{"id":716,"date":"2020-02-18T10:34:40","date_gmt":"2020-02-18T13:34:40","guid":{"rendered":"https:\/\/micheladrianomedeiros.com.br\/blog\/?p=716"},"modified":"2020-02-18T10:34:43","modified_gmt":"2020-02-18T13:34:43","slug":"data-science-com-python-3","status":"publish","type":"post","link":"https:\/\/micheladrianomedeiros.com.br\/blog\/data-science-com-python-3\/","title":{"rendered":"Data Science Com Python #3"},"content":{"rendered":"\n
Uma tupla \u00e9 uma sequ\u00eancia de\nobjetos imut\u00e1veis de Python cercado por par\u00eanteses.<\/p>\n\n\n\n
Diferente de listas, tuplas\nn\u00e3o podem ser mudadas. Tuplas s\u00e3o fun\u00e7\u00f5es convenientes que retornam m\u00faltiplos\nvalores.<\/p>\n\n\n\n
O c\u00f3digo a seguir cria uma tupla, a divide, cria uma lista e cria uma matriz de tuplas e listas:<\/p>\n\n\n\n
import numpy as np\n\nif __name__ == \"__main__\":\n ls = ['laranja', 'banana', 'uva', 'ma\u00e7\u00e3', 'uva']\n print('tamanho da tupla: ', len(ls), ' itens')\n print('quantidade de uva: ', ls.count('uva'))\n print('\\ntupla dividida: ')\n print ('Os 3 primeiros elementos da tupla: ', ls[:3])\n print ('Os \u00faltimos de 3 elementos da tupla: ', ls[3:])\n print ('Come\u00e7a no 2\u00ba vai at\u00e9 o 5\u00ba valor: ', ls[1:5])\n print ('Come\u00e7a no do 3\u00ba \u00edndice e vai at\u00e9 o fim da tupla: ', ls[-3:])\n print ('Come\u00e7a do 2\u00ba e vai at\u00e9 o pen\u00faltimo valor da tupla: ', ls[1:-1])\n print ('\\ncriar lista e criar matriz a partir da lista e da tupla: ')\n fruta = ['pera', 'toranja', 'cantaloupe', 'kiwi', 'ameixa']\n matriz = np.array([ls, fruta])\n print (matriz)<\/code><\/pre>\n\n\n\n
Sa\u00edda:<\/p>\n\n\n\n
<\/figure><\/div>\n\n\n\n
O c\u00f3digo come\u00e7a importando o\nNumPy. O bloco principal come\u00e7a criando uma tupla com o nome ls, depois mostra o\ntamanho da tupla, o n\u00famero de elementos que h\u00e1 na tupla com o nome uva.<\/p>\n\n\n\n
O c\u00f3digo continua repartindo a\ntupla em peda\u00e7os. Primeiro, mostra os 3 primeiros elementos da tupla ls[:3].<\/p>\n\n\n\n
Segundo, mostra os \u00faltimos 3\nelementos da tupla ls[3:].<\/p>\n\n\n\n
Terceiro, mostra o segundo\nelemento da tupla e vai at\u00e9 o quinto ls[1:5].<\/p>\n\n\n\n
Quarto, mostra o terceiro elemento\ne vai at\u00e9 o fim da tupla ls[-3:].<\/p>\n\n\n\n
Quinto, mostra o segundo\nelemento e vai at\u00e9 o pen\u00faltimo elemento da tupla ls[1:-1].<\/p>\n\n\n\n
O c\u00f3digo continua e cria uma\nlista e cria uma matriz a partir da lista de da tupla.<\/p>\n\n\n\n
Um dicion\u00e1rio \u00e9 uma cole\u00e7\u00e3o\nn\u00e3o ordenada de itens identificadas por uma chave\/valor.<\/p>\n\n\n\n
\u00c9 uma estrutura de dados\nextremamente importante para trabalhar com dados.<\/p>\n\n\n\n
O exemplo a seguir \u00e9 muito\nsimples, mas a pr\u00f3xima se\u00e7\u00e3o apresenta um exemplo mais complexo com base em um\nconjunto de dados.<\/p>\n\n\n\n
O c\u00f3digo de exemplo cria um dicion\u00e1rio, remove um elemento, adiciona um elemento, cria uma lista de elementos do dicion\u00e1rio e percorre a lista:<\/p>\n\n\n\n
import numpy as np\n\nif __name__ == \"__main__\":\n audio = {'amp':'Linn', 'preamp':'Luxman', 'speakers':'Energy', \n\t'ic':'Crystal Ultra', 'pc':'JPS', 'power':'Equi-tech',\n\t'sp':'Crystal Ultra', 'cdp':'Nagra', 'up':'Esoteric'}\n del audio['up']\n print('elemento \"removido\";')\n print(audio, '\\n')\n print('elemento \"adicionado\";')\n audio['up'] = 'Oppo'\n print (audio, '\\n')\n print ('universal player:', audio['up'], '\\n')\n dict_ls = [audio]\n video = {'tv':'LG 65C7 OLED', 'stp':'DISH', 'HDMI':'DH Labs',\n\t'cable':'coax'}\n print ('listas de elementos;')\n dict_ls.append(video)\n for i, row in enumerate(dict_ls):\n print ('linha' , i, ':')\n print (row)\n<\/code><\/pre>\n\n\n\n
Sa\u00edda:<\/p>\n\n\n\n
<\/figure><\/div>\n\n\n\n
O bloco principal come\u00e7a a\ncriar um dicion\u00e1rio de \u00e1udio com v\u00e1rios elementos.<\/p>\n\n\n\n
Logo ap\u00f3s remove um elemento\ncom a chave de nome up e valor Esoteric e imprime o resultado.<\/p>\n\n\n\n
A seguir um novo elemento com\na chave up com o valor Oppo \u00e9 adicionado e imprime o resultado.<\/p>\n\n\n\n
Depois cria um dicion\u00e1rio de \u00e1udio,\ncria um dicion\u00e1rio de v\u00eddeo, e cria um dicion\u00e1rio com os dicion\u00e1rios de v\u00eddeo e\nde \u00e1udio.<\/p>\n\n\n\n
No final utilizamos um loop\npara percorrer a lista e mostrar os dois dicion\u00e1rios.<\/p>\n\n\n\n
