{ "cells": [ { "cell_type": "markdown", "id": "given-replication", "metadata": {}, "source": [ "# Estrutura de dados (2) e strings\n", "\n", "Na aula passada aprendemos a mexer com listas, uma das estruturas de dados mais importantes e flexíveis que existe.\n", "\n", "Nesta aula veremos outras estruturas de dados importantes como os dicionários, os conjuntos e as tuplas.\n", "\n", "Antes disso vamos rever o que aprendemos com as listas.\n", "\n", "Também vamos ver mais funcionalidades de strings." ] }, { "cell_type": "markdown", "id": "qualified-paragraph", "metadata": {}, "source": [ "## Revisão de listas\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "mechanical-mouse", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L = [1,2,3,4] # Como criar uma lista\n", "L" ] }, { "cell_type": "code", "execution_count": 2, "id": "growing-constitutional", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(L) # Comprimento de uma lista" ] }, { "cell_type": "code", "execution_count": 3, "id": "surrounded-college", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L[0] # Acessar os elementos da lista" ] }, { "cell_type": "code", "execution_count": 4, "id": "laden-wheat", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2, 3]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L[1:3] # Fatiamento das listas" ] }, { "cell_type": "code", "execution_count": 5, "id": "developing-ancient", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L.append(5) # Adicionar um elemento ao final da lista\n", "L" ] }, { "cell_type": "code", "execution_count": 6, "id": "potential-disclosure", "metadata": {}, "outputs": [], "source": [ "M = [1,2,3,] + [4,5,6,7,8] # Concatenar listas" ] }, { "cell_type": "code", "execution_count": 7, "id": "homeless-fever", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7, 8]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L.extend([6,7,8]) # Extender lista\n", "L" ] }, { "cell_type": "code", "execution_count": 8, "id": "lesser-transport", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "del L[5:] # Remover elementos da lista\n", "L" ] }, { "cell_type": "code", "execution_count": 9, "id": "proprietary-suspension", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L.pop(-1) # Retirar o último elemento da lista e retornar seu valor" ] }, { "cell_type": "code", "execution_count": 10, "id": "under-gathering", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L" ] }, { "cell_type": "code", "execution_count": 11, "id": "retained-treasury", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L.pop(0) # Retirar o primeiro elemento da lista e retornar seu valor" ] }, { "cell_type": "code", "execution_count": 12, "id": "yellow-cambridge", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2, 3, 4]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L" ] }, { "cell_type": "code", "execution_count": 13, "id": "knowing-haiti", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2, 3, 4, 2, 3, 4, 2, 3, 4]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Algo que não vimos!\n", "L*3" ] }, { "cell_type": "markdown", "id": "retained-avenue", "metadata": {}, "source": [ "### Vimos que existem vários métodos\n", "\n", "Estes métodos podem ser bastante úteis" ] }, { "cell_type": "code", "execution_count": 14, "id": "regional-lithuania", "metadata": {}, "outputs": [], "source": [ "L = [32,3456,124,687,523,456,23,634,576]\n", "M = L[:] # Cuidado ao copiar a lista!!!" ] }, { "cell_type": "code", "execution_count": 15, "id": "concrete-syntax", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L.index(124) # Buscar índice de elemento da lista" ] }, { "cell_type": "code", "execution_count": 16, "id": "bound-advertiser", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[2,34,3,2,3,6,6,6,6,32,4,4,3,2].count(6)" ] }, { "cell_type": "code", "execution_count": 17, "id": "indian-aberdeen", "metadata": {}, "outputs": [], "source": [ "L.sort() # Reordenar" ] }, { "cell_type": "code", "execution_count": 18, "id": "english-exposure", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[23, 32, 124, 456, 523, 576, 634, 687, 3456]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L" ] }, { "cell_type": "markdown", "id": "registered-kitty", "metadata": {}, "source": [ "### Vimos o algorítimo de Bubble Sort para reordenar listas" ] }, { "cell_type": "markdown", "id": "diagnostic-nature", "metadata": {}, "source": [ "## Dicionários\n", "\n", "Estrutura de dados semelhante à lista mas com acesso diferente.\n", "\n", "Os dicionários em Python são compostos por\n", " \n", " * Chaves\n", " * Valores\n", " \n", "O dicionário relaciona uma chava a um valor. A cada chave, existe um valor associado.\n", "\n", "De certo modo a lista pode ser vista como um dicionário: as chaves são números inteiros ordenados. \n", "\n", "\n", "Para se criar um dicionário, utilizam-se chaves ({})\n", "\n", "Imagine como exemplo, uma lista de preços de um supermercado:\n", "\n", "| Produto | Preço |\n", "| ------- | ------- |\n", "| Alface | R\\$ 0,45 |\n", "| Batata | R\\$ 1,20 |\n", "| Tomate | R\\$ 2,30 |\n", "| Feijão | R\\$ 1,50 |\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "binding-cross", "metadata": {}, "outputs": [], "source": [ "tabela = {\"Alface\": 0.45, \n", " \"Batata\": 1.20, \n", " \"Tomate\": 2.30, \n", " \"Feijão\": 1.50}" ] }, { "cell_type": "code", "execution_count": 20, "id": "northern-secretariat", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Alface': 0.45, 'Batata': 1.2, 'Tomate': 2.3, 'Feijão': 1.5}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabela" ] }, { "cell_type": "markdown", "id": "republican-clone", "metadata": {}, "source": [ "Em um dicionário, para se acessar um valor, usa-se a chave:" ] }, { "cell_type": "code", "execution_count": 21, "id": "naval-enclosure", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.45" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabela[\"Alface\"]" ] }, { "cell_type": "markdown", "id": "functional-chick", "metadata": {}, "source": [ "Também podemos atribuir um valor à chave:" ] }, { "cell_type": "code", "execution_count": 22, "id": "polish-crowd", "metadata": {}, "outputs": [], "source": [ "tabela[\"Alface\"] = 0.55 # Aumentamos o preço" ] }, { "cell_type": "code", "execution_count": 23, "id": "sized-conservation", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Alface': 0.55, 'Batata': 1.2, 'Tomate': 2.3, 'Feijão': 1.5}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabela" ] }, { "cell_type": "markdown", "id": "retained-storm", "metadata": {}, "source": [ "Se atribuirmos um valor a uma chave que **não** existe, essa nova chave com este valor será adicionada ao dicionário:" ] }, { "cell_type": "code", "execution_count": 24, "id": "bigger-program", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Alface': 0.55, 'Batata': 1.2, 'Tomate': 2.3, 'Feijão': 1.5, 'Cebola': 1.35}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabela[\"Cebola\"] = 1.35\n", "tabela" ] }, { "cell_type": "markdown", "id": "physical-camping", "metadata": {}, "source": [ "Se a chave não existir, ocorrerá um erro:" ] }, { "cell_type": "code", "execution_count": 25, "id": "regulated-kingston", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'Picanha'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_19331/2276766913.