{"id":473,"date":"2025-07-23T20:07:37","date_gmt":"2025-07-24T00:07:37","guid":{"rendered":"https:\/\/literaciadigital.ufms.br\/?page_id=473"},"modified":"2025-09-29T11:50:47","modified_gmt":"2025-09-29T15:50:47","slug":"7-3","status":"publish","type":"page","link":"https:\/\/literaciadigital.ufms.br\/en\/data8\/7-0\/7-3\/","title":{"rendered":"Cap\u00edtulo 7.3"},"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<pre><code><span style=\"color: black\">from datascience import *\r\nimport numpy as np\r\npath_data = '..\/..\/..\/assets\/data\/'\r\n%matplotlib inline\r\nimport matplotlib.pyplot as plots\r\nplots.style.use('fivethirtyeight')<\/span><\/code><\/pre>\n<h1 id=\"gr-ficos-sobrepostos\" style=\"text-align: center\">Gr\u00e1ficos Sobrepostos<\/h1>\n<p style=\"text-align: justify\">Neste cap\u00edtulo, aprendemos como visualizar dados desenhando gr\u00e1ficos. Um uso comum dessas visualiza\u00e7\u00f5es \u00e9 comparar dois conjuntos de dados. Nesta se\u00e7\u00e3o, veremos como <em>sobrepor<\/em> gr\u00e1ficos, ou seja, desenh\u00e1-los em um \u00fanico gr\u00e1fico em um par comum de eixos.<\/p>\n<p style=\"text-align: justify\">Para que a sobreposi\u00e7\u00e3o fa\u00e7a sentido, os gr\u00e1ficos que est\u00e3o sendo sobrepostos devem representar as mesmas vari\u00e1veis e serem medidos nas mesmas unidades.<\/p>\n<p style=\"text-align: justify\">Para desenhar gr\u00e1ficos sobrepostos, os m\u00e9todos <code>scatter<\/code>, <code>plot<\/code> e <code>barh<\/code> podem ser chamados da mesma maneira. Para <code>scatter<\/code> e <code>plot<\/code>, uma coluna deve servir como o eixo horizontal comum para todos os gr\u00e1ficos sobrepostos. Para <code>barh<\/code>, uma coluna deve servir como o eixo comum que \u00e9 o conjunto de categorias. A chamada geral parece assim:<\/p>\n<p><code>name_of_table.method(column_label_of_common_axis, array_of_labels_of_variables_to_plot)<\/code><\/p>\n<p style=\"text-align: justify\">Mais comumente, primeiro selecionaremos apenas as colunas necess\u00e1rias para nosso gr\u00e1fico e depois chamaremos o m\u00e9todo especificando apenas a vari\u00e1vel no eixo comum:<\/p>\n<p><code>name_of_table.method(column_label_of_common_axis)<\/code><\/p>\n<h2 id=\"gr-ficos-de-dispers-o-sobrepostos\">Gr\u00e1ficos de Dispers\u00e3o Sobrepostos<\/h2>\n<p style=\"text-align: justify\">A tabela <code>sons_heights<\/code> faz parte de um conjunto de dados hist\u00f3ricos sobre as alturas de pais e seus filhos. Especificamente, a popula\u00e7\u00e3o consiste em 179 homens que foram os primeiros nascidos em suas fam\u00edlias. Os dados s\u00e3o suas pr\u00f3prias alturas e as alturas de seus pais. Todas as alturas foram medidas em polegadas.<\/p>\n<pre><code><span style=\"color: black\">sons_heights = Table.read_table(path_data + 'sons_heights.csv')\r\nsons_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\">father<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">mother<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">son<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\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\">73.2<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\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\">73.5<\/td>\n<\/tr>\n<tr>\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\">71.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\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\">70.5<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">75.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">58.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.0<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">74.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">68.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">76.5<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">74.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">62.0<\/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\">73.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.0<\/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\">73.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">66.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">71.0<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd;text-align: center;font-style: italic\" colspan=\"3\">&#8230; (169 rows omitted)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">O m\u00e9todo <code>scatter<\/code> nos permite visualizar como as alturas dos filhos est\u00e3o relacionadas \u00e0s alturas de ambos os pais. No gr\u00e1fico, as alturas dos filhos formar\u00e3o o eixo horizontal comum.<\/p>\n<pre><code><span style=\"color: black\">sons_heights.scatter('son')<\/span><\/code><\/pre>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone wp-image-477\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-1-300x219.png\" alt=\"\" width=\"347\" height=\"253\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-1-300x219.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-1-438x320.png 438w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-1.png 472w\" sizes=\"(max-width: 347px) 100vw, 347px\" \/><\/p>\n<p style=\"text-align: justify\">Repare como especificamos apenas a vari\u00e1vel (alturas dos filhos) no eixo horizontal comum. O Python desenhou dois gr\u00e1ficos de dispers\u00e3o: um para a rela\u00e7\u00e3o entre essa vari\u00e1vel e as outras duas.