
+Cuando quieres una pivot table (`index` x `columns` -> `agg(value)`) de una tabla
+DuckDB con MUCHAS filas y necesitas que el resultado quepa entero en un informe: un
+PDF abierto en el movil o un slide PPTX, donde una matriz de 50x30 se cortaria. La
+agregacion se hace push-down en el motor (no traes las filas a RAM) y el resultado se
+limita a las `top_rows` x `top_cols` combinaciones con mas observaciones. Encaja en el
+flujo `eda` para resumir el cruce de dos categoricas (sexo x clase, region x producto)
+contra una metrica. Para pivotar un `list[dict]` ya cargado en memoria usa la funcion
+pura `pivot_py_datascience`; esta es la version push-down sobre disco.
+
+## Gotchas
+
+- Funcion impura: lee un archivo DuckDB del disco (read_only, nunca lo modifica).
+- Recorta a `top_rows` x `top_cols` por numero de observaciones (suma de `COUNT(*)`),
+  NO por magnitud del valor agregado. Si habia mas filas/columnas, `truncated_rows` /
+  `truncated_cols` quedan en True y esas combinaciones NO aparecen en la matriz.
+- Las celdas sin datos (combinacion `index` x `columns` que no existe en la tabla) se
+  rellenan con `None`, no con 0: distinguir "cero medido" de "sin observaciones".
+- `agg='count'` cuenta filas por celda con `COUNT(*)` e ignora `value` (puedes pasar
+  cualquier nombre de columna). Para el resto de aggs, `value` debe ser una columna
+  numerica real o la query fallara con `{status:'error'}`.
+- `agg` solo admite mean, sum, count, min, max, median; cualquier otro valor devuelve
+  `{status:'error'}` sin tocar la base.
+- Orden de `row_labels` / `col_labels`: por numero de observaciones descendente, con
+  desempate estable por etiqueta. No es orden alfabetico ni el de aparicion.
+- La query se ejecuta con `sandbox=False` en `duckdb_query_readonly` (uso interno
+  confiable: el SQL lo construye esta funcion, no un cliente externo).

python/functions/datascience/pivot_table_duckdb.py

@@ -0,0 +1,176 @@
+"""pivot_table_duckdb — pivot table (index x columns -> agg(value)) con push-down SQL.
+
+Funcion impura: lee de disco a traves de DuckDB reusando la primitiva read-only del
+grupo `duckdb` (`duckdb_query_readonly`). Pertenece al grupo de capacidad `eda`
+(exploratory data analysis).
+
+A diferencia de la funcion pura `pivot` (que pivota un `list[dict]` ya cargado en
+memoria), esta version empuja la agregacion al motor de DuckDB (push-down): el
+GROUP BY lo resuelve DuckDB y solo se traen los valores agregados, nunca las filas
+crudas. Esto la hace apta para tablas grandes.
+
+Ademas reduce el resultado a las `top_rows` filas y `top_cols` columnas con mas
+observaciones, de modo que la pivot quepa entera en un PDF movil / slide PPTX sin
+cortarse. Marca `truncated_rows`/`truncated_cols` cuando hubo recorte.
+
+Estilo dict-no-throw del grupo duckdb: nunca lanza; captura cualquier error y
+devuelve {status:'error', error:str}.
+"""
+
+from collections import defaultdict
+
+from infra import duckdb_query_readonly
+
+# Funciones de agregacion permitidas y su nombre en SQL DuckDB.
+# mean -> avg; el resto mapea directo. count se trata aparte (COUNT(*), sin value).
+_AGG_SQL = {
+    "mean": "avg",
+    "sum": "sum",
+    "count": "count",
+    "min": "min",
+    "max": "max",
+    "median": "median",
+}
+
+
+def _quote_ident(ident: str) -> str:
+    """Cita un identificador SQL con dobles comillas, escapando `"` -> `""`.
+
+    DuckDB no admite parametros posicionales para nombres de tabla/columna, asi que
+    hay que interpolarlos. El quoting con `"` y el doblado de comillas internas evita
+    que un nombre rompa la sentencia (mismo patron que correlation_matrix_duckdb).
+    """
+    return '"' + str(ident).replace('"', '""') + '"'
+
+
+def pivot_table_duckdb(
+    db_path: str,
+    table: str,
+    index: str,
+    columns: str,
+    value: str,
+    agg: str = "mean",
+    top_rows: int = 10,
+    top_cols: int = 8,
+) -> dict:
+    """Pivot table push-down en DuckDB, recortada a top_rows x top_cols.
+
+    Construye una pivot (filas = valores de `index`, columnas = valores de `columns`,
+    celda = `agg(value)`) agregando en el motor de DuckDB, y la reduce a las filas y
+    columnas con mas observaciones para que quepa en un PDF / slide.
+
+    Args:
+        db_path: ruta al archivo DuckDB. Debe existir (read_only NO crea la base).
+        table: nombre de la tabla a pivotar.
+        index: columna cuyos valores forman las filas de la pivot.
+        columns: columna cuyos valores forman las columnas de la pivot.
+        value: columna numerica a agregar. Ignorada cuando agg="count".
+        agg: funcion de agregacion. Una de: "mean", "sum", "count", "min", "max",
+            "median". mean se traduce a avg(); count a COUNT(*).
+        top_rows: numero maximo de filas a conservar, elegidas por mayor numero de
+            observaciones (suma de COUNT(*) por valor de index). Default 10.
+        top_cols: numero maximo de columnas a conservar, elegidas por mayor numero de
+            observaciones (suma de COUNT(*) por valor de columns). Default 8.
+
+    Returns:
+        dict. En exito:
+            {status:'ok',
+             index, columns, value, agg,
+             row_labels:[...],                 # valores de index, en orden de freq desc
+             col_labels:[...],                 # valores de columns, en orden de freq desc
+             matrix:[[...], ...],              # len == len(row_labels); cada fila
+                                               # len == len(col_labels); celda = agg o None
+             truncated_rows:bool, truncated_cols:bool,
+             note:str}
+        En error (sin lanzar): {status:'error', error:str}.
+    """
+    try:
+        if not isinstance(agg, str) or agg not in _AGG_SQL:
+            return {
+                "status": "error",
+                "error": "invalid agg "
+                + repr(agg)
+                + "; allowed: "
+                + ", ".join(sorted(_AGG_SQL)),
+            }
+
+        # Paso 1 (push-down): agregar (index, columns) -> agg(value) + COUNT(*).
+        if agg == "count":
+            agg_expr = "COUNT(*)"
+        else:
+            agg_expr = f"{_AGG_SQL[agg]}({_quote_ident(value)})"
+
+        sql = (
+            f"SELECT {_quote_ident(index)} AS r, "
+            f"{_quote_ident(columns)} AS c, "
+            f"{agg_expr} AS v, "
+            f"COUNT(*) AS n "
+            f"FROM {_quote_ident(table)} "
+            f"GROUP BY {_quote_ident(index)}, {_quote_ident(columns)}"
+        )
+
+        # max_rows alto: queremos todos los grupos (index x columns) para elegir el
+        # top con criterio global. sandbox=False igual que correlation_matrix_duckdb,
+        # porque db_path es una ruta interna de confianza.
+        result = duckdb_query_readonly(
+            db_path, sql, max_rows=1_000_000, sandbox=False
+        )
+        if result.get("status") != "ok":
+            return {
+                "status": "error",
+                "error": "pivot query failed: "
+                + str(result.get("error", "unknown")),
+            }
+
+        # Paso 2 (en python): contar observaciones por fila y por columna, y guardar
+        # el valor agregado de cada celda (r, c).
+        row_obs: dict = defaultdict(int)
+        col_obs: dict = defaultdict(int)
+        cell: dict = {}
+        for row in result.get("rows", []):
+            r = row.get("r")
+            c = row.get("c")
+            n = row.get("n") or 0
+            row_obs[r] += n
+            col_obs[c] += n
+            cell[(r, c)] = row.get("v")
+
+        def _top(obs: dict, limit: int):
+            # Orden: mas observaciones primero; desempate estable por etiqueta.
+            ranked = sorted(obs.items(), key=lambda kv: (-kv[1], str(kv[0])))
+            selected = [label for label, _ in ranked[:limit]]
+            return selected, len(ranked) > limit
+
+        row_labels, truncated_rows = _top(row_obs, top_rows)
+        col_labels, truncated_cols = _top(col_obs, top_cols)
+
+        # Paso 3: materializar la matriz; None donde la combinacion no existe.
+        matrix = [
+            [cell.get((r, c)) for c in col_labels] for r in row_labels
+        ]
+
+        note = (
+            f"pivot {agg}({value}) reducida a {len(row_labels)}x{len(col_labels)} "
+            "(top por observaciones) para caber en PDF/slide"
+        )
+        if agg == "count":
+            note = (
+                f"pivot count(*) reducida a {len(row_labels)}x{len(col_labels)} "
+                "(top por observaciones) para caber en PDF/slide"
+            )
+
+        return {
+            "status": "ok",
+            "index": index,
+            "columns": columns,
+            "value": value,
+            "agg": agg,
+            "row_labels": row_labels,
+            "col_labels": col_labels,
+            "matrix": matrix,
+            "truncated_rows": truncated_rows,
+            "truncated_cols": truncated_cols,
+            "note": note,
+        }
+    except Exception as e:  # noqa: BLE001
+        return {"status": "error", "error": str(e)}

python/functions/datascience/pivot_table_duckdb_test.py

@@ -0,0 +1,115 @@
+"""Tests para pivot_table_duckdb."""
+
+import os
+import sys
+
+import duckdb
+
+# Permitir importar funciones del registry (from infra import ..., from datascience import ...).
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "functions"))
+
+from datascience.pivot_table_duckdb import pivot_table_duckdb
+
+
+def _make_db(tmp_name: str) -> str:
+    """Crea una DuckDB con dos categoricas (a, b) y un valor numerico conocido.
+
+    Filas:
+        a='x', b='y', val=10
+        a='x', b='y', val=20   -> mean(x,y) = 15, count(x,y) = 2
+        a='x', b='z', val=5    -> mean(x,z) = 5
+        a='w', b='y', val=100  -> mean(w,y) = 100
+    Observaciones por a: x=3, w=1.  Por b: y=3, z=1.
+    La combinacion (w, z) no existe -> celda None.
+    """
+    db = os.path.join("/tmp", tmp_name)
+    if os.path.exists(db):
+        os.remove(db)
+    con = duckdb.connect(db)
+    con.execute("CREATE TABLE t (a VARCHAR, b VARCHAR, val DOUBLE)")
+    con.execute(
+        "INSERT INTO t VALUES "
+        "('x','y',10),('x','y',20),('x','z',5),('w','y',100)"
+    )
+    con.close()
+    return db
+
+
+def test_pivot_mean_labels_y_celda_conocida():
+    db = _make_db("pivot_test_mean.duckdb")
+    res = pivot_table_duckdb(db, "t", index="a", columns="b", value="val", agg="mean")
+    assert res["status"] == "ok", res
+    # Filas ordenadas por observaciones desc: x (3) antes que w (1).
+    assert res["row_labels"] == ["x", "w"], res["row_labels"]
+    # Columnas ordenadas por observaciones desc: y (3) antes que z (1).
+    assert res["col_labels"] == ["y", "z"], res["col_labels"]
+    # matrix[0][0] = mean(a='x', b='y') = (10 + 20) / 2 = 15.
+    assert abs(res["matrix"][0][0] - 15.0) < 1e-9, res["matrix"]
+    # matrix[0][1] = mean(a='x', b='z') = 5.
+    assert abs(res["matrix"][0][1] - 5.0) < 1e-9, res["matrix"]
+    # matrix[1][0] = mean(a='w', b='y') = 100.
+    assert abs(res["matrix"][1][0] - 100.0) < 1e-9, res["matrix"]
+    # (w, z) no existe -> None.
+    assert res["matrix"][1][1] is None, res["matrix"]
+    # Sin truncado con los defaults (top_rows=10, top_cols=8).
+    assert res["truncated_rows"] is False
+    assert res["truncated_cols"] is False
+    # La matriz es rectangular consistente con las etiquetas.
+    assert len(res["matrix"]) == len(res["row_labels"])
+    for fila in res["matrix"]:
+        assert len(fila) == len(res["col_labels"])
+
+
+def test_pivot_trunca_a_top_rows_y_top_cols():
+    db = _make_db("pivot_test_trunc.duckdb")
+    res = pivot_table_duckdb(
+        db, "t", index="a", columns="b", value="val", agg="mean",
+        top_rows=1, top_cols=1,
+    )
+    assert res["status"] == "ok", res
+    # Solo la fila/columna mas frecuente sobrevive.
+    assert res["row_labels"] == ["x"], res["row_labels"]
+    assert res["col_labels"] == ["y"], res["col_labels"]
+    assert res["matrix"] == [[15.0]], res["matrix"]
+    # Habia mas de 1 fila y mas de 1 columna -> truncado en ambos ejes.
+    assert res["truncated_rows"] is True
+    assert res["truncated_cols"] is True
+
+
+def test_pivot_count_no_necesita_value_real():
+    db = _make_db("pivot_test_count.duckdb")
+    # value apunta a una columna real pero count(*) la ignora; tambien valdria un
+    # nombre cualquiera. Verificamos que count funciona igualmente.
+    res = pivot_table_duckdb(
+        db, "t", index="a", columns="b", value="val", agg="count"
+    )
+    assert res["status"] == "ok", res
+    assert res["row_labels"] == ["x", "w"]
+    assert res["col_labels"] == ["y", "z"]
+    # count(a='x', b='y') = 2 observaciones.
+    assert res["matrix"][0][0] == 2, res["matrix"]
+    # count(a='x', b='z') = 1.
+    assert res["matrix"][0][1] == 1, res["matrix"]
+    # count(a='w', b='y') = 1.
+    assert res["matrix"][1][0] == 1, res["matrix"]
+    # (w, z) no existe -> None.
+    assert res["matrix"][1][1] is None, res["matrix"]
+
+
+def test_pivot_db_inexistente_devuelve_error_sin_lanzar():
+    res = pivot_table_duckdb(
+        "/nonexistent/path/does_not_exist.duckdb",
+        "t", index="a", columns="b", value="val", agg="mean",
+    )
+    assert res["status"] == "error", res
+    assert isinstance(res["error"], str)
+
+
+def test_pivot_agg_invalido_devuelve_error():
+    db = _make_db("pivot_test_badagg.duckdb")
+    res = pivot_table_duckdb(
+        db, "t", index="a", columns="b", value="val", agg="stddev"
+    )
+    assert res["status"] == "error", res
+    assert "invalid agg" in res["error"]