Uma fun\u00e7\u00e3o \u00fatil que pode ser utilizada no loop\n\u00e9 o enumerate(). Essa fun\u00e7\u00e3o adiciona um contador ao iterable.<\/p>\n\n\n\n
Um iterable \u00e9 um objeto que pode ser iterado. A fun\u00e7\u00e3o enumerate() \u00e9 muito \u00fatil porque um contador \u00e9 automaticamente criado e incrementado, isso significa menos c\u00f3digo.<\/p>\n\n\n\n
Fonte livro:\u00a0Data Science Fundamentals for Python\u00a0and\u00a0MongoDB\u00a0<\/strong> <\/p>\n\n\n\n
Uma tupla \u00e9 uma sequ\u00eancia de objetos imut\u00e1veis de Python cercado por par\u00eanteses. Diferente de listas, tuplas n\u00e3o podem ser mudadas. Tuplas s\u00e3o fun\u00e7\u00f5es convenientes que retornam m\u00faltiplos valores. O c\u00f3digo a seguir cria uma tupla, a divide, cria uma lista e cria uma matriz de tuplas e listas: Sa\u00edda: O c\u00f3digo come\u00e7a importando o […]<\/p>\n","protected":false},"author":1,"featured_media":111,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[31],"tags":[],"class_list":["post-716","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-datascience"],"yoast_head":"\n
Data Science Com Python #3 - Viciados em Aprender<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/micheladrianomedeiros.com.br\/blog\/data-science-com-python-3\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Science Com Python #3\" \/>\n<meta property=\"og:description\" content=\"Uma tupla \u00e9 uma sequ\u00eancia de objetos imut\u00e1veis de Python cercado por par\u00eanteses. Diferente de listas, tuplas n\u00e3o podem ser mudadas. Tuplas s\u00e3o fun\u00e7\u00f5es convenientes que retornam m\u00faltiplos valores. O c\u00f3digo a seguir cria uma tupla, a divide, cria uma lista e cria uma matriz de tuplas e listas: Sa\u00edda: O c\u00f3digo come\u00e7a importando o […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/micheladrianomedeiros.com.br\/blog\/data-science-com-python-3\/\" \/>\n<meta property=\"og:site_name\" content=\"Viciados em Aprender\" \/>\n<meta property=\"article:published_time\" content=\"2020-02-18T13:34:40+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-02-18T13:34:43+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/micheladrianomedeiros.com.br\/blog\/wp-content\/uploads\/2019\/06\/Pyhton-for-data-science-0.png\" \/>\n\t<meta property=\"og:image:width\" content=\"815\" \/>\n\t<meta property=\"og:image:height\" content=\"476\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"blackzig\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"blackzig\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tempo estimado de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/data-science-com-python-3\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/data-science-com-python-3\\\/\"},\"author\":{\"name\":\"blackzig\",\"@id\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/#\\\/schema\\\/person\\\/687a6d17428a0f0e040aefbd858ebc1e\"},\"headline\":\"Data Science Com Python #3\",\"datePublished\":\"2020-02-18T13:34:40+00:00\",\"dateModified\":\"2020-02-18T13:34:43+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/data-science-com-python-3\\\/\"},\"wordCount\":421,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/#\\\/schema\\\/person\\\/687a6d17428a0f0e040aefbd858ebc1e\"},\"image\":{\"@id\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/data-science-com-python-3\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/wp-content\\\/uploads\\\/2019\\\/06\\\/Pyhton-for-data-science-0.png\",\"articleSection\":[\"Data Science\"],\"inLanguage\":\"pt-PT\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/data-science-com-python-3\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/data-science-com-python-3\\\/\",\"url\":\"https:\\\/\\\/micheladrianomedeiros.com.br\\\/blog\\\/data-science-com-python-3\\\/\",\"name\":\"Data Science Com Python #3 - 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Viciados em Aprender","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/micheladrianomedeiros.com.br\/blog\/data-science-com-python-3\/","og_locale":"pt_PT","og_type":"article","og_title":"Data Science Com Python #3","og_description":"Uma tupla \u00e9 uma sequ\u00eancia de objetos imut\u00e1veis de Python cercado por par\u00eanteses. Diferente de listas, tuplas n\u00e3o podem ser mudadas. Tuplas s\u00e3o fun\u00e7\u00f5es convenientes que retornam m\u00faltiplos valores. 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