feat(eda): capítulo text_distr (TEXTO/NLP) — primer capítulo de datos no tabulares

Añade el capítulo `text_distr` al motor AutomaticEDA: perfila columnas de texto
libre largo (reseñas, descripciones, comentarios) que la distribución categórica
no resume bien. Sigue el patrón de cat_distr/num_distr (build_text_distr(profile,
ctx) -> Chapter | None) y se registra en CHAPTER_ORDER tras cat_distr.

Activación en dos fases: gate barato desde el perfil (columna no numérica con
len_mean >= 50 chars) + confirmación con muestra cruda (mediana de palabras >= 20).
Un dataset sin texto largo (p.ej. titanic) devuelve None sin tocar el informe.

Bloques por columna (Group con page_break): resumen (longitudes, vocabulario con
TTR y % hapax, idioma dominante, % duplicados, legibilidad), histograma de
longitudes, top términos (tabla + barras), bigramas/trigramas, idiomas detectados
y nube de palabras opcional. Términos ttr/hapax enganchados al glosario clicable.

Lógica delegada a 7 funciones nuevas del registry (datascience, tag eda),
estilo dict-no-throw:
- extract_text_sample (impura, push-down SQL DuckDB/Postgres)
- compute_text_length_stats, compute_vocabulary_stats, compute_top_ngrams (puras, stdlib)
- detect_corpus_language (langdetect opcional), compute_text_readability (textstat
  opcional), compute_text_duplicates (hash + datasketch opcional)

Versión barata sin modelos pesados: las piezas que dependen de una librería
opcional (langdetect, textstat, wordcloud, datasketch) degradan a omitidas sin
lanzar. Añade langdetect y textstat (ligeras) al pyproject + uv.lock.

Verificado: golden sobre dataset de reviews multi-idioma (capítulo presente en
PDF+PPTX+MD con métricas reales), titanic sin capítulo (None), degradación sin
libs, suite automatic_eda + pipeline verde (128 passed), fn index OK.

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

python/functions/datascience/__init__.py

@@ -64,6 +64,7 @@ from .exploratory_caveats import exploratory_caveats
 from .render_eda_pdf import render_eda_pdf, render_eda_pdf_relational
 from .render_automatic_eda_pdf import render_automatic_eda_pdf
 from .render_automatic_eda_pptx import render_automatic_eda_pptx
+from .render_automatic_eda_markdown import render_automatic_eda_markdown
 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
@@ -82,6 +83,7 @@ __all__ = [
     "resample_timeseries",
     "render_automatic_eda_pdf",
     "render_automatic_eda_pptx",
+    "render_automatic_eda_markdown",
     "decode_qr_image",
     "adf_kpss_stationarity",
     "acf_pacf",

python/functions/datascience/automatic_eda/__init__.py

@@ -36,6 +36,7 @@ from .model import (  # noqa: F401
 from .chapters_registry import CHAPTER_ORDER, build_chapter, build_document  # noqa: F401
 from .render_pdf_impl import render_pdf  # noqa: F401
 from .render_pptx_impl import render_pptx  # noqa: F401
+from .render_md_impl import render_md  # noqa: F401
 
 __all__ = [
     "ENGINE_NAME",
@@ -60,4 +61,5 @@ __all__ = [
     "build_document",
     "render_pdf",
     "render_pptx",
+    "render_md",
 ]

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

@@ -561,13 +561,11 @@ def _intro_blocks(gloss=None, mark_term: bool = False) -> list:
     t_groupby = _term(mark_term, "groupby", "**por grupos** (split-apply-combine)")
     t_pivot = _term(mark_term, "pivot_table", "**tablas dinámicas** (pivot)")
     text = (
-        f"Este capítulo analiza la tabla {t_groupby}: "
-        "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 "
-        f"(conteo, media, mediana, desviación). Las {t_pivot} "
-        "cruzan dos categóricas sobre una medida, y los **gráficos de barras** "
-        "(siempre desde cero) comparan los grupos de un vistazo."
+        f"Este capítulo analiza la tabla {t_groupby}: elige las columnas "
+        "categóricas más informativas (por cardinalidad y relevancia, no todas "
+        "contra todas) y resume las variables numéricas dentro de cada grupo "
+        f"(conteo, media, mediana, desviación). Se añaden {t_pivot} y "
+        "**gráficos de barras** (siempre desde cero) para comparar los grupos."
     )
     return [model.Heading(text=CHAPTER_TITLE, level=1),
             model.Markdown(text=text)]

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

@@ -3,12 +3,13 @@
 Builds the quality chapter from a ``TableProfile`` of the ``eda`` group. The
 chapter implements the quality model of report 2046:
 
-1. **En qué se basa la calidad** — an intro paragraph explaining the two scored
+1. **En qué se basa la calidad** — a concise intro naming the two scored
    dimensions and their weights (completitud 60%, validez 40%) plus the
-   table-level row uniqueness, BEFORE any number, and stating explicitly that
-   outliers are reported as observations and do **not** lower the score. The
-   criteria terms (calidad de datos, completitud, validez, unicidad de registro)
-   are hooked into the shared glossary as clickable jumps.
+   table-level row uniqueness, BEFORE any number, and stating that outliers are
+   reported as observations and do **not** lower the score. The criteria terms
+   (calidad de datos, completitud, validez, unicidad de registro) are hooked
+   into the shared glossary as clickable jumps; their full definitions live in
+   the GLOSARIO chapter, not inline here.
 2. **Scores por columna** — a table with, per column, the total quality score and
    its breakdown into completeness / validity (no consistency dimension).
 3. **Problemas de calidad** — a table listing ONLY real quality defects
@@ -309,30 +310,22 @@ def _term(key: str, label: str, mark: bool) -> str:
 
 
 def _criteria_intro(mark: bool) -> str:
-    """Intro paragraph explaining the two scored dimensions and the principle."""
+    """Intro: how the score is composed, with every term marked clickable.
+
+    Concise on purpose: the definitions of each term (calidad de datos,
+    completitud, validez, unicidad de registro) now live in the GLOSARIO
+    chapter, so the body no longer repeats them — it only states how the score
+    is composed and keeps each term marked so it stays a clickable jump.
+    """
     calidad = _term("calidad_datos", "calidad de datos", mark)
-    completitud = _term("completitud", "Completitud (peso 60%)", mark)
-    validez = _term("validez", "Validez (peso 40%, cuando es medible)", mark)
+    completitud = _term("completitud", "completitud", mark)
+    validez = _term("validez", "validez", mark)
     unicidad = _term("unicidad_registro", "unicidad de registro", mark)
     return (
-        f"La {calidad} de cada columna es un score de 0 a 100 que combina solo "
-        "dimensiones medibles desde el perfil de la tabla, sin fuente externa "
-        "de verdad:\n\n"
-        f"- {completitud}: proporción de valores presentes (1 − % de nulos; en "
-        "texto, las celdas vacías cuentan como faltantes). Los nulos y vacíos "
-        "bajan el score.\n"
-        f"- {validez}: proporción de valores que encajan con su tipo o formato "
-        "(un número que parsea, una fecha legible, un email con forma de email). "
-        "Si una columna es texto libre sin formato esperado, la validez no se "
-        "mide y el score se basa solo en la completitud.\n\n"
-        f"Score de columna = 100 × (0,6·completitud + 0,4·validez), "
-        "renormalizado cuando la validez no aplica. A nivel de tabla se añade "
-        f"la {unicidad} (1 − % de filas duplicadas).\n\n"
-        "**Los valores atípicos (outliers) NO bajan la calidad.** Un valor "
-        "extremo puede ser real y correcto; detectar atípicos es parte del "
-        "análisis de la distribución, no un juicio de corrección. Por eso, junto "
-        "con las columnas constantes y los identificadores, se listan aparte "
-        "como **observaciones analíticas** que no afectan al score."
+        f"La {calidad} de cada columna es un score de 0 a 100 que combina "
+        f"{completitud} (peso 60%) y {validez} (peso 40%, cuando es medible); "
+        f"a nivel de tabla se añade la {unicidad}. Los valores atípicos no "
+        "bajan el score: se listan aparte como **observaciones analíticas**."
     )
 
 

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

@@ -72,14 +72,16 @@ def test_golden_chapter_estructura_y_version():
     assert "markdown" in kinds and "kv_table" in kinds and "data_table" in kinds
 
 
-def test_golden_intro_explica_dos_dimensiones_y_pesos():
+def test_golden_intro_nombra_dos_dimensiones_y_pesos():
+    # La intro nombra las dos dimensiones, sus pesos y la unicidad, pero ya NO
+    # repite sus definiciones largas: estas viven ahora en el capítulo GLOSARIO.
     ch = build_calidad(_profile(), {})
     intro = [b for b in ch.blocks if b.kind == "markdown"][0].text
-    for needle in ("Completitud", "Validez", "60%", "40%",
+    for needle in ("completitud", "validez", "60%", "40%",
                    "unicidad de registro"):
         assert needle in intro, f"falta {needle!r} en la intro de criterios"
     # El principio: los outliers NO bajan la calidad.
-    assert "atípicos" in intro and "NO bajan" in intro
+    assert "atípicos" in intro and "no bajan" in intro
     # Ya no se menciona la dimensión consistencia eliminada.
     assert "20%" not in intro
 

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

@@ -356,12 +356,11 @@ def build_correlacion(profile: dict, ctx: dict):
     t_cramers = _term(mark_term, "cramers_v", "Cramér's V")
     t_corr_ratio = _term(mark_term, "correlation_ratio", "razón de correlación")
     blocks.append(model.Markdown(text=(
-        "Asociación entre columnas. Cada par se evalúa con la métrica adecuada a "
-        f"sus tipos ({t_pearson}/{t_spearman} entre numéricas — con **signo**; "
-        f"{t_cramers} entre categóricas; {t_corr_ratio} 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.")))
+        "Asociación entre columnas. Cada par se evalúa con la métrica adecuada "
+        f"a sus tipos: {t_pearson}/{t_spearman} (numéricas), {t_cramers} "
+        f"(categóricas), {t_corr_ratio} (num-categórica) e información mutua. "
+        "Sólo las correlaciones **num-num** llevan **signo** (dirección): por "
+        "eso los pares **negativos** son siempre num-num.")))
 
     # 1) Association matrix (heatmap).
     labels, trimmed = _ordered_labels(pairs)

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

@@ -6,15 +6,16 @@ normality}``). It renders, as structured markdown/tables/figures that the core
 paginator never cuts:
 
 1. **Normalization note** — every multivariate model below standardizes the
-   columns with z-score first; the chapter explains why (different scales would
-   otherwise dominate distance/variance).
+   columns with z-score first (the term is marked clickable; its definition
+   lives in the GLOSARIO chapter, not inline).
 2. **PCA** — a scree plot (explained + cumulative variance, single Y axis) plus
    variance and top-loadings tables.
 3. **KMeans segments** — a PCA scatter **coloured by cluster** (its own
    page/slide), the cluster-size table, and a per-cluster LLM micro-analysis
    with a title for each segment.
-4. **Isolation Forest outliers** — a short explanation of how anomalous rows are
-   isolated multivariately and how the threshold is chosen, plus the counts.
+4. **Isolation Forest outliers** — the multivariate anomaly counts and decision
+   threshold (the method is marked clickable; its definition lives in the
+   GLOSARIO chapter, not inline).
 5. **Normality** — per-column Jarque-Bera / D'Agostino / Shapiro verdicts.
 
 The raw numeric data needed to colour the cluster scatter is **not** in the
@@ -314,12 +315,8 @@ def _normalization_intro(gloss=None, mark_term: bool = False) -> list:
     text = (
         "Estos modelos son **no supervisados**: buscan estructura latente sin "
         "una variable objetivo. Antes de aplicarlos, todas las columnas "
-        f"numéricas se {zscore} (cada valor menos la media, dividido por la "
-        "desviación típica). Sin esta normalización, una variable con escala "
-        "grande (p.ej. ingresos en euros) dominaría las distancias y la varianza "
-        "frente a otra de escala pequeña (p.ej. un ratio entre 0 y 1), sesgando "
-        "tanto el PCA como el KMeans. Tras la estandarización todas las variables "
-        "pesan por igual."
+        f"numéricas se {zscore}, para que todas pesen por igual con "
+        "independencia de su escala."
     )
     return [model.Heading(text="Modelos no supervisados", level=1),
             model.Markdown(text=text)]
@@ -334,11 +331,11 @@ def _pca_section(pca: dict, gloss=None, mark_term: bool = False) -> list:
     n_used = pca.get("n_rows_used")
     n_feat = pca.get("n_features")
     intro = (
-        f"El {_term(mark_term, 'pca', 'PCA')} resume {_fmt_num(n_feat)} variables "
-        "numéricas en componentes ortogonales ordenados por la varianza que "
-        f"capturan ({_fmt_num(n_used)} filas usadas tras eliminar nulos). El "
-        "gráfico de sedimentación (scree) muestra cuánta varianza aporta cada "
-        "componente y su acumulado: un codo marca cuántos componentes bastan."
+        f"El {_term(mark_term, 'pca', 'PCA')} se aplica sobre "
+        f"{_fmt_num(n_feat)} variables numéricas ({_fmt_num(n_used)} filas "
+        "usadas tras eliminar nulos). El gráfico de sedimentación (scree) "
+        "muestra cuánta varianza aporta cada componente y su acumulado: un "
+        "codo marca cuántos componentes bastan."
     )
     blocks.append(model.Markdown(text=intro))
 
@@ -403,9 +400,8 @@ def _kmeans_section(kmeans: dict, projection: dict, titles,
     t_sil = _term(mark_term, "silhouette", "*silhouette*")
     intro = (
         f"{t_kmeans} agrupa las filas en **{_fmt_num(best_k)} segmentos** "
-        f"elegidos automáticamente maximizando el coeficiente de {t_sil} "
-        f"(**{_fmt_num(sil)}**, rango −1 a 1: cuanto más alto, segmentos más "
-        "compactos y separados). Los segmentos se proyectan sobre el plano de "
+        f"elegidos automáticamente por el coeficiente de {t_sil} "
+        f"(**{_fmt_num(sil)}**). Los segmentos se proyectan sobre el plano de "
         "los dos primeros componentes principales para visualizarlos."
     )
     blocks.append(model.Markdown(text=intro))
@@ -469,14 +465,10 @@ def _outliers_section(outliers: dict, gloss=None, mark_term: bool = False) -> li
                             level=2)]
     isof = _term(mark_term, "isolation_forest", "**Isolation Forest**")
     explain = (
-        f"{isof} detecta filas anómalas de forma *multivariante*: "
-        "construye árboles que parten el espacio con cortes aleatorios y mide "
-        "cuántos cortes hacen falta para aislar cada fila. Las filas raras "
-        "(combinaciones de valores poco frecuentes considerando **todas las "
-        "columnas a la vez**, no una sola) se aíslan con muy pocos cortes y "
-        "obtienen un score bajo. El **umbral** de decisión separa las filas "
-        "normales de las anómalas según la contaminación esperada del modelo: "
-        "una fila es outlier cuando su score queda por debajo de ese umbral."
+        f"{isof} marca filas anómalas de forma *multivariante*: combinaciones "
+        "de valores poco frecuentes considerando **todas las columnas a la "
+        "vez**, no una sola. La tabla resume cuántas se detectaron y el umbral "
+        "de decisión empleado."
     )
     blocks.append(model.Markdown(text=explain))
     blocks.append(model.KVTable(rows=[

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

@@ -256,14 +256,14 @@ def _pk_candidates_section(profile: dict, mark: bool) -> list:
     pk = ("[[term:pk]]**clave primaria**[[/term]]" if mark
           else "**clave primaria**")
     intro = (
-        f"Estas columnas son **candidatas a {pk}**: su "
-        "[[term:cardinalidad]]cardinalidad[[/term]] iguala al número de filas y no "
-        "tienen nulos, así que cada valor identifica una fila distinta. Son "
-        "candidatas, no una clave declarada: la base no las marca como tal."
+        f"Columnas **candidatas a {pk}**: su "
+        "[[term:cardinalidad]]cardinalidad[[/term]] iguala al número de filas y "
+        "no tienen nulos. Son candidatas, no una clave declarada: la base no "
+        "las marca como tal."
         if mark else
-        "Estas columnas son **candidatas a clave primaria**: su cardinalidad "
-        "iguala al número de filas y no tienen nulos, así que cada valor "
-        "identifica una fila distinta.")
+        "Columnas **candidatas a clave primaria**: su cardinalidad iguala al "
+        "número de filas y no tienen nulos. Son candidatas, no una clave "
+        "declarada.")
 
     rows = []
     for name in keys:
@@ -320,10 +320,10 @@ def _inter_table_section(db_path: str, tables: list, mark: bool) -> list:
     blocks = [
         model.Heading(text="Claves foráneas candidatas (inter-tabla)", level=2),
         model.Markdown(text=(
-            f"La fuente tiene varias tablas. Estas {fk_term} candidatas se infieren "
-            f"por señal de nombre y por {containment}: una columna de una tabla cuyos "
-            "valores están contenidos en la clave de otra. No están declaradas por "
-            "la base; son la relación más probable según los datos.")),
+            f"La fuente tiene varias tablas. Estas {fk_term} candidatas se "
+            f"infieren por señal de nombre y por {containment}. No están "
+            "declaradas por la base; son la relación más probable según los "
+            "datos.")),
     ]
 
     shown = candidates[:MAX_FK_ROWS]
@@ -441,13 +441,12 @@ def _intro_blocks(mark: bool) -> list:
     pk = "[[term:pk]]clave primaria[[/term]]" if mark else "clave primaria"
     fk = "[[term:fk]]clave foránea[[/term]]" if mark else "clave foránea"
     text = (
-        f"Este capítulo analiza las **relaciones de clave** de la tabla: qué columna "
-        f"identifica cada fila (la {pk}) y qué columnas referencian a otra tabla (las "
-        f"{fk}). Cuando la base las **declara** como restricciones del esquema, se "
-        "muestran tal cual; cuando no, se proponen las más probables a partir de los "
-        "datos —por inclusión de valores entre tablas (containment) o, en una sola "
-        "tabla, por una heurística de nombre y cardinalidad— siempre marcadas como "
-        "candidatas, nunca como hechos.")
+        f"Este capítulo analiza las **relaciones de clave** de la tabla: cuál es "
+        f"la {pk} y cuáles son las {fk}. Cuando la base las **declara** como "
+        "restricciones del esquema, se muestran tal cual; cuando no, se proponen "
+        "las más probables a partir de los datos —por containment entre tablas o, "
+        "en una sola tabla, por una heurística de nombre y cardinalidad— siempre "
+        "marcadas como candidatas, nunca como hechos.")
     return [model.Heading(text=CHAPTER_TITLE, level=1), model.Markdown(text=text)]
 
 