python/functions/datascience/select_groupby_keys.md

@@ -0,0 +1,158 @@
+---
+id: select_groupby_keys_py_datascience
+name: select_groupby_keys
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def select_groupby_keys(profile: dict, max_keys: int = 3, max_card: int = 20, max_measures: int = 4) -> dict"
+description: "Elige deterministicamente las columnas categoricas mas interesantes para GROUP BY, las numericas medida y pares pivote a partir de un TableProfile del grupo eda. Respaldo cuantitativo para el capitulo de agregacion/OLAP de un EDA. Funcion pura, no muta el input, nunca lanza."
+tags: [eda, aggregation, groupby, olap, profiling, datascience]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+example: |
+  from datascience import select_groupby_keys
+  profile = {
+      "n_rows": 891,
+      "key_candidates": ["passenger_id"],
+      "columns": [
+          {"name": "sex", "inferred_type": "categorical", "distinct_count": 2,
+           "unique_pct": 0.002, "null_pct": 0.0, "flags": [],
+           "categorical": {"imbalance": 1.8}, "numeric": None},
+          {"name": "pclass", "inferred_type": "categorical", "distinct_count": 3,
+           "unique_pct": 0.003, "null_pct": 0.0, "flags": [],
+           "categorical": {"imbalance": 2.5}, "numeric": None},
+          {"name": "fare", "inferred_type": "numeric", "distinct_count": 200,
+           "unique_pct": 0.2, "null_pct": 0.0, "flags": [],
+           "numeric": {"std": 49.7, "cv": 1.54}, "categorical": None},
+      ],
+  }
+  select_groupby_keys(profile)
+  # {"group_keys": [{"col": "sex", ...}, {"col": "pclass", ...}],
+  #  "measures": ["fare"],
+  #  "pivots": [{"index": "sex", "columns": "pclass", "value": "fare"}],
+  #  "note": "2 clave(s) de grupo: sex, pclass; 1 medida(s): fare; 1 pivot(s)."}
+tested: true
+tests:
+  - "test_titanic_picks_good_cats_excludes_id_and_constant"
+  - "test_titanic_measures_exclude_id_constant_and_keep_numerics"
+  - "test_titanic_generates_one_pivot"
+  - "test_empty_profile_returns_all_empty_and_does_not_crash"
+  - "test_none_profile_does_not_crash"
+  - "test_only_numerics_yields_empty_group_keys_and_no_pivots"
+  - "test_high_cardinality_and_max_card_are_excluded"
+  - "test_max_keys_limits_group_keys"
+  - "test_three_keys_cap_pivots_to_two"
+  - "test_does_not_mutate_input"
+test_file_path: "python/functions/datascience/select_groupby_keys_test.py"
+file_path: "python/functions/datascience/select_groupby_keys.py"
+params:
+  - name: profile
+    desc: >
+      TableProfile dict del grupo eda (p.ej. salida de summarize_table_duckdb).
+      Se lee de forma defensiva (.get / or [] / isinstance). Claves usadas:
+      columns (list[ColumnProfile]), key_candidates (list de nombres de columna
+      o dicts {name}), n_rows. Cada ColumnProfile usa: name, inferred_type
+      ("numeric"|"categorical"|"datetime"|"text"|"boolean"), distinct_count,
+      unique_pct (0..1), null_pct (0..1), flags (list[str], reconoce
+      "possible_id"/"high_cardinality"/"constant"), numeric ({std, cv, ...}|None)
+      y categorical ({imbalance, mode_pct, ...}|None).
+  - name: max_keys
+    desc: "Numero maximo de claves de grupo (group_keys) a devolver. Default 3."
+  - name: max_card
+    desc: >
+      Cardinalidad maxima (distinct_count) que una columna categorica puede
+      tener para seguir siendo candidata a clave de grupo. Default 20.
+  - name: max_measures
+    desc: "Numero maximo de columnas medida (nombres) a devolver. Default 4."
+output: >
+  dict con group_keys (list de {col, cardinality, score} ordenada por score
+  desc), measures (list[str] de nombres de columnas numericas ordenadas por
+  dispersion), pivots (list de {index, columns, value}, hasta 2 pares
+  categorica x categorica con la primera measure como valor) y note (str,
+  resumen corto en espanol de lo elegido). Ante profile vacio/None devuelve
+  todas las listas vacias y una note descriptiva; nunca lanza.
+---
+
+## Ejemplo
+
+```python
+from datascience import select_groupby_keys
+
+# TableProfile estilo titanic: 2 categoricas buenas, 1 numerica medida,
+# 1 id secuencial (descartado) y un key_candidate declarado.
+profile = {
+    "n_rows": 891,
+    "key_candidates": ["passenger_id"],
+    "columns": [
+        {"name": "sex", "inferred_type": "categorical", "distinct_count": 2,
+         "unique_pct": 0.002, "null_pct": 0.0, "flags": [],
+         "categorical": {"imbalance": 1.8}, "numeric": None},
+        {"name": "pclass", "inferred_type": "categorical", "distinct_count": 3,
+         "unique_pct": 0.003, "null_pct": 0.0, "flags": [],
+         "categorical": {"imbalance": 2.5}, "numeric": None},
+        {"name": "fare", "inferred_type": "numeric", "distinct_count": 200,
+         "unique_pct": 0.2, "null_pct": 0.0, "flags": [],
+         "numeric": {"std": 49.7, "cv": 1.54}, "categorical": None},
+        {"name": "passenger_id", "inferred_type": "numeric", "distinct_count": 891,
+         "unique_pct": 1.0, "null_pct": 0.0, "flags": ["possible_id"],
+         "numeric": {"std": 257.4, "cv": 0.58}, "categorical": None},
+    ],
+}
+
+select_groupby_keys(profile)
+# {
+#   "group_keys": [
+#       {"col": "sex",    "cardinality": 2, "score": 0.5556},
+#       {"col": "pclass", "cardinality": 3, "score": 0.4},
+#   ],
+#   "measures": ["fare"],            # passenger_id excluido (id secuencial)
+#   "pivots": [{"index": "sex", "columns": "pclass", "value": "fare"}],
+#   "note": "2 clave(s) de grupo: sex, pclass; 1 medida(s): fare; 1 pivot(s).",
+# }
+```
+
+## Cuando usarla
+
+Cuando hayas perfilado una tabla con el grupo `eda` (p.ej.
+`summarize_table_duckdb`) y necesites decidir, sin mirar los datos, por qué
+columnas merece la pena agrupar (GROUP BY) y qué métricas numéricas agregar:
+el respaldo cuantitativo del capítulo de agregación/OLAP de un AutomaticEDA, o
+para proponer pivotes en un dashboard. Es la capa de selección sobre el
+TableProfile crudo: lee el perfil, ordena candidatos de forma determinista y
+no toca los datos.
+
+## Notas
+
+Función pura, sin I/O ni dependencias externas (solo stdlib), no muta
+`profile`. Lectura defensiva total (`.get`, `or []`, `isinstance`): un `{}` o
+`None` produce `{"group_keys": [], "measures": [], "pivots": [], "note": ...}`
+y nunca lanza.
+
+Criterios de selección (deterministas):
+
+- **group_keys** — candidatas con `inferred_type` en `("categorical","boolean")`.
+  Se descartan las que estén en `key_candidates`, con flag
+  `possible_id`/`high_cardinality`/`constant`, con `distinct_count` fuera de
+  `[2, max_card]`, o all-null (`null_pct >= 0.999`). `score = card_score *
+  balance_score`: `card_score` mantiene un plateau para cardinalidad moderada
+  (2..12) y decae hacia `max_card`; `balance_score = 1/imbalance` usando
+  `categorical.imbalance` si está, aproximando con `mode_pct` si no, o un valor
+  neutro (0.5) en último caso. Devuelve hasta `max_keys`, ordenadas por score
+  desc (empates por orden de columna).
+- **measures** — candidatas con `inferred_type` en
+  `("numeric","integer","float")`. Se descartan id-like (flag `possible_id` y
+  `unique_pct >= 0.99`) y constantes (`numeric.std` == 0 o None). Se rankean por
+  dispersión informativa: `abs(cv)` si está, si no `abs(std)`. Devuelve hasta
+  `max_measures` **nombres** (strings).
+- **pivots** — hasta 2 pares `(group_keys[i].col, group_keys[j].col)` con i<j y
+  la primera measure como valor. Vacío si hay menos de 2 group_keys.
+
+Caveat de ranking de measures: mezclar `cv` (adimensional) con `std` (en
+unidades de la columna) cuando una columna carece de `cv` puede dar órdenes
+poco comparables entre columnas; se prefiere `cv` siempre que esté disponible.

