ml_pln_0.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GuidoGuarda/data_science/blob/main/ml_pln_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Algebra Linear - Revisão 1\n",
        "\n",
        "\n",
        "\n",
        "1.   A equação linear tem x¹\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "s5zvqP7sqBf1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kzn_H1oZpk36"
      },
      "outputs": [],
      "source": [
        "def verifica_equacao(base,expoente):\n",
        "     msg = \"\"\n",
        "     if expoente == 1:\n",
        "         msg = \"equação linear\"\n",
        "     else:\n",
        "         msg = \"equação não linear\"\n",
        "\n",
        "     return f\" res-potência:{pow(base,expoente)} - {msg}\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#chamar a função (paradigma estruturado)\n",
        "# overflow - estouro de pilha de memória\n",
        "verifica_equacao(5,1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "-h8zdwftvP8T",
        "outputId": "be5acf10-5ce6-476e-f551-5cd104edf793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' res-potência:5 - equação linear'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3 é coeficiente da variável X\n",
        "# 5 (sem letra e variável) é o termo  independente\n",
        "# Se o termo independente for igual a 0 = Equação Linear Homogênea\n",
        "# Quando não tem coeficiente é igual a 1\n"
      ],
      "metadata": {
        "id": "UUEHHsiYtV7O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# POO - Paradigma Orientado a Objetos\n",
        "\n",
        "- Classe\n",
        "- Objeto\n",
        "- Método\n",
        "- Construtor\n",
        "\n",
        "**Biblioteca abc**\n",
        "\n",
        "- Classes Abstratas\n",
        "- Métodos Abstratos\n",
        "- Polimorfismo"
      ],
      "metadata": {
        "id": "IKfsapJuwsZ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# classe concreta : possui métodos concretos\n",
        "# cabeça e corpo -\n",
        "\"\"\"\n",
        "def somar(x,y): #assinatura\n",
        "    return x+y # implementação\n",
        "\n",
        "\"\"\"\n",
        "# Chamamos de classe concreta a classe que possui métodos concretos.\n",
        "# método concreto: que tem cabeça e corpo.\n",
        "\"\"\"\n",
        "   # cabeçalho - assinatura do método\n",
        "   def somar(x,y):\n",
        "      return x+y  # corpo do método - implementação\n",
        "\n",
        "   @abstractmethod\n",
        "   def fazer_som(): - assinatura (cabeça )\n",
        "      pass          - não tem implementação ( corpo )\n",
        "\n",
        "   @abstractmethod\n",
        "   def nao_ir_para_o_forro():\n",
        "       pass\n",
        "\n",
        "   @abstractmethod\n",
        "   def nao_mudar_para_rj_casa_da_mamae():\n",
        "       pass\n",
        "\n",
        "\n",
        "   def casamento_aberto():\n",
        "       return \"pode tudo\"\n",
        "\"\"\"\n",
        "# classe concreta\n",
        "class EquacaoLinear:\n",
        "    # atributo - base e expoente\n",
        "    # atributos o objeto\n",
        "    def __init__(self,base,expoente):\n",
        "        self.base = base\n",
        "        self.expoente = expoente\n",
        "    def verifica_equacao(self):\n",
        "    #variáveis - msg\n",
        "     msg = \"\"\n",
        "     if self.expoente == 1:\n",
        "         msg = \"equação linear\"\n",
        "     else:\n",
        "         msg = \"equação não linear\"\n",
        "\n",
        "     return f\" res-potência:{pow(self.base,self.expoente)} - {msg}\""
      ],
      "metadata": {
        "id": "unfmyZLRx2jm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "fIrwNMB1tU2u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = EquacaoLinear(6,1)"
      ],
      "metadata": {
        "id": "Vw9_CYTl0FuN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a.verifica_equacao()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "llAA5bhm0mit",
        "outputId": "e58f4b1d-8fee-4fd1-dca9-a2134be62ed7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' res-potência:6 - equação linear'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "verifica_equacao_l = lambda x : pow(x,1)"
      ],
      "metadata": {
        "id": "vX8TdCs9AJ22"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(verifica_equacao_l(2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-hkGjrPAhuk",
        "outputId": "16968271-4349-474f-a3a4-ad1ca1bab8c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "verifica_equacao_l2 = lambda base, expoente: \" equação linear\" if expoente == 1 else \"equação não linear\"\n"
      ],
      "metadata": {
        "id": "bMosuQXtA4Hq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "verifica_equacao_l2(2,2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MvO5H5DNC79c",
        "outputId": "0501ef83-8eed-482c-a020-1a9192d09251"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'equação não linear'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "verifica_equacao_l3 = lambda base, expoente: f'res-potencia: {pow(base, expoente)} - {\"equação linear\" if expoente == 1 else \"equação não linear\"}'"
      ],
      "metadata": {
        "id": "Bzp9GzOIDsKL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "verifica_equacao_l3(2,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "IF9xOv5TEECz",
        "outputId": "668faf2f-aa34-401e-d7e2-0579ee51094d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'res-potencia: 8 - equação não linear'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importou uma biblioteca matemática\n",
        "import math\n",
        "# criou uma função com um parâmetro x\n",
        "def raiz_quadrada(x):\n",
        "    return math.sqrt(x)"
      ],
      "metadata": {
        "id": "0nsh3DdfFm7n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PwLpokO1Mn7x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lsmrwFOPMoRh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"a raiz_quadrada de x é: {raiz_quadrada(81)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CNHkIjRGmCq",
        "outputId": "e31d9197-a012-4c86-c05f-3455fbcecfa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a raiz_quadrada de x é: 9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exercício 01 (sozinho):  Considere a função criada anteriormente da raiz quadrada. E crie duas versões multiparadigma: uma solução com orientação a objetos em uma classe que terá o nome de Matematica. E outra solução usando lambda e ai você define o nome."
