{"id":530,"date":"2025-07-28T12:34:21","date_gmt":"2025-07-28T16:34:21","guid":{"rendered":"https:\/\/literaciadigital.ufms.br\/?page_id=530"},"modified":"2025-10-06T16:41:11","modified_gmt":"2025-10-06T20:41:11","slug":"8-1","status":"publish","type":"page","link":"https:\/\/literaciadigital.ufms.br\/en\/data8\/8-0\/8-1\/","title":{"rendered":"Cap\u00edtulo 8.1"},"content":{"rendered":"<div style=\"position: relative\">\n<div style=\"float: left;width: 300px;background-color: #f5f5f5;border: 1px solid #ddd;border-radius: 5px;padding: 15px;margin-right: 20px;margin-bottom: 5px;overflow: hidden\">\n<h3 style=\"margin: 0 0 10px 0;padding-bottom: 8px;border-bottom: 1px solid #ddd\">\u00cdndice<\/h3>\n<ol style=\"margin: 0;padding-left: 0;list-style-type: none\">\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/\">1. O que \u00e9 Ci\u00eancia de Dados?<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-1\/\">1.1. Introdu\u00e7\u00e3o<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-1\/1-1\/\">1.1.1. Ferramentas Computacionais<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-1\/1-2\/\">1.1.2. T\u00e9cnicas Estat\u00edsticas<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-2\/\">1.2. Por que Ci\u00eancia de Dados?<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-3\/\">1.3. Tra\u00e7ando os Cl\u00e1ssicos<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-3\/3-1\/\">1.3.1. Personagens Liter\u00e1rios<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-3\/3-2\/\">1.3.2. Outro Tipo de Personagem<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/\">2. Causalidade e Experimentos<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-1\/\">2.1. John Snow e a Bomba da Broad Street<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-2\/\">2.2. O &#8220;Grande Experimento&#8221; de Snow<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-3\/\">2.3. Estabelecendo Causalidade<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-4\/\">2.4. Randomiza\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-5\/\">2.5. Notas Finais<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/\">3. Progamando em Python<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-1\/\">3.1. Express\u00f5es<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-2\/\">3.2. Nomes<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-2\/2-1\/\">3.2.1. Exemplo: Taxas de Crescimento<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-3\/\">3.3. Chamadas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-4\/\">3.4. Introdu\u00e7\u00e3o \u00e0s Tabelas<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/\">4. Tipos de Dados<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/4-1\/\">4.1. N\u00fameros<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/4-2\/\">4.2. Strings<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/4-2\/2-1\/\">4.2.1. M\u00e9todos de Strings<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/4-3\/\">4.3. Compara\u00e7\u00f5es<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/5-0\/\">5. Sequ\u00eancias<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/5-0\/5-1\/\">5.1. Arrays<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/5-0\/5-2\/\">5.2. Ranges<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/5-0\/5-3\/\">5.3. Mais sobre Arrays<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/\">6. Tabelas<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/6-1\/\">6.1. Ordenando Linhas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/6-2\/\">6.2. Selecionando Linhas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/6-3\/\">6.3. Exemplo: Tend\u00eancias Populacionais<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/6-4\/\">6.4. Examplo: Propor\u00e7\u00f5es de Sexos<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/\">7. Visualiza\u00e7\u00e3o<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/7-1\/\">7.1. Visualizando Distribui\u00e7\u00f5es<br \/>\nCateg\u00f3ricas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/7-2\/\">7.2. Visualizando Distribui\u00e7\u00f5es Num\u00e9ricas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/7-3\/\">7.3. Gr\u00e1ficos Sobrepostos<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/\">8. Fun\u00e7\u00f5es e Tabelas<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-1\/\">8.1. Aplicando Fun\u00e7\u00e3o a uma Coluna<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-2\/\">8.2. Classificando por uma Vari\u00e1vel<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-3\/\">8.3. Classifica\u00e7\u00e3o Cruzada<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-4\/\">8.4. Unindo Tabelas por Colunas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-5\/\">8.5. Compartilhamento de Bicicletas<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/\">9. Aleatoriedade<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-1\/\">9.1. Declara\u00e7\u00f5es Condicionais<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-2\/\">9.2. Itera\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-3\/\">9.3. Simula\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-4\/\">9.4. O