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

@@ -0,0 +1,559 @@
+"""Free-text / NLP distributions chapter (TEXT DISTR) for AutomaticEDA.
+
+First chapter for **non-tabular** content: it profiles the linguistic content of
+any column holding long free text (reviews, descriptions, comments, tickets) that
+the categorical chapter cannot meaningfully summarize (high cardinality, many
+words per value). It is the cheap, model-free counterpart to ``cat_distr`` for
+columns that are prose rather than discrete labels.
+
+Activation (returns ``None`` when it does not apply):
+
+1. Cheap gate from the aggregated profile: at least one non-numeric column whose
+   ``categorical.len_mean`` (mean character length) is ``>= _MIN_LEN_CHARS``.
+   A dataset whose only string columns are short labels (e.g. titanic's
+   ``Name``, ~27 chars) never passes this gate, so the chapter disappears with
+   zero extra work and the existing report is untouched.
+2. Confirmation from a raw sample: each candidate column is sampled (push-down
+   ``extract_text_sample`` over ``ctx['db_path']``/``ctx['table']``, or an
+   in-memory ``ctx['text_raw']`` for tests) and kept only if the **median word
+   count is ``>= _MIN_WORDS``** — i.e. it is genuinely long text, not a long
+   single token. If no column survives, the chapter returns ``None``.
+
+Per surviving column the chapter emits, kept together on its own page/slide
+(``Group(page_break_before=...)``):
+
+- a key/value summary (documents, length percentiles, vocabulary richness with
+  **[[term:ttr]]TTR[[/term]]** and **[[term:hapax]]hapax legomena[[/term]]**,
+  dominant language, exact-duplicate %, readability when available);
+- a word-count histogram figure;
+- a top-terms table + a horizontal bar figure;
+- bigram and trigram frequency tables;
+- a detected-language bar figure (when ``langdetect`` is available);
+- an optional word-cloud figure (only when ``wordcloud`` is installed);
+- a closing note on duplicates / readability degradation.
+
+Every metric is delegated to pure ``eda`` registry functions
+(``compute_text_length_stats``, ``compute_vocabulary_stats``,
+``compute_top_ngrams``, ``detect_corpus_language``, ``compute_text_duplicates``,
+``compute_text_readability``) and the raw sample to ``extract_text_sample``; all
+are imported defensively so a missing function or optional library degrades that
+single piece to a note instead of aborting the chapter. Optional libraries
+(``langdetect``, ``textstat``, ``wordcloud``, ``datasketch``) are never required:
+the piece is silently omitted when they are absent.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+"""
+
+from __future__ import annotations
+
+from .. import model
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "text_distr"
+CHAPTER_TITLE = "Texto libre (NLP)"
+
+# Cheap activation gate (characters): a non-numeric column whose mean string
+# length reaches this is a candidate for "long text". Short labels (titanic's
+# Name ≈ 27 chars) stay below it, so the chapter does not fire on them.
+_MIN_LEN_CHARS = 50
+# Confirmation gate (words): a candidate is kept only if its median document has
+# at least this many words — genuine prose, not a long id/URL token.
+_MIN_WORDS = 20
+# Bound the document so very wide datasets stay readable.
+_MAX_TEXT_COLS = 5
+# Raw text rows to sample per column when the chapter must extract them itself.
+_SAMPLE_ROWS = 2000
+# Rows shown in the frequency tables.
+_TOP_TERMS = 15
+_TOP_NGRAMS = 10
+
+# Glossary terms this chapter explains (registered in the shared collector and
+# marked clickable on first appearance — same mechanism as cat_distr's entropía).
+_TERMS = {
+    "ttr": (
+        "TTR (type-token ratio)",
+        "Riqueza léxica de un texto: número de palabras distintas (tipos) "
+        "dividido por el número total de palabras (tokens). Vale 1 cuando no se "
+        "repite ninguna palabra (máxima variedad) y baja hacia 0 cuando el "
+        "vocabulario se repite mucho. Depende de la longitud del corpus, así que "
+        "compara mejor textos de tamaño parecido."),
+    "hapax": (
+        "Hapax legomena",
+        "Palabras que aparecen una sola vez en todo el corpus. Un porcentaje "
+        "alto de hapax indica vocabulario muy variado o, a veces, ruido "
+        "(erratas, identificadores, tokens raros). Se expresa como porcentaje "
+        "sobre el número de palabras distintas."),
+}
+
+
+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 = 2) -> 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, decimals: int = 1) -> str:
+    if value is None:
+        return "—"
+    try:
+        return f"{float(value):.{decimals}f}%"
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def _truncate(text, limit: int = 40) -> str:
+    s = model._safe_str(text)
+    return s if len(s) <= limit else s[: max(1, limit - 1)].rstrip() + "…"
+
+
+# --------------------------------------------------------------------------- #
+# Defensive wrappers around the registry functions: each returns the function's
+# output dict or a safe empty default, never raising and never importing at
+# module load (so the chapter stays importable even if a function is missing).
+# --------------------------------------------------------------------------- #
+def _length_stats(texts) -> dict:
+    try:
+        from datascience.compute_text_length_stats import compute_text_length_stats
+        out = compute_text_length_stats(texts)
+        if isinstance(out, dict):
+            return out
+    except Exception:  # noqa: BLE001
+        pass
+    return {}
+
+
+def _vocab_stats(texts) -> dict:
+    try:
+        from datascience.compute_vocabulary_stats import compute_vocabulary_stats
+        out = compute_vocabulary_stats(texts, top_k=_TOP_TERMS)
+        if isinstance(out, dict):
+            return out
+    except Exception:  # noqa: BLE001
+        pass
+    return {}
+
+
+def _ngrams(texts, n) -> list:
+    try:
+        from datascience.compute_top_ngrams import compute_top_ngrams
+        out = compute_top_ngrams(texts, n=n, top_k=_TOP_NGRAMS)
+        if isinstance(out, dict):
+            return out.get("top") or []
+    except Exception:  # noqa: BLE001
+        pass
+    return []
+
+
+def _language(texts) -> dict:
+    try:
+        from datascience.detect_corpus_language import detect_corpus_language
+        out = detect_corpus_language(texts)
+        if isinstance(out, dict):
+            return out
+    except Exception:  # noqa: BLE001
+        pass
+    return {"available": False, "distribution": [], "dominant": None}
+
+
+def _duplicates(texts) -> dict:
+    try:
+        from datascience.compute_text_duplicates import compute_text_duplicates
+        out = compute_text_duplicates(texts)
+        if isinstance(out, dict):
+            return out
+    except Exception:  # noqa: BLE001
+        pass
+    return {}
+
+
+def _readability(texts) -> dict:
+    try:
+        from datascience.compute_text_readability import compute_text_readability
+        out = compute_text_readability(texts)
+        if isinstance(out, dict):
+            return out
+    except Exception:  # noqa: BLE001
+        pass
+    return {"available": False, "flesch": {}}
+
+
+# --------------------------------------------------------------------------- #
+# Candidate detection + raw sample acquisition.
+# --------------------------------------------------------------------------- #
+def _candidate_columns(profile: dict) -> list:
+    """Cheap gate: non-numeric columns whose mean char length reaches the
+    threshold. Returns the list of column names (possibly empty)."""
+    out = []
+    for col in profile.get("columns") or []:
+        if not isinstance(col, dict):
+            continue
+        if col.get("inferred_type") == "numeric":
+            continue
+        cat = col.get("categorical")
+        if not isinstance(cat, dict):
+            continue
+        len_mean = cat.get("len_mean")
+        if isinstance(len_mean, (int, float)) and not isinstance(len_mean, bool) \
+                and len_mean >= _MIN_LEN_CHARS:
+            name = col.get("name")
+            if name:
+                out.append(str(name))
+    return out
+
+
+def _get_samples(profile: dict, ctx: dict, columns: list) -> dict:
+    """Return {col: [str, ...]} raw text samples for the candidate columns.
+
+    Prefers an in-memory ``ctx['text_raw']`` (used by tests); otherwise pushes a
+    sample down to the database via ``extract_text_sample`` using ctx db_path /
+    table. Never raises: returns {} when no sample can be obtained."""
+    text_raw = ctx.get("text_raw")
+    if isinstance(text_raw, dict) and text_raw:
+        return {c: [str(v) for v in (text_raw.get(c) or []) if v is not None]
+                for c in columns if text_raw.get(c)}
+
+    db_path = ctx.get("db_path")
+    table = ctx.get("table")
+    if not db_path or not table:
+        return {}
+    backend = ctx.get("backend") or "duckdb"
+    sample = ctx.get("sample") or _SAMPLE_ROWS
+    try:
+        from datascience.extract_text_sample import extract_text_sample
+        out = extract_text_sample(db_path, table, columns, backend=backend,
+                                  sample=sample)
+        if isinstance(out, dict) and out.get("status") == "ok":
+            cols = out.get("columns")
+            if isinstance(cols, dict):
+                return {c: list(v) for c, v in cols.items() if v}
+    except Exception:  # noqa: BLE001 — dict-no-throw: no sample → chapter omits.
+        pass
+    return {}
+
+
+def _confirm_long_text(samples: dict) -> dict:
+    """Keep only columns whose median word count reaches _MIN_WORDS. Returns
+    {col: length_stats_dict} for the survivors, in input order."""
+    survivors = {}
+    for col, texts in samples.items():
+        stats = _length_stats(texts)
+        words = stats.get("words") if isinstance(stats, dict) else None
+        median = words.get("p50") if isinstance(words, dict) else None
+        if isinstance(median, (int, float)) and not isinstance(median, bool) \
+                and median >= _MIN_WORDS:
+            survivors[col] = stats
+    return survivors
+
+
+# --------------------------------------------------------------------------- #
+# Figures (lazy matplotlib, scaled by the renderers — same style as num_distr).
+# --------------------------------------------------------------------------- #
+def _hist_figure(name: str, length_stats: dict):
+    def make():
+        import matplotlib
+        matplotlib.use("Agg")
+        from matplotlib.figure import Figure
+        fig = Figure(figsize=(6.2, 3.0))
+        ax = fig.add_subplot(111)
+        bins = (length_stats or {}).get("word_hist") or []
+        drew = False
+        for b in bins:
+            if not isinstance(b, dict):
+                continue
+            lo, hi, count = b.get("lo"), b.get("hi"), b.get("count") or 0
+            if lo is None or hi is None:
+                continue
+            width = (hi - lo) if hi > lo else max(abs(lo) * 1e-3, 1e-6)
+            ax.bar(lo, count, width=width, align="edge", color="#9ec6df",
+                   edgecolor="#5b8aa6", linewidth=0.4)
+            drew = True
+        if not drew:
+            ax.text(0.5, 0.5, "(sin datos de longitud)", ha="center",
+                    va="center", color="#8a8a8a", transform=ax.transAxes)
+        ax.set_xlabel("palabras por documento", fontsize=8)
+        ax.set_ylabel("nº de documentos", fontsize=8)
+        ax.tick_params(labelsize=7)
+        for spine in ("top", "right"):
+            ax.spines[spine].set_visible(False)
+        ax.set_title(f"Longitud de «{_truncate(name, 30)}»", fontsize=10,
+                     loc="left")
+        fig.tight_layout()
+        return fig
+    return make
+
+
+def _barh_figure(title: str, items: list, label_key: str, value_key: str,
+                 xlabel: str):
+    """Horizontal bar chart from [{label_key:..., value_key:...}, ...]."""
+    def make():
+        import matplotlib
+        matplotlib.use("Agg")
+        from matplotlib.figure import Figure
+        rows = [it for it in (items or []) if isinstance(it, dict)
+                and isinstance(it.get(value_key), (int, float))]
+        rows = rows[:12]
+        fig = Figure(figsize=(6.2, max(2.2, 0.32 * len(rows) + 0.8)))
+        ax = fig.add_subplot(111)
+        if not rows:
+            ax.text(0.5, 0.5, "(sin datos)", ha="center", va="center",
+                    color="#8a8a8a", transform=ax.transAxes)
+            ax.axis("off")
+            return fig
+        labels = [_truncate(r.get(label_key), 28) for r in rows][::-1]
+        values = [float(r.get(value_key) or 0) for r in rows][::-1]
+        ypos = range(len(rows))
+        ax.barh(list(ypos), values, color="#9ec6df", edgecolor="#5b8aa6",
+                linewidth=0.4)
+        ax.set_yticks(list(ypos))
+        ax.set_yticklabels(labels, fontsize=7)
+        ax.set_xlabel(xlabel, fontsize=8)
+        ax.tick_params(labelsize=7)
+        for spine in ("top", "right"):
+            ax.spines[spine].set_visible(False)
+        ax.set_title(_truncate(title, 44), fontsize=10, loc="left")
+        fig.tight_layout()
+        return fig
+    return make
+
+
+def _wordcloud_figure(texts):
+    """Word-cloud figure callable, or None if wordcloud is not installed."""
+    try:
+        import wordcloud  # noqa: F401
+    except Exception:  # noqa: BLE001 — optional dependency: omit the figure.
+        return None
+
+    def make():
+        import matplotlib
+        matplotlib.use("Agg")
+        from matplotlib.figure import Figure
+        from wordcloud import WordCloud
+        fig = Figure(figsize=(6.2, 3.2))
+        ax = fig.add_subplot(111)
+        joined = " ".join(t for t in texts if isinstance(t, str))
+        try:
+            wc = WordCloud(width=800, height=400, background_color="white",
+                           colormap="viridis").generate(joined)
+            ax.imshow(wc, interpolation="bilinear")
+        except Exception:  # noqa: BLE001
+            ax.text(0.5, 0.5, "(nube de palabras no disponible)", ha="center",
+                    va="center", color="#8a8a8a", transform=ax.transAxes)
+        ax.axis("off")
+        fig.tight_layout()
+        return fig
+    return make
+
+
+# --------------------------------------------------------------------------- #
+# Per-column block assembly.
+# --------------------------------------------------------------------------- #
+def _summary_kv(n_docs, length_stats, vocab, lang, dup, read):
+    chars = (length_stats or {}).get("chars") or {}
+    words = (length_stats or {}).get("words") or {}
+    sents = (length_stats or {}).get("sentences") or {}
+    rows = [
+        ("Documentos", _fmt_int(n_docs)),
+        ("Caracteres (media · p50 · p90 · p99)",
+         f"{_fmt_num(chars.get('mean'))} · {_fmt_int(chars.get('p50'))} · "
+         f"{_fmt_int(chars.get('p90'))} · {_fmt_int(chars.get('p99'))}"),
+        ("Palabras (media · p50 · p90 · p99)",
+         f"{_fmt_num(words.get('mean'))} · {_fmt_int(words.get('p50'))} · "
+         f"{_fmt_int(words.get('p90'))} · {_fmt_int(words.get('p99'))}"),
+        ("Frases (media · máx)",
+         f"{_fmt_num(sents.get('mean'))} · {_fmt_int(sents.get('max'))}"),
+        ("Vocabulario (tokens · tipos · TTR)",
+         f"{_fmt_int(vocab.get('n_tokens'))} · {_fmt_int(vocab.get('n_types'))} "
+         f"· {_fmt_num(vocab.get('ttr'), 3)}"),
+        ("Hapax legomena",
+         f"{_fmt_int(vocab.get('n_hapax'))} ({_fmt_pct(vocab.get('hapax_pct'))})"),
+    ]
+    if isinstance(lang, dict) and lang.get("available"):
+        dom = lang.get("dominant")
+        n_langs = len(lang.get("distribution") or [])
+        rows.append(("Idioma dominante · nº idiomas",
+                     f"{model._safe_str(dom) or '—'} · {_fmt_int(n_langs)}"))
+    if isinstance(dup, dict) and dup.get("n_docs"):
+        rows.append(("Duplicados exactos",
+                     f"{_fmt_int(dup.get('n_exact_dup'))} "
+                     f"({_fmt_pct(dup.get('exact_dup_pct'))})"))
+    if isinstance(read, dict) and read.get("available"):
+        flesch = read.get("flesch") or {}
+        rows.append(("Legibilidad Flesch (media)",
+                     _fmt_num(flesch.get("mean"), 1)))
+    return model.KVTable(rows=rows, title="Resumen del texto")
+
+
+def _terms_table(vocab) -> "model.DataTable | None":
+    top = (vocab or {}).get("top_terms") or []
+    rows = [[_truncate(t.get("term"), 32), _fmt_int(t.get("count")),
+             _fmt_pct(t.get("pct"))]
+            for t in top[:_TOP_TERMS] if isinstance(t, dict)]
+    if not rows:
+        return None
+    return model.DataTable(header=["Término", "Conteo", "% tokens"], rows=rows,
+                           title="Términos más frecuentes",
+                           note="stopwords ES+EN eliminadas")
+
+
+def _ngram_table(items, n_label) -> "model.DataTable | None":
+    rows = [[_truncate(it.get("ngram"), 40), _fmt_int(it.get("count"))]
+            for it in (items or [])[:_TOP_NGRAMS] if isinstance(it, dict)]
+    if not rows:
+        return None
+    return model.DataTable(header=[n_label, "Conteo"], rows=rows,
+                           title=f"{n_label} más frecuentes")
+
+
+def _dup_note(dup, lang, read) -> "model.Note | None":
+    bits = []
+    if isinstance(dup, dict):
+        nd = dup.get("near_dup") or {}
+        if nd.get("available"):
+            bits.append(
+                f"casi-duplicados detectados (MinHash, umbral "
+                f"{_fmt_num(nd.get('threshold'))}): "
+                f"{_fmt_int(nd.get('n_near_dup_docs'))} documentos")
+        else:
+            bits.append("near-duplicados no calculados (datasketch no instalado; "
+                        "se reportan solo los duplicados exactos por hash)")
+    if isinstance(lang, dict) and not lang.get("available"):
+        bits.append("detección de idioma omitida (langdetect no instalado)")
+    if isinstance(read, dict) and not read.get("available"):
+        bits.append("legibilidad omitida (textstat no instalado)")
+    if not bits:
+        return None
+    return model.Note(" · ".join(bits))
+
+
+def _column_group(name, texts, length_stats, idx, mark_terms):
+    vocab = _vocab_stats(texts)
+    lang = _language(texts)
+    dup = _duplicates(texts)
+    read = _readability(texts)
+    n_docs = (length_stats or {}).get("n_docs")
+
+    blocks = [
+        model.Heading(text=str(name), level=2),
+        _summary_kv(n_docs, length_stats, vocab, lang, dup, read),
+        model.Figure(make=_hist_figure(name, length_stats),
+                     caption=f"Distribución de la longitud (palabras) de "
+                             f"«{_truncate(name, 30)}»."),
+    ]
+
+    terms_tbl = _terms_table(vocab)
+    if terms_tbl is not None:
+        blocks.append(terms_tbl)
+        blocks.append(model.Figure(
+            make=_barh_figure(f"Top términos de «{_truncate(name, 24)}»",
+                              vocab.get("top_terms"), "term", "count",
+                              "conteo"),
+            caption="Términos más frecuentes (barras)."))
+
+    bi_tbl = _ngram_table(_ngrams(texts, 2), "Bigrama")
+    if bi_tbl is not None:
+        blocks.append(bi_tbl)
+    tri_tbl = _ngram_table(_ngrams(texts, 3), "Trigrama")
+    if tri_tbl is not None:
+        blocks.append(tri_tbl)
+
+    if isinstance(lang, dict) and lang.get("available") \
+            and lang.get("distribution"):
+        blocks.append(model.Figure(
+            make=_barh_figure(f"Idiomas detectados en «{_truncate(name, 24)}»",
+                              lang.get("distribution"), "lang", "count",
+                              "documentos"),
+            caption="Distribución de idiomas detectados (langdetect)."))
+
+    wc = _wordcloud_figure(texts)
+    if wc is not None:
+        blocks.append(model.Figure(
+            make=wc, caption=f"Nube de palabras de «{_truncate(name, 30)}»."))
+
+    note = _dup_note(dup, lang, read)
+    if note is not None:
+        blocks.append(note)
+
+    return model.Group(blocks=blocks, page_break_before=(idx > 0))
+
+
+def _intro_blocks(n_cols, mark_terms):
+    ttr = ("[[term:ttr]]TTR[[/term]]" if mark_terms else "TTR")
+    hapax = ("[[term:hapax]]hapax legomena[[/term]]" if mark_terms
+             else "hapax legomena")
+    text = (
+        f"Este capítulo perfila las columnas de **texto libre largo** del "
+        f"dataset (reseñas, descripciones, comentarios): contenido lingüístico "
+        f"que la distribución categórica no resume bien. Para cada columna se "
+        f"muestran la longitud de los documentos, la riqueza de vocabulario "
+        f"(incluido el {ttr} y el porcentaje de {hapax}), los términos y "
+        f"n-gramas más frecuentes, los idiomas detectados y el nivel de "
+        f"duplicación. Las métricas son baratas y sin modelos pesados; las "
+        f"piezas que dependen de una librería opcional se omiten si no está "
+        f"instalada.")
+    return [
+        model.Heading(text=CHAPTER_TITLE, level=1),
+        model.Markdown(text=text),
+    ]
+
+
+def build_text_distr(profile: dict, ctx: dict):
+    """Build the free-text Chapter, or None if no long-text column applies."""
+    profile = profile or {}
+    ctx = ctx or {}
+
+    # 1) Cheap gate from the profile (no DB access yet).
+    candidates = _candidate_columns(profile)
+    if not candidates:
+        return None
+
+    # 2) Raw sample + 3) confirm genuine long text (median words >= threshold).
+    samples = _get_samples(profile, ctx, candidates)
+    if not samples:
+        return None
+    survivors = _confirm_long_text(samples)
+    if not survivors:
+        return None
+
+    # Register glossary terms (clickable) once we know the chapter applies.
+    glossary = ctx.get("glossary")
+    mark_terms = False
+    if isinstance(glossary, model.GlossaryCollector):
+        for key, (label, definition) in _TERMS.items():
+            glossary.add(key, label, definition)
+        mark_terms = True
+
+    blocks = list(_intro_blocks(len(survivors), mark_terms))
+
+    rendered = list(survivors.items())[:_MAX_TEXT_COLS]
+    for idx, (name, length_stats) in enumerate(rendered):
+        texts = samples.get(name) or []
+        blocks.append(_column_group(name, texts, length_stats, idx, mark_terms))
+
+    if len(survivors) > len(rendered):
+        omitted = len(survivors) - len(rendered)
+        blocks.append(model.Note(
+            f"Se muestran las primeras {len(rendered)} columnas de texto; "
+            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/text_distr_test.py

@@ -0,0 +1,256 @@
+"""Tests for the TEXT DISTR chapter — DoD: golden + edges + degradation.
+
+Self-contained: builds synthetic TableProfiles and feeds the raw text sample
+in-memory through ``ctx['text_raw']`` (no DuckDB needed), so the suite is fast
+and deterministic. Verifies that ``build_text_distr``:
+
+- GOLDEN: with a long-text column, emits the chapter with its key blocks
+  (length summary, word histogram, top-terms table, n-gram tables, language
+  bars) and registers the clickable glossary terms; and that it renders inside
+  the full document to both PDF and PPTX showing that content.
+- EDGE (None): a dataset whose only string column is short labels (titanic-like
+  ``Name``) yields ``None`` without raising — the existing report is untouched.
+- EDGE (None): a column that passes the cheap char gate but whose documents are
+  short (median words below the threshold) is rejected at the confirmation step.
+- DEGRADATION: with ``langdetect`` / ``textstat`` / ``wordcloud`` unavailable,
+  the chapter still builds (those pieces are omitted) and never raises.
+"""
+
+import builtins
+import os
+import tempfile
+
+from pypdf import PdfReader
+from pptx import Presentation
+
+from datascience.automatic_eda.model import (
+    DataTable, Figure, GlossaryCollector, Group, Heading, KVTable, Markdown,
+    Note,
+)
+from datascience.automatic_eda.chapters.text_distr import (
+    CHAPTER_ID, CHAPTER_VERSION, build_text_distr,
+)
+from datascience.automatic_eda.chapters_registry import build_document
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+# --------------------------------------------------------------------------- #
+# Synthetic corpus + profiles.
+# --------------------------------------------------------------------------- #
+_ES = [
+    "El producto llegó en perfecto estado y mucho antes de lo previsto por la tienda",
+    "La calidad de los materiales es realmente excelente y se nota la diferencia al usarlo",
+    "No me convenció del todo porque esperaba bastante más por el precio que pagué finalmente",
+    "El servicio de atención al cliente fue rápido amable y resolvió mi problema sin demora",
+    "Lo recomiendo totalmente ya que ha superado con creces todas mis expectativas iniciales",
+]
+_EN = [
+    "The product arrived in perfect condition and much earlier than the store had promised me",
+    "The build quality is genuinely outstanding and you can really feel the difference using it",
+    "I was not fully convinced because I expected quite a lot more for the price i finally paid",
+    "Customer support was fast friendly and solved my whole problem without any delay at all",
+    "I highly recommend it since it has exceeded by far every one of my initial expectations",
+]
+
+
+def _long_reviews(n=40) -> list:
+    """A corpus of long multi-sentence reviews (>= 20 words each), mixing two
+    languages and including a few exact duplicates."""
+    out = []
+    for i in range(n):
+        base = _ES if i % 3 != 0 else _EN  # mostly ES, some EN
+        a = base[i % len(base)]
+        b = base[(i + 2) % len(base)]
+        out.append(f"{a}. {b}.")
+    # Inject a couple of exact duplicates.
+    out.append(out[0])
+    out.append(out[1])
+    return out
+
+
+def _text_profile() -> dict:
+    """Profile with a long free-text column (review) + a numeric + a short cat."""
+    return {
+        "table": "reviews",
+        "source": "/data/reviews.duckdb",
+        "profiled_at": "2026-06-30T10:00:00+00:00",
+        "n_rows": 42,
+        "n_cols": 3,
+        "quality_score": 88.0,
+        "columns": [
+            {
+                "name": "review",
+                "inferred_type": "categorical",
+                "categorical": {
+                    "top": [{"value": "x", "count": 2, "pct": 0.05}],
+                    "n_distinct": 40,
+                    "len_mean": 180.0,
+                    "len_min": 80,
+                    "len_max": 220,
+                },
+            },
+            {
+                "name": "rating",
+                "inferred_type": "numeric",
+                "numeric": {"mean": 3.1, "median": 3.0, "std": 1.2,
+                            "min": 1, "max": 5},
+            },
+            {
+                "name": "product",
+                "inferred_type": "categorical",
+                "categorical": {
+                    "top": [{"value": "teclado", "count": 10, "pct": 0.25}],
+                    "n_distinct": 6,
+                    "len_mean": 7.0,
+                    "len_min": 5, "len_max": 11,
+                },
+            },
+        ],
+    }
+
+
+def _no_text_profile() -> dict:
+    """titanic-like: the only string column is short labels (Name ≈ 27 chars)."""
+    return {
+        "table": "titanic",
+        "n_rows": 891,
+        "n_cols": 3,
+        "columns": [
+            {"name": "Age", "inferred_type": "numeric",
+             "numeric": {"mean": 29.7, "median": 28.0, "std": 14.5}},
+            {"name": "Name", "inferred_type": "categorical",
+             "categorical": {"top": [{"value": "Braund, Mr. Owen Harris",
+                                      "count": 1, "pct": 0.001}],
+                             "n_distinct": 891, "len_mean": 27.0,
+                             "len_min": 12, "len_max": 82}},
+            {"name": "Sex", "inferred_type": "categorical",
+             "categorical": {"top": [{"value": "male", "count": 577,
+                                      "pct": 0.65}],
+                             "n_distinct": 2, "len_mean": 4.6,
+                             "len_min": 4, "len_max": 6}},
+        ],
+    }
+
+
+def _flatten(blocks) -> list:
+    """Recursively flatten Group blocks so tests can inspect leaf blocks."""
+    out = []
+    for b in blocks:
+        if isinstance(b, Group):
+            out.extend(_flatten(b.blocks))
+        else:
+            out.append(b)
+    return out
+
+
+# --------------------------------------------------------------------------- #
+# Golden.
+# --------------------------------------------------------------------------- #
+def test_golden_activa_con_texto():
+    glossary = GlossaryCollector()
+    ctx = {"text_raw": {"review": _long_reviews()}, "glossary": glossary}
+    ch = build_text_distr(_text_profile(), ctx)
+
+    assert ch is not None, "el capítulo debe activarse con una columna de texto largo"
+    assert ch.id == CHAPTER_ID
+    assert ch.version == CHAPTER_VERSION
+    leaves = _flatten(ch.blocks)
+    kinds = [b.kind for b in leaves]
+    assert "heading" in kinds
+    assert "kv_table" in kinds          # summary
+    assert "figure" in kinds            # histogram / bars
+    assert "data_table" in kinds        # top terms + n-grams
+
+    # KV summary mentions vocabulary metrics.
+    kv = next(b for b in leaves if isinstance(b, KVTable))
+    labels = " ".join(str(r[0]) for r in kv.rows)
+    assert "TTR" in labels
+    assert "Hapax" in labels or "hapax" in labels
+
+    # There is a terms table and at least one n-gram table.
+    titles = [getattr(b, "title", "") or "" for b in leaves
+              if isinstance(b, DataTable)]
+    assert any("Términos" in t for t in titles)
+    assert any("Bigrama" in t for t in titles)
+
+    # Glossary terms were registered (clickable destinations).
+    assert glossary.has("ttr")
+    assert glossary.has("hapax")
+
+
+def test_golden_render_pdf_pptx():
+    profile = _text_profile()
+    ctx = {"text_raw": {"review": _long_reviews()},
+           "dataset_name": "reviews"}
+    chapters = build_document(profile, ctx)
+    ids = [c.id for c in chapters]
+    assert "text_distr" in ids, f"text_distr ausente en {ids}"
+
+    with tempfile.TemporaryDirectory() as d:
+        pdf = os.path.join(d, "t.pdf")
+        pptx = os.path.join(d, "t.pptx")
+        rp = render_automatic_eda_pdf(profile, pdf, {"title": "EDA", "ctx": ctx})
+        rx = render_automatic_eda_pptx(profile, pptx, {"title": "EDA", "ctx": ctx})
+        assert rp.get("path") and os.path.exists(pdf)
+        assert rx.get("path") and os.path.exists(pptx)
+
+        text = "\n".join(p.extract_text() or "" for p in PdfReader(pdf).pages)
+        assert "Texto libre" in text or "TTR" in text
+
+        prs = Presentation(pptx)
+        ptext = []
+        for slide in prs.slides:
+            for shp in slide.shapes:
+                if shp.has_text_frame:
+                    ptext.append(shp.text_frame.text)
+        joined = "\n".join(ptext)
+        assert "Texto libre" in joined or "TTR" in joined
+
+
+# --------------------------------------------------------------------------- #
+# Edges — None.
+# --------------------------------------------------------------------------- #
+def test_edge_none_sin_texto_largo():
+    # titanic-like: short labels only → chapter must not apply.
+    assert build_text_distr(_no_text_profile(), {}) is None
+
+
+def test_edge_none_palabras_cortas():
+    # Char gate passes (len_mean high) but documents are short → confirmation
+    # rejects them (median words below threshold).
+    profile = _text_profile()
+    short = ["palabra " * 3] * 30  # 3 words each, < _MIN_WORDS
+    ctx = {"text_raw": {"review": short}}
+    assert build_text_distr(profile, ctx) is None
+
+
+def test_edge_none_empty_profile():
+    assert build_text_distr({}, {}) is None
+    assert build_text_distr(None, None) is None
+
+
+# --------------------------------------------------------------------------- #
+# Degradation — optional libs absent.
+# --------------------------------------------------------------------------- #
+def test_degradacion_sin_libs(monkeypatch):
+    real_import = builtins.__import__
+    blocked = ("langdetect", "textstat", "wordcloud", "datasketch")
+
+    def fake_import(name, *a, **k):
+        if name in blocked or any(name.startswith(b + ".") for b in blocked):
+            raise ImportError(f"simulado: {name}")
+        return real_import(name, *a, **k)
+
+    monkeypatch.setattr(builtins, "__import__", fake_import)
+
+    ctx = {"text_raw": {"review": _long_reviews()}}
+    ch = build_text_distr(_text_profile(), ctx)
+    # Still builds (the cheap, stdlib-only pieces remain) and never raises.
+    assert ch is not None
+    leaves = _flatten(ch.blocks)
+    assert any(isinstance(b, KVTable) for b in leaves)
+    assert any(isinstance(b, DataTable) for b in leaves)
+    # A degradation note is present mentioning the missing optional libs.
+    notes = " ".join(b.text for b in leaves if isinstance(b, Note))
+    assert "langdetect" in notes or "textstat" in notes or "datasketch" in notes