      ],
      "metadata": {
        "id": "mKuWc643G0_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# criando a classe\n",
        "\n",
        "import math\n",
        "\n",
        "class Matematica:\n",
        "  def __init__(self, x):\n",
        "    self.x = x\n",
        "\n",
        "  def raiz_quadrada(self):\n",
        "    return math.sqrt(self.x) # math - biblioteca  sqrt - função da biblioteca math - self.x é o atributo\n",
        "\n",
        "  def calcular_fatorial(self):\n",
        "    return math.factorial(self.x)\n",
        "\n",
        "  def angulo(self):\n",
        "    pass"
      ],
      "metadata": {
        "id": "xKc1mBO_JzYG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando a instância da classe (objeto)\n",
        "\n",
        "# calculadora é um objeto\n",
        "calculadora = Matematica(25) # Matematica( ) - construtor - 25 é valor\n",
        "\n",
        "\n",
        "print(f\"A raiz quadrada de {calculadora.x} é: {calculadora.raiz_quadrada()}\")\n",
        "\n",
        "fat = Matematica(5)\n",
        "print(f\"o fatorial de 5 é: {fat.calcular_fatorial()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w79zmppqKAtV",
        "outputId": "b8879c64-45e0-4e83-daae-642b68a08b0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A raiz quadrada de 25 é: 5.0\n",
            "o fatorial de 5 é: 120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Potênciação e Potência\n",
        "\n",
        "Potênciação é a multiplicação sucessiva de números iguais.\n",
        "\n",
        "Potência é o resultado da potênciação\n",
        "\n",
        "Base - é a parte de baixo\n",
        "Expotente - é a parte de cima\n",
        "\n",
        "\n",
        "- Todo número elevado a 1 é igual a ele mesmo.\n",
        "- Todo número decimal não nulo elevado a zero é igual a 1\n",
        "- A propriedade de que qualquer número multiplicado por zero resulta em zero é uma regra fundamental da matemática."
      ],
      "metadata": {
        "id": "e-aOV7kJMw0U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Versão Programação Orientada a Objetos Power\n",
        "\n",
        "\n",
        "\n",
        "1.  Interface (polimorfismo)\n",
        "2.  Classe Abstrata (polimorfismo)\n",
        "3.  Classe\n",
        "4.  Construtor\n",
        "5.  get e set (encapsulamento)\n",
        "6.  Cria um objeto\n",
        "7.  Chame o construtor\n",
        "8.  Chame a função\n",
        "\n"
      ],
      "metadata": {
        "id": "6PEguN2YOK5D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Interface\n",
        "\n",
        "- As interfaces possui um nível muito alto de abstração\n",
        "- Possui métodos abstratos que assinam contratos em quem os implementam\n",
        "- Aqui você implementa o polimorfismo\n"
      ],
      "metadata": {
        "id": "KcxBTWAVMwwl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: crie uma interface em python para calcular_potencia( )\n",
        "# abc\n",
        "from abc import ABC, abstractmethod\n",
        "\n",
        "# Interface  ou I\n",
        "# PotenciaInteface ou IPotencia\n",
        "# Orientada a Objetos - padrão para os objetos.\n",
        "class IPotencia(ABC): # herança\n",
        "  @abstractmethod # assinatura com contrato\n",
        "  def calcular_potencia(self, base, expoente):\n",
        "     pass\n"
      ],
      "metadata": {
        "id": "OBwSnYmoQMBJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Potencia(IPotencia):\n",
        "  def calcular_potencia(self, base, expoente):\n",
        "    return pow(base, expoente)"
      ],
      "metadata": {
        "id": "_gO0Wmu7SzqY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p = Potencia()"
      ],
      "metadata": {
        "id": "uPuUBuFuVmtJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"potência: {p.calcular_potencia(0.8,3):.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pfE2Mh3Vtnj",
        "outputId": "c9ddb13f-4587-4c8c-9c21-c4d6cf878af3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "potência: 0.5120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from abc import ABC, abstractmethod\n",
        "\n",
        "class ICalculadora(ABC):\n",
        "\n",
        "  @abstractmethod\n",
        "  def multiplicar(self):\n",
        "    pass\n"
      ],
      "metadata": {
        "id": "aqXnS_RVc2up"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CalculadoraPadrao(ICalculadora):\n",
        "  def __init__(self, num1=None, num2=None,num3=None):\n",
        "    self.__num1 = num1\n",
        "    self.__num2 = num2\n",
        "    self.__num3 = num3\n",
        "\n",
        "  def multiplicar(self):\n",
        "    return self.__num1 * self.__num2 * self.__num3\n",
        "\n",
        "  @property\n",
        "  def num1(self):\n",
        "    return self.__num1\n",
        "\n",
        "  @num1.setter\n",
        "  def num1(self, value):\n",
        "    self.__num1 = value\n",
        "\n",
        "  @property\n",
        "  def num2(self):\n",
        "    return self.__num2\n",
        "\n",
        "  @num2.setter\n",
        "  def num2(self, value):\n",
        "    self.__num2 = value\n",
        "\n",
        "  @property\n",
        "  def num3(self):\n",
        "    return self.__num3\n",
        "\n",
        "  @num3.setter\n",
        "  def num3(self, value):\n",
        "    self.__num3 = value"
      ],
      "metadata": {
        "id": "GM4tUALzfNse"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Usando o construtor\n",
        "calculadora1 = CalculadoraPadrao(0.8,0.8,0.8)\n",
        "\n",
        "resultado1 = calculadora1.multiplicar()\n",
        "print(f\"Resultado da multiplicação (construtor): {resultado1 : .3f}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bd1F6kWFe9AZ",
        "outputId": "93a46aac-c1fb-4c96-8d63-53467575314d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado da multiplicação (construtor):  0.512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Usando get e set\n",