python/functions/datascience/select_groupby_keys.py

@@ -0,0 +1,310 @@
+"""Pure EDA helper: pick GROUP BY keys and measures from a TableProfile.
+
+Given a ``TableProfile`` of the ``eda`` group (the dict produced by, e.g.,
+``summarize_table_duckdb``), this function deterministically selects the most
+interesting categorical columns to group by (GROUP BY), the numeric measure
+columns to aggregate, and a couple of categorical x categorical pivot pairs.
+
+It is the quantitative backbone for the aggregation / OLAP chapter of an
+AutomaticEDA: a pure, deterministic ranking over the profile, with no I/O, no
+mutation of the input and no external dependencies (stdlib only). It never
+raises — a missing or malformed profile yields an empty, well-formed result.
+"""
+
+
+def select_groupby_keys(
+    profile: dict,
+    max_keys: int = 3,
+    max_card: int = 20,
+    max_measures: int = 4,
+) -> dict:
+    """Select GROUP BY keys, measures and pivot pairs from a TableProfile.
+
+    Reads everything defensively (``.get(...)``, ``or []``, ``isinstance``) and
+    never raises. With an empty/None profile it returns every list empty.
+
+    Selection rules (deterministic):
+
+    - **group_keys** (categorical columns to group by): candidates have
+      ``inferred_type`` in ``("categorical", "boolean")``. Discarded if they are
+      in ``profile['key_candidates']``, carry a ``possible_id`` /
+      ``high_cardinality`` / ``constant`` flag, have ``distinct_count`` outside
+      ``[2, max_card]``, or are all-null (``null_pct >= 0.999``). Each survivor
+      gets ``score = card_score * balance_score`` where ``card_score`` keeps a
+      plateau for moderate cardinality (2..12) and decays towards ``max_card``,
+      and ``balance_score = 1 / imbalance`` (``categorical.imbalance`` if
+      present, else approximated from ``mode_pct``, else a neutral default).
+      The top ``max_keys`` by score (desc, ties by column order) are returned.
+
+    - **measures** (numeric columns to aggregate): candidates have
+      ``inferred_type`` in ``("numeric", "integer", "float")``. Discarded if
+      id-like (``possible_id`` flag *and* ``unique_pct >= 0.99``) or constant
+      (``numeric.std`` is ``0`` or ``None``). Ranked by informative dispersion:
+      ``abs(cv)`` when available, else ``abs(std)``. The top ``max_measures``
+      **names** are returned.
+
+    - **pivots**: up to 2 ``(group_keys[i].col, group_keys[j].col)`` pairs with
+      ``i < j``, using the first measure as the aggregated value. Empty when
+      fewer than 2 group keys were selected.
+
+    Args:
+        profile: TableProfile dict of the ``eda`` group. Relevant keys:
+            ``columns`` (list[ColumnProfile]), ``key_candidates`` (list of
+            column names or ``{name}`` dicts), ``n_rows``. Each ColumnProfile
+            uses: ``name``, ``inferred_type``, ``distinct_count``,
+            ``unique_pct`` (0..1), ``null_pct`` (0..1), ``flags`` (list[str]),
+            ``numeric`` ({std, cv, ...}|None), ``categorical``
+            ({imbalance, mode_pct, ...}|None).
+        max_keys: Maximum number of group-by keys to return. Default 3.
+        max_card: Maximum cardinality (``distinct_count``) a categorical column
+            may have to still qualify as a group key. Default 20.
+        max_measures: Maximum number of measure names to return. Default 4.
+
+    Returns:
+        dict with:
+          group_keys (list[{col, cardinality, score}], ordered by score desc),
+          measures   (list[str], numeric column names ordered by dispersion),
+          pivots     (list[{index, columns, value}], up to 2 pairs),
+          note       (str, short summary of what was chosen).
+    """
+    if not isinstance(profile, dict):
+        profile = {}
+
+    try:
+        max_keys = int(max_keys)
+    except (TypeError, ValueError):
+        max_keys = 3
+    try:
+        max_card = int(max_card)
+    except (TypeError, ValueError):
+        max_card = 20
+    try:
+        max_measures = int(max_measures)
+    except (TypeError, ValueError):
+        max_measures = 4
+    max_keys = max(max_keys, 0)
+    max_card = max(max_card, 2)
+    max_measures = max(max_measures, 0)
+
+    columns = profile.get("columns") or []
+    if not isinstance(columns, (list, tuple)):
+        columns = []
+
+    key_names = _key_candidate_names(profile.get("key_candidates"))
+
+    group_keys = _select_group_keys(columns, key_names, max_keys, max_card)
+    measures = _select_measures(columns, max_measures)
+    pivots = _select_pivots(group_keys, measures)
+
+    return {
+        "group_keys": group_keys,
+        "measures": measures,
+        "pivots": pivots,
+        "note": _build_note(group_keys, measures, pivots),
+    }
+
+
+# ---------------------------------------------------------------------------
+# group_keys
+# ---------------------------------------------------------------------------
+
+_GROUP_TYPES = ("categorical", "boolean")
+_DISQUALIFYING_FLAGS = frozenset({"possible_id", "high_cardinality", "constant"})
+_CARD_PLATEAU_HI = 12  # cardinalities 2..12 are all "moderate" (best).
+
+
+def _select_group_keys(columns, key_names, max_keys, max_card) -> list:
+    """Rank categorical/boolean columns suitable for GROUP BY."""
+    scored = []
+    for idx, col in enumerate(columns):
+        if not isinstance(col, dict):
+            continue
+        if (col.get("inferred_type") or "") not in _GROUP_TYPES:
+            continue
+
+        name = col.get("name")
+        if name is None:
+            continue
+        if name in key_names:
+            continue
+
+        flags = _as_set(col.get("flags"))
+        if flags & _DISQUALIFYING_FLAGS:
+            continue
+
+        if _num(col.get("null_pct"), 0.0) >= 0.999:
+            continue
+
+        card = _num(col.get("distinct_count"), 0.0)
+        if card < 2 or card > max_card:
+            continue
+        card_i = int(card)
+
+        score = _card_score(card_i, max_card) * _balance_score(col.get("categorical"))
+        scored.append((round(score, 6), idx, name, card_i))
+
+    # Deterministic: higher score first, ties broken by original column order.
+    scored.sort(key=lambda t: (-t[0], t[1]))
+
+    out = []
+    for score, _idx, name, card_i in scored[:max_keys]:
+        out.append({"col": name, "cardinality": card_i, "score": score})
+    return out
+
+
+def _card_score(card: int, max_card: int) -> float:
+    """Prefer moderate cardinality; plateau at 2..12, decay towards max_card."""
+    if card <= 1:
+        return 0.0
+    if card <= _CARD_PLATEAU_HI:
+        return 1.0
+    denom = max(max_card - _CARD_PLATEAU_HI, 1)
+    over = card - _CARD_PLATEAU_HI
+    return max(0.1, 1.0 - over / denom)
+
+
+def _balance_score(categorical) -> float:
+    """1.0 for a perfectly balanced category, decaying as imbalance grows.
+
+    Uses ``categorical.imbalance`` (max_count/min_count, >= 1) when available;
+    otherwise approximates from ``mode_pct`` (top-class dominance); otherwise a
+    neutral default so the column is still selectable.
+    """
+    if isinstance(categorical, dict):
+        imbalance = categorical.get("imbalance")
+        if isinstance(imbalance, (int, float)) and imbalance >= 1.0:
+            return 1.0 / float(imbalance)
+        mode_pct = categorical.get("mode_pct")
+        if isinstance(mode_pct, (int, float)):
+            return _clamp(1.0 - float(mode_pct), 0.0, 1.0)
+    return 0.5
+
+
+# ---------------------------------------------------------------------------
+# measures
+# ---------------------------------------------------------------------------
+
+_NUMERIC_TYPES = ("numeric", "integer", "float")
+
+
+def _select_measures(columns, max_measures) -> list:
+    """Rank numeric columns by informative dispersion (cv, else std)."""
+    scored = []
+    for idx, col in enumerate(columns):
+        if not isinstance(col, dict):
+            continue
+        if (col.get("inferred_type") or "") not in _NUMERIC_TYPES:
+            continue
+
+        name = col.get("name")
+        if name is None:
+            continue
+
+        flags = _as_set(col.get("flags"))
+        unique_pct = _num(col.get("unique_pct"), 0.0)
+        if "possible_id" in flags and unique_pct >= 0.99:
+            continue  # sequential id, not a measure.
+
+        numeric = col.get("numeric")
+        std = numeric.get("std") if isinstance(numeric, dict) else None
+        if not isinstance(std, (int, float)) or std == 0:
+            continue  # constant or unknown spread -> not informative.
+
+        cv = numeric.get("cv") if isinstance(numeric, dict) else None
+        if isinstance(cv, (int, float)):
+            dispersion = abs(float(cv))
+        else:
+            dispersion = abs(float(std))
+
+        scored.append((dispersion, idx, name))
+
+    # Higher dispersion first, ties broken by original column order.
+    scored.sort(key=lambda t: (-t[0], t[1]))
+    return [name for _disp, _idx, name in scored[:max_measures]]
+
+
+# ---------------------------------------------------------------------------
+# pivots
+# ---------------------------------------------------------------------------
+
+
+def _select_pivots(group_keys, measures) -> list:
+    """Up to 2 (cat_a, cat_b) pairs from the chosen group keys."""
+    if not isinstance(group_keys, list) or len(group_keys) < 2:
+        return []
+    value = measures[0] if measures else None
+    pairs = []
+    n = len(group_keys)
+    for i in range(n):
+        for j in range(i + 1, n):
+            pairs.append({
+                "index": group_keys[i].get("col"),
+                "columns": group_keys[j].get("col"),
+                "value": value,
+            })
+            if len(pairs) >= 2:
+                return pairs
+    return pairs
+
+
+# ---------------------------------------------------------------------------
+# helpers
+# ---------------------------------------------------------------------------
+
+
+def _build_note(group_keys, measures, pivots) -> str:
+    """One-line Spanish summary of the selection."""
+    parts = []
+    if group_keys:
+        cols = ", ".join(str(g.get("col")) for g in group_keys)
+        parts.append(f"{len(group_keys)} clave(s) de grupo: {cols}")
+    else:
+        parts.append("sin categóricas agrupables")
+    if measures:
+        parts.append(f"{len(measures)} medida(s): " + ", ".join(str(m) for m in measures))
+    else:
+        parts.append("sin medidas numéricas")
+    if pivots:
+        parts.append(f"{len(pivots)} pivot(s)")
+    return "; ".join(parts) + "."
+
+
+def _key_candidate_names(key_candidates) -> set:
+    """Normalize ``key_candidates`` (strings or ``{name}`` dicts) to a name set."""
+    names = set()
+    if not isinstance(key_candidates, (list, tuple)):
+        return names
+    for entry in key_candidates:
+        if isinstance(entry, str):
+            names.add(entry)
+        elif isinstance(entry, dict):
+            nm = entry.get("name") or entry.get("col")
+            if nm is not None:
+                names.add(nm)
+    return names
+
+
+def _as_set(flags) -> set:
+    """Coerce a flags value into a set, tolerating None / non-iterables."""
+    if isinstance(flags, (list, tuple, set)):
+        return set(flags)
+    return set()
+
+
+def _num(value, default: float) -> float:
+    """Best-effort float conversion with a fallback default."""
+    if value is None:
+        return default
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _clamp(x: float, lo: float, hi: float) -> float:
+    """Recorta x al rango [lo, hi]."""
+    if x < lo:
+        return lo
+    if x > hi:
+        return hi
+    return x