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtabela\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Picanha\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyError\u001b[0m: 'Picanha'" ] } ], "source": [ "tabela[\"Picanha\"]" ] }, { "cell_type": "markdown", "id": "convinced-toilet", "metadata": {}, "source": [ "Para se verificar se um chave faz parte do dicionário, use o operador `in`:" ] }, { "cell_type": "code", "execution_count": 26, "id": "aquatic-orbit", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"Feijão\" in tabela" ] }, { "cell_type": "code", "execution_count": 27, "id": "empty-memphis", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"Manga\" in tabela" ] }, { "cell_type": "markdown", "id": "severe-disclaimer", "metadata": {}, "source": [ "O método `.keys()` retorna uma lista (algo parecido) com as chaves:" ] }, { "cell_type": "code", "execution_count": 28, "id": "exceptional-memphis", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['Alface', 'Batata', 'Tomate', 'Feijão', 'Cebola'])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabela.keys()" ] }, { "cell_type": "code", "execution_count": 29, "id": "certified-foster", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Entre com o nome do produto, fim para terminar: Alface\n", "Preço 0.45\n", "Entre com o nome do produto, fim para terminar: Feijão\n", "Preço 1.50\n", "Entre com o nome do produto, fim para terminar: fim\n" ] } ], "source": [ "# Programa para obter o preço da tabela:\n", "tabela = {\"Alface\": 0.45, \n", " \"Batata\": 1.20, \n", " \"Tomate\": 2.30, \n", " \"Feijão\": 1.50}\n", "\n", "while True:\n", " produto = input(\"Entre com o nome do produto, fim para terminar: \")\n", " if produto == 'fim':\n", " break\n", " if produto in tabela:\n", " print(f\"Preço {tabela[produto]:5.2f}\")\n", " else:\n", " print(\"Produto não encontrado!\")\n", " " ] }, { "cell_type": "markdown", "id": "historical-necklace", "metadata": {}, "source": [ "Assim como nas listas, podemos apagar uma chave com a instrução `del`:" ] }, { "cell_type": "code", "execution_count": 30, "id": "technical-lancaster", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Alface': 0.45, 'Batata': 1.2, 'Feijão': 1.5}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabela = {\"Alface\": 0.45, \n", " \"Batata\": 1.20, \n", " \"Tomate\": 2.30, \n", " \"Feijão\": 1.50}\n", "\n", "del tabela[\"Tomate\"]\n", "tabela" ] }, { "cell_type": "markdown", "id": "informational-provision", "metadata": {}, "source": [ "### Quando usar listas ou dicionários?\n", "\n", "Quando você quer acessar os dados pela chave. Observe que nas versões mais antigas de Python (antes da versão 3.7) **a ordem de inserção não\n", "era preservada**!\n", "\n", "O acesso de elementos de listas pode ser mais rápido. " ] }, { "cell_type": "markdown", "id": "annual-feelings", "metadata": {}, "source": [ "**Cuidado** ao usar os f-strings com dicionários:" ] }, { "cell_type": "code", "execution_count": 31, "id": "illegal-folks", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Banana: R$ 2.00 Maçã: R$ 5.00'" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {\"banana\": 2.00, \"maçã\": 5.00}\n", "f\"Banana: R${d['banana']:5.2f} Maçã: R${d['maçã']:5.2f}\"" ] }, { "cell_type": "markdown", "id": "controlling-dressing", "metadata": {}, "source": [ "### O que pode ser usado como chave em um dicionário?\n", "\n", " * Strings\n", " * Números\n", " * Tuplas (veremos mais tarde)" ] }, { "cell_type": "markdown", "id": "settled-magnet", "metadata": {}, "source": [ "### Os elementos de um dicionário podem ser qualquer coisa..." ] }, { "cell_type": "code", "execution_count": 32, "id": "familiar-donor", "metadata": {}, "outputs": [], "source": [ "estoque = {\"tomate\": [1000, 2.30],\n", " \"alface\": [500, 0.45],\n", " \"batata\": [2001, 1.20], \n", " \"feijão\": [100, 1.5]}" ] }, { "cell_type": "code", "execution_count": 33, "id": "finished-disability", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vendas:\n", "\n", "tomate : 5 x 2.30 = 11.50\n", "batata : 10 x 1.20 = 12.00\n", "alface : 5 x 0.45 = 2.25\n", " Custo total: 25.75\n", "\n", "Estoque:\n", "\n", "Descrição: tomate\n", "Quantidade: 995\n", "Preço: 2.30\n", "\n", "Descrição: alface\n", "Quantidade: 495\n", "Preço: 0.45\n", "\n", "Descrição: batata\n", "Quantidade: 1991\n", "Preço: 1.20\n", "\n", "Descrição: feijão\n", "Quantidade: 100\n", "Preço: 1.50\n", "\n" ] } ], "source": [ "# Programa para atualizar estoque após venda\n", "venda = [[\"tomate\", 5], [\"batata\",10], [\"alface\",5]]\n", "total = 0\n", "print(\"Vendas:\\n\")\n", "for operação in venda:\n", " produto, quantidade = operação\n", " preço = estoque[produto][1]\n", " custo = preço * quantidade\n", " print(f\"{produto:12s}: {quantidade:3d} x {preço:6.2f} = {custo:6.2f}\")\n", " estoque[produto][0] -= quantidade\n", " total += custo\n", "print(f\" Custo total: {total:21.2f}\\n\")\n", "print(\"Estoque:\\n\")\n", "for chave, dados in estoque.items():\n", " print(\"Descrição: \", chave)\n", " print(\"Quantidade: \", dados[0])\n", " print(f\"Preço: {dados[1]:6.2f}\\n\")\n" ] }, { "cell_type": "markdown", "id": "hungry-corps", "metadata": {}, "source": [ "### Exercício 1\n", "Altere o programa anterior de modo que o produto e a quantidade vendida seja solicitada ao usuário. Verifique se o produto digitado existe e só então efetue a baixa no estoque. Também verifique se o estoque é o suficiente para atender ao pedido.\n" ] }, { "cell_type": "markdown", "id": "permanent-testing", "metadata": {}, "source": [ "### Exercício 2\n", "Escreva um programa que que gere um dicionário a partir de uma string onde a chave seja um caracter e o valor o número de vezes que este caracter aparece no dicionário\n", "\n", "o rolo -> {'o': 3, ' ': 1, 'l': 1}\n" ] }, { "cell_type": "markdown", "id": "guided-thanks", "metadata": {}, "source": [ "### Dicionário com valores padrão\n", "\n", "Algo comum é que ao se recuperar o valor de uma chave, se essa chave não existir, utilizar um valor padrão" ] }, { "cell_type": "code", "execution_count": 34, "id": "united-alert", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'A': 1, 'B': 2}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {}\n", "d[\"A\"] = 1\n", "d[\"B\"] = 2\n", "d" ] }, { "cell_type": "code", "execution_count": 35, "id": "virtual-spell", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'C'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_19331/852664300.