        "calculadora2 = CalculadoraPadrao()\n",
        "calculadora2.num1 = float(input(\"Digite uma variável númerica contínua (fracionária)\"))\n",
        "calculadora2.num2 = float(input(\"Digite uma variável númerica contínua (fracionária)\"))\n",
        "calculadora2.num3 = float(input(\"Digite uma variável númerica contínua (fracionária)\"))\n",
        "resultado2 = calculadora2.multiplicar()\n",
        "print(f\"Resultado da multiplicação (get e set): {resultado2: .3f} \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8XDJ3X5fe-ui",
        "outputId": "aa4235cb-9777-4ad8-8050-3ef1c4dd7ec8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite uma variável númerica contínua (fracionária)0.6\n",
            "Digite uma variável númerica contínua (fracionária)0.5\n",
            "Digite uma variável númerica contínua (fracionária)0.7\n",
            "Resultado da multiplicação (get e set):  0.210 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Revisão - Static"
      ],
      "metadata": {
        "id": "kcJX3GKS_UQG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# static : é da classe\n",
        "\n",
        "# no-static : é de instância\n",
        "\n",
        "class MinhaClasse:\n",
        "    \n",
        "    # Atributo de classe para contar instâncias\n",
        "    \n",
        "    contagem = 0\n",
        "\n",
        "    def __init__(self):\n",
        "        # Incrementa a contagem sempre que um objeto é criado\n",
        "        MinhaClasse.contagem += 1\n",
        "\n",
        "    @staticmethod\n",
        "    def obter_contagem(self):\n",
        "        \"\"\"Método estático para retornar a contagem de objetos criados.\"\"\"\n",
        "        return MinhaClasse.contagem\n",
        "\n",
        "# Exemplo de uso\n",
        "objeto1 = MinhaClasse()\n",
        "objeto2 = MinhaClasse()\n",
        "objeto3 = MinhaClasse()\n",
        "objeto4 = MinhaClasse()\n",
        "\n",
        "print(MinhaClasse.obter_contagem())  # Saída: 3\n"
      ],
      "metadata": {
        "id": "S1jb3KyJWqD8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# static : é da classe\n",
        "\n",
        "# no-static : é de instância\n",
        "\n",
        "class MinhaClasse:\n",
        "\n",
        "    # Atributo de classe para contar instâncias\n",
        "\n",
        "    contagem = 0\n",
        "\n",
        "    def __init__(self):\n",
        "        # Incrementa a contagem sempre que um objeto é criado\n",
        "        MinhaClasse.contagem += 1\n",
        "\n",
        "    @staticmethod\n",
        "    def obter_contagem():\n",
        "        \"\"\"Método estático para retornar a contagem de objetos criados.\"\"\"\n",
        "        return MinhaClasse.contagem\n",
        "\n",
        "# Exemplo de uso\n",
        "objeto1 = MinhaClasse()\n",
        "objeto2 = MinhaClasse()\n",
        "objeto3 = MinhaClasse()\n",
        "objeto4 = MinhaClasse()\n",
        "\n",
        "\n",
        "\n",
        "print(MinhaClasse.obter_contagem())  # Saída: 3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7BTJ-XhILfg",
        "outputId": "170a7552-94af-484e-b26e-02f9a2da69cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def conectar():\n",
        "  return \"conexao\"\n",
        "\n",
        "@staticmethod\n",
        "def conectar():\n",
        "  return \"conectei\""
      ],
      "metadata": {
        "id": "nxBLnpBvQXtv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Aula sobre Static no Python"
      ],
      "metadata": {
        "id": "QKhB3IDcXdys"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. O que é static?\n",
        "\n",
        "- Em Python, o termo \"static\" geralmente se refere a atributos e métodos estáticos.\n",
        "- Esses elementos pertencem à classe, não a instâncias individuais da classe.\n",
        "- Eles são usados quando você quer que um método ou atributo esteja disponível sem precisar criar uma instância da classe.\n",
        "-"
      ],
      "metadata": {
        "id": "m_jouSvlXpqG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Exemplo:\n",
        "    @staticmethod\n",
        "    def metodo_estatico():\n",
        "        print(\"Este é um método estático.\")\n",
        "\n",
        "    def metodo_instancia(self):\n",
        "        print(\"Este é um método de instância.\")\n",
        "\n",
        "# Chamando os métodos\n",
        "Exemplo.metodo_estatico()  # Saída: Este é um método estático.\n",
        "\n",
        "objeto = Exemplo()\n",
        "objeto.metodo_instancia()  # Saída: Este é um método de instância.\n"
      ],
      "metadata": {
        "id": "Bsm4T2G9YkNq",
        "outputId": "38078674-7eee-4db2-a113-98ae9ffd6a4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Este é um método estático.\n",
            "Este é um método de instância.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gostou do Python?\n",
        "\n",
        "- Lançada em 1991 por Guido Van Rossum, o Python é uma linguagem de alto nível, interpretada, imperativa, orientada a objetos, funcional, de tipagem dinâmica e forte - ou seja, o Python é uma linguagem completa e pode ser utilizada em uma vasta quantidade de softwares.\n",
        "- Além disso, o Python é uma linguagem multiplataforma, podendo ser executada em diversos sistemas operacionais, como o Windows, Linux, Mac e até dispositivos móveis, fazendo do Python uma linguagem amplamente utilizada em diversos segmentos e por empresas como Google, NASA, Amazon, Facebook e diversas outras.\n",
        "- Um outro ponto muito interessante sobre o Python são seus Frameworks, fazendo com que seja possível desenvolver aplicativos em Python para Web (através do Django ou Flask) ou aplicativos híbridos em Flet.\n",
        "- Sendo assim é fácil perceber o grande poder do Python e o que ele provê para o desenvolvimento de aplicações, fazendo do Python uma das linguagens mais utilizadas mundialmente."
