egutierrez
9cdde4a341
Introduce la capa intermedia entre el contenido de un EDA y su formato de
salida. Un documento es una lista de capítulos versionados; cada capítulo es
un conjunto ordenado de bloques (heading, markdown, kv_table, data_table,
figure, image, caption, note) independientes del formato.
Núcleo (paquete de soporte python/functions/datascience/automatic_eda/):
- model.py: dataclasses de bloques + Chapter, normalizadores defensivos
(aceptan dataclass o dict, nunca lanzan), ENGINE_VERSION y el manifiesto
por capítulo (automatic_eda_manifest.json).
- text_layout.py: medición/wrapping por rejilla de caracteres compartida.
- chapters_registry.py: CHAPTER_ORDER pre-declarado + build_document con
auto-discovery de capítulos por convención (permite añadir capítulos en
paralelo sin editar el registro).
- render_pdf_impl.py: paginador A5 retrato móvil que MIDE cada bloque y nunca
corta: texto a líneas completas, tablas largas partidas por filas repitiendo
cabecera, figuras/imágenes escaladas para caber enteras. Pie versionado por
capítulo.
- render_pptx_impl.py: mismo principio sobre slides 16:9 (continúa en slide
"(cont.)"; tablas repiten cabecera; figuras exportadas a PNG escaladas).
- chapters/portada.py y chapters/overview.py: capítulos de referencia. Portada
con nombre, rótulo Automatic-EDA, fuente, almacenamiento (inferido de
source), fecha europea, filas×cols, descripción, granularidad y calidad con
criterios. Overview con df.head (placeholder honesto si falta head_rows),
diccionario de columnas (tipo/nulos/ejemplos) y describe numérico.
Funciones públicas del registry (grupo eda, dict-no-throw):
- render_automatic_eda_pdf / render_automatic_eda_pptx: aceptan capítulos o un
TableProfile (construyen los capítulos con build_document) y escriben el
manifiesto. Aditivas — no reemplazan render_eda_pdf.
Tests self-contained (sin DuckDB) para ambos renderers: golden (portada +
overview), partición de tablas largas repitiendo cabecera, no-corte de celdas
y markdown largos, profile None/{} válido de 1 página/slide, y error path en
directorio no escribible. 23 tests verdes (incluye los previos de
render_eda_pdf, intactos).
Dependencia nueva python-pptx>=1.0.2 declarada en python/pyproject.toml.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
from .datascience import (
|
|
pearson,
|
|
standardize,
|
|
min_max_scale,
|
|
clip,
|
|
detect_outliers,
|
|
impute,
|
|
histogram,
|
|
rolling_window,
|
|
autocorrelation,
|
|
linspace,
|
|
)
|
|
from .scrape_amazon_bestsellers import scrape_amazon_bestsellers
|
|
from .scrape_google_trends import scrape_google_trends
|
|
from .scrape_competitor_prices import scrape_competitor_prices
|
|
from .scrape_tiktok_creative import scrape_tiktok_creative
|
|
from .scrape_aliexpress_trending import scrape_aliexpress_trending
|
|
from .fetch_reddit_search import fetch_reddit_search
|
|
from .fetch_hackernews_search import fetch_hackernews_search
|
|
from .score_demand_signal import score_demand_signal
|
|
from .pull_gsc_search_analytics import pull_gsc_search_analytics
|
|
from .summarize_table_duckdb import summarize_table_duckdb
|
|
from .summarize_table_pg import summarize_table_pg
|
|
from .describe_numeric import describe_numeric
|
|
from .summarize_categorical import summarize_categorical
|
|
from .infer_semantic_type import infer_semantic_type
|
|
from .column_quality_score import column_quality_score
|
|
from .render_eda_markdown import render_eda_markdown
|
|
from .detect_distribution_type import detect_distribution_type
|
|
from .spearman_corr import spearman_corr
|
|
from .cramers_v import cramers_v
|
|
from .theils_u import theils_u
|
|
from .correlation_ratio import correlation_ratio
|
|
from .mutual_info_columns import mutual_info_columns
|
|
from .infer_fk_containment_duckdb import infer_fk_containment_duckdb
|
|
from .build_join_graph import build_join_graph
|
|
from .association_matrix import association_matrix
|
|
from .correlation_matrix_duckdb import correlation_matrix_duckdb
|
|
from .pca_explained import pca_explained
|
|
from .kmeans_segments import kmeans_segments
|
|
from .isolation_forest_outliers import isolation_forest_outliers
|
|
from .normality_tests import normality_tests
|
|
from .trend_slope import trend_slope
|
|
from .run_eda_models import run_eda_models
|
|
from .eda_llm_insights import eda_llm_insights
|
|
from .build_eda_notebook import build_eda_notebook
|
|
from .decode_qr_image import decode_qr_image
|
|
from .adf_kpss_stationarity import adf_kpss_stationarity
|
|
from .acf_pacf import acf_pacf
|
|
from .stl_decompose import stl_decompose
|
|
from .to_returns import to_returns
|
|
from .fdr_correction import fdr_correction
|
|
from .suggest_reexpression import suggest_reexpression
|
|
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
|
|
|
|
__all__ = [
|
|
"render_automatic_eda_pdf",
|
|
"render_automatic_eda_pptx",
|
|
"decode_qr_image",
|
|
"adf_kpss_stationarity",
|
|
"acf_pacf",
|
|
"stl_decompose",
|
|
"to_returns",
|
|
"fdr_correction",
|
|
"suggest_reexpression",
|
|
"exploratory_caveats",
|
|
"render_eda_pdf",
|
|
"render_eda_pdf_relational",
|
|
"summarize_table_duckdb",
|
|
"summarize_table_pg",
|
|
"spearman_corr",
|
|
"cramers_v",
|
|
"theils_u",
|
|
"correlation_ratio",
|
|
"mutual_info_columns",
|
|
"infer_fk_containment_duckdb",
|
|
"build_join_graph",
|
|
"association_matrix",
|
|
"correlation_matrix_duckdb",
|
|
"pca_explained",
|
|
"kmeans_segments",
|
|
"isolation_forest_outliers",
|
|
"normality_tests",
|
|
"trend_slope",
|
|
"run_eda_models",
|
|
"eda_llm_insights",
|
|
"build_eda_notebook",
|
|
"describe_numeric",
|
|
"summarize_categorical",
|
|
"infer_semantic_type",
|
|
"column_quality_score",
|
|
"render_eda_markdown",
|
|
"detect_distribution_type",
|
|
"pull_gsc_search_analytics",
|
|
"scrape_amazon_bestsellers",
|
|
"scrape_google_trends",
|
|
"scrape_competitor_prices",
|
|
"scrape_tiktok_creative",
|
|
"scrape_aliexpress_trending",
|
|
"fetch_reddit_search",
|
|
"fetch_hackernews_search",
|
|
"score_demand_signal",
|
|
"pearson",
|
|
"standardize",
|
|
"min_max_scale",
|
|
"clip",
|
|
"detect_outliers",
|
|
"impute",
|
|
"histogram",
|
|
"rolling_window",
|
|
"autocorrelation",
|
|
"linspace",
|
|
]
|