Problema de Monty Hall<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-5\/\">9.5. Encontrando Probabilidades<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/\">10. Amostragem e Distribui\u00e7\u00f5es Emp\u00edricas<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-1\/\">10.1. Distribui\u00e7\u00f5es Emp\u00edricas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-2\/\">10.2. Amostragem de uma Popula\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-3\/\">10.3. Distribui\u00e7\u00e3o Emp\u00edrica de uma<br \/>\nEstat\u00edstica<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-4\/\">10.4. Amostragem Aleat\u00f3ria em Python <\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/\">11. Testando Hip\u00f3teses<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/11-1\/\">11.1. Avaliando um Modelo<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/11-2\/\">11.2. M\u00faltiplas Categorias<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/11-3\/\">11.3. Decis\u00f5es e Incertezas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/11-4\/\">11.4. Probabilidades de Erro<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/12-0\/\">12. Comparando Duas Amostras<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/12-0\/12-1\/\">12.1. Teste A\/B<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/12-0\/12-2\/\">12.2. Causalidade<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/12-0\/12-3\/\">12.3. Esvaziar<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/\">13. Estima\u00e7\u00e3o<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/13-1\/\">13.1. Percentis<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/13-2\/\">13.2. O Bootstrap<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/13-3\/\">13.3. Intervalos de Confian\u00e7a<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/13-4\/\">13.4. Usando Intervalos de Confian\u00e7a<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/\">14. Por que a M\u00e9dia \u00e9 Importante<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-1\/\">14.1. Propriedades da M\u00e9dia<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-2\/\">14.2. Variabilidade<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-3\/\">14.3. O DP e a Curva Normal<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-4\/\">14.4. Teorema Central do Limite<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-5\/\">14.5. Variabilidade da M\u00e9dia da Amostra<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-6\/\">14.6. Escolhendo um Tamanho de Amostra<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/\">15. Previs\u00e3o<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-1\/\">15.1. Correla\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-2\/\">15.2. Linha de Regress\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-3\/\">15.3. M\u00e9todo dos M\u00ednimos Quadrados<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-4\/\">15.4. Regress\u00e3o de M\u00ednimos Quadrados<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-5\/\">15.5. Diagn\u00f3sticos Visuais<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-6\/\">15.6. Diagn\u00f3stico Num\u00e9rico<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<\/div>\n<p><!-- Main Content --><\/p>\n<div style=\"overflow: hidden\">\n<p><!--###########################################################################################################################################################--><\/p>\n<h1 id=\"aplicando-uma-fun-o-a-uma-coluna\" style=\"text-align: center\">Aplicando uma fun\u00e7\u00e3o a uma coluna<\/h1>\n<p style=\"text-align: justify\">Temos visto muitos exemplos de cria\u00e7\u00e3o de novas colunas de tabelas aplicando fun\u00e7\u00f5es a colunas existentes ou a outros arrays. Todas essas fun\u00e7\u00f5es tomavam arrays como argumentos. Mas frequentemente desejaremos converter as entradas em uma coluna por uma fun\u00e7\u00e3o que n\u00e3o recebe um array como seu argumento. Por exemplo, ele pode usar apenas um n\u00famero como argumento, como na fun\u00e7\u00e3o <code>cut_off_at_100<\/code> definida abaixo.<\/p>\n<pre><code><span style=\"color: black\">def cut_off_at_100(x):\r\n    \"\"\"O menor entre x e 100\"\"\"\r\n    return min(x, 100)<\/span><\/code><\/pre>\n<pre><code><span style=\"color: black\">cut_off_at_100(17)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[1]:<\/td>\n<td style=\"text-align: left\">17<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\">cut_off_at_100(117)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[2]:<\/td>\n<td style=\"text-align: left\">100<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\">cut_off_at_100(100)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[3]:<\/td>\n<td style=\"text-align: left\">100<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">A fun\u00e7\u00e3o <code>cut_off_at_100<\/code> simplesmente retorna seu argumento se o argumento for menor ou igual a 100. Mas se o argumento for maior que 100, ela retorna 100.