      ],
      "metadata": {
        "id": "C6p2mjliWuVA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Qual a diferença de uma IDE e um Editor de Texto\n",
        "\n",
        "- IDE significa ambiente integrado de desenvolvimento, nesse ambiente temos debugs integrados, recursos pré-instalados, ferremantas integradas.\n",
        "- Já no Editor, temos que instalar as ferramentas e configura-las manualmente.\n",
        "- Abaixo um print de uma IDE, PyCharm Community da empresa Jetbrains.\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "xSafDSgLXinK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Int: Números inteiros;\n",
        "# Str: Cadeia de caracteres;\n",
        "# Bool: Dados booleanos (True ou False);\n",
        "# List: Estrutura de dados para armazenamento de conjunto de elementos;\n",
        "# Dict: Dicionários;\n",
        "# Files: Arquivos.\n",
        "\n",
        "class Funcionario:\n",
        "    def __init__(self, nome: str, idade: int, ativo: bool, salario: float):\n",
        "        self.nome = nome\n",
        "        self.idade = idade\n",
        "        self.ativo = ativo\n",
        "        self.salario = salario\n"
      ],
      "metadata": {
        "id": "3xeRMfy6YOyY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f = Funcionario('Rômulo Cesar',45,1,20000.00)\n",
        "print(f.nome)\n",
        "print(f.idade)\n",
        "print(f.ativo)\n",
        "print(f.salario)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZCXB8FPd1Wl",
        "outputId": "1c4774c4-e1db-4dc9-ff94-914ba80a1312"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rômulo Cesar\n",
            "45\n",
            "1\n",
            "20000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# String"
      ],
      "metadata": {
        "id": "KdI3oYcGe0SV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f.nome[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "XV3w3o5ke8Dx",
        "outputId": "9d65ba82-b56d-4d4a-a1fa-ea70b8b7b58c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'R'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.nome[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Dyq07whafAeE",
        "outputId": "6cf399ea-f429-4d97-dcae-9462891de8d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ô'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.nome[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "s1TFsstdfC5D",
        "outputId": "da6046f6-13bb-47d5-e692-8702f16d4aba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Rôm'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.nome[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_RNr9Ka_fHtV",
        "outputId": "617499e8-e6fa-4f6a-a216-86418a9d05f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'r'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.nome[-2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "tpeCxWsgfPXv",
        "outputId": "3a1ec17f-fb16-4995-dba2-e5a7c52a0091"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'a'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.nome[-3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "NaDhO6OQfTNr",
        "outputId": "0356e4a5-b986-4e98-cad9-42388ee301db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'s'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.nome.capitalize()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "1rdhNtr8fhwG",
        "outputId": "e00013fd-0b00-4dc9-a670-084a87c9f973"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Rômulo cesar'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f.nome.casefold()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "bOQuQ6dyfpqW",
        "outputId": "c103c7e4-2e3a-4e1f-cb4f-199ef1966a57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'rômulo cesar'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " casefold( ) é um método que converte a string para uma forma em minúsculas, útil para comparações de strings que não diferenciam maiúsculas de minúsculas."
      ],
      "metadata": {
        "id": "0gD7Olwihpn5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Hoje é o seu aniversário, parabéns colaborador \"+f.nome)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tbBbbhvh1tt",
        "outputId": "4c8cb9e5-ed7b-4150-f257-0eb0cb7ef660"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hoje é o seu aniversário, parabéns colaborador Rômulo Cesar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "# URL da imagem\n",
        "url = 'https://raw.githubusercontent.com/romulosilvestre/semanamachinelearning-datasets/main/identacao.png'\n",
        "\n",
        "# Exibir a imagem\n",
        "display(Image(url=url))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "m9esSkH3isBC",
        "outputId": "55a8050d-9d51-46d2-bf9b-28ea1830a7df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<img src=\"https://raw.githubusercontent.com/romulosilvestre/semanamachinelearning-datasets/main/identacao.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imagem muito importante que mostra a identação do python. No python a identação é obrigatória.\n",
        "\n",
        "# Vamos falar de identação em alguns algoritmos com o tema probabilidade:\n",
        "\n",
        "pergunta = input(\"Estudos sobre probabilidades fornecem certezas sobre qualquer evento?\")\n",
        "if pergunta == \"sim\":\n",
        "    print(\"resposta errada\")\n",
        "else:\n",
        "    print(\"resposta certa\")\n",
        "\n",
        "# observe que o comando print() se encontra com uma identação , ou seja, tem uma tabulação do código\n",
        "# if else é uma estrutura condicional composta.\n",
        "# apenas o if é uma estrutura condicional simples.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddZnnIyFiuL7",
        "outputId": "f25cc9e5-a0ce-4e1f-bbd5-1a712b0260f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estudos sobre probabilidades fornecem certezas sobre qualquer evento?n\n",
            "resposta certa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Matemáticamente, o valor atribuído à probabilidade de um evento é a razão entre o número de possibilidades favoráveis\n",
        "e o número total de possibilidades\n",
        "\"\"\"\n",
        "\n",
        "calcular_probabilidade = lambda num_possibilidades_favoraveis,num_total_possibilidades: num_possibilidades_favoraveis/num_total_possibilidades"
      ],
      "metadata": {
        "id": "2Feti6JEB4jW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exemplo o lançamento de duas moedas, há quatro casos possíveis\n",
        "\n",
        "\"\"\"\n",
        "A probabilidade de as duas moedas caírem com a face voltada para cima é de 1/4, pois essa é uma possibilidade\n",
        "em um total de quatro.\n",
        "\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "JhDnvZoVChQY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# cara ou coroa..\n",
        "\n",
        "calcular_probabilidade(1,4)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTxUNl9pDp4M",
        "outputId": "39d244d0-1ce9-4d8a-8973-6a0c877b514c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.25"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# O sinal de \"=\" é o sinal de atribuição\n",
        "# Nesse sinal guardamos uma variável\n",
        "\n",
        "x = 10"
      ],
      "metadata": {
        "id": "osuAnYciJCWn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"O valor de x é  {x}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaUO6pRlJKy5",