python/functions/datascience/automatic_eda/chapters_registry.py

@@ -31,6 +31,7 @@ CHAPTER_ORDER = [
     "analisis_llm",  # LLM interpretation — sits next to overview (user request)
     "num_distr",     # numeric distributions
     "cat_distr",     # categorical distributions
+    "text_distr",    # free-text / NLP distributions (non-tabular content)
     "calidad",       # data quality
     "correlacion",   # correlations / associations
     "relaciones",    # key relations: declared/candidate PK + FK (inter/intra-table)

python/functions/datascience/automatic_eda/render_md_impl.py

@@ -0,0 +1,458 @@
+"""AutomaticEDA Markdown serializer — one self-contained file to paste to an LLM.
+
+Same document model as the PDF/PPTX renderers (an ordered list of
+:class:`Chapter`, each a list of format-independent blocks) but emitted as plain
+**Markdown** instead of a binary. The goal is different from the other two
+renderers: a Markdown EDA is meant to be *pasted into an LLM*, so it prioritises
+TEXT and DATA over visuals. Tables become Markdown tables (every row dumped, no
+pagination — nothing is cut because there are no pages); a ``Figure`` becomes its
+caption plus, when possible, the underlying bar/histogram data as a Markdown
+table (an LLM cannot see the image); glossary term markers are stripped while
+``**bold**`` is kept (it is valid Markdown).
+
+dict-no-throw (the ``eda`` group style): :func:`render_md` never raises. On a
+fatal error it returns ``{path: None, ...}`` with a ``note`` explaining why; a
+malformed block degrades to a readable note rather than crashing the document.
+"""
+
+from __future__ import annotations
+
+import os
+import re
+
+from . import model
+
+# Glossary span markers (kept text, dropped markers). We intentionally do NOT use
+# ``text_layout.strip_inline_md`` for Markdown blocks because that also removes
+# ``**bold**`` — valid Markdown we want to preserve when pasting to an LLM.
+_TERM_OPEN_RE = re.compile(r"\[\[term:[A-Za-z0-9_]+\]\]")
+_MAX_BAR_ROWS = 100
+
+
+# --------------------------------------------------------------------------- #
+# Small helpers.
+# --------------------------------------------------------------------------- #
+def _clean_terms(s) -> str:
+    """Drop glossary term markers, keeping the visible text (and any **bold**)."""
+    s = model._safe_str(s)
+    s = _TERM_OPEN_RE.sub("", s)
+    return s.replace("[[/term]]", "")
+
+
+def _cell(v) -> str:
+    """Render a value as a safe Markdown table cell.
+
+    Escapes pipes (``|`` -> ``\\|``) so they do not break the column layout and
+    folds newlines to ``<br>`` so a multi-line value stays inside one cell. None
+    becomes an empty string.
+    """
+    s = model._safe_str(v)
+    s = s.replace("|", "\\|")
+    s = s.replace("\r\n", "\n").replace("\r", "\n").replace("\n", "<br>")
+    return s
+
+
+def _slug(text: str) -> str:
+    """GitHub-style heading anchor: lowercase, spaces->'-', drop other symbols."""
+    s = model._safe_str(text).strip().lower()
+    out = []
+    for ch in s:
+        if ch.isalnum():
+            out.append(ch)
+        elif ch in " -":
+            out.append("-")
+        # any other symbol is dropped.
+    slug = "".join(out)
+    while "--" in slug:
+        slug = slug.replace("--", "-")
+    return slug.strip("-")
+
+
+def _fmt_num(v) -> str:
+    """Compact number for the figure data tables (ints as ints, else 4 sig figs)."""
+    try:
+        f = float(v)
+    except Exception:  # noqa: BLE001
+        return model._safe_str(v)
+    if f != f:  # NaN
+        return "NaN"
+    if f == int(f) and abs(f) < 1e15:
+        return str(int(f))
+    return f"{f:.4g}"
+
+
+def _fmt_int(v) -> str:
+    try:
+        return str(int(v))
+    except Exception:  # noqa: BLE001
+        return model._safe_str(v)
+
+
+def _now_iso() -> str:
+    from datetime import datetime, timezone
+    return datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
+
+
+# --------------------------------------------------------------------------- #
+# Document header (title + metadata blockquote + numbered index).
+# --------------------------------------------------------------------------- #
+def _meta_block(meta: dict) -> list:
+    """Build the metadata lines for the header blockquote (omitting absentees)."""
+    ctx = meta.get("ctx") if isinstance(meta.get("ctx"), dict) else {}
+    lines: list = []
+
+    def add(label, value) -> None:
+        if value is None:
+            return
+        s = model._safe_str(value).strip()
+        if s and s.lower() != "none":
+            lines.append(f"**{label}:** {s}")
+
+    add("Dataset", ctx.get("dataset_name") or meta.get("dataset_name"))
+    add("Fuente", ctx.get("source_origin") or meta.get("source_origin"))
+    add("Almacenamiento", ctx.get("storage") or meta.get("storage"))
+    n_rows = ctx.get("n_rows", meta.get("n_rows"))
+    n_cols = ctx.get("n_cols", meta.get("n_cols"))
+    if n_rows is not None and n_cols is not None:
+        lines.append(
+            f"**Dimensiones:** {_fmt_int(n_rows)} filas × {_fmt_int(n_cols)} columnas")
+    add("Generado", meta.get("generated_at") or _now_iso())
+    lines.append(f"**Motor:** {model.ENGINE_NAME} v{model.ENGINE_VERSION}")
+    return lines
+
+
+# --------------------------------------------------------------------------- #
+# Per-block serializers. Each returns a Markdown string (no surrounding blanks;
+# the caller separates blocks with a blank line).
+# --------------------------------------------------------------------------- #
+def _md_heading(block) -> str:
+    level = int(getattr(block, "level", 1) or 1)
+    hashes = "#" * min(level + 2, 6)  # level1 -> ###; '#'/'##' reserved for doc/chapter.
+    text = _clean_terms(getattr(block, "text", "")).strip()
+    return f"{hashes} {text}"
+
+
+def _md_markdown(block) -> str:
+    # Keep the text verbatim, dropping only glossary markers (keep **bold**).
+    return _clean_terms(getattr(block, "text", "")).rstrip("\n")
+
+
+def _md_kv_table(block) -> str:
+    lines: list = []
+    title = getattr(block, "title", None)
+    if title:
+        lines.append(f"**{_clean_terms(title).strip()}**")
+        lines.append("")
+    lines.append("| Campo | Valor |")
+    lines.append("| --- | --- |")
+    for row in (getattr(block, "rows", []) or []):
+        try:
+            label, value = row[0], row[1]
+        except Exception:  # noqa: BLE001
+            label, value = row, ""
+        lines.append(f"| {_cell(label)} | {_cell(value)} |")
+    return "\n".join(lines)
+
+
+def _md_data_table(block) -> str:
+    lines: list = []
+    title = getattr(block, "title", None)
+    if title:
+        lines.append(f"**{_clean_terms(title).strip()}**")
+        lines.append("")
+    header = list(getattr(block, "header", []) or [])
+    rows = list(getattr(block, "rows", []) or [])
+    if not header:
+        ncol = max((len(r) for r in rows), default=1)
+        header = [f"col{i + 1}" for i in range(ncol)]
+    ncol = len(header)
+    lines.append("| " + " | ".join(_cell(h) for h in header) + " |")
+    lines.append("| " + " | ".join(["---"] * ncol) + " |")
+    for r in rows:  # dump every row — no pagination, nothing cut.
+        cells = [_cell(r[c]) if c < len(r) else "" for c in range(ncol)]
+        lines.append("| " + " | ".join(cells) + " |")
+    note = getattr(block, "note", None)
+    if note:
+        lines.append("")
+        lines.append(f"*{_clean_terms(note).strip()}*")
+    return "\n".join(lines)
+
+
+def _bars_table(bars: list) -> str:
+    """Render extracted bar/histogram data as a Markdown table (Desde/Hasta/Frec)."""
+    lines = ["| Desde | Hasta | Frecuencia |", "| --- | --- | --- |"]
+    shown = bars[:_MAX_BAR_ROWS]
+    for x0, x1, h in shown:
+        lines.append(f"| {_fmt_num(x0)} | {_fmt_num(x1)} | {_fmt_num(h)} |")
+    out = "\n".join(lines)
+    extra = len(bars) - len(shown)
+    if extra > 0:
+        out += f"\n\n*… ({extra} filas más)*"
+    return out
+
+
+def _extract_bars(fig) -> list:
+    """Collect (x_from, x_to, height) of the rectangular bars of a matplotlib fig.
+
+    Histogram / bar-chart bars are ``matplotlib.patches.Rectangle`` with positive
+    width and height; spines, legends and zero-area artists are skipped. Never
+    raises — returns ``[]`` on any problem.
+    """
+    bars: list = []
+    try:
+        for ax in fig.get_axes():
+            # Collect this axes' positive-area rectangles, then keep only the ones
+            # that look like actual histogram/bar bins. Reference shapes that
+            # matplotlib also stores in ``ax.patches`` — most notably the ``±1σ``
+            # band drawn by ``axvspan`` (a single rectangle far wider than a bin)
+            # and a lone Tukey boxplot box — would otherwise show up as fake
+            # "bins". A histogram axes has several near-equal-width bars, so we
+            # drop any rectangle whose width is more than twice the median width
+            # of that axes' rectangles (the σ-band spans many bins; uniform bins
+            # all sit at the median width and stay).
+            ax_bars: list = []
+            for patch in list(getattr(ax, "patches", []) or []):
+                try:
+                    w = patch.get_width()
+                    h = patch.get_height()
+                    x = patch.get_x()
+                except Exception:  # noqa: BLE001 — not a Rectangle-like patch.
+                    continue
+                if w and w > 0 and h and h > 0:
+                    ax_bars.append((x, x + w, h))
+            if len(ax_bars) >= 3:
+                widths = sorted(b[1] - b[0] for b in ax_bars)
+                median_w = widths[len(widths) // 2]
+                if median_w > 0:
+                    ax_bars = [b for b in ax_bars
+                               if (b[1] - b[0]) <= 2.0 * median_w]
+            bars.extend(ax_bars)
+    except Exception:  # noqa: BLE001
+        return []
+    return bars
+
+
+def _md_figure(block, meta: dict, out_path: str, counter: list) -> str:
+    """Serialize a Figure prioritising TEXT + DATA (an LLM cannot see the image).
+
+    Emits the caption, then — if the matplotlib figure has bars — a Markdown table
+    of the underlying (Desde, Hasta, Frecuencia) values. Optionally (when
+    ``meta['embed_figures']`` is True) also exports a PNG beside the .md and adds
+    an image link; off by default so the Markdown stays self-contained.
+    """
+    caption = model._safe_str(getattr(block, "caption", "")).strip()
+    parts = [f"*Figura: {caption}*" if caption else "*Figura*"]
+    fig = None
+    try:
+        import matplotlib
+        matplotlib.use("Agg")  # defensive: headless rasterization backend.
+        fig = getattr(block, "fig", None)
+        make = getattr(block, "make", None)
+        if fig is None and callable(make):
+            fig = make()
+        if fig is not None:
+            bars = _extract_bars(fig)
+            if bars:
+                parts.append(_bars_table(bars))
+            if meta.get("embed_figures"):
+                png = _embed_png(fig, out_path, counter)
+                if png:
+                    parts.append(f"![{caption}]({png})")
+    except Exception:  # noqa: BLE001 — a bad figure degrades to just its caption.
+        pass
+    finally:
+        if fig is not None:
+            try:
+                import matplotlib.pyplot as plt
+                plt.close(fig)
+            except Exception:  # noqa: BLE001
+                pass
+    return "\n\n".join(parts)
+
+
+def _embed_png(fig, out_path: str, counter: list) -> str:
+    """Export the figure to ``<basename>_figN.png`` beside the .md; return its name."""
+    try:
+        counter[0] += 1
+        base = os.path.splitext(os.path.basename(out_path))[0] or "figura"
+        name = f"{base}_fig{counter[0]}.png"
+        path = os.path.join(os.path.dirname(os.path.abspath(out_path)), name)
+        fig.savefig(path, format="png", dpi=120, bbox_inches="tight")
+        return name
+    except Exception:  # noqa: BLE001
+        return ""
+
+
+def _md_image(block) -> str:
+    path = model._safe_str(getattr(block, "path", ""))
+    caption = model._safe_str(getattr(block, "caption", "")).strip()
+    out = f"![{caption}]({path})"
+    if caption:
+        out += f"\n\n*{caption}*"
+    return out
+
+
+def _md_caption(block) -> str:
+    return f"*{_clean_terms(getattr(block, 'text', '')).strip()}*"
+
+
+def _md_note(block) -> str:
+    text = _clean_terms(getattr(block, "text", "")).strip()
+    lines = text.split("\n")
+    return "\n".join((f"> {ln}" if ln.strip() else ">") for ln in lines)
+
+
+def _md_group(block, meta: dict, out_path: str, counter: list) -> str:
+    parts: list = []
+    title = getattr(block, "title", None)
+    if title:
+        parts.append(f"### {_clean_terms(title).strip()}")
+    for b in (getattr(block, "blocks", []) or []):
+        try:
+            seg = _serialize_block(b, meta, out_path, counter)
+        except Exception:  # noqa: BLE001
+            seg = ""
+        if seg:
+            parts.append(seg)
+    return "\n\n".join(parts)
+
+
+def _md_glossary_entry(block) -> str:
+    label = (model._safe_str(getattr(block, "label", "")).strip()
+             or model._safe_str(getattr(block, "key", "")).strip())
+    definition = _clean_terms(getattr(block, "definition", "")).strip()
+    out = f"### {label}"
+    if definition:
+        out += f"\n\n{definition}"
+    return out
+
+
+def _serialize_block(block, meta: dict, out_path: str, counter: list) -> str:
+    """Dispatch a single block to its Markdown serializer. Unknown -> note."""
+    kind = getattr(block, "kind", "")
+    if kind == "heading":
+        return _md_heading(block)
+    if kind == "markdown":
+        return _md_markdown(block)
+    if kind == "kv_table":
+        return _md_kv_table(block)
+    if kind == "data_table":
+        return _md_data_table(block)
+    if kind == "figure":
+        return _md_figure(block, meta, out_path, counter)
+    if kind == "image":
+        return _md_image(block)
+    if kind == "caption":
+        return _md_caption(block)
+    if kind == "note":
+        return _md_note(block)
+    if kind == "group":
+        return _md_group(block, meta, out_path, counter)
+    if kind == "glossary_entry":
+        return _md_glossary_entry(block)
+    # Unknown content -> readable note (mirrors the model's defensive coercion).
+    return _md_note(model.Note(text=model._safe_str(block)))
+
+
+# --------------------------------------------------------------------------- #
+# Entry point.
+# --------------------------------------------------------------------------- #
+def render_md(chapters: list, out_path: str, meta: dict = None) -> dict:
+    """Serialize a list of Chapters into a single self-contained Markdown file.
+
+    The output leads with ``# <title>``, a metadata blockquote and a numbered
+    ``## Índice`` linking each chapter, then one ``## N. <title>`` section per
+    chapter with its blocks. Tables become Markdown tables (every row dumped),
+    figures become caption + underlying data table, glossary markers are stripped
+    while ``**bold**`` is kept. Designed to be pasted into an LLM.
+
+    Args:
+        chapters: a list of ``Chapter`` (dataclasses or dicts); normalized
+            defensively with ``model.as_chapters``.
+        out_path: filesystem path for the ``.md`` (parent dirs are created).
+        meta: optional dict. Recognised keys: ``title``, ``ctx`` (dict with
+            ``dataset_name``/``source_origin``/``storage``/``n_rows``/``n_cols``),
+            ``generated_at``, ``embed_figures`` (export PNGs beside the .md,
+            default False).
+
+    Returns:
+        dict (never raises): ``{path: str|None, n_chars: int,
+        chapters: list[{id, version}], note: str}``. On a fatal error ``path`` is
+        None and ``note`` explains why.
+    """
+    meta = meta or {}
+    chapters = model.as_chapters(chapters)
+    title = model._safe_str(meta.get("title")) or model.ENGINE_NAME
+
+    # Edge: nothing to render -> a minimal but valid Markdown document.
+    if not chapters:
+        content = (f"# {title}\n\n"
+                   "*(documento vacío — sin capítulos aplicables)*\n")
+        return _write(out_path, content, [], "documento vacío")
+
+    counter = [0]  # document-wide figure counter for unique PNG names.
+    notes: list = []
+    segments: list = [f"# {title}"]
+
+    meta_lines = _meta_block(meta)
+    if meta_lines:
+        segments.append("\n".join(f"> {ln}" for ln in meta_lines))
+
+    # Numbered index. The anchor matches the chapter heading emitted below
+    # (``## N. <title>``) in GitHub slug style.
+    chap_heads = []
+    idx_lines = ["## Índice"]
+    for i, ch in enumerate(chapters, 1):
+        head_text = f"{i}. {model._safe_str(ch.title)}"
+        anchor = _slug(head_text)
+        chap_heads.append((head_text, anchor))
+        idx_lines.append(f"{i}. [{model._safe_str(ch.title)}](#{anchor})")
+    segments.append("\n".join(idx_lines))
+
+    chapters_meta = []
+    for i, ch in enumerate(chapters, 1):
+        segments.append("---")
+        head_text, _anchor = chap_heads[i - 1]
+        segments.append(f"## {head_text}")
+
+        blocks = list(ch.blocks or [])
+        # Omit a leading level-1 Heading that just repeats the chapter title.
+        if blocks:
+            b0 = blocks[0]
+            if (getattr(b0, "kind", "") == "heading"
+                    and int(getattr(b0, "level", 1) or 1) == 1
+                    and _clean_terms(getattr(b0, "text", "")).strip()
+                    == model._safe_str(ch.title).strip()):
+                blocks = blocks[1:]
+
+        for block in blocks:
+            try:
+                seg = _serialize_block(block, meta, out_path, counter)
+            except Exception as e:  # noqa: BLE001
+                seg = _md_note(model.Note(text=model._safe_str(block)))
+                notes.append(
+                    f"bloque '{getattr(block, 'kind', '?')}' del capítulo "
+                    f"'{ch.id}' degradado: {e}")
+            if seg:
+                segments.append(seg)
+        chapters_meta.append({"id": ch.id, "version": ch.version})
+
+    content = "\n\n".join(segments) + "\n"
+    note = f"{len(content)} caracteres"
+    if notes:
+        note += " · " + "; ".join(notes)
+    return _write(out_path, content, chapters_meta, note)
+
+
+def _write(out_path: str, content: str, chapters_meta: list, note: str) -> dict:
+    """Write the Markdown to disk (creating parents). dict-no-throw."""
+    try:
+        parent = os.path.dirname(os.path.abspath(out_path))
+        os.makedirs(parent, exist_ok=True)
+        with open(out_path, "w", encoding="utf-8") as fh:
+            fh.write(content)
+    except Exception as e:  # noqa: BLE001 — never raise from the writer.
+        return {"path": None, "n_chars": 0, "chapters": [],
+                "note": f"no se pudo escribir el Markdown: {e}"}
+    return {"path": out_path, "n_chars": len(content),
+            "chapters": chapters_meta, "note": note}