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"C\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyError\u001b[0m: 'C'" ] } ], "source": [ "d[\"C\"]" ] }, { "cell_type": "code", "execution_count": 36, "id": "adjustable-wrong", "metadata": {}, "outputs": [], "source": [ "\n", "x = d.get(\"C\")" ] }, { "cell_type": "code", "execution_count": 37, "id": "numerical-negative", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x is None" ] }, { "cell_type": "code", "execution_count": 38, "id": "cathedral-dress", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "999" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = d.get(\"C\", 999)\n", "x" ] }, { "cell_type": "markdown", "id": "metropolitan-offer", "metadata": {}, "source": [ "### Iterando um dicionário com `for`\n", "\n", "Também dá usar for em um dicionário." ] }, { "cell_type": "code", "execution_count": 39, "id": "exact-soviet", "metadata": {}, "outputs": [], "source": [ "estoque = {\"tomate\": [1000, 2.30],\n", " \"alface\": [500, 0.45],\n", " \"batata\": [2001, 1.20], \n", " \"feijão\": [100, 1.5]}" ] }, { "cell_type": "code", "execution_count": 40, "id": "stretch-detroit", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tomate\n", "alface\n", "batata\n", "feijão\n" ] } ], "source": [ "for x in estoque:\n", " print(x)" ] }, { "cell_type": "markdown", "id": "parental-annex", "metadata": {}, "source": [ "E se você precisa da chave e do valor? Use o método `.items()`" ] }, { "cell_type": "code", "execution_count": 41, "id": "opened-participant", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------\n", "Chave: tomate\n", "Valor: [1000, 2.3]\n", "------------\n", "Chave: alface\n", "Valor: [500, 0.45]\n", "------------\n", "Chave: batata\n", "Valor: [2001, 1.2]\n", "------------\n", "Chave: feijão\n", "Valor: [100, 1.5]\n" ] } ], "source": [ "for c,v in estoque.items():\n", " print(\"------------\")\n", " print(f\"Chave: {c}\")\n", " print(f\"Valor: {v}\")\n", " " ] }, { "cell_type": "code", "execution_count": 42, "id": "advanced-sheep", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_items([('tomate', [1000, 2.3]), ('alface', [500, 0.45]), ('batata', [2001, 1.2]), ('feijão', [100, 1.5])])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "estoque.items()" ] }, { "cell_type": "markdown", "id": "surgical-drinking", "metadata": {}, "source": [ "## Tuplas\n", "\n", "Muito parecida com a lista mas com uma diferença: são imutáveis!\n", "\n", "Tem alguma semelhança com as strings. Strings se *parecem* com listas que não podem ser modificadas. Mas as strings são otimizadas para caracteres. \n", "\n", "Apesar de imutáveis, os elementos da tupla podem ter tipos distintos.\n", "\n" ] }, { "cell_type": "markdown", "id": "frozen-canvas", "metadata": {}, "source": [ "### Tuplas são parecidas com listas!" ] }, { "cell_type": "code", "execution_count": 43, "id": "fresh-titanium", "metadata": {}, "outputs": [], "source": [ "tupla = (\"a\", \"b\", \"c\")" ] }, { "cell_type": "code", "execution_count": 44, "id": "certified-leisure", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('a', 'b', 'c')" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tupla" ] }, { "cell_type": "code", "execution_count": 45, "id": "individual-whale", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'a'" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tupla[0]" ] }, { "cell_type": "code", "execution_count": 46, "id": "becoming-removal", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'b'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tupla[1]" ] }, { "cell_type": "code", "execution_count": 47, "id": "varied-cancer", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 1, 2, 3]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[1,2,3]*2 # repetição" ] }, { "cell_type": "code", "execution_count": 48, "id": "touched-proportion", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('b', 'c')" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tupla[1:]" ] }, { "cell_type": "code", "execution_count": 49, "id": "handed-junior", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(tupla)" ] }, { "cell_type": "code", "execution_count": 50, "id": "polished-empire", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n" ] } ], "source": [ "for e in tupla:\n", " print(e)\n", " " ] }, { "cell_type": "code", "execution_count": 51, "id": "informational-humanity", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_19331/2537307119.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Lembre-se as tuplas são imutáveis!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtupla\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'd'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], "source": [ "# Lembre-se as tuplas são imutáveis!\n", "tupla[1] = 'd'" ] }, { "cell_type": "markdown", "id": "brief-philippines", "metadata": {}, "source": [ "### Tuplas como múltiplos valores, empacotamento e desempacotamento\n" ] }, { "cell_type": "code", "execution_count": 52, "id": "distinct-anderson", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(100, 200, 300)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tupla = 100,200,300\n", "tupla" ] }, { "cell_type": "code", "execution_count": 53, "id": "broken-tender", "metadata": {}, "outputs": [], "source": [ "a, b, c = tupla" ] }, { "cell_type": "code", "execution_count": 54, "id": "hispanic-lodge", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = 100, b = 200, c = 300\n" ] } ], "source": [ "print(f\"a = {a}, b = {b}, c = {c}\")" ] }, { "cell_type": "code", "execution_count": 55, "id": "editorial-calculator", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10, 20)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a, b = 10, 20\n", "a,b" ] }, { "cell_type": "code", "execution_count": 56, "id": "guided-vietnamese", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20, 10)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Operação realizada de maneira 'paralela'\n", "a, b = b, a\n", "a,b" ] }, { "cell_type": "markdown", "id": "mexican-freight", "metadata": {}, "source": [ "Se lembram do bubble sort?" ] }, { "cell_type": "code", "execution_count": 57, "id": "american-ferry", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2, 3, 4, 6, 8, 9]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L = [9, 2, 4, 3, 8, 6]\n", "N = len(L)\n", "for i in range(N):\n", " trocou = False\n", " for k in range(N-i-1):\n", " if L[k] > L[k+1]: # Inverter a posição\n", " trocou = True\n", " # Como era antes\n", " # tmp = L[k]\n", " # L[k] = L[k+1]\n", " #L[k+1] = tmp\n", " L[k], L[k+1] = L[k+1], L[k] # Olha só que simples!\n", " if not trocou:\n", " break\n", "\n", "L\n", " " ] }, { "cell_type": "markdown", "id": "presidential-positive", "metadata": {}, "source": [ "### Tuplas com um elemento" ] }, { "cell_type": "code", "execution_count": 58, "id": "grateful-joint", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t1 = (1)\n", "t1" ] }, { "cell_type": "code", "execution_count": 59, "id": "figured-capture", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1,)" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t2 = (1,)\n", "t2" ] }, { "cell_type": "code", "execution_count": 60, "id": "passing-harvest", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "()" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Tupla vazia\n", "t3 = ()\n", "t3" ] }, { "cell_type": "markdown", "id": "ranging-yellow", "metadata": {}, "source": [ "### Tuplas a partir de listas (e vice e versa):\n" ] }, { "cell_type": "code", "execution_count": 61, "id": "cooperative-chance", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 2, 3)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L = [1,2,3]\n", "T = tuple(L)\n", "T" ] }, { "cell_type": "code", "execution_count": 62, "id": "numeric-istanbul", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T = (1,2,3)\n", "L = list(T)\n", "L" ] }, { "cell_type": "markdown", "id": "optional-omaha", "metadata": {}, "source": [ "\n", "### Concatenação\n" ] }, { "cell_type": "code", "execution_count": 