revisao_funcoes_py.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "revisao_funcoes_py.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO0B7suCnRbVR5B5gXctsl7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gabifc/data_python_viz/blob/master/revisao_funcoes_py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtLjQ4sAu4YQ"
      },
      "source": [
        "# **Funções**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdyviIODrOZx"
      },
      "source": [
        "def seu_nome():\n",
        "  nome = input(\"Qual seu nome?\")\n",
        "  print(f\"Olá {nome}!!!\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8ew_RMpsGl0",
        "outputId": "b6247b56-3d56-462d-eefb-a9241c946b38"
      },
      "source": [
        "seu_nome()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Qual seu nome?Gabi\n",
            "Olá Gabi!!!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qomnU2DTtC31"
      },
      "source": [
        "def nome_completo():\n",
        " primeiro_nome = input('Qual seu primeiro nome? ')\n",
        " sobrenome = input('Qual seu sobrenome? ')\n",
        " nome_inteiro = primeiro_nome + ' ' + sobrenome\n",
        " print(nome_inteiro)\n",
        "\n",
        "nome_completo()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPORNa1MuQFF"
      },
      "source": [
        "# Função com Parametros\n",
        "def saudacao_parametros(nome_pessoa):\n",
        "  print(f\"Olá {nome_pessoa}\")\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4pHO14NuVJ3"
      },
      "source": [
        "nome = \"Gabi\""
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Amv9FguiunzB",
        "outputId": "04b61579-c8a6-4cd2-99c5-874dfa80e274"
      },
      "source": [
        "saudacao_parametros(nome)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Olá Gabi\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvPO-u6FusjZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVM4Z3HRu9zV"
      },
      "source": [
        "# Condicionais"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8G-HtAb6wJsA"
      },
      "source": [
        "### com parâmetros"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-i8gts_vIp0"
      },
      "source": [
        "idade = 20\n",
        "\n",
        "def verifica_idade(idade):\n",
        "  if idade >= 18:\n",
        "    print(\"Maior de idade\")\n",
        "  else:\n",
        "    print(\"Menor idade\")"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EaRSUC0CvQN9",
        "outputId": "ecd36f2b-6b5b-4d75-babb-85813ddaa7c4"
      },
      "source": [
        " verifica_idade(idade)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maior de idade\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umSYwZnXwZsK"
      },
      "source": [
        "### sem parâmetro e convertendo string para int\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdlGb3zDw9Y-"
      },
      "source": [
        "def verica_idade_sem_parametro():\n",
        "  idade = input(\"Digite sua idade: \")\n",
        "  idade = int(idade)\n",
        "  if idade >= 18:\n",
        "    print(\"Maior de idade\")\n",
        "  else:\n",
        "    print(\"Menor idade\")"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtBMxql7xL71",
        "outputId": "ce125f18-1842-4fbb-95e6-75eba5e95ad1"
      },
      "source": [
        "verica_idade_sem_parametro()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Digite sua idade: 15\n",
            "Menor idade\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbQ-OluVxOHk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjsChtIix9s2"
      },
      "source": [
        "### exercício\n",
        "\n",
        "Uma pessoa vai até a padaria todos os dias. A distância de sua casa até o estabelecimento, é de 100 metros e todo percurso é feito em 20 segundos.\n",
        "\n",
        "Para calcular a velocidade média e descobrir a razão do espaço pelo tempo, podemos dividir o espaço percorrido pelo tempo.\n",
        "\n",
        "Sabendo disso, a pessoa deseja criar uma função chamada velocidade, que recebe 2 parâmetros chamados espaco e tempo, realizar o cálculo e exibir uma saída semelhante ao exemplo abaixo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiTIS9P8ybBo"
      },