python/functions/datascience/select_groupby_keys_test.py

@@ -0,0 +1,213 @@
+"""Tests para select_groupby_keys (grupo eda, dominio datascience)."""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from select_groupby_keys import select_groupby_keys
+
+
+def _cat_col(name, card, *, imbalance=2.0, flags=None, null_pct=0.0):
+    """ColumnProfile categorico minimo con bloque categorical."""
+    return {
+        "name": name,
+        "inferred_type": "categorical",
+        "distinct_count": card,
+        "unique_pct": card / 1000.0,
+        "null_pct": null_pct,
+        "flags": flags or [],
+        "numeric": None,
+        "categorical": {"imbalance": imbalance, "mode_pct": 0.5, "n_distinct": card},
+    }
+
+
+def _num_col(name, *, std, cv, flags=None, unique_pct=0.1):
+    """ColumnProfile numerico minimo con bloque numeric."""
+    return {
+        "name": name,
+        "inferred_type": "numeric",
+        "distinct_count": 200,
+        "unique_pct": unique_pct,
+        "null_pct": 0.0,
+        "flags": flags or [],
+        "numeric": {"std": std, "cv": cv},
+        "categorical": None,
+    }
+
+
+def _titanic_like_profile() -> dict:
+    """Perfil estilo titanic: 2 categoricas buenas, 2 numericas, 1 id, 1 constante."""
+    return {
+        "n_rows": 891,
+        "key_candidates": ["passenger_id"],
+        "columns": [
+            _cat_col("sex", 2, imbalance=1.8),
+            _cat_col("pclass", 3, imbalance=2.5),
+            _num_col("age", std=14.5, cv=0.49),
+            _num_col("fare", std=49.7, cv=1.54),
+            # id secuencial: flag possible_id + unique_pct alto.
+            {
+                "name": "passenger_id",
+                "inferred_type": "numeric",
+                "distinct_count": 891,
+                "unique_pct": 1.0,
+                "null_pct": 0.0,
+                "flags": ["possible_id"],
+                "numeric": {"std": 257.4, "cv": 0.58},
+                "categorical": None,
+            },
+            # columna constante: flag constant + std 0.
+            {
+                "name": "embarked_const",
+                "inferred_type": "categorical",
+                "distinct_count": 1,
+                "unique_pct": 0.001,
+                "null_pct": 0.0,
+                "flags": ["constant"],
+                "numeric": None,
+                "categorical": {"imbalance": 1.0},
+            },
+        ],
+    }
+
+
+def test_titanic_picks_good_cats_excludes_id_and_constant():
+    out = select_groupby_keys(_titanic_like_profile())
+
+    # Elige las dos categoricas buenas.
+    chosen_cols = {g["col"] for g in out["group_keys"]}
+    assert chosen_cols == {"sex", "pclass"}
+
+    # Excluye la constante y el key_candidate.
+    assert "embarked_const" not in chosen_cols
+    assert "passenger_id" not in chosen_cols
+
+    # Cada group key trae col, cardinality y score.
+    for g in out["group_keys"]:
+        assert set(g.keys()) == {"col", "cardinality", "score"}
+        assert isinstance(g["score"], float)
+    by_col = {g["col"]: g for g in out["group_keys"]}
+    assert by_col["sex"]["cardinality"] == 2
+    assert by_col["pclass"]["cardinality"] == 3
+
+    # Ordenadas por score descendente.
+    scores = [g["score"] for g in out["group_keys"]]
+    assert scores == sorted(scores, reverse=True)
+
+
+def test_titanic_measures_exclude_id_constant_and_keep_numerics():
+    out = select_groupby_keys(_titanic_like_profile())
+
+    # Solo nombres (strings) de numericas informativas, sin el id secuencial.
+    assert all(isinstance(m, str) for m in out["measures"])
+    assert "passenger_id" not in out["measures"]
+    assert set(out["measures"]) == {"age", "fare"}
+
+    # fare tiene mayor cv (1.54 > 0.49) -> primero.
+    assert out["measures"][0] == "fare"
+
+
+def test_titanic_generates_one_pivot():
+    out = select_groupby_keys(_titanic_like_profile())
+
+    # Con 2 group keys -> exactamente 1 pivot.
+    assert len(out["pivots"]) == 1
+    pivot = out["pivots"][0]
+    assert set(pivot.keys()) == {"index", "columns", "value"}
+    assert {pivot["index"], pivot["columns"]} == {"sex", "pclass"}
+    # El valor es la primera measure (fare).
+    assert pivot["value"] == "fare"
+
+
+def test_empty_profile_returns_all_empty_and_does_not_crash():
+    out = select_groupby_keys({})
+    assert out["group_keys"] == []
+    assert out["measures"] == []
+    assert out["pivots"] == []
+    assert isinstance(out["note"], str)
+
+
+def test_none_profile_does_not_crash():
+    out = select_groupby_keys(None)
+    assert out == {
+        "group_keys": [],
+        "measures": [],
+        "pivots": [],
+        "note": out["note"],
+    }
+    assert isinstance(out["note"], str)
+
+
+def test_only_numerics_yields_empty_group_keys_and_no_pivots():
+    profile = {
+        "n_rows": 500,
+        "key_candidates": [],
+        "columns": [
+            _num_col("price", std=12.0, cv=0.6),
+            _num_col("weight", std=3.0, cv=0.2),
+        ],
+    }
+    out = select_groupby_keys(profile)
+    assert out["group_keys"] == []
+    assert out["pivots"] == []
+    # Las numericas si se eligen como measures.
+    assert set(out["measures"]) == {"price", "weight"}
+    assert out["measures"][0] == "price"  # mayor cv.
+
+
+def test_high_cardinality_and_max_card_are_excluded():
+    profile = {
+        "n_rows": 1000,
+        "key_candidates": [],
+        "columns": [
+            _cat_col("city", 50, flags=["high_cardinality"]),  # flag -> fuera.
+            _cat_col("zone", 35),  # card 35 > max_card 20 -> fuera.
+            _cat_col("region", 5),  # valida.
+        ],
+    }
+    out = select_groupby_keys(profile, max_card=20)
+    assert {g["col"] for g in out["group_keys"]} == {"region"}
+
+
+def test_max_keys_limits_group_keys():
+    profile = {
+        "n_rows": 1000,
+        "key_candidates": [],
+        "columns": [
+            _cat_col("a", 4, imbalance=1.0),
+            _cat_col("b", 5, imbalance=1.2),
+            _cat_col("c", 6, imbalance=1.5),
+            _cat_col("d", 7, imbalance=2.0),
+        ],
+    }
+    out = select_groupby_keys(profile, max_keys=2)
+    assert len(out["group_keys"]) == 2
+    # Hasta 2 pivots con >=2 keys (aqui exactamente 1 par posible entre 2 keys).
+    assert len(out["pivots"]) == 1
+
+
+def test_three_keys_cap_pivots_to_two():
+    profile = {
+        "n_rows": 1000,
+        "key_candidates": [],
+        "columns": [
+            _cat_col("a", 4, imbalance=1.0),
+            _cat_col("b", 5, imbalance=1.1),
+            _cat_col("c", 6, imbalance=1.2),
+            _num_col("m", std=10.0, cv=0.5),
+        ],
+    }
+    out = select_groupby_keys(profile, max_keys=3)
+    assert len(out["group_keys"]) == 3
+    # 3 keys -> 3 pares posibles, capado a 2.
+    assert len(out["pivots"]) == 2
+    for p in out["pivots"]:
+        assert p["value"] == "m"
+
+
+def test_does_not_mutate_input():
+    profile = _titanic_like_profile()
+    before = repr(profile)
+    select_groupby_keys(profile)
+    assert repr(profile) == before

python/functions/datascience/suggest_aggregations_llm.md

@@ -0,0 +1,96 @@
+---
+name: suggest_aggregations_llm
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def suggest_aggregations_llm(profile: dict, candidates: dict, max_aggs: int = 4, model: str = \"claude-haiku-4-5-20251001\") -> dict"
+description: "MUST-11.1 del capitulo AGREGACION del AutomaticEDA (grupo eda). Dado el TableProfile de una tabla y los candidatos cuantitativos de select_groupby_keys ({group_keys:[{col,cardinality,score}], measures:[str], pivots:[{index,columns,value}]}), con UNA sola llamada al LLM elige y ordena las K agregaciones (GROUP BY categorica x medidas numericas) y los pivots MAS INFORMATIVOS para un analisis de grupos, con una razon corta cada uno, evitando la explosion combinatoria (no todo contra todo). Privacidad/coste: NO envia filas crudas, solo el resumen AGREGADO de los candidatos (tabla, columnas categoricas con cardinalidad/score, medidas, pivots). Reusa ask_llm del grupo claude-direct (API directa con token OAuth de Claude). Impura, dict-no-throw: NUNCA lanza y SIEMPRE devuelve algo usable; si el LLM falla, el JSON no parsea o no hay seleccion valida, cae a un fallback determinista construido desde los candidatos (source='fallback'). Toda columna que el LLM invente se descarta."
+tags: [eda, claude-direct, llm, aggregation, groupby, pivot, datascience, automatic-eda]
+params:
+  - name: profile
+    desc: "TableProfile del grupo eda. Solo se usa profile['table'] para nombrar la tabla en el prompt; puede ir vacio o sin esa clave (se usa '(tabla sin nombre)')."
+  - name: candidates
+    desc: "Salida de select_groupby_keys: {group_keys:[{col, cardinality, score}], measures:[str], pivots:[{index, columns, value}]}. group_keys = columnas categoricas candidatas para GROUP BY; measures = columnas numericas a agregar (sum/avg); pivots = cruces index x columns -> value sugeridos. Cualquier columna que el LLM elija debe existir aqui o se descarta. None o no-dict se trata como vacio."
+  - name: max_aggs
+    desc: "Tope de agregaciones a devolver. Default 4. Valores <1 o no-int se normalizan a 4. Limita tanto la seleccion del LLM como el fallback determinista, para evitar la explosion combinatoria."
+  - name: model
+    desc: "id del modelo Anthropic a usar en la unica llamada. Default 'claude-haiku-4-5-20251001' (haiku, coste bajo, ~2-3s). Para razones mas finas, pasar p.ej. 'claude-opus-4-8'."
+output: "dict dict-no-throw: {status:'ok', source:'llm'|'fallback', aggregations:[{group_by:str, measures:[str], why:str}], pivots:[{index:str, columns:str, value:str|None, why:str}], note:str}. source=='llm' si el LLM produjo al menos una agregacion valida (columnas existentes en candidates); en cualquier otro caso (LLM caido, JSON invalido, seleccion vacia, sin candidatos) source=='fallback' y aggregations/pivots se derivan de candidates con why='selección cuantitativa (sin LLM)'. NUNCA lanza."
+uses_functions: [ask_llm_py_core, select_groupby_keys_py_datascience]
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: []
+tested: true
+tests: ["test_extract_json_object", "test_extract_json_wrapped_in_fences_and_junk", "test_extract_json_non_json_returns_none", "test_validate_aggregations_drops_invalid_columns", "test_llm_path_uses_selection", "test_llm_path_respects_max_aggs", "test_llm_invented_column_is_discarded", "test_fallback_on_empty_llm_response", "test_fallback_on_unparseable_response", "test_fallback_respects_max_aggs", "test_fallback_when_llm_raises", "test_no_candidates_returns_empty_fallback", "test_non_dict_candidates_does_not_raise"]
+test_file_path: "python/functions/datascience/suggest_aggregations_llm_test.py"
+file_path: "python/functions/datascience/suggest_aggregations_llm.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+
+from datascience.suggest_aggregations_llm import suggest_aggregations_llm
+
+profile = {"table": "ventas"}
+
+# candidates = salida de select_groupby_keys (aqui literal de ejemplo).
+candidates = {
+    "group_keys": [
+        {"col": "categoria", "cardinality": 8, "score": 0.91},
+        {"col": "region", "cardinality": 5, "score": 0.74},
+        {"col": "canal", "cardinality": 3, "score": 0.60},
+    ],
+    "measures": ["importe", "unidades"],
+    "pivots": [
+        {"index": "categoria", "columns": "region", "value": "importe"},
+    ],
+}
+
+out = suggest_aggregations_llm(profile, candidates, max_aggs=4)   # haiku por defecto
+
+print("fuente:", out["source"])        # "llm" o "fallback" si no hay red
+for agg in out["aggregations"]:
+    print(f"GROUP BY {agg['group_by']} -> {agg['measures']}  ({agg['why']})")
+for piv in out["pivots"]:
+    print(f"pivot {piv['index']} x {piv['columns']} = {piv['value']}  ({piv['why']})")
+```
+
+## Cuando usarla
+
+Justo despues de `select_groupby_keys` en el capitulo AGREGACION del AutomaticEDA:
+cuando ya tienes los candidatos cuantitativos (columnas categoricas con cardinalidad,
+medidas numericas y pivots posibles) y quieres que un LLM se quede con las K
+agregaciones y pivots MAS INFORMATIVOS en vez de generar "todo contra todo". Usala para
+priorizar el plan de analisis de grupos antes de materializar las tablas con
+`aggregate_by_group` / pivots, manteniendo el coste y el ruido bajos. Si no hay red o
+credenciales, sigue funcionando con un fallback determinista, asi que es seguro
+ponerla en un pipeline.
+
+## Gotchas
+
+- **Impura: hace 1 llamada de red al LLM.** No es determinista ni gratis. Latencia
+  tipica ~2-3s con haiku. Una sola llamada cubre toda la seleccion.
+- **Requiere token OAuth de Claude** en `~/.claude/.credentials.json` (via `ask_llm` /
+  grupo `claude-direct`). Sin token / sin red NO lanza: cae al **fallback
+  determinista** (`source="fallback"`) construido desde `candidates`
+  (group_keys x measures hasta `max_aggs`, pivots tal cual) con
+  `why="selección cuantitativa (sin LLM)"`. Comprueba `out["source"]` para saber si la
+  seleccion vino del LLM o del fallback.
+- **NO envia filas crudas al LLM**, solo el resumen AGREGADO de los candidatos. Esto
+  exige que `candidates` venga ya calculado por `select_groupby_keys` (cardinalidades,
+  scores, medidas, pivots).
+- **Valida columnas inventadas**: si el LLM propone un `group_by`/`measure`/`index`/
+  `columns` que no esta en `candidates`, esa entrada se descarta (las medidas se
+  recortan a las validas). Si tras validar no queda ninguna agregacion, cae al
+  fallback completo.
+- **`max_aggs` acota la explosion combinatoria** tanto en el camino LLM como en el
+  fallback. Subirlo demasiado reintroduce el ruido que esta funcion evita.
+- **Modelo `haiku` por defecto** para coste bajo; sube a `claude-opus-4-8` si necesitas
+  razones (`why`) mas finas (mas caro y lento).