63, "id": "scientific-template", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 2, 3, 4, 5, 6)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(1,2,3) + (4,5,6)" ] }, { "cell_type": "markdown", "id": "generous-individual", "metadata": {}, "source": [ "### A tupla é imutável, não seus elementos (pode ou não ser!)" ] }, { "cell_type": "code", "execution_count": 64, "id": "choice-sheffield", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, [2, 3, 4])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T = (1,[2,3,4])\n", "T" ] }, { "cell_type": "code", "execution_count": 65, "id": "peripheral-marathon", "metadata": {}, "outputs": [], "source": [ "T[1].append(5)" ] }, { "cell_type": "code", "execution_count": 66, "id": "still-theta", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, [2, 3, 4, 5])" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T" ] }, { "cell_type": "markdown", "id": "severe-willow", "metadata": {}, "source": [ "### Exercício 3\n", "Explore os métodos disponíveis aos dicionários. Brinque com eles" ] }, { "cell_type": "markdown", "id": "focal-radar", "metadata": {}, "source": [ "### Mais empacotamento e desempacotamento (listas também podem)" ] }, { "cell_type": "code", "execution_count": 67, "id": "lovely-overview", "metadata": {}, "outputs": [], "source": [ "tupla = 1,2\n", "a,b = tupla" ] }, { "cell_type": "code", "execution_count": 68, "id": "satisfactory-jerusalem", "metadata": {}, "outputs": [], "source": [ "a,b = 1,2" ] }, { "cell_type": "code", "execution_count": 69, "id": "ultimate-settlement", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "c = 3, d = 4\n" ] } ], "source": [ "c,d = [3,4]\n", "print(f\"c = {c}, d = {d}\")" ] }, { "cell_type": "code", "execution_count": 70, "id": "ideal-final", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "*a, b = [1,2,3,4,5]\n", "b" ] }, { "cell_type": "code", "execution_count": 71, "id": "systematic-thousand", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "code", "execution_count": 72, "id": "realistic-gasoline", "metadata": {}, "outputs": [], "source": [ "*a,b = (1,2,3,4,5)" ] }, { "cell_type": "code", "execution_count": 73, "id": "fifteen-string", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "code", "execution_count": 74, "id": "medieval-sharp", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 75, "id": "assisted-memorial", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a,*b = [1,2,3,4,5]\n", "a" ] }, { "cell_type": "code", "execution_count": 76, "id": "broad-schema", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2, 3, 4, 5]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 77, "id": "blocked-karma", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a,*b,c = [1,2,3,4,5]\n", "a" ] }, { "cell_type": "code", "execution_count": 78, "id": "driven-serve", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2, 3, 4]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 79, "id": "plastic-dispute", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 80, "id": "contrary-median", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "*a,b,c = [1,2,3,4,5]\n", "a" ] }, { "cell_type": "code", "execution_count": 81, "id": "micro-jewel", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 82, "id": "major-ecuador", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 83, "id": "major-magnet", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a,b,*c = [1,2,3,4,5]\n", "a\n" ] }, { "cell_type": "code", "execution_count": 84, "id": "balanced-packing", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 85, "id": "alike-danish", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 4, 5]" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "markdown", "id": "ahead-shuttle", "metadata": {}, "source": [ "## Conjuntos (`set`)\n", "\n", "Os conjuntos em Python são uma estrutura de dados que funcionam como conjuntos que aprendemos na escola. \n", "\n", "Um conjunto é uma coleção de elementos que não são repetidos. \n", "\n", "Ah, podemos fazer as operações união e intersecção!" ] }, { "cell_type": "code", "execution_count": 86, "id": "binary-linux", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3}" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = {1,2,3} # Notação para conjuntos\n", "a" ] }, { "cell_type": "code", "execution_count": 87, "id": "thirty-stretch", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3, 4, 5, 6}" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.add(4)\n", "a.add(5)\n", "a.add(6)\n", "a" ] }, { "cell_type": "code", "execution_count": 88, "id": "tropical-round", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3, 4, 5, 6}" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.add(1)\n", "a" ] }, { "cell_type": "code", "execution_count": 89, "id": "informed-portable", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{-1, 1, 2, 3, 4, 5, 6}" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.add(-1)\n", "a" ] }, { "cell_type": "code", "execution_count": 90, "id": "sexual-pollution", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(a)" ] }, { "cell_type": "markdown", "id": "bigger-means", "metadata": {}, "source": [ "### Ver se um elemento pertence a um conjunto:" ] }, { "cell_type": "code", "execution_count": 91, "id": "embedded-olympus", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ " 2 in a" ] }, { "cell_type": "code", "execution_count": 92, "id": "progressive-studio", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "123 in a" ] }, { "cell_type": "markdown", "id": "constitutional-parameter", "metadata": {}, "source": [ "### Criando conjuntos a partir de listas" ] }, { "cell_type": "code", "execution_count": 93, "id": "august-profit", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3, 4, 5}" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = set([1,2,3,4,5])\n", "b" ] }, { "cell_type": "code", "execution_count": 94, "id": "sublime-witness", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3, 4, 5}" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = set([1,2,3,4,5,1,2,3,4,5])\n", "b" ] }, { "cell_type": "markdown", "id": "collaborative-general", "metadata": {}, "source": [ "### Operações com conjuntos\n", "\n", "Diferença entre conjuntos:\n", "```python\n", "a - b\n", "```\n", "O resultado é um conjunto com os elementos de `a` que não estão em `b`\n" ] }, { "cell_type": "code", "execution_count": 95, "id": "overhead-algorithm", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{-1, 0, 1}" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = {0, 1, 2, 3, -1}\n", "b = {2, 3}\n", "a-b" ] }, { "cell_type": "code", "execution_count": 96, "id": "southern-services", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{-1, 0, 1, 4, 5}" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = {0, 1, 2, 3, -1, 4, 5}\n", "b = {2, 3}\n", "a-b" ] }, { "cell_type": "code", "execution_count": 97, "id": "amino-mapping", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{-1, 0, 1}" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = {0, 1, 2, 3, -1}\n", "b = {2, 3, 4}\n", "a-b" ] }, { "cell_type": "markdown", "id": "conservative-yukon", "metadata": {}, "source": [ "União entre conjuntos:\n", "```python\n", "a | b\n", "```" ] }, { "cell_type": "code", "execution_count": 98, "id": "advised-harrison", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{-1, 