<\/p>\n<p style=\"text-align: justify\">Em nossos exemplos anteriores usando dados do censo, vimos que a vari\u00e1vel <code>AGE<\/code> tinha um valor de 100 que significava &#8220;100 anos ou mais&#8221;. Cortar idades em 100 desta maneira \u00e9 exatamente o que <code>cut_off_at_100<\/code> faz.<\/p>\n<p style=\"text-align: justify\">Para usar esta fun\u00e7\u00e3o em muitas idades de uma vez, precisaremos ser capazes de <em>referenciar<\/em> a fun\u00e7\u00e3o em si, sem realmente cham\u00e1-la. Analogamente, poder\u00edamos mostrar uma receita de bolo a um chef e pedir a ele para us\u00e1-la para assar 6 bolos. Nesse cen\u00e1rio, n\u00e3o estamos usando a receita para assar nenhum bolo n\u00f3s mesmos; nosso papel \u00e9 apenas referir o chef \u00e0 receita. Da mesma forma, podemos pedir a uma tabela para chamar <code>cut_off_at_100<\/code> em 6 n\u00fameros diferentes em uma coluna.<\/p>\n<p style=\"text-align: justify\">Primeiro, criamos a tabela <code>ages<\/code> com uma coluna para as pessoas e outra para suas idades. Por exemplo, a pessoa <code>C<\/code> tem 52 anos.<\/p>\n<pre><code><span style=\"color: black\">ages = Table().with_columns(\r\n    'Person', make_array('A', 'B', 'C', 'D', 'E', 'F'),\r\n    'Age', make_array(17, 117, 52, 100, 6, 101)\r\n)\r\nages<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Person<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Age<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">A<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">17<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">B<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">117<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">C<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">52<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">D<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">100<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">E<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">6<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">F<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">101<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>apply<\/h2>\n<p style=\"text-align: justify\">Para cortar cada uma das idades em 100, usaremos um novo m\u00e9todo Table. O m\u00e9todo <code>apply<\/code> chama uma fun\u00e7\u00e3o em cada elemento de uma coluna, formando um novo array de valores de retorno. Para indicar qual fun\u00e7\u00e3o chamar, basta nome\u00e1-la (sem aspas ou par\u00eanteses). O nome da coluna de valores de entrada \u00e9 uma string que ainda deve aparecer entre aspas.<\/p>\n<pre><code><span style=\"color: black\">ages.apply(cut_off_at_100, 'Age')<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[5]:<\/td>\n<td style=\"text-align: left\">array([ 17, 100, 52, 100, 6, 100])<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">O que fizemos aqui foi <code>apply<\/code> a fun\u00e7\u00e3o <code>cut_off_at_100<\/code> a cada valor na coluna <code>Age<\/code> da tabela <code>ages<\/code>. A sa\u00edda \u00e9 a matriz de valores de retorno correspondentes da fun\u00e7\u00e3o. Por exemplo, 17 permaneceu 17, 117 se tornou 100, 52 permaneceu 52, e assim por diante.<\/p>\n<p style=\"text-align: justify\">Esta matriz, que tem o mesmo comprimento que a coluna <code>Age<\/code> original da tabela <code>ages<\/code>, pode ser usada como os valores em uma nova coluna chamada <code>Cut Off Age<\/code> junto com as colunas existentes <code>Person<\/code> e <code>Age<\/code>.<\/p>\n<pre><code><span style=\"color: black\">ages.with_column(\r\n    'Cut Off Age', ages.apply(cut_off_at_100, 'Age')\r\n)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Person<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Age<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Cut Off Age<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">A<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">17<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">17<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">B<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">117<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">100<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">C<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">52<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">52<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">D<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">100<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">100<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">E<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">6<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">6<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">F<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">101<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">100<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Fun\u00e7\u00f5es como Valores<\/h2>\n<p style=\"text-align: justify\">J\u00e1 vimos que o Python possui muitos tipos de valores. Por exemplo, <code>6<\/code> \u00e9 um valor num\u00e9rico, <code>\"bolo\"<\/code> \u00e9 um valor de texto, <code>Table()<\/code> \u00e9 uma tabela vazia, e <code>ages<\/code> \u00e9 um nome para um valor de tabela (j\u00e1 que o definimos acima).