python/functions/datascience/compute_text_duplicates.md

@@ -0,0 +1,102 @@
+---
+id: compute_text_duplicates_py_datascience
+name: compute_text_duplicates
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def compute_text_duplicates(texts, near_threshold=0.85, sample_max=2000) -> dict"
+description: "Detecta documentos duplicados en un corpus de texto. Los duplicados EXACTOS se calculan siempre con la stdlib: cada documento se normaliza (colapsa espacios, strip, lower) y se hashea con SHA-1; n_exact_dup es cuántos docs repiten uno ya visto y exact_dup_pct su porcentaje. Los CASI-duplicados (near-dup) usan la dependencia OPCIONAL datasketch (MinHash + LSH sobre 3-shingles de palabras); si no está instalada, esa parte degrada a available:False sin afectar al resto. Estilo dict-no-throw del grupo eda — nunca lanza."
+tags: [eda, datascience, text, nlp, duplicates, minhash, pure, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [hashlib, re]
+example: |
+  from datascience.compute_text_duplicates import compute_text_duplicates
+  texts = ["El gato come pescado", "El gato come pescado", "Un perro ladra"]
+  result = compute_text_duplicates(texts)
+  # {"n_docs": 3, "n_exact_dup": 1, "exact_dup_pct": 33.33, "n_unique": 2,
+  #  "near_dup": {"available": False, "n_near_dup_docs": 0}}
+tested: true
+tests:
+  - "test_duplicados_exactos"
+  - "test_sin_duplicados"
+  - "test_vacio"
+  - "test_near_dup_degrada"
+test_file_path: "python/functions/datascience/compute_text_duplicates_test.py"
+file_path: "python/functions/datascience/compute_text_duplicates.py"
+params:
+  - name: texts
+    desc: "Lista de documentos de texto. Los elementos None o que no sean str se descartan silenciosamente; n_docs cuenta solo los documentos válidos. None como argumento se trata como lista vacía."
+  - name: near_threshold
+    desc: "Umbral de similitud Jaccard (0–1) para considerar dos documentos casi-duplicados en el cálculo near-dup vía MinHashLSH. Solo aplica si datasketch está instalada. Default 0.85."
+  - name: sample_max
+    desc: "Número máximo de documentos muestreados (los primeros) para el cálculo near-dup, que es O(n) en memoria de MinHashes. No afecta al conteo de duplicados exactos, que siempre recorre todo el corpus. Default 2000."
+output: "Dict con exactamente 5 claves, siempre presentes: n_docs (int, docs válidos), n_exact_dup (int, docs que repiten un texto normalizado ya visto = n_docs - n_unique), exact_dup_pct (float a 2 decimales = n_exact_dup/n_docs*100, o None si el corpus está vacío), n_unique (int, nº de textos normalizados distintos), y near_dup (sub-dict con available:bool y n_near_dup_docs:int; cuando available es True incluye además threshold con el near_threshold usado). La función nunca lanza: captura toda excepción y degrada."
+---
+
+## Ejemplo
+
+```python
+from datascience.compute_text_duplicates import compute_text_duplicates
+
+# Tres copias del mismo texto (con espacios/casing distintos) + dos únicos.
+texts = [
+    "El gato come pescado",
+    "El gato come pescado",
+    "el  GATO   come pescado",   # mismo tras normalizar
+    "Un perro ladra",
+    "La luna brilla",
+]
+
+compute_text_duplicates(texts)
+# {
+#   "n_docs": 5,
+#   "n_exact_dup": 2,          # 3 copias del primer texto => 2 repeticiones
+#   "exact_dup_pct": 40.0,     # 2 / 5 * 100
+#   "n_unique": 3,             # 3 textos normalizados distintos
+#   "near_dup": {"available": False, "n_near_dup_docs": 0},  # datasketch ausente
+# }
+
+# Corpus vacío: contrato estable, exact_dup_pct None, sin excepción.
+compute_text_duplicates([])
+# {"n_docs": 0, "n_exact_dup": 0, "exact_dup_pct": None, "n_unique": 0,
+#  "near_dup": {"available": False, "n_near_dup_docs": 0}}
+```
+
+## Cuando usarla
+
+Úsala en la fase de calidad de un EDA de texto, cuando quieras saber cuánto de
+tu corpus es ruido duplicado antes de entrenar, vectorizar o muestrear: te da
+el porcentaje de duplicados exactos (`exact_dup_pct`), el número de documentos
+únicos (`n_unique`) y, si tienes `datasketch` instalada, una estimación de
+casi-duplicados (paráfrasis, copias con pequeñas ediciones) vía MinHash + LSH.
+Pásale directamente la columna/lista de textos crudos; la función filtra None y
+no-str por ti y nunca lanza, así que es segura para encadenar en pipelines de
+perfilado.
+
+## Gotchas
+
+- **Near-dup requiere `datasketch` (opcional).** Si la librería no está
+  instalada, `near_dup` degrada a `{"available": False, "n_near_dup_docs": 0}`
+  (sin clave `threshold`) y el resto del resultado se calcula igual. Los
+  duplicados **exactos** funcionan siempre porque solo usan la stdlib (hash).
+- **Normalización de exactos.** Dos textos cuentan como el mismo duplicado
+  exacto si coinciden tras `" ".join(doc.split()).strip().lower()`: se colapsan
+  espacios/tabuladores/saltos, se recortan extremos y se ignora el caso. Cambios
+  de puntuación o acentos SÍ los distinguen (no se eliminan).
+- **`n_exact_dup` cuenta repeticiones, no grupos.** Con 3 copias de un mismo
+  texto, `n_exact_dup` es 2 (las dos copias extra), no 1. Equivale a
+  `n_docs - n_unique`.
+- **`exact_dup_pct` es `None` con corpus vacío** (no `ZeroDivisionError`); en
+  cualquier otro caso es un float redondeado a 2 decimales.
+- **`sample_max` solo limita el near-dup.** El conteo de duplicados exactos
+  recorre todo el corpus; el near-dup muestrea los primeros `sample_max`
+  documentos para acotar memoria. Si el corpus está ordenado, considera barajar
+  antes para que la muestra sea representativa.
+- **Elementos no-str se descartan.** `True`/`False` no cuentan como str y se
+  ignoran igual que `None`; `n_docs` refleja solo los documentos válidos.

python/functions/datascience/compute_text_duplicates.py

@@ -0,0 +1,128 @@
+"""Detección de documentos duplicados en un corpus de texto.
+
+Función pura, estilo dict-no-throw del grupo `eda`: nunca lanza, siempre
+devuelve el mismo contrato de claves. Los duplicados EXACTOS se calculan
+siempre con la stdlib (normalización + hash SHA-1). Los CASI-duplicados
+(near-dup) requieren la dependencia opcional `datasketch`; si no está
+instalada, esa parte degrada limpiamente a ``available: False`` sin afectar
+al resto del cálculo.
+"""
+
+import hashlib
+import re
+
+
+def _compute_near_dup(valid, near_threshold, sample_max):
+    """Cuenta documentos con al menos otro casi-duplicado vía MinHash + LSH.
+
+    Import perezoso de ``datasketch``. Si la librería no está disponible (o
+    cualquier paso falla), degrada a ``{"available": False, "n_near_dup_docs": 0}``
+    sin propagar la excepción.
+
+    Args:
+        valid: lista de str ya filtrada (sin None ni no-str).
+        near_threshold: umbral de similitud Jaccard para LSH.
+        sample_max: número máximo de documentos a muestrear.
+
+    Returns:
+        dict con ``available`` (bool) y ``n_near_dup_docs`` (int). Cuando
+        ``available`` es True, incluye además ``threshold``.
+    """
+    try:
+        from datasketch import MinHash, MinHashLSH
+    except Exception:
+        return {"available": False, "n_near_dup_docs": 0}
+
+    try:
+        docs = valid[:sample_max]
+        num_perm = 128
+        lsh = MinHashLSH(threshold=near_threshold, num_perm=num_perm)
+        minhashes = {}
+
+        for i, doc in enumerate(docs):
+            tokens = re.findall(r"\w+", doc.lower())
+            shingles = set()
+            for j in range(len(tokens) - 2):
+                shingles.add(" ".join(tokens[j:j + 3]))
+            # Documentos con menos de 3 tokens no generan 3-shingles: caemos a
+            # los tokens sueltos para no perderlos del todo.
+            if not shingles:
+                shingles = set(tokens)
+            if not shingles:
+                # Documento sin tokens (cadena vacía / solo símbolos): se omite.
+                continue
+            m = MinHash(num_perm=num_perm)
+            for sh in shingles:
+                m.update(sh.encode("utf-8"))
+            key = "d{}".format(i)
+            minhashes[key] = m
+            lsh.insert(key, m)
+
+        n_near = 0
+        for key, m in minhashes.items():
+            matches = lsh.query(m)
+            if len(matches) > 1:
+                n_near += 1
+
+        return {
+            "available": True,
+            "n_near_dup_docs": int(n_near),
+            "threshold": near_threshold,
+        }
+    except Exception:
+        return {"available": False, "n_near_dup_docs": 0}
+
+
+def compute_text_duplicates(texts, near_threshold=0.85, sample_max=2000) -> dict:
+    """Detecta duplicados exactos y casi-duplicados en un corpus de texto.
+
+    Args:
+        texts: lista de documentos. Los elementos None o que no sean str se
+            descartan; ``n_docs`` cuenta solo los válidos.
+        near_threshold: umbral de similitud Jaccard para considerar dos
+            documentos casi-duplicados (solo near-dup, requiere datasketch).
+        sample_max: tope de documentos muestreados para el cálculo near-dup.
+
+    Returns:
+        dict con las claves ``n_docs``, ``n_exact_dup``, ``exact_dup_pct``
+        (float redondeado a 2 decimales, o None si el corpus está vacío),
+        ``n_unique`` y ``near_dup`` (sub-dict con ``available`` y
+        ``n_near_dup_docs``, más ``threshold`` cuando está disponible).
+        Nunca lanza: captura toda excepción y degrada.
+    """
+    # Filtrado defensivo de documentos válidos.
+    try:
+        valid = [t for t in texts if isinstance(t, str)] if texts is not None else []
+    except Exception:
+        valid = []
+
+    n_docs = len(valid)
+
+    # Duplicados exactos: normalizar + hash SHA-1 (stdlib, siempre disponible).
+    try:
+        seen = set()
+        n_exact_dup = 0
+        for doc in valid:
+            norm = " ".join(doc.split()).strip().lower()
+            digest = hashlib.sha1(norm.encode("utf-8")).hexdigest()
+            if digest in seen:
+                n_exact_dup += 1
+            else:
+                seen.add(digest)
+        n_unique = len(seen)
+    except Exception:
+        n_exact_dup = 0
+        n_unique = 0
+
+    exact_dup_pct = round(n_exact_dup / n_docs * 100, 2) if n_docs > 0 else None
+
+    # Casi-duplicados: opcional vía datasketch, degrada solo.
+    near_dup = _compute_near_dup(valid, near_threshold, sample_max)
+
+    return {
+        "n_docs": n_docs,
+        "n_exact_dup": n_exact_dup,
+        "exact_dup_pct": exact_dup_pct,
+        "n_unique": n_unique,
+        "near_dup": near_dup,
+    }

python/functions/datascience/compute_text_duplicates_test.py

@@ -0,0 +1,77 @@
+"""Tests para compute_text_duplicates.
+
+Importa el modulo hoja directamente (`datascience.compute_text_duplicates`)
+para no depender de que el paquete reexporte la funcion en su __init__.
+datasketch normalmente NO esta instalada en el venv, asi que near_dup
+degrada a available=False; los tests no requieren la libreria.
+"""
+
+from datascience.compute_text_duplicates import compute_text_duplicates
+
+
+EXPECTED_KEYS = {"n_docs", "n_exact_dup", "exact_dup_pct", "n_unique", "near_dup"}
+
+
+def test_duplicados_exactos():
+    """3 copias del mismo texto + 2 únicos: n_exact_dup=2, pct>0."""
+    texts = [
+        "El gato come pescado",
+        "El gato come pescado",
+        "el  GATO   come pescado",  # mismo tras normalizar (espacios + case)
+        "Un perro ladra",
+        "La luna brilla",
+    ]
+    result = compute_text_duplicates(texts)
+
+    assert set(result.keys()) == EXPECTED_KEYS
+    assert result["n_docs"] == 5
+    # 3 copias del primer texto (2 son repeticion) + 2 textos unicos.
+    assert result["n_exact_dup"] == 2
+    assert result["n_unique"] == 3
+    assert result["exact_dup_pct"] is not None
+    assert result["exact_dup_pct"] > 0
+    # 2 / 5 * 100 = 40.0
+    assert abs(result["exact_dup_pct"] - 40.0) < 1e-9
+
+
+def test_sin_duplicados():
+    """Corpus sin repeticiones: n_exact_dup=0, n_unique==n_docs."""
+    texts = [
+        "primero documento distinto",
+        "segundo documento distinto",
+        "tercero documento distinto",
+    ]
+    result = compute_text_duplicates(texts)
+
+    assert result["n_docs"] == 3
+    assert result["n_exact_dup"] == 0
+    assert result["n_unique"] == 3
+    assert abs(result["exact_dup_pct"] - 0.0) < 1e-9
+
+
+def test_vacio():
+    """Corpus vacio: n_docs 0, exact_dup_pct None, no lanza."""
+    result = compute_text_duplicates([])
+
+    assert set(result.keys()) == EXPECTED_KEYS
+    assert result["n_docs"] == 0
+    assert result["n_exact_dup"] == 0
+    assert result["exact_dup_pct"] is None
+    assert result["n_unique"] == 0
+    assert result["near_dup"]["n_near_dup_docs"] == 0
+
+
+def test_near_dup_degrada():
+    """near_dup expone 'available' (bool) y no lanza aunque falte datasketch."""
+    texts = ["uno dos tres cuatro", "uno dos tres cuatro cinco", "algo distinto"]
+    result = compute_text_duplicates(texts)
+
+    near = result["near_dup"]
+    assert "available" in near
+    assert isinstance(near["available"], bool)
+    assert "n_near_dup_docs" in near
+    assert isinstance(near["n_near_dup_docs"], int)
+    # Tambien tolera None y entradas no-str sin lanzar.
+    mixed = compute_text_duplicates(["hola", None, 123, "hola"])
+    assert mixed["n_docs"] == 2
+    assert mixed["n_exact_dup"] == 1

python/functions/datascience/compute_text_length_stats.md

@@ -0,0 +1,86 @@
+---
+id: compute_text_length_stats_py_datascience
+name: compute_text_length_stats
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def compute_text_length_stats(texts, n_bins=20) -> dict"
+description: "Profiles the length distribution of a corpus of text documents for EDA: per-document characters, words (unicode \\w+ tokens) and sentences (segments split on .!?… with a minimum of 1 per non-empty doc), each summarized with mean/p50/p90/p99/min/max (nearest-rank percentiles), plus an equal-width histogram of per-document word counts. None and non-str items are discarded. Dict-no-throw: never raises. Stdlib only (re)."
+tags: [eda, datascience, text, nlp, length, statistics, pure, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [re, math]
+example: |
+  from datascience.compute_text_length_stats import compute_text_length_stats
+  result = compute_text_length_stats(["Hola mundo.", "Una frase mas larga aqui."], n_bins=5)
+tested: true
+tests:
+  - "test_basico"
+  - "test_vacio"
+  - "test_descarta_none"
+  - "test_un_documento"
+test_file_path: "python/functions/datascience/compute_text_length_stats_test.py"
+file_path: "python/functions/datascience/compute_text_length_stats.py"
+params:
+  - name: texts
+    desc: "List of text documents (str). None entries and any non-str items (ints, floats, etc.) are discarded before any computation. An empty string \"\" is kept (chars 0, words 0, sentences 0)."
+  - name: n_bins
+    desc: "Number of equal-width bins for the per-document word-count histogram. Default 20. When all docs have the same word count, there are <2 docs, or n_bins < 1, a single covering bin is returned instead."
+output: "Dict with keys n_docs (int), chars, words, sentences and word_hist. Each of the three axis sub-dicts has the exact keys mean (float, 2 decimals), p50, p90, p99, min, max (ints). When there are no valid documents, n_docs is 0, every axis statistic is None and word_hist is []. word_hist is a list of {lo: float, hi: float, count: int} bins; the sum of all bin counts equals n_docs."
+---
+
+## Ejemplo
+
+```python
+from datascience.compute_text_length_stats import compute_text_length_stats
+
+compute_text_length_stats(
+    [
+        "Hola mundo.",
+        "Una frase mas larga con varias palabras aqui.",
+        "Esto. Tiene. Tres frases distintas!",
+    ],
+    n_bins=5,
+)
+# {
+#   "n_docs": 3,
+#   "chars":     {"mean": 30.33, "p50": 35, "p90": 45, "p99": 45, "min": 11, "max": 45},
+#   "words":     {"mean": 5.0,   "p50": 5,  "p90": 8,  "p99": 8,  "min": 2,  "max": 8},
+#   "sentences": {"mean": 1.67,  "p50": 1,  "p90": 3,  "p99": 3,  "min": 1,  "max": 3},
+#   "word_hist": [
+#     {"lo": 2.0, "hi": 3.2, "count": 1},
+#     {"lo": 3.2, "hi": 4.4, "count": 0},
+#     {"lo": 4.4, "hi": 5.6, "count": 1},
+#     {"lo": 5.6, "hi": 6.8, "count": 0},
+#     {"lo": 6.8, "hi": 8.0, "count": 1},
+#   ],
+# }
+```
+
+## Cuando usarla
+
+Úsala al perfilar una columna o corpus de texto libre en un EDA: cuando
+necesites saber lo largos que son los documentos (en caracteres, palabras y
+frases) y cómo se reparte esa longitud antes de tokenizar, vectorizar o decidir
+truncados/ventanas para un modelo. Pásale la lista de strings crudos de la
+columna; `None` y valores no-texto se descartan solos. Encaja en el grupo `eda`
+como bloque de longitud junto a `summarize_categorical`.
+
+## Gotchas
+
+- Función pura, solo stdlib (`re`). No usa numpy, pandas ni sklearn.
+- Percentiles por método **nearest-rank** (devuelven un valor real de la lista,
+  no interpolan); por eso p50/p90/p99/min/max son enteros y `mean` es el único
+  float (redondeado a 2 decimales).
+- El conteo de frases es una **aproximación** por puntuación (`.!?…`): un texto
+  sin esa puntuación cuenta como 1 frase si no está vacío; abreviaturas o
+  ellipsis pueden inflar o reducir el conteo.
+- `word_hist` es equal-width entre min y max de palabras: con todos los docs
+  del mismo tamaño, menos de 2 docs, o `n_bins < 1`, devuelve un único bin.
+- Dict-no-throw: ante input inesperado devuelve la forma vacía
+  (`n_docs` 0, ejes `None`, `word_hist` []) en vez de lanzar.

python/functions/datascience/compute_text_length_stats.py

@@ -0,0 +1,168 @@
+"""Pure EDA helper: document length distribution for the `eda` group.
+
+Given a list of text documents, computes the length distribution along three
+axes (characters, words and sentences) plus an equal-width histogram of the
+per-document word counts. Stdlib only (``re`` + ``statistics`` semantics via a
+hand-rolled nearest-rank percentile). No numpy, no sklearn.
+
+The function is dict-no-throw: it never raises. On any unexpected input it
+degrades to the empty-shape result.
+"""
+
+import math
+import re
+
+_WORD_RE = re.compile(r"\w+", re.UNICODE)
+_SENT_RE = re.compile(r"[.!?…]+")
+
+
+def _empty_axis() -> dict:
+    """Return an axis sub-dict with every statistic set to ``None``."""
+    return {"mean": None, "p50": None, "p90": None, "p99": None, "min": None, "max": None}
+
+
+def _pct(sorted_vals, q):
+    """Nearest-rank percentile of an already-sorted list.
+
+    Args:
+        sorted_vals: List of numbers sorted ascending.
+        q: Percentile in the 0..100 range.
+
+    Returns:
+        The value at the nearest rank, or ``None`` for an empty list.
+    """
+    n = len(sorted_vals)
+    if n == 0:
+        return None
+    if q <= 0:
+        return sorted_vals[0]
+    rank = math.ceil(q / 100.0 * n)
+    if rank < 1:
+        rank = 1
+    if rank > n:
+        rank = n
+    return sorted_vals[rank - 1]
+
+
+def _axis_stats(values) -> dict:
+    """Compute mean/p50/p90/p99/min/max over a list of integer counts.
+
+    ``mean`` is rounded to 2 decimals; every other statistic is an integer
+    (they are counts). Returns an all-``None`` axis for an empty list.
+    """
+    if not values:
+        return _empty_axis()
+    sv = sorted(values)
+    return {
+        "mean": round(sum(sv) / len(sv), 2),
+        "p50": int(_pct(sv, 50)),
+        "p90": int(_pct(sv, 90)),
+        "p99": int(_pct(sv, 99)),
+        "min": int(sv[0]),
+        "max": int(sv[-1]),
+    }
+
+
+def _word_hist(word_counts, n_bins) -> list:
+    """Equal-width histogram of per-document word counts.
+
+    Builds ``n_bins`` bins between ``min`` and ``max`` of the word counts. When
+    every document has the same number of words, there are fewer than 2
+    documents, or ``n_bins`` is not at least 1, a single covering bin is
+    returned. With no documents the result is ``[]``. The sum of bin ``count``
+    always equals ``len(word_counts)``.
+    """
+    if not word_counts:
+        return []
+    wmin = min(word_counts)
+    wmax = max(word_counts)
+    if wmax == wmin or len(word_counts) < 2 or n_bins < 1:
+        return [{"lo": float(wmin), "hi": float(wmax), "count": len(word_counts)}]
+
+    width = (wmax - wmin) / n_bins
+    bins = []
+    for i in range(n_bins):
+        lo = wmin + i * width
+        hi = wmin + (i + 1) * width
+        bins.append({"lo": float(lo), "hi": float(hi), "count": 0})
+    # Pin the last upper edge to the real maximum to avoid float drift.
+    bins[-1]["hi"] = float(wmax)
+
+    for wc in word_counts:
+        if wc >= wmax:
+            idx = n_bins - 1
+        else:
+            idx = int((wc - wmin) / width)
+            if idx < 0:
+                idx = 0
+            elif idx >= n_bins:
+                idx = n_bins - 1
+        bins[idx]["count"] += 1
+    return bins
+
+
+def compute_text_length_stats(texts, n_bins=20) -> dict:
+    """Summarize the length distribution of a corpus of text documents.
+
+    For each document three lengths are measured: characters (``len(doc)``),
+    words (count of ``\\w+`` unicode tokens) and sentences (non-empty segments
+    after splitting on ``.!?…``, with a minimum of 1 for any non-empty
+    document). For each axis the mean, p50, p90, p99, min and max are reported,
+    plus an equal-width histogram of the per-document word counts.
+
+    ``None`` entries and any non-``str`` items in ``texts`` are discarded.
+    The function never raises: on empty/``None`` input or any internal error it
+    returns the empty-shape result (``n_docs`` 0, all-``None`` axes, ``[]``
+    histogram).
+
+    Args:
+        texts: List of text documents (``str``). ``None`` and non-``str``
+            items are dropped.
+        n_bins: Number of equal-width bins for the word-count histogram.
+            Default 20.
+
+    Returns:
+        Dict with keys ``n_docs``, ``chars``, ``words``, ``sentences`` and
+        ``word_hist``. Each of the three axes is a sub-dict with ``mean``
+        (float, 2 decimals), ``p50``, ``p90``, ``p99``, ``min`` and ``max``
+        (ints), all ``None`` when there are no documents. ``word_hist`` is a
+        list of ``{lo, hi, count}`` bins whose ``count`` sums to ``n_docs``.
+    """
+    empty_axis = _empty_axis()
+    fallback = {
+        "n_docs": 0,
+        "chars": dict(empty_axis),
+        "words": dict(empty_axis),
+        "sentences": dict(empty_axis),
+        "word_hist": [],
+    }
+    try:
+        if not texts:
+            return fallback
+
+        docs = [t for t in texts if isinstance(t, str)]
+        n_docs = len(docs)
+        if n_docs == 0:
+            return fallback
+
+        char_counts = [len(d) for d in docs]
+        word_counts = [len(_WORD_RE.findall(d)) for d in docs]
+
+        sent_counts = []
+        for d in docs:
+            segments = [s for s in _SENT_RE.split(d) if s.strip()]
+            n = len(segments)
+            if d and n == 0:
+                # Non-empty document with no detectable sentence: count as 1.
+                n = 1
+            sent_counts.append(n)
+
+        return {
+            "n_docs": n_docs,
+            "chars": _axis_stats(char_counts),
+            "words": _axis_stats(word_counts),
+            "sentences": _axis_stats(sent_counts),
+            "word_hist": _word_hist(word_counts, n_bins),
+        }
+    except Exception:
+        return fallback