python/functions/datascience/suggest_aggregations_llm.py

@@ -0,0 +1,405 @@
+"""suggest_aggregations_llm — el LLM elige las agregaciones mas informativas (grupo `eda`).
+
+MUST-11.1 del capitulo AGREGACION del AutomaticEDA. Dado el `TableProfile` de una
+tabla y los CANDIDATOS cuantitativos que produce `select_groupby_keys`
+(`{group_keys:[{col,cardinality,score}], measures:[str], pivots:[{index,columns,value}]}`),
+con UNA sola llamada al LLM elige y ordena las K agregaciones (GROUP BY categorica x
+medidas numericas) y los pivots MAS INFORMATIVOS para un analisis de grupos, con una
+razon corta cada uno. El objetivo es evitar la explosion combinatoria: en vez de
+"todo contra todo", el LLM se queda con lo que mas informa.
+
+Privacidad y coste: NO se envian filas crudas al LLM. El prompt solo lleva el resumen
+AGREGADO de los candidatos (nombre de la tabla, columnas categoricas con su
+cardinalidad/score, medidas y pivots posibles). Una sola llamada barata.
+
+Reusa `ask_llm` del registry (grupo claude-direct, API directa con el token OAuth de
+Claude en ~/.claude/.credentials.json, arranque 0). Impura: una llamada de red.
+
+Estilo dict-no-throw con FALLBACK DETERMINISTA: la funcion NUNCA lanza y SIEMPRE
+devuelve algo usable. Si `ask_llm` falla (devuelve ""), el JSON no parsea, o el LLM no
+produce ninguna seleccion valida, se construye la respuesta directamente desde los
+candidatos (group_keys x measures hasta max_aggs, pivots tal cual) con
+`source="fallback"`. Ademas, toda columna que el LLM invente (no presente en los
+candidatos) se descarta.
+"""
+
+import json
+
+from core.ask_llm import ask_llm
+
+_SYSTEM = (
+    "Eres un analista de datos conciso. Te dan los CANDIDATOS AGREGADOS de una tabla "
+    "(columnas categoricas para GROUP BY con su cardinalidad, medidas numericas y "
+    "pivots posibles) y eliges las agregaciones y pivots MAS INFORMATIVOS para "
+    "entender los grupos, evitando la explosion combinatoria (no todo contra todo). "
+    "No recibes filas crudas. Responde en espanol. Responde SIEMPRE y SOLO con un "
+    "unico objeto JSON valido, sin texto alrededor ni fences de markdown, con la forma "
+    '{"aggregations": [{"group_by": "<col categorica>", "measures": ["<medida>", ...], '
+    '"why": "<razon corta>"}], "pivots": [{"index": "<col>", "columns": "<col>", '
+    '"value": "<medida o null>", "why": "<razon corta>"}]}. Usa SOLO nombres de columna '
+    "que aparezcan en los candidatos; no inventes nombres."
+)
+
+
+def _fmt_num(value) -> str:
+    """Formatea un numero de forma compacta para el prompt (None -> '?')."""
+    if value is None:
+        return "?"
+    if isinstance(value, bool):
+        return str(value)
+    if isinstance(value, float):
+        if value == int(value):
+            return str(int(value))
+        return f"{value:.4g}"
+    return str(value)
+
+
+def _candidate_view(candidates: dict):
+    """Extrae las vistas utiles de los candidatos. Funcion interna PURA.
+
+    Devuelve la tupla (group_cols, measures, measure_set, pivots, group_keys):
+    - group_cols: set de nombres de columna categorica validas (de group_keys[].col).
+    - measures: lista de medidas numericas (str) preservando orden.
+    - measure_set: set de las medidas para validar pertenencia rapido.
+    - pivots: lista de pivots candidatos (dicts) tal cual vienen.
+    - group_keys: lista de dicts {col, cardinality, score} ya filtrada a entradas validas.
+
+    Tolera estructuras incompletas o de tipo incorrecto sin lanzar.
+    """
+    candidates = candidates if isinstance(candidates, dict) else {}
+
+    gk_raw = candidates.get("group_keys")
+    group_keys = []
+    if isinstance(gk_raw, list):
+        for gk in gk_raw:
+            if isinstance(gk, dict) and isinstance(gk.get("col"), str):
+                group_keys.append(gk)
+    group_cols = {gk["col"] for gk in group_keys}
+
+    m_raw = candidates.get("measures")
+    measures = [m for m in m_raw if isinstance(m, str)] if isinstance(m_raw, list) else []
+    measure_set = set(measures)
+
+    p_raw = candidates.get("pivots")
+    pivots = p_raw if isinstance(p_raw, list) else []
+
+    return group_cols, measures, measure_set, pivots, group_keys
+
+
+def _sorted_group_cols(group_keys: list) -> list:
+    """Nombres de columna categorica ordenados por score descendente. PURA."""
+
+    def _score(gk):
+        s = gk.get("score")
+        if isinstance(s, (int, float)) and not isinstance(s, bool):
+            return s
+        return 0.0
+
+    return [gk["col"] for gk in sorted(group_keys, key=_score, reverse=True)]
+
+
+def _build_prompt(profile: dict, candidates: dict, max_aggs: int) -> str:
+    """Construye el prompt compacto SOLO con agregados. Funcion interna PURA.
+
+    No toca red ni disco: testeable sin credenciales. Incluye el nombre de la tabla,
+    las columnas categoricas candidatas (con cardinalidad y score), las medidas
+    numericas y los pivots candidatos. Nunca filas crudas.
+
+    Args:
+        profile: TableProfile (se usa solo profile['table'] para nombrar la tabla).
+        candidates: salida de select_groupby_keys.
+        max_aggs: tope de agregaciones a pedir.
+
+    Returns:
+        El texto del prompt.
+    """
+    profile = profile if isinstance(profile, dict) else {}
+    candidates = candidates if isinstance(candidates, dict) else {}
+
+    table = profile.get("table")
+    table = str(table) if table is not None else "(tabla sin nombre)"
+
+    lines = [
+        f"Tabla: {table}",
+        (
+            "Tarea: elegir las agregaciones (GROUP BY categorica x medidas numericas) y "
+            "los pivots MAS INFORMATIVOS para un analisis de grupos. Evita la explosion "
+            "combinatoria: NO combines todo contra todo, prioriza lo que mas informa."
+        ),
+        f"Devuelve a lo sumo {max_aggs} agregaciones.",
+        "",
+        "Columnas categoricas candidatas para GROUP BY (col: cardinalidad, score):",
+    ]
+
+    group_keys = candidates.get("group_keys") or []
+    for gk in group_keys:
+        if not isinstance(gk, dict) or not isinstance(gk.get("col"), str):
+            continue
+        lines.append(
+            f"  - {gk['col']}: cardinalidad={_fmt_num(gk.get('cardinality'))}, "
+            f"score={_fmt_num(gk.get('score'))}"
+        )
+
+    measures = candidates.get("measures") or []
+    lines.append("")
+    lines.append("Medidas numericas disponibles (para sum/avg por grupo):")
+    lines.append("  " + ", ".join(str(m) for m in measures if isinstance(m, str)))
+
+    pivots = candidates.get("pivots") or []
+    if pivots:
+        lines.append("")
+        lines.append("Pivots candidatos (index x columns -> value):")
+        for p in pivots:
+            if not isinstance(p, dict):
+                continue
+            lines.append(
+                f"  - index={p.get('index')}, columns={p.get('columns')}, "
+                f"value={p.get('value')}"
+            )
+
+    lines.append("")
+    lines.append(
+        "Usa SOLO columnas de las listas anteriores; no inventes nombres. Responde "
+        "SOLO con el JSON descrito en las instrucciones del sistema."
+    )
+    return "\n".join(lines)
+
+
+def _extract_json(text: str):
+    """Extrae el primer bloque JSON (objeto o array) de la respuesta. PURA.
+
+    Localiza el bloque que empieza antes (el primer '{' o el primer '[') y, para ese
+    delimitador, hace json.loads del rango hasta su ultimo cierre. Tolera texto basura
+    alrededor y fences ```json. NUNCA lanza: ante cualquier fallo devuelve None.
+
+    Args:
+        text: respuesta cruda del LLM.
+
+    Returns:
+        El objeto/lista deserializado, o None si no se pudo parsear.
+    """
+    if not text or not isinstance(text, str):
+        return None
+
+    opens = []
+    i_obj = text.find("{")
+    if i_obj != -1:
+        opens.append((i_obj, "{", "}"))
+    i_arr = text.find("[")
+    if i_arr != -1:
+        opens.append((i_arr, "[", "]"))
+    opens.sort()
+
+    for _, open_c, close_c in opens:
+        start = text.find(open_c)
+        end = text.rfind(close_c)
+        if start != -1 and end != -1 and end > start:
+            try:
+                return json.loads(text[start : end + 1])
+            except (ValueError, TypeError):
+                continue
+    return None
+
+
+def _validate_aggregations(raw_aggs, group_cols: set, measure_set: set, max_aggs: int) -> list:
+    """Filtra las agregaciones del LLM a las que usan SOLO columnas candidatas. PURA.
+
+    Descarta cualquier agregacion cuyo group_by no este en group_cols o que no tenga
+    al menos una medida valida. Recorta las medidas a las presentes en measure_set.
+    Limita el resultado a max_aggs entradas.
+    """
+    out = []
+    if not isinstance(raw_aggs, list):
+        return out
+    for item in raw_aggs:
+        if not isinstance(item, dict):
+            continue
+        gb = item.get("group_by")
+        if not isinstance(gb, str) or gb not in group_cols:
+            continue  # columna inventada -> se descarta
+        raw_measures = item.get("measures")
+        if isinstance(raw_measures, str):
+            raw_measures = [raw_measures]
+        if not isinstance(raw_measures, list):
+            continue
+        measures = [m for m in raw_measures if isinstance(m, str) and m in measure_set]
+        if not measures:
+            continue  # sin medidas validas -> agregacion inutil
+        why = item.get("why")
+        why = str(why) if why is not None else ""
+        out.append({"group_by": gb, "measures": measures, "why": why})
+        if len(out) >= max_aggs:
+            break
+    return out
+
+
+def _validate_pivots(raw_pivots, group_cols: set, measure_set: set) -> list:
+    """Filtra los pivots del LLM a los que usan SOLO columnas candidatas. PURA.
+
+    Descarta el pivot si index o columns no son columnas categoricas validas. Si el
+    value no es una medida valida, lo deja en None (un pivot de conteo sigue siendo util).
+    """
+    out = []
+    if not isinstance(raw_pivots, list):
+        return out
+    for item in raw_pivots:
+        if not isinstance(item, dict):
+            continue
+        idx = item.get("index")
+        cols = item.get("columns")
+        if not (isinstance(idx, str) and idx in group_cols):
+            continue
+        if not (isinstance(cols, str) and cols in group_cols):
+            continue
+        val = item.get("value")
+        if not (isinstance(val, str) and val in measure_set):
+            val = None
+        why = item.get("why")
+        why = str(why) if why is not None else ""
+        out.append({"index": idx, "columns": cols, "value": val, "why": why})
+    return out
+
+
+def _fallback_aggregations(group_cols_sorted: list, measures: list, max_aggs: int) -> list:
+    """Agregaciones deterministas: cada columna categorica x todas las medidas. PURA."""
+    out = []
+    for col in group_cols_sorted:
+        out.append(
+            {
+                "group_by": col,
+                "measures": list(measures),
+                "why": "selección cuantitativa (sin LLM)",
+            }
+        )
+        if len(out) >= max_aggs:
+            break
+    return out
+
+
+def _fallback_pivots(cand_pivots: list) -> list:
+    """Normaliza los pivots candidatos a la forma de salida (tal cual + why). PURA."""
+    out = []
+    if not isinstance(cand_pivots, list):
+        return out
+    for p in cand_pivots:
+        if not isinstance(p, dict):
+            continue
+        idx = p.get("index")
+        cols = p.get("columns")
+        if not (isinstance(idx, str) and isinstance(cols, str)):
+            continue
+        val = p.get("value")
+        if not isinstance(val, str):
+            val = None
+        out.append(
+            {
+                "index": idx,
+                "columns": cols,
+                "value": val,
+                "why": "selección cuantitativa (sin LLM)",
+            }
+        )
+    return out
+
+
+def suggest_aggregations_llm(
+    profile: dict,
+    candidates: dict,
+    max_aggs: int = 4,
+    model: str = "claude-haiku-4-5-20251001",
+) -> dict:
+    """Elige las agregaciones y pivots mas informativos con UNA llamada al LLM.
+
+    MUST-11.1 del capitulo AGREGACION del AutomaticEDA. Toma el perfil de la tabla y
+    los candidatos cuantitativos (salida de select_groupby_keys) y deja que el LLM
+    seleccione/ordene las K agregaciones (GROUP BY categorica x medidas) y los pivots
+    mas utiles, con una razon corta cada uno, evitando la explosion combinatoria.
+
+    Privacidad/coste: solo viaja al LLM el resumen AGREGADO de los candidatos, nunca
+    filas crudas. Una sola llamada barata.
+
+    dict-no-throw con fallback determinista: NUNCA lanza. Si el LLM falla, el JSON no
+    parsea, o no produce seleccion valida -> construye la respuesta desde los candidatos
+    (group_keys x measures hasta max_aggs, pivots tal cual) con source="fallback". Las
+    columnas que el LLM invente (no presentes en los candidatos) se descartan.
+
+    Args:
+        profile: TableProfile del grupo eda. Solo se usa profile['table'] para nombrar
+            la tabla en el prompt; puede ir vacio.
+        candidates: salida de select_groupby_keys, con la forma
+            {group_keys:[{col,cardinality,score}], measures:[str],
+            pivots:[{index,columns,value}]}.
+        max_aggs: tope de agregaciones a devolver. Default 4. Valores <1 o no-int se
+            normalizan a 4.
+        model: id del modelo Anthropic. Default 'claude-haiku-4-5-20251001' (haiku,
+            coste bajo, ~2-3s).
+
+    Returns:
+        dict {status:"ok", source:"llm"|"fallback",
+        aggregations:[{group_by:str, measures:[str], why:str}],
+        pivots:[{index:str, columns:str, value:str|None, why:str}], note:str}.
+        source=="llm" si el LLM produjo al menos una agregacion valida; en cualquier
+        otro caso "fallback". NUNCA lanza.
+    """
+    if not isinstance(candidates, dict):
+        candidates = {}
+    if isinstance(max_aggs, bool) or not isinstance(max_aggs, int) or max_aggs < 1:
+        max_aggs = 4
+
+    group_cols, measures, measure_set, cand_pivots, group_keys = _candidate_view(candidates)
+    group_cols_sorted = _sorted_group_cols(group_keys)
+
+    # Sin material suficiente para agregar: no merece la pena llamar al LLM.
+    if not group_cols or not measures:
+        return {
+            "status": "ok",
+            "source": "fallback",
+            "aggregations": [],
+            "pivots": _fallback_pivots(cand_pivots),
+            "note": "sin candidatos suficientes para agregar",
+        }
+
+    prompt = _build_prompt(profile, candidates, max_aggs)
+    try:
+        text = ask_llm(prompt, model=model, system=_SYSTEM, echo=False)
+    except Exception:  # noqa: BLE001 — degradacion: cualquier fallo de red/LLM.
+        text = ""
+
+    parsed = _extract_json(text)
+    if parsed is not None:
+        if isinstance(parsed, dict):
+            raw_aggs = parsed.get("aggregations")
+            raw_pivots = parsed.get("pivots")
+        elif isinstance(parsed, list):
+            raw_aggs = parsed
+            raw_pivots = None
+        else:
+            raw_aggs = None
+            raw_pivots = None
+
+        aggs = _validate_aggregations(raw_aggs, group_cols, measure_set, max_aggs)
+        if aggs:
+            pivots = _validate_pivots(raw_pivots, group_cols, measure_set)
+            if not pivots:
+                pivots = _fallback_pivots(cand_pivots)
+            return {
+                "status": "ok",
+                "source": "llm",
+                "aggregations": aggs,
+                "pivots": pivots,
+                "note": f"{len(aggs)} agregaciones y {len(pivots)} pivots seleccionados por el LLM",
+            }
+
+    # Fallback determinista.
+    note = (
+        "LLM no disponible; selección cuantitativa determinista"
+        if not text
+        else "LLM sin selección válida; selección cuantitativa determinista"
+    )
+    return {
+        "status": "ok",
+        "source": "fallback",
+        "aggregations": _fallback_aggregations(group_cols_sorted, measures, max_aggs),
+        "pivots": _fallback_pivots(cand_pivots),
+        "note": note,
+    }