<\/p>\n<p style=\"text-align: justify\">Em Python, cada fun\u00e7\u00e3o, incluindo <code>cut_off_at_100<\/code>, tamb\u00e9m \u00e9 um valor. Voltando \u00e0 analogia anterior, iremos pensar novamente sobre receitas. Uma receita para bolo \u00e9 uma coisa real, distinta de bolos ou ingredientes, e voc\u00ea pode dar a ela um nome como &#8220;Receita de bolo da Ani&#8221;. Quando definimos <code>cut_off_at_100<\/code> com uma instru\u00e7\u00e3o <code>def<\/code>, na verdade fizemos duas coisas separadas: criamos uma fun\u00e7\u00e3o que corta n\u00fameros em 100, e a nomeamos de <code>cut_off_at_100<\/code>.<\/p>\n<p style=\"text-align: justify\">Podemos nos referir a qualquer fun\u00e7\u00e3o escrevendo seu nome, sem os par\u00eanteses ou argumentos necess\u00e1rios para realmente cham\u00e1-la. Fizemos isso quando chamamos <code>apply<\/code> acima. Quando escrevemos o nome de uma fun\u00e7\u00e3o sozinho como a \u00faltima linha em uma c\u00e9lula, o Python produz uma representa\u00e7\u00e3o de texto da fun\u00e7\u00e3o, assim como imprimiria um n\u00famero ou um valor de string.<\/p>\n<pre><code><span style=\"color: black\">cut_off_at_100<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[7]:<\/td>\n<td style=\"text-align: left\">&lt;function __main__.cut_off_at_100(x)&gt;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Observe que n\u00e3o escrevemos <code>\"cut_off_at_100\"<\/code> com aspas (que \u00e9 apenas um peda\u00e7o de texto), ou <code>cut_off_at_100()<\/code> (que \u00e9 uma chamada de fun\u00e7\u00e3o, e uma inv\u00e1lida). Simplesmente escrevemos <code>cut_off_at_100<\/code> para nos referirmos \u00e0 fun\u00e7\u00e3o.<\/p>\n<p style=\"text-align: justify\">Assim como podemos definir novos nomes para outros valores, podemos definir novos nomes para fun\u00e7\u00f5es. Por exemplo, suponha que queiramos nos referir \u00e0 nossa fun\u00e7\u00e3o como <code>cut_off<\/code> em vez de <code>cut_off_at_100<\/code>. Podemos simplesmente escrever isso:<\/p>\n<pre><code><span style=\"color: black\">cut_off = cut_off_at_100<\/span><\/code><\/pre>\n<p style=\"text-align: justify\">Agora <code>cut_off<\/code> \u00e9 o nome de uma fun\u00e7\u00e3o. \u00c9 a mesma fun\u00e7\u00e3o que <code>cut_off_at_100<\/code>, ent\u00e3o o valor impresso \u00e9 exatamente o mesmo.<\/p>\n<pre><code><span style=\"color: black\">cut_off<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[8]:<\/td>\n<td style=\"text-align: left\">&lt;function __main__.cut_off_at_100(x)&gt;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Vejamos outra aplica\u00e7\u00e3o de <code>apply<\/code>.<\/p>\n<h2>Exemplo: Previs\u00e3o<\/h2>\n<p style=\"text-align: justify\">A ci\u00eancia de dados \u00e9 frequentemente usada para fazer previs\u00f5es sobre o futuro. Se estivermos tentando prever um resultado para um determinado indiv\u00edduo \u2013 por exemplo, como ele responder\u00e1 a um tratamento ou se ele comprar\u00e1 um produto \u2013 \u00e9 natural basear-se a previs\u00e3o dos resultados de outros indiv\u00edduos semelhantes.<\/p>\n<p style=\"text-align: justify\">A tabela abaixo foi adaptada de um conjunto de dados hist\u00f3ricos sobre a altura dos pais e de seus filhos adultos. Cada linha corresponde a um filho adulto. As vari\u00e1veis \u200b\u200bs\u00e3o um c\u00f3digo num\u00e9rico para a fam\u00edlia, as alturas (em polegadas) do pai e da m\u00e3e , o n\u00famero de filhos na fam\u00edlia, bem como a classe de nascimento da crian\u00e7a (1 = mais velho), sexo (codificado apenas como &#8220;masculino&#8221; ou &#8220;feminino&#8221;) e altura em polegadas.<\/p>\n<pre><code><span style=\"color: black\"># Dados sobre a altura dos pais e dos filhos adultos\r\nfamily_heights = Table.read_table(path_data + 'family_heights.csv').drop(3)\r\nfamily_heights<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">family<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">father<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">mother<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">children<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">childNum<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">sex<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">childHeight<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">78.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">male<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.2<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">78.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">female<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.2<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">78.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">female<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">78.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">female<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.0<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">75.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">66.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">male<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">75.