fn_registry/python/functions/datascience/best_central_tendency.md

id, name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, example, tested, tests, test_file_path, file_path, params, output, source_repo, source_license, source_file

id	name	kind	lang	domain	version	purity	signature	description	tags	uses_functions	uses_types	returns	returns_optional	error_type	imports	example	tested	tests	test_file_path	file_path	params	output	source_repo	source_license	source_file
best_central_tendency_py_datascience	best_central_tendency	function	py	datascience	1.0.0	pure	def best_central_tendency(values: list[float], dist_type: str) -> tuple[str, float]	Selects the most appropriate central tendency measure for a given distribution type. Returns (label, value) pair.	statistics central-tendency distribution robust mean median pendiente-usar	geometric_mean_py_datascience trimmed_mean_py_datascience			false		math numpy	from best_central_tendency import best_central_tendency label, value = best_central_tendency([1, 2, 3, 4, 5], "normal-ish") # ("mean", 3.0)	true	test_best_central_tendency_normal_ish test_best_central_tendency_right_skewed test_best_central_tendency_left_skewed test_best_central_tendency_lognormal_ish test_best_central_tendency_heavy_tail test_best_central_tendency_empty test_best_central_tendency_default	python/functions/datascience/tests/test_best_central_tendency.py	python/functions/datascience/best_central_tendency.py	name desc values List of numeric values to summarize. name desc dist_type Distribution type string, typically from detect_distribution_type. One of: normal-ish, lognormal-ish, heavy-tail, right-skewed, left-skewed, other, too_few_samples.	Tuple (label, value) where label is one of "mean", "median", "geometric_mean", "trimmed_mean_5%", and value is the computed central tendency. Returns ("median", math.nan) for empty input.	internal:footprint_aurgi	internal-aurgi	aurgi_mapas/generar_pdf_reporte.py:196

Ejemplo

from best_central_tendency import best_central_tendency

best_central_tendency([1, 2, 3, 4, 5], "normal-ish")    # ("mean", 3.0)
best_central_tendency([1, 2, 3, 4, 5], "right-skewed")  # ("median", 3.0)
best_central_tendency([1, 2, 4, 8], "lognormal-ish")    # ("geometric_mean", ~2.83)
best_central_tendency([1, 2, 3, 100], "heavy-tail")     # ("trimmed_mean_5%", ...)

Mapeo de tipos a medidas

dist_type	Medida	Funcion interna
normal-ish	mean	numpy.mean
lognormal-ish	geometric_mean	geometric_mean()
heavy-tail	trimmed_mean_5%	trimmed_mean(0.05)
right-skewed	median	numpy.median
left-skewed	median	numpy.median
otros / default	median	numpy.median