python/functions/datascience/suggest_aggregations_llm_test.py

@@ -0,0 +1,198 @@
+"""Tests para suggest_aggregations_llm.
+
+NO acceden a red ni a credenciales: las funciones internas (_build_prompt,
+_extract_json, _validate_*, _fallback_*) son puras y testeables aisladas; la unica
+via que llamaria al LLM (suggest_aggregations_llm) se prueba reemplazando el simbolo
+`ask_llm` del modulo bajo prueba con una funcion simulada. Los candidatos van
+literales en el test: NO se importa select_groupby_keys.
+
+Cubre golden (LLM ok con columnas validas), edge (max_aggs respetado, sin candidatos)
+y error (LLM caido -> fallback, JSON invalido -> fallback, columna inventada -> se
+descarta). Todos sin tocar la red.
+"""
+
+import json
+
+import datascience.suggest_aggregations_llm as M
+from datascience.suggest_aggregations_llm import (
+    _extract_json,
+    _validate_aggregations,
+    suggest_aggregations_llm,
+)
+
+# Candidatos de ejemplo con la forma que produce select_groupby_keys (literales).
+_CANDIDATES = {
+    "group_keys": [
+        {"col": "categoria", "cardinality": 8, "score": 0.91},
+        {"col": "region", "cardinality": 5, "score": 0.74},
+        {"col": "canal", "cardinality": 3, "score": 0.60},
+    ],
+    "measures": ["importe", "unidades"],
+    "pivots": [
+        {"index": "categoria", "columns": "region", "value": "importe"},
+    ],
+}
+_PROFILE = {"table": "ventas"}
+
+
+def _fake_returner(text):
+    """Devuelve un ask_llm simulado que ignora args y retorna `text`."""
+
+    def _fake(prompt, model="x", system="", echo=True, **kwargs):
+        return text
+
+    return _fake
+
+
+# --- _extract_json (parser puro, sin red) ---
+
+
+def test_extract_json_object():
+    obj = {"aggregations": [{"group_by": "categoria", "measures": ["importe"], "why": "x"}]}
+    assert _extract_json(json.dumps(obj)) == obj
+
+
+def test_extract_json_wrapped_in_fences_and_junk():
+    obj = {"aggregations": [], "pivots": []}
+    text = "Claro, aqui tienes:\n```json\n" + json.dumps(obj) + "\n```\nFin."
+    assert _extract_json(text) == obj
+
+
+def test_extract_json_non_json_returns_none():
+    assert _extract_json("no hay json aqui") is None
+    assert _extract_json("") is None
+    assert _extract_json(None) is None
+
+
+# --- _validate_aggregations (puro) ---
+
+
+def test_validate_aggregations_drops_invalid_columns():
+    group_cols = {"categoria", "region"}
+    measure_set = {"importe", "unidades"}
+    raw = [
+        {"group_by": "categoria", "measures": ["importe", "inventada"], "why": "ok"},
+        {"group_by": "no_existe", "measures": ["importe"], "why": "mala"},
+        {"group_by": "region", "measures": ["solo_inventada"], "why": "sin medidas"},
+    ]
+    out = _validate_aggregations(raw, group_cols, measure_set, max_aggs=4)
+    # Solo sobrevive la primera, con las medidas recortadas a las validas.
+    assert out == [{"group_by": "categoria", "measures": ["importe"], "why": "ok"}]
+
+
+# --- suggest_aggregations_llm: camino LLM (golden) ---
+
+
+def test_llm_path_uses_selection(monkeypatch):
+    llm_obj = {
+        "aggregations": [
+            {"group_by": "categoria", "measures": ["importe"], "why": "ventas por familia"},
+            {"group_by": "region", "measures": ["importe", "unidades"], "why": "reparto geografico"},
+        ],
+        "pivots": [
+            {"index": "categoria", "columns": "region", "value": "importe", "why": "cruce clave"},
+        ],
+    }
+    monkeypatch.setattr(M, "ask_llm", _fake_returner(json.dumps(llm_obj)))
+
+    out = suggest_aggregations_llm(_PROFILE, _CANDIDATES)
+    assert out["status"] == "ok"
+    assert out["source"] == "llm"
+    assert out["aggregations"] == llm_obj["aggregations"]
+    assert out["pivots"][0]["index"] == "categoria"
+    assert out["pivots"][0]["why"] == "cruce clave"
+
+
+def test_llm_path_respects_max_aggs(monkeypatch):
+    llm_obj = {
+        "aggregations": [
+            {"group_by": "categoria", "measures": ["importe"], "why": "a"},
+            {"group_by": "region", "measures": ["importe"], "why": "b"},
+            {"group_by": "canal", "measures": ["unidades"], "why": "c"},
+        ],
+        "pivots": [],
+    }
+    monkeypatch.setattr(M, "ask_llm", _fake_returner(json.dumps(llm_obj)))
+
+    out = suggest_aggregations_llm(_PROFILE, _CANDIDATES, max_aggs=2)
+    assert out["source"] == "llm"
+    assert len(out["aggregations"]) == 2
+
+
+def test_llm_invented_column_is_discarded(monkeypatch):
+    # El LLM mezcla una agregacion valida con otra de columna inexistente.
+    llm_obj = {
+        "aggregations": [
+            {"group_by": "categoria", "measures": ["importe"], "why": "valida"},
+            {"group_by": "columna_fantasma", "measures": ["importe"], "why": "inventada"},
+        ],
+        "pivots": [
+            {"index": "fantasma", "columns": "region", "value": "importe", "why": "mala"},
+        ],
+    }
+    monkeypatch.setattr(M, "ask_llm", _fake_returner(json.dumps(llm_obj)))
+
+    out = suggest_aggregations_llm(_PROFILE, _CANDIDATES)
+    assert out["source"] == "llm"
+    # La agregacion inventada se descarta; queda solo la valida.
+    assert [a["group_by"] for a in out["aggregations"]] == ["categoria"]
+    # El pivot con index fantasma se descarta -> cae a los pivots de candidates.
+    assert all(p["index"] in {"categoria", "region", "canal"} for p in out["pivots"])
+
+
+# --- suggest_aggregations_llm: fallback determinista (error paths) ---
+
+
+def test_fallback_on_empty_llm_response(monkeypatch):
+    monkeypatch.setattr(M, "ask_llm", _fake_returner(""))
+
+    out = suggest_aggregations_llm(_PROFILE, _CANDIDATES, max_aggs=4)
+    assert out["status"] == "ok"
+    assert out["source"] == "fallback"
+    # Las agregaciones se derivan de candidates (una por group_key, con todas las medidas).
+    assert out["aggregations"][0]["group_by"] in {"categoria", "region", "canal"}
+    assert out["aggregations"][0]["measures"] == ["importe", "unidades"]
+    assert out["aggregations"][0]["why"] == "selección cuantitativa (sin LLM)"
+    # Pivots tal cual de candidates.
+    assert out["pivots"][0]["index"] == "categoria"
+
+
+def test_fallback_on_unparseable_response(monkeypatch):
+    monkeypatch.setattr(M, "ask_llm", _fake_returner("esto no es JSON {roto"))
+
+    out = suggest_aggregations_llm(_PROFILE, _CANDIDATES)
+    assert out["source"] == "fallback"
+    assert len(out["aggregations"]) >= 1
+
+
+def test_fallback_respects_max_aggs(monkeypatch):
+    monkeypatch.setattr(M, "ask_llm", _fake_returner(""))
+
+    out = suggest_aggregations_llm(_PROFILE, _CANDIDATES, max_aggs=2)
+    assert out["source"] == "fallback"
+    assert len(out["aggregations"]) == 2
+
+
+def test_fallback_when_llm_raises(monkeypatch):
+    def _boom(*args, **kwargs):
+        raise RuntimeError("sin red")
+
+    monkeypatch.setattr(M, "ask_llm", _boom)
+
+    out = suggest_aggregations_llm(_PROFILE, _CANDIDATES)
+    assert out["source"] == "fallback"
+    assert out["aggregations"]  # no vacio, no lanza
+
+
+def test_no_candidates_returns_empty_fallback():
+    # Sin red porque ni siquiera se llama al LLM (no hay material).
+    out = suggest_aggregations_llm(_PROFILE, {"group_keys": [], "measures": [], "pivots": []})
+    assert out["status"] == "ok"
+    assert out["source"] == "fallback"
+    assert out["aggregations"] == []
+
+
+def test_non_dict_candidates_does_not_raise():
+    out = suggest_aggregations_llm(_PROFILE, None)
+    assert out["status"] == "ok"
+    assert out["aggregations"] == []