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">66.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">male<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.5<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">75.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">66.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">female<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">65.5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">75.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">66.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">female<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">65.5<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">75.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">64.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">male<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">75.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">64.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">female<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Um dos principais motivos para coletar os dados era poder prever a altura adulta de uma crian\u00e7a nascida de pais semelhantes aos da base de dados. Vamos tentar fazer isso, usando a m\u00e9dia simples da altura dos pais como a vari\u00e1vel na qual basearemos nossa previs\u00e3o.<\/p>\n<p style=\"text-align: justify\">Esta altura m\u00e9dia dos pais \u00e9 nossa vari\u00e1vel <em>preditora<\/em>. Na c\u00e9lula abaixo, seus valores est\u00e3o no array <code>parent_averages<\/code>.<\/p>\n<p style=\"text-align: justify\">A tabela <code>heights<\/code> consiste apenas das alturas m\u00e9dias dos pais e das alturas das crian\u00e7as. O gr\u00e1fico de dispers\u00e3o das duas vari\u00e1veis mostra uma associa\u00e7\u00e3o positiva, como esperado para essas vari\u00e1veis.<\/p>\n<pre><code><span style=\"color: black\">parent_averages = (family_heights.column('father') + family_heights.column('mother'))\/2\r\nheights = Table().with_columns(\r\n    'Parent Average', parent_averages,\r\n    'Child', family_heights.column('childHeight')\r\n)\r\nheights<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Parent Average<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Child<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.75<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.2<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.75<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.2<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.75<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.75<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.0<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.5<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">65.5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">65.5<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<pre><code><span style=\"color: black\">heights.scatter('Parent Average')<\/span><\/code><\/pre>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-533\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-1.png\" alt=\"\" width=\"367\" height=\"346\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-1.png 367w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-1-300x283.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-1-339x320.png 339w\" sizes=\"(max-width: 367px) 100vw, 367px\" \/><\/p>\n<p style=\"text-align: justify\">Agora, suponha que os pesquisadores encontrem um novo casal, semelhante aos da base de dados, e se perguntem qual seria a altura de seu filho. Qual seria uma boa maneira para eles preverem a altura da crian\u00e7a, dado que a altura m\u00e9dia dos pais era, digamos, 68 polegadas?<\/p>\n<p style=\"text-align: justify\">Uma abordagem razo\u00e1vel seria basear a previs\u00e3o em todos os pontos que correspondem a uma altura m\u00e9dia dos pais de cerca de 68 polegadas. A previs\u00e3o seria igual \u00e0 m\u00e9dia da altura das crian\u00e7as calculada apenas a partir desses pontos.<\/p>\n<p style=\"text-align: justify\">Vamos executar esse plano. Por enquanto, faremos uma defini\u00e7\u00e3o razo\u00e1vel do que significa &#8220;cerca de 68 polegadas&#8221; e trabalharemos com isso. Mais tarde no curso, examinaremos as consequ\u00eancias de tais escolhas.<\/p>\n<p style=\"text-align: justify\">Vamos considerar &#8220;pr\u00f3ximo&#8221; como &#8220;dentro de meia polegada&#8221;. A figura abaixo mostra todos os pontos correspondentes a uma altura m\u00e9dia dos pais entre 67,5 polegadas e 68,5 polegadas. Esses s\u00e3o todos os pontos na faixa entre as linhas vermelhas. Cada um desses pontos corresponde a uma crian\u00e7a; nossa previs\u00e3o da altura do filho do novo casal \u00e9 a altura m\u00e9dia de todas as crian\u00e7as na faixa. Isso \u00e9 representado pelo ponto dourado.<\/p>\n<p style=\"text-align: justify\">Ignore o c\u00f3digo e concentre-se apenas em entender o processo mental de chegar a esse ponto dourado.