      "source": [
        "def vel_med(espaco, tempo):\n",
        "  velocidade = espaco / tempo\n",
        "  print(f\"Velocidade: {velocidade} m/s\")"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tb5F7EkIzeWi",
        "outputId": "c3cf3322-462c-46a9-b5a7-6bdde3caa51a"
      },
      "source": [
        "vel_med(100,20)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Velocidade: 5.0 m/s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofMO1ahd1SGh"
      },
      "source": [
        "# **Listas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8lIli_mztDp",
        "outputId": "c7c46929-33e6-4e82-b97f-1e61ff027eaa"
      },
      "source": [
        "idade = 22\n",
        "idade"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gDT7FuG0sJ5",
        "outputId": "a6bb2741-d035-47b8-9a3d-88e8f925b33f"
      },
      "source": [
        "type(idade)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZa7QlLh1bIP"
      },
      "source": [
        "idades = [18, 22, 15, 50]"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvzeBCe11ioM",
        "outputId": "968fb3a5-a932-479d-e2ab-1c0323dddb5c"
      },
      "source": [
        "idades[2]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jcyp-EUH2HEc"
      },
      "source": [
        "# Loops"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hy5MkepA1khi",
        "outputId": "b7e06d8d-1a02-4789-cf87-a8ddbb2b1b9e"
      },
      "source": [
        "# for fora da def\n",
        "def verifica_se_pode_dirigir(idades):\n",
        "  if idade >= 18:\n",
        "    print(f\"{idades} anos de idade, TEM permissão para dirigir\")\n",
        "  else:\n",
        "    print(f\"{idades} anos de idade, NÃO TEM permissão para dirigir\")\n",
        "\n",
        "for idade in idades:\n",
        "  verifica_se_pode_dirigir(idade)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18 anos de idade, TEM permissão para dirigir\n",
            "22 anos de idade, TEM permissão para dirigir\n",
            "15 anos de idade, NÃO TEM permissão para dirigir\n",
            "50 anos de idade, TEM permissão para dirigir\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxVCyjl93VTC"
      },
      "source": [
        "# for dentro da def - quando chamar a função chamar idades e não idade\n",
        "\n",
        "def verifica_se_pode_dirigir(idades):\n",
        "  for idade in idades:\n",
        "    if idade >= 18:\n",
        "      print(f\"{idades} anos de idade, TEM permissão para dirigir\")\n",
        "    else:\n",
        "      print(f\"{idades} anos de idade, NÃO TEM permissão para dirigir\")\n",
        "\n"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Jh3QpQw350f",
        "outputId": "8f9710a6-514f-4b10-b0f2-4a3524014aa9"
      },
      "source": [
        "verifica_se_pode_dirigir(idades)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[18, 22, 15, 50] anos de idade, TEM permissão para dirigir\n",
            "[18, 22, 15, 50] anos de idade, TEM permissão para dirigir\n",
            "[18, 22, 15, 50] anos de idade, NÃO TEM permissão para dirigir\n",
            "[18, 22, 15, 50] anos de idade, TEM permissão para dirigir\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujhi2UjW4QgO"
      },
      "source": [
        "# Boleano\n",
        "função identifica qual idade é > que 18 e add na lista permissões"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50uzLh0Y4Cxk"
      },
      "source": [
        "permissoes = []\n",
        "idades = [20, 15, 45, 77, 18, 21]\n",
        "\n",
        "def lista_pode_dirigir(idades, permissoes):\n",
        "  for idade in idades:\n",
        "    if idade>= 18:\n",
        "      permissoes.append(True)\n",
        "    else:\n",
        "      permissoes.append(False)\n",
        "lista_pode_dirigir(idades,permissoes)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oh-J3YDn5TgX",
        "outputId": "c01e60d6-6ff2-4487-9018-645fd871b43a"
      },
      "source": [
        "permissoes"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[True, False, True, True, True, True]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdwyslGX5ej2",
        "outputId": "51670e88-c58d-4b9d-bc04-a5851beb1c52"
      },
      "source": [
        "for permissao in permissoes:\n",