python/functions/datascience/compute_text_length_stats_test.py

@@ -0,0 +1,70 @@
+"""Tests para compute_text_length_stats.
+
+Inserta `python/functions` en sys.path (relativo a este archivo) para importar
+el modulo hoja por su paquete `datascience`, sin depender de que el paquete lo
+reexporte en su __init__.
+"""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from datascience.compute_text_length_stats import compute_text_length_stats
+
+
+def test_basico():
+    """Varios textos de longitudes distintas: stats y histograma coherentes."""
+    texts = [
+        "Hola mundo.",                      # 2 words, 1 sentence
+        "Una frase mas larga con varias palabras aqui.",  # 8 words, 1 sentence
+        "Corto.",                           # 1 word, 1 sentence
+        "Esto. Tiene. Tres frases distintas!",            # 5 words, 3 sentences
+    ]
+    result = compute_text_length_stats(texts)
+
+    assert result["n_docs"] == 4
+    # Diferentes longitudes en palabras -> max estrictamente mayor que min.
+    assert result["words"]["max"] > result["words"]["min"]
+    # El histograma de palabras no esta vacio.
+    assert result["word_hist"] != []
+    # La suma de counts del histograma cubre todos los documentos.
+    assert sum(b["count"] for b in result["word_hist"]) == result["n_docs"]
+    # mean es float redondeado; min/max son enteros.
+    assert isinstance(result["words"]["mean"], float)
+    assert isinstance(result["words"]["min"], int)
+    assert isinstance(result["words"]["max"], int)
+    # El documento con 3 frases empuja el max de sentences a >= 3.
+    assert result["sentences"]["max"] >= 3
+
+
+def test_vacio():
+    """Lista vacia: n_docs 0, subdicts None, word_hist []."""
+    result = compute_text_length_stats([])
+    assert result["n_docs"] == 0
+    for axis in ("chars", "words", "sentences"):
+        for key in ("mean", "p50", "p90", "p99", "min", "max"):
+            assert result[axis][key] is None
+    assert result["word_hist"] == []
+
+
+def test_descarta_none():
+    """None y valores no-str se descartan del computo."""
+    result = compute_text_length_stats(["hello world", None, 123, 4.5, "foo bar baz"])
+    # Solo dos strings validos.
+    assert result["n_docs"] == 2
+    assert result["words"]["min"] == 2  # "hello world"
+    assert result["words"]["max"] == 3  # "foo bar baz"
+    assert sum(b["count"] for b in result["word_hist"]) == 2
+
+
+def test_un_documento():
+    """Un solo documento: word_hist tiene exactamente un bin con count 1."""
+    result = compute_text_length_stats(["solo un documento aqui"])
+    assert result["n_docs"] == 1
+    assert len(result["word_hist"]) == 1
+    assert result["word_hist"][0]["count"] == 1
+    # Con un unico documento, p50 == min == max == su numero de palabras (4).
+    assert result["words"]["min"] == 4
+    assert result["words"]["max"] == 4
+    assert result["words"]["p50"] == 4

python/functions/datascience/compute_text_readability.md

@@ -0,0 +1,88 @@
+---
+id: compute_text_readability_py_datascience
+name: compute_text_readability
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def compute_text_readability(texts, sample_max=500) -> dict"
+description: "Calcula la legibilidad Flesch Reading Ease de un corpus de texto usando textstat con import perezoso y degradación. Filtra None/no-str/vacíos, muestrea hasta sample_max documentos (los primeros) y agrega los scores Flesch en {mean, p50, min, max}. Si textstat no está instalada devuelve available=False sin lanzar. Estilo dict-no-throw del grupo eda — nunca lanza."
+tags: [eda, datascience, text, nlp, readability, flesch, textstat, pure, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [math, textstat]
+example: |
+  from datascience.compute_text_readability import compute_text_readability
+  out = compute_text_readability(["The cat sat on the mat. It was warm and sunny."])
+  # {"available": True, "n_scored": 1, "flesch": {"mean": 109.0, "p50": 109.0, "min": 108.96..., "max": 108.96...}}
+tested: true
+tests:
+  - "test_prosa_ingles"
+  - "test_vacio"
+  - "test_degradacion"
+test_file_path: "python/functions/datascience/compute_text_readability_test.py"
+file_path: "python/functions/datascience/compute_text_readability.py"
+params:
+  - name: texts
+    desc: "Lista de str (documentos del corpus). Los elementos None, no-str o vacíos tras strip() se descartan silenciosamente. El orden se respeta: el muestreo toma los primeros documentos válidos."
+  - name: sample_max
+    desc: "Número máximo de documentos válidos a puntuar (los primeros). Default 500. Acota el coste en corpus grandes. Valores no convertibles a int caen a 500; negativos se tratan como 0."
+output: "Dict con exactamente 3 claves siempre presentes: available (bool: True si textstat se pudo importar), n_scored (int: nº de documentos efectivamente puntuados), flesch (dict con mean, p50, min, max). mean y p50 redondeados a 1 decimal; p50 por nearest-rank sobre los scores ordenados; min/max son los scores extremos sin redondear. Todos los valores de flesch son None cuando n_scored es 0. La función nunca lanza: cualquier excepción global (incluida ImportError de textstat) degrada a available=False, n_scored=0 y flesch todo None."
+---
+
+## Ejemplo
+
+```python
+from datascience.compute_text_readability import compute_text_readability
+
+textos = [
+    "The cat sat on the mat. It was a warm and sunny day in the park.",
+    "Reading is a wonderful habit. Books open doors to new worlds and ideas.",
+    "He ran quickly to the store to buy some fresh bread and a bottle of milk.",
+]
+
+compute_text_readability(textos)
+# {
+#   "available": True,
+#   "n_scored": 3,
+#   "flesch": {"mean": 91.4, "p50": 95.4, "min": 70.08..., "max": 108.83...}
+# }
+
+# Corpus vacío (textstat presente): available True pero nada que puntuar.
+compute_text_readability([])
+# {"available": True, "n_scored": 0,
+#  "flesch": {"mean": None, "p50": None, "min": None, "max": None}}
+```
+
+## Cuando usarla
+
+Úsala en un EDA de texto cuando necesites una métrica única y comparable de
+**lo fácil que es de leer** un corpus de documentos (descripciones, reviews,
+artículos, tickets). Devuelve el resumen Flesch Reading Ease agregado
+(`mean`/`p50`/`min`/`max`) listo para un report o un bloque del notebook, sin
+tener que iterar `textstat` a mano. Pásale la lista de textos crudos y, si el
+corpus es grande, limita el coste con `sample_max`. El estilo dict-no-throw
+permite incrustarla en pipelines del grupo `eda` sin envolver en try/except.
+
+## Gotchas
+
+- **`textstat` es una dependencia opcional.** Si no está instalada (o falla al
+  importar) la función NO lanza: devuelve `available=False`, `n_scored=0` y
+  `flesch` todo `None`. Comprueba `available` antes de interpretar los números.
+- **Flesch Reading Ease está pensado para prosa en inglés.** Aplicado a otros
+  idiomas o a texto no-prosa (código, listas, tablas, cadenas muy cortas) los
+  scores no son interpretables, aunque se calculen sin error.
+- **Escala Flesch:** valores **altos** = más fácil de leer (≈90–100 muy fácil),
+  valores **bajos** = más difícil (puede ser negativo en texto muy denso). No
+  se recortan a ningún rango: se reportan tal cual los devuelve `textstat`.
+- **`available=True` con `n_scored=0`** significa que `textstat` está presente
+  pero el corpus no aportó documentos puntuables (vacío, solo None/no-str, o
+  todos los docs fallaron al puntuar). Es distinto de `available=False`.
+- **Muestreo = los primeros `sample_max`**, no aleatorio. Si el orden del corpus
+  está sesgado, el resumen reflejará ese sesgo.
+- **`mean` y `p50` redondean a 1 decimal**; `min`/`max` se devuelven sin
+  redondear (los scores extremos reales).

python/functions/datascience/compute_text_readability.py

@@ -0,0 +1,121 @@
+"""Legibilidad Flesch Reading Ease de un corpus de texto.
+
+Función pura del grupo `eda`, estilo dict-no-throw: nunca lanza. Usa la
+librería `textstat` con import perezoso y degradación: si `textstat` no está
+instalada (o falla al importar), devuelve un resultado con `available=False`
+en lugar de propagar el error.
+"""
+
+
+def _percentile_nearest_rank(sorted_values, pct):
+    """Percentil por nearest-rank sobre una lista ya ordenada ascendente.
+
+    rank = ceil(pct/100 * n); índice 1-based recortado a [1, n].
+    Devuelve None si la lista está vacía.
+    """
+    n = len(sorted_values)
+    if n == 0:
+        return None
+    import math
+
+    rank = math.ceil((pct / 100.0) * n)
+    if rank < 1:
+        rank = 1
+    if rank > n:
+        rank = n
+    return sorted_values[rank - 1]
+
+
+def compute_text_readability(texts, sample_max=500) -> dict:
+    """Calcula la legibilidad Flesch Reading Ease de un corpus.
+
+    Args:
+        texts: lista de str. Los elementos None, no-str o vacíos (tras strip)
+            se descartan. Se muestrean los primeros `sample_max` documentos
+            válidos.
+        sample_max: número máximo de documentos a puntuar (los primeros).
+
+    Returns:
+        Dict con la forma exacta::
+
+            {"available": bool, "n_scored": int,
+             "flesch": {"mean": float|None, "p50": float|None,
+                        "min": float|None, "max": float|None}}
+
+        `available` es True si `textstat` se pudo importar. La función nunca
+        lanza: cualquier excepción global degrada a `available=False`.
+    """
+    empty = {
+        "available": False,
+        "n_scored": 0,
+        "flesch": {"mean": None, "p50": None, "min": None, "max": None},
+    }
+    try:
+        # Import perezoso con degradación: textstat es una dependencia opcional.
+        try:
+            import textstat
+        except Exception:
+            return {
+                "available": False,
+                "n_scored": 0,
+                "flesch": {"mean": None, "p50": None, "min": None, "max": None},
+            }
+
+        # Filtrar y muestrear documentos válidos (los primeros sample_max).
+        docs = []
+        if texts is not None:
+            try:
+                limit = int(sample_max)
+            except Exception:
+                limit = 500
+            if limit < 0:
+                limit = 0
+            for item in texts:
+                if not isinstance(item, str):
+                    continue
+                if item.strip() == "":
+                    continue
+                docs.append(item)
+                if len(docs) >= limit:
+                    break
+
+        scores = []
+        for doc in docs:
+            try:
+                score = textstat.flesch_reading_ease(doc)
+            except Exception:
+                continue
+            try:
+                score = float(score)
+            except Exception:
+                continue
+            scores.append(score)
+
+        n_scored = len(scores)
+        if n_scored == 0:
+            # textstat presente pero corpus vacío / sin puntuar.
+            return {
+                "available": True,
+                "n_scored": 0,
+                "flesch": {"mean": None, "p50": None, "min": None, "max": None},
+            }
+
+        mean_val = round(sum(scores) / n_scored, 1)
+        sorted_scores = sorted(scores)
+        p50_raw = _percentile_nearest_rank(sorted_scores, 50)
+        p50_val = round(p50_raw, 1) if p50_raw is not None else None
+        min_val = sorted_scores[0]
+        max_val = sorted_scores[-1]
+
+        return {
+            "available": True,
+            "n_scored": n_scored,
+            "flesch": {
+                "mean": mean_val,
+                "p50": p50_val,
+                "min": min_val,
+                "max": max_val,
+            },
+        }
+    except Exception:
+        return empty

python/functions/datascience/compute_text_readability_test.py

@@ -0,0 +1,74 @@
+"""Tests para compute_text_readability."""
+
+import sys
+import os
+import builtins
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
+
+from datascience.compute_text_readability import compute_text_readability
+
+
+EXPECTED_KEYS = {"available", "n_scored", "flesch"}
+FLESCH_KEYS = {"mean", "p50", "min", "max"}
+
+
+def test_prosa_ingles():
+    """Varios textos en prosa inglesa: available True, n_scored>0, mean no None."""
+    texts = [
+        "The cat sat on the mat. It was a warm and sunny day in the park.",
+        "She sells sea shells by the sea shore. The shells she sells are surely sea shells.",
+        "Reading is a wonderful habit. Books open doors to new worlds and ideas.",
+        "He ran quickly to the store to buy some fresh bread and a bottle of milk.",
+    ]
+    out = compute_text_readability(texts)
+
+    assert set(out.keys()) == EXPECTED_KEYS
+    assert out["available"] is True
+    assert out["n_scored"] > 0
+    assert set(out["flesch"].keys()) == FLESCH_KEYS
+    assert out["flesch"]["mean"] is not None
+    assert out["flesch"]["p50"] is not None
+    assert out["flesch"]["min"] is not None
+    assert out["flesch"]["max"] is not None
+    # min <= mean/p50 <= max coherente.
+    assert out["flesch"]["min"] <= out["flesch"]["max"]
+
+
+def test_vacio():
+    """Corpus vacío con textstat presente: available True, n_scored 0, flesch None."""
+    out = compute_text_readability([])
+
+    assert set(out.keys()) == EXPECTED_KEYS
+    assert out["available"] is True
+    assert out["n_scored"] == 0
+    assert out["flesch"]["mean"] is None
+    assert out["flesch"]["p50"] is None
+    assert out["flesch"]["min"] is None
+    assert out["flesch"]["max"] is None
+
+    # Elementos no-str / vacíos también se descartan -> n_scored 0.
+    out2 = compute_text_readability([None, "", "   ", 123])
+    assert out2["available"] is True
+    assert out2["n_scored"] == 0
+
+
+def test_degradacion(monkeypatch):
+    """Sin textstat (ImportError forzado): degrada a available False sin lanzar."""
+    import datascience.compute_text_readability as m
+
+    real = builtins.__import__
+
+    def fake(name, *a, **k):
+        if name == "textstat" or name.startswith("textstat."):
+            raise ImportError("simulado")
+        return real(name, *a, **k)
+
+    monkeypatch.setattr(builtins, "__import__", fake)
+    out = m.compute_text_readability(["The cat sat on the mat. It was happy and warm."])
+    assert out["available"] is False
+    assert out["n_scored"] == 0
+    assert out["flesch"]["mean"] is None
+    assert out["flesch"]["p50"] is None
+    assert out["flesch"]["min"] is None
+    assert out["flesch"]["max"] is None

python/functions/datascience/compute_top_ngrams.md

@@ -0,0 +1,103 @@
+---
+id: compute_top_ngrams_py_datascience
+name: compute_top_ngrams
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def compute_top_ngrams(texts, n=2, top_k=15, remove_stopwords=True) -> dict"
+description: "Calcula los n-gramas de palabras más frecuentes de un corpus de texto (n=1 unigramas, 2 bigramas, 3 trigramas...). Tokeniza a minúsculas con re.findall(r'\\w+', ...), descarta tokens numéricos y, si remove_stopwords=True, elimina stopwords ES+EN ANTES de formar los n-gramas (n-gramas contiguos sobre la secuencia de tokens de contenido, sin cruzar documentos). Pura y autocontenida con collections.Counter, sin sklearn. Estilo dict-no-throw del grupo eda: nunca lanza."
+tags: [eda, datascience, text, nlp, ngrams, bigrams, trigrams, pure, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [re, collections]
+example: |
+  from datascience.compute_top_ngrams import compute_top_ngrams
+  texts = ["machine learning rocks", "we love machine learning"]
+  compute_top_ngrams(texts, n=2, top_k=5)
+  # {"n": 2, "top": [{"ngram": "machine learning", "count": 2}, ...]}
+tested: true
+tests:
+  - "test_bigramas"
+  - "test_trigramas"
+  - "test_vacio"
+  - "test_stopwords"
+test_file_path: "python/functions/datascience/compute_top_ngrams_test.py"
+file_path: "python/functions/datascience/compute_top_ngrams.py"
+params:
+  - name: texts
+    desc: "Lista (o tupla) de cadenas. Los elementos None o que no sean str se descartan silenciosamente. Cada documento se tokeniza por separado; los n-gramas no cruzan la frontera entre documentos."
+  - name: n
+    desc: "Tamaño del n-grama: 1 unigramas, 2 bigramas, 3 trigramas, etc. Valores < 1 o no enteros producen top vacío (se conserva tal cual en la clave 'n' del retorno)."
+  - name: top_k
+    desc: "Número máximo de n-gramas a devolver, ordenados por frecuencia descendente con desempate alfabético determinista. Default 15. Valores negativos se tratan como 0."
+  - name: remove_stopwords
+    desc: "Si True (default) elimina las stopwords ES+EN de una lista inline (~130 términos de altísima frecuencia) ANTES de formar los n-gramas, de modo que los n-gramas se construyen sobre la secuencia de tokens de contenido."
+output: "Dict con exactamente 2 claves: n (el n recibido, sin normalizar) y top (lista de dicts {'ngram': str, 'count': int} ordenada por count descendente, longitud <= top_k). ngram es la unión de los tokens del n-grama por un espacio. Corpus vacío, tokens insuficientes para formar n-gramas o cualquier excepción interna degradan a {'n': n, 'top': []}. La función nunca lanza."
+---
+
+## Ejemplo
+
+```python
+from datascience.compute_top_ngrams import compute_top_ngrams
+
+texts = [
+    "machine learning rocks",
+    "machine learning is fun",
+    "we love machine learning",
+]
+
+# Bigramas (n=2): "machine learning" aparece en los 3 documentos.
+compute_top_ngrams(texts, n=2, top_k=5)
+# {
+#   "n": 2,
+#   "top": [
+#       {"ngram": "machine learning", "count": 3},
+#       {"ngram": "learning fun",     "count": 1},
+#       {"ngram": "learning rocks",   "count": 1},
+#       {"ngram": "love machine",     "count": 1},
+#   ],
+# }
+
+# Unigramas con stopwords fuera (default): solo palabras de contenido.
+compute_top_ngrams(["the cat sat on the mat"], n=1, top_k=3)
+# {"n": 1, "top": [{"ngram": "cat", "count": 1},
+#                  {"ngram": "mat", "count": 1},
+#                  {"ngram": "sat", "count": 1}]}
+```
+
+## Cuando usarla
+
+Úsala en la fase de EDA de texto cuando, además del vocabulario suelto, necesites
+ver qué **combinaciones de palabras contiguas** dominan un corpus: colocaciones,
+frases técnicas recurrentes ("machine learning", "data analyst"), o patrones de
+trigramas en titulares/descripciones. Es el complemento natural de un perfil de
+vocabulario: pasa de "qué palabras aparecen" a "qué secuencias aparecen". Llámala
+con `n=1` para unigramas, `n=2` para bigramas y `n=3` para trigramas, y ajusta
+`top_k` al tamaño de la tabla que vas a renderizar. Deja `remove_stopwords=True`
+para que los n-gramas reflejen contenido y no conectores gramaticales.
+
+## Gotchas
+
+- **Las stopwords se eliminan ANTES de formar los n-gramas.** Con
+  `remove_stopwords=True` la frase "data of analysis" produce el bigrama
+  "data analysis" (el "of" intermedio desaparece y los tokens de contenido se
+  vuelven contiguos), no "data of" ni "of analysis". Si quieres preservar la
+  adyacencia literal del texto original, pasa `remove_stopwords=False`.
+- **Los n-gramas NO cruzan documentos.** Cada elemento de `texts` se tokeniza y
+  recorre por separado; el último token de un documento nunca se combina con el
+  primero del siguiente.
+- **Tokens puramente numéricos se descartan** (`tok.isdigit()`), pero los
+  alfanuméricos mixtos no: "3d" o "covid19" sí cuentan como tokens. Un decimal
+  como "3.5" se parte en "3" y "5" por `\w+` y ambos se descartan por numéricos.
+- **La lista de stopwords es inline ES+EN**, pensada para textos generales en
+  esos dos idiomas. Para otros idiomas o jerga específica de dominio puede dejar
+  pasar conectores; en ese caso filtra el corpus aguas arriba o usa
+  `remove_stopwords=False` y posfiltra.
+- **`top` puede tener menos de `top_k` elementos** si el corpus no tiene tantos
+  n-gramas distintos. El desempate por frecuencia es alfabético (determinista),
+  no por orden de aparición.