<\/p>\n<pre><code><span style=\"color: black\">heights.scatter('Parent Average')\r\nplots.plot([67.5, 67.5], [50, 85], color='red', lw=2)\r\nplots.plot([68.5, 68.5], [50, 85], color='red', lw=2)\r\nplots.scatter(68, 67.62, color='gold', s=40);<\/span><\/code><\/pre>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-534\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-2.png\" alt=\"\" width=\"367\" height=\"342\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-2.png 367w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-2-300x280.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-2-343x320.png 343w\" sizes=\"(max-width: 367px) 100vw, 367px\" \/><\/p>\n<p style=\"text-align: justify\">Para calcular exatamente onde o ponto dourado deveria estar, primeiro precisamos identificar todos os pontos na faixa. Eles correspondem \u00e0s linhas onde a <code>Parente Average<\/code> est\u00e1 entre 67,5 polegadas e 68,5 polegadas.<\/p>\n<pre><code><span style=\"color: black\">close_to_68 = heights.where('Parent Average', are.between(67.5, 68.5))\r\nclose_to_68<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Parent Average<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Child<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">74.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.0<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.0<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">66.0<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">63.5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">63.0<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">65.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">62.7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">A altura prevista de uma crian\u00e7a cuja altura m\u00e9dia dos pais \u00e9 de 68 polegadas \u00e9 a altura m\u00e9dia das crian\u00e7as nessas fileiras. Isso \u00e9 67,62 polegadas.<\/p>\n<pre><code><span style=\"color: black\">np.average(close_to_68.column('Child'))<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[12]:<\/td>\n<td style=\"text-align: left\">67.625<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Agora temos uma maneira de prever a altura de uma crian\u00e7a, dado qualquer valor da altura m\u00e9dia dos pais pr\u00f3ximo daqueles em nosso conjunto de dados. Podemos definir uma fun\u00e7\u00e3o <code>predict_child<\/code> que faz isso. O corpo da fun\u00e7\u00e3o consiste no c\u00f3digo nas duas c\u00e9lulas acima, al\u00e9m das escolhas de nomes.<\/p>\n<pre><code><span style=\"color: black\">def predict_child(p_avg):\r\n    \"\"\"Preveja a altura de uma crian\u00e7a cujos pais t\u00eam uma altura m\u00e9dia dos pais de p_avg.\r\n\r\n    A previs\u00e3o \u00e9 a altura m\u00e9dia das crian\u00e7as cuja altura m\u00e9dia dos pais est\u00e1\r\n    no intervalo p_avg mais ou menos 0,5.\r\n    \"\"\"\r\n\r\n    close_points = heights.where('Parent Average', are.between(p_avg-0.5, p_avg + 0.5))\r\n    return np.average(close_points.column('Child'))<\/span><\/code><\/pre>\n<p style=\"text-align: justify\">Dada uma altura m\u00e9dia dos pais de 68 polegadas, a fun\u00e7\u00e3o <code>predict_child<\/code> retorna a mesma previs\u00e3o (67,62 polegadas) que obtivemos anteriormente. A vantagem de definir a fun\u00e7\u00e3o \u00e9 que podemos facilmente alterar o valor do preditor e obter uma nova previs\u00e3o.<\/p>\n<pre><code><span style=\"color: black\">predict_child(68)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[13]:<\/td>\n<td style=\"text-align: left\">67.625<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<pre><code><span style=\"color: black\">predict_child(66)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[14]:<\/td>\n<td style=\"text-align: left\">66.83333333333333<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Qu\u00e3o boas s\u00e3o essas previs\u00f5es? Podemos ter uma ideia disso comparando as previs\u00f5es com os dados que j\u00e1 temos. Para fazer isso, primeiro aplicamos a fun\u00e7\u00e3o <code>predict_child<\/code> \u00e0 coluna de alturas <code>Parent Average<\/code> e coletamos os resultados em uma nova coluna chamada <code>Prediction<\/code>.<\/p>\n<pre><code><span style=\"color: black\"># Aplique predict_child a todas as alturas m\u00e9dias dos pais\r\n\r\nheights_with_predictions = heights.with_column(\r\n    'Prediction', heights.apply(predict_child, 'Parent Average')\r\n)<\/span><\/code><\/pre>\n<pre><code><span style=\"color: black\">heights_with_predictions<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Parent Average<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Child<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Prediction<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.75<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.1<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.75<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.1<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.75<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.1<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.75<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.1<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.4158<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.4158<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">65.