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Overview

Project website docs.python.org
Instructions for translators

See devguide.python.org/documentation/translating.
Project maintainers User avatar rafaelff User avatar josix User avatar m-aciek
Translation license Creative Commons Zero v1.0 Universal
Translation process
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  • 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-126911: Update credits output (GH-126913) (#126974) bc124c1
github-actions[bot] authored 19 hours ago
Last commit in Weblate [3.12] gh-126911: Update credits output (GH-126913) (#126974) bc124c1
github-actions[bot] authored 19 hours ago
Weblate repository https://hosted.weblate.org/git/python-docs/latest/bugs/
File mask */latest/library/statistics.po
Translation file Download pt-br/latest/library/statistics.po
Last change Nov. 11, 2023, 11:41 p.m.
Last change made by None
Language Portuguese (Brazil)
Language code pt_BR
Text direction Left to right
Number of speakers 200,247,320
Number of plurals 3
Plural type Unknown
Plurals Singular 0, 1
Plural 1000000, 2000000
Plural form 2 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, …
Plural formula (n == 0 || n == 1) ? 0 : n != 0 && n % 1000000 == 0 ? 1 : 2
a month ago

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 181 3,703 25,290
Translated 77% 140 75% 2,807 74% 18,834
Needs editing 0% 0 0% 0 0% 0
Read-only 0% 0 0% 0 0% 0
Failing checks 2% 5 2% 94 2% 615
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 22% 41 24% 896 25% 6,456
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Resource updated
Parsing of the “pt-br/latest/library/statistics.po” file was enforced. a year ago
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Resource updated
File “pt-br/latest/library/statistics.po” was added. a year ago
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String updated in the repository
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:
A previsão final vai para a probabilidade posterior maior. Isso é conhecido como `máximo a posteriori <https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation>`_ ou MAP:
a year ago
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String updated in the repository
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:
Começando com uma `probabilidade a priori de 50% <https://pt.wikipedia.org/wiki/Probabilidade_a_priori>`_ de ser homem ou mulher, calculamos a posteriori como a priori vezes o produto das probabilidade para as características de medidas dado o gênero:
a year ago
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String updated in the repository
Next, we encounter a new person whose feature measurements are known but whose gender is unknown:
Em seguida, encontramos uma nova pessoa cujas características de medidas são conhecidas, mas cujo gênero é desconhecido:
a year ago
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String updated in the repository
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`:
Recebemos um conjunto de dados de treinamento com medições para oito pessoas. As medidas são consideradas normalmente distribuídas, então resumimos os dados com :class:`NormalDist`:
a year ago
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String updated in the repository
Wikipedia has a `nice example of a Naive Bayesian Classifier <https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Person_classification>`_. The challenge is to predict a person's gender from measurements of normally distributed features including height, weight, and foot size.
 
a year ago
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Normal distributions commonly arise in machine learning problems.
Distribuições normais geralmente surgem em problemas de aprendizado de máquina.
a year ago
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String updated in the repository
For example, an open source conference has 750 attendees and two rooms with a 500 person capacity. There is a talk about Python and another about Ruby. In previous conferences, 65% of the attendees preferred to listen to Python talks. Assuming the population preferences haven't changed, what is the probability that the Python room will stay within its capacity limits?
Por exemplo, uma conferência de código aberto tem 750 participantes e duas salas com capacidade para 500 pessoas. Há uma palestra sobre Python e outra sobre Ruby. Em conferências anteriores, 65% dos participantes preferiram ouvir palestras sobre Python. Supondo que as preferências da população não tenham mudado, qual é a probabilidade da sala de Python permanecer dentro de seus limites de capacidade?
a year ago
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String updated in the repository
Normal distributions can be used to approximate `Binomial distributions <https://mathworld.wolfram.com/BinomialDistribution.html>`_ when the sample size is large and when the probability of a successful trial is near 50%.
 
a year ago
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