python/functions/datascience/compute_top_ngrams.py

@@ -0,0 +1,94 @@
+"""Top n-gramas de palabras más frecuentes de un corpus de texto.
+
+Función pura, autocontenida (solo stdlib: re + collections.Counter). No depende
+de scikit-learn ni de ninguna otra librería externa. Estilo dict-no-throw del
+grupo `eda`: ante cualquier entrada degenerada o excepción interna devuelve
+``{"n": n, "top": []}`` en vez de lanzar.
+"""
+
+import re
+from collections import Counter
+
+# Lista inline de stopwords ES + EN (~80 términos de altísima frecuencia).
+# Se eliminan ANTES de formar los n-gramas: los n-gramas se construyen sobre la
+# secuencia de tokens de contenido, no sobre el texto original.
+_STOPWORDS = frozenset({
+    # Español
+    "de", "la", "que", "el", "en", "y", "a", "los", "del", "se", "las", "por",
+    "un", "para", "con", "no", "una", "su", "al", "lo", "como", "más", "mas",
+    "pero", "sus", "le", "ya", "o", "este", "sí", "si", "porque", "esta",
+    "entre", "cuando", "muy", "sin", "sobre", "también", "tambien", "me",
+    "hasta", "hay", "donde", "quien", "desde", "todo", "nos", "durante",
+    "todos", "uno", "les", "ni", "contra", "otros", "ese", "eso", "ante",
+    "ellos", "e", "esto", "mí", "antes", "algunos", "qué", "unos", "yo",
+    "otro", "otras", "otra", "él", "tanto", "esa", "estos", "mucho", "quienes",
+    "nada", "muchos", "cual", "poco", "ella", "estar", "estas", "algunas",
+    "algo", "nosotros",
+    # Inglés
+    "the", "of", "and", "to", "in", "is", "it", "for", "on", "with", "as",
+    "are", "was", "be", "this", "that", "by", "an", "or", "at", "from", "but",
+    "not", "have", "has", "had", "they", "you", "we", "he", "she", "his",
+    "her", "their", "its", "i", "my", "me", "our", "us", "do", "does", "did",
+    "will", "would", "can", "could", "should", "there", "which", "who", "what",
+    "when", "where", "how", "all", "if", "so", "than", "then", "out", "up",
+})
+
+
+def compute_top_ngrams(texts, n=2, top_k=15, remove_stopwords=True) -> dict:
+    """Calcula los n-gramas de palabras más frecuentes de un corpus.
+
+    Args:
+        texts: lista de cadenas. Los elementos ``None`` o que no sean ``str`` se
+            descartan silenciosamente.
+        n: tamaño del n-grama (1 = unigramas, 2 = bigramas, 3 = trigramas...).
+            Valores < 1 o no enteros producen ``top`` vacío.
+        top_k: número máximo de n-gramas a devolver, ordenados por frecuencia
+            descendente (con desempate alfabético determinista).
+        remove_stopwords: si ``True`` elimina las stopwords ES+EN ANTES de
+            formar los n-gramas, de modo que los n-gramas se construyen sobre la
+            secuencia de tokens de contenido (no cruzando documentos).
+
+    Returns:
+        ``{"n": n, "top": [{"ngram": "w1 w2", "count": int}, ...]}``. Corpus
+        vacío, sin tokens suficientes o cualquier excepción interna degrada a
+        ``{"n": n, "top": []}``. Nunca lanza.
+    """
+    try:
+        if not isinstance(n, int) or n < 1:
+            return {"n": n, "top": []}
+
+        try:
+            limit = int(top_k)
+        except (TypeError, ValueError):
+            limit = 0
+        if limit < 0:
+            limit = 0
+
+        if not isinstance(texts, (list, tuple)):
+            return {"n": n, "top": []}
+
+        counter = Counter()
+        for doc in texts:
+            if not isinstance(doc, str):
+                continue
+            tokens = [
+                tok
+                for tok in re.findall(r"\w+", doc.lower(), re.UNICODE)
+                if not tok.isdigit()
+            ]
+            if remove_stopwords:
+                tokens = [tok for tok in tokens if tok not in _STOPWORDS]
+            if len(tokens) < n:
+                continue
+            for i in range(len(tokens) - n + 1):
+                ngram = " ".join(tokens[i:i + n])
+                counter[ngram] += 1
+
+        if not counter:
+            return {"n": n, "top": []}
+
+        ordered = sorted(counter.items(), key=lambda kv: (-kv[1], kv[0]))
+        top = [{"ngram": ngram, "count": count} for ngram, count in ordered[:limit]]
+        return {"n": n, "top": top}
+    except Exception:
+        return {"n": n, "top": []}

python/functions/datascience/compute_top_ngrams_test.py

@@ -0,0 +1,65 @@
+"""Tests para compute_top_ngrams."""
+
+import sys
+import os
+
+# sys.path estándar: añade `python/functions/` para importar por paquete raíz.
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
+
+from datascience.compute_top_ngrams import compute_top_ngrams
+
+
+def test_bigramas():
+    # "machine learning" se repite en cada documento -> bigrama más frecuente.
+    texts = [
+        "machine learning rocks",
+        "machine learning is fun",
+        "we love machine learning",
+    ]
+    result = compute_top_ngrams(texts, n=2, top_k=5)
+    assert result["n"] == 2
+    assert result["top"], "esperaba al menos un bigrama"
+    assert result["top"][0]["ngram"] == "machine learning"
+    assert result["top"][0]["count"] == 3
+    # Cada entrada respeta el contrato {"ngram": str, "count": int}.
+    for item in result["top"]:
+        assert isinstance(item["ngram"], str)
+        assert isinstance(item["count"], int)
+
+
+def test_trigramas():
+    texts = [
+        "alpha beta gamma delta",
+        "alpha beta gamma omega",
+    ]
+    # Con stopwords desactivadas para no descartar tokens de contenido.
+    result = compute_top_ngrams(texts, n=3, top_k=5, remove_stopwords=False)
+    assert result["n"] == 3
+    ngrams = {item["ngram"]: item["count"] for item in result["top"]}
+    # "alpha beta gamma" aparece en ambos documentos.
+    assert ngrams.get("alpha beta gamma") == 2
+    # Trigramas únicos de cada documento.
+    assert ngrams.get("beta gamma delta") == 1
+    assert ngrams.get("beta gamma omega") == 1
+
+
+def test_vacio():
+    assert compute_top_ngrams([], n=2) == {"n": 2, "top": []}
+    # Documentos no-str / None se descartan -> corpus efectivamente vacío.
+    assert compute_top_ngrams([None, 123, {"a": 1}], n=2) == {"n": 2, "top": []}
+
+
+def test_stopwords():
+    # "the cat" debería desaparecer al quitar stopwords ("the" es stopword EN).
+    texts = ["the cat the cat the cat"]
+    con = compute_top_ngrams(texts, n=2, top_k=10, remove_stopwords=True)
+    sin = compute_top_ngrams(texts, n=2, top_k=10, remove_stopwords=False)
+
+    con_ngrams = {item["ngram"] for item in con["top"]}
+    sin_ngrams = {item["ngram"] for item in sin["top"]}
+
+    # Sin filtrar, el bigrama dominante es "the cat".
+    assert "the cat" in sin_ngrams
+    # Al filtrar stopwords, ya no aparece "the cat" (queda solo "cat cat").
+    assert "the cat" not in con_ngrams
+    assert con_ngrams != sin_ngrams

python/functions/datascience/compute_vocabulary_stats.md

@@ -0,0 +1,91 @@
+---
+id: compute_vocabulary_stats_py_datascience
+name: compute_vocabulary_stats
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def compute_vocabulary_stats(texts: list, top_k: int = 20, remove_stopwords: bool = True) -> dict"
+description: "Profiles the vocabulary of a text corpus for EDA: tokenises a list of documents, counts term frequencies and derives lexical-richness measures — total tokens, unique types, type-token ratio (TTR), hapax legomena and the top-k most frequent terms. Pure, stdlib only (re + collections.Counter); no nltk, no sklearn. Inline ES+EN stopword list, opt-out via remove_stopwords. Never raises: empty/degenerate input returns the zeroed result."
+tags: [eda, datascience, text, nlp, vocabulary, ttr, hapax, pure, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [re, collections]
+example: |
+  from datascience.compute_vocabulary_stats import compute_vocabulary_stats
+  result = compute_vocabulary_stats(["el gato y el perro", "gato veloz"], top_k=5)
+tested: true
+tests:
+  - "test_basico"
+  - "test_vacio"
+  - "test_stopwords_quitadas"
+  - "test_stopwords_conservadas"
+test_file_path: "python/functions/datascience/compute_vocabulary_stats_test.py"
+file_path: "python/functions/datascience/compute_vocabulary_stats.py"
+params:
+  - name: texts
+    desc: "List of documents (strings) forming the corpus. Entries that are None or not a str are silently discarded. Tokens are extracted per document with re.findall(r'\\w+', doc.lower(), re.UNICODE); purely numeric tokens (tok.isdigit()) are dropped."
+  - name: top_k
+    desc: "Maximum number of most-frequent terms to return in top_terms. Default 20. Does not affect n_tokens/n_types/ttr/hapax — only the length of the top_terms list."
+  - name: remove_stopwords
+    desc: "When True (default) common Spanish+English stopwords from the inline _STOPWORDS set (~120 entries) are removed from the token stream before any counting. Set False to keep every word (raw lexical profile)."
+output: "Dict with the exact keys n_tokens (int), n_types (int), ttr (float|None, n_types/n_tokens rounded to 4 dp), n_hapax (int, terms occurring exactly once), hapax_pct (float|None, n_hapax/n_types*100 rounded to 2 dp) and top_terms (list of {term, count, pct} sorted by count descending, pct = count/n_tokens*100 rounded to 2 dp). For an empty corpus (no tokens after filtering): n_tokens=0, n_types=0, ttr=None, n_hapax=0, hapax_pct=None, top_terms=[]. Any exception degrades to that same empty result — the function never throws."
+---
+
+## Ejemplo
+
+```python
+from datascience.compute_vocabulary_stats import compute_vocabulary_stats
+
+compute_vocabulary_stats(
+    ["el gato y el perro", "gato veloz corre", "perro perro perro"],
+    top_k=5,
+)
+# {
+#   "n_tokens": 6,        # stopwords (el, y) eliminadas por defecto
+#   "n_types": 3,         # gato, perro, veloz, corre -> tras quitar stopwords
+#   "ttr": 0.5,           # n_types / n_tokens
+#   "n_hapax": 2,         # veloz, corre (1 aparicion cada uno)
+#   "hapax_pct": 50.0,    # n_hapax / n_types * 100
+#   "top_terms": [
+#     {"term": "perro", "count": 4, "pct": 44.44},
+#     {"term": "gato",  "count": 2, "pct": 22.22},
+#     ...
+#   ],
+# }
+
+# Perfil lexico crudo (sin filtrar stopwords):
+compute_vocabulary_stats(["the cat and the dog"], remove_stopwords=False)
+```
+
+## Cuando usarla
+
+Úsala al perfilar una columna o corpus de texto libre en un EDA del grupo `eda`:
+cuando necesites medir la riqueza léxica (cuántos tokens y cuántas palabras
+distintas, type-token ratio, porcentaje de palabras que solo aparecen una vez) y
+ver qué términos dominan el vocabulario (top-k frecuencias). Pásale la lista de
+documentos crudos (filas de la columna); `None` y valores no-string se ignoran
+solos. Es el equivalente para texto largo de `summarize_categorical`, que perfila
+categorías cortas.
+
+## Gotchas
+
+- Función pura y stdlib-only, pero el resultado depende del **idioma**: la lista
+  `_STOPWORDS` cubre español e inglés. Para otros idiomas pon
+  `remove_stopwords=False` o filtra fuera, o el perfil mezclará stopwords no
+  reconocidas en `top_terms`.
+- La tokenización es `\w+` con `re.UNICODE`: separa por puntuación y conserva
+  acentos/ñ, pero NO hace stemming ni lematización — "gato" y "gatos" cuentan
+  como tipos distintos. Tampoco hace stripping de acentos, así que "más" (con
+  tilde) y "mas" son tokens diferentes (ambos están en la stoplist).
+- Los tokens **puramente numéricos** (`"123"`) se descartan siempre; un token
+  alfanumérico mixto (`"covid19"`) se conserva.
+- `ttr` baja artificialmente en corpus grandes (más texto, más repetición): no
+  compares TTR entre corpus de tamaños muy distintos sin normalizar.
+- Nunca lanza: entrada vacía, `None`, o cualquier excepción interna devuelven el
+  resultado con ceros/`None`/`[]`. Comprueba `n_tokens == 0` para detectar el
+  caso degenerado.

python/functions/datascience/compute_vocabulary_stats.py

@@ -0,0 +1,99 @@
+"""Profile the vocabulary of a text corpus for EDA (pure, stdlib only).
+
+Tokenises a list of documents, counts term frequencies and derives lexical
+richness measures (type-token ratio, hapax legomena) plus the top-k terms.
+No external NLP dependencies (no nltk, no sklearn) — only ``re`` and
+``collections`` from the standard library.
+"""
+
+import re
+from collections import Counter
+
+# Common Spanish + English stopwords. Inline, lowercase, no accents stripped
+# beyond what already appears here. Filtering is opt-in via remove_stopwords.
+_STOPWORDS = {
+    # Spanish
+    "de", "la", "que", "el", "en", "y", "a", "los", "del", "se", "las", "por",
+    "un", "para", "con", "no", "una", "su", "al", "es", "lo", "como", "mas",
+    "más", "pero", "sus", "le", "ya", "o", "este", "si", "sí", "porque",
+    "esta", "entre", "cuando", "muy", "sin", "sobre", "tambien", "también",
+    "me", "hasta", "hay", "donde", "quien", "desde", "todo", "nos", "durante",
+    "todos", "uno", "les", "ni", "contra", "otros", "ese", "eso", "ante",
+    "ellos", "e", "esto", "antes", "algunos", "que", "unos", "yo", "otro",
+    "otras", "otra", "el", "tanto", "esa", "estos", "mucho", "nada", "muchos",
+    # English
+    "the", "of", "and", "to", "in", "is", "it", "for", "on", "with", "as",
+    "was", "but", "are", "this", "that", "an", "be", "by", "or", "not", "at",
+    "from", "my", "i", "you", "he", "she", "we", "they", "his", "her", "its",
+    "our", "their", "what", "which", "who", "whom", "has", "have", "had", "do",
+    "does", "did", "will", "would", "can", "could", "should", "may", "might",
+    "must", "if", "then", "than", "so", "too", "very", "just", "also", "were",
+    "been", "being", "there", "here", "all", "any", "some", "more", "most",
+    "out", "up", "down", "into", "over", "such", "only", "own", "same",
+}
+
+
+def compute_vocabulary_stats(texts, top_k=20, remove_stopwords=True) -> dict:
+    """Profile the vocabulary of a corpus of documents.
+
+    Args:
+        texts: List of strings (the corpus). Entries that are None or not a
+            string are discarded silently.
+        top_k: Maximum number of most-frequent terms to include in
+            ``top_terms``. Default 20. Does not affect the other measures.
+        remove_stopwords: When True (default) common ES+EN stopwords are
+            dropped from the token stream before any counting.
+
+    Returns:
+        A dict with the exact keys ``n_tokens``, ``n_types``, ``ttr``,
+        ``n_hapax``, ``hapax_pct`` and ``top_terms``. For an empty corpus (no
+        tokens after filtering): n_tokens=0, n_types=0, ttr=None, n_hapax=0,
+        hapax_pct=None, top_terms=[]. Never raises — any exception degrades to
+        the empty-corpus result.
+    """
+    empty = {
+        "n_tokens": 0,
+        "n_types": 0,
+        "ttr": None,
+        "n_hapax": 0,
+        "hapax_pct": None,
+        "top_terms": [],
+    }
+    try:
+        tokens = []
+        for doc in texts or []:
+            if not isinstance(doc, str):
+                continue
+            for tok in re.findall(r"\w+", doc.lower(), re.UNICODE):
+                if tok.isdigit():
+                    continue
+                if remove_stopwords and tok in _STOPWORDS:
+                    continue
+                tokens.append(tok)
+
+        n_tokens = len(tokens)
+        if n_tokens == 0:
+            return dict(empty)
+
+        counts = Counter(tokens)
+        n_types = len(counts)
+        ttr = round(n_types / n_tokens, 4)
+
+        n_hapax = sum(1 for c in counts.values() if c == 1)
+        hapax_pct = round(n_hapax / n_types * 100, 2)
+
+        top_terms = [
+            {"term": term, "count": count, "pct": round(count / n_tokens * 100, 2)}
+            for term, count in counts.most_common(top_k)
+        ]
+
+        return {
+            "n_tokens": n_tokens,
+            "n_types": n_types,
+            "ttr": ttr,
+            "n_hapax": n_hapax,
+            "hapax_pct": hapax_pct,
+            "top_terms": top_terms,
+        }
+    except Exception:
+        return dict(empty)

python/functions/datascience/compute_vocabulary_stats_test.py

@@ -0,0 +1,74 @@
+"""Tests para compute_vocabulary_stats."""
+
+import os
+import sys
+
+sys.path.insert(
+    0, os.path.join(os.path.dirname(__file__), "..", "..", "functions")
+)
+
+from datascience.compute_vocabulary_stats import compute_vocabulary_stats
+
+
+def test_basico():
+    # Corpus con repeticiones y hapax. Stopwords desactivadas para controlar
+    # exactamente que tokens entran.
+    texts = ["gato gato perro", "perro perro raton", "elefante"]
+    r = compute_vocabulary_stats(texts, top_k=10, remove_stopwords=False)
+
+    # n_types < n_tokens cuando hay repeticiones.
+    assert r["n_types"] < r["n_tokens"]
+    assert r["n_tokens"] == 7
+    assert r["n_types"] == 4  # gato, perro, raton, elefante
+
+    # ttr en (0, 1].
+    assert 0 < r["ttr"] <= 1
+    assert r["ttr"] == round(4 / 7, 4)
+
+    # top_terms ordenado por count descendente.
+    counts = [t["count"] for t in r["top_terms"]]
+    assert counts == sorted(counts, reverse=True)
+    assert r["top_terms"][0]["term"] == "perro"
+    assert r["top_terms"][0]["count"] == 3
+
+    # hapax: raton y elefante aparecen exactamente una vez.
+    assert r["n_hapax"] == 2
+    assert r["hapax_pct"] == round(2 / 4 * 100, 2)
+
+    # pct coherente con count/n_tokens.
+    assert r["top_terms"][0]["pct"] == round(3 / 7 * 100, 2)
+
+
+def test_vacio():
+    # Sin documentos validos -> ceros / None / [].
+    for arg in ([], None, [None, 123, ""], ["123 456"]):
+        r = compute_vocabulary_stats(arg)
+        assert r["n_tokens"] == 0
+        assert r["n_types"] == 0
+        assert r["ttr"] is None
+        assert r["n_hapax"] == 0
+        assert r["hapax_pct"] is None
+        assert r["top_terms"] == []
+
+
+def test_stopwords_quitadas():
+    texts = ["the gato the perro", "de la casa azul"]
+    r = compute_vocabulary_stats(texts, remove_stopwords=True)
+    terms = {t["term"] for t in r["top_terms"]}
+    # Stopwords ES+EN no deben aparecer.
+    assert "the" not in terms
+    assert "de" not in terms
+    assert "la" not in terms
+    # Palabras de contenido si.
+    assert "gato" in terms
+    assert "casa" in terms
+
+
+def test_stopwords_conservadas():
+    texts = ["the gato the perro", "de la casa azul"]
+    r = compute_vocabulary_stats(texts, remove_stopwords=False)
+    terms = {t["term"] for t in r["top_terms"]}
+    # Con el filtro desactivado, las stopwords se conservan.
+    assert "the" in terms
+    assert "de" in terms
+    assert "la" in terms

python/functions/datascience/detect_corpus_language.md

@@ -0,0 +1,80 @@
+---
+name: detect_corpus_language
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def detect_corpus_language(texts, top_k=10, sample_max=1000) -> dict"
+description: "Estima la distribucion de idiomas de un corpus de textos con la libreria langdetect (import perezoso). Funcion pura y defensiva del grupo eda: filtra documentos None/no-str/vacios, muestrea hasta sample_max docs, clasifica cada uno con detect() ignorando los que langdetect no puede resolver (LangDetectException), y devuelve la distribucion top_k por frecuencia mas el idioma dominante. Si langdetect no esta instalada o algo falla, degrada a {available: False, ...} y NUNCA lanza (dict-no-throw). Seed fija (DetectorFactory.seed=0) para deteccion determinista."
+tags: [eda, datascience, text, nlp, language-detection, langdetect, pure, python]
+params:
+  - name: texts
+    desc: "Lista de strings (documentos). Los elementos None, no-str o vacios tras strip se descartan antes de clasificar."
+  - name: top_k
+    desc: "Numero maximo de idiomas a devolver en distribution, ordenados por count descendente (desempate por codigo ISO ascendente). Default 10."
+  - name: sample_max
+    desc: "Numero maximo de documentos a clasificar (se toman los primeros del corpus) para acotar el coste. Default 1000."
+output: >
+  Dict con forma fija (dict-no-throw, nunca lanza):
+  {"available": bool, "n_detected": int,
+   "distribution": [{"lang": str, "count": int, "pct": float}, ...],
+   "dominant": str|None}.
+  available=True si langdetect es importable; lang son codigos ISO 639-1 ("es","en","fr",...);
+  pct = count/n_detected*100 redondeado a 2 decimales; n_detected = docs clasificados con exito;
+  dominant = idioma mas frecuente (None si no hubo detecciones). Corpus vacio con langdetect
+  presente -> available True, n_detected 0, distribution [], dominant None. Sin langdetect (o
+  fallo global) -> available False y el resto de campos a su valor vacio.
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [langdetect]
+tested: true
+tests: ["test_mixto_es_en", "test_vacio", "test_degradacion"]
+test_file_path: "python/functions/datascience/detect_corpus_language_test.py"
+file_path: "python/functions/datascience/detect_corpus_language.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience.detect_corpus_language import detect_corpus_language
+
+corpus = [
+    "este es un texto bastante largo en español para detectar el idioma correctamente",
+    "la inteligencia artificial transforma la manera en que trabajamos cada dia",
+    "this is a fairly long english text to detect the language correctly without issues",
+]
+out = detect_corpus_language(corpus)
+# {"available": True, "n_detected": 3,
+#  "distribution": [{"lang": "es", "count": 2, "pct": 66.67},
+#                   {"lang": "en", "count": 1, "pct": 33.33}],
+#  "dominant": "es"}
+```
+
+## Cuando usarla
+
+Cuando perfiles una columna o corpus de texto en un EDA y necesites saber en
+que idioma(s) esta escrito antes de elegir tokenizadores, stopwords, modelos
+NLP o stemmers. Util tambien como check de calidad: detectar corpus mezclados
+o un idioma inesperado. Llamala con la lista de textos crudos; la funcion
+limpia, muestrea y resume sola.
+
+## Gotchas
+
+- `langdetect` es **opcional**: si no esta instalada, la funcion no lanza —
+  devuelve `{"available": False, "n_detected": 0, "distribution": [], "dominant": None}`.
+  Comprueba `out["available"]` antes de usar la distribucion.
+- **Textos cortos** (pocas palabras o sin features lingüisticas) pueden no
+  detectarse: langdetect lanza `LangDetectException`, que se ignora y el doc no
+  cuenta en `n_detected`. Pasa frases razonablemente largas para resultados fiables.
+- **Determinismo**: se fija `DetectorFactory.seed = 0` en cada llamada para que la
+  deteccion sea reproducible; sin esa semilla langdetect puede dar resultados
+  ligeramente distintos entre ejecuciones.
+- `distribution` esta truncada a `top_k`; si el corpus tiene mas idiomas que
+  `top_k`, la suma de los `count` mostrados puede ser menor que `n_detected`
+  (pero `dominant` siempre refleja el idioma mas frecuente del corpus completo).