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.4158<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">65.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">70.4158<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.5025<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.5025<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Para ver onde as previs\u00f5es est\u00e3o em rela\u00e7\u00e3o aos dados observados, podemos desenhar gr\u00e1ficos de dispers\u00e3o sobrepostos com <code>Parent Average<\/code> como o eixo horizontal comum.<\/p>\n<pre><code><span style=\"color: black\">heights_with_predictions.scatter('Parent Average')<\/span><\/code><\/pre>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-535\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-3.png\" alt=\"\" width=\"491\" height=\"349\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-3.png 491w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-3-300x213.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/8-1-3-450x320.png 450w\" sizes=\"(max-width: 491px) 100vw, 491px\" \/><\/p>\n<p style=\"text-align: justify\">O gr\u00e1fico de pontos dourados \u00e9 chamado de <em>gr\u00e1fico de m\u00e9dias<\/em>, porque cada ponto dourado \u00e9 o centro de uma faixa vertical como a que desenhamos anteriormente. Cada um fornece uma previs\u00e3o da altura de uma crian\u00e7a dada a altura m\u00e9dia dos pais. Por exemplo, o gr\u00e1fico de dispers\u00e3o mostra que para uma altura m\u00e9dia dos pais de 65 polegadas, a altura prevista da crian\u00e7a seria pouco acima de 65 polegadas, e de fato <code>predict_child(65)<\/code> avalia para cerca de 65.84.<\/p>\n<pre><code><span style=\"color: black\">predict_child(65)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[16]:<\/td>\n<td style=\"text-align: left\">65.84<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Observe que o gr\u00e1fico de m\u00e9dias segue aproximadamente uma linha reta. Esta linha reta \u00e9 agora chamada de <em>linha de regress\u00e3o<\/em> e \u00e9 um dos m\u00e9todos mais comuns de fazer previs\u00f5es. O c\u00e1lculo que acabamos de fazer \u00e9 muito semelhante ao c\u00e1lculo que levou ao desenvolvimento do m\u00e9todo de regress\u00e3o, usando os mesmos dados.<\/p>\n<p style=\"text-align: justify\">Este exemplo, assim como o exemplo da an\u00e1lise das mortes por c\u00f3lera de John Snow, mostra como alguns dos conceitos fundamentais da ci\u00eancia de dados moderna t\u00eam ra\u00edzes que remontam a muito tempo atr\u00e1s. O m\u00e9todo usado aqui foi um precursor dos m\u00e9todos de previs\u00e3o <em>vizinho mais pr\u00f3ximo<\/em> que agora t\u00eam aplica\u00e7\u00f5es poderosas em diversos contextos. O campo moderno de <em>aprendizado de m\u00e1quina<\/em> inclui a automa\u00e7\u00e3o de tais m\u00e9todos para fazer previs\u00f5es com base em conjuntos de dados vastos e em r\u00e1pida evolu\u00e7\u00e3o.<\/p>\n<p><!--###########################################################################################################################################################--><\/p>\n<table width=\"100%\">\n<tbody>\n<tr>\n<td align=\"left\"><a class=\"next-page-link\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/\">\u2190 Cap\u00edtulo 8 &#8211; Fun\u00e7\u00f5es e Tabelas<\/a><\/td>\n<td align=\"right\"><a class=\"next-page-link\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-2\/\">Cap\u00edtulo 8.2 &#8211; Classificando por uma Vari\u00e1vel \u2192<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><!--###########################################################################################################################################################--><\/p>\n<\/div>\n<\/div>\n<div style=\"clear: both;height: 1px;margin-top: -1px\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u00cdndice 1. O que \u00e9 Ci\u00eancia de Dados? 1.1. Introdu\u00e7\u00e3o 1.1.1. Ferramentas Computacionais 1.1.2. T\u00e9cnicas Estat\u00edsticas 1.2. Por que Ci\u00eancia de Dados? 1.3. Tra\u00e7ando os Cl\u00e1ssicos 1.3.1. Personagens Liter\u00e1rios 1.3.2. Outro Tipo de Personagem 2. Causalidade e Experimentos 2.1. John Snow e a Bomba da Broad Street 2.2. O &#8220;Grande Experimento&#8221; de Snow 2.3. Estabelecendo [&hellip;]<\/p>\n","protected":false},"author":21894,"featured_media":0,"parent":484,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"coauthors":[14],"class_list":["post-530","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/530","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/users\/21894"}],"replies":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/comments?post=530"}],"version-history":[{"count":11,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/530\/revisions"}],"predecessor-version":[{"id":971,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/530\/revisions\/971"}],"up":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/484"}],"wp:attachment":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/media?parent=530"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/coauthors?post=530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}