        "outputId": "6b46d0c3-7ef2-445b-a75e-3443aabaeda8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O valor de x é  10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calcular_probabilidade(15,29)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fV3t9YsrKOG4",
        "outputId": "d6361bb4-74af-4f93-c1f0-bb54a80ac529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5172413793103449"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calcular_probabilidade(14,29)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iaIO2q7nRdLK",
        "outputId": "9174781b-816b-4fc8-f27a-b7066febf570"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4827586206896552"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# declarando variáveis quantitativas discreta\n",
        "\n",
        "idade = 10\n",
        "print(idade)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPhBvFmRdYWH",
        "outputId": "74324ae2-4b69-427d-c201-556e6669d013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# declarando variáveis quantitativas discretas\n",
        "\n",
        "altura = 1.70\n",
        "print(altura)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxUYVI8Od4fL",
        "outputId": "2ec6a389-a8a8-44ca-e29c-0d51f1c67168"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# declarando variável qualitativa nominal\n",
        "\n",
        "sexo = \"Masculino\"\n",
        "print(sexo)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmvDWjD2eMPa",
        "outputId": "716c651b-11f7-48e4-ba1d-0b9088daf355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Masculino\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# declarando variável qualitativa ordinal\n",
        "\n",
        "nivel_educacao=\"Ensino Médio\"\n",
        "print(nivel_educacao)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTYiPAw5eXE5",
        "outputId": "cbed5b58-826b-43f6-b5c3-3371d98ee9b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ensino Médio\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# qual a a probabilidadde de obter o número 1 em um dado equilibrado\n",
        "dado_lancado = lambda pp,pt: pp/pt"
      ],
      "metadata": {
        "id": "a58vFcaDemdT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dado_lancado(1,6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3vNxJBZexev",
        "outputId": "1da2eff3-a2fb-4bcb-ceeb-88385611935a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.16666666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# estrutura de condição simples\n",
        "def verificar_idade(idade):\n",
        "    if idade >= 18:\n",
        "        return \"Você é maior de idade.\""
      ],
      "metadata": {
        "id": "jInTnUNi0LTL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "idade = input(\"Digite sua idade: \")\n",
        "idade = int(idade)\n",
        "resultado = verificar_idade(idade)\n",
        "print(resultado)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jIJ_R990Oqg",
        "outputId": "220215d4-2faa-4ec7-db46-f6e9fe989438"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite sua idade: 18\n",
            "Você é maior de idade.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# estrutura de condição composta\n",
        "\n",
        "def verificar_idade_total(idade):\n",
        "    if idade >= 18:\n",
        "        return \"Você é maior de idade.\"\n",
        "    else:\n",
        "        return \"Você é menor de idade.\""
      ],
      "metadata": {
        "id": "fs_uKFVa0tnO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "idade = input(\"Digite sua idade: \")\n",
        "idade = int(idade)\n",
        "resultado = verificar_idade_total(idade)\n",
        "print(resultado)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruApqzV-003l",
        "outputId": "1c9f5037-254f-4226-ab11-607324a12842"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite sua idade: 19\n",
            "Você é maior de idade.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# estrutura de condição composta encadeada\n",
        "def verificar_numero(numero):\n",
        "    if numero > 0:\n",
        "        return \"O número é positivo.\"\n",
        "    elif numero < 0:\n",
        "        return \"O número é negativo.\"\n",
        "    else:\n",
        "        return \"O número é nulo.\""
      ],
      "metadata": {
        "id": "WP828D8xz22M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exemplo de uso\n",
        "numero = float(input(\"Digite um número: \"))\n",
        "resultado = verificar_numero(numero)\n",
        "print(resultado)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1W7s1Gzz5vV",
        "outputId": "9f763059-3fe2-4f91-dfb9-cf0542c1a860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite um número: -5\n",
            "O número é negativo.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# estrutura de seleção múltipla\n",
        "\n",
        "def mostrar_mes(numero):\n",
        "    match numero:\n",
        "        case 1:\n",
        "            return \"Janeiro\"\n",
        "        case 2:\n",
        "            return \"Fevereiro\"\n",
        "        case 3:\n",
        "            return \"Março\"\n",
        "        case 4:\n",
        "            return \"Abril\"\n",
        "        case 5:\n",
        "            return \"Maio\"\n",
        "        case 6:\n",
        "            return \"Junho\"\n",
        "        case 7:\n",
        "            return \"Julho\"\n",
        "        case 8:\n",
        "            return \"Agosto\"\n",
        "        case 9:\n",
        "            return \"Setembro\"\n",
        "        case 10:\n",
        "            return \"Outubro\"\n",
        "        case 11:\n",
        "            return \"Novembro\"\n",
        "        case 12:\n",
        "            return \"Dezembro\"\n",
        "        case _:\n",
        "            return \"Número inválido! Digite um número de 1 a 12.\"\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "XSuQ16LUfzc-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exemplo de uso de função com estrutura múltipla\n",
        "numero = int(input(\"Digite um número de 1 a 12: \"))\n",
        "mes = mostrar_mes(numero)\n",
        "print(mes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuznFti8zETs",
        "outputId": "2a50abef-360c-48e2-8b6b-6a62946d6ae9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite um número de 1 a 12: 9\n",
            "Setembro\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Revisão de PLN na prática"
      ],
      "metadata": {
        "id": "bLIll5em8yUF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install textblob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBGp1wX78iyS",
        "outputId": "3bf3392c-2dd1-4d42-bb1b-d902964f40d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: textblob in /usr/local/lib/python3.10/dist-packages (0.17.1)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.10/dist-packages (from textblob) (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (2024.9.11)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (4.66.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texto = \"Hello Student. During this chapter, we will talk about the types of houses, name of the rooms and some objects we can normally find in them, and also the meaning and  how to use question word correctly in this context.\""
      ],
      "metadata": {
        "id": "qTc-61Vy8mhV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Módulos - usando from e import para trabalhar com módulos e classes."