python/functions/datascience/detect_corpus_language.py

@@ -0,0 +1,91 @@
+"""Detecta la distribucion de idiomas de un corpus de textos.
+
+Funcion pura y defensiva: el computo es determinista y local (sin I/O de red).
+La libreria opcional `langdetect` se importa de forma perezosa dentro de la
+funcion; si no esta instalada (o cualquier paso falla), la funcion degrada
+limpiamente a `available=False` y NUNCA lanza excepciones.
+"""
+
+
+def detect_corpus_language(texts, top_k=10, sample_max=1000) -> dict:
+    """Estima la distribucion de idiomas de un corpus con `langdetect`.
+
+    Args:
+        texts: lista de strings (documentos). Los elementos None, no-str o
+            vacios tras strip se descartan.
+        top_k: numero maximo de idiomas a devolver en `distribution`,
+            ordenados por frecuencia descendente.
+        sample_max: numero maximo de documentos a clasificar (se toman los
+            primeros) para acotar el coste.
+
+    Returns:
+        dict con la forma fija (dict-no-throw):
+        {
+            "available": bool,   # True si langdetect es importable
+            "n_detected": int,   # documentos clasificados con exito
+            "distribution": [{"lang": str, "count": int, "pct": float}, ...],
+            "dominant": str | None,
+        }
+    """
+    degraded = {
+        "available": False,
+        "n_detected": 0,
+        "distribution": [],
+        "dominant": None,
+    }
+    try:
+        # Import perezoso con degradacion: si langdetect no esta disponible,
+        # devolvemos el dict degradado sin lanzar.
+        try:
+            from langdetect import detect, DetectorFactory
+
+            # Semilla fija -> deteccion determinista entre ejecuciones.
+            DetectorFactory.seed = 0
+        except Exception:
+            return dict(degraded)
+
+        # Normaliza y filtra el corpus.
+        docs = []
+        if texts:
+            for t in texts:
+                if isinstance(t, str):
+                    s = t.strip()
+                    if s:
+                        docs.append(s)
+
+        # Muestreo de los primeros `sample_max` documentos.
+        if sample_max is not None and sample_max >= 0:
+            docs = docs[:sample_max]
+
+        # Conteo por idioma; langdetect lanza LangDetectException en textos
+        # sin features detectables -> se ignora y se sigue.
+        counts: dict = {}
+        for doc in docs:
+            try:
+                lang = detect(doc)
+            except Exception:
+                continue
+            counts[lang] = counts.get(lang, 0) + 1
+
+        n_detected = sum(counts.values())
+
+        # Orden estable: por count descendente, desempate por codigo de idioma.
+        ordered = sorted(counts.items(), key=lambda kv: (-kv[1], kv[0]))
+
+        k = top_k if (top_k is not None and top_k >= 0) else len(ordered)
+        distribution = []
+        for lang, count in ordered[:k]:
+            pct = round(count / n_detected * 100, 2) if n_detected else 0.0
+            distribution.append({"lang": lang, "count": count, "pct": pct})
+
+        dominant = ordered[0][0] if ordered else None
+
+        return {
+            "available": True,
+            "n_detected": n_detected,
+            "distribution": distribution,
+            "dominant": dominant,
+        }
+    except Exception:
+        # Cualquier fallo global degrada a available False sin lanzar.
+        return dict(degraded)

python/functions/datascience/detect_corpus_language_test.py

@@ -0,0 +1,58 @@
+"""Tests para detect_corpus_language."""
+
+import builtins
+import os
+import sys
+
+# Anade python/functions a sys.path para importar el paquete `datascience`.
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+from datascience.detect_corpus_language import detect_corpus_language
+
+_ES = [
+    "este es un texto bastante largo en español para detectar el idioma correctamente sin problemas",
+    "la inteligencia artificial transforma la manera en que trabajamos cada dia en muchos sectores",
+]
+_EN = [
+    "this is a fairly long english text to detect the language correctly without any length issues",
+    "machine learning models can classify documents into many different categories quite reliably",
+]
+
+
+def test_mixto_es_en():
+    """Golden: corpus mixto ES+EN claro -> available True, >=2 idiomas, counts coherentes."""
+    out = detect_corpus_language(_ES + _EN)
+    assert out["available"] is True
+    assert out["dominant"] in {"es", "en"}
+    assert len(out["distribution"]) >= 2
+    total = sum(item["count"] for item in out["distribution"])
+    assert total == out["n_detected"]
+    assert out["n_detected"] == 4
+
+
+def test_vacio():
+    """Edge: lista vacia con langdetect presente -> available True, sin detecciones."""
+    out = detect_corpus_language([])
+    assert out["available"] is True
+    assert out["n_detected"] == 0
+    assert out["distribution"] == []
+    assert out["dominant"] is None
+
+
+def test_degradacion(monkeypatch):
+    """Error path: si langdetect no es importable -> degrada a available False sin lanzar."""
+    import datascience.detect_corpus_language as m
+
+    real_import = builtins.__import__
+
+    def fake_import(name, *a, **k):
+        if name == "langdetect" or name.startswith("langdetect."):
+            raise ImportError("simulado")
+        return real_import(name, *a, **k)
+
+    monkeypatch.setattr(builtins, "__import__", fake_import)
+    out = m.detect_corpus_language(["hola mundo", "hello world"])
+    assert out["available"] is False
+    assert out["n_detected"] == 0
+    assert out["distribution"] == []
+    assert out["dominant"] is None

python/functions/datascience/extract_text_sample.md

@@ -0,0 +1,102 @@
+---
+name: extract_text_sample
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def extract_text_sample(db_path: str, table: str, columns: list, backend: str = 'duckdb', sample: int = 2000) -> dict"
+description: "Muestrea columnas de texto de una tabla DuckDB/Postgres con push-down SQL (LIMIT sample), SIN traer la tabla entera a RAM. Funcion impura del grupo de capacidad `eda`: la usan los capitulos de texto/NLP del AutomaticEDA que necesitan valores crudos de texto (longitudes, tokens, ejemplos) sobre una muestra acotada. Construye el lector read-only query_fn(sql)->dict igual que build_eda_render_ctx (closure sobre duckdb_query_readonly / pg_query importados perezosamente desde infra). Escapa los identificadores con comillas dobles y lanza una sola query SELECT \"c1\", \"c2\" FROM \"table\" LIMIT n. Por columna, la lista de strings solo contiene valores NO None y NO vacios: cada celda no nula se convierte con str(...) y se descarta si queda cadena vacia. Estilo dict-no-throw del grupo eda: NUNCA lanza; ante cualquier fallo (query, conversion, backend desconocido) devuelve {status:'error', error:str, columns:{}, n:0}. La clave n reporta el numero de FILAS leidas por la query (antes de filtrar None/vacios)."
+tags: [eda, datascience, text, nlp, extraction, read-only, duckdb, postgres, python]
+uses_functions: [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 inyecta en el closure query_fn. No se valida aqui: si la base no existe o el DSN es invalido, la query devuelve status error y el resultado es {status:'error', ...} (no lanza)."
+  - name: table
+    desc: "nombre de la tabla. Se escapa con comillas dobles en la query (SELECT ... FROM \"table\")."
+  - name: columns
+    desc: "lista de nombres de columna de texto a muestrear. Se filtra a las entradas que sean str no vacio; cada nombre se escapa con comillas dobles. Si tras filtrar queda vacia -> {status:'ok', columns:{}, n:0} sin tocar la base."
+  - name: backend
+    desc: "'duckdb' (default) o 'postgres'. Selecciona el lector read-only del registry (duckdb_query_readonly / pg_query). Cualquier otro valor -> {status:'error', error:'backend desconocido: <valor>', columns:{}, n:0}."
+  - name: sample
+    desc: "maximo de filas a muestrear (clausula LIMIT). Default 2000. Acota memoria y tiempo: con tablas grandes obtienes el primer tramo por orden fisico (sin ORDER BY), no un muestreo uniforme."
+output: "dict dict-no-throw (NUNCA lanza): {status:'ok'|'error', columns:{col_name:[str,...]}, n:int, error:str}. En exito (status='ok') columns mapea cada columna pedida a la lista de sus valores de texto NO None y NO vacios (cada celda convertida con str(...)); n es el numero de FILAS leidas por la query (antes de filtrar None/vacios). columns vacio -> {status:'ok', columns:{}, n:0}. En error (backend desconocido, query con status!='ok', o cualquier excepcion) -> {status:'error', error:str, columns:{}, n:0}; la clave error solo aparece en este caso."
+tested: true
+tests: ["test_extract_basic", "test_backend_desconocido", "test_columns_vacio", "test_sample_limit"]
+test_file_path: "python/functions/datascience/extract_text_sample_test.py"
+file_path: "python/functions/datascience/extract_text_sample.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+# Import directo del submodulo (no requiere export en datascience/__init__.py).
+from datascience.extract_text_sample import extract_text_sample
+
+# Muestrea hasta 2000 filas de dos columnas de texto de una tabla DuckDB.
+res = extract_text_sample(
+    "data/reviews.duckdb", "reviews", ["title", "body"],
+    backend="duckdb", sample=2000,
+)
+# res == {
+#   "status": "ok",
+#   "columns": {
+#     "title": ["Gran producto", "No funciona", ...],   # solo no-None, no-""
+#     "body":  ["Lo uso a diario...", ...],
+#   },
+#   "n": 2000,   # filas leidas por la query (antes de filtrar None/vacios)
+# }
+
+# Postgres: db_path es el DSN.
+res_pg = extract_text_sample(
+    "postgresql://user:pass@localhost:5433/trends", "comentarios", ["texto"],
+    backend="postgres", sample=500,
+)
+```
+
+## Cuando usarla
+
+Cuando necesites valores CRUDOS de texto de una o varias columnas para analisis
+NLP/texto (distribucion de longitudes, conteo de tokens, ejemplos representativos,
+deteccion de idioma) pero NO quieras cargar la tabla entera en memoria. Es el
+muestreador de texto del grupo `eda`: una sola llamada con push-down `LIMIT`
+devuelve listas de strings por columna, limpias de None y vacios, listas para
+alimentar un capitulo de texto del AutomaticEDA o cualquier rutina de tokenizado.
+Usala junto a `profile_table` / `build_eda_render_ctx` cuando el perfil agregado
+no basta y hace falta el texto real.
+
+## 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 devuelve `{status:'error', error:str, columns:{}, n:0}`.
+- **`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 dict de error. Es metadata, no comportamiento.
+- **Backend desconocido**: con un `backend` que no sea `duckdb` ni `postgres`
+  devuelve `{status:'error', error:'backend desconocido: <valor>', columns:{},
+  n:0}` sin tocar la base.
+- **Las listas NO incluyen None ni cadenas vacias**: cada celda no nula se pasa
+  por `str(...)` y se descarta si queda `""`. Por eso `len(columns[col])` puede ser
+  menor que `n` (que cuenta las filas leidas). Si necesitas alineacion por fila
+  (una entrada por fila aunque sea None), usa `build_eda_render_ctx` (raw_numeric),
+  no esta funcion.
+- **`LIMIT sample` sin `ORDER BY`**: con tablas grandes obtienes el primer tramo
+  por orden fisico del backend, no un muestreo uniforme ni reproducible. Sube
+  `sample` para mas cobertura, o pre-ordena/aleatoriza la tabla si necesitas
+  representatividad.
+- **DuckDB en sandbox por defecto**: `duckdb_query_readonly` abre la conexion con
+  `enable_external_access=False`, asi que la query solo puede leer la propia base
+  (no `read_csv`/`httpfs`/`ATTACH` a paths externos). Lee tablas ya existentes en
+  el archivo DuckDB sin problema.
+- **No loguear los datos crudos**: las listas de `columns` pueden contener texto
+  sensible (reviews, comentarios, PII). En trazas usa solo conteos (`n`,
+  `len(columns[col])`) y nombres de columna, no el dict completo.

python/functions/datascience/extract_text_sample.py

@@ -0,0 +1,112 @@
+"""extract_text_sample — muestrea columnas de texto de una tabla sin cargarla en RAM.
+
+Funcion impura (lee de la base de datos) del grupo de capacidad `eda`. Dado un
+``db_path`` + ``table`` (DuckDB o PostgreSQL) y una lista de ``columns`` de texto,
+trae una MUESTRA de esas columnas con push-down SQL (``LIMIT sample``), nunca la
+tabla entera. La usan los capitulos de texto/NLP del AutomaticEDA que necesitan
+valores crudos de texto (longitudes, tokens, ejemplos) sin materializar millones
+de filas en memoria.
+
+El lector read-only ``query_fn(sql) -> dict`` se construye igual que en
+``build_eda_render_ctx`` / ``profile_table``: un closure sobre el wrapper del
+registry (``duckdb_query_readonly`` / ``pg_query``), importado perezosamente
+dentro de la funcion para no crear ciclos al cargar el ``__init__`` del paquete
+``datascience``. Nunca abre conexiones fuera de esos wrappers.
+
+Estilo dict-no-throw del grupo `eda`: la funcion NUNCA lanza. Captura cualquier
+excepcion (query, conversion) y devuelve ``{"status":"error", "error":str(e),
+"columns":{}, "n":0}``. Si la query subyacente devuelve ``status != "ok"``, se
+propaga como error con el mensaje del wrapper.
+
+Por columna, la lista de strings solo contiene valores NO nulos y NO vacios:
+cada celda no-None se convierte con ``str(...)`` y se descarta si queda ``""``.
+La clave ``n`` reporta el numero de FILAS leidas por la query (antes de filtrar
+los None/vacios), util para saber cuanto se muestreo realmente.
+"""
+
+
+def extract_text_sample(db_path, table, columns, backend="duckdb", sample=2000):
+    """Muestrea columnas de texto de una tabla DuckDB/Postgres con push-down SQL.
+
+    Args:
+        db_path: ruta al archivo DuckDB, o DSN PostgreSQL si backend="postgres".
+            Se inyecta en el closure query_fn. No se valida aqui: si la base no
+            existe o el DSN es invalido, la query devuelve status error y el
+            resultado es {status:'error', ...} (no lanza).
+        table: nombre de la tabla. Se escapa con comillas dobles en la query.
+        columns: lista de nombres de columna de texto a muestrear. Se filtra a las
+            entradas que sean str no vacio; cada nombre se escapa con comillas
+            dobles. Si tras filtrar queda vacia -> {status:'ok', columns:{}, n:0}.
+        backend: "duckdb" (default) o "postgres". Selecciona el lector read-only
+            del registry (duckdb_query_readonly / pg_query). Cualquier otro valor
+            -> {status:'error', error:'backend desconocido: ...', columns:{}, n:0}.
+        sample: maximo de filas a muestrear (clausula LIMIT). Default 2000. Acota
+            memoria y tiempo: con tablas grandes obtienes el primer tramo por
+            orden fisico, no un muestreo uniforme.
+
+    Returns:
+        dict (dict-no-throw, NUNCA lanza):
+        {"status": "ok"|"error",
+         "columns": {col_name: [str, str, ...], ...},  # solo no-None, no-""
+         "n": int,        # nº de filas leidas por la query (antes de filtrar)
+         "error": str}    # solo presente si status == "error"
+    """
+    try:
+        # 1) Lector read-only del backend activo, construido como en
+        # build_eda_render_ctx (closure sobre el wrapper del registry). Imports
+        # perezosos: este modulo vive en el paquete `datascience`, importar a
+        # `infra` a nivel de modulo crearia un ciclo al cargar el __init__.
+        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:
+            return {
+                "status": "error",
+                "error": f"backend desconocido: {backend}",
+                "columns": {},
+                "n": 0,
+            }
+
+        # 2) Columnas validas (str no vacio). Si no queda ninguna, nada que
+        # muestrear: ok con columns vacio.
+        cols = []
+        if isinstance(columns, (list, tuple)):
+            cols = [c for c in columns if isinstance(c, str) and c != ""]
+        if not cols:
+            return {"status": "ok", "columns": {}, "n": 0}
+
+        # 3) Push-down: una sola query con LIMIT. Identificadores escapados con
+        # comillas dobles, igual que build_eda_render_ctx.
+        cols_sql = ", ".join(f'"{c}"' for c in cols)
+        sql = f'SELECT {cols_sql} FROM "{table}" LIMIT {int(sample)}'
+        q = query_fn(sql)
+        if not isinstance(q, dict) or q.get("status") != "ok":
+            err = q.get("error") if isinstance(q, dict) else "query sin resultado"
+            return {"status": "error", "error": str(err), "columns": {}, "n": 0}
+
+        rows = q.get("rows") or []
+        out = {c: [] for c in cols}
+        for row in rows:
+            if not isinstance(row, dict):
+                continue
+            for c in cols:
+                value = row.get(c)
+                if value is None:
+                    continue
+                s = str(value)
+                if s == "":
+                    continue
+                out[c].append(s)
+
+        return {"status": "ok", "columns": out, "n": len(rows)}
+    except Exception as exc:  # noqa: BLE001 - dict-no-throw del grupo eda
+        return {"status": "error", "error": str(exc), "columns": {}, "n": 0}

python/functions/datascience/extract_text_sample_test.py

@@ -0,0 +1,83 @@
+"""Tests para extract_text_sample.
+
+Self-contained: crea un DuckDB temporal pequeño con una columna de texto (algunas
+filas con NULL) y una numerica, y verifica que la muestra de texto trae solo los
+valores no nulos, que el backend desconocido y la lista de columnas vacia se
+manejan dict-no-throw, y que sample acota el numero de filas leidas.
+"""
+
+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 datascience.extract_text_sample import extract_text_sample  # noqa: E402
+
+_TABLE = "t"
+# 6 filas: txt VARCHAR con dos NULL, other INT siempre presente.
+_ROWS = [
+    ("alpha", 1),
+    ("beta", 2),
+    (None, 3),
+    ("gamma", 4),
+    (None, 5),
+    ("delta", 6),
+]
+_TXT_NON_NULL = {"alpha", "beta", "gamma", "delta"}
+
+
+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), "text_sample.duckdb")
+    con = duckdb.connect(db_path)
+    try:
+        con.execute(f'CREATE TABLE "{_TABLE}" (txt VARCHAR, other INTEGER)')
+        con.executemany(f'INSERT INTO "{_TABLE}" VALUES (?, ?)', _ROWS)
+    finally:
+        con.close()
+    return db_path
+
+
+def test_extract_basic(tmp_path):
+    db_path = _make_db(tmp_path)
+    res = extract_text_sample(db_path, _TABLE, ["txt"])
+    assert res["status"] == "ok"
+    # n = filas leidas por la query (6), antes de filtrar None.
+    assert res["n"] == len(_ROWS)
+    # columns["txt"] trae solo los strings no nulos (los dos NULL fuera).
+    assert "txt" in res["columns"]
+    assert set(res["columns"]["txt"]) == _TXT_NON_NULL
+    assert len(res["columns"]["txt"]) == len(_TXT_NON_NULL)
+    # No se pidio "other", no debe aparecer.
+    assert "other" not in res["columns"]
+
+
+def test_backend_desconocido(tmp_path):
+    db_path = _make_db(tmp_path)
+    res = extract_text_sample(db_path, _TABLE, ["txt"], backend="mysql")
+    assert res["status"] == "error"
+    assert "backend desconocido" in res["error"]
+    assert res["columns"] == {}
+    assert res["n"] == 0
+
+
+def test_columns_vacio(tmp_path):
+    db_path = _make_db(tmp_path)
+    res = extract_text_sample(db_path, _TABLE, [])
+    assert res["status"] == "ok"
+    assert res["columns"] == {}
+    assert res["n"] == 0
+
+
+def test_sample_limit(tmp_path):
+    db_path = _make_db(tmp_path)
+    res = extract_text_sample(db_path, _TABLE, ["txt"], sample=2)
+    assert res["status"] == "ok"
+    # sample=2 -> la query lee como mucho 2 filas.
+    assert res["n"] == 2
+    assert len(res["columns"]["txt"]) <= 2

python/functions/datascience/render_automatic_eda_markdown.md

@@ -0,0 +1,89 @@
+---
+name: render_automatic_eda_markdown
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def render_automatic_eda_markdown(chapters_or_profile, out_path: str, meta: dict = None) -> dict"
+description: "Renderiza un documento AutomaticEDA por CAPÍTULOS (modelo de bloques independiente del formato) en un único MARKDOWN autocontenido pensado para PEGAR A UN LLM. Acepta una lista de capítulos del modelo o directamente un TableProfile del grupo eda (construye los capítulos canónicos con build_document). Prioriza TEXTO + DATOS sobre lo visual: las tablas se vuelcan como tablas markdown con TODAS las filas (sin paginar — no hay páginas que cortar), una figura matplotlib se reduce a su caption más la tabla de datos subyacente (Desde/Hasta/Frecuencia de las barras del histograma) porque un LLM no ve la imagen, y los marcadores de glosario se eliminan conservando el **negrita**. Lleva cabecera (# título), bloque de metadatos en blockquote e índice numerado con anclas GitHub. Espejo de render_automatic_eda_pdf/render_automatic_eda_pptx pero SIN manifest (KISS, el markdown es un único artefacto de texto). dict-no-throw: nunca lanza, devuelve {path, n_chars, chapters, note}; en error fatal path es None y note explica la causa. Flag opcional meta['embed_figures'] exporta PNGs junto al .md (off por defecto)."
+tags: [eda, markdown, render, report, llm, automatic-eda, chapters, versioned, no-cut, text, datascience, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [os, re, matplotlib, "datascience.automatic_eda"]
+params:
+  - name: chapters_or_profile
+    desc: "una lista de capítulos del modelo AutomaticEDA (dataclasses Chapter o dicts {id,title,version,blocks}) O un TableProfile dict del grupo eda. Si es un TableProfile, los capítulos canónicos se construyen con build_document(profile, meta['ctx']). Bloques soportados: heading, markdown, kv_table, data_table, figure, image, caption, note, group, glossary_entry. Lectura defensiva: lo no reconocido se degrada a Note, nunca lanza."
+  - name: out_path
+    desc: "ruta del archivo .md de salida. Los directorios padre se crean si faltan. Directorio no escribible → {path:None, note:<causa>} sin lanzar."
+  - name: meta
+    desc: "dict opcional. Claves: title (título del documento), ctx (dict con dataset_name→Dataset, source_origin→Fuente, storage→Almacenamiento, n_rows/n_cols→Dimensiones; también lo consumen los builders de capítulo cuando se da un profile), generated_at (timestamp; si falta se genera ISO UTC), embed_figures (True para exportar PNGs <basename>_figN.png junto al .md; por defecto False y el markdown queda autocontenido)."
+output: "dict (nunca lanza): {path: str|None, n_chars: int, chapters: list[{id,version}], note: str}. En error fatal (p.ej. directorio no escribible) path es None y note explica la causa. Un documento sin capítulos aplicables produce un markdown mínimo válido con 'documento vacío' y chapters=[]."
+tested: true
+tests: ["test_golden_bloques_sinteticos_serializa_todo_a_markdown", "test_edge_documento_vacio_no_revienta", "test_profile_path_construye_capitulos_y_escribe"]
+test_file_path: "python/functions/datascience/render_automatic_eda_markdown_test.py"
+file_path: "python/functions/datascience/render_automatic_eda_markdown.py"
+---
+
+## Ejemplo
+
+```python
+from datascience import render_automatic_eda_markdown
+
+# Desde un TableProfile del grupo eda (mismo modelo que los renderers PDF/PPTX).
+profile = {
+    "table": "ventas", "source": "/data/ventas.csv",
+    "n_rows": 1000, "n_cols": 2, "quality_score": 92.5,
+    "columns": [
+        {"name": "precio", "inferred_type": "numeric", "null_pct": 0.01,
+         "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,
+         "categorical": {"top": [{"value": "neumaticos", "count": 500}]}},
+    ],
+}
+res = render_automatic_eda_markdown(
+    profile, "reports/ventas_aeda.md",
+    {"title": "EDA — ventas",
+     "ctx": {"dataset_name": "Ventas", "source_origin": "ERP export",
+             "n_rows": 1000, "n_cols": 2}})
+print(res["path"], res["n_chars"], res["chapters"])
+# -> reports/ventas_aeda.md 4123 [{'id':'portada','version':'1.0.0'}, ...]
+```
+
+## Cuando usarla
+
+Cuando quieras **pegar el EDA a un LLM** (ChatGPT, Claude, ...) o tenerlo en texto
+plano versionable: mismo documento por capítulos que el PDF/PPTX, pero serializado a
+Markdown sin binarios. Úsala como tercera salida junto a `render_automatic_eda_pdf`
+(móvil) y `render_automatic_eda_pptx` (compartir) desde el MISMO modelo de capítulos.
+A diferencia de esas dos, no hay páginas ni slides: todas las filas de cada tabla se
+vuelcan (nada se corta) y cada figura se reduce a su caption + la tabla de datos
+subyacente, que es lo que un LLM puede leer. Para añadir capítulos al documento, ver
+`docs/capabilities/automatic_eda.md`.
+
+## Gotchas
+
+- **Impura**: escribe el `.md` en `out_path` (crea los directorios padre). Con
+  `meta['embed_figures']=True` además exporta un PNG `<basename>_figN.png` por figura
+  junto al `.md`; por defecto NO exporta nada y el markdown queda autocontenido.
+- **Nunca lanza** (dict-no-throw): un bloque que falle se degrada a una nota y se anota
+  en `note`; el documento se escribe igual. Un profile/lista vacíos producen un markdown
+  mínimo válido con `*(documento vacío …)*` y `chapters=[]`.
+- **Figuras = datos, no imagen**: un bloque `figure` se serializa como `*Figura: caption*`
+  más, si la figura matplotlib trae barras (histograma / barras), una tabla
+  `| Desde | Hasta | Frecuencia |` extraída de los `Rectangle` patches (máx 100 filas;
+  el resto se trunca con `*… (N filas más)*`). Si no hay barras o algo falla, solo sale
+  el caption. La figura se cierra (`plt.close`) tras leerla.
+- **Glosario vs negrita**: se eliminan SOLO los marcadores de glosario
+  `[[term:key]]visible[[/term]]` (queda `visible`); el `**negrita**` markdown SE
+  CONSERVA (es válido). No se usa `strip_inline_md` aquí porque ese también quita el bold.
+- **Anclas del índice**: el `## Índice` enlaza cada capítulo con un ancla estilo GitHub
+  del encabezado `## N. Título` (minúsculas, espacios→`-`, sin signos). Si dos capítulos
+  comparten título exacto sus anclas colisionan (caso raro; los capítulos canónicos tienen
+  títulos únicos).
+- **Tablas**: las celdas escapan `|` (→ `\|`) y pliegan saltos de línea a `<br>` para no
+  romper la columna. No hay reparto por ancho — un LLM no lo necesita.

