egutierrez
a69d14d38e
Capítulo nuevo build_timeseries(profile, ctx) -> Chapter|None del motor AutomaticEDA. Cuando la tabla tiene columna de fecha/datetime, grafica la evolución de cada columna numérica por periodo (valor agregado + conteo de filas) y los paneles de descomposición STL y autocorrelación (ACF), con el análisis de la serie: estacionariedad (ADF+KPSS), autocorrelación (Ljung-Box), fuerzas de tendencia/estacionalidad (Hyndman) y la transformación sugerida (retornos o diferencias) para evitar correlaciones espurias. Sin columna temporal devuelve None. Consolida series OHLC casi idénticas en un único gráfico conservando el análisis de cada columna. La serie cruda llega por ctx['timeseries_raw'] (mismo patrón que modelos con raw_numeric); las figuras son perezosas (Figure.make) y el paginador del núcleo garantiza no-corte en PDF y PPTX. CHAPTER_VERSION 1.0.0. Cubre los MUST del diseño (report 2043): MUST-9.1 (línea valor-vs-tiempo + conteo por periodo), MUST-9.2 (paneles STL + ACF), MUST-9.3 (perfil datetime + consolidación OHLC). Funciones nuevas del registry (grupo eda), delegadas a fn-constructor, no inline: - detect_time_column (pure): detecta la columna temporal y las numéricas - profile_datetime (pure): rango/frecuencia/regularidad/huecos de la fecha - resample_timeseries (pure): agrega la serie por periodo + conteo - extract_timeseries_raw (impure): lee la serie cruda ordenada de DuckDB/PG Verificación: 69 tests verdes (capítulo 9 + funciones 28 + núcleo/renderers); golden real sobre seattle-weather (estacional) y aapl (OHLC) con PDF+PPTX sin cortar nada (cols_cortadas=[]). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
from .datascience import (
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pearson,
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standardize,
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min_max_scale,
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clip,
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detect_outliers,
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impute,
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histogram,
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rolling_window,
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autocorrelation,
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linspace,
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)
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from .scrape_amazon_bestsellers import scrape_amazon_bestsellers
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from .scrape_google_trends import scrape_google_trends
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from .scrape_competitor_prices import scrape_competitor_prices
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from .scrape_tiktok_creative import scrape_tiktok_creative
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from .scrape_aliexpress_trending import scrape_aliexpress_trending
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from .fetch_reddit_search import fetch_reddit_search
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from .fetch_hackernews_search import fetch_hackernews_search
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from .score_demand_signal import score_demand_signal
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from .pull_gsc_search_analytics import pull_gsc_search_analytics
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from .summarize_table_duckdb import summarize_table_duckdb
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from .summarize_table_pg import summarize_table_pg
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from .describe_numeric import describe_numeric
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from .summarize_categorical import summarize_categorical
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from .infer_semantic_type import infer_semantic_type
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from .column_quality_score import column_quality_score
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from .render_eda_markdown import render_eda_markdown
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from .detect_distribution_type import detect_distribution_type
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from .spearman_corr import spearman_corr
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from .cramers_v import cramers_v
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from .theils_u import theils_u
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from .correlation_ratio import correlation_ratio
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from .mutual_info_columns import mutual_info_columns
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from .infer_fk_containment_duckdb import infer_fk_containment_duckdb
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from .build_join_graph import build_join_graph
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from .association_matrix import association_matrix
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from .correlation_matrix_duckdb import correlation_matrix_duckdb
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from .pca_explained import pca_explained
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from .kmeans_segments import kmeans_segments
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from .isolation_forest_outliers import isolation_forest_outliers
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from .normality_tests import normality_tests
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from .trend_slope import trend_slope
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from .run_eda_models import run_eda_models
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from .project_clusters_2d import project_clusters_2d
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from .describe_clusters_llm import describe_clusters_llm
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from .eda_llm_insights import eda_llm_insights
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from .build_eda_notebook import build_eda_notebook
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from .decode_qr_image import decode_qr_image
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from .adf_kpss_stationarity import adf_kpss_stationarity
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from .acf_pacf import acf_pacf
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from .stl_decompose import stl_decompose
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from .to_returns import to_returns
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from .fdr_correction import fdr_correction
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from .suggest_reexpression import suggest_reexpression
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from .exploratory_caveats import exploratory_caveats
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from .render_eda_pdf import render_eda_pdf, render_eda_pdf_relational
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from .render_automatic_eda_pdf import render_automatic_eda_pdf
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from .render_automatic_eda_pptx import render_automatic_eda_pptx
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from .detect_time_column import detect_time_column
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from .extract_timeseries_raw import extract_timeseries_raw
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from .profile_datetime import profile_datetime
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from .resample_timeseries import resample_timeseries
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__all__ = [
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"detect_time_column",
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"extract_timeseries_raw",
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"profile_datetime",
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"resample_timeseries",
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"render_automatic_eda_pdf",
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"render_automatic_eda_pptx",
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"decode_qr_image",
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"adf_kpss_stationarity",
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"acf_pacf",
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"stl_decompose",
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"to_returns",
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"fdr_correction",
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"suggest_reexpression",
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"exploratory_caveats",
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"render_eda_pdf",
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"render_eda_pdf_relational",
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"summarize_table_duckdb",
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"summarize_table_pg",
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"spearman_corr",
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"cramers_v",
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"theils_u",
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"correlation_ratio",
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"mutual_info_columns",
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"infer_fk_containment_duckdb",
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"build_join_graph",
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"association_matrix",
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"correlation_matrix_duckdb",
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"pca_explained",
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"kmeans_segments",
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"isolation_forest_outliers",
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"normality_tests",
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"trend_slope",
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"run_eda_models",
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"project_clusters_2d",
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"describe_clusters_llm",
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"eda_llm_insights",
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"build_eda_notebook",
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"describe_numeric",
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"summarize_categorical",
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"infer_semantic_type",
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"column_quality_score",
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"render_eda_markdown",
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"detect_distribution_type",
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"pull_gsc_search_analytics",
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"scrape_amazon_bestsellers",
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"scrape_google_trends",
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"scrape_competitor_prices",
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"scrape_tiktok_creative",
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"scrape_aliexpress_trending",
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"fetch_reddit_search",
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"fetch_hackernews_search",
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"score_demand_signal",
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"pearson",
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"standardize",
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"min_max_scale",
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"clip",
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"detect_outliers",
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"impute",
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"histogram",
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"rolling_window",
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"autocorrelation",
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"linspace",
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]
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