<\/p>\n<p style=\"text-align: justify\">Cada ponto representa uma linha da tabela, ou seja, um trio &#8220;pai, m\u00e3e, filho&#8221;. Para todos os pontos, o eixo horizontal representa a altura do filho. Nos pontos azuis, o eixo vertical representa a altura do pai. Nos pontos dourados, o eixo vertical representa as alturas da m\u00e3e.<\/p>\n<p style=\"text-align: justify\">Tanto o gr\u00e1fico de dispers\u00e3o em dourado quanto o azul t\u00eam inclina\u00e7\u00e3o para cima e mostram uma associa\u00e7\u00e3o positiva entre as alturas dos filhos e as alturas de ambos os pais. O gr\u00e1fico azul (dos pais) \u00e9 geralmente mais alto que o dourado, porque os pais eram, em geral, mais altos que as m\u00e3es.<\/p>\n<h2 id=\"gr-ficos-de-linhas-sobrepostas\">Gr\u00e1ficos de linhas sobrepostas<\/h2>\n<p style=\"text-align: justify\">O pr\u00f3ximo exemplo envolve dados sobre crian\u00e7as de tempos mais recentes. Voltaremos \u00e0 tabela de dados do Censo <code>us_pop<\/code>, criada novamente abaixo para refer\u00eancia. A partir desta tabela, extrairemos a contagem de todas as crian\u00e7as em cada uma das faixas et\u00e1rias de 0 a 18 anos.<\/p>\n<pre><code><span style=\"color: black\"># Leia a tabela completa do Censo\r\ndata = 'http:\/\/www2.census.gov\/programs-surveys\/popest\/technical-documentation\/file-layouts\/2010-2019\/nc-est2019-agesex-res.csv'\r\nfull_census_table = Table.read_table(data)\r\n\r\n# Selecione colunas da tabela completa e renomeie algumas delas\r\npartial_census_table = full_census_table.select('SEX', 'AGE', 'POPESTIMATE2014', 'POPESTIMATE2019')\r\nus_pop = partial_census_table.relabeled('POPESTIMATE2014', '2014').relabeled('POPESTIMATE2019', '2019')\r\n\r\n# Acesse as linhas correspondentes a todas as crian\u00e7as de 0 a 18 anos\r\nchildren = us_pop.where('SEX', are.equal_to(0)).where('AGE', are.below(19)).drop('SEX')\r\nchildren.show()<\/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\">AGE<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">2014<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">2019<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3954787<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3783052<\/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\">3948891<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3829599<\/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\">3958711<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3922044<\/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\">4005928<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3998665<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4004032<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4043323<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4004576<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4028281<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">6<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4133372<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4017227<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">7<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4152666<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4022319<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">8<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4118349<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4066194<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">9<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4106068<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4061874<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">10<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4114558<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4060940<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">11<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4084457<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4189261<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">12<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4067187<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4208387<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">13<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4168095<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4175221<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">14<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4231353<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4164459<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">15<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4162828<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4175459<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">16<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4165925<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4150420<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">17<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4181940<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4142425<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">18<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4221344<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4255827<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Podemos agora desenhar dois gr\u00e1ficos de linhas sobrepostas, mostrando o n\u00famero de crian\u00e7as nas diferentes faixas et\u00e1rias para cada um dos anos de 2014 e 2019. A chamada \u00e9 an\u00e1loga \u00e0 chamada de <code>scatter<\/code> no exemplo anterior.