      ],
      "metadata": {
        "id": "MHERBTshC2MU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import TextBlob"
      ],
      "metadata": {
        "id": "LaXRfrEr82T0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "blob = TextBlob(texto)"
      ],
      "metadata": {
        "id": "T3vtQCWY8sUO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# traduzindo o texto\n",
        "blob.translate(from_lang='en', to='pt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZcidLvN9AT-",
        "outputId": "c444e93c-9ce6-405f-bb7b-b5d1cb63c789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TextBlob(\"Olá aluno. Durante este capítulo, falaremos sobre os tipos de casas, o nome das salas e alguns objetos que normalmente podemos encontrar nelas e também o significado e como usar a palavra de perguntas corretamente neste contexto.\")"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(texto)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7m-0X5U9cU1",
        "outputId": "055cbdeb-1d58-4cc1-8230-69f9aacd0d1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello Student. During this chapter, we will talk about the types of houses, name of the rooms anda some objects we can normally find in them, and also the meaning and  how to use question word correctly in this context.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# crie uma função que faça o que as células anteriores estão fazendo, uma função que receba o texto em portugues e mostre traduzido.\n",
        "\n",
        "def traduzir_texto(texto):\n",
        "    blob = TextBlob(texto)\n",
        "    traducao = blob.translate(from_lang='en', to='pt-br')\n",
        "    return traducao"
      ],
      "metadata": {
        "id": "kLD3Kixr95SE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "texto_entrada = input(\"Digite o texto: \")\n",
        "print(traduzir_texto(texto_entrada))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WP7rtcAC-HJU",
        "outputId": "0d5bf474-78c6-4057-8400-0c497fb16b02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite o texto: Do you think houses around the world are similiar to yours\n",
            "Você acha que casas em todo o mundo são semelhantes para a sua\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Uma estrutura de repetição é responsável por permitir que um determinado bloco de código seja executado diversas vezes, até que uma determinada condição seja atendida. No Python, possuímos duas estruturas de repetição:\n",
        "\n",
        "    While\n",
        "    For\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "numero = 0\n",
        "soma = 0\n",
        "while numero < 5:\n",
        "    numero = int(input(\"Digite um número\"))\n",
        "    soma = soma + numero\n",
        "\n",
        "print(soma)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7CLMzsNBVQi",
        "outputId": "88da404d-16be-45b6-f534-4ff49f82667c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite um número1\n",
            "Digite um número2\n",
            "Digite um número3\n",
            "Digite um número4\n",
            "Digite um número5\n",
            "15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x in range(1, 10):\n",
        "    print(x)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjwPmxU7CF_T",
        "outputId": "96e1ea1d-5f48-40d3-990d-14f6438e69fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x in range(1, 10):\n",
        "    print(x)\n",
        "else:\n",
        "    print(\"loop encerrado com sucesso\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3sK8F6vxCabq",
        "outputId": "8f7b39d9-5514-4d12-8f18-dd4875404a23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "loop encerrado com sucesso\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numero = 0\n",
        "soma = 0\n",
        "while numero < 20:\n",
        "    numero = int(input(\"Digite um número menor que 20\"))\n",
        "    soma = soma + numero\n",
        "else:\n",
        "    print(\"loop encerrado com sucesso\")\n",
        "print(soma)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4AlxZCCvChcP",
        "outputId": "2a97d385-6e75-4dda-ff7c-edb4ec12e907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite um número menor que 205\n",
            "Digite um número menor que 205\n",
            "Digite um número menor que 205\n",
            "Digite um número menor que 2021\n",
            "loop encerrado com sucesso\n",
            "36\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "\n",
        "- Uma lista é uma estrutura de dados representada como uma sequência de objetos separados por vírgula.\n",
        "- Podendo armazenar qualquer tipo de dado e ser alterada a qualquer momento.\n",
        "-\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "lista = [\"Arroz\", \"Feijão\"]\n",
        "lista.append(\"Macarrão\")\n",
        "print(lista) #Arroz, Feijão, Macarrão"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKQo0BnfDAAQ",
        "outputId": "d01ce9e7-22c2-4cae-a546-456a3444f518"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Arroz', 'Feijão', 'Macarrão']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# criar uma função para ir adicionando palavras em inglês que não entendo\n",
        "\n",
        "lista = []\n",
        "def adicionar_palavra(palavra):\n",
        "    lista.append(palavra)\n",
        "\n",
        "def mostrar_palavras():\n",
        "    return lista"
      ],
      "metadata": {
        "id": "Fb5YzrXrDTyc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# crie um loop para ir perguntando se deseja adicionar alguma palavra\n",
        "\n",
        "while True:\n",
        "    palavra = input(\"Digite uma palavra ou sair para finalizar: \")\n",
        "    if palavra == \"sair\":\n",
        "        break\n",
        "    adicionar_palavra(palavra)\n",
        "\n",
        "\n",
        "print(mostrar_palavras())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tsbLktRXDf1j",
        "outputId": "031aa9e6-024f-4784-90f4-27a557d46a3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Digite uma palavra ou sair para finalizar: the\n",
            "Digite uma palavra ou sair para finalizar: house\n",
            "Digite uma palavra ou sair para finalizar: jesus\n",
            "Digite uma palavra ou sair para finalizar: cristo\n",