0, 1, 2, 3}" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = {0,1,2,3,-1}\n", "b = {2,3}\n", "a | b" ] }, { "cell_type": "code", "execution_count": 99, "id": "prerequisite-bradford", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{-1, 0, 1, 2, 3, 4, 5, 6}" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = {4,5,6}\n", "a | c" ] }, { "cell_type": "markdown", "id": "south-mission", "metadata": {}, "source": [ "Interseção de conjuntos" ] }, { "cell_type": "code", "execution_count": 100, "id": "analyzed-evans", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{2, 3}" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a & b" ] }, { "cell_type": "code", "execution_count": 101, "id": "talented-second", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "set()" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a & c" ] }, { "cell_type": "markdown", "id": "numerical-museum", "metadata": {}, "source": [ "### Exercício 4\n", "Veja os métodos disponíveis aos conjuntos. Brinque com eles" ] }, { "cell_type": "markdown", "id": "acoustic-contractor", "metadata": {}, "source": [ "### Exercício 5\n", "Escreva um programa que compare duas listas. Utilizando operações com conjuntos, imprima:\n", "\n", " * Os valores comuns às duas listas\n", " * Os valores que só existem na primeira\n", " * Os valores que existem apenas na segunda\n", " * Uma lista com os elementos não repetidos das duas listas\n", " * A primeira lista sem os elementos repetidos na segunda" ] }, { "cell_type": "markdown", "id": "chemical-professional", "metadata": {}, "source": [ "## Qual estrutura de dados utilizar?\n", "\n", "| | Listas | Tuplas | Dicionários | Conjuntos |\n", "| ----------- | ---------- | --------- | ------------- | ----------- |\n", "| Ordem dos elementos | Fixa | Fixa | Fixa a partir da versão 3.7 | Indeterminada |\n", "| Tamanho | Variável | Fixo | Variável | Variável |\n", "| Elementos repetidos | Sim | Sim | Valores mas não chaves| Não |\n", "| Pesquisa | Sequencial | Sequencial | Direto pela chave | Direta pelo valor |\n", "| Alterações | Sim | Não | Sim | Sim |\n", "| Uso primário | Sequências | Sequências constantes | Dados indexados por chave | Verificação de unicidade |" ] }, { "cell_type": "markdown", "id": "historical-cheese", "metadata": {}, "source": [ "# Trabalhando com strings\n", "\n", "Temos usado strings desde a primeira aula. Strings são úteis e extremamente importantes. Vimos que existem algumas semelhanças entre strings e listas e entre strings e tuplas: strings são sequências *imutáveis* de caracteres." ] }, { "cell_type": "code", "execution_count": 102, "id": "phantom-speech", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'A'" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"Alô mundo\"\n", "s[0]\n" ] }, { "cell_type": "code", "execution_count": 103, "id": "likely-letter", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_19331/2311957138.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'X'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], "source": [ "s[0] = 'X'" ] }, { "cell_type": "code", "execution_count": 104, "id": "absent-fight", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['A', 'l', 'ô', ' ', 'm', 'u', 'n', 'd', 'o']" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L = list(s)\n", "L" ] }, { "cell_type": "markdown", "id": "severe-washer", "metadata": {}, "source": [ "### Strings também têm métodos! " ] }, { "cell_type": "code", "execution_count": 105, "id": "exclusive-wilderness", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Alô mundo'" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = \"\".join(L)\n", "s1" ] }, { "cell_type": "code", "execution_count": 106, "id": "located-theta", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abc\n", "a b c\n", "a.b.c\n", "a_|_b_|_c\n" ] } ], "source": [ "L = ['a','b','c']\n", "print(\"\".join(L))\n", "print(\" \".join(L))\n", "print(\".\".join(L))\n", "print(\"_|_\".join(L))" ] }, { "cell_type": "code", "execution_count": null, "id": "fantastic-index", "metadata": {}, "outputs": [], "source": [ "s." ] }, { "cell_type": "markdown", "id": "unsigned-firmware", "metadata": {}, "source": [ "### Verificação parcial de strings\n" ] }, { "cell_type": "code", "execution_count": 108, "id": "hairy-bread", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nome = \"Instituto de Pesquisas Tecnológicas\"\n", "nome.startswith(\"Inst\")" ] }, { "cell_type": "code", "execution_count": 109, "id": "delayed-truth", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nome.startswith(\"inst\")" ] }, { "cell_type": "markdown", "id": "brown-aspect", "metadata": {}, "source": [ "### Maíusculas e minúsculas\n" ] }, { "cell_type": "code", "execution_count": 110, "id": "jewish-bahrain", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'INSTITUTO DE PESQUISAS TECNOLÓGICAS'" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nome.upper()" ] }, { "cell_type": "code", "execution_count": 111, "id": "handmade-topic", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'instituto de pesquisas tecnológicas'" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nome.lower()" ] }, { "cell_type": "code", "execution_count": 112, "id": "honest-refund", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nome.lower().startswith(\"inst\")" ] }, { "cell_type": "code", "execution_count": 113, "id": "fabulous-symphony", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nome.upper().startswith(\"INST\")" ] }, { "cell_type": "code", "execution_count": 114, "id": "chinese-omega", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Uma frase simples com letras minúsculas'" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"uma frase simples com letras minúsculas\".capitalize()" ] }, { "cell_type": "markdown", "id": "practical-category", "metadata": {}, "source": [ "### Pesquisando em strings" ] }, { "cell_type": "code", "execution_count": 115, "id": "mysterious-carbon", "metadata": {}, "outputs": [], "source": [ "s = \"o rato roeu a roupa do rei de roma\"" ] }, { "cell_type": "code", "execution_count": 116, "id": "worldwide-effect", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"rato\" in s" ] }, { "cell_type": "code", "execution_count": 117, "id": "earlier-marble", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"Rato\" in s" ] }, { "cell_type": "code", "execution_count": 118, "id": "cloudy-discussion", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"u a r\" in s" ] }, { "cell_type": "code", "execution_count": 119, "id": "equivalent-whale", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"santos\" not in s" ] }, { "cell_type": "code", "execution_count": 120, "id": "internal-stress", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'roupa' not in s" ] }, { "cell_type": "markdown", "id": "deadly-sheriff", "metadata": {}, "source": [ "Cuidado com acentos!!!" ] }, { "cell_type": "code", "execution_count": 121, "id": "herbal-halifax", "metadata": {}, "outputs": [], "source": [ "s = \"além da cúpula do trovão\"" ] }, { "cell_type": "code", "execution_count": 122, "id": "fitted-cheat", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"além\" in s" ] }, { "cell_type": "code", "execution_count": 123, "id": "vanilla-masters", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"alem\" in