<\/p>\n<pre><code><span style=\"color: black\">children.plot('AGE')<\/span><\/code><\/pre>\n<p><img decoding=\"async\" class=\"alignnone wp-image-478\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-2-300x237.png\" alt=\"\" width=\"341\" height=\"269\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-2-300x237.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-2-404x320.png 404w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-2.png 527w\" sizes=\"(max-width: 341px) 100vw, 341px\" \/><\/p>\n<p style=\"text-align: justify\">Embora os r\u00f3tulos do eixo horizontal incluam alguns n\u00fameros meio-inteiros, \u00e9 importante lembrar que s\u00f3 temos dados nas idades de 0, 1, 2, e assim por diante. Os gr\u00e1ficos de linha &#8220;unem os pontos&#8221; entre eles.<\/p>\n<p style=\"text-align: justify\">Os dois gr\u00e1ficos se cruzam em alguns lugares. Por exemplo, houve mais crian\u00e7as de 6 anos em 2014 do que em 2019, e houve mais crian\u00e7as de 12 anos em 2019 do que em 2014.<\/p>\n<p style=\"text-align: justify\">\u00c9 claro que as crian\u00e7as de 12 anos em 2019 consistem principalmente nas crian\u00e7as que tinham 7 anos em 2014. Para ver isso nos gr\u00e1ficos, compare o gr\u00e1fico dourado na <code>AGE<\/code> 12 e o gr\u00e1fico azul na <code>AGE<\/code> 7. Voc\u00ea perceber\u00e1 que o gr\u00e1fico dourado (2019) se parece muito com o gr\u00e1fico azul (2014) deslocado para a direita em 5 anos. O deslocamento \u00e9 acompanhado por um leve aumento devido ao efeito l\u00edquido das crian\u00e7as que entraram no pa\u00eds entre 2014 e 2019 superando aquelas que sa\u00edram. Felizmente, nessas idades, n\u00e3o h\u00e1 muita perda de vida.<\/p>\n<h2 id=\"gr-ficos-de-barras\" style=\"text-align: justify\">Gr\u00e1ficos de Barras<\/h2>\n<p style=\"text-align: justify\">A Funda\u00e7\u00e3o Kaiser Family compilou dados do Censo sobre a distribui\u00e7\u00e3o de ra\u00e7a e etnia nos EUA. O site da Funda\u00e7\u00e3o fornece compila\u00e7\u00f5es de dados para <a href=\"http:\/\/kff.org\/other\/state-indicator\/distribution-by-raceethnicity\/\">toda a popula\u00e7\u00e3o dos EUA<\/a> em 2019, bem como para <a href=\"http:\/\/kff.org\/other\/state-indicator\/children-by-raceethnicity\/\">crian\u00e7as nos EUA<\/a> que tinham menos de 18 anos naquele ano.<\/p>\n<p style=\"text-align: justify\">A tabela <code>usa_ca<\/code> \u00e9 adaptada de seus dados para os Estados Unidos e Calif\u00f3rnia. As colunas representam todos nos EUA, todos na Calif\u00f3rnia, crian\u00e7as nos EUA e crian\u00e7as na Calif\u00f3rnia.<\/p>\n<p style=\"text-align: justify\">O corpo da tabela cont\u00e9m porcentagens nas diferentes categorias. Cada coluna mostra a distribui\u00e7\u00e3o da vari\u00e1vel <code>Ethnicity\/Race<\/code> no grupo de pessoas correspondente a essa coluna. Ent\u00e3o em cada coluna, as entradas somam 100. A categoria <code>API<\/code> consiste em asi\u00e1ticos e ilh\u00e9us do Pac\u00edfico, incluindo havaianos nativos. A categoria <code>Other<\/code> inclui nativos americanos, nativos do Alasca e pessoas que se identificam com m\u00faltiplas ra\u00e7as.<\/p>\n<pre><code><span style=\"color: black\">usa_ca = Table.read_table(path_data + 'usa_ca_2019.csv')\r\nusa_ca<\/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\">Ethnicity\/Race<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">USA All<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">CA All<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">USA Children<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">CA Children<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">API<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">5.8<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">15.1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4.9<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">11.5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Black<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">12.2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">5.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">13.4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4.9<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Hispanic<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">18.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">39.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">25.6<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">52.1<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">White<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">60.1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">36.4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">50.0<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">25.5<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Other<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3.4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3.7<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">6.1<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">6.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">\u00c9 natural querer comparar essas distribui\u00e7\u00f5es. Faz sentido comparar as colunas diretamente, porque todas as entradas s\u00e3o porcentagens e, portanto, est\u00e3o na mesma escala.