            "Digite uma palavra ou sair para finalizar: sair\n",
            "['the', 'house', 'jesus', 'cristo']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tratamento de exceções no Python\n",
        "\n",
        "É comum que, no desenvolvimento de uma aplicação, queiramos prever situações em que um erro possa ocorrer, com a finalidade de tratarmos estas situações para que a execução do nosso software não seja interrompida.\n",
        "\n",
        "Por exemplo: imagine que temos um programa responsável por realizar as quatro operações básicas da matemática. Sabemos que há alguns casos que são impossíveis de serem realizados, como a divisão por 0. Neste caso, nosso software precisa sempre verificar se esta situação está ocorrendo e, caso esteja, exibir uma mensagem de erro.\n",
        "\n",
        "No Python, os seguintes comandos são utilizados para o tratamento de exceções:\n",
        "\n",
        "- try/except: tenta executar o determinado bloco de código definido no try e, caso este gere um erro, executa o bloco de código definido no except;\n",
        "- try/finally: após o bloco try ser executado, com sucesso ou não, o código definido dentro do finally é executado;\n",
        "- Raise: se, por algum motivo, precisarmos gerar uma exceção em nosso script, utilizamos o comando raise;\n",
        "- Assert: assim como o comando raise, o assert serve para gerar exceções, porém é necessário definir uma condição para isso.\n"
      ],
      "metadata": {
        "id": "ZHCcJzfbEezq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    numero = 15/0\n",
        "except:\n",
        "    print(\"Divisão por zero não existe\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XV_3m73HE0w7",
        "outputId": "9dbfedbf-5724-4b38-a4c1-358a8bb28421"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Divisão por zero não existe\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    numero = 15/0\n",
        "except ZeroDivisionError:\n",
        "    print(\"Divisão por zero não existe\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vq2DQgu3E5O_",
        "outputId": "843db7c4-a11b-4932-f8ed-7d9a92df762c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Divisão por zero não existe\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    numero = 15/0\n",
        "except ZeroDivisionError:\n",
        "    print(\"Divisão por zero não existe\")\n",
        "finally:\n",
        "    print(\"Execução finalizada\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ukNudurE8SD",
        "outputId": "7125310f-3199-47f0-b52a-61491456e1a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Divisão por zero não existe\n",
            "Execução finalizada\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    raise ZeroDivisionError\n",
        "except ZeroDivisionError:\n",
        "    print(\"Divisão por zero não existe\")\n",
        "finally:\n",
        "    print(\"Execução finalizada\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2U0khssFHir",
        "outputId": "cb34bfe8-8d93-4140-f71e-071d9e2a9713"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Divisão por zero não existe\n",
            "Execução finalizada\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Relançar exceções: Use raise para adicionar contexto a exceções que você deseja relançar.\n",
        "\n",
        "def dividir(a, b):\n",
        "    if b == 0:\n",
        "        # Lança uma exceção personalizada se o divisor for zero\n",
        "        raise ValueError(\"Erro: O divisor não pode ser zero.\")\n",
        "    return a / b\n",
        "\n",
        "try:\n",
        "    resultado = dividir(5, 0)  # Tenta dividir 5 por 0\n",
        "except ValueError as e:\n",
        "    print(e)  # Saída: Erro: O divisor não pode ser zero.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l2xeV4Y9FHUL",
        "outputId": "7b8c6783-5496-43da-f884-3646cecc1f2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erro: O divisor não pode ser zero.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lançar exceção: Se b for zero, a linha raise é executada. Isso gera uma exceção do tipo ValueError com uma mensagem personalizada. Essa linha interrompe a execução da função e \"lança\" a exceção para o código que chamou a função.\n",
        "\n",
        "# Gerando exceções com Assert\n",
        "\n",
        "Assim como o raise, utilizamos o assert para gerar exceções na execução do nosso sistema. Porém, o assert irá gerar essa exceção a depender de uma condição. Por exemplo:\n"
      ],
      "metadata": {
        "id": "4eXhTF2PGPhH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    numero = 15\n",
        "    divisor = 0\n",
        "    assert divisor != 0\n",
        "except:\n",
        "    print(\"Divisão por zero não existe\")\n",
        "finally:\n",
        "    print(\"Execução finalizada\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "798uvW3fGurB",
        "outputId": "213a0ce5-28df-4098-d72a-dfcf40ebcfee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Divisão por zero não existe\n",
            "Execução finalizada\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Destrutores"
      ],
      "metadata": {
        "id": "3kNZ2j_cHzj5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# classe Pessoa\n",
        "class Pessoa():\n",
        "  def __init__(self, nome, idade, sexo):\n",
        "    self.nome = nome\n",
        "    self.idade = idade\n",
        "    self.sexo = sexo\n",
        "  def __del__(self):\n",
        "    print(\"Destruindo objeto\")"
      ],
      "metadata": {
        "id": "nC7NB0_bH1lI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pessoa1 = Pessoa(\"Rômulo\",45, \"Masculino\")\n",
        "print(pessoa1.nome) #João #Destruindo objeto"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GkdDgWmCH4Bc",
        "outputId": "cb21dc94-272e-4e3c-eec3-20d5e2b6fd35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rômulo\n",
            "Destruindo objeto\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "No dia-a-dia, o método de desconstrução (__del__) não é muito utilizado.\n",
        "Ele só será implementado caso a classe aloque algum recurso/elemento que não será\n",
        "removido da memória quando a mesma for \"destruída\".\n",
        "Por exemplo, um acesso ao banco de dados que é aberto no método construtor ou de inicialização."