s" ] }, { "cell_type": "markdown", "id": "compound-referral", "metadata": {}, "source": [ "Algumas vezes queremos saber onde se encontra uma substring" ] }, { "cell_type": "code", "execution_count": 124, "id": "foreign-republic", "metadata": {}, "outputs": [], "source": [ "s = \"o rato roeu a roupa do rei de roma\"" ] }, { "cell_type": "code", "execution_count": 125, "id": "annoying-walnut", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i = s.find(\"roeu\")\n", "i" ] }, { "cell_type": "code", "execution_count": 126, "id": "pleasant-leadership", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'roeu'" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[i:i+4]" ] }, { "cell_type": "code", "execution_count": 127, "id": "golden-voice", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.find(\"gato\")" ] }, { "cell_type": "markdown", "id": "indonesian-interaction", "metadata": {}, "source": [ "Buscando pelo fim\n" ] }, { "cell_type": "code", "execution_count": 128, "id": "brilliant-being", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"o rato roeu a roupa do rato de roma\"\n", "i = s.find(\"rato\")\n", "i" ] }, { "cell_type": "code", "execution_count": 129, "id": "living-marriage", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'rato'" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[i:i+4]" ] }, { "cell_type": "code", "execution_count": 130, "id": "therapeutic-promise", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "23" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i = s.rfind(\"rato\")\n", "i" ] }, { "cell_type": "code", "execution_count": 131, "id": "close-housing", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'rato'" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[i:i+4]" ] }, { "cell_type": "markdown", "id": "heard-mileage", "metadata": {}, "source": [ "Dá para falar de onde queremos começar a procurar!\n" ] }, { "cell_type": "code", "execution_count": 132, "id": "introductory-diary", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "23" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.find(\"rato\", 3)" ] }, { "cell_type": "code", "execution_count": 133, "id": "greenhouse-bachelor", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Posição: 3\n", "Posição: 15\n", "Posição: 28\n" ] } ], "source": [ "# Programa que pesquisa *todas* as ocorrênicas\n", "s = \"um tigre, dois tigres, três tigres\"\n", "ss = \"tigre\"\n", "p = 0\n", "while p > -1:\n", " p = s.find(ss, p)\n", " if p >= 0:\n", " print(f\"Posição: {p}\")\n", " p += 1\n" ] }, { "cell_type": "markdown", "id": "apart-classroom", "metadata": {}, "source": [ "### Exercício 6\n", "Teste a diferença entre os métodos `.find` e `.rfind` com `.index` e `.rindex`" ] }, { "cell_type": "markdown", "id": "norwegian-translation", "metadata": {}, "source": [ "### Exercício 7\n", "Escreva um programa que leia duas strings. Verifique se a segunda ocorre dentro da primeira e imprima sua posição de início" ] }, { "cell_type": "markdown", "id": "wrapped-russia", "metadata": {}, "source": [ "### Exercício 8\n", "Escreva um programa que leia duas strings e gere uma terceira string com todos os caracteres que aparecem nas duas strings. A ordem não interessa." ] }, { "cell_type": "markdown", "id": "military-resort", "metadata": {}, "source": [ "### Exercício 9\n", "Escreva um programa que leia uma string e imprima quantas vezes cada caracter aparece nessa string" ] }, { "cell_type": "markdown", "id": "modular-namibia", "metadata": {}, "source": [ "### Justificação de strings" ] }, { "cell_type": "code", "execution_count": 134, "id": "outstanding-branch", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "' bola '" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = 'bola'\n", "s.center(20)" ] }, { "cell_type": "code", "execution_count": 135, "id": "nutritional-minority", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'X bola X'" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'X' + s.center(10) + 'X'" ] }, { "cell_type": "code", "execution_count": 136, "id": "general-luxembourg", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'X...bola...X'" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'X' + s.center(10, '.') + 'X'" ] }, { "cell_type": "code", "execution_count": 137, "id": "composed-logan", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'bola '" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.ljust(10)" ] }, { "cell_type": "code", "execution_count": 138, "id": "voluntary-arrangement", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'bola......'" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.ljust(10,'.')" ] }, { "cell_type": "code", "execution_count": 139, "id": "unknown-palestinian", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "' bola'" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.rjust(10)" ] }, { "cell_type": "code", "execution_count": 140, "id": "expanded-navigator", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'......bola'" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.rjust(10,'.')" ] }, { "cell_type": "markdown", "id": "floating-stick", "metadata": {}, "source": [ "### Remoção de espaços em branco" ] }, { "cell_type": "code", "execution_count": 141, "id": "annual-employer", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'IPT'" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \" IPT \"\n", "s.strip()" ] }, { "cell_type": "code", "execution_count": 142, "id": "biological-tutorial", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "' IPT'" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.rstrip()" ] }, { "cell_type": "code", "execution_count": 143, "id": "endangered-generator", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'IPT '" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.lstrip()" ] }, { "cell_type": "code", "execution_count": 144, "id": "stylish-judge", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "' IPT '" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.lstrip('.')" ] }, { "cell_type": "code", "execution_count": 145, "id": "wireless-cabin", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "' IPT ... '" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = '.... IPT ... .'\n", "s.strip('.')" ] }, { "cell_type": "code", "execution_count": 146, "id": "honest-classroom", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'IPT'" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.strip('. ')\n" ] }, { "cell_type": "code", "execution_count": 147, "id": "brown-lover", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'.... IPT'" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.rstrip(' .')" ] }, { "cell_type": "code", "execution_count": 148, "id": "relative-terrorist", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'IPT ... .'" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.lstrip(' .')" ] }, { "cell_type": "markdown", "id": "remarkable-malaysia", "metadata": {}, "source": [ "### Quebrando strings" ] }, { "cell_type": "code", "execution_count": 149, "id": "criminal-kansas", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['1', '2', '3', '4', '5', '6']" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"1,2,3,4,5,6\"\n", "s.split(\",\")" ] }, { "cell_type": "code", "execution_count": 150, "id": "smooth-movement", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['1', '2', '3', '4', '5', '6']" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"1 2 3 4 5 6\".split(\" \")" ] }, { "cell_type": "code", "execution_count": 151, "id": "circular-pattern", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['1', '2', '3', '4', '5', '6']" ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"1 e 2 e 3 e 4 e 5 e 6\".split(\" e \")" ] }, { "cell_type": "markdown", "id": "therapeutic-rates", "metadata": {}, "source": [ "### Substituindo strings" ] }, { "cell_type": "code", "execution_count": 152, "id": "based-satellite", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'um gato, dois gatos, três gatos'" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"um tigre, dois tigres, três tigres\"\n", "s.replace(\"tigre\", \"gato\")" ] }, { "cell_type": "code", "execution_count": 153, "id": "fantastic-career", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'um gato, dois tigres, três tigres'" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.replace(\"tigre\", \"gato\", 1)" ] }, { "cell_type": "code", "execution_count": 154, "id": "recent-venezuela", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'um gato, dois gatos, três tigres'" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.replace(\"tigre\", \"gato\", 2)" ] }, { "cell_type": "code", "execution_count": 155, "id": "senior-claim", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'um , dois s, três s'" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.replace(\"tigre\", \"\")" ] }, { "cell_type": "code", "execution_count": 156, "id": "illegal-lottery", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'-u-m- -t-i-g-r-e-,- -d-o-i-s- -t-i-g-r-e-s-,- -t-r-ê-s- -t-i-g-r-e-s-'" ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.replace(\"\", \"-\")" ] }, { "cell_type": "code", "execution_count": null, "id": "future-float", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "unsigned-survivor", "metadata": {}, "source": [ "### Validação por tipo de conteúdo" ] }, { "cell_type": "code", "execution_count": 157, "id": "auburn-butter", "metadata": {}, "outputs": [], "source": [ "s = \"125\"\n", "p = \"uma pequena frase\"" ] }, { "cell_type": "code", "execution_count": 158, "id": "interested-appearance", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.isalnum()" ] }, { "cell_type": "code", "execution_count": 159, "id": "automotive-competition", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p.isalnum()" ] }, { "cell_type": "code", "execution_count": 160, "id": "identical-carroll", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"abc123\".isalnum()" ] }, { "cell_type": "code", "execution_count": 161, "id": "familiar-tulsa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"125\".isalpha()" ] }, { "cell_type": "code", "execution_count": 162, "id": "advance-option", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"uma frase pequena\".isalpha()" ] }, { "cell_type": "code", "execution_count": 163, "id": "detected-department", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"palavra\".isalpha()" ] }, { "cell_type": "code", "execution_count": 164, "id": "graphic-public", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"123\".isdigit()" ] }, { "cell_type": "code", "execution_count": 165, "id": "isolated-construction", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\"3.14\".isdigit()" ] }, { "cell_type": "code", "execution_count": 166, "id": "alert-yacht", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"-123\".isdigit()" ] }, { "cell_type": "code", "execution_count": 167, "id": "parallel-cooper", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"123\".isnumeric()" ] }, { "cell_type": "code", "execution_count": 168, "id": "embedded-binding", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"3.14\".isnumeric()" ] }, { "cell_type": "code", "execution_count": 169, "id": "together-transparency", "metadata": {}, "outputs": [], "source": [ "# .isdigit() -> caracteres numéricos em Unicode" ] }, { "cell_type": "code", "execution_count": 170, "id": "geographic-finland", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'⅓'" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"\\u2153\"\n", "s" ] }, { "cell_type": "code", "execution_count": 171, "id": "provincial-domestic", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.isnumeric()" ] }, { "cell_type": "code", "execution_count": 172, "id": "neural-ordinary", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.isdigit()" ] }, { "cell_type": "markdown", "id": "professional-lying", "metadata": {}, "source": [ "Como reconhecer se o número é ponto flutuante? Isso é mais chato do que parece. Mas temos aqui uma solução. Aqui introduzimos uma nova funcionalidade: exceções:\n" ] }, { "cell_type": "code", "execution_count": 173, "id": "terminal-snapshot", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"123e23\"\n", "\n", "try:\n", " float(s)\n", " isfloat = True\n", "except:\n", " isfloat = False\n", " \n", "isfloat\n" ] }, { "cell_type": "code", "execution_count": 174, "id": "metropolitan-companion", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\" \".isspace()" ] }, { "cell_type": "code", "execution_count": 175, "id": "adequate-apache", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\\n\\r\\t \".isspace()" ] }, { "cell_type": "code", "execution_count": 176, "id": "unnecessary-marker", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\\n\\t\\r\".isprintable()" ] }, { "cell_type": "code", "execution_count": 177, "id": "ethical-convertible", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"um testo simples\".isprintable()" ] }, { "cell_type": "code", "execution_count": 178, "id": "divine-prize", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 178, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"ABC\".isupper()" ] }, { "cell_type": "code", "execution_count": 179, "id": "future-trinidad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 179, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"ABC\".islower()" ] }, { "cell_type": "code", "execution_count": 180, "id": "funded-canberra", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 180, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"abc\".isupper()" ] }, { "cell_type": "code", "execution_count": 181, "id": "worth-catalyst", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"abc\".islower()" ] }, { "cell_type": "code", "execution_count": 182, "id": "theoretical-curtis", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 182, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"Abc\".istitle()" ] }, { "cell_type": "code", "execution_count": 183, "id": "female-decline", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"ABC\".istitle()" ] }, { "cell_type": "code", "execution_count": 184, "id": "endangered-miracle", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 184, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"abc\".istitle()" ] }, { "cell_type": "markdown", "id": "crude-jersey", "metadata": {}, "source": [ "### Formatação de strings\n", "Já temos usado bastante isso. \n" ] }, { "cell_type": "code", "execution_count": 185, "id": "scenic-commitment", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Oi