python/functions/datascience/render_automatic_eda_markdown.py

@@ -0,0 +1,55 @@
+"""render_automatic_eda_markdown — chapter-based EDA report as one Markdown file.
+
+Public ``eda``-group entry point that serializes an AutomaticEDA document (a list
+of chapters, or an ``eda`` TableProfile from which the canonical chapters are
+built) into a single self-contained Markdown file optimised to be **pasted into
+an LLM**: plain text, Markdown tables (every row dumped — there are no pages to
+cut), figures reduced to caption + underlying data, no binaries. It mirrors
+``render_automatic_eda_pdf`` / ``render_automatic_eda_pptx`` but for text output;
+unlike those it writes no manifest (KISS — Markdown is a single text artefact).
+
+dict-no-throw: never raises. Returns ``{path, n_chars, chapters, note}``; on a
+fatal error ``path`` is None and ``note`` explains why.
+"""
+
+from __future__ import annotations
+
+from datascience.automatic_eda import build_document, render_md
+from datascience.automatic_eda.model import as_chapter, as_chapters
+
+
+def _coerce_chapters(chapters_or_profile, meta: dict) -> list:
+    """Accept chapters OR an eda profile and return a list of Chapter."""
+    arg = chapters_or_profile
+    if isinstance(arg, (list, tuple)):
+        return as_chapters(list(arg))
+    if isinstance(arg, dict):
+        if "blocks" in arg and "columns" not in arg:
+            ch = as_chapter(arg)
+            return [ch] if ch is not None else []
+        return build_document(arg, (meta or {}).get("ctx"))
+    return []
+
+
+def render_automatic_eda_markdown(chapters_or_profile, out_path: str,
+                                  meta: dict = None) -> dict:
+    """Render an AutomaticEDA document into a single self-contained Markdown file.
+
+    Args:
+        chapters_or_profile: a list of chapters (``Chapter`` dataclasses or
+            dicts) or an ``eda`` TableProfile dict (chapters built via
+            ``build_document(profile, meta['ctx'])``).
+        out_path: filesystem path for the ``.md`` (parent dirs are created).
+        meta: optional dict. Recognised keys: ``title``, ``ctx`` (dict with
+            ``dataset_name``/``source_origin``/``storage``/``n_rows``/``n_cols``),
+            ``generated_at``, ``embed_figures`` (export PNGs beside the .md,
+            default False — off keeps the Markdown self-contained).
+
+    Returns:
+        dict (never raises): ``{path: str|None, n_chars: int,
+        chapters: list[{id, version}], note: str}``. On a fatal error ``path`` is
+        None and ``note`` explains the cause.
+    """
+    meta = dict(meta or {})
+    chapters = _coerce_chapters(chapters_or_profile, meta)
+    return render_md(chapters, out_path, meta)

python/functions/datascience/render_automatic_eda_markdown_test.py

@@ -0,0 +1,168 @@
+"""Tests for render_automatic_eda_markdown — DoD: golden + edge + profile path.
+
+Self-contained synthetic blocks (no DuckDB). Verifies every block kind serializes
+to Markdown (heading, markdown with glossary+bold, kv/data tables, a figure whose
+histogram bars become a data table, caption, note, group, glossary entry), that a
+leading level-1 heading equal to the chapter title is omitted, that an empty
+document degrades to a valid minimal Markdown without raising, and that passing a
+minimal TableProfile builds chapters and writes the file.
+"""
+
+import os
+import tempfile
+
+from datascience.render_automatic_eda_markdown import render_automatic_eda_markdown
+from datascience.automatic_eda.model import (
+    Caption, Chapter, DataTable, Figure, GlossaryEntry, Group, Heading, KVTable,
+    Markdown, Note,
+)
+
+
+def _hist_fig():
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    fig, ax = plt.subplots()
+    ax.hist([1, 1, 2, 2, 2, 3, 4, 4, 5, 5, 5, 5], bins=5)
+    return fig
+
+
+def _chapters() -> list:
+    blocks = [
+        Heading("Demo", 1),                       # == chapter title -> omitted.
+        Heading("Seccion dos", 2),                # -> ####
+        Markdown("Texto con [[term:ent]]entropia[[/term]] y **bold** aqui."),
+        KVTable(rows=[("Filas", 1000), ("Columnas", 5)], title="Resumen"),
+        DataTable(header=["col", "valor"],
+                  rows=[["alpha", "111"], ["beta", "222"], ["gamma", "333"]],
+                  title="Datos", note="nota inferior"),
+        Figure(make=_hist_fig, caption="Histograma demo"),
+        Caption("pie de figura"),
+        Note("una nota aparte"),
+        Group(title="Grupo X", blocks=[Markdown("dentro del grupo")]),
+        GlossaryEntry(key="ent", label="Entropia",
+                      definition="Medida de incertidumbre."),
+    ]
+    return [Chapter(id="demo", title="Demo", version="1.0.0", blocks=blocks)]
+
+
+def _read(path: str) -> str:
+    with open(path, "r", encoding="utf-8") as fh:
+        return fh.read()
+
+
+def test_golden_bloques_sinteticos_serializa_todo_a_markdown():
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "demo.md")
+        res = render_automatic_eda_markdown(
+            _chapters(), out,
+            {"title": "EDA Demo",
+             "ctx": {"dataset_name": "Demo", "n_rows": 12, "n_cols": 2}})
+        assert res["path"] == out
+        assert os.path.exists(out)
+        assert res["n_chars"] > 0
+        assert res["chapters"] == [{"id": "demo", "version": "1.0.0"}]
+
+        content = _read(out)
+        # Document structure.
+        assert content.startswith("# ")
+        assert "## Índice" in content
+        # A Markdown table is present (header + separator row).
+        assert "| " in content and "| --- " in content
+        # DataTable values are all dumped.
+        for v in ("alpha", "111", "beta", "222", "gamma", "333"):
+            assert v in content
+        # Glossary markers stripped, bold kept.
+        assert "[[term" not in content
+        assert "[[/term]]" not in content
+        assert "**bold**" in content
+        assert "entropia" in content  # visible glossary text preserved.
+        # Figure histogram bars became a data table.
+        assert "| Desde | Hasta | Frecuencia |" in content
+        # Glossary entry rendered as a level-3 heading.
+        assert "### Entropia" in content
+        # Level-2 heading -> ####.
+        assert "#### Seccion dos" in content
+        # Leading level-1 heading equal to the title was omitted.
+        assert "### Demo" not in content
+        # Group title rendered.
+        assert "### Grupo X" in content
+
+
+def _hist_fig_with_span():
+    """Histogram with a wide ``axvspan`` (±1σ band) over it.
+
+    Reproduces the num_distr figure shape: matplotlib keeps the span as a lone
+    Rectangle in ``ax.patches`` alongside the bin bars; it must NOT leak into the
+    extracted bins table as a fake bin (it is ~5x wider than a bin)."""
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    fig, ax = plt.subplots()
+    data = [1, 1, 2, 2, 2, 3, 4, 4, 5, 5, 5, 5]
+    ax.hist(data, bins=5)
+    ax.axvspan(2.0, 4.0, alpha=0.2)   # mean±σ band — a wide stray rectangle.
+    return fig
+
+
+def test_figura_descarta_axvspan_de_la_tabla_de_bins():
+    """The ±1σ band rectangle must not appear as a row in the bins table."""
+    blocks = [Figure(make=_hist_fig_with_span, caption="Hist con banda")]
+    chapters = [Chapter(id="f", title="Fig", version="1.0.0", blocks=blocks)]
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "fig.md")
+        render_automatic_eda_markdown(chapters, out, {"title": "T"})
+        content = _read(out)
+        assert "| Desde | Hasta | Frecuencia |" in content
+        # Extract the rows of the bins table: lines between the header/separator
+        # and the next blank line.
+        lines = content.splitlines()
+        hi = next(i for i, ln in enumerate(lines)
+                  if ln.startswith("| Desde | Hasta | Frecuencia |"))
+        rows = []
+        for ln in lines[hi + 2:]:           # skip header + separator
+            if not ln.startswith("|"):
+                break
+            rows.append(ln)
+        # 5 histogram bins, no extra wide span row.
+        assert len(rows) == 5, rows
+        # No row spans a width of ~2.0 (the axvspan from x=2 to x=4).
+        for ln in rows:
+            cells = [c.strip() for c in ln.strip("|").split("|")]
+            lo, hi_v = float(cells[0]), float(cells[1])
+            assert (hi_v - lo) < 1.5, f"wide span leaked: {ln}"
+
+
+def test_edge_documento_vacio_no_revienta():
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "empty.md")
+        res = render_automatic_eda_markdown([], out, {})
+        assert res["path"] == out
+        assert os.path.exists(out)
+        assert res["chapters"] == []
+        content = _read(out)
+        assert "documento vacío" in content
+        assert content.startswith("# ")
+
+
+def test_profile_path_construye_capitulos_y_escribe():
+    profile = {
+        "table": "mini",
+        "source": "/data/mini.csv",
+        "n_rows": 10,
+        "n_cols": 1,
+        "quality_score": 88.0,
+        "columns": [
+            {"name": "x", "inferred_type": "numeric", "null_pct": 0.0,
+             "null_count": 0,
+             "numeric": {"mean": 1.0, "median": 1.0, "min": 0.0, "max": 2.0,
+                         "std": 0.5}},
+        ],
+    }
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "mini.md")
+        res = render_automatic_eda_markdown(
+            profile, out, {"title": "Mini", "ctx": {"dataset_name": "Mini"}})
+        assert res["path"] == out  # not None — no exception, file written.
+        assert os.path.exists(out)
+        assert res["n_chars"] > 0

python/functions/pipelines/render_automatic_eda.py

@@ -1,9 +1,10 @@
-"""render_automatic_eda — EDA completo one-shot: perfil → ctx → PDF + PPTX.
+"""render_automatic_eda — EDA completo one-shot: perfil → ctx → PDF + PPTX + MD.
 
 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:
+PostgreSQL), produce el informe AutomaticEDA COMPLETO en sus tres formatos a la
+vez (PDF móvil A5 + PPTX 16:9 + Markdown autocontenido para pegar a un LLM) con
+los capítulos POBLADOS, en una sola llamada. Compone, sin reimplementar su
+lógica, varias funciones del registry:
 
   - profile_table          : perfila la tabla end-to-end (TableProfile agregado),
                              opcionalmente con modelos baratos y análisis de serie.
@@ -12,8 +13,11 @@ llamada. Compone, sin reimplementar su lógica, cuatro funciones del registry:
                              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.
+  - render_automatic_eda_pdf      : renderiza el documento por capítulos a PDF.
+  - render_automatic_eda_pptx     : renderiza el mismo documento a PPTX.
+  - render_automatic_eda_markdown : serializa el mismo documento a Markdown
+                                    autocontenido (texto + tablas markdown, sin
+                                    binarios) para incorporar a un LLM.
 
 El TableProfile agregado basta para portada/overview/distribuciones/calidad/
 correlación, pero los capítulos `modelos`, `timeseries`, `geospatial` y
@@ -32,6 +36,7 @@ from datetime import datetime, timezone
 
 from datascience import (
     build_eda_render_ctx,
+    render_automatic_eda_markdown,
     render_automatic_eda_pdf,
     render_automatic_eda_pptx,
     run_eda_models,
@@ -93,6 +98,7 @@ def render_automatic_eda(
     out_dir: str = "reports",
     basename: str = None,
     ctx_extra: dict = None,
+    emit_md: bool = True,
 ) -> dict:
     """Perfila una tabla y emite el informe AutomaticEDA completo (PDF + PPTX).
 
@@ -140,13 +146,19 @@ def render_automatic_eda(
         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.
+        emit_md: además del PDF y el PPTX, emite un Markdown autocontenido del
+            MISMO documento por capítulos (texto plano + tablas markdown, sin
+            binarios), pensado para pegar a un LLM. Default True. La ruta sale en
+            la clave de retorno ``aeda_md_path``. No altera las demás salidas.
 
     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>}
+             "aeda_md_path": str|None, "manifest_path": str|None,
+             "n_pages": int, "n_slides": int, "md_chars": int|None,
+             "pdf_note": str, "pptx_note": str, "md_note": str|None,
+             "profile": <TableProfile>}
 
         En error: {"status": "error", "error": str}.
     """
@@ -243,15 +255,26 @@ def render_automatic_eda(
         rpdf = render_automatic_eda_pdf(prof, pdf_path, meta) or {}
         rpptx = render_automatic_eda_pptx(prof, pptx_path, meta) or {}
 
+        # Salida Markdown autocontenida (mismo documento por capítulos) para
+        # pegar a un LLM. Aditiva: no afecta a PDF/PPTX/manifest. dict-no-throw.
+        rmd = {}
+        md_path = None
+        if emit_md:
+            md_path = os.path.join(out_dir, base + ".md")
+            rmd = render_automatic_eda_markdown(prof, md_path, meta) or {}
+
         return {
             "status": "ok",
             "pdf_path": rpdf.get("path"),
             "pptx_path": rpptx.get("path"),
+            "aeda_md_path": rmd.get("path"),
             "manifest_path": rpdf.get("manifest_path"),
             "n_pages": rpdf.get("n_pages"),
             "n_slides": rpptx.get("n_slides"),
+            "md_chars": rmd.get("n_chars"),
             "pdf_note": rpdf.get("note"),
             "pptx_note": rpptx.get("note"),
+            "md_note": rmd.get("note"),
             "profile": prof,
         }
     except Exception as e:  # noqa: BLE001 — dict-no-throw: degradar, nunca lanzar.

python/pyproject.toml

@@ -18,6 +18,7 @@ dependencies = [
     "google-cloud-bigquery-storage>=2.27",
     "google-cloud-storage>=3.10.1",
     "httpx",
+    "langdetect>=1.0.9",
     "matplotlib>=3.10.9",
     "opencv-contrib-python-headless>=4.13.0.92",
     "openpyxl>=3.1.5",
@@ -40,6 +41,7 @@ dependencies = [
     "seaborn>=0.13.2",
     "shapely>=2.1.2",
     "statsmodels>=0.14.6",
+    "textstat>=0.7.13",
     "trimesh>=4.12.2",
     "xlrd>=2.0.2",
 ]

python/uv.lock

@@ -899,6 +899,7 @@ dependencies = [
     { name = "google-cloud-bigquery-storage" },
     { name = "google-cloud-storage" },
     { name = "httpx" },
+    { name = "langdetect" },
     { name = "matplotlib" },
     { name = "opencv-contrib-python-headless" },
     { name = "openpyxl" },
@@ -906,9 +907,11 @@ dependencies = [
     { name = "polars" },
     { name = "pymeshlab" },
     { name = "pymssql" },
+    { name = "pymupdf" },
     { name = "pypdf" },
     { name = "pyproj" },
     { name = "python-docx" },
+    { name = "python-pptx" },
     { name = "pyyaml" },
     { name = "qrcode", extra = ["pil"] },
     { name = "rapidfuzz" },
@@ -919,6 +922,7 @@ dependencies = [
     { name = "seaborn" },
     { name = "shapely" },
     { name = "statsmodels" },
+    { name = "textstat" },
     { name = "trimesh" },
     { name = "xlrd" },
 ]
@@ -959,6 +963,7 @@ requires-dist = [
     { name = "jupyter-collaboration", marker = "extra == 'jupyter'", specifier = ">=2.0" },
     { name = "jupyter-mcp-server", marker = "extra == 'jupyter'" },
     { name = "jupyterlab", marker = "extra == 'jupyter'", specifier = ">=4.0" },
+    { name = "langdetect", specifier = ">=1.0.9" },
     { name = "matplotlib", specifier = ">=3.10.9" },
     { name = "opencv-contrib-python-headless", specifier = ">=4.13.0.92" },
     { name = "openpyxl", specifier = ">=3.1.5" },
@@ -966,9 +971,11 @@ requires-dist = [
     { name = "polars", specifier = ">=1.40.1" },
     { name = "pymeshlab", specifier = ">=2025.7.post1" },
     { name = "pymssql", specifier = ">=2.3.13" },
+    { name = "pymupdf", specifier = ">=1.28.0" },
     { name = "pypdf", specifier = ">=6.10.0" },
     { name = "pyproj", specifier = ">=3.7.2" },
     { name = "python-docx", specifier = ">=1.2.0" },
+    { name = "python-pptx", specifier = ">=1.0.2" },
     { name = "pyyaml", specifier = ">=6.0.3" },
     { name = "qrcode", extras = ["pil"], specifier = ">=8.2" },
     { name = "rapidfuzz", specifier = ">=3.14.5" },
@@ -979,6 +986,7 @@ requires-dist = [
     { name = "seaborn", specifier = ">=0.13.2" },
     { name = "shapely", specifier = ">=2.1.2" },
     { name = "statsmodels", specifier = ">=0.14.6" },
+    { name = "textstat", specifier = ">=0.7.13" },
     { name = "trimesh", specifier = ">=4.12.2" },
     { name = "xlrd", specifier = ">=2.0.2" },
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 ]
 
+[[package]]
+name = "langdetect"
+version = "1.0.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0e/72/a3add0e4eec4eb9e2569554f7c70f4a3c27712f40e3284d483e88094cc0e/langdetect-1.0.9.tar.gz", hash = "sha256:cbc1fef89f8d062739774bd51eda3da3274006b3661d199c2655f6b3f6d605a0", size = 981474, upload-time = "2021-05-07T07:54:13.562Z" }
+
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 version = "1.3.1"
@@ -2699,6 +2716,21 @@ wheels = [
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 ]
 
+[[package]]
+name = "nltk"
+version = "3.9.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "joblib" },
+    { name = "regex" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/74/a1/b3b4adf15585a5bc4c357adde150c01ebeeb642173ded4d871e89468767c/nltk-3.9.4.tar.gz", hash = "sha256:ed03bc098a40481310320808b2db712d95d13ca65b27372f8a403949c8b523d0", size = 2946864, upload-time = "2026-03-24T06:13:40.641Z" }
+wheels = [
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+]
+
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 name = "notebook-shim"
 version = "0.2.4"
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 ]
 
+[[package]]
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+version = "1.28.0"
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+    { url = "https://files.pythonhosted.org/packages/33/f4/23775bbda0781b61fc398cc75079a2b0e64696d8fcf93271748883e9627e/pymupdf-1.28.0-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:4d692dcf44d3566ae96bc6f6346c6ad432274a29ba617bf7a9fe18009e24adb4", size = 23828292, upload-time = "2026-06-29T09:03:46.129Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/f5/bf75fc7a415722f8b33662054f82d88520c0cbfd4c36d0e08aeaec605e49/pymupdf-1.28.0-cp310-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:47a5c29ed4eb0744de9c4e37bb49b1259b18d4d75fcc8a7c130f7c9fa15956f6", size = 25045507, upload-time = "2026-06-29T09:04:03.86Z" },
+    { url = "https://files.pythonhosted.org/packages/58/69/5d12c9f1f2d76f28383d6110a069c79fbfced5a4f97bb1ee6e8354f52bb7/pymupdf-1.28.0-cp310-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:44f0973f5e5edbaec95bc34b64e71d1959d4ee90b1328de1b4f4f5b4fa78673f", size = 25716599, upload-time = "2026-06-29T09:04:19.367Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/b4/ec0e017bc42857cc86bd651441dbc41cc18be48d4698ecd27aac491e0c9a/pymupdf-1.28.0-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:4d61ec323a706e153a12e262e51febfb43eeaa20977785ace135d18d48bcdc83", size = 25940489, upload-time = "2026-06-29T09:04:36.624Z" },
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+    { url = "https://files.pythonhosted.org/packages/2e/5d/1a03f53eb0449900469335fcfc742ca28e3ba159b7d650e0921d50b8b308/pymupdf-1.28.0-cp310-abi3-win_amd64.whl", hash = "sha256:e01e90fd86abfeb37ceb921eddb951f988a11d45ff6ce6b7664f2039849068ec", size = 19773102, upload-time = "2026-06-29T09:04:49.773Z" },
+    { url = "https://files.pythonhosted.org/packages/72/f6/1e52ce243ca792254f6223b4017c5667194c146ce9b88baf37bc5eb3d1c9/pymupdf-1.28.0-cp313-abi3-pyemscripten_2025_0_wasm32.whl", hash = "sha256:74c6d00ba2a9aad3a635db73b07c15db462b480741d831a34a75a56535ebc22b", size = 18357011, upload-time = "2026-06-29T20:50:50.353Z" },
+    { url = "https://files.pythonhosted.org/packages/62/b1/46b5b3d8ef3cc71114667cf10c4d8b33f39af97253af32e9a0986775b638/pymupdf-1.28.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:b3e1399c7a64c6914239116a369efcdaac4cfb9e838bde2656d7accc4a85c72d", size = 25753599, upload-time = "2026-06-29T09:05:09.398Z" },
+]
+
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 name = "pyogrio"
 version = "0.12.1"
@@ -3811,6 +3860,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "pyphen"
+version = "0.17.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/69/56/e4d7e1bd70d997713649c5ce530b2d15a5fc2245a74ca820fc2d51d89d4d/pyphen-0.17.2.tar.gz", hash = "sha256:f60647a9c9b30ec6c59910097af82bc5dd2d36576b918e44148d8b07ef3b4aa3", size = 2079470, upload-time = "2025-01-20T13:18:36.296Z" }
+wheels = [
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+]
+
 [[package]]
 name = "pyproj"
 version = "3.7.2"
@@ -3935,6 +3993,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1c/fd/0318007beb234790993d3ec5afd051d1dbceb733e81e3afe2b981ece3f37/python_multipart-0.0.30-py3-none-any.whl", hash = "sha256:830964def8c90607ac5daa00514e3987815865713ade8d20febc9177ac0c3c5b", size = 29730, upload-time = "2026-05-31T19:24:53.814Z" },
 ]
 
+[[package]]
+name = "python-pptx"
+version = "1.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "lxml" },
+    { name = "pillow" },
+    { name = "typing-extensions" },
+    { name = "xlsxwriter" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/52/a9/0c0db8d37b2b8a645666f7fd8accea4c6224e013c42b1d5c17c93590cd06/python_pptx-1.0.2.tar.gz", hash = "sha256:479a8af0eaf0f0d76b6f00b0887732874ad2e3188230315290cd1f9dd9cc7095", size = 10109297, upload-time = "2024-08-07T17:33:37.772Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d9/4f/00be2196329ebbff56ce564aa94efb0fbc828d00de250b1980de1a34ab49/python_pptx-1.0.2-py3-none-any.whl", hash = "sha256:160838e0b8565a8b1f67947675886e9fea18aa5e795db7ae531606d68e785cba", size = 472788, upload-time = "2024-08-07T17:33:28.192Z" },
+]
+
 [[package]]
 name = "pywin32"
 version = "311"
@@ -4936,6 +5009,20 @@ wheels = [
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 ]
 
+[[package]]
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+version = "0.7.13"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "nltk" },
+    { name = "pyphen" },
+    { name = "setuptools" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8c/0f/b673fcec5ad6e976b2e8368ef3651fe0fea3348a1191bacfcd41a17ddec6/textstat-0.7.13.tar.gz", hash = "sha256:a88d1da76287cd27ca4ce7bcba1ebaf2890544a5f0bb6a5758fa84cef3bceccb", size = 138932, upload-time = "2026-02-18T21:07:39.525Z" }
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 version = "3.6.0"
@@ -5312,6 +5399,15 @@ wheels = [
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 ]
 
+[[package]]
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