<\/p>\n<p style=\"text-align: justify\">O m\u00e9todo <code>barh<\/code> nos permite visualizar as compara\u00e7\u00f5es desenhando v\u00e1rios gr\u00e1ficos de barras nos mesmos eixos. A chamada \u00e9 an\u00e1loga \u00e0quelas para <code>scatter<\/code> e <code>plot<\/code>: temos que especificar o eixo comum das categorias.<\/p>\n<pre><code><span style=\"color: black\">usa_ca.barh('Ethnicity\/Race')<\/span><\/code><\/pre>\n<p><img decoding=\"async\" class=\"alignnone wp-image-479\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-3-300x124.png\" alt=\"\" width=\"399\" height=\"165\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-3-300x124.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-3.png 643w\" sizes=\"(max-width: 399px) 100vw, 399px\" \/><\/p>\n<p style=\"text-align: justify\">Embora desenhar os gr\u00e1ficos de barras sobrepostos seja direto, h\u00e1 informa\u00e7\u00f5es em excesso neste gr\u00e1fico para conseguirmos separar semelhan\u00e7as e diferen\u00e7as entre as popula\u00e7\u00f5es. \u00c9 muito mais f\u00e1cil comparar as popula\u00e7\u00f5es um par de cada vez.<\/p>\n<p style=\"text-align: justify\">Vamos come\u00e7ar comparando as popula\u00e7\u00f5es inteiras dos EUA e da Calif\u00f3rnia.<\/p>\n<pre><code><span style=\"color: black\">usa_ca.select('Ethnicity\/Race', 'USA All', 'CA All').barh('Ethnicity\/Race')<\/span><\/code><\/pre>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-480\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-4-300x132.png\" alt=\"\" width=\"368\" height=\"162\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-4-300x132.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-4.png 602w\" sizes=\"(max-width: 368px) 100vw, 368px\" \/><\/p>\n<p style=\"text-align: justify\">As duas distribui\u00e7\u00f5es s\u00e3o bastante diferentes. A Calif\u00f3rnia tem porcentagens mais altas nas categorias <code>API<\/code> e <code>Hispanic<\/code>, e correspondentes porcentagens mais baixas nas categorias <code>Black<\/code> e <code>White<\/code>. As porcentagens na categoria <code>Other<\/code> s\u00e3o bastante semelhantes nas duas popula\u00e7\u00f5es. As diferen\u00e7as se devem em grande parte \u00e0 localiza\u00e7\u00e3o geogr\u00e1fica da Calif\u00f3rnia e aos padr\u00f5es de imigra\u00e7\u00e3o e migra\u00e7\u00e3o, tanto historicamente quanto nas \u00faltimas d\u00e9cadas.<\/p>\n<p style=\"text-align: justify\">Como voc\u00ea pode ver no gr\u00e1fico, quase 40% da popula\u00e7\u00e3o da Calif\u00f3rnia em 2019 era <code>Hispanic<\/code>. Uma compara\u00e7\u00e3o com a popula\u00e7\u00e3o de crian\u00e7as do estado indica que a propor\u00e7\u00e3o <code>Hispanic<\/code> provavelmente ser\u00e1 maior nos pr\u00f3ximos anos. Entre as crian\u00e7as californianas em 2019, mais de 50% estavam na categoria <code>Hispanic<\/code>.<\/p>\n<pre><code><span style=\"color: black\">usa_ca.select('Ethnicity\/Race', 'CA All', 'CA Children').barh('Ethnicity\/Race')<\/span><\/code><\/pre>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-481\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-5-300x125.png\" alt=\"\" width=\"389\" height=\"162\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-5-300x125.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-5-555x233.png 555w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/7-3-5.png 634w\" sizes=\"(max-width: 389px) 100vw, 389px\" \/><\/p>\n<p style=\"text-align: justify\">Conjuntos de dados mais complexos naturalmente d\u00e3o origem a visualiza\u00e7\u00f5es variadas e interessantes, incluindo gr\u00e1ficos sobrepostos de diferentes tipos. Para analisar esses dados, \u00e9 \u00fatil ter mais habilidades em manipula\u00e7\u00e3o de dados, para que possamos colocar os dados em uma forma que nos permita usar m\u00e9todos como os desta se\u00e7\u00e3o. No pr\u00f3ximo cap\u00edtulo, desenvolveremos algumas dessas habilidades.<\/p>\n<p style=\"text-align: justify\"><!--###########################################################################################################################################################--><\/p>\n<table width=\"100%\">\n<tbody>\n<tr>\n<td align=\"left\"><a class=\"next-page-link\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/7-2\/\">\u2190 Cap\u00edtulo 7.2 &#8211; Visualizando Distribui\u00e7\u00f5es Num\u00e9ricas<\/a><\/td>\n<td align=\"right\"><a class=\"next-page-link\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/\">Cap\u00edtulo 8 &#8211; Fun\u00e7\u00f5es e Tabelas \u2192<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\"><!--###########################################################################################################################################################--><\/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":425,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"coauthors":[14],"class_list":["post-473","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/473","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=473"}],"version-history":[{"count":8,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/473\/revisions"}],"predecessor-version":[{"id":955,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/473\/revisions\/955"}],"up":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/425"}],"wp:attachment":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/media?parent=473"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/coauthors?post=473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}