Python documentation/3.12/latest/library/statistics

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Source strings

183	Strings	100%	Browse Translate
3,757	Words	100%
25,740	Characters	100%

This translation is being used as source strings within this component.

Strings status

Strings Words Characters
183 3,757 25,740	All strings	Browse Translate Zen
183 3,757 25,740	Read-only strings	Browse Translate Zen
2 45 254	Strings with any failing checks	Browse Translate Zen
2 45 254	Failing check: Multiple failing checks	Browse Translate Zen

Overview

Project website	docs.python.org
Instructions for translators	See devguide.python.org/documentation/translating.
Project maintainers	rafaelff josix m-aciek
Translation license	Creative Commons Zero v1.0 Universal
Translation process	Translations can be made directly. Translation suggestions can be made. Any authenticated user can contribute. The translation uses bilingual files.
Source code repository	`git@github.com:m-aciek/python-docs-weblate.git`
Repository branch	main
Last remote commit	[3.12] gh-115145: Update documentation about ``PyThreadState_DeleteCurrent`` (gh-124920) (gh-124931) `0d727af` github-actions[bot] authored 17 hours ago
Last commit in Weblate	[3.12] gh-115145: Update documentation about ``PyThreadState_DeleteCurrent`` (gh-124920) (gh-124931) `0d727af` github-actions[bot] authored 17 hours ago
Weblate repository	`https://hosted.weblate.org/git/python-docs/latest/bugs/`
File mask	`*/latest/library/statistics.po`
Translation file	Not available
Last change	Nov. 8, 2023, 4:32 p.m.
Last author	None

2 weeks ago

String statistics

	Strings percent	Hosted strings	Words percent	Hosted words	Characters percent	Hosted characters
Total		183		3,757		25,740
Translated	100%	183	100%	3,757	100%	25,740
Needs editing	0%	0	0%	0	0%	0
Read-only	100%	183	100%	3,757	100%	25,740
Failing checks	1%	2	1%	45	1%	254
Strings with suggestions	0%	0	0%	0	0%	0
Untranslated strings	0%	0	0%	0	0%	0

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3,757

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183

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None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only Added support for Spearman's rank correlation coefficient. 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only Example with `Kepler's laws of planetary motion <https://en.wikipedia.org/wiki/Kepler's_laws_of_planetary_motion>`_: 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only Spearman's correlation coefficient is appropriate for ordinal data or for continuous data that doesn't meet the linear proportion requirement for Pearson's correlation coefficient. 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only If method is "ranked", computes `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_ for two inputs. The data is replaced by ranks. Ties are averaged so that equal values receive the same rank. The resulting coefficient measures the strength of a monotonic relationship. 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only Return the `Pearson's correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_ for two inputs. Pearson's correlation coefficient r takes values between -1 and +1. It measures the strength and direction of a linear relationship. 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only Pearson and Spearman's correlation coefficients. 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only The final prediction goes to the largest posterior. This is known as the `maximum a posteriori <https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation>`_ or MAP: 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only Starting with a 50% `prior probability <https://en.wikipedia.org/wiki/Prior_probability>`_ of being male or female, we compute the posterior as the prior times the product of likelihoods for the feature measurements given the gender: 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only Next, we encounter a new person whose feature measurements are known but whose gender is unknown: 11 months ago
None Source string added Python documentation 3.12 latest/library/statistics English	English 0 characters edited Read only We're given a training dataset with measurements for eight people. The measurements are assumed to be normally distributed, so we summarize the data with :class:`NormalDist`: 11 months ago

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