python/functions/pipelines/profile_table.md

@@ -5,7 +5,7 @@ lang: py
 domain: pipelines
 purity: impure
 version: "1.0.0"
-signature: "def profile_table(db_path: str, table: str, backend: str = \"duckdb\", sample: int = 5000, run_models: bool = False, run_llm: bool = False, run_series: bool = False, emit_pdf: bool = False, report_dir: str = \"reports\", write_report: bool = True) -> dict"
+signature: "def profile_table(db_path: str, table: str, backend: str = \"duckdb\", sample: int = 5000, run_models: bool = False, run_llm: bool = False, run_series: bool = False, emit_pdf: bool = False, emit_automatic: bool = False, report_dir: str = \"reports\", write_report: bool = True) -> dict"
 description: "Orquestador one-shot del grupo de capacidad eda: perfila UNA tabla (DuckDB o PostgreSQL) end-to-end componiendo las funciones del grupo (perfil base SQL + muestreo read-only + inferencia semantica + promocion de tipo + estadistica numerica/categorica + score de calidad + correlaciones con correccion FDR + re-expresion de Tukey + avisos exploratorios) y, opcional, modelos baratos (run_models), interpretacion LLM (run_llm) y analisis de serie temporal por columna (run_series: estacionariedad ADF+KPSS, ACF/PACF, STL, retornos). Emite el TableProfile completo mas (opcional) report markdown + JSON sidecar + PDF movil (emit_pdf). Es la composicion canonica para hazme un EDA de esta tabla."
 tags: [eda, duckdb, postgres, profiling, data-quality, pipeline, dataops, timeseries]
 uses_functions:
@@ -26,6 +26,9 @@ uses_functions:
   - exploratory_caveats_py_datascience
   - render_eda_markdown_py_datascience
   - render_eda_pdf_py_datascience
+  - build_eda_render_ctx_py_datascience
+  - render_automatic_eda_pdf_py_datascience
+  - render_automatic_eda_pptx_py_datascience
   - duckdb_query_readonly_py_infra
   - pg_query_py_infra
 uses_types: []
@@ -55,11 +58,13 @@ params:
     desc: "Si True (default False) calcula por columna numerica un bloque de serie temporal (estacionariedad ADF+KPSS, ACF/PACF, STL y, si parece de niveles, retornos). Ordena por la primera columna datetime si existe; si no, por el orden fisico. Guardado en col['series'] y agregado en prof['series']."
   - name: emit_pdf
     desc: "Si True (default False) renderiza un PDF multipagina vertical (legible en movil) del perfil junto al report markdown y devuelve su ruta en pdf_path."
+  - name: emit_automatic
+    desc: "Si True (default False) emite ADEMAS el informe AutomaticEDA completo en PDF (A5 movil) Y PPTX (16:9) con los 11 capitulos del motor; construye el ctx de datos crudos con build_eda_render_ctx para que modelos/timeseries/geospatial/agregacion salgan poblados. Aditivo: no sustituye a emit_pdf. Rutas en aeda_pdf_path / aeda_pptx_path / aeda_manifest_path."
   - name: report_dir
     desc: "Directorio donde escribir los reports si write_report (y el PDF si emit_pdf). Default 'reports'. Se crea si no existe."
   - name: write_report
     desc: "Si True (default) escribe report markdown + JSON sidecar timestamped en report_dir; si False no toca disco y los paths markdown/json del retorno son None (emit_pdf es independiente)."
-output: "dict {status:'ok', profile:<TableProfile enriquecido con quality_score, key_candidates, type_breakdown recalculado, correlaciones con FDR, reexpression por columna numerica, caveats, y (con run_series) series>, report_md_path:str|None, report_json_path:str|None, pdf_path:str|None} o {status:'error', error:str} (dict-no-throw)."
+output: "dict {status:'ok', profile:<TableProfile enriquecido con quality_score, key_candidates, type_breakdown recalculado, correlaciones con FDR, reexpression por columna numerica, caveats, y (con run_series) series>, report_md_path:str|None, report_json_path:str|None, pdf_path:str|None, aeda_pdf_path:str|None, aeda_pptx_path:str|None, aeda_manifest_path:str|None (estos tres solo con emit_automatic)} o {status:'error', error:str} (dict-no-throw)."
 ---
 
 ## Ejemplo

python/functions/pipelines/profile_table.py

@@ -32,11 +32,14 @@ from datascience import (
     acf_pacf,
     adf_kpss_stationarity,
     association_matrix,
+    build_eda_render_ctx,
     column_quality_score,
     describe_numeric,
     eda_llm_insights,
     exploratory_caveats,
     infer_semantic_type,
+    render_automatic_eda_pdf,
+    render_automatic_eda_pptx,
     render_eda_markdown,
     render_eda_pdf,
     run_eda_models,
@@ -385,6 +388,7 @@ def profile_table(
     run_llm: bool = False,
     run_series: bool = False,
     emit_pdf: bool = False,
+    emit_automatic: bool = False,
     report_dir: str = "reports",
     write_report: bool = True,
 ) -> dict:
@@ -412,6 +416,15 @@ def profile_table(
         emit_pdf: si True (default False) renderiza un PDF multipagina vertical
             (legible en movil) del perfil junto al report markdown y devuelve su
             ruta en pdf_path.
+        emit_automatic: si True (default False) emite ademas el informe
+            AutomaticEDA COMPLETO en sus dos formatos (PDF A5 movil + PPTX 16:9)
+            con los 11 capitulos del motor por capitulos. Construye el contexto
+            de datos crudos con build_eda_render_ctx (raw_numeric para modelos/
+            geo, timeseries_raw para series, geo_points para el mapa, db_path/
+            table para la agregacion push-down) para que los capitulos modelos/
+            timeseries/geospatial/agregacion salgan poblados, no degradados. Es
+            ADITIVO: no sustituye a emit_pdf (render_eda_pdf). Sus rutas vuelven
+            en aeda_pdf_path / aeda_pptx_path / aeda_manifest_path.
         report_dir: directorio donde escribir los reports si write_report.
             Default "reports". Se crea si no existe.
         write_report: si True (default), escribe un report markdown + un JSON
@@ -727,12 +740,51 @@ def profile_table(
             except Exception:  # noqa: BLE001
                 pdf_path = None
 
+        # Informe AutomaticEDA completo (PDF + PPTX por capitulos). Aditivo:
+        # convive con emit_pdf (render_eda_pdf) sin sustituirlo. Construye el ctx
+        # con los datos crudos para que modelos/timeseries/geospatial/agregacion
+        # salgan poblados; degrada por clave si build_eda_render_ctx falla.
+        aeda_pdf_path = None
+        aeda_pptx_path = None
+        aeda_manifest_path = None
+        if emit_automatic:
+            try:
+                os.makedirs(report_dir, exist_ok=True)
+                base_ctx = {
+                    "dataset_name": table,
+                    "source_origin": db_path,
+                    "storage": "DuckDB" if backend == "duckdb" else (
+                        "PostgreSQL" if backend == "postgres" else backend),
+                }
+                if run_llm:
+                    base_ctx.update({"run_cluster_llm": True,
+                                     "run_geo_llm": True, "run_agg_llm": True})
+                ctx = build_eda_render_ctx(
+                    db_path, table, prof, backend=backend, sample=sample,
+                    base_ctx=base_ctx)
+                meta = {"title": f"EDA — {table}", "ctx": ctx}
+                aeda_pdf_target = os.path.join(report_dir,
+                                               f"aeda_{table}_{ts}.pdf")
+                aeda_pptx_target = os.path.join(report_dir,
+                                                f"aeda_{table}_{ts}.pptx")
+                rpdf = render_automatic_eda_pdf(prof, aeda_pdf_target, meta) or {}
+                rpptx = render_automatic_eda_pptx(
+                    prof, aeda_pptx_target, meta) or {}
+                aeda_pdf_path = rpdf.get("path")
+                aeda_pptx_path = rpptx.get("path")
+                aeda_manifest_path = rpdf.get("manifest_path")
+            except Exception:  # noqa: BLE001
+                pass
+
         return {
             "status": "ok",
             "profile": prof,
             "report_md_path": report_md_path,
             "report_json_path": report_json_path,
             "pdf_path": pdf_path,
+            "aeda_pdf_path": aeda_pdf_path,
+            "aeda_pptx_path": aeda_pptx_path,
+            "aeda_manifest_path": aeda_manifest_path,
         }
     except Exception as e:  # noqa: BLE001
         return {"status": "error", "error": str(e)}

python/functions/pipelines/render_automatic_eda.md

@@ -0,0 +1,91 @@
+---
+name: render_automatic_eda
+kind: pipeline
+lang: py
+domain: pipelines
+purity: impure
+version: "1.0.0"
+signature: "def render_automatic_eda(db_path: str, table: str, backend: str = \"duckdb\", sample: int = 5000, run_models: bool = True, run_series: bool = True, run_llm: bool = False, out_dir: str = \"reports\", basename: str = None, ctx_extra: dict = None) -> dict"
+description: "Informe AutomaticEDA COMPLETO one-shot de una tabla DuckDB/PostgreSQL: perfila con profile_table, construye el ctx con los datos crudos (build_eda_render_ctx: raw_numeric para modelos/geo, timeseries_raw para series, geo_points para el mapa, db_path/table para la agregacion push-down) y emite PDF (A5 movil) Y PPTX (16:9) del mismo documento por capitulos, con los 11 capitulos POBLADOS de verdad (clusters pintados sobre el PCA, evolucion temporal, mapa geografico y tablas de agregacion), no degradados. Devuelve las rutas de PDF/PPTX y el manifiesto de versiones por capitulo."
+tags: [eda, duckdb, postgres, profiling, pipeline, dataops, report, pdf, pptx]
+uses_functions:
+  - profile_table_py_pipelines
+  - build_eda_render_ctx_py_datascience
+  - render_automatic_eda_pdf_py_datascience
+  - render_automatic_eda_pptx_py_datascience
+uses_types: []
+returns: []
+returns_optional: false
+error_type: error_go_core
+imports: []
+tested: true
+tests:
+  - "render end-to-end sobre DuckDB sintetico con categoricas + fecha + lat/lon emite PDF y PPTX con paginas/slides"
+test_file_path: "python/functions/pipelines/render_automatic_eda_test.py"
+file_path: "python/functions/pipelines/render_automatic_eda.py"
+params:
+  - name: db_path
+    desc: "Ruta al archivo DuckDB (read-only, debe existir) o DSN PostgreSQL si backend='postgres'."
+  - name: table
+    desc: "Nombre de la tabla a perfilar e informar."
+  - name: backend
+    desc: "'duckdb' (default) o 'postgres'. Selecciona el motor de perfilado y muestreo."
+  - name: sample
+    desc: "Maximo de filas/valores muestreados por columna para el perfil y para los datos crudos del ctx (LIMIT). Default 5000."
+  - name: run_models
+    desc: "Si True (default) corre los modelos baratos (PCA/KMeans/IsolationForest/normalidad); necesario para que el capitulo modelos pinte los clusters sobre el plano PCA."
+  - name: run_series
+    desc: "Si True (default) calcula el analisis de serie temporal por columna numerica; necesario para el analisis del capitulo timeseries (la grafica de evolucion sale de los datos crudos del ctx aunque sea False)."
+  - name: run_llm
+    desc: "Si True (default False) hace la interpretacion LLM del perfil y ACTIVA la narrativa LLM de los capitulos modelos/geospatial/agregacion (titulos de segmento, descripcion de zona, seleccion de agregaciones). Con False usan su derivacion cuantitativa sin red."
+  - name: out_dir
+    desc: "Directorio de salida (se crea si no existe). Default 'reports'."
+  - name: basename
+    desc: "Nombre base de los archivos sin extension. Default 'aeda_<table>_<timestamp>'."
+  - name: ctx_extra
+    desc: "Dict opcional con claves de presentacion/contexto extra que se mezclan en el ctx (dataset_name, description, source_origin, ...); no pisan las claves de datos calculadas por build_eda_render_ctx."
+output: "dict {status:'ok', pdf_path:str, pptx_path:str, manifest_path:str|None, n_pages:int, n_slides:int, pdf_note:str, pptx_note:str, profile:<TableProfile>} o {status:'error', error:str} (dict-no-throw)."
+---
+
+## Ejemplo
+
+```python
+from pipelines.render_automatic_eda import render_automatic_eda
+
+# Tabla DuckDB con categoricas + fecha + numericas: informe completo a reports/.
+r = render_automatic_eda("/tmp/ventas.duckdb", "ventas",
+                         run_models=True, run_series=True, out_dir="reports")
+print(r["status"], r["pdf_path"], r["pptx_path"], r["n_pages"], r["n_slides"])
+# ok reports/aeda_ventas_20260630-120500.pdf reports/aeda_ventas_20260630-120500.pptx 14 16
+
+# Con narrativa LLM (titulos de segmento, descripcion geografica, etc.):
+r = render_automatic_eda("/tmp/ventas.duckdb", "ventas", run_llm=True)
+```
+
+## Cuando usarla
+
+Cuando quieras el informe AutomaticEDA COMPLETO (PDF + PPTX) de una tabla en una
+sola llamada, con los capitulos de modelos, series, geoespacial y agregacion ya
+poblados (no degradados). Es el reemplazo de "perfila + monta el ctx a mano +
+llama a los dos renderers": este pipeline orquesta `profile_table` ->
+`build_eda_render_ctx` -> `render_automatic_eda_pdf`/`_pptx`. Usalo como
+entregable para compartir un EDA, o como el motor detras de `profile_table(
+emit_automatic=True)` y del skill `/eda`.
+
+## Gotchas
+
+- Impura: ESCRIBE el PDF, el PPTX y `automatic_eda_manifest.json` en `out_dir`.
+- `db_path` debe existir: DuckDB read-only no crea la base.
+- `run_models=True` y `run_series=True` por defecto encarecen el perfil (PCA/
+  KMeans/IsolationForest + ADF/KPSS/STL por columna). Para un informe mas barato
+  ponlos a False: los capitulos modelos/timeseries se omiten o se reducen, pero
+  el resto del informe sale igual.
+- `run_llm=True` hace llamadas de red (interpretacion del perfil + narrativa por
+  capitulo). Sin red, dejalo en False: los capitulos siguen completos con su
+  derivacion cuantitativa (titulos de segmento derivados, nota geografica
+  derivada, seleccion de agregaciones cuantitativa).
+- El PPTX requiere `python-pptx`; si no esta instalado, `pptx_path` sera None y
+  `pptx_note` lo explica (el PDF se emite igual).
+- Los datos crudos del ctx se muestrean con `sample` (LIMIT), no se trae la tabla
+  entera a RAM; con tablas enormes sube `sample` si quieres mas representatividad
+  (coste: mas memoria).