        "  if permissao == True:\n",
        "    print(\"Pode\")\n",
        "  else:\n",
        "    print(\"Não pode\")"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pode\n",
            "Não pode\n",
            "Pode\n",
            "Pode\n",
            "Pode\n",
            "Pode\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUlzkG7j7FHO"
      },
      "source": [
        "# **Tipos em uma lista**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9zFq2sH6LLu",
        "outputId": "cc255505-d7d3-4622-8099-4b6d01a7c5c8"
      },
      "source": [
        "lista_2 = [\"Gabi\", 28, True, \"18\"]\n",
        "\n",
        "for elemento in lista_2:\n",
        "  print(f\"O elemento é do tipo: \", type(elemento))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O elemento é do tipo:  <class 'str'>\n",
            "O elemento é do tipo:  <class 'int'>\n",
            "O elemento é do tipo:  <class 'bool'>\n",
            "O elemento é do tipo:  <class 'str'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCAzFKRt8OvC"
      },
      "source": [
        "Armazenando notas aleatorias em uma lista com **range**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgENsczs74YV"
      },
      "source": [
        "from random import randrange, seed"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gttg4byn-92y"
      },
      "source": [
        "seed(9) # para manipular os numeros gerados"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2JSn2_Rx9BL0",
        "outputId": "beaaf342-40eb-40c6-f728-785fec8197fe"
      },
      "source": [
        "randrange(0,10)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h18qyMQl9R5p",
        "outputId": "8f93ef63-c3aa-4b96-da27-093792ac2b4f"
      },
      "source": [
        "notas_matematica = []\n",
        "\n",
        "# para cada nota no range adicione 8 notas de 0 até 10\n",
        "for nota in range(8):\n",
        "  notas_matematica.append(randrange(0,11))\n",
        "\n",
        "notas_matematica"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 5, 4, 2, 2, 10, 0, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Vnj6FiK_Ynx"
      },
      "source": [
        "MATPLOTLIB\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pAy1kra-CEP"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "pFLkK2QU_emJ",
        "outputId": "fecc74e4-fd1f-47f5-97ec-e1cc0ed8d769"
      },
      "source": [
        "# 8 posições de notas no eixo x\n",
        "x = list(range(1,9))\n",
        "# as notas geradas acima são o eixo y\n",
        "y = notas_matematica\n",
        "#criando grafico\n",
        "plt.plot(x,y,\"g\", marker=\"*\")\n",
        "plt.xlabel(\"provas\")\n",
        "plt.ylabel(\"notas\")\n",
        "plt.title(\"Notas do aluno João\")"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Notas do aluno João')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNSgL9TvAUTU",
        "outputId": "6bdb1dc4-1519-4f99-ece7-5d3a52552128"
      },
      "source": [
        "# conferindo\n",
        "notas_matematica"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 5, 4, 2, 2, 10, 0, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74CgmulVBb3X"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxXRBKnCA4QZ"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "notas_matematica = ['Matemática',8,7,6,6,7,7,8,10]\n",
        "notas_portugues = ['Português',9,9,9,8,5,6,8,5]\n",
        "notas_geografia = ['Geografia',10,10,6,7,7,7,8,7]\n",
        "\n",
        "notas = [notas_matematica, notas_portugues, notas_geografia]"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "id": "1t0WGIFyBdhR",
        "outputId": "0af44994-4228-4ae7-f198-7449f270aa47"
      },
      "source": [
        "for nota in notas:\n",
        " x = list(range(1, 9))\n",
        " y = nota[1:]\n",
        " plt.plot(x, y, marker='o')\n",
        " plt.xlabel('Provas')\n",
        " plt.ylabel('Notas')\n",
        " plt.title(nota[0])\n",
        " plt.show()\n",
        "\n",
        " # variável y irá armazenar a nota de cada matéria a partir do índice 1 e o título, \n",
        " # armazenado no índice 0 será incluso no gráfico."
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tys9HYXBfr2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}