      ],
      "metadata": {
        "id": "k-U4iXa6Ie4c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# herança\n",
        "\n",
        "\"\"\"\n",
        "Em orientação a objetos, a herança é um mecanismo pelo qual uma classe consegue\n",
        "obter métodos e atributos de uma outra classe, para isso,\n",
        "dizemos que uma classe filha \"herda\" de uma classe pai,\n",
        "onde a classe Pai possui os métodos e atributos que serão herdados.\n",
        "\"\"\"\n",
        "\n"
      ],
      "metadata": {
        "id": "nF420GmCI6X-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "# URL da imagem\n",
        "url = \"https://github.com/romulosilvestre/semanamachinelearning-datasets/raw/main/orientacao_aobjetos.png\"\n",
        "\n",
        "# Exibir a imagem\n",
        "display(Image(url=url))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "nKjn8V8sJdSR",
        "outputId": "fb1ba046-a560-4a14-8b5d-c1cbf8e3ca11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<img src=\"https://github.com/romulosilvestre/semanamachinelearning-datasets/raw/main/orientacao_aobjetos.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "# URL da imagem (com a correção para usar o link direto)\n",
        "url = \"https://github.com/romulosilvestre/semanamachinelearning-datasets/raw/main/oo.png\"\n",
        "\n",
        "# Exibir a imagem\n",
        "display(Image(url=url))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "lxNu3PftJz2G",
        "outputId": "83e94e85-3e85-457b-84ba-abbfe3e521f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<img src=\"https://github.com/romulosilvestre/semanamachinelearning-datasets/raw/main/oo.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Definindo um interface"
      ],
      "metadata": {
        "id": "Ea1ULx5CLOyW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import abc\n",
        "\n",
        "# Definindo uma interface simples\n",
        "class InterfaceVeiculo(abc.ABC):\n",
        "    @abc.abstractmethod\n",
        "    def ligar(self):\n",
        "        pass\n",
        "\n",
        "    @abc.abstractmethod\n",
        "    def desligar(self):\n",
        "        pass\n",
        "\n",
        "    @abc.abstractmethod\n",
        "    def acelerar(self, velocidade):\n",
        "        pass\n"
      ],
      "metadata": {
        "id": "G1YNT5PRLCGv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Veiculo(InterfaceVeiculo, abc.ABC):\n",
        "    \"\"\"Essa é a classe veículo. Esta classe é utilizada para instanciar novos veículos em nosso programa.\"\"\"\n",
        "\n",
        "    def __init__(self, cor, tipo_combustivel, potencia):\n",
        "        self._cor = cor\n",
        "        self.__tipo_combustivel = tipo_combustivel\n",
        "        self.__potencia = potencia\n",
        "        self._qtd_combustivel = 0\n",
        "        self.__is_ligado = False\n",
        "        self.__velocidade = 0\n",
        "\n",
        "    def __del__(self):\n",
        "        print(\"O objeto foi removido da memória. :)\")\n",
        "\n",
        "    @property\n",
        "    def cor(self):\n",
        "        return self._cor\n",
        "\n",
        "    def pintar(self, cor):\n",
        "        self._cor = cor\n",
        "\n",
        "    def abastecer(self, qtd_combustivel):\n",
        "        \"\"\"O método abastecer recebe como parâmetro a quantidade de combustível e incrementa no atributo qtd_combustivel do objeto veículo.\"\"\"\n",
        "        self._qtd_combustivel += qtd_combustivel\n",
        "\n",
        "    def ligar(self):\n",
        "        if self.__is_ligado:\n",
        "            print(\"O veículo já está ligado.\")\n",
        "        else:\n",
        "            self.__is_ligado = True\n",
        "            print(\"O veículo foi ligado.\")\n",
        "\n",
        "    def desligar(self):\n",
        "        if not self.__is_ligado:\n",
        "            print(\"O veículo já está desligado.\")\n",
        "        else:\n",
        "            self.__is_ligado = False\n",
        "            print(\"O veículo foi desligado.\")\n",
        "\n",
        "    def acelerar(self, velocidade=10):\n",
        "        if self.__is_ligado:\n",
        "            self.__velocidade += velocidade\n",
        "            print(f\"Velocidade atual: {self.__velocidade} km/h\")\n",
        "        else:\n",
        "            print(\"O veículo precisa estar ligado para ser acelerado.\")"
      ],
      "metadata": {
        "id": "tBC9Vb6CLLwI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exemplo de uso\n",
        "carro = Veiculo(\"vermelho\", \"gasolina\", 120)\n",
        "\n",
        "# Testando os métodos\n",
        "carro.abastecer(20)\n",
        "carro.ligar()\n",
        "carro.acelerar(30)\n",
        "carro.desligar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_aDxc0zgLEUR",
        "outputId": "39796f56-fa02-469c-d6a9-77ceba11237e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O veículo foi ligado.\n",
            "Velocidade atual: 30 km/h\n",
            "O veículo foi desligado.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Encapsulamento\n",
        "\n"
      ],
      "metadata": {
        "id": "8la-lfg9LyiP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Em Python, o uso de dois underscores (__) antes de um atributo (como self.__atributo) é uma forma de nomeação com \"name mangling\". Isso é usado para encapsular o atributo e evitar que ele seja acessado diretamente de fora da classe. Aqui está o que isso significa e como funciona:\n",
        "O que significa __atributo\n",
        "\n",
        "- Encapsulamento:\n",
        "        O uso de __ indica que o atributo é \"privado\" ou destinado a ser usado apenas dentro da classe. Embora Python não tenha um verdadeiro modificador de acesso como algumas outras linguagens (por exemplo, private em Java), essa convenção sugere que o atributo não deve ser acessado diretamente fora da classe.\n",
        "\n",
        "- Name Mangling:\n",
        "        Quando você define um atributo com dois underscores, Python altera o nome do atributo internamente para incluir o nome da classe. Por exemplo, se você tiver uma classe chamada Veiculo e um atributo __potencia, ele será renomeado para _Veiculo__potencia. Isso ajuda a evitar conflitos de nome em subclasses."
      ],
      "metadata": {
        "id": "Hy67NZTNL3Gw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exercício\n",
        "\n",
        "# Aplique os conhecimentos e códigos (o quanto for possível) do Notebook do Professor Rômulo\n",
        "# Aplique conhecimentos do professor Karython (esta no notebook)\n",
        "\n",
        "#objeto - classe - método abstrato - construtor - static\n",
        "# match\n",
        "\n",
        "Claudio aqui:\n",
        "Exercicio 3:\n",
        "Alguém aqui consegue fazer um código, que precisa de duas assinaturas: Uma do Presidente, e outra do tesoureiro.\n",
        "Os dois PRECISAM ASSINAR para liberar o pagamento, mas o presidente não pode assinar pelo tesoureiro,\n",
        "e o tesoureiro nao pode assinar pelo presidente.\n",
        "Não pode ter assinatura em branco ( ou seja, nao pode deixar sem assinar )\n",
        "Quem fizer ( sem chat gpt e i.a, me chama )\n",
        "\n",
        "Exercicio 2:\n",
        "Quero ( quem quiser, é claro! )\n",
        "Um algoritmo que mostre a cor de dois cavalos, um preto e outro branco, mas nao pode aceitar qualquer outra cor.\n",
        "Quem fizer, me chama :D\n",
        "\n",
        "\n",
        "# para cada recurso aplicado - 1 ponto\n"
      ],
      "metadata": {
        "id": "j_1q6YT1Tzsd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}