python/functions/pipelines/render_automatic_eda.py

@@ -0,0 +1,157 @@
+"""render_automatic_eda — EDA completo one-shot: perfil → ctx → PDF + PPTX.
+
+Pipeline impuro del grupo de capacidad `eda`. Dada UNA tabla DuckDB (o
+PostgreSQL), produce el informe AutomaticEDA COMPLETO en sus dos formatos a la
+vez (PDF móvil A5 + PPTX 16:9) con los 11 capítulos POBLADOS, en una sola
+llamada. Compone, sin reimplementar su lógica, cuatro funciones del registry:
+
+  - profile_table          : perfila la tabla end-to-end (TableProfile agregado),
+                             opcionalmente con modelos baratos y análisis de serie.
+  - build_eda_render_ctx   : construye el `ctx` con los DATOS CRUDOS que el
+                             TableProfile agregado no incluye (raw_numeric para
+                             modelos/geo, timeseries_raw para series, geo_points
+                             para el mapa, db_path/table para la agregación
+                             push-down). Sin él, esos capítulos degradan.
+  - render_automatic_eda_pdf  : renderiza el documento por capítulos a PDF.
+  - render_automatic_eda_pptx : renderiza el mismo documento a PPTX.
+
+El TableProfile agregado basta para portada/overview/distribuciones/calidad/
+correlación, pero los capítulos `modelos`, `timeseries`, `geospatial` y
+`agregacion` necesitan datos crudos (los clusters proyectados sobre el PCA, la
+serie valor-vs-tiempo, los puntos lat/lon, las filas para el groupby/pivot).
+`build_eda_render_ctx` los muestrea (LIMIT + push-down, sin traer la tabla
+entera a RAM) y los entrega en `ctx`; este pipeline los pasa como `meta['ctx']`
+a ambos renderers para que el informe salga completo.
+
+Estilo dict-no-throw del grupo `eda`: nunca lanza; captura cualquier error y
+degrada a `{"status": "error", "error": str}`.
+"""
+
+import os
+from datetime import datetime, timezone
+
+from datascience import (
+    build_eda_render_ctx,
+    render_automatic_eda_pdf,
+    render_automatic_eda_pptx,
+)
+from pipelines.profile_table import profile_table
+
+# Tokens de almacenamiento por backend (para la portada del informe).
+_STORAGE = {"duckdb": "DuckDB", "postgres": "PostgreSQL"}
+
+
+def render_automatic_eda(
+    db_path: str,
+    table: str,
+    backend: str = "duckdb",
+    sample: int = 5000,
+    run_models: bool = True,
+    run_series: bool = True,
+    run_llm: bool = False,
+    out_dir: str = "reports",
+    basename: str = None,
+    ctx_extra: dict = None,
+) -> dict:
+    """Perfila una tabla y emite el informe AutomaticEDA completo (PDF + PPTX).
+
+    Args:
+        db_path: ruta al archivo DuckDB, o DSN PostgreSQL si backend="postgres".
+        table: nombre de la tabla a perfilar.
+        backend: "duckdb" (default) o "postgres".
+        sample: máximo de filas/valores muestreados por columna para el perfil
+            y para los datos crudos del ctx (LIMIT). Default 5000.
+        run_models: si True (default) corre los modelos baratos
+            (PCA/KMeans/IsolationForest/normalidad). Necesario para que el
+            capítulo `modelos` pinte los clusters sobre el plano PCA.
+        run_series: si True (default) calcula el análisis de serie temporal por
+            columna numérica. Necesario para el análisis del capítulo
+            `timeseries` (la gráfica de evolución sale de los datos crudos del
+            ctx aunque run_series sea False).
+        run_llm: si True (default False) hace la interpretación LLM del perfil y
+            ACTIVA además la narrativa LLM de los capítulos modelos/geospatial/
+            agregacion (títulos de segmento, descripción de la zona, selección de
+            agregaciones). Con False esos capítulos usan su derivación
+            cuantitativa (siguen completos, sin llamadas de red).
+        out_dir: directorio de salida (se crea si no existe). Default "reports".
+        basename: nombre base de los archivos sin extensión. Default
+            "aeda_<table>_<timestamp>".
+        ctx_extra: dict opcional con claves de presentación/contexto extra que se
+            mezclan en el ctx (p.ej. dataset_name, description, source_origin).
+            No pisan las claves de datos calculadas por build_eda_render_ctx.
+
+    Returns:
+        dict (nunca lanza). En éxito::
+
+            {"status": "ok", "pdf_path": str, "pptx_path": str,
+             "manifest_path": str|None, "n_pages": int, "n_slides": int,
+             "pdf_note": str, "pptx_note": str, "profile": <TableProfile>}
+
+        En error: {"status": "error", "error": str}.
+    """
+    try:
+        # 1) Perfil base + modelos/serie opcionales. No escribe report propio
+        # (write_report=False): este pipeline emite su propio par PDF/PPTX.
+        pres = profile_table(
+            db_path,
+            table,
+            backend=backend,
+            sample=sample,
+            run_models=run_models,
+            run_llm=run_llm,
+            run_series=run_series,
+            emit_pdf=False,
+            write_report=False,
+        )
+        if pres.get("status") != "ok":
+            return {"status": "error",
+                    "error": f"profile_table falló: {pres.get('error')}"}
+        prof = pres.get("profile") or {}
+
+        # 2) Contexto de presentación + datos crudos para los 4 capítulos que los
+        # necesitan. Las claves de presentación van en base_ctx; build_eda_render_ctx
+        # añade raw_numeric / timeseries_raw / geo_points / db_path / table.
+        base_ctx = {
+            "dataset_name": table,
+            "source_origin": db_path,
+            "storage": _STORAGE.get(backend, backend),
+        }
+        if run_llm:
+            # Activa la narrativa LLM de los capítulos que la soportan.
+            base_ctx.update({
+                "run_cluster_llm": True,
+                "run_geo_llm": True,
+                "run_agg_llm": True,
+            })
+        if ctx_extra:
+            base_ctx.update(ctx_extra)
+
+        ctx = build_eda_render_ctx(
+            db_path, table, prof, backend=backend, sample=sample,
+            base_ctx=base_ctx,
+        )
+
+        # 3) Render a ambos formatos desde el MISMO documento por capítulos.
+        os.makedirs(out_dir, exist_ok=True)
+        ts = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S")
+        base = basename or f"aeda_{table}_{ts}"
+        pdf_path = os.path.join(out_dir, base + ".pdf")
+        pptx_path = os.path.join(out_dir, base + ".pptx")
+        meta = {"title": f"EDA — {table}", "ctx": ctx}
+
+        rpdf = render_automatic_eda_pdf(prof, pdf_path, meta) or {}
+        rpptx = render_automatic_eda_pptx(prof, pptx_path, meta) or {}
+
+        return {
+            "status": "ok",
+            "pdf_path": rpdf.get("path"),
+            "pptx_path": rpptx.get("path"),
+            "manifest_path": rpdf.get("manifest_path"),
+            "n_pages": rpdf.get("n_pages"),
+            "n_slides": rpptx.get("n_slides"),
+            "pdf_note": rpdf.get("note"),
+            "pptx_note": rpptx.get("note"),
+            "profile": prof,
+        }
+    except Exception as e:  # noqa: BLE001 — dict-no-throw: degradar, nunca lanzar.
+        return {"status": "error", "error": str(e)}

python/functions/pipelines/render_automatic_eda_test.py

@@ -0,0 +1,91 @@
+"""Test del pipeline render_automatic_eda — EDA completo a PDF + PPTX.
+
+Self-contained: crea un DuckDB temporal pequeño con categóricas + fecha + lat/lon
++ varias numéricas, corre el pipeline (sin LLM) y verifica que emite PDF y PPTX
+con páginas/slides, manifest, y que los capítulos dependientes de ctx quedan
+POBLADOS (sin la nota de degradación).
+"""
+
+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)
+
+import duckdb  # noqa: E402
+
+from pipelines.render_automatic_eda import render_automatic_eda  # noqa: E402
+
+
+def _make_db(path):
+    con = duckdb.connect(path)
+    con.execute(
+        "CREATE TABLE sales (d DATE, region VARCHAR, channel VARCHAR, "
+        "amount DOUBLE, units INTEGER, lat DOUBLE, lon DOUBLE)"
+    )
+    from datetime import date, timedelta
+
+    regions = ["norte", "sur", "este"]
+    channels = ["web", "tienda"]
+    centers = {"norte": (43.0, -3.0), "sur": (37.0, -5.0), "este": (39.5, -0.4)}
+    rows = []
+    d0 = date(2024, 1, 1)
+    for i in range(180):
+        r = regions[i % 3]
+        ch = channels[i % 2]
+        clat, clon = centers[r]
+        rows.append((
+            d0 + timedelta(days=i), r, ch,
+            round(100 + (i % 7) * 13.5 + (5 if ch == "web" else 0), 2),
+            10 + (i % 5),
+            round(clat + (i % 3) * 0.1, 4),
+            round(clon + (i % 4) * 0.1, 4),
+        ))
+    con.executemany("INSERT INTO sales VALUES (?,?,?,?,?,?,?)", rows)
+    con.close()
+
+
+def test_pipeline_emits_pdf_and_pptx_with_chapters(tmp_path):
+    db = str(tmp_path / "sales.duckdb")
+    _make_db(db)
+    out = str(tmp_path / "out")
+
+    r = render_automatic_eda(db, "sales", run_models=True, run_series=True,
+                             run_llm=False, out_dir=out, basename="test_sales")
+    assert r["status"] == "ok", r.get("error")
+
+    # Both formats produced.
+    assert r["pdf_path"] and os.path.exists(r["pdf_path"])
+    assert r["pptx_path"] and os.path.exists(r["pptx_path"])
+    assert (r["n_pages"] or 0) > 0
+    assert (r["n_slides"] or 0) > 0
+    # Per-chapter manifest written next to the output.
+    assert r["manifest_path"] and os.path.exists(r["manifest_path"])
+
+
+def test_pipeline_chapters_populated_not_degraded(tmp_path):
+    """The 4 ctx-dependent chapters build with real data (no degradation note)."""
+    import json
+
+    db = str(tmp_path / "sales.duckdb")
+    _make_db(db)
+    out = str(tmp_path / "out")
+    r = render_automatic_eda(db, "sales", run_models=True, run_series=True,
+                             run_llm=False, out_dir=out, basename="t2")
+    assert r["status"] == "ok"
+
+    # The manifest lists the ctx-dependent chapters as actually rendered.
+    with open(r["manifest_path"], encoding="utf-8") as fh:
+        man = json.load(fh)
+    chapters = man.get("chapters") or {}
+    for cid in ("modelos", "timeseries", "geospatial", "agregacion"):
+        assert cid in chapters, f"capítulo {cid} ausente del manifest: {list(chapters)}"
+
+
+def test_pipeline_bad_db_degrades_without_raising(tmp_path):
+    r = render_automatic_eda(str(tmp_path / "nope.duckdb"), "ghost",
+                             out_dir=str(tmp_path / "o"))
+    assert r["status"] == "error"
+    assert "error" in r