mundo'" ] }, "execution_count": 185, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"{0} {1}\".format(\"Oi\", \"mundo\")" ] }, { "cell_type": "code", "execution_count": 186, "id": "opening-moldova", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1 2 3'" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"{} {} {}\".format(1,2,3)" ] }, { "cell_type": "code", "execution_count": 187, "id": "convertible-arbitration", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'3 2 1'" ] }, "execution_count": 187, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"{2} {1} {0}\".format(1,2,3)" ] }, { "cell_type": "code", "execution_count": 188, "id": "announced-fitting", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'XIPT X'" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"X{0:<10}X\".format(\"IPT\")" ] }, { "cell_type": "code", "execution_count": 189, "id": "ecological-drill", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'X IPTX'" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"X{0:>10}X\".format(\"IPT\")" ] }, { "cell_type": "code", "execution_count": 190, "id": "nearby-campbell", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'X IPT X'" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"X{0:^10}X\".format(\"IPT\")" ] }, { "cell_type": "code", "execution_count": 191, "id": "trained-montreal", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'XIPT.......X'" ] }, "execution_count": 191, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"X{0:.<10}X\".format(\"IPT\")" ] }, { "cell_type": "code", "execution_count": 192, "id": "accessible-fetish", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'X...IPT....X'" ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"X{0:.^10}X\".format(\"IPT\")" ] }, { "cell_type": "markdown", "id": "sensitive-andrews", "metadata": {}, "source": [ "Os parâmetros de `format` podem ser uma lista:\n" ] }, { "cell_type": "code", "execution_count": 193, "id": "widespread-closer", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1 2 3 4'" ] }, "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\"{0[0]} {0[1]} {1[0]} {1[1]}\".format([1,2], [3,4])" ] }, { "cell_type": "markdown", "id": "fabulous-optics", "metadata": {}, "source": [ "Até mesmo um dicionário:" ] }, { "cell_type": "code", "execution_count": 194, "id": "printable-latin", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'João 3767-0000 1 2'" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"{0[nome]} {0[telefone]} {1[0]} {1[1]}\".format({\"telefone\": \"3767-0000\", \"nome\": \"João\"}, [1,2])" ] }, { "cell_type": "markdown", "id": "compound-maximum", "metadata": {}, "source": [ "Tem como formatar números. Na verdade esta formatação é quase uma minilinguagem. Para maiores informações, veja a documentação oficial do Python:\n", "\n", " * " ] }, { "cell_type": "markdown", "id": "devoted-chuck", "metadata": {}, "source": [ "### Jogo da forca" ] }, { "cell_type": "code", "execution_count": 195, "id": "motivated-liabilities", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Digite a plavra secreta: rato\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "....\n", "\n", "Digite uma letra: t\n", "||==:==\n", "|| : \n", "||\n", "||\n", "||\n", "||\n", "==========\n", "..t.\n", "\n", "Digite uma letra: a\n", "||==:==\n", "|| : \n", "||\n", "||\n", "||\n", "||\n", "==========\n", ".at.\n", "\n", "Digite uma letra: o\n", "||==:==\n", "|| : \n", "||\n", "||\n", "||\n", "||\n", "==========\n", ".ato\n", "\n", "Digite uma letra: r\n", "||==:==\n", "|| : \n", "||\n", "||\n", "||\n", "||\n", "==========\n", "rato\n", "Você acertou!\n" ] } ], "source": [ "palavra = input(\"Digite a plavra secreta: \").strip().lower()\n", "for x in range(100): \n", " print()\n", "\n", "digitadas = []\n", "acertos = []\n", "erros = 0\n", "\n", "while True:\n", " senha = \"\"\n", " for letra in palavra:\n", " senha += letra if letra in acertos else \".\"\n", " print(senha)\n", " if senha == palavra:\n", " print(\"Você acertou!\")\n", " break\n", " tentativa = input(\"\\nDigite uma letra: \").strip().lower()\n", " if tentativa in digitadas:\n", " print(\"Você já tentou esta letra!\")\n", " continue\n", " else:\n", " digitadas += tentativa\n", " if tentativa in palavra:\n", " acertos += tentativa\n", " else:\n", " erros += 1\n", " print(\"Você errou!\")\n", " print(\"||==:==\\n|| : \")\n", " print(\"|| O \" if erros >= 1 else \"||\")\n", " linha2 = \"\"\n", " if erros == 2:\n", " linha2 = \" | \"\n", " elif erros == 3:\n", " linha2 = \" \\| \" \n", " elif erros >= 4:\n", " linha2 = \" \\|/ \"\n", " print(f\"||{linha2}\")\n", " linha3 = \"\"\n", " if erros == 5:\n", " linha3 += \" / \"\n", " elif erros >= 6:\n", " linha3 += \" / \\ \"\n", " print(f\"||{linha3}\")\n", " print(\"||\\n==========\")\n", " if erros == 6:\n", " print(\"Enforcado!\")\n", " break" ] }, { "cell_type": "markdown", "id": "sunrise-success", "metadata": {}, "source": [ "## Escrevendo scripts\n", "\n", "Scripts são pequenos programas que fazem alguma tarefa específica. Python surgiu como uma linguagem de script e é muito usada para isso.\n", "\n", "Um exemplo comum é executar um programa repetidas vezes mudando algum dado de entrada.\n", "\n", "Aqui, vamos fazer um script com o jogo de forca acima. Convenhamos, usar isso no notebook jupyter não é a melhor coisa do mundo...\n", "\n", "Para se fazer o script, você vai precisar usar um editor de texto. Pode ser o spyder, notepad++, geany, visual studio code, emacs ou qualquer editor de texto. Não processador de texto. Não vai funcionar com o Word!!!\n", "\n", "Crie um arquivo novo e copie e cole o programa acima. Chame o arquivo de `forca.py`\n", "\n", "Agora basta você executar \n", "```\n", "python forca.py\n", "```\n", "\n", "Tem como tornar isso um pouco melhor.\n", "\n", "### No Linux ou Unix\n", "\n", "Em linux (e mac OS?) é interessante adicionar a seguinte linha no topo:\n", "```\n", "#!/usr/bin/env python3\n", "```\n", "aí você torna o arquivo executável com a seguinte linha de comando\n", "```\n", "chmod +x forca.py\n", "```\n", "\n", "Agora o arquivo funciona como se fosse um executável!\n", "\n", "### No windows\n", "Se você instalou o python, clique duplo no arquivo forca.py já deveria funcionar. Se a associação da extensão do arquivo estiver correta. \n", "\n", "Na linha de comando você pode fazer algo parecido com o Linux. Na primeira linha, coloque o caminho para o executável do Python. No meu computador, esta primeira linha é:\n", "\n", "```\n", "#!\"C:\\ProgramData\\Anaconda3\\python.exe\"\n", "```\n", "\n", "Aí você pode usar o programa como se fosse um executável.\n", "\n" ] }, { "cell_type": "markdown", "id": "consecutive-antenna", "metadata": {}, "source": [ "### Exercício 10\n", "Modifique o jogo da forca de forma que a palavra secreta seja escrita caso o jpgador perca" ] }, { "cell_type": "markdown", "id": "fifty-amsterdam", "metadata": {}, "source": [ "### Exercício 11\n", "Modifique o jogo da forca de forma a utilizar uma lista de palavras. No início pergunte um número e calcule o índice da palavra a utilizar pela fórmula:\n", "```python\n", "índice = (número * 776) % len(lista